urban environment research articles

• A-Z Publications

Annual Review of Environment and Resources

Volume 42, 2017, review article, linking urbanization and the environment: conceptual and empirical advances.

• Xuemei Bai 1 , Timon McPhearson 2,3 , Helen Cleugh 4 , Harini Nagendra 5 , Xin Tong 6 , Tong Zhu 7 , and Yong-Guan Zhu 8,9
• View Affiliations Hide Affiliations Affiliations: 1 Fenner School of Environment and Society, Australian National University, Canberra ACT 0200, Australia; email: [email protected] 2 Urban Systems Lab, The New School, New York, NY 10003, USA 3 Cary Institute of Ecosystem Studies, Millbrook, New York 12545, USA 4 Climate Science Centre, CSIRO, Canberra ACT 2601, Australia 5 School of Development, Azim Premji University, Bangalore 560100, India 6 Department of Urban and Economic Geography, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China 7 BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China 8 Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China 9 Research Center for Eco-environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
• Vol. 42:215-240 (Volume publication date October 2017) https://doi.org/10.1146/annurev-environ-102016-061128
• First published as a Review in Advance on August 14, 2017
• © Annual Reviews

Urbanization is one of the biggest social transformations of modern time, driving and driven by multiple social, economic, and environmental processes. The impacts of urbanization on the environment are profound, multifaceted and are manifested at the local, regional, and global scale. This article reviews recent advances in conceptual and empirical knowledge linking urbanization and the environment, focusing on six core aspects: air pollution, ecosystems, land use, biogeochemical cycles and water pollution, solid waste management, and the climate. We identify several emerging trends and remaining questions in urban environmental research, including ( a ) increasing evidence on the amplified or accelerated environmental impacts of urbanization; ( b ) varying distribution patterns of impacts along geographical and other socio-economic gradients; ( c ) shifting focus from understanding and quantifying the impacts of urbanization toward understanding the processes and underlying mechanisms; ( d ) increasing focus on understanding complex interactions and interlinkages among different environmental, social, economic, and cultural processes; and ( e ) conceptual advances that call for articulating and using a systems approach in cities. In terms of governing the urban environment, there is an increasing focus on public participation and coproduction of knowledge with stakeholders. Cities are actively experimenting toward sustainability under a plethora of guiding concepts that manifests their aspirational goals, with varying levels of implementation and effectiveness.

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Perspectives on urban transformation research: transformations in , of , and by cities

• Katharina Hölscher   ORCID: orcid.org/0000-0002-4504-3368 1 &
• Niki Frantzeskaki 2  

Urban Transformations volume  3 , Article number:  2 ( 2021 ) Cite this article

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The narrative of ‘urban transformations’ epitomises the hope that cities provide rich opportunities for contributing to local and global sustainability and resilience. Urban transformation research is developing a rich yet consistent research agenda, offering opportunities for integrating multiple perspectives and disciplines concerned with radical change towards desirable urban systems. We outline three perspectives on urban transformations in , of and by cities as a structuring approach for integrating knowledge about urban transformations. We illustrate how each perspective helps detangle different questions about urban transformations while also raising awareness about their limitations. Each perspective brings distinct insights about urban transformations to ultimately support research and practice on transformations for sustainability and resilience. Future research should endeavour to bridge across the three perspectives to address their respective limitations.

Science highlights

We outline three perspectives on urban transformations for explaining, structuring and integrating the emerging urban transformations research field.

Transformation in cities focuses on unravelling the diverse factors, processes and dynamics driving place-based transformations in cities. This perspective represents research that aims to examine and explain why transformations occur and are supported in some places and not others.

Transformation of cities examines the outcomes of transformative changes in urban (sub-)systems. It serves to understand and evaluate the emergence of new urban functions, new interactions and their implications for sustainability and resilience.

Transformation by cities looks at the changes taking place on global and regional levels as a result of urbanisation and urban development approaches. The perspective emphasises the agency of cities on a global scale and how transformation concepts travel between places.

Future research should aim to bridge across the perspectives to address their respective limitations, for example by bringing in place-based knowledge (‘in’) into global discussions (‘by’) to facilitate cross-city learning.

Policy and practice recommendations

Experimental, collaborative and place-based governance approaches facilitate the integration of local knowledge, the development of inspiring narratives that boost sense of place and empower local communities to boost transformations in cities.

To assess and coordinate urban transformations, transformations, policy and practice actors need to employ systemic concepts and visions that advance solutions with multiple benefits for synergies and  minimal trade-offs.

Multi-level partnerships and (transnational) networks for policy knowledge exchange between cities help mobilising the potential of cities as agents of change for sustainability at a global scale.

Introduction

The notion of ‘urban transformation’ has been gaining ground in science and policy debates. Urban transformations to sustainability and resilience are enshrined in the 2030 United Nations Sustainable Development Goals (SDGs) (UN 2016 ) and the New Urban Agenda (UN-Habitat 2016a ). A rich research field around questions of urban transformations has started to emerge, combining multiple scientific disciplines, ontologies and methods (Elmqvist et al. 2018 , 2019 ; Wolfram et al. 2017 ; Vojnovic 2014 ). Key to these debates is the aim to put cities on a central stage for accelerating change towards local and global sustainability and resilience.

Urban transformation narratives have been driven by the recognition of the need and opportunity for radical change towards sustainable and resilient cities. Cities constantly experience changes, but contemporary urban change processes are unparalleled. Cities grapple with a variety of interrelated challenges, including pollution, poverty and inequality, ageing infrastructure and climate change (Haase et al. 2018 ; UN-Habitat 2016b ; Seto et al. 2017 ). Urbanisation in its current form causes significant changes in land use, energy demand, biodiversity and lifestyles and raises questions about the contribution of cities to global environmental change (Haase et al. 2018 ; Alberti et al. 2018 ; Elmqvist et al. 2013 ; Seto et al. 2017 ). At the same time, cities concentrate the conditions and resources for realising the fundamental changes in energy, transportation, water use, land use, housing, consumption and lifestyles that are needed to ensure liveability, wellbeing and sustainability of our (urban) future (Romero-Lankao et al. 2018 ; Koch et al. 2016 ; Elmqvist et al. 2018 ). The potential and momentum in cities is visible in for example the ‘climate emergency’ declarations of local governments that call for accelerated climate action in view of international stalemate.

The notion of urban transformation guides and formulates a better understanding of urban change. On the one hand, ‘transformation’ serves as an analytical lens to describe and understand the continuous, complex and contested processes and dynamics manifesting in cities, as well as how these dynamics alter urban functions, local needs and interactions between cities and their surroundings (McCormick et al. 2013 ; Iwaniec et al. 2019 ). On the other hand, the transformation perspective provides a normative orientation that emphasises the need for radical and systemic change in order to overcome persistent social, environmental and economic problems and to purposefully move towards sustainable and resilient cities in the long-term (Hölscher et al. 2019 ; Kabisch et al. 2018 ). Accordingly, sustainability and resilience are complementary concepts to asses and orient urban transformation processes (Elmqvist et al. 2019 ; Pickett et al. 2014 ; Simon et al. 2018 ).

In this paper, we distinguish three perspectives on urban transformations to structure and guide research and practice on urban transformations. Urban transformation research is an emergent, loosely connected interdisciplinary field combining urban studies and complex system studies. Various research fields and disciplines converge in urban transformation research; the multitude of disciplines has been systematically reviewed in Wolfram et al. ( 2017 ) and Wolfram and Frantzeskaki ( 2016 ). This diversity engenders multiple entry points and provides complementary concepts, theories and insights. However, the diversity causes ambiguities in ontologies, use of concepts and fragmented knowledge about how urban transformations unfold and can be supported.

Urban transformation research would benefit from “gradual interconnection, and the articulation of a certain range of research perspectives” (Wolfram and Frantzeskaki 2016 : 2). To facilitate this, we distinguish and describe three perspectives on urban transformations that provide areas of convergence across diverse research approaches. Each perspective provides distinct starting points to generate, structure and integrate knowledge along certain questions. Ultimately, the perspectives outline an agenda for advancing theory and practice on urban transformations for sustainability and resilience: they generate implications for urban policy and practice and a way forward to bridge across the perspectives to address the respective limitations.

Perspectives on transformations in, of and by cities

We distinguish between perspectives on urban transformations in , of and by cities. The perspectives provide entry points for formulating and structuring research questions on urban transformations, integrating research approaches and knowledge, and deriving implications for practice.

The three perspectives start from similar assumptions about cities and urban transformations. They focus on urban transformations as complex processes of radical, systemic change across multiple dimensions (e.g. social, institutional, cultural, political, economic, technological, ecological) (Hölscher et al. 2018 ; Frantzeskaki et al. 2018a ; McCormick et al. 2013 ). Cities are understood as complex, adaptive and open systems (Alberti et al. 2018 ; McPhearson and Wijsman, 2017 ; Ernstson et al., 2010 ; Collier et al. 2013 ). This implies that urban transformations are not spatially limited, and driven by and driving cross-scale and cross-sectoral dynamics: cities are “local nodes within multiple overlapping social, economic, ecological, political and physical networks, continuously shaping and shaped by flows of people, matter and information across scales” (Wolfram and Frantzeskaki 2016 : 143; see also Hansen and Coenen 2015 ; Chelleri et al. 2015 ). To describe, explain and evaluate urban transformations, cities are increasingly approached as social-ecological-technical systems (SETS), including (1) socio-economic, political and institutional dimensions (social); (2) natural resource flows and physical phenomena (ecological); (3) as well as the manmade surroundings (technological) (McPhearson 2020 ; Alberti et al. 2018 ; Bai et al. 2017 ). Actors have a central position within urban systems, influencing how cities are organised and resources are produced and consumed. Given the open character of urban systems, actors are diverse and include household members, local governments, and entrepreneurs also regional and national governments, international bodies and multinational companies, amongst others (Glaas et al. 2019 ; Webb et al. 2018 ).

Urban transformations can be desirable or undesirable (Elmqvist et al. 2019 ; Hölscher 2019 ). A shared aim across urban transformation research perspectives and approaches is to generate actionable knowledge to intervene in urban transformation processes and support radical change towards sustainable and resilient urban systems (cf. Wittmayer and Hölscher 2017 ).

Despite these shared starting points and aims, the perspectives ask distinct questions about transformations vis-à-vis urban systems. They look at systemic change dynamics taking place in cities (“in”), the outcomes of systemic change of cities (“of”), or systemic change on global and regional levels driven by cities (“by”). These entry points and corresponding questions manifest in differences along key descriptors of urban transformations (cf. Hölscher et al. 2018 ). The differences are not contradictory: they generate complementary insights for understanding and supporting urban transformations given the different level of aggregation, analysis and understanding of system dynamics and points of intervention (Table 1 ). 

The main aim of the perspectives is to facilitate structuring of urban transformation research along shared themes and questions. Specifically, in articulating these, we show the actionable knowledge generated through each perspective to support urban transformations for sustainability and resilience. We also show that the perspectives offer bridges across knowledge to strengthen research and practice.

Transformation in cities: cities as places of transformations

Transformation in cities focuses on unravelling the diverse, local, regional and global factors, processes and interactions that converge in cities as places of transformations, thus driving or constraining place-based transformations.

The perspective zooms in on cities as spaces and places. Cities are geolocated in an objective, abstracted point, i.e. space, which is for example demarcated by geographical and administrative boundaries. Cities as places are defined by the physical (i.e. urban form) and philosophical (i.e. imagination and representation) relationships between people and place (Roche, 2016 ; Knox 2005 ). Thus, cities as places are both “a centre of meaning and the external context of people’s actions” (Knox 2005 : 2). As spaces and places of transformations, cities harbour specific potentials, driving forces and barriers (Hansen and Coenen 2015 ).

Place-based transformations are the result of the social construction by people responding to the opportunities and constraints of their particular locality (Fratini and Jensen 2017 ; Späth and Rohracher 2014 ). Endogenous conditions and developments include geographic location, climate, local economic structure, population dynamics and the built environment. For example, urban segregation and inequality result from and are reinforced by interactions between residential choices, personal preferences, job markets, land and real estate markets and public policies (Alberti et al. 2018 ). The construction of place-based transformations does not take place independently of societal norms and representations of the world. Economic and cultural globalisation and the resulting ‘network society’ becomes manifest in cities and shape place-based transformation dynamics (Roche, 2016 ). Scholars seeking to understand the ‘geography in transitions’ emphasise that cities are positioned within cross-scale spatial and institutional contexts that influence local change dynamics (Hansen and Coenen 2015 ; Truffer et al. 2015 ; Coenen et al. 2012 ; Hodson et al. 2017 ; McLean et al. 2016 ). Along similar lines, Loorbach et al. ( 2020 ) show the translocal character of social innovations that are locally rooted but globally connected.

This perspective positions transformative agency as deeply embedded in socio-spatial contexts. A central research focus is on urban niches that experiment with and scale new solutions (McLean et al. 2016 ; Ehnert et al. 2018 ), governance arrangements (Wolfram 2019 ; Hölscher et al. 2019a ) and ways of relating and knowing (Frantzeskaki and Rok 2018 ). Urban experimentation or real-world laboratories have become process tools to facilitate co-creative and innovative solution finding processes that empower actors to deal with urban problems, for example related to mobility, regeneration, community resilience or green job creation (Bulkeley et al. 2019 ; von Wirth et al. 2019 ; Hölscher et al. 2019c ). Such approaches represent situated manners of  place-making to co-develop inspiring ‘narratives of place’, empower local communities and foster urban transformative capacities (Wolfram 2019 ; Jensen et al. 2016 ; Ziervogel, 2019 ; Castán Broto et al. 2019 ). The idea of place-specificity recognises the particular role of ‘sense of place’ and ‘place attachment’, which can be an outcome of experimentation and in turn drive transformative change (Frantzeskaki et al., 2016 ; di Masso et al. 2019 ; Brink and Wamsler 2019 ). Ryan ( 2013 ) describes how multiple small ‘eco-acupuncture’ interventions can shift the community’s ideas of what is permissible, desirable and possible.

A key value of this perspective lies on its embedded research inquiry into the ‘black box’ of a city, including social, economic and ecological situated and contextual knowledge. A main implication for urban policy and planning practice is to facilitate place-based innovation by going beyond sectoral infrastructuring and top-down masterplanning towards situated and cross-sectoral place-making. Experimental and co-creative governance approaches help recognise and mobilise place-specific capacities. The need for place-based innovation further calls for higher-level policies to be centred on the local dimension. For example, the current European Union Cohesion Policy puts a place-based approach into practice that recognises place variety (Solly 2016 ) and further extends it to a governance capacity building programme that engages with cities on the ground through the URBACT program ( www.urbact.eu ).

A limitation of this perspective is that knowledge about and actions instigating transformations in a specific city context are very entrenched in context dynamics. This can  limit transferability or scaling other than ‘scaling deep’ pathway (Moore et al.  2015 ; Lam et al. 2020 ) if not connected with mechanisms for global and transnational learning and knowledge transfer (Section 2.3). In (Moore et al. 2015 ; Lam et al. 2020 ) addition, neighbourhood-level interventions need to be connected to knowledge about city-level outcomes. This calls for critical evaluations of systemic outcomes in urban systems (Section 2.2).

Transformation of cities: outcomes of transformation dynamics in urban systems

Transformation of cities examines and evaluates the outcomes of transformation dynamics in urban (sub-)systems in terms of new urban functions, local needs and interactions and implications for sustainability and resilience.

This perspective focuses on urban (sub-)systems defined by specific functions (e.g. economy, energy, transport, food, healthcare, housing). Compared to the other perspectives, it most explicitly applies socio-technical and social-ecological, and increasingly SETS, frameworks to describe urban (sub-)systems. Urban transformations are the outcome of radical changes of dominant structures (e.g. infrastructures, regulations), cultures (e.g. values) and practices (e.g. mobility behaviours) of such urban (sub-)systems. As a result of these changes, what kind of and how system functions are delivered is fundamentally altered (Ernst et al. 2016 ).

The main aim of this perspective is to explain and evaluate how transformation dynamics affect urban systems’ functions. Frameworks and models to investigate how transformation dynamics influence urban (sub-)systems pay attention to the complex processes and feedback loops within, across and beyond urban systems and the accumulated effects on the urban system level. For example, studying social-ecological-technical infrastructure systems in cities advances understanding of urban structure-function relationships between green space availability, wellbeing, biodiversity and climate adaptation (McPhearson 2020 ). Similarly, urban metabolism analysis and ecosystem studies seek to understand energy and material flows (Bai 2016 ; Dalla Fontana and Boas 2019 ). An emerging perspective on cities as ‘multi-regime’ configurations investigates dynamics across different functional systems (e.g. energy, water, mobility, food) (Grin et al. 2017 ; Irvine and Bai 2019 ). This provides opportunities to unveil interactions across multiple urban systems and scales. For instance, rapid changes in electricity systems can have knock-on effects for urban mobility or heat systems (Chen and Chen 2016 ; Chelleri et al. 2015 ). The relational geography perspective puts forth a differentiated view of urban systems: it zooms in on different boroughs, districts or neighbourhoods and raises questions such as how innovation and change in one location affects neighbouring locations (Wachsmuth et al. 2016 ).

This perspective most explicitly addresses prescriptive, ‘goal’-driven and recently mission-driven orientations for reinventing cities to be more sustainable, resilient, inclusive, attractive, prosperous, safe and environmentally healthy (Elmqvist et al. 2018 ; Kabisch et al. 2018 ; Rudd et al. 2018 ). Researchers and urban practitioners and planners employ concepts like ‘sustainability’ and ‘resilience’ as frames to evaluate the state of urban systems and to inform urban planning and regeneration programmes (Elmqvist et al. 2019 ). The systemic focus and application of such concepts also helps to identify synergies and trade-offs across urban systems and goals. For example, the sustainability paradigm of maximising efficiency in mobility or energy systems might result in vulnerability to natural disasters when systems lack parallel or redundant back-up systems (ibid.). Similarly, scholars point to the risks of green gentrification: while urban greening interventions have multiple benefits for the environment and climate adaptation, if not planned and governed inclusively, they can create unintended dynamics of exclusion, polarisation and segregation (Anguelovski et al. 2019 ; Haase et al. 2017 ).

This perspective takes a meta-level view on the agency and governance in cities, highlighting strategic partnerships and interventions based on desired system-level outcomes. From this perspective, cities may act as coherent strategic entities based on systemic understandings of city-specific and long-term effects to pursue managed transitions of their large-scale (sub-)systems (Jensen et al. 2016 ; Hodson et al., 2017 ). Urban transformation governance needs to facilitate alignment, foresight and reflexive learning to recognise, anticipate and shape transformation dynamics and leverage points (Hölscher et al. 2019b ). Key starting points are shared definitions of what ‘desirability’ means in specific contexts. Orchestration can align priorities and connect emerging alternatives, ideas, people and solutions (ibid.; Hodson et al., 2017 ). Shared and long-term visions re-orient short-term decisions and interventions that create synergies across multiple priorities. For example, Galvin and Maassen ( 2020 ) analyse Medellín’s (Columbia) mobility transformation that also contributed to inclusiveness and public safety. Transition management is a practice-oriented framework to co-develop shared visions, pathways and experiments in an ongoing learning-by-doing and doing-by-learning way (Frantzeskaki et al. 2018b ; Loorbach et al. 2015 ).

In summary, this perspective provides a view on interpreting transformation dynamics and developing orientations and practical guidance for intervention. It becomes visible in urban planning and policy practice through the development of systemic urban concepts as ‘anchor points’ or attractors for urban transformations such as ‘sharing cities’, ‘circular cities’, or ‘renaturing cities’. Cities like Rotterdam in the Netherlands and New York City in the USA are using such concepts to formulate long-term climate, sustainability and resilience agendas and establish cross-cutting city-level partnerships for their implementation (Hölscher et al. 2019a ). A main implication of this perspective is about the need to institutionalise and prioritise such long-term agendas into policy and planning across sectors and scales (ibid.).

A limitation of this perspective is that it overlooks place-specific implications and can nuance or be agnostic to politics and contestations at local sub-system level. Strategically linking place-based initiatives (Section 2.1) with systemic urban concepts and visions provides a powerful tool to align the multitude of activities taking place in cities and to coordinate urban transformations on (sub-)system scale. Additionally, this perspective requires explicit attention to the relationships between urban systems and their hinterlands or other distant territories, which affect and are affected by urban system’s functioning (Section 2.3).

Transformation by cities: cities as agents of change at global scale

The third perspective on transformation by cities draws attention to the changes taking place on global and regional levels as a result of urbanisation and urban development.

The main emphasis is here placed on cities as “agents of change at global scale” (Acuto 2016 ). As open systems, cities are not just influenced by developments outside their spatial boundaries (see Section 2.1). Urban transformations also have implications on global resources, environmental conditions, commodities and governance.

On the one hand, cities – including their social-ecological-technological configurations and the diversity of actors influencing them – can be viewed as culprits driving global high emissions, resource depletion and unsustainability. This raises critical questions about the relationship between current and unprecedented urbanisation and global sustainability (Seto et al. 2017 ; Haase et al. 2018 ). For example, the expansion of cities will triple land cover by 2030, compared to 2000, with severe implications on biodiversity (Alberti et al. 2018 ; Elmqvist et al. 2013 ). Different frameworks and concepts are employed to describe and assess the linkages between cities and their hinterland and other distant territories, including ‘urban land teleconnections’ (Seto et al. 2012 ), ‘regenerative cities’ (Girardet 2016 ) and ‘urban ecological footprint’ (Folke et al. 1997 ; Hoornweg et al. 2016 ; Rees and Wackernagel 2008 ).

On the other hand, cities have become key loci for trialling sustainable approaches and solutions that inform the global sustainability agenda (UN-Habitat 2016b ; Seto et al. 2017 ; Bai et al. 2018 ). Cities – especially local governments – play key roles in shaping global sustainability programmes and discourses and in developing and sharing knowledge and best practices. Local governments have also become celebrated for taking action when the national government is not (van der Heijden 2018 ; Acuto 2016 ). Governance strategies such as experimentation, best practices or imaginaries have been taken up globally (Haarstad 2016 ; McCann 2011 ; van der Heijden 2016 ). This raises questions about how the experiences and best practices showcased in cities become knowledge to be diffused and shared, as well as how transformations travel between places and across scales (Lam et al. 2020 ).

This perspective supports a polycentric and multi-level approach to global environmental governance. Global environmental governance is becoming increasingly decentralised and polycentric, which is visible for example in climate governance (Ostrom 2014 ; Jordan et al. 2018 ; Hölscher and Frantzeskaki 2020 ) and the urban SDG (UN 2016 ). The recent ‘city charters’ of global organisations such as the IPCC Cities and Climate Change, the Convention on Biological Diversity and Cities and Future Earth Urban Knowledge Network, showcase the recognition of ‘cities’ as key players on a global level. While urban sustainability governance has often proliferated without leadership at national levels, the nestedness of local governance in legal and institutional frameworks at regional, national and international levels requires alignment of priorities and legislation across governance levels (Hughes et al. 2017 ; Keskitalo et al. 2016 ).

In summary, this perspective creates knowledge about the role of cities in contributing to global change and what it means for governance, policy and planning at global, national, metropolitan and regional levels. It provides and requires big data from cities and their resource footprints, flows and dynamics so as to draw on patterns and pathways for change that can inform and reinforce global agendas for action. A key mechanism for urban practitioners is to strengthen policy knowledge exchange across frontrunning cities (Hölscher et al. 2019a ). Transnational city networks such as the International Council for Local Environmental Initiatives (ICLEI), C40 and 100 Resilient Cities facilitate knowledge exchange and inter-city learning, foster the creation of collective goals, lobby for international attention, and enable the transplantation of innovative, sustainable and resilient policy and planning approaches (Acuto et al. 2017 ; Lee 2018 ; Mejía-Dugand et al. 2016 ; Frantzeskaki et al. 2019 ; Davidson et al. 2019 ).

A danger of this perspective is that this global discourse is mainly focused on ‘global cities’. Medium-sized and middle-income cities are leaders in terms of actual sustainability performance and need to be actively acknowledged and considered (Vojnovic 2014 ). Florida ( 2017 ) criticises how “winner-take-all cities” reinforce inequality, while many cities stagnate and middle-class neighbourhoods disappear. This requires more research into how resources and opportunities are distributed and made accessible across different cities, for example ‘global’ cities, metropolitan cities and developing countries’ cities (Coenen et al. 2012 ; Gavin et al. 2013 ). Additionally, cities are not necessarily a united front: priorities and interpretations differ across cities (Growe and Freytag 2019 ). To address these issues, this perspective would benefit from a more critical and contextual research approach on place-based transformations (Section 2.1), questioning why transformations occur and are supported in some places and not others. Comparative analyses into the factors and dynamics influencing place-based transformations can facilitate transnational knowledge transfer and upscaling of place-based initiatives.

Conclusions

We offer three perspectives on urban transformations research as a means to cherish and celebrate, but also structure the diversity of the growing urban transformations research field. Our paper is a first attempt to distinguish these perspectives, by discussing key questions, entry points, practical implications and limitations. We show that the perspectives help converge research approaches and clarify how different perspectives provide evidence for urban policy and planning.

The perspectives are not merely conceptual devices: they show up in cities’ agendas, programmes and approaches and give guidance to practitioners. The ‘transformation in cities’ perspective asks practitioners to experiment with collaborative place-making approaches like urban living labs to integrate local knowledge and strengthen a sense of place and empowerment. The ‘transformation of cities’ perspective appears as underlying integrative systems’ approach for core urban strategies such as climate change and biodiversity strategies. The ‘transformation by cities’ perspective highlights the need to invest in policy knowledge exchange between cities, for example through transnational city networks.

The three perspectives on urban transformation do not exist in isolation from one another. We have shown how the perspectives can feed into and complement each other to address respective research gaps and practical challenges. The main future research direction we put forth is to bridge across the perspectives to address their respective limitations and generate comprehensive actionable knowledge. This means to formulate integrative research questions bridging across perspectives: How do place-making initiatives in a specific neighbourhood affect urban systems’ functioning? How can place-based transformation knowledge be transferred to other city contexts? How can place-based experiments and transformation initiatives or projects inform policy at city and city-network level? What are the conditions for downscaling strategic initiatives from global level – for example, post-Aichi biodiversity targets – considering capacities of urban sub-systems?

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Abbreviations

US Department of Housing and Urban Development

International Council for Local Environmental Initiatives

International Panel on Climate Change

Sustainable Development Goal

Social-ecological-technological system

United NationsMeerow, S

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Hölscher, K., Frantzeskaki, N. Perspectives on urban transformation research: transformations in , of , and by cities. Urban Transform 3 , 2 (2021). https://doi.org/10.1186/s42854-021-00019-z

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Challenges and Opportunities for Urban Environmental Health and Sustainability: the HEALTHY-POLIS initiative

• Sotiris Vardoulakis 1 , 3 ,
• Keith Dear 2 &
• Paul Wilkinson 3  

Environmental Health volume  15 , Article number:  S30 ( 2016 ) Cite this article

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Cities around the world face many environmental health challenges including contamination of air, water and soil, traffic congestion and noise, and poor housing conditions exacerbated by unsustainable urban development and climate change. Integrated assessment of these risks offers opportunities for holistic, low carbon solutions in the urban environment that can bring multiple benefits for public health. The Healthy-Polis consortium aims to protect and promote urban health through multi-disciplinary, policy-relevant research on urban environmental health and sustainability. We are doing this by promoting improved methods of health risk assessment, facilitating international collaboration, contributing to the training of research scientists and students, and engaging with key stakeholders in government, local authorities, international organisations, industry and academia. A major focus of the consortium is to promote and support international research projects coordinated between two or more countries. The disciplinary areas represented in the consortium are many and varied, including environmental epidemiology, modelling and exposure assessment, system dynamics, health impact assessment, multi-criteria decision analysis, and other quantitative and qualitative approaches. This Healthy-Polis special issue presents a range of case studies and reviews that illustrate the need for a systems-based understanding of the urban environment.

Rapid urbanization, combined with rapid improvement in standards of living is stretching natural resources and threatening environmental quality in many countries. Population density has reached unprecedented levels in most parts of the high, medium and low income world. The urban population in 2014 was 54 % of the total global population, up from 30 % in 1950, and is projected to account for around 66 % of the global population by 2050 [ 1 ]. Urban areas are facing a range of environmental health challenges including contamination of air, water and soil. Sprawling urban areas contribute to traffic congestion, with associated air pollution, noise and long commuting times affecting public health and productivity across the world.

In addition, climate change is likely to aggravate certain urban health risks and inequalities by increasing the frequency and severity of extreme weather events (heatwaves, storms and floods), potentially contributing to air pollution episodes (ground-level ozone and pollen) and disturbing urban ecology [ 2 ], [ 3 ]. The urban heat island effect (i.e. the difference in temperatures between a city centre and the surrounding countryside) also exacerbates heat stress in built up areas [ 4 ]. This has knock-on effects on the indoor environment, energy demand (for ventilation and cooling) and public health [ 5 ], [ 6 ].

However, there is also an opportunity here: climate change mitigation and adaptation measures can deliver a range of health benefits. These health benefits are likely to result from “low carbon” policies aimed at lowering greenhouse gas emissions by improving energy efficiency in buildings (enhancing thermal comfort for occupants) [ 5 ], reducing dependency on private car use (improving physical activity levels and local air quality) [ 7 ], increasing renewable energy generation (improving ambient air quality) [ 8 ], and reducing meat and dairy consumption (reducing saturated fat intake) [ 9 ]. Accounting for the health co-benefits of climate change mitigation strengthens the case for reductions in greenhouse gas emissions from many sectors. However, attention should also be paid to the unintended harmful effects of certain carbon reduction policies. For example, home energy efficiency measures have the potential to worsen indoor air quality if steps are not taken to maintain good ventilation [ 10 ]; and the promotion of active travel has the potential to increase road injury risks without separation of cyclists and pedestrians from other road traffic [ 7 ].

Cities are complex systems. Research to elucidate pathways to better health and wellbeing demands systems-based, interdisciplinary methods involving epidemiologists, toxicologists, urban planners, environmental scientists, mathematical modellers, engineers, IT experts, social scientists, public health researchers and health care professionals. Importantly, local communities need to be involved in research projects aiming to inform local policies from an early stage. This can be achieved through genuine stakeholder engagement [ 11 ], citizen science and knowledge co-generation approaches [ 12 ], which raise awareness, provide valuable information and improve acceptability of interventions.

Methodological innovation in epidemiology, exposure assessment and risk analysis, and standardization of methods across countries, are needed to address complex environmental health challenges in the context of climate change and sustainable development. Relevant areas include the assessment and reduction of the health risks and impacts of weather extremes, air pollution, water contamination and other forms of environmental hazard, especially in the context of climate change, and evaluating mitigation and adaptation options [ 13 ]. These challenges highlight the need for integrated assessment methods that account for the complex interactions (including feedback loops) between climatic, environmental and behavioural factors, and the urban fabric [ 14 ]. This is particularly the case in global megacities where exposure to environmental stressors (such air pollution, congestion, heat and noise) can be substantially higher than in rural areas. Particular opportunities for influencing development pathways may arise in the multitude of rapidly developing cities in low and middle income countries. System dynamics approaches [ 15 ] and multi-criteria decision analysis methods [ 16 ] integrating quantitative and qualitative evidence can help characterise the likely overall impacts of policy options in urban environments.

This is the approach adopted by Healthy-Polis ( www.healthy-polis.org ), a new international consortium for urban environmental health and sustainability which aims to: (1) promote innovation and standardization in research methods (including exposure modelling, environmental epidemiology, risk analysis and integrated assessment methods), (2) facilitate international, multi-disciplinary research collaborations, (3) provide training and promote capacity building especially in rapidly urbanizing countries, and (4) evaluate and promote environmental interventions to improve public health in cities.

A particular emphasis of Healthy-Polis is on engendering and supporting a growing community of young researchers in the field of urban environmental health, climate change and sustainability, who will push the research agenda forward through global collaborations in the coming critical decades.

Methods and case studies

The disciplinary areas represented in Healthy-Polis are many and varied, including environmental epidemiology, modelling and exposure assessment, system dynamics, health impact assessment, multi-criteria decision analysis, and other quantitative and qualitative approaches. Key areas of interest (Fig.  1 ) were discussed at the 1 st Healthy-Polis workshop in Manchester, U.K. (6 March 2014).

Healthy-Polis. Key areas of scientific research and inter-linkages in the urban environment

In this special issue of Environmental Health, we present twelve contributions that address the aims of the Healthy-Polis consortium using methods from many disciplines. Perhaps the most familiar connection between climate change and health is the impact of extreme weather events such as heatwaves. A systematic review by Arbuthnott et al. [ 17 ] covers the important question of whether susceptibility to heat and cold has changed over time. It appears that various populations did become less susceptible to heat, although attribution to a specific cause is difficult. Heaviside et al. [ 18 ] consider the attribution of mortality to the Urban Heat Island effect during heatwaves, finding an appreciable contribution of this effect to the excess mortality experienced in the West Midlands region of England in the 2003 European heatwave. In regard to the co-benefits of climate change mitigation, Sabel et al. [ 19 ] report on the health benefits of several European and Chinese cities’ actual mitigation efforts, finding mixed results but with relatively modest health gains. The significant contribution of this study was in additionally considering climate change impacts on positive health outcomes, such as wellbeing.

We include a set of papers that address various aspects of disease in the urban environment. Asikainen et al. [ 20 ] focus on the calculation of the annual burden of disease caused by exposure to indoor air pollution in EU countries, and how best to ventilate with outdoor air, which may also be polluted. Considering various measures of urban form in 50 urban areas in England, Fecht et al. [ 21 ] intriguingly report higher rates of premature cardiovascular mortality in cities with higher densities of road junctions. Turning to infectious disease, Semenza et al. [ 22 ] present a predictive model of West Nile Virus infections based on ambient temperature and other environmental determinants. Higher rates are projected under climate change which has implications for the safety of the blood supply. Analysing the consequences of China’s massive ongoing migration and rapid urbanisation, Li et al. [ 23 ] show that action to protect and improve health in cities can be taken at multiple scales from national to individual.

Many of the Healthy-Polis papers address the broad area of urbanisation and planning. Macmillan et al. [ 24 ] report a project in which over 50 stakeholders collaboratively built causal diagrams to capture the complexities of housing, energy and wellbeing and developed criteria for assessing housing policy, while Nieuwenhuijsen [ 25 ] surveys new concepts and methods developed to address the complexity of urban environmental health in the wider context of urban and transport planning. Turning to specifics, Woods et al. [ 26 ] show how multi-criteria decision analysis can be used to prioritize environmental health hazards in a city. Salmond et al. [ 27 ] consider the ecosystem services and disservices provided by planting street trees as an urban planning tool, and argue that a holistic approach is necessary to ensure a net benefit. Finally, Rietveld et al. [ 28 ] argue for a systems approach to water and waste management in cities, illustrating their points with case studies from three continents.

Conclusions and vision

The range of risks and opportunities for urban environmental health explored in this special issue clearly demonstrates the complexity of the challenge cities are facing in the 21 st century in the context of climate, land use and demographic change. As the planet becomes increasingly urbanised, pressure on natural resources (air, water, soil), urban infrastructure (housing and transport) and health care systems increases, but so does our capacity to address risks though technological innovation, international co-operation, and participatory decision-making at city level. Solutions may involve advanced “smart” systems (e.g. controlling energy consumption, temperature and ventilation in houses) as well as more traditional approaches (e.g. urban greening, promoting walking and cycling) to improve health and wellbeing. Importantly, these solutions need to be assessed in a holistic way to maximise the benefits (“win-win”, e.g. reducing energy consumption and improving thermal comfort and air quality in buildings) and avoid unintended trade-offs (“win-lose”, e.g. planting tree species that are aesthetically appealing but require high energy input for maintenance). Methods such as multi-criteria decision analysis, participatory system dynamics modelling and quantitative health impacts assessment can help avoid pitfalls of the past and create healthier and more sustainable cities. Healthy-Polis is committed to capitalizing on these opportunities by supporting international collaboration, building research capacity, and promoting dialogue between researchers, policy-makers and local communities.

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Acknowledgements

We are grateful to the Healthy-Polis scientific advisory committee, and to all authors and reviewers who contributed to this special issue.

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Public Health England has provided funding for the publication fee of this article.

This article has been published as part of Environmental Health Volume 15 Suppl 1, 2016: Healthy-Polis: Challenges and Opportunities for Urban Environmental Health and Sustainability. The full contents of the supplement can be found at http://www.ehjournal.net/supplements/15/S1 .

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Vardoulakis, S., Dear, K. & Wilkinson, P. Challenges and Opportunities for Urban Environmental Health and Sustainability: the HEALTHY-POLIS initiative. Environ Health 15 (Suppl 1), S30 (2016). https://doi.org/10.1186/s12940-016-0096-1

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Environmental determinants of population health in urban settings. A systematic review

• Marta Salgado   ORCID: orcid.org/0000-0003-0164-7271 1 ,
• Joana Madureira 2 , 3 ,
• Ana Sofia Mendes 2 , 3 ,
• Anália Torres 4 ,
• João Paulo Teixeira 2 , 3 &
• Mónica Duarte Oliveira 5  

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Population health is influenced by interactions between environmental determinants, which are captured by dimensions and indicators. This study aims to systematically review key environmental determinants and respective dimensions and indicators, relevant to evaluate population health in urban settings, and to understand their potential implications into policies.

A search of literature published between 2008 and 2018 was conducted in PubMed, Web of Science, Scopus and SciELO Portugal databases, on studies with evidence on association between an environmental determinant and a health outcome in urban contexts. Health determinants, dimensions and indicators researched in the selected studies were synthetized, and associations analyzed. An independent assessment of quality of the studies was performed. Key conclusions and policy recommendations were extracted to build a framework to analyze environment related population health and policies in urban settings.

Ninety four studies of varied methodological approaches and quality met the inclusion criteria. The review identified positive associations between all environmental determinants -socioeconomic, built environment, natural environment, healthcare, behaviors, and health outcomes - overall mortality and morbidity, in urban settings. Improvements in income, education, air quality, occupation status, mobility and smoking habits indicators have positive impact in overall mortality and chronic diseases morbidity indicators. Initiatives to improve population health in which policymakers can be more evidence-informed include socioeconomic, natural environment and built environment determinants.

Conclusions

There is scope and need to further explore which environmental determinants and dimensions most contribute to population health to create a series of robust evidence-based measures to better inform urban planning policies.

Peer Review reports

Assuring the health of the public goes beyond focusing on the health status of individuals; it requires a population health approach. Population health refers to “health outcomes and their distribution in a population. These outcomes are achieved by patterns of health determinants” [ 1 ]. Recent studies and socio-ecological models have been demonstrating that population health is influenced by economic factors, employment, education status, access to green spaces, walkability, water and air quality and individual behavior [ 2 , 3 , 4 , 5 , 6 , 7 ]. This wide range of factors can be considered as environment because formally, everything other than the genome is or can be connoted as part of the environment [ 8 ]. Taking this broad perspective of environment and perceiving it as relevant to population health [ 9 ], environmental determinants include the physical, chemical and biological factors external to the individual, as well as all the other factors impacting behaviors in order to prevent diseases and create healthy environments [ 10 ]. Thus, including socioeconomic dimensions- education, employment, income, racial segregation, healthcare dimensions- access to hospital care, health insurance, and behavior dimensions- alcohol consumption, nutrition, physical activity, and smoking habits. The complex and dynamic interaction between environmental determinants and health outcomes are known to affect the development of good livelihood, the building of a sustainable workforce and resilient communities [ 11 , 12 , 13 , 14 ].

The impact of urban settings on population health has been increasing as more people live in cities and towns than in rural areas [ 15 , 16 ]. As reported by the United Nations [ 17 ], in 2018 about half of the world’s population lived in urban areas but, by 2030, the numbers are expected to increase to two-thirds. Hartley (2004) [ 18 ] has documented a difference between urban and rural health frequently expressed in terms of determinants as medical care, built environment, natural environment, and socioeconomic status. Urban settings offer a high variety of opportunities, jobs and services, but the diversity, urban segregation and heterogeneous socioeconomic characteristics contribute to inequalities in health [ 19 ]. Population health has changed as the cities become bigger leading to changes in population heterogeneity, environment and society with impact on health and have for long been a serious health policy concern in many countries because there is no consensus on what can be routinely done to overcome intra-urban inequalities in health, their distributions within the country and with other countries [ 20 ]. Population health equity is also often dependent on political decision-making [ 21 ]. The increasing concern about the influence of context on health [ 16 ] requests for the integration of population health into urban planning as an essential goal to improve related-policymaking decisions, to foster healthier lifestyles and to avoid major health risks [ 22 , 23 ].

An integrated and holistic overview is necessary to facilitate a systematic examination of population health and its multiple environmental determinants in urban contexts, so that it is possible to track new evidence [ 24 , 25 ] and to foster adapted research and policy development into sustainability [ 26 ].

Therefore, we conducted a systematic review of literature to identify which key environmental determinants (socioeconomic, built environment, natural environment, health behaviors and healthcare) and respective dimensions and indicators (used to operationalize the measurement of determinants) are associated with human health outcomes, entailing overall mortality and morbidity, in urban settings. The review enables an informed discussion about relevant environmental health determinants, dimensions, and indicators for urban settings and how these factors interrelate and how they may be tackled through policies defined for the urban context.

This review was conducted according to the recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [ 27 ]. A systematic search of PubMed, Web of Science, Scopus and SciELO Portugal database was conducted. Bibliographies of included articles were also searched for possible relevant articles (using the article title). Articles were eligible if they reported a relationship between at least one indicator operationalizing a dimension relevant for an environmental determinant (with socioeconomic, built environment, natural environment, health behaviors and/or healthcare determinants being considered) and at least one indicator operationalizing an health outcome (entailing all causes mortality and/or morbidity) in urban settings, areas with high density of population and build-up area [ 11 ]. In the adopted nomenclature, determinant – e.g. natural environment - is divided into dimensions like air quality and noise, which are then operationalized through indicators, such as, concentration of particulate matter (PM) or day-evening-night level (L den ). A representation of the environmental determinants and dimensions relevant to evaluate population health in urban settings is depicted in Fig.  1 .

Illustrative representation of environmental determinants to evaluate population health in urban settings, adapted from [ 28 , 29 , 30 ]. The common dimensions were organize within the considered determinants with differences in: i) education and racial segregation are included in socioeconomic determinants; ii) physical environment is named natural environment, and the dimensions divided in air quality, water quality, noise and soil instead of natural resources; iii) built environment included green spaces and iv) behaviors included physical activity

PRISMA flow chart with literature search

Search strategy

The period covered in the search was from 2008 to 2018 and the following syntax was used: (#1) (“population health”[All fields]) AND (#2) (city OR cities OR town OR “metropolitan area” OR “urban environment”[Title/Abstract]) AND (#3) (indicators OR determinants [Title/Abstract]).

As inclusion criteria each study had to: (1) be written in English or Portuguese; (2) report a quantitative relationship between at least one environmental determinant, and one health outcome and (3) population health should be analyzed in urban settings at city level, council or metropolitan area (studies performed in Brazil municipalities must state if the municipality is an urban environment).

The exclusion criteria were: (1) specific populations as migrants or indigenous populations or population living in slums; (2) genetic studies or studies using animal models, as well as studies evaluating the applications of tools or indexes and studies comparing rural and urban environments; (3) qualitative studies, systematic reviews and meta-analyses and (4) studies that were only published in abstract form. Although, we recognize the value of grey literature, in the current systematic review this form of publication was not considered due to potential risk of bias.

Study selection and data extraction

Two authors (MS and JM) independently screened all included titles and abstracts of the entire list of studies identified and reviewed full texts of articles that met predetermined inclusion criteria.

All the references identified through the search were uploaded into citation manager software ENDNOTE (X7, Thomson Reuters) and duplicates were removed. Data extracted for each publication was organized by environmental determinant, grouped by category of heath outcome and included: author and date, aim of the study, study population, study design, association measure, dimension and respective indicators, and type of relation between indicator and health outcome (Additional file 1 ).

The visualization of the relationships between environmental determinants dimensions and health outcomes evidenced in the extracted data was made using Sankey diagram ( http://sankeymatic.com/build/ ). Key conclusions and policy recommendations were extracted to inform the construction of a final framework to analyze environment related population health in urban settings. Discrepancies were solved through a review by a third coauthor (AM).

Quality assessment

Acknowledging the relevance of assessing the quality of studies, we evaluated the risk of bias of the sampled studies by means of a checklist previously used in reviews assessing the impact of environmental determinants on health [ 31 , 32 ]. For each study, two investigators (MS and JM) independently evaluated the risk of bias associated with exposure assessment, confounding, selection of participants, and health outcome assessment, leading to a risk classification for each bias and globally (low, high and unclear). The studies that did not obtain the same risk of bias class from the two investigators were discussed with the third author (AM) to reach consensus. The classes set in [ 31 ] and the respective assessment of the sampled studies are shown in Additional file  2 .

The literature search identified 1369 records. After removing the duplicate records, 1262 studies were screened based on title and abstract and 1019 records were excluded, leaving 243 articles for full-text screening. Ninety-four records met the inclusion criteria and were included in this review while 149 were excluded. Figure 2 provides the flow diagram of articles included and excluded from the review.

Environmental determinants were divided in socioeconomic status, natural environment, built environment, healthcare, and behaviors. Health outcomes were divided into 5 major categories: 1) overall mortality, 2) morbidity related to birth outcomes (low birth weight, preterm, low height and weight for gestational age), 3) morbidity related with overall chronic diseases outcomes (e.g. cancer, cardiovascular, impairment, HIV, oral diseases and respiratory) [ 33 ], 4) morbidity related with mental illness and 5) morbidity caused by obesity health conditions provides the flow diagram of articles included and excluded from the review.

Environmental determinants were divided in socioeconomic status, natural environment, built environment, healthcare, and behaviors. Health outcomes were divided into 5 major categories: 1) overall mortality, 2) morbidity related to birth outcomes (low birth weight, preterm, low height and weight for gestational age), 3) morbidity related with overall chronic diseases outcomes (e.g. cancer, cardiovascular, impairment, HIV, oral diseases and respiratory) [ 33 ], 4) morbidity related with mental illness and 5) morbidity caused by obesity health conditions.

Out of the sample of 94 studies, the largest number of included studies were published between 2012 and 2016. Predominantly the referred studies analyzed the impact of an environmental determinant and/or dimension making use of more than one indicator; and more than half focused on adult populations (18–64-years-old). Most of the studies had a cross-sectional (56%) and cohort (37%) design and the association measures were mainly odds ratio, relative risk, β coefficient and prevalence ratio.

The 94 studies explored 24-paired associations between 45 indicators (within 5 environmental determinants) and the 5 categories of health outcomes. The multilevel mapping Sankey diagram displayed in Fig.  3 shows the relationships between environmental determinants and the major categories of health outcomes of the 94 studies. The characteristics of each included study were systematically analyzed and summarized in Tables 1–10 (see Additional file  1 ), in which the relationship between the environmental indicator and health outcome was categorized as follows:

positive (+), if a desirable improvement in the indicator was associated with an improvement of population health (i.e. a decrease in unemployment is associated with better health), or if a population subgroup is associated with higher population health (i.e. in case White has comparatively higher health than other groups);

negative (−), if a desirable improvement in the indicator was associated with a deterioration of population health (i.e. a decrease in unemployment is associated with worse health), or if a population subgroup is associated with worse population health (i.e. in case a Black has comparatively worse health than other groups);

Sankey diagram of studies exploring relationships between environmental determinants and health outcomes ( N  = 94 studies)

Such cases of positive or negative associations are presented in Tables 1, 3, 5, 7, 9 in Additional file 1 . If the study reported an association not statistically significant (for the defined statistical level) between an indicator and the health outcome, it was categorized as null (0) (as in Tables 2, 4, 6, 8, 10 in Additional file 1 ). The published research was conducted in various locations with a high contribution of studies conducted in Europe (35%) followed by Brazil (26%) and USA (16%).

Looking into specific environmental health determinants, from the 57 studies evaluating the impact of socioeconomic determinant, 81 indicators showed association with population health. All improvements in socioeconomic determinant indicators were found to positively impact population health.

Of the 36 indicators used to understand the relationship between natural environment and population health, obtained from 18 studies, the evidence showed that increases in the quality of water and decreases in all air pollution and noise indicators are associated with improved on overall mortality, birth outcomes, chronic diseases (cardiovascular, cancer, and respiratory) and mental disorders.

Results from the 18 indicators of built environment show that improvements in mobility and green spaces would improve population health related with overall mortality, birth complications, chronic diseases, mental disorders and obesity outcomes. Sanitation and safety improvements are associated with improvements in birth outcomes, mental disorders, and obesity outcomes.

Only 5 studies assessed the healthcare determinant showing that increases in hospital supply and infrastructures have positive associations with overall mortality, while dental care use and health infrastructures showed null associations with any health outcome.

Among health behaviors determinant, from the 21 indicators referred in the 14 studies included, improvements in human behavior indicators translated into improved population health but no association was found with birth outcomes and morbidity related with HIV and respiratory diseases.

Contrasting with the initial framework defined for analysis, there was no study assessing the impact of soil indicators, housing indicators and health insurance indicators on mortality and morbidity outcomes. From the reviewed studies, 78% of the studies were found to have overall high risk of bias (Additional file 2 ), mostly because of a high bias due to blinded health outcome assessment.

Lastly, Fig.  4 systematizes the determinants and dimensions hierarchy relevant to analyze environmental population health in urban settings, based on findings of this review and on recommendations extracted from the studies included. The urban context and exposure boxes present environmental health dimensions ranked by evidence of association with health as captured by the number of studies providing evidence of association (dimensions without evidence of association were excluded). The health outcomes box displays the main outcomes dimensions relevant to measure environmental population health in urban settings. The straight arrows show generic impact associations.

Summary of environmental determinants and dimension based upon the review, deemed as relevant for urban contexts, and synthesis of preventive recommendations to promote population health in urban contexts

Sample of reviewed studies

This systematic review was performed to elucidate the nature and state of current evidence on the relationship between environmental determinants and indicators and health outcomes in urban settings. It was based on 94 studies with a clear heterogeneity of methodological approaches, targeted populations and association measures which can explain why a high percentage of studies entailed high risk of bias (78%) with risk of bias being mainly attributed to issues in outcome assessment. Most of the studies were performed with populations from USA, Brazil, and Europe. This predominance can be explained by the fact that the most urbanized regions include Northern America (82% living in urban areas in 2014), Latin America (80%), and Europe (73%). African and Asian countries remain mostly rural, with 40 and 48% of their respective populations living in urban areas [ 11 ].

Our strict inclusion criteria guaranteed that only studies assessing a clear relationship between an environmental determinant and an outcome were included. This was to objectively appraise that relationship, as well as the risk of bias and facilitate the interpretation of the evidence to increase validity of results and constancy across the data extraction.

Evidence on environmental determinants and associations

The evidence presented in the studies included in this systematic review demonstrated the importance of understanding the complex interdependency of health, society, socioeconomic condition, built and natural environment [ 34 , 35 , 36 , 37 ], as well as an increasing consensus about the repercussions of surrounding environment on population health, and also on the specificities of environmental population health measurement in urban contexts.

The overall findings suggest that socioeconomic determinant have been the most studied area, evidencing strong and consistent associations with all health outcomes appraised in this review. Lorenzoni (2019) and Pickett (2015) [ 38 , 39 ] shows that income inequality, measured mainly as median household income, has a strong impact on health what is aligned with the inverse associations found in this review, that indicate that improvements in indicators like income, education, employment status and racial inclusion, could result in a reduction in mortality and morbidity outcomes improving overall population health. Indeed, lower mortality and morbidity rates among socioeconomically advantaged people have been observed for hundreds of years, and in recent decades these observations have been replicated using various indicators of socioeconomic (percentage of people working or ranking like blue vs white collar) status and while considering multiple disease outcomes. A careful analysis of the results revealed an additive influence on the impact of these indicators with the outcome, meaning that improving more than one indicator simultaneously could result in a higher improvement on health. From a policy perspective, as well as from an etiological perspective, it is important to understand which of the components is critical - for instance, if education is found to be important, the policies that may be implemented would differ from the policies needed if income was found to be the most influential factor. In fact, most research has not tested such competing hypotheses directly, although the indicators used in each study are explicitly identified.

The constant need to monitor the state of the natural environment to check if the international targets are being achieved and if policy actions are having the desired effects [ 40 ] can explain natural environment determinant emerging as the second area with the most evidence on associations with population health. Evidence was found that improvements in ambient air pollution (PM2.5, PM10, NO 2 , SO 2 , O 3 , total suspended particles (TSP)) and noise levels (L den , L night ) resulted in lower rates of mortality, as well as in decreased numbers of birth complications, chronic diseases such as cardiovascular, cancer and respiratory, and mental outcomes. In general, the studies reviewed evaluated separately the impact of air pollution and noise on health supporting the evidence that environmental noise should be considered an independent risk factor to health separated from air pollution [ 41 , 42 ]. Another perspective shows that there is a relationship between air pollution and noise generated by traffic road traffic in cities [ 43 ]. In fact, depending on which health outcome is being analyzed and which types of pollutants are being measured the effect could be independent or cumulative. These perspectives should lead to the adoption of common measures for each category of health outcomes and of common mitigation strategies in urban environments. It was not found any evidence relating soil quality indicators and health, in urban settings. The restriction to cities, where agriculture has few expression in daily life can explain the lack of evidence or as mention by Morrison (2014) [ 44 ] there is a link between soil and air pollutants, but the associations between air quality and health are more pronounced. The studies assessing the impact of built environment indicators on health are heterogeneous. This could be related to variations in measures and tools used across studies, making difficult to compare findings and obtain uniform results [ 45 ]. There has been a weak evidence that improving built environment indicators is associated with improvement of health outcomes, but it is necessary more information to infer a causal relation between them [ 46 ]. Within a context of increasing urbanization, urban green spaces are gaining a growing interest for their role as an important element for sustainable and healthy societies in an urban context [ 47 ]. Green spaces contributes to the urban ecosystem through air purification, water and climate regulation, reduce air pollution by absorbing certain airborne pollutants from the atmosphere, biodiversity, providing benefits to urban residents (recreation, social interaction and inclusion, health benefits and wellbeing), produces economic value by improving the quality of landscapes and the attractiveness of the city within the context of increasing competition [ 48 , 49 ]. Additionally, green areas, including urban gardening, parks and other natural areas, have been associated with lower stress scores, decrease of obesity rates [ 50 ], increased physical activity, and improved well-being and health in general [ 31 , 51 , 52 ]. No study proved that housing conditions have a relation with health outcomes, and sanitation indicators was analyzed only by Cau (2016) and de Souza (2012) [ 53 , 54 ] showing that increasing wastewater treatment and quality of drinking water are associated with mental health improvements.

The evidence about relationship between behavior indicators and population health shows many positive associations, especially in studies in which improvements in more than one indicator of behavior were analyzed - improvements in behavior-related indicators should improve health outcomes like mortality, chronic diseases, mental and obesity disorders.

Lastly, a scarce number of studies reported the relationship between healthcare indicators and mortality outcomes - evidence was reported only in American and Brazilian populations and showed a positive association between improvements in hospital care and improvements on population health.

Jia (2017) [ 55 ] suggests that the role of health behaviors and healthcare indicators are tied to demographic characteristics and socioeconomic inequality, acting as an indirect pathway with impact on health and the results of this review can be explained by this. As a healthcare determinant works as a mediating pathway of inequality to mortality, the evidence about the association of healthcare indicators and population health is limited.

Evidence on health outcomes

Overall mortality and chronic diseases morbidity were the most studied outcomes in the reviewed studies. Associations with all the determinants evaluated were found, as well as for mental outcomes. Obesity outcomes appear as the fourth health consequence in population health more influenced by environmental determinants, followed by birth outcomes. Within all the outcomes included in chronic diseases, HIV indicators were referred to be influenced only by socioeconomic indicators. Given that, the number of individuals newly infected with HIV has declined over the years but some groups remain at high risk [ 56 ], this can be an explanation for the present evaluation of the impact of environmental determinants on HIV indicators. However, the small number of studies may act as a bias and an indication that using HIV indicators to evaluate population health should be carefully discussed considering the specific urban context under analysis.

Evidence also showed that different measures to assess overall morbidity were used among the literature and in fact it may be a cofounding aspect that can generate divergences. To overcome this divergence, disability-adjusted life years (DALYs) or other health related quality of life metrics can be used to define and quantify the burden of disease, so as to measure the gap between current health status and an ideal situation free of disease and while combining mortality and morbidity indicators [ 57 ].

Implications for environmental health analyses and policies in urban settings

The health of people living in cities is deeply determined by their living conditions. While there are considerable inequalities across regions, there are also inequalities within cities among various dimensions. The health challenges that need to be tackled to reduce population health inequities in urban environments are different from the ones found in rural environments in terms of, for example, air quality or access to health infrastructures, and must be analyzed differently [ 58 ]. These geographic differences reinforce the need for a differentiated environmental health assessment, using the right indicators and determinants to evaluate population health in urban environments and improve equity [ 59 , 60 ]. Acknowledging the complexity and interconnectedness of population health assessment and their specificities for urban contexts, the purpose of collecting data related to determinants of population health in cities was to facilitate more evidence-based, rational, and prioritizing policy making.

Our results from the framework (Fig. 4 ) are consistent with the fact that health policies of tobacco control, alcohol control, food policy, and air pollution control have made significant contribution to advances in population health over the past decades, and remain an integral part of the political decision-making process in the context of urban settings [ 21 ]. To improve the link between evidence and policy actions, an extra box is added with recommendations measures [ 61 , 62 , 63 , 64 , 65 , 66 , 67 ] which are also aligned with recommendations from recent international reports and studies [ 68 , 69 , 70 , 71 , 72 ] for urban settings to promote population health.

Strengths and limitations

This review has several strengths. As the main aim of this review is to report on associations, not to prove or refute causality, it presents an analysis of a wide and exhaustive range of influences between environmental determinants and health outcomes in urban settings. This urban settings focus enables an up to date identification of potential risks to population health. Although 25% of the reviewed studies were from Portuguese-speaking countries, the limitation to only select English and Portuguese written studies could have limited the evidence appraised in this review and to introduce a geographic location bias. Also, cities are spatially dynamic and can include suburban areas or slums contributing to the inherent complexity in mapping and in evaluating the quality of the heterogeneous data. Different populations, different methods and measures of evaluation, from the included studies and the variability of existing definitions of each determinant/dimension and outcome that were not standardized as might be expected may contribute to a misclassification bias. This heterogeneity should be considered when interpreting the high risk of bias of the reviewed studies. To minimize these issues, only peer review publications were included, and grey literature was excluded for the analysis.

Our results provide a comprehensive synthesis of environment health determinants and indicators, outcomes and of associations between determinants and outcomes in urban settings, as well as identifies important gaps and methodological limitations in this field of research. Environmental health indices should be redesigned to reach consensus on definitions and measurements and to be meaningful to planners, policymakers, and researchers.

Ultimately, this review helps to identify those aspects of a city that influences and contribute to improve population health and suggests a hierarchy of determinants where actions to improve them should be taken to promote population health in urban settings.

Future work should look to improve flexible tools capable of evaluate modifications in environmental health determinants related to population health taking into account the dynamic of the urban setting to help target action areas, allocate resources and provide information to improve interventions and policies and to support decision making about health services and urban planning policies.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

Human Immunodeficiency Virus

United States of America

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Acknowledgements

Marta Salgado research was supported by Fundação para a Ciência e a Tecnologia (FCT) and Valorsul S.A under the scholarship number PDE/BDE/120465/2016. Joana Madureira research was supported by FCT through the scholarship SFRH/BPD/115112/2016. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Marta Salgado and Mónica Oliveira acknowledge support from the Centre for Management Studies of Instituto Superior Técnico (CEGIST, University of Lisbon, FCT project UIDB/GES/00097/2020).

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Salgado, M., Madureira, J., Mendes, A.S. et al. Environmental determinants of population health in urban settings. A systematic review. BMC Public Health 20 , 853 (2020). https://doi.org/10.1186/s12889-020-08905-0

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In order to assess sustainability, it is necessary to monitor social conditions on environmental, global, national and urban scales. Therefore, the objective of this study is to develop a scale to determine PSUE. In addition, it is also examined whether there is a significant difference between the components that determine the perception of sustainable urban environment according to the characteristics of the participants. For this purpose, Exploratory Factor Analysis, Spearman–Brown correlation test, Cronbach alpha test, Mann–Whitney test and Kruskal–Wallis test are implemented. According to the findings acquired, it has been detected that the components that determine PSUE are “spatial strategies related to sustainable environment and transportation”, “spatial strategies related to ecological sustainability and solid waste management” and “spatial strategies related to social and economic sustainability” respectively. In this study, differences have been identified in the perspectives on social and economic sustainability strategies according to gender. However, participant’s viewpoints regarding strategies related to sustainable environment and transportation, as well as ecological sustainability and solid waste management, are similar. Additionally, significant differences exist in social and economic sustainability strategies based on the age of the participants. The urban or rural character of the settlement where the students families live or grew up influences their perspectives on strategies based on social sustainability and economic relations.

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Introduction

A significant portion of the worlds population lives in urban areas and most of the economic, social and environmental processes that affect human societies occur in these areas. Urbanization is currently regarded as one of the most important social processes and also has a considerable impact on the environment on a local, regional and global scale. Thus, the growth of cities appears to have numerous and complex consequences, both in terms of global sourcing and peoples living conditions (Hiremath et al. 2013 , p.555). For this reason, in recent years, discussions have started on what can be done to ensure the sustainability of natural and cultural resources by preventing the uncontrolled growth of cities in the management and planning of urban areas (Bebber et al. 2021 ). In this scope, the concept of sustainable development has come to the fore with the objective of providing a more balanced and planned economic development and urbanization to ensure the ecological balance (Uğuz Yedievli 2021 , p.20).

Sustainability is an approach that aims to protect resources and the environment, the efficiency of economic activities, environmental quality and social welfare in order to meet the needs of future generations, and these three areas should be considered equally important (Mori and Christodoulou 2012 , p. 95). This multidimensional structure of sustainability causes its concept to be addressed in different ways by many professional disciplines. For example, in social sciences, sustainable cities are generally defined in terms of social sustainability goals, while in engineering, they are explained by focusing on the efficient use of resources. It is clearly seen that the concept of social equality or justice, which constitutes another fundamental area of sustainability, is addressed within the discussion of sustainable urban form (Williams 2009 , p. 2). Hiremath et al, ( 2013 , p.555) defined urban sustainability as “providing a balance between the development of urban areas and the protection of the environment, as well as the procurement of justice in urban areas in terms of income, employment, housing, basic services, social infrastructure and transportation”. In other words, for sustainability, an internal balance between the factors such as economic activity, population growth, infrastructure and services, pollution, waste and noise in urban areas must be maintained. In this way, the urban system and its dynamics evolve in harmony and its negative impacts on the natural environment are limited internally as much as possible. In this context, measurable indicators that can set forth the status at different spatial scales are attempted to be determined in order to assess the status and progress of sustainability. These indicators can provide an opportunity for the quality and performance of a place to be assessed and monitored effectively (Ahvenniemi et al. 2017 , p. 235; Taecharungroj et al. 2018 ). The underlying leading motive behind these efforts is to assist decision makers on what actions should be taken or not in short-term and long-term perspectives in order to create sustainable cities (Ness et al. 2007 , p. 499).

In order to assess sustainability, it is necessary to monitor social conditions on environmental, global, national and urban scales. In this context, the number of studies related to the theories, methods and practices set forth to assess the sustainability has been gradually increasing in the last 20 years. These studies can sometimes be built on incompatible conceptual frameworks due to the multidimensional structure of the concept. In this context, while some of the assessment methods that are set forth address the sustainability in a very narrow scope, some of them can be comprehensive enough to include the multidimensional structure of sustainability (Winfield et al. 2010 , p. 4116). Similarly, many indicator tools related to urban sustainability have been tested in cities by various organizations and research staff and have been developed to serve different purposes. Since each measure set creates standards that are in line with its own priority targets and indicators, there is no definite consensus between these methods and standards (Yazar 2006 ; Moraes et al. 2019 , p.142; Bebber et al. 2021 ; Erdem 2022 , p. 185). Although there are many studies in the literature examining urban development in terms of various aspects of sustainability, (Tanguay et al. 2010 ; Hiremath et al. 2013 , p. 556) the number of studies to measure the perception of sustainable urban environment (PSUE) is quite limited (Kaya 2013 ; Yıldız, 2018 ; Larimian and Sadeghi 2019 ; Wang et al. 2020 ; Michael et al. 2020 ; Topal et al. 2021 ; Biasutti and Frate 2017 , p. 220; Taecharungroj et al. 2018 ).Therefore, in this research, the authors use the scale development method to set forth the factors that constitute the perception of sustainable urban environment (PSUE)(Fig.  1 ).

Studies in the literature that examine urban development in terms of various dimensions of sustainability

In the study; since it is attempted to determine on what extent the strategies related to sustainability contribute to the perception of sustainable urban environment, it has been assessed that it is necessary for the target group to have a certain level of knowledge about the concepts of sustainability and sustainable urban environment. Therefore, the target group is determined as students who are having education within the scale of planning and urban design. In this way, it will be possible to determine the level of awareness and consciousness about the subjects of sustainable development and urban environment of the students having education in occupational groups of architecture. For this purpose, the questionnaire form was applied to students studying at X University Faculty of Architecture in the autumn semester of 2021–2022.

Performance indicators for the sustainable urban development

World commission on environment and development (WCED), defines sustainable development context as “a process that meets the needs of today without compromising the ability of future generations to meet their needs”. This approach, brings forward that each generation will achieve a sustainable future by preserving the biophysical and social conditions that will support economic activity and human welfare for future generations. In this context, WCED puts a strong emphasis on justice issues, especially the goal of poverty alleviation, and aims to provide intergenerational equity in the realization of these goals (WCED cit. from 1987 Howarth 2012 , p. 32). Considering that sustainability basically has three dimensions which are economic, environmental and social sustainability, it can be said that the most basic statement of the concept of sustainable development is to find the balance between economy, environment and society (Ness et al. 2007 , p. 498).

Sustainable urbanization is accepted as one of the key elements of sustainable development. There are many definitions of sustainable urban development in the related literature. Regional Environment Centre (REC) defines the sustainable city as “a city that guarantees access to resources and reusability of these resources for future generations, social equality, economic development and welfare through action plans and politics” (Gedikli 2017 , p. 147). However, an integrated approach is required in the precautions taken and solutions in terms of planning and coordination in order to ensure sustainable urban development (Kaur and Garg 2019 , p. 147). For example, while most practices related to sustainable development focus primarily on economic development, the use of advanced technologies such as information and communication technologies, energy production and optimization, waste management and public transportation, subjects such as the preservation of the natural environment and the sustainability of socio-cultural elements can be overlooked (Kaur and Garg 2019 , p. 147; Larimian and Sadeghi 2019 , p. 3).

Although the necessity of the sustainability of urban development has been accepted for a long period of time, the present urban development tendencies that have emerged due to common planning and design practices determined by economic and demographic concerns, leads to high consumption of non-renewable natural resources and large amounts of waste generation, high population levels and the formation of urban heat islands. Therefore, in order to ensure sustainable urban development, it is necessary to understand the sustainability concept in a holistic sense; which requires an adequate understanding of the suitable concepts, approaches, methods, tools and techniques used to assess the sustainability of urban development. The assessment of urban sustainability is important in terms of providing a method and tools that can assist the decision makers and policy makers on what they must do and/or must not do to create a more socially sustainable society (Kaur and Garg 2019 , p. 147–148). Urban sustainability can be assessed through quantitative and qualitative indicators on different spatial scales. Just as this assessment contains the assessments of elements starting with material selection on the building scale to energy and internal air quality analysis; site selection and planning, transportation planning, society development and social welfare, governance and innovation are also addressed on a wide framework. For this reason, the systems set forth in order to assess the sustainability are quite different in terms of the date they developed, strategy selections, assessment structures, assessment criteria and local criteria (Yalçın and Can 2015 , p. 19; Yıldız 2018 ; Kaur and Garg 2019 , p. 148; Erdem 2022 ). In other words, just as it is possible to examine sustainable urban development subjects by focusing on spatial planning and design subjects and associating them with the form of the city, urban land uses and with the field of urban design; it is also possible to examine them under urban politics and programs oriented for environmental, economic and social dimensions (Gedikli 2017 , p. 604; Karakut Tosun 2019 , p. 70).

The European Commission stated in the European Sustainable Cities Report that sustainability indicators are needed as a tool to measure sustainability performance. In other words, if sustainability is a consistent politics objective, then it should be possible to measure whether we are moving towards the goal or not (Hiremath et al. 2013 , p. 556). Indicators set forth for this objective are of vital importance on the subjects such as; assessing the expected and unexpected effects related to sustainability-oriented politics, having an educative role about the latest developments, assisting of the follow up on processes, being a tool for verifying/assessing what is done in the past, and transferring the correct information for the decision-making process (Mori and Christodoulou 2012 , p. 96–97; Hiremath et al. 2013 , p. 556; Memiş, 2020 , p. 248).

The first group of assessment tools for urban sustainability consist of indicators and indices (Ness et al. 2007 , p. 499). For example, in the year of 2012, UN-Habitat created a monitoring tool named “City prosperity index (CPI)” in order to measure sustainability at the city level. Considered both as a measurement index and a politics dialogue, the CPI consists of five dimensions: productivity, infrastructure, quality of life, equality and environmental sustainability (UN Habitat 2013 , p. 12). Similarly, sustainable cities, which are included in the sustainability and development goals, and indicators that set forth the life goal are also addressed under ten main topics (UNESCO 2023 ). As a result, when the concepts set forth in the literature related to the sustainability of cities are examined, it is seen that these concepts are defined differently in the context of parameters that vary depending on the sustainability goals or elements and serve some of them or all of them (OECD 2001 , 2008 , 2012 ; Yazar 2006 ; Balaban 2017 ; Gedikli 2017 ; Ulubaş Hamurcu and Aysan Buldurur 2017 , p. 226–228; Yalçıner Ercoşkun 2018 ; Pınarcıoğlu and Kanbak 2020 ). As is seen, there is a wide range of literature with treatments, similarities and differences related to the urban sustainability approach. It is not possible to cover this wide axis in a single article; therefore, in this study, performance indicators related to urban sustainability are addressed in the axis of strategies regarding the spatial reflection of the three pillars of sustainable development. The sustainability components defined within the scope of the study and the performance indicators determined in relation to these components have a developable structure. In this context, rather than having precise and absolute values, the defined framework is open to development and as a result of assessment, it is open to change (Table 1 ).

Evaluation of scales for sustainable development

In the literature, many methods are used to evaluate individuals environmental attitudes and behaviors (Altanlar et al. 2023 ). These methods are based on models such as “New Environmental Paradigm (NEP)” (Dunlap et al. 2000 ) “norm activation model” (Steg and De Groot 2010 ) and the “value-belief-norm model” (Stern 2000 ). However, the scales put forward in these studies are theoretically and conceptually (Turaga et al. 2010 ; Karayeğen Balent 2020 ; Drescher et al. 2017 ; Manoli et al. 2019 ) different than the sustainable development scales (Biasutti and Frate 2017 , p. 218; Wang et al., 2020 , p. 14–15). Scales for measuring environmental attitudes and behavior generally aim to evaluate the environmental knowledge, attitudes and behaviors of primary, secondary school and university students as well as their teachers (Altanlar et al. 2023 ; Altanlar and Özdemir 2022 ; Müderrisoğlu and Altanlar 2011 ). However, it is observed that a limited number of scales related to sustainable development have been developed in the literature (Biasutti and Frate 2017 , p. 218; Yakışık and Mustafazade 2023 ; Haldar et al. 2022 ; Homer 2023 ). The scales developed in this context focus on environment, economy, society and education (Yakışık and Mustafazade 2023 ; Michalos et al. 2012 ; Balakrishnan et al. 2020 ). For example, Biasutti and Frate ( 2017 ) developed a 20-item scale consisting of environment, economy, society and education factors to measure university students attitudes towards sustainable development (SD). The environmental dimension at scale focuses on natural resources, climate change, rural development, sustainable urbanization, disaster prevention and mitigation. The economic aspect is associated with poverty alleviation, corporate responsibility to avoid wasting resources and the market economy. The community aspect focuses on human rights, gender equality, peace and human security, health, governance, cultural diversity and intercultural understanding. The education aspect includes the promotion of knowledge and skills in student-centered teaching methods, future-oriented thinking, high-level thinking skills, thinking critically, interdisciplinary work, and integrating local and global issues in the context of sustainable development (Biasutti and Frate 2017 , p. 219). Atabek Yiğit and Balkan Kıyıcı, ( 2022 , p.655) developed an environmental awareness scale in the context of sustainable development. The first factor in the scale is named “Positive environmental awareness” and the second factor is named “Negative environmental awareness”.

In the study conducted by Wang et al. ( 2020 ), the scale developed to determine university students views on sustainable development (SD) includes four factors: commitment to sustainable development, knowledge of sustainability issues, attitudes towards sustainable development, and practices related to sustainability. The first factor includes issues such as that environmental protection is more important than economic growth, that resources should be preserved for future generations, and that the depletion of natural resources and environmental degradation are a matter of concern. The second factor aims to scale individuals level of knowledge on sustainable development. The third factor focuses on the role of university students in sustainability, the importance of sustainability-related education and research, and student support and engagement in sustainable campus practices. The fourth factor aims to examine students attitudes towards recycling, energy saving, the use of environmentally friendly products and green transportation (Wang et al., 2020 , p. 14–15).

Michael et al ( 2020 , p. 114) studied university students general awareness, attitudes and possibility of taking action on sustainability. Accordingly, three aspects that promote sustainable development are examined: awareness, attitude and action. Awareness aspect refers to the awareness and sensitivity of social groups and individuals towards the environment. Attitude aspect aims to evaluate students positive or negative attitudes towards environmental events. Action aspect includes things that an individual intentionally does for a sustainable environment (Michael et al. 2020 , p. 114).

As can be observed from the literature, sustainable development scales generally include economic, environmental and social aspects. These scales address a wide range of factors such as economic growth, natural resource use, environmental quality, equity and justice. However, it is observed in the literature study that the scales developed to evaluate the sustainability of the urban environment are quite limited (Memiş 2020 , pp. 256–257; Yıldız 2018 , p. 182; Homer 2023 ). Therefore, this study aims to develop a scale to determine the perception of sustainable urban environment. For this purpose, while creating the item pool of the scale, we focused on the goals and strategies for economic development, protection of natural sites, solid waste management, poverty alleviation, health and welfare, improvement of environmental quality, sustainable built environment and sustainable development. It is thought that the scale put forward in this way can guide the establishment of policies and strategies related to sustainable development and comprehensive decision-making processes.

Materials and methods

Sample selection and method.

Research data are collected in the 2021–2022 fall semester during November and December months by benefitting from the questionnaire technique. The designed questionnaire is implemented face to face with the students, who are having their education in X from Faculty of Architecture, Department of Urban and Regional Planning (CRP) and Department of Urban Design and Landscape Architecture (UDLA). The total number of samples is determined as 201 for 342 people with a 95% of confidence interval and a sampling error of ± 0.05 (Ural and Kılıç 2005 ). In the literature, it is stated that the number of items in EFA analysis should be at least five times the number of items, and if it is accessible, it should be at least 10 times the number of items (Demir 2021 , p. 423). On the other hand, Kline ( 1994 ) emphasizes that a sample of 200 people would be sufficient to extract reliable factors, and that this number can be reduced to 100 in cases where the factor structure is clear and few, but it would be useful to work with a larger sample for better results (Kline 1994 ). Based on these data, it can be stated that the sample size is sufficient for factor analysis.

Questionnaire form and its content

The questionnaire questions consist of three parts. In the first part of the questionnaire, there are 9 questions to determine the socio-demographic characteristics of the students; in the second part, there are 2 questions that are asked to determine whether the students took courses related to the subjects of environment and ecology prior to their undergraduate education. In the third part, a structure of 51 articles developed in order to determine the students perceptions of the sustainable urban environment is applied. The articles used in the scale are created by benefitting from the indicators that include a range of objectives, strategies, planning and design elements set forth for the sustainability of urban development (OECD 2001 , 2008 , 2012 ; Yazar 2006 ; Balaban 2017 ; Gedikli 2017 ; Yalçıner Ercoşkun 2018 ). In addition, the statements in the scale are questioned according to the 6-point Likert scale.

Measurement method and techniques

Firstly, a literature review was conducted and an item pool was prepared for the PSUE. Policies and spatial strategies for sustainable development were utilised in the preparation of the item pool. The factors for PSUE were determined as struggle against climate change, preservation of the natural areas and rural areas, economic development, solid waste management, health and welfare, struggle against poverty, environmental quality and environmental health and sustainable built environment and transportation (Table 1 ). The items addressed within the scope of these factors reveal a more inclusive and innovative approach compared to other scales as they include environmental, economic and social dimensions of sustainable urban environment as well as spatial strategies ( Biasutti and Frate 2017 , p. 219; Wang et al., 2020 , p. 14–15; Michael et al. 2020 , p. 114; Memiş, 2020 , p. 256–257; Yıldız, 2018 , p. 182).

In order to determine the content validity of the PSUE scale, first of all, an expert opinion has been referred. For this purpose, the plot scale of 51 articles is delivered to 5 lecturers and 1 measurement and assessment expert working in the Department of Urban and Regional Planning, and their opinions and suggestions are received. In the study of content validity, “Davis Technique” is implemented for the assessment of expert opinions (Davis 1992 , p. 196). A value of 0.80 is accepted as a criteria for the content validity index (CVI) of the technique. CVI values of the plot scale vary between 0.50 and 1.00 (Karadağlı and Ecevit Alpar 2017 , p. 178). Based on the expert opinions received, the articles that are stated to be problematic are revised in line with the suggestions.

Exploratory Factor Analysis (EFA) is applied to detect the structure validity of the PSUE scale and to set forth the factor structure. For this purpose, direct oblimin method, which is one of the rotation methods, is applied in the analysis of principial components. Kaiser–Meyer–Olkin (KMO) test is applied to examine the suitability of the data structure for the factor analysis in terms of sample size (Çokluk et al. 2012 , p. 204–207).

Within the scope of the reliability studies of the scale, Spearman–Brown correlation test and Cronbach alpha and reliability analysis in the context of stability are applied (Çokluk et al. 2012 , p. 182). The coefficient of skewness analysis is performed to determine whether all the data is suitable to normal distribution at the significance level of 0.05. As a result of the test, if the skewness values obtained are higher than 0.96 at the significance level of 0.05, the variables are accepted to have a normal distribution (Bursal 2019 , p. 225). In order to detect the external consistency of the scale test-retest reliability analysis is performed.

Finally, the Mann–Whitney test and Kruskal–Wallis test are implemented to detect whether there is a significant difference between the socio-demographic characteristics of the participants and the scale factors.

Restrictions of the research

In this study, strategies for economic, environmental and social sustainability have been examined in order to determine the sustainable development and urban environmental consciousness. The elements set forth within the scope of the study are restricted with the accessible resources related to the body of literature. In addition, in view of the developments in the body of literature, it should be taken into account that the importance of the strategies set forth regarding the sustainable development and urban environment may change in the process. Similarly, the level of knowledge of the target group on the subject and the level of importance they give to sustainable urban development strategies may change depending on the social, economic and environmental conditions they are in. Therefore, from this study, it is not possible to generalize on behalf of all stakeholders who play a role in sustainable urban development and are affected by this development. In addition, the scale that is set forth includes indicators related to the spatial reflection of the three pillars of sustainable development.

Problem and hypotheses of the research

In this study, it is aimed to develop a scale in order to measure the perception of sustainable urban environment. In line with this objective, it is aimed to answer the following research questions:

What are the components that determine the perception of sustainable urban environment?

How are the components that determine the perception of sustainable urban environment line up according to their level of importance?

Does the level of importance of the components determining the perception of sustainable urban environment differ according to the characteristics of the participants?

In this study, the following hypotheses are questioned within the scope of the research problems:

H1. There is a significant difference between the students perception of sustainable urban environment and the variables of age, gender, households level of income and accommodation unit.

H2. There is a significant difference between the students perception of sustainable urban environment and the variables of departments in which they have their education and their classes.

H3. The students perception of the urban environment significantly differ from each other according to the courses related to the environmental subjects they have taken before.

Results and discussion

Findings on individuals demographic characteristics.

53.7% of the students participating in the research are female and 46.3% of them are male. 52.2% of the participants are having their education on CRP department and 47.8% of them are having their education on UDLA department. 37.5% of the participants are first grade and second grade students, 27.5% of them are third grade and 35% of them are fourth grade students. It is observed that 57.1% of the participants are aged 21 and younger and 42.9% of them are aged 22 and older. It is seen that the household income of 37.4% of the participants is less than the minimum wage Footnote 1 and 13.5% of them live in rural areas (Appendix 1).

Findings on item analysis and factor structure of the scale

Prior to the implementation of the exploratory factor analysis, item analysis of the scale is performed and the total item score correlations of 51 items in the scale are examined. It is detected that the items in the scale are in the positive direction and statistically on significant level between the correlation coefficients of r  = 0.200 and r  = 0.730. For this reason, it is found appropriate to include all the items in the test. Kaiser–Meyer–Olkin (KMO) test is implemented in order to test the suitability of the sample size for factorization. As a result of the analysis, it is determined that the KMO value is 0.877. In line with this fact, it is concluded that the sample size is “well enough” to perform factor analysis (Çokluk et al. 2012 , p. 207). When the results of the Bartlett global test (BGT) are examined, it is determined that the chi-square value obtained is significant (X 2(1275)  = 4615.885; p  = 0.000 < 0.01). Accordingly, it is accepted that the data is obtained from a normal distribution with multivariate. As a result of the analysis performed, it is seen that there are 12 components with their eigenvalues are above 1 for the 51 items that are selected as the baseline of the analysis. The contribution that these components made to the total variance is 69.237%. According to the results of the analysis, it is detected that the anti-image r values of the plot scale are between 0.543 and 0.948. When the scree plot graphic is examined, since it is understood that there is a high accelerated decrease after the first factor and a less accelerated decrease after the third factor, it is decided that the scale could have three factors (Fig.  2 ). Within this framework, it is decided to repeat the analysis for three factors.

Perception of the sustainable urban environment scales scree plot graphic

In the exploratory factor analysis performed in order to develop the PSUE scale, the level of acceptance for factor weight values is accepted as 0.400. The items that constitute the scale are examined in terms of whether they exhibit overlapping item and they met the factor weight values or not, and it is decided for the items that did not meet the conditions to be removed from the scale. For this purpose, a total of 11 EFA is performed. As a result of the assessment of the first analysis, since the factor load is below 0.400, article 41 of “Reducing private parking spaces and garages in residential areas”; and after the second analysis, the statement 38 of “establishing park-and-ride stations” is removed from the scale and proceeded with the analysis. As a result of the third and fourth analyzes, the statements in which their factor load are below 0.400, statement 37 of “Reservation of private parking spaces for bike and car sharing” and statement 8 of “supporting the use of fossil fuels in settlements” are removed from the scale respectively. In the fifth, sixth, seventh and eighth stages, the statements in which their factor load are below 0.400; statement 40 of “Reduction of private automobile dependency”, statement 13 of “Ensuring effective use of public spaces”, statement 36 of “Dissemination of car sharing systems”, statement 34 of “Using local tree species that produce edible fruits in and around the city” are removed from the scale respectively. In the tenth stage, the article 15 of “Providing accessibility of public transportation all over the city at low cost” which exhibits an overlapping item is removed from the scale (Appendix 1). In the last EFA practices, it is determined that each article in the scale met the acceptance conditions. It is determined that the KMO value is 0.898. In addition, it is detected that the chi-square value obtained in line with the result of the Bartlett global test (BGT) is significant (X 2(820)  = 3854.797; p  = 0.000 < 0.01). According to the data obtained, first factor of the scale set forth the PSUE define the 35.897% of, the second factor 6.734% of and the third factor 5.007% of the total variance. The total variance rate defined by the three factors is 47.463%. The defined variance in Social Sciences to be between 40% and 60% is accepted as enough (Çokluk et al. 2012 , p. 207–209). Accordingly, it is concluded that the PSUE scale is valid. In addition, common factor variance is calculated for each one of the variables that constitute the scale. It is observed that the common variances of the four factors defined related to the articles vary between 0.224 and 0.740. The maximum score that can be obtained from the scale is 246 and the minimum score is 0. After this stage, the scale articles included in the factors that formed are examined and the sub-dimensions were named (Table 2 ).

The first factor is named as “Spatial strategies related to sustainable environment and transportation (F1)” as it covers the subjects of preservation and development of natural and cultural heritage, effective and resource-conserving management of the urban eco-system, development of environmentally friendly types of transportation, waste management and local governance.

The second factor is conceptualized as “Spatial strategies related to ecological sustainability and solid waste management (F2)” as it includes interventions such as reducing environmental pressures arising from economic growth, protection from environmental hazards including chemical pollution in residence, workplace and living environment, ensuring the sustainability and quality of water systems, effective and resource-conserving management of urban eco-system and development of environmentally friendly types of transport.

The third factor is stated as “Strategies related to social and economic sustainability (F3)” as it includes forms of intervention such as providing residence and housing right so that the humane needs can be met, regulating the daily lives of citizens, providing better management of urban programs, increasing resources that will ensure the need of urban public services are met, and solving transportation related problems (Table 2 ).

A test-retest method was implemented to assess the invariance of the scale with respect to the time. For this purpose, the scale was applied to 25 students from the scale universe with an interval of 10 weeks of time. As a result of the test-retest process, it is seen that the correlations are providing highly stable measurements ( r : 0.893, p  < 0.05). In addition, in order to detect the reliability of the PSUE scale, Cronbach Alpha reliability analysis is performed and the reliability of the scale is found as α  = 0.938. In addition to that, it is detected that the reliability of the F1 dimension is α  = 0.945, reliability of the F2 dimension is α  = 0.792 and reliability of the F3 dimension is α  = 0.753. Since its Cronbach alpha value is 0.70 and above, it was accepted that the scale is a reliable measurement tool, including its sub-dimensions (Büyüköztürk 2021 , p. 183).

The relation between the scale and its sub-dimensions was assessed with the Spearman–Brown correlation analysis technique, and a statistically significant ( p  < 0.05) correlation with positive direction was found between the total score of the scale (STS) and its sub-dimension scores. For example, it is seen that there is a midlevel positive ( r  = 0.419) and significant ( p  < 0.01) relation between the first factor and the second factor. However, it is detected that there is a low level ( r  = 0.205) and significant ( p  < 0.05) relation between the first factor and the third factor. Similarly, it is seen that there is a low level ( r  = 0.248) and significant ( p  < 0.01) relation between the second factor and the third factor. In conclusion, it can be said that each of all three factors increased significantly together (Table 3 ).

Coefficient of skewness analysis is performed in order to analyze whether the data is providing normal distribution or not. According to the data obtained, it is seen that the residential area, life span, income level variables and the factors that constitute the scale do not meet the condition of normal distribution (Appendix 1).

Sustainable urban environment consciousness according to socio-demographic characteristics

No statistically significant difference is found between the variables of average household income, to be able to budget for hobbies, class and previous environmental education received and the variables of the sub dimensions of the scale. According to the age variable it is detected that there is no significant difference between F1 factor (X 2  = 2.567, df  = 2000, p  = 0.277) and F2 factor (X 2  = 2.653, df  = 2000, p  = 0.265), but there is a significant difference with F3 factor (X 2  = 11.610, df  = 2000, p  = 0.003). When we consider about which age groups difference originate from, it is observed that there is a statistically significant difference between the age groups of 20 or younger and 21 (Mann–Whitney U = 925.000, Z = − 2.244, p  = 0.025) and between the age groups of 21 and 22 or older (Mann–Whitney U = 501.500, Z = − 2.384, p  = 0.017). It is understood that the average score of the “spatial strategies based on social sustainability and economic relations” factor of the participants aged 21 and younger is higher than the individuals older than the age of 22 (Tables 4 , 5 ).

It is detected that the participants perspectives on spatial strategies related to social and economic sustainability differ from each other significantly according to gender and residential area. The findings obtained show that the average of the scores, given by the people living in the urban environment related to the factor of strategies based on social sustainability and economic relations is higher. Similarly, it is determined that the average score given by females on strategies related to social and economic sustainability is higher than that of males (Table 6 ).

As a result, studies in the literature show that the scales for the evaluation of sustainable urban environment are limited. Therefore, developing a scale to determine the perception of sustainable urban environment is aimed in this study. While creating the item pool of the scale, we focused on the goals and strategies for economic development, protection of natural sites, solid waste management, poverty alleviation, health and welfare, improvement of environmental quality, sustainable built environment and sustainable development. The scale set forth in this study is determined by examining the planning principles and indicators of the urban models in the literature related to the politics and strategies developed for sustainable urban development. While some of the indicators are used as they are, purposive revisions are performed in order to make some of them suitable for this study. In the study, it is determined that the factors that constitute the urban environment consciousness are; “spatial strategies related to sustainable environment and transportation”, “spatial strategies related to ecological sustainability and solid waste management” and “spatial strategies related to social and economic sustainability”. The scale put forward in this way can be a tool that can guide the establishment of policies and strategies related to sustainable development and comprehensive decision-making processes. Considering the variance rates of the relevant factors, it is detected that the most important factor in determining the perception of sustainable urban environment is “spatial strategies related to sustainable environment and transportation”. It is seen that spatial strategies related to ecological sustainability and solid waste management (6.734 %) take the second place and the spatial strategies related to social and economic sustainability (5.007 %) take the third place. The findings obtained from the study correspond with Yıldız ( 2018 )s study on the subject of the creation of a sustainable urban transformation assessment model, which she carried out with the participation of experts. Yıldız ( 2018 ) determined the main factors in determining the sustainability of the urban transformation project as economic sustainability (19 %), environmental sustainability (46 %) and social sustainability (35 %). In Memiş ( 2020 )s study on the perceptions of sustainability of the urban actors; it is seen that the strongest consensus among local governments is related to playgrounds for children within the scope of social sustainability and the participation of disadvantaged individuals and communities in social life. It is understood that the subjects agreed between non-governmental organizations (NGOs) are mostly related to environmental sustainability. When local governments and NGOs are addressed together, it has been determined that they primarily agree on the elements given on social sustainability, and secondly, they agree on environmental sustainability subjects such as waste management and preservation of agricultural areas and water resources (Memiş, 2020 , p. 256–257). As a result, the findings obtained in all three studies set forth that the participants expressed their opinions on the need to focus on environmental and social subjects prior to the economic sustainability for a sustainable urban environment.

In the study, while it was detected that the participants perspectives on strategies related to sustainable environment and transportation and strategies related to ecological sustainability and solid waste management were similar, it was detected that the perspectives on strategies related to social and economic sustainability differ from each other according to gender. However, it is also seen that the average of the subscale scores of females for all three factors is higher than the averages of males. Thus, it can be said that the awareness of females on sustainable urban environment is higher than males. The finding obtained is corresponded with the studies of Tuncer ( 2008 , p. 220) and Müderrisoǧlu and Altanlar ( 2011 ). For example, Müderrisoğlu and Altanlar ( 2011 ) state that female students adopt ecocentric attitudes while male students adopt technocentric attitudes. Similarly, Tuncer ( 2008 , p. 220) also states that females perception of sustainable development is higher. On the contrary of these findings, Syed Azhar et al. ( 2022 , p. 17) set forth that there is no differentiation between students attitudes and perceptions related to sustainability according to their gender.

In the study, it is detected that there is a significant difference between the age of the participants and the factor of strategies related to social and economic sustainability. The findings obtained show that students aged 21 and under give more importance to strategies related to the subjects of social and economic sustainability than the students over the age of 21. This finding obtained is corresponded with Alnıaçık ( 2010 , p. 527). Alnıaçık ( 2010 , p. 527) set forth that the participants in the older age groups exhibit a more pro-environmental attitude. In addition, this study set forth that the students views on the sustainability of the urban environment do not differ according to the variables that determine socio-economic status such as average household income and ability to budget for hobbies. On the contrary to this data, Alnıaçık ( 2010 , p. 527) set forth that as the income level of the answerers families decreases, their pro-environmental attitude scores also decrease.

In this study, it was determined that the students views related to strategies based on social sustainability and economic relations differ from each other depending on the residential area that their families live in or whether the residential area they grew up in carry the characteristics of urban or rural areas. The findings show that urban-based participants are more conscious of the strategies related to social and economic sustainability than that of the rural-based participants. Similarly, Kanbak ( 2015 , p. 86) detected in his study that, students who grew up in large settlements with activities and movements on the environment around them had more awareness related to environmental problems.

This study set forth that the students who took courses on environmental subjects prior to their undergraduate education and those who did not took them do not differ from each other in terms of their perceptive towards the sustainable development. Topal et al. ( 2021 ) detected in their study that, even though the participants stated that they did not receive any education on environmental subjects, they also have knowledge related to urban sustainability. However, it is seen in the relevant study that the participants declared that they read things about sustainable cities in various news channels. In this context, it can be said that the participants learned about acts of urban sustainability via press or the internet, which explain how they gained their awareness related to sustainability even though they did not received education on the subject. Tuncer ( 2008 , p. 220) detected that females who received education on the subject of environment are more aware of sustainable development than those who did not. Brody and Ryu ( 2006 ) set forth that students check in at post graduate courses related to sustainable development have significantly increased sustainable behaviors. Similarly, Hay and Eagle ( 2020 , p. 134) emphasized that the inclusion of sustainability-related content in the curriculum of business management departments is beneficial in terms of increasing students environmental awareness and for them to exhibit environmentally friendly behaviors. Even though at first glance the findings obtained in this study do not seem to correspond with those of Topal et al. ( 2021 ), Brody and Ryu ( 2006 ); Tuncer ( 2008 , p. 220) and Hay and Eagle ( 2020 , p. 134), if we consider that the theoretical and practical courses they took during their vocational education were focused on the sustainability of the natural, cultural and physical environment, it is not a surprising result that the students have similar opinions related to urban sustainability during their undergraduate educations.

In this study, it was determined that there was no difference between CRP and UDLA department students perception of sustainable urban environment. It is being considered that this situation is occurred as there are theoretical and practical courses related to sustainable environment in the education curriculum of both departments. However, it is understood that CRP department students show a better performance than UDLA students in terms of environmental, ecological and social sustainability. Biasutti and Frate ( 2017 , p. 225) determined in their study that agriculture students show better performance in terms of environmental factor than psychology students, whereas psychology students show better performance than agriculture students in terms of social factor. Similarly, Oğuz et al. ( 2011 , p. 37) set forth that university students attitudes and sensitivities related to the environment do not differ from each other according to their grade level. In addition, in this study, it is determined that the sustainable urban environmental consciousness of the students do not differ from each other according to their grade level. This finding is considered to be occurred due to the first-year students under-representation in the total. On the contrary to this finding, Michael et al. ( 2020 , p. 117) set forth that third grade students have the highest average scores on attitudes and actions related to sustainability awareness compared to the first and second grade students.

It should be considered that the scale developed in this study is applied only to the students having their education at the faculty of architecture. Therefore, the results of the research are not to be generalized to all university students and the whole society. It will be able to provide beneficial information on how the students level of consciousness on sustainable cities develops in the axis of the theoretical and practical courses they took related to the sustainable urbanization. However, for a more comprehensive and precise results, it would be beneficial to perform long term follow-up studies. In this way, it will be possible to measure how permanent the behavioral changes are related to the sustainable urban environmental awareness in reality. In other words, further research performed on this subject will help us to have a better understanding on the impact of undergraduate and postgraduate education on sustainable development related to sustainable urban environmental consciousness and planning decisions. In fact, measurements to be made at certain periods can provide an idea on whether the point of view gained by the students in planning and design approaches is permanent. As a result, the scale set forth in this study will be useful in detecting to what extent such fields that provide undergraduate and postgraduate education can achieve the program objectives and in the development of educational curriculum. The scale set forth will be very useful in terms of detecting the awareness of the actors (public, private and civil society) and city residents on the subject of sustainability and learning how would they assess the place they live in their private lives in terms of sustainability.

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Acknowledgement

Research data are collected in the 2021–2022 fall semester during November and December months by benefitting from the questionnaire technique. The designed questionnaire is implemented face to face with the students, who are having their education in X from Faculty of Architecture, Department of Urban and Regional Planning (CRP) and Department of Urban Design and Landscape Architecture (UDLA). Thanks to the students who participated in the survey.

Open access funding provided by the Scientific and Technological Research Council of Türkiye (TÜBİTAK). The authors have not received any support from any institution or organization for the research, writing, and/or publication of this article.

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Altanlar, A., Özdemir, Z. A scale development study on the perception of the sustainable urban environment. Int. J. Environ. Sci. Technol. (2024). https://doi.org/10.1007/s13762-024-05914-z

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• Systematic Review
• Open access
• Published: 02 August 2024

Understanding the synergy between heat waves and the built environment: a three-decade systematic review informing policies for mitigating urban heat island in cities

• Ketaki Joshi 1 ,
• Ansar Khan 2 ,
• Prashant Anand 1 &
• Joy Sen 1  

Sustainable Earth Reviews volume  7 , Article number:  25 ( 2024 ) Cite this article

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The escalating frequencies and intensities of heat waves have become a global concern in the face of climate change. Cities are increasingly vulnerable to overheating due to the amplification of urban heat island (UHI) during heat waves. Factors influencing the synergetic impact of UHI and heat waves on the built environment are complex, mainly including the degree of urbanization, land use patterns, building morphology, thermal properties of construction materials, and variations in moisture fluxes and heat sinks. Researchers worldwide are extensively exploring the characteristics of heat waves, the factors influencing heat waves in urban areas, and the impact of heat waves on built environments, as well as possible mitigation measures. However, the existing literature lacks a holistic and comprehensive understanding of the complexities between heat waves and the built environment that is needed for planning and implementing effective mitigation measures in the future. This study systematically presents a comprehensive overview of the global literature of the past three decades related to heat waves and urban built environments, spanning variations in heat wave definitions, factors influencing heat waves in urban areas, heat wave impacts on buildings, energy, occupant health, and infrastructure, mitigation measures, case studies, best practices, future considerations, and challenges. The objective is to synthesize current knowledge and highlight gaps in understanding, providing a foundation for future research. The review suggests that implementing a combination of strategies across various scales, from individual buildings to entire neighborhoods and cities, can contribute to effectively mitigating heat. This includes prioritizing compact and mid-rise buildings with light-colored exteriors, integrating large parks and green spaces, utilizing cool and super cool materials, ensuring effective insulation, employing passive and mixed-mode cooling and ventilation systems in buildings, and incorporating sustainable technology and innovation. Additionally, community participation and social equity are crucial for addressing vulnerabilities at a local level. It highlights the complexity of the relationship between heat waves and the built environment, emphasizing the need for interdisciplinary approaches for sustainable urban development in the face of heat waves. The outcomes can contribute to the formulation of informed policies to mitigate the adverse impacts of heat waves on built surroundings.

Introduction

Heat waves, characterized by prolonged periods of excessively high temperatures, pose a formidable challenge to the built environment in cities in various ways. As climate change intensifies, the frequency and severity of heat waves are on the rise [ 1 , 2 ]. UHI in cities wherein the temperature of urban areas is higher compared to surrounding rural areas is one of the major influencing factors for heat waves [ 3 ]. Heat waves have a synergetic effect with UHI phenomena further intensifying the magnitude of urban overheating globally up to 5–10 °C during heat waves [ 4 , 5 ]. Land use planning and heat retention materials in built-up areas have a huge implication on the magnitude of UHI and subsequently the potential impacts of heat waves in cities [ 6 , 7 , 8 , 9 ]. As the cities become more urbanized the severity of impacts of combined effects of heat wave and UHI on built environment can exacerbate [ 10 ].

Understanding the synergistic impacts of heat waves and UHI on public health, building energy consumption, and infrastructure is essential for addressing related challenges and formulating effective mitigation strategies, given the crucial role of the built environment in mediating these impacts. Many studies have extensively documented increased health risk and excess mortality among vulnerable populations resulting from high ambient temperatures during the heat wave period, both in outdoor and indoor settings [ 11 , 12 ]. For instance, there is an observed rise in heat-related morbidity during summer ranging from 0.05 to 4.6% per degree of temperature increase in Mekong River Delta, Madrid and Brisbane with an escalation ranging from 1 to 11% during heat wave period [ 4 ]. Moreover, increased ambient temperatures during heat wave periods raise the demand for building cooling energy in cities, resulting in an additional UHI induced Global Energy Penalty of up to 237 (± 130) kWh per person [ 13 ]. This could further result in elevated health risks for economically disadvantaged populations due to inequitable consumption of energy resources and substandard housing conditions [ 4 , 14 , 15 ]. Furthermore, elevated temperature levels during heat waves negatively impact infrastructure across power, healthcare, transportation, and water sectors in multiple ways [ 16 , 17 ]. The interconnectedness of these sectors could further affect liveability of urban residents in the absence of incorporating eco-design principles, implementing sustainable operational practices and prioritizing heatwave-resilient maintenance strategies.

The exploration of heat waves and their interconnectedness with UHI has garnered significant attention from researchers worldwide. This inquiry extends to predictive studies, urban planning strategies, material engineering advancements, and innovative architectural designs, all aimed at mitigating the impacts of rising temperatures and enhancing urban resilience [ 5 , 12 , 18 , 19 , 20 ]. Predictive studies have forecasted a significant increase in days, frequency and duration of heat waves by the end of the century [ 1 , 2 ]. At the urban planning and design level, substantial research is being done to demonstrate the effectiveness of various heat mitigation strategies aiming at limiting heat sources and enhancing heat sinks [ 10 ]. These strategies mainly include sustainable land use planning that integrates green infrastructure (GI) and blue infrastructure (BI) across various spatial scales, as well as increasing the albedo of roofs and pavements [ 4 ]. Advancements in material engineering with the development of supercool materials such as doped reflecting surfaces containing nano Phase Change Materials (PCM), quantum dots and fluorescent materials have demonstrated tremendous cooling potential with few practical challenges [ 21 ]. Architects have successfully experimented with innovative building design with contemporary applications of passive cooling strategies like vertical forests, supercool wind catchers, green facades, façade shading etc. that can significantly reduce peak cooling energy demands in buildings thereby providing promising solutions for heat wave mitigation at building scale [ 22 , 23 ]. All these advancements represent a further grain of microclimatic responses and the clearer understanding on synergistic effects of heat waves and UHI on built surroundings can be obtained from them.

Despite substantial research and development on heat waves and UHI, a critical gap remains in understanding the complex interplay between these phenomena and their impacts on built environments. Perhaps, a connection may be established through co-evolutionary studies that adopt an eco-systemic approach, combining macroclimatic concerns with the microclimatic phenomena of UHI in diverse geographic and demographic settings. Current research often seems confined to individual domains such as urban planning, material engineering, and architectural design, lacking integration across various spatial scales. Consequently, there is a pressing need for interdisciplinary research that bridges these domains, fostering a comprehensive understanding necessary for developing effective mitigation measures tailored to the complexities of urban environments.

To address the aforementioned gap, this study systematically reviews global literature from 1990 to 2023 concerning heat waves and urban built environments. The primary objective is to synthesize existing knowledge, identify knowledge gaps, and discuss advancements in mitigation strategies. Through this, the study aims to lay the groundwork for future research and contribute to the formulation of effective policies to mitigate the impacts of combined effects of heat waves and UHI on built environments. The scope of the study includes a comprehensive review of various aspects related to heat waves and built environments. This encompasses discussions on heat wave definitions, urban factors influencing heat waves, their multifaceted impacts on health and energy, potential mitigation strategies, including successful applications at building and city scales and considerations for anticipated climate change impacts. Additionally, the study explores urban planning interventions that prioritize community resilience and social equity, emphasizing the need for interdisciplinary approaches to foster sustainable urban development in the face of heat waves.

The review is based on the articles and reviews as referred from the Scopus database between 1990 to 2023 internationally concerning heat waves and built environment. The search strings used for searching the literature mainly included TITLE-ABS-KEY search for the key term “heat waves” with the “Boolean AND” operation with other keywords that included “definitions”, “built environment”, “buildings”, “infrastructure”, “health”, “building performance”, “energy consumption”, “indoor thermal comfort”, “heat mitigation”, “architectural designs”, “technologies”, “community resilience”, “social equity”. After removing the duplicate records, the remaining ones were screened mainly based on the titles, keywords and abstracts. The inclusion criteria at this stage were the relevance to the scope and objective of the study. The remaining records were then categorized into several thematic areas to facilitate discussion. Firstly, the variations in definitions based on thresholds, and characteristics of heat waves were examined. Secondly, factors influencing heat waves in urban areas were explored, including UHI, the contribution of building materials to heat retention and the implications of land use planning. Subsequently, the study delved into the multifaceted impacts of heat waves on the built environment, encompassing building performance, energy consumption, occupant health, and infrastructure. Following this, heat mitigation measures were considered, with a focus on strategies such as GI and BI, high albedo materials and sustainable building practices. Additionally, the study included case studies and best practices to provide real-world examples of successful interventions. Lastly, future prospects and challenges were addressed, including the anticipated effects of climate change on heat waves and built environments, potential advancements in technology, and the integration of community resilience and social equity in urban planning. The accessible full texts were then referred to and included in the discussion under identified themes. The inclusion criteria at this stage were robust methodology, relevant data and heterogeneous outcomes (Fig.  1 ).

ROSES flow diagram for literature screening

The narrative synthesis involved the final sample size of 153 peer-reviewed articles. Figure  2 illustrates the distribution of these articles across different countries worldwide. Notably, research activities in this field were predominantly concentrated in the USA (40), Australia (26), China (24), and the UK (16).

The country-wise global distribution of the literature considered in the study

Definition and characteristics of heat waves

Researchers have been investigating thresholds to define heat waves and their profound impacts on human health and the environment for several decades. However, the lack of a standardized definition for heat waves poses challenges in assessing patterns, trends, and impacts, leading to diverse perspectives among experts. Prevention measures and protective temperature thresholds are often defined based on descriptive analyses and expert judgment [ 24 ]. The correlation between mortality and heat waves varies significantly depending on how ‘heat wave’ is defined [ 25 ]. Despite these variations, numerous studies have examined patterns, trends, and disparities in the impacts of heat waves on mortality and morbidity across different demographics and geographic areas [ 18 , 19 , 20 ]. Variations in definitions and thresholds arise from the use of different air temperature metrics, including daily maximum, minimum, absolute, average, heat index, as well as climatic and bioclimatic indices as mentioned in Table  1 .

Before 2010, there were limited studies critically examining the appropriateness of heat wave thresholds that were predominantly based on expert judgment. One of the earliest investigations occurred in 2001, evaluating and testing the effectiveness of existing thresholds in the United States [ 28 ]. In Nanjing, China, from 2007 to 2013, among the different heat wave definitions examined, those defined as lasting for four consecutive days or more with a daily average temperature demonstrated the highest suitability [ 25 ]. According to another study conducted in Wuhan, China, defining heat waves based on daily mean temperature with a duration exceeding three days demonstrated the highest predictive capability in assessing the mortality impacts of heat waves [ 19 ]. In the Coastal and Piedmont regions of North California, heat wave definitions were found to be most sensitive to daily maximum and daily minimum temperatures respectively for two or more consecutive days [ 31 ]. A comparison of official heat wave definitions derived from weather data for sixteen cities in France revealed that the most significant heat-related mortality impact was indicated by the Excess Heat Factor (EHF). At the national level, the EHF identified heat waves with 2.46–8.18 times the global burden compared to the climatological indicator of the French National Weather Service and the heat wave indicator of the French National Heat Warning System, respectively [ 29 ]. Analysing daily maximum temperatures recorded at 587 surface observation stations in China from 1959 to 2013, it is suggested that, given the notable variations in regional climatology, employing relative thresholds is more meaningful for identifying local extremes [ 26 ]. The study sought to define heat waves in five Chinese cities and discovered a positive non-linear correlation between extremely high temperatures and mortality. City-specific definitions were established, such as Beijing and Tianjin defining heat waves as two or more consecutive days with daily mean temperatures exceeding 30.2 and 29.5 °C, respectively. For Nanjing, Shanghai, and Changsha, heat waves were defined as lasting three or more consecutive days with daily mean temperatures higher than 32.9, 32.3, and 34.5 °C, respectively [ 33 ]. Adjusting national-level thresholds to city-specific thresholds can provide a more accurate understanding of heat wave characteristics, particularly concerning demographic, climatic, and geographical differences.

The National Weather Service (NWS) HI thresholds derived from both ambient temperature and humidity are a general estimate indicating the onset of human physiological stress. In regions characterized by high heat and humidity, adjustments to these thresholds may be necessary for physical, social, and cultural adaptations, ensuring that only events perceived as stressful are accurately identified. For example, following the analysis and testing of various thresholds in the United States for the period 1951–1990, a heat wave was defined as a duration of at least 48 hours during which neither the overnight low nor the daytime high falls below the NWS heat stress thresholds (80 and 105 °F, respectively) [ 28 ]. An examination of the temporal trends of heat waves in China spanning from 1961 to 2014 revealed divergent patterns related to relative and absolute heat indices. Notable correlations were identified among various HIs within the same category (either absolute or relative), but the connections were weak when comparing relative and absolute threshold HIs [ 27 ]. A study in South Korea recommends WBGT and its associated thresholds as the most appropriate for establishing connections between heat waves and heat-related diseases. When heat waves were defined using WBGT and HI, the study region experienced the highest total number of heat wave days, while the longest duration of heat waves was observed in the same region based on air temperature [ 32 ]. According to [ 30 ], using the UTCI is a meaningful threshold for a potential heat-health watch warning system [ 34 ]. proposed bioclimatic indices like the UTCI and Physiologically Equivalent Temperature (PET) as a new basis for heat wave definitions.

While mortality impacts are a significant focus of these studies, they also explore demographic, climatic, and geographical differences, employing diverse methodologies. It’s essential to recognize that the implications of these findings extend beyond human mortality to include the built environment. Changes in the built environment over time affect climatic parameters, which in turn influence these indicators. Continuous monitoring and adjustment of thresholds are vital for informing effective heat mitigation measures for built environments, ultimately enhancing resilience.

Furthermore, understanding the association of long-term trends with different definitions of heat waves is crucial for comprehending the temporal and spatial characteristics of heat waves [ 35 ], especially in the context of the built environment. For example, on average, in Spain, the temperature threshold related to mortality has shown an increase at a rate of 0.57 ºC per decade, over the period from 1983 to 2018 [ 36 ]. The research in the eastern Mediterranean region, from 1900 to 2019 concludes that changes in the timing of heat waves, consistently observed across 15 definitions, have led to the extension of the heat wave season by up to 7 days per decade since the 1960s [ 34 ]. This extension has critical implications for the built environment, emphasizing the necessity for buildings and infrastructure to mitigate prolonged periods of extreme heat.

A study based on the synoptic conditions characterizes a combination of daytime and night-time heat waves to form compound heat waves. Night-time and compound heat waves show more significant rises in both occurrence and proportion compared to daytime heat waves. Daytime heat waves are linked to heightened solar radiation during dry periods and decreased cloud cover and humidity under clear skies. On the other hand, night-time heat waves usually coincide with humid conditions, along with higher cloud coverage, humidity, and longwave radiation at night [ 37 ]. Understanding these dynamics is pivotal for devising effective heat mitigation strategies considering both daytime and night-time thermal comfort in the built environment in the face of heat waves. Conventional heat wave definitions have consistently centered around external conditions, yet it is the conditions within buildings that pose a substantial threat to human lives, leading to significant casualties. There is a need for a definition specifically addressing the performance of buildings in terms of overheating. Very few studies have addressed this concern [ 38 ]. has introduced a method to define heat waves based on their impact on indoor conditions and occupant well-being, shifting from an external to an internal perspective. This human-centric approach aims to assess building resilience and establish early warning systems for health emergencies. A case study based on four heat wave events in China finds that the severity of heat wave events, especially the most intense events based on the relative heat wave event definition using average temperature thresholds, is a more suitable definition that poses the greatest threat to indoor thermal conditions. Selection of extreme hot years based on heat wave severity indicates a potential 1–2 months of indoor overheating in passive buildings in specific cities by the century’s end, emphasizing the necessity for adaptive measures in building design and urban planning [ 39 ].

Although there is an agreement in outcomes of various studies projecting a significant increase in days, frequency and duration of heat waves by the end of the century [ 1 , 2 ], these studies differ significantly in the methods such as the duration of the period under consideration, assessment approaches.

A consistent framework for defining heat waves is necessary to compare and understand heat waves on a global scale, especially given the increasing concerns about global warming and its potential effects on the frequency, severity and duration of heat waves. However, examining the temporal progression of heat wave threshold temperatures, which exhibit geographic diversity, is crucial to devise heat adaptation plans. This evolution can be influenced by local demographic, socioeconomic and microclimatic diversities. Therefore, it is essential to acquire a thorough understanding of the specific local mechanisms, such as variations in moisture fluxes that contribute to various types of heat waves. This knowledge is crucial for informing future assessments of risks and impacts. Additionally, more research is needed to define occupant-centric heat wave thresholds for indoor built environments.

Factors influencing heat waves in urban areas

Numerous studies have demonstrated that urban areas are more vulnerable to heat wave impacts [ 12 , 40 , 41 ], compared to rural areas. For instance, notable increases in the frequency of heat waves were observed during 1973–2012 in 217 urban locations worldwide as studied by Mishra et al. [ 42 ] with almost half of the locations experiencing extremely hot days and two third locations experiencing increases in the frequency of extremely hot nights. July 1987 heat wave in Athens had a modifying impact by dense built structures in central urban areas than sub-urban regions causing high night time heat stress on people [ 43 ]. The morphological and construction features of cities, urban landscapes, land use patterns, and anthropogenic heat emissions play significant roles in determining the thermal equilibrium and local ambient temperature increase in urban areas [ 10 ]. This section examines these factors, focusing on UHI, the contribution of building materials in heat retention and the implications of land use configurations. By exploring these aspects, we aim to provide a comprehensive understanding of the complex dynamics contributing to heat wave vulnerability in urban areas.

The UHI phenomenon has been reported as a crucial factor that influences heat waves and their impacts on built environment in urban areas [ 3 , 4 ]. Many studies conclude that UHI intensifies summer heat waves and raises energy usage and the risk of heat-related illnesses and deaths for vulnerable populations such as the elderly, young children, and low-income residents, who are more susceptible to extreme heat stress due to various physical, social, and economic factors [ 7 , 44 , 45 , 46 ]. The extent of UHI, as measured in 101 cities and regions across Australia and Asia, is noteworthy, ranging between 0.4 to 11.0 K [ 47 ]. A study that mapped surface temperatures during the August 2003 heat wave in Paris supported the association of elevated night-time surface temperatures with high mortality risk attributed to UHI phenomena [ 3 ]. In China, night-time heat wave frequency, intensity and duration were observed to be increased with urbanization accounting for nearly 50% of the extended duration and nearly 40% of the enhanced intensity and frequency of night-time heat waves in urban areas relative to rural areas [ 48 ]. During the 1998 heat wave, the mortality rate in the urban zone in Shanghai was approximately 27.3 per 100,000 which was significantly higher than the rate of 7 per 100,000 in the outlying districts. The study concluded the direct influence of UHI on increased hot days and heat waves in urban areas compared to rural areas [ 49 ]. The primary contributors to UHI in urban areas are increased anthropogenic heat emissions, excess release of sensible heat from building materials, increased incoming long-wave radiation due to pollution, lowered evaporative heat loss, decreased turbulent transfer, and reduced longwave radiation losses from street canyons [ 6 ] as illustrated in Fig.  3 . These are mainly dictated by the degree of urbanization, urban morphology, construction characteristics land use and land cover patterns in urban areas.

Synergistic relationship between heat waves and UHI (Adapted from Santamouris [ 4 ] and Oke et al. [ 6 ])

Heat waves and UHI share a synergistic relationship [ 5 , 50 ]. Ambient temperatures in urban areas are intensified due to the combined effect of UHI and heat waves resulting in overheating. Also, heat waves interact with UHI, amplifying the temperature contrast between urban and rural areas and consequently leading to elevated heat-related consequences in cities [ 5 ]. A recent study in Beijing demonstrated seven times increase in the frequency of compound heat wave with the intensity increasing from 0.65 to 2.47 °C resulting from enhanced UHI during 2000–2018 [ 51 ]. The intensity of UHI was raised to about 0.9–1.3 °C during daytime under heat wave conditions studied over Mediterranean towns Nicosia, Cyprus during 2007–2014 [ 50 ]. Upper top layer soil moisture that dictates the evaporation losses in rural areas is mainly responsible for the enhancement or suppression effects of heat waves on urban heat islands. Reduced soil moisture in urban areas lowers evaporation losses thus enhancing the UHI [ 5 , 50 , 51 ]. In Beijing, daytime heat waves were noted to heighten the night-time UHI, primarily due to an escalation in the urban-rural contrast in sensible heat and a decrease in latent heat differences. Conversely, night-time heat waves were observed to suppress the daytime UHI. During the 1995 heat wave in Chicago, the UHI was observed to be prominent. Daytime maximum temperatures in the city centre (Midway) were 1.6 °C higher compared to nearby suburban and rural areas. At night, the city centre experienced a temperature difference of 2.0–2.5 °C, being warmer. Additionally, the city air was much drier during the daytime and moist during early mornings, compared to the surrounding rural areas [ 52 ]. Lower wind speed is another crucial attribute responsible for the enhanced synergetic impact of heat waves and UHI in urban areas [ 5 ], as wind speeds get altered due to urban morphology that often creates dense urban canyons hindering natural wind flows. The depletion of groundwater in urban areas can affect the intensity and frequency of heat waves, as the urban climate is influenced by near-surface weather conditions [ 10 ].

Multiple investigations indicate that the types and configurations of land use have a direct impact through elevated local temperatures, and an indirect impact through air pollution, on the thermal comfort and health of urban residents during heat wave days [ 7 , 8 , 9 , 53 ]. A study in Berlin city by Dugord et al. [ 54 ] for a small temporal scale of 12 h revealed that the warmest land uses at both 10 am and 10 pm are associated with high construction and population density in industrial, commercial, residential and mixed-land use areas. Additionally, the type of residential construction also contributes to temperature differences, with closed constructions like multi-story tenement blocks exhibiting lower nocturnal cooling compared to open areas such as residential parks and sports, leisure, and recreation zones [ 55 ]. Another study in Shanghai city found that non-built-up open land uses such as agricultural, unfrosted green lands and urban brownfields exhibit elevated mean Land Surface Temperatures (LST) at 10 am, accompanied by significant variability in LST. However, water bodies display the least variability attributable to water’s substantial specific heat capacity, resulting in a slower cooling rate [ 56 ]. City dwellers residing in densely populated areas at potentially the highest heat-stress risk independently from their location in the city, the natural characteristic of its surroundings, the state of the surfaces and the characteristics of the inhabitants [ 54 ]. A spatial heat stress pattern exhibited during heat waves in Beijing from 2008 to 2011, showed an elevated risk in urban areas, a moderate risk in the transition zone between urban and rural areas, and the lowest risk in rural areas [ 57 ]. Furthermore, meta-analysis of eleven studies focussing on intra-urban microclimate variations, estimated that individuals residing in warmer regions within cities experience a 6% increased risk of mortality or morbidity compared to those in cooler areas. Similarly, those living in less vegetated areas face a 5% higher risk compared to their counterparts in more vegetated areas [ 58 ].

In urban environments, the combination of high ambient temperatures and poor air quality during heat waves poses significant challenges to public health, exacerbated by the types and configurations of land use. For example, Thiassion, a Greek industrial area, experienced exceptionally high ambient temperatures and poor air quality causing prolonged thermal stress throughout the day in the 2007 heat wave. The Air Quality Stress Index (AQSI) indicating significant stress, ranged from 1.41 to 6.58 due to increased ozone concentration. Residential areas, especially those away from the seashore, experienced intense heat, with temperatures reaching up to 47.7 °C [ 59 ]. A GIS-based study conducted in urban areas in Germany identified augmented concentrations of health-relevant airborne substances such as metals and polycyclic aromatic hydrocarbons within the zone where there is an increased risk of the simultaneous occurrence of temperature stress and particulate matter pollution [ 8 ].

Built-up surfaces in urban areas including building envelopes (walls and roofs), roads, pathways, etc. retain, reflect and release heat depending on their thermal properties [ 6 ]. Built-up surfaces especially dark coloured asphalt roads and pavements having low albedo absorb more and reflect less incident solar radiation [ 60 ], resulting in excess heat retention in urban fabric. Asphalt pavements which are popularly used observed to have extremely high surface temperatures in open spaces in tropical environments during the daytime ranging from the lowest 24.6 °C in the morning to the highest 60.4 °C in the afternoon, indicating 35.8 °C temperature increase during the 6 hr period from 6.00 a.m. to 12.00 p.m [ 60 ]. Urban surfaces due to their roughness, tend to trap 10–40% excess solar radiation compared to flat surfaces composed of the same material [ 61 , 62 ]. This is because roughness reduces the solar reflectivity of materials.

Materials having high thermal capacitance and low emissivity, absorb and store sensible heat during daytime and do not readily release to offset the surface radiative loss. This excess sensible heat is released at night thus adding to the nocturnal UHI [ 6 ]. During the heat wave period, this can result in the persistence of constantly high ambient temperatures during day and night time giving vulnerable populations less opportunity to recover from heat-related morbidity. This also results in increased cooling energy demand as cooling systems are operated for longer durations day and night for maintaining indoor thermal comfort conditions. A heavyweight building wall possesses distinct characteristics compared to a lightweight building wall, particularly concerning the processes of heat storage and release. A recent study demonstrated that, among the most commonly used wall façade systems in tropical climates, conventional uninsulated heavy-weight brick walls increase afternoon UHI compared to lightweight façade systems such as Aluminium Cladding Panel (ACP) and low-E glazing. This is because, due to their high thermal inertia, they store and release a significant amount of thermal energy outdoors in the afternoon. Additionally, the application of ACP insulation on brick walls can raise surrounding outdoor temperatures by up to 4 °C [ 63 ]. This can exacerbate outdoor heat in tropical urban areas during heat wave events in contrast to temperate urban areas where the effects may be less pronounced due to different climatic conditions. Also high reflectivity of lightweight walling systems can raise surrounding ambient temperatures in urban areas during heat wave periods leading to outdoor heat exposure for pedestrians. In practical terms, the interplay between building geometry and the thermal characteristics of diverse building materials occurs across a broad spectrum of combinations. Optimal pairings of these factors influence thermal characteristics within urban fabric specifically influencing night-time heat island conditions. The combination of these factors that maximizes the UHI effect can lead to an increase in temperature of up to 10 °C after sunset [ 6 ]. Tis facet needs to be investigated in detail in various urban climate settings. Furthermore, it’s essential to recognize the distinctive challenges posed by heat waves in tropical urban areas compared to temperate regions. These challenges highlight the importance of tailored strategies for mitigating heatwave impacts in diverse urban contexts.

In summary, the factors influencing heat waves in urban areas encompass the complex interplay of UHI and heat waves that is influenced by the geographical location of an urban area, proximity to the city centre and water bodies, land use patterns, population density, built-up area density, building typology and thermal characteristics of building materials. However, research towards a comprehensive understanding of contributory aspects of these factors in varying urban settings is needed at micro-climate and macro-climate scales for effective urban planning and mitigating the adverse impacts of heat waves on urban residents.

Impacts of heat waves on the built environment

Numerous studies have assessed the implications of heat waves on various aspects of urban built environments such as building performance, building energy consumption, occupant health and infrastructure [ 11 , 16 , 64 , 65 , 66 ]. Technological advancements since 2010 have transformed research methodologies, shifting from reliance on field experiments to computational techniques, enabling simulation of diverse scenarios for indoor and outdoor thermal comfort conditions and energy consumption in buildings across historical, current, and future contexts. Figure  4 , illustrates the impacts of synergies between heat waves and UHI on built environment.

Impacts of synergistic effects of heat waves and UHI on built environment (Adapted from Santamouris [ 4 ])

Heat waves can significantly impact health within the built environment, especially in vulnerable populations [ 67 , 68 , 69 ]. Particularly in urban areas, the built environment aggravates the health impacts of heat waves by intensifying UHI [ 4 ]. This is primarily caused by dense construction, scarce green spaces, widespread use of heat-absorbing materials and poorly ventilated buildings, all of which contribute to heightened exposure of urban residents to extreme heat levels both indoors and outdoors [ 9 , 70 , 71 ]. Consequently, several studies have explored the impact of high ambient temperatures, especially during heat waves in urban areas, on overall and cause-specific morbidity and mortality in urban areas [ 11 , 12 ]. Some of the common impacts are sleep disturbances, fatigue, exacerbation of medical conditions, and fatalities attributed to heatstroke [ 72 ]. There is an observed rise in heat-related morbidity ranging from 0.05 to 4.6% per degree of temperature increase, with a higher increase during heat waves ranging from 1 to 11% [ 4 ]. Older women in urban areas are more susceptible during heat waves attributed to factors such as age, chronic illness, social isolation, inadequate access to and usage of air conditioning, and lack of awareness [ 11 , 67 , 73 ]. However, a study in Maricopa County found that the impact of extreme heat is nearly as significant among younger individuals as it is among older individuals, partly due to their frequent outdoor exposure [ 74 ]. Pediatric health issues linked to heat waves encompass renal disease, respiratory disease, electrolyte imbalance, and fever [ 75 ]. Moreover, many researchers have found a positive correlation between heatwave conditions and hospital admissions related to cardiovascular and respiratory diseases [ 68 ], mental, behavioural, and cognitive disorders [ 76 ]. Low-income group people show significantly high vulnerability to heatwave-induced mortality and morbidity due to poor indoor environment, deprivation of adequate mechanical cooling and healthcare facilities [ 4 , 69 ]. High temperatures during heat waves have also been reported to reduce cognitive abilities and work efficiency of laborers due to limitations of human psychological mechanisms to cope with high-temperature conditions beyond the threshold [ 77 ]. A meta-analysis of nine studies found that individuals working in conditions of heat stress were nearly four times more prone to experiencing occupational heat strain compared to those working under thermally regulated conditions [ 77 ]. Additionally, occupants of poorly ventilated office buildings can get exposed to indoor air pollutants for a longer time thus affecting their health [ 9 ].

In this context, understanding how building characteristics and design influence resilience to heat waves is crucial for mitigating their impacts on indoor thermal discomfort and overall building performance, as heat waves pose significant challenges to buildings, leading to prolonged heat exposure for occupants [ 15 , 78 ]. Research indicates that the performance of a building during heat waves is predominantly influenced by factors such as its envelope characteristics, insulation attributes, and ventilation systems [ 71 , 79 , 80 ]. Understanding how building characteristics and design influence resilience to heat waves is crucial for mitigating their impacts.

Findings of several case studies and experimental studies demonstrate variations in building resilience to heat waves based on envelope characteristics such as construction materials, building age, degree of insulation and envelope design. For example, research conducted during the August 2003 heat wave revealed notably uncomfortable indoor conditions in flats in London compared to two storey houses in Manchester during much of the heat wave period with variation in different rooms [ 14 ]. In another study in Australia during the summer of 2012, indoor temperatures among dwellings varied significantly attributed to structural features such as age, roof pitch, insulation where dwellings constructed with brick and veneer exhibited lesser diurnal temperature fluctuations compared to other building materials [ 71 ]. Traditional masonry wards in hospitals in UK National Health Service (NHS) hospital exhibited resilience to elevated temperatures for 2006 hot summer weather conditions in contrast to lightweight modular buildings that were projected to face a hazardous risk of overheating [ 81 ]. The findings of the study revealed that conventional uninsulated heavyweight brick walls, with their high thermal inertia, can moderate indoor temperatures by absorbing heat during the day and releasing it at night, though this might result in warmer indoor conditions during night-time in tropical climates. Conversely, lightweight façade systems such as ACP and low-E glazing heat up and cool down more rapidly, which can enhance indoor comfort if well-insulated [ 63 ]. Simulation results from a case study of typical residential buildings in Houston and Phoenix, indicated that age-old constructions are prone to reaching hazardous indoor thermal conditions in heatwave scenarios compared to newer buildings attributed to improved envelope characteristics such as exterior wall insulation, U-value and Solar Heat Gain Co-efficient of windows, roof absorptivity and infiltration rate [ 82 ]. Another case study on a 1960s building in Lisbon, Portugal, also highlighted the importance of insulation along with building orientation and occupancy patterns in assessing the vulnerability of occupants to indoor heat exposure [ 78 ].

Low-income housing is particularly vulnerable to heat waves due to factors such as poor building quality, inadequate ventilation, and low thermal capacitance. Studies from various regions highlight the challenges faced by low-income communities during heat waves, including elevated indoor temperatures and prolonged discomfort [ 15 , 83 ]. For example, in Athens, low-income, naturally ventilated houses exhibited considerably elevated indoor temperatures, reaching up to 40 °C, during the extremely hot 2007 summer [ 15 ]. Another recent case study in a low-income neighbohood in La Pampa, central Argentina observed inefficiency of houses to effectively handle heat waves [ 83 ].

Insufficient ventilation within the buildings significantly contributes to the uncomfortable indoor conditions during heat waves, mainly due to poor naturally ventilated buildings, reliance on passive cooling systems, unaffordability of occupants to air conditioners (AC) and frequent power cuts [ 64 , 80 , 83 ]. Air-driven passively cooled office buildings in Germany is observed to have faced challenges in maintaining thermal comfort during heatwave periods [ 80 ]. Simulation findings from a study conducted in Zurich, Switzerland, showed that depending solely on night ventilation is inadequate for sufficiently reducing indoor temperatures during heatwave periods due to UHI [ 84 ]. Another simulation study for Barcelona, highlights the fact that, in the coming years, heat waves are expected to diminish the effectiveness of passive cooling methods [ 64 ].

However, insulation and ventilation strategies if not appropriately addressed throughout all seasons, can exacerbate indoor living conditions during heat waves. For example, a study on newly constructed and renovated social housing during the 2018 heatwave in the U.K., revealed significantly high levels of thermal discomfort due to insulation and ventilation strategies, which primarily aimed to improve resilience against winter conditions but overlooked extreme heat events [ 79 ]. Similarly, the modeling of residential buildings in four representative cold-climate cities in China highlighted the prevalence of overheating issues during hot summers [ 85 ]. All these studies highlight the importance of understanding building performance during heat waves and the necessity of building design to ensure effective passive ventilation in conjunction with mechanical cooling systems creating more resilient buildings that can withstand the challenges posed by heat waves. Also, thoughtful insulation measures in buildings need to be ensured for comfort in diverse weather conditions.

The current peak demand of buildings is calculated based on peak load, which can be considered as high-probability events based on previous years’ weather data. However, events like extreme heat are generally not considered for peak load calculation because of their low probability. This can lead to a scenario where existing building systems are unable to meet occupants’ thermal comfort requirements, even when relying heavily on active cooling systems with increased energy consumption. Widespread usage of AC emerges as one of the crucial factors in reducing heat related mortality and morbidity during heat waves [ 11 , 86 , 87 , 88 ]. Research conducted worldwide suggests that the global urban burden of additional electricity demand per degree of temperature rise ranges from 0.45 to 12.3% [ 65 ]. For each degree of temperature rise, the surge in peak electricity load ranges from 0.45 to 4.6% and an extra electricity penalty of approximately 21 (±10.4) watts per person [ 65 ]. Another study that focused on the San Juan Metropolitan Area in Puerto Rico, for a tropical coastal environment highlighted that energy per capita in urban areas could increase by up to 21% during a heatwave event compared to normal days [ 89 ]. Building cooling energy consumption during heat waves is influenced by factors such as UHI intensity, building age, degree of insulation and usage of cooling devices by building occupants [ 13 , 90 , 91 , 92 ]. The estimated additional energy penalty attributed to the UHI phenomenon at the city scale is approximately 70 kilowatt-hours per person per degree Celsius of UHI intensity [ 13 ]. The study of four representative office buildings in Vienna, Austria, reveals a notable overall increase in cooling requirements that varies based on different construction periods, and the location within the city, further implying need for a widespread installation of AC [ 90 ]. Another numerical investigation of typical Italian residential buildings, during extreme hot periods demonstrated more than threefold increase in cooling requirements of insulated buildings compared to traditional non-insulated buildings [ 91 ]. A study focussing on an office building in Zurich during a heatwave indicated that the closed courtyard exhibited a 20% increase, while the open courtyard showed a 9% rise in cooling demand. This attributed to higher air temperatures and lower heat transfer coefficients resulting from reduced local airflow speeds [ 93 ]. Recent simulation study conducted in Beijing in the context of global warming and the rising occurrence of extreme heat wave events demonstrated an association of increase of one Cooling Degree Day (CDD) with a growth of 0.053 million kWh in power consumption, further projecting that by 2060, electricity consumption in Beijing will range between 219 and 290.4 billion kWh [ 94 ]. All these studies underscore the urgent need to anticipate a potential hike in AC usage and subsequent energy consumption during heat waves in the future.

However, it is also important to note that the escalating demand for AC in buildings can further exacerbate UHI during heat waves, attributed to increased anthropogenic heat generation from indoor electricity systems and the additional heat released by outdoor units of AC. One notable study in Paris for the 2003 heatwave, revealed that the local temperature fluctuations attributed to UHI are directly proportional to the locally released sensible heat by AC systems [ 95 ]. In this context, another study in Jiangsu Province, China demonstrated that adjusting the indoor AC target temperature to 25–27 °C could lead to a 12.66% reduction in the total energy release of the AC system [ 96 ].

Heat waves, exacerbated by UHI, disproportionately impact multiple urban infrastructure sectors through direct and indirect interconnections. Significant impacts are observed in electricity, healthcare, transport, water distribution systems, and building structures [ 16 , 17 ]. This causes inconvenience to people by depriving them of basic civic amenities like traffic light failures, traffic congestion, cancellations of train or flight services, water supply shortages, black-out situations, electricity price hikes, etc. Limited studies have evaluated the specific impact outcomes of heatwave events across all sectors and their interconnectedness.

The electricity sector is particularly susceptible to the impacts of synergistic effects of heat waves and UHI, leading to a range of operational challenges and risks [ 16 ]. The rise in peak electricity demand affects power generation, transmission networks, and distribution networks [ 4 , 16 , 97 ]. This can result in frequent breakdowns of the system leading to increased frequency of blackout situations [ 98 ]. Heat waves pose challenges to power stations by impairing the efficient generation and transmission of power through factors such as diminished insulator capacity and increased breakdown risks [ 16 ]. Additionally, nuclear and coal-fired thermal power plants experience operational difficulties mainly due to warming of cooling water during heatwave days, affecting their capacity and efficiency [ 4 ]. During the summer of 2009 in France, approximately one-third of nuclear power stations had to be shut down due to cooling water shortages [ 97 ]. Moreover, heightened power demand leading to the necessity for extra infrastructure and other operational challenges can escalate prices, particularly impacting lower-income populations who may struggle to afford adequate cooling due to increased costs [ 4 ].

The healthcare sector encounters notable difficulties during heat waves, with increased demand for medical services and infrastructure strain. Many epidemiological studies across Europe [ 99 ], US [ 100 , 101 ], Australia [ 102 , 103 ] and Asia [ 104 ] have reported an increase in hospital admissions, emergency department visits, emergency dispatches and ambulance attendances in urban areas, attributed to heat-related mortality and morbidity during heatwave period. The situation can result in overcrowding in hospitals straining available infrastructure related to space, electricity supply, water supply, waste management and critical healthcare services. For example, a recent study forecasts that future heat waves in Phoenix may overwhelm regional emergency departments, as nearly half of the population could require medical care for heat-related illnesses, exceeding the capacity of these facilities [ 98 ].

Furthermore, transportation, water resources, and building structural integrity are adversely impacted by elevated temperatures resulting from the combined effects of heat waves and UHI. These high temperatures during heat waves affect road transport by impairing engine and tire performance, increasing air conditioning usage, causing pavement rutting and bridge expansion [ 16 , 105 ]. Rail infrastructure faces challenges such as track buckling and electrical faults during heat waves [ 106 ], while flights may get cancelled due to elevated fuel consumption [ 105 ]. Heat waves increase water demand, strain municipal water resources, and elevate the risk of infrastructure damage such as burst pipes and groundwater depletion [ 17 , 105 , 107 ]. Dry weather and high temperatures during heat waves can exacerbate issues like subsidence-related structural damage in buildings, emphasizing the need for further investigation and the application of appropriate construction regulations in subsidence-prone areas. The impacts of heat waves on urban heritage remain largely unexplored [ 108 ].

In summary, addressing the complexities of interconnectedness between heat waves, UHI, building performance, increased cooling energy, public health and critical infrastructure requires holistic approaches that encompass urban planning strategies, building design considerations, equitable access to cooling technologies, and community resilience measures. Furthermore, proactive measures to mitigate the impact of heat waves, such as reducing UHI, improving indoor thermal comfort, enhancing ventilation in buildings, and ensuring access to healthcare facilities, are essential to safeguard the health and well-being of urban populations in the face of escalating heat wave risk. By incorporating eco-design principles, implementing sustainable operational practices, and prioritizing heatwave-resilient maintenance strategies, infrastructure across power, healthcare, transportation, and water sectors can better withstand the challenges posed by heat waves, ultimately enhancing the liveability for urban residents.

Mitigation measures for heat waves in the built environment

This section discusses strategies like GI, BI, high albedo materials, and sustainable building practices that can potentially mitigate high ambient temperatures in building indoors and outdoors, caused by the combined effects of heat waves and UHI, thus aiding in reducing heat wave severity. These strategies are mainly aimed at limiting heat sources and enhancing heat sinks within the built environments [ 10 ]. Detailed investigation of various heat wave mitigation strategies to examine their performance and effectiveness mainly gained attraction in the research community in the past decade.

The integration of GI into urban design stands out as one of the most effective measures for mitigating heat waves particularly within the built environments. GI plays a key role in enhancing carbon sinks by sequestering carbon [ 109 , 110 ], which can mitigate global warming and subsequently reduce the frequency and intensity of heat waves. Moreover, it contributes to moderate the microclimate through processes like evapotranspiration and shading effects [ 10 ], thus aiding in UHI mitigation by lowering surface temperatures [ 111 , 112 ]. Additionally, GI helps improve air quality by reducing urban pollutants [ 110 ]. This multifaceted approach reduces heat-related mortality, lowers peak cooling energy demand in buildings, thereby addressing the combined effects of heat waves and UHI in built environments [ 113 , 114 , 115 ]. For example, the reduction in UHI in Shanghai, resulting from an increase in urban green area coverage from 19.1 to 35.2%, is believed to have played a role in the decrease in heat-related mortality during the 2003 heat wave compared to that of 1998 [ 113 ]. GI’s effectiveness in mitigating the impacts of combined effects of heat waves and UHI in urban areas involves the construction and conservation of green infrastructure elements that significantly improve the cooling effect, mainly through shading and evapotranspiration [ 10 ]. This can be done by increasing vegetation within the urban landscape at various levels, including parks, streetscapes, neighborhood open spaces, building roofs, and facades. Table  2 , lists the heat wave mitigation potential of various GI and BI strategies demonstrated by studies at different spatial scales.

Urban trees are crucial GI elements particularly in mitigating the effects of hot extremes. Green spaces featuring urban trees demonstrate significantly higher cooling potential, ranging from 2 to 4 times greater compared to treeless green spaces in urban environments depending on their size [ 54 , 114 ]. However, the cooling impact of GI is influenced by factors such as plant species, time of year, seasonal variations, size and morphology of plants, scale and geometry of the green infrastructure, and the prevailing climate [ 10 , 120 ]. Recent research findings suggest that during extreme heat waves with very high temperatures, the cooling capacity of transpiration for most plant species is adversely affected [ 114 ]. Another study conducting in Munich found that the cooling effect of urban vegetation decreased during the 2003 heat wave, potentially due to vegetation dieback caused by water scarcity, resulting in reduced cooling through shading and evapotranspiration [ 120 ]. Thus, long-term irrigation strategies need to be integrated into urban design to improve evapotranspiration in the regions facing groundwater depletion thus reducing heat wave intensity and frequency [ 114 ]. Although increasing ground cover with significant trees has a greater cooling effect on street temperatures, green roofs are more effective for reducing energy consumption during heat waves [ 119 , 121 ]. Integrating green living systems into building envelopes, encompassing horizontal surfaces with green roofs and vertical greening systems for facades is crucial in dense urban areas [ 109 ]. GI improves air quality during the heatwave period [ 122 ]. However, more urban GI could also increase the emission of biogenic volatile organic compounds (BVOCs), leading to elevated ground-level ozone concentrations, which pose a significant threat to human health [ 110 ]. Hence, urban design solutions should seek to maximize mitigation potential from GI strategies by combining them with other heat mitigation measures. In hot and humid urban climates, integrating BI into urban design can improve pedestrian-level thermal conditions by reducing ambient temperatures, energy consumption and enhancing urban ventilation during thermal stress periods [ 112 ].

Many studies have demonstrated that widespread implementations of cool and supercool materials with highly reflective properties on roofs and pavements in urban areas can prove to be an effective and affordable technology for combating high ambient temperatures [ 123 ]. The highest peak roof temperature reductions achieved by cool and supercool materials are up to 10.7 and 13.4 °C respectively during the summer [ 124 ]. These effects can play a crucial role in mitigating UHI, maintaining indoor thermal comfort, lowering cooling energy demands during heat waves, when outdoor temperatures are very high [ 118 , 125 , 126 , 127 ]. Additionally cool materials help to decrease ozone and PM 2.5 levels [ 126 ], thereby maintaining air quality during heat wave periods. Policy interventions to enhance urban albedo at city-scale by utilizing cool pavements, permeable pavements, high albedo roads, cool roofs, and high albedo walls can significantly contribute towards lowering the heat related mortality in cities [ 127 ]. As per the information retrieved from Wang et al. [ 128 ], California has undertaken various demonstration projects for cool pavements in cities like Chula Vista, Merced, and Sacramento. In 2001, the initial demonstration project of a parking lot at Bannister Park in Fair Oaks marked California’s pioneering use of permeable pavements as a strategy to mitigate UHI and heat waves. Table  3 , lists the heat wave mitigation potential of high albedo materials and solar panel roof application demonstrated by various studies at different spatial scales.

All these studies endorse increased albedo of roof, walls, roads and pavements in urban areas as an effective mitigation strategy to reduce impacts of combined effect of heat waves and UHI. However, a study in Milan, Italy suggests that the use of high albedo materials requires cautious implementation, particularly during heat wave periods when ambient temperatures are already elevated since high albedo materials have the potential to exacerbate pedestrian thermal discomfort [ 129 ]. Deployment of solar panels on rooftops serves a dual purpose of reducing indoor temperatures and conserving energy through renewable sources during heat waves [ 118 ].

Literature on recent advancements in material engineering has demonstrated a notable cooling potential of doped reflecting surfaces containing nano PCM and quantum dots, fluorescent materials, thermodynamic materials and daytime radiative cooling, facilitated by photonic materials as they have the ability to attain sub-ambient temperatures, maintaining an average surface that is 5 to 10 °C, lower than that of cooler white materials with a negligible or negative sensible heat release to the atmosphere [ 21 ]. Consequently, they can prove valuable in alleviating elevated daytime temperatures, especially during heat wave periods.

Combination of heat mitigation measures in urban design can yield better results in combating impacts of synergies between heat waves and UHI. For example, a study in Darwin, Australia, demonstrated a reduction of 2.7 °C in peak ambient temperature, 2% in peak electricity demand, 7.2% in the total yearly cooling load as well as the potential to prevent 9.66 excess deaths annually per 100,000 people in Darwin district resulting from the combination of cool materials, shading, and greenery [ 130 ]. Designing compact, mid-rise buildings with a light-coloured exterior, along with incorporating large parks, green spaces with significant amount of trees in proximity to the buildings could prove to be an optimum urban design solution [ 122 ]. These strategies primarily target UHI by reducing heat absorption and promoting cooling, while also contributing to heatwave mitigation through carbon sequestration via vegetation. Additionally, managing building heights to enhance airflow and reduce heat retention within built environments indirectly aids in mitigating heat waves by preventing extreme temperature spikes. While specific studies in humidity-dominated tropical cities emphasize the importance of integrating shading, urban ventilation, vegetation, water bodies, and albedo modifications to lower air temperatures [ 131 ], the underlying principles of these strategies remain applicable across various urban contexts. Adaptations may be necessary to suit the specific climatic conditions and urban landscapes, but the overarching goal of mitigating UHI and heat waves remains consistent.

Furthermore, sustainable building materials and technology can ensure better mitigation effects of heat waves in urban areas. Studies have examined the performance of buildings under various envelope materials and ventilation scenarios when subjected to very high temperatures during the heat wave period [ 84 , 132 ]. Table  4 lists the heat wave mitigation potential of sustainable building materials and design practices.

The outcomes of studies in Table  4 , highlight the effectiveness of utilizing passive envelpe design and ventilation as a promising solution to maintain indoor thermal comfort and safeguard occupants from health risks during heat waves and blackouts [ 84 , 117 , 133 , 134 ]. However, studies have also found that passively designed air-driven systems such as night ventilation may get strained during hot extremes necessitating the adoption of advanced natural ventilation strategies or mixed mode ventilation systems alongside appropriate adaptive comfort criteria for heat wave resilience [ 80 , 137 ]. Additionally, as per [ 80 ], water-driven cooling systems utilizing ground cooling outperform air-based alternatives when the levels of outdoor temperatures are high. For low-income housing, adopting building design strategies such as providing shade through elements like trees, climbing plants, green walls, or installing ventilated opaque facades, along with enhancing roofs through light-colored coatings and the addition of thermal insulation, can prove advantageous when combined with night ventilation [ 83 ].

Modern buildings need to be designed to optimize glazing areas to curtail intense solar radiation during high ambient temperature days [ 90 ]. For buildings where conventional materials like bricks, cement, concrete, and wood, limit envelope cooling potential during heat waves, integrating PCM enhances the sensible thermal energy storage of the building envelope to control indoor temperatures [ 115 , 135 , 136 ]. In conclusion,research progress in applying PCMs in existing buildings (Table  4 ) offers a viable option for adapting urban building stock to heat waves through convenient and cost-effective retrofit measures.

Many studies have suggested the application of insulating materials for old and new building stock for improved envelop thermal performance during the heat wave period [ 138 ]. However improper application of insulation materials without consideration of seasonal variability can have an inverse impact [ 79 ]. Also, the external application of insulation materials can add to the surrounding UHI during high ambient temperature conditions [ 63 ].

Furthermore, it is essential to consider ecological implications associated with the use of construction materials for combating heat waves. One study in this context has demonstrated that the lightweight Autoclaved Aerated Concrete with the optimal sugar sediment content could effectively delay the propagation of heat waves from the outer wall to the inner wall [ 139 ], thus exhibiting the ecological, financial, and health implications associated with diverting a significant volume of industrial waste from landfills.

The review of existing literature underscores diverse approaches to mitigate the impacts of combined effects of heat waves and UHI in built environments. While many strategies primarily target UHI rather than directly addressing heat waves, it’s essential to recognize their distinct contributions in mitigating both phenomena. This understanding helps in better responding to the synergies between heat waves and UHI. Heat mitigation approaches span across various spatial scales, from city-wide initiatives to neighborhood-level interventions and building-specific measures. Key findings emphasize the importance of tailored solutions at the urban design level, incorporating a combination of urban trees, blue infrastructure, and high albedo materials to effectively mitigate heat during heat waves. At the building design scale, advanced mixed-mode ventilation systems and emerging technologies like PCMs offer promise in enhancing building thermal performance. Challenges identified include cautious implementation of high albedo materials and ecological implications of construction materials. Future research should optimize synergies among mitigation measures across various spatial scales such as city, neighborhood and buildings to address effectiveness across urban contexts, and prioritize ecological sustainability. Building regulations need to integrate region-specific adaptive criteria specifically for mixed mode ventilation systems, while careful development of energy benchmarking policies is essential to manage increased cooling demands. Policy responses need to consider population dynamics, temperature shifts, and economic factors, urging tailored strategies for each city. Future research needs to focus on aligning regulations and policies with both environmental and societal needs to foster sustainable urban development amidst heat wave challenges.

Case studies and best practices

Cities around the world have implemented various successful heat wave and UHI mitigation strategies to address the challenges posed by rising temperatures. Existing literature on these case study examples highlights valuable insights regarding success as well as challenges at the implementation level. For example, Chicago and Melbourne have extensively implemented green roofs, tree-planting initiatives, and increased green spaces to combat UHI [ 22 , 140 ]. In response to the 1995 heat wave and increasing urban heat challenges, Chicago implemented various green infrastructure interventions, including incentivizing vegetated roofs and mandating reflective roof implementation on new buildings through zoning requirements [ 22 ]. The Green Alley initiative in Chicago, Illinois utilized a range of strategies to mitigate UHI. This included the implementation of cool pavements and green roofs. Over the period from 2001 to 2017, more than 300 Green Alleys were established as part of this initiative [ 128 ]. Melbourne, Australia, exemplifies successful heat mitigation through urban greening initiatives characterized by effective governance. These initiatives operate on diverse scales and involve collaborative efforts from various stakeholders from administrators, policymakers, and urban planners to non-governmental organizations, community groups, and private landowners [ 140 ]. The Urban Forest Strategy implemented by Melbourne’s central city municipality serves as an exemplary case study in mitigating climate change impacts induced by heat waves and UHI through urban greening initiatives. This strategy demonstrates the importance of informed decision-making and collaborative co-creation in addressing these climate challenges [ 141 ]. These measures often lead to temperature reductions and improved microclimates. However, critical evaluations should delve into the long-term viability as well as their equitable distribution across diverse urban landscapes. However, a comprehensive review should assess the environmental consequences of such technologies and their adaptability to different urban settings. It’s crucial to examine the durability of cool pavements and their effectiveness over extended periods.

Similarly, there is significant experimentation taking place in the development of innovative architectural designs and technologies aimed at mitigating the impacts of combined effect of heat waves and UHI. A critical review of selected designs and technologies underscores both their merits and the need for careful consideration of their application. Daramu House, constructed in 2019 in Barangaroo, Sydney, Australia, boasts a green roof with around 15,000 native plants and PV panel coverage. It showcased notable reductions in rooftop surface temperatures, up to 20 °C during ambient temperatures over 40 °C, and improved heat flow efficiency by up to 55.54% compared to conventional buildings lacking green roofs [ 142 ]. In a Hong Kong study, turf-based vegetation cladding on an elevated facade wall of a public housing apartment demonstrated a notable reduction in interior temperatures and delayed solar heat transfer leading to lower air-conditioning power consumption compared to a building with exposed concrete. However, the study emphasizes the crucial role of maintaining a healthy plant cover and a supportive substrate, as the cooling effect through transpiration relies on their robust presence sustained by proper irrigation practices [ 143 ]. Bosco Verticale in Milan, the “vertical forest” that incorporates over 13,000 plants across 90 + species, including full-sized trees, on its towers’ facades, showcases innovative engineering for cultivating trees on high-rise balconies, serving as a model for urban greening to mitigate impacts of higher temperatures during heat waves [ 144 ]. However, successful implementation of such innovative technologies requires collaboration among experts in architecture, structural engineering, botany and climatology to address issues like adapting to altered growing conditions, tree stability, irregular growth, planting restraint safety systems and regular maintenance. The combination of multiple passive strategies in buildings based on the local climatic conditions can be an innovative architectural design approach to reduce building energy consumption and thereby mitigate heat wave impacts on the energy sector. The Pearl River Tower in Guangzhou employs a double-skin facade with integrated wind turbines and solar panels. The facade design allows for natural ventilation and passive cooling, utilizing wind pressure differences between the outer and inner layers [ 145 ]. Traditional passive ventilation concepts equipped with modern technologies prove to save significant amounts of energy in buildings providing promising solutions to electricity breakdown situations during heat waves. For example, as mentioned by Saadatian et al. [ 23 ], Council House 2 (CH2), Melbourne, Australia utilizes advanced wind catcher technology with five installations on its southern façade connected to the basement tanks filled with phase-change material balls that freeze at 15 °C, providing efficient cooling for circulating water through convection thereby achieving 80% reduction in energy consumption. However, the system’s effectiveness could be influenced by changes in wind patterns and solar exposure, posing challenges in maintaining consistent energy efficiency across different seasons and weather conditions. Also, addressing potential challenges of initial costs, appearance concerns, etc. is essential for the long-term success and widespread adoption of similar designs in the future.

While case studies and innovative practices provide valuable insights into successful urban heat mitigation strategies to lower the impacts of heat waves and UHI, they should be critically reviewed for longevity, scalability, and equity considerations. The interconnected nature of climate challenges demands a nuanced understanding of the social, economic, and environmental implications of each strategy to inform future urban planning and heat mitigation efforts.

Future prospects and challenges

The future impact of climate change on heat waves and urban areas is a critical concern that demands careful examination to develop effective mitigation and adaptation measures. Outcomes of several studies have projected a significant increase in days, frequency and duration of heat waves by the end of the century [ 1 , 2 ]. The synergetic impact of UHI and heat waves is predicted to pose significant challenges towards increased health risks, elevated energy demand for cooling systems, and strain on urban infrastructure due to overheating in cities. Many research studies have explored the correlation between anticipated future temperature rises and corresponding levels of heat related mortality under climate change scenario [ 146 ]. For example, Examination of 144 articles focused on assessing the influence of climate change on the prospective energy consumption of commercial buildings across 40 cities for the timeframe spanning 2030–2100, and considering temperature increases in the range of 0.4–5 °C, has revealed that the anticipated rise in cooling demand varies from 1 to 86 kWh/m 2 /year. This variation is influenced by factors such as future climatic scenarios, existing climatic conditions, and specific building characteristics [ 147 ]. The research gaps identified in the existing knowledge and methods that need to be addressed in the context of impacts of synergistic relationship between heat waves and UHI on built environments have been listed below. However, the list is not exhaustive.

Heat vulnerability index (HVI) and related thresholds often fail to integrate demographic, socioeconomic and climatic attributes with physical building attributes. Additionally, indoor heat wave thresholds need to be developed with occupant-centric approaches that consider personal factors, adaptive comfort and behavioural patterns.

There is a need to gain a more comprehensive understanding of the capabilities of the current building stock with a consideration of a wider array of samples to encompass various construction typologies and ventilation scenarios in varying climatic and urban settings.

The resilience of low-income group populations to heat waves, particularly in relation to mitigation strategies within the built environment, is still an important area to be investigated.

Research on the impacts of heat waves on heritage areas needs to be explored further to comprehensively understand this aspect.

Future research needs to focus on building energy optimization and promising solutions to combat heat wave impacts that combine passive and active systems.

There is also an urgent need to consider the impact of increased anthropogenic heat generation from indoor electricity systems during extreme hot events on the outdoor thermal environment.

The gap between recent scientific advancements in material technologies exhibiting high heat mitigation capabilities and their application in buildings needs to be bridged.

Future research exploring nature-based solutions should holistically consider challenges such as initial capital requirements and the absence of comprehensive policies.

Researchers should consider effective water management for the irrigation of GI during heat waves to enhance cooling effects, particularly in anticipation of future challenges related to water scarcity and groundwater depletion in urban areas.

Studies on mitigation measures should consider variations in local UHI and moisture fluxes at various spatial scales.

The critical role of community resilience and social equity in urban planning and infrastructure development to mitigate the impacts of heat waves requires greater attention.

The utilization of advanced technologies in future studies could play a pivotal role in addressing the challenges posed by the impact of climate change on heat waves and the built environment. For example, Space-based Earth observation for the long-term monitoring and quantification of gradual changes in the climate system resulting from the accumulation of greenhouse gases in the atmosphere and the subsequent rise in surface temperatures [ 148 ]. This technological approach will be useful in tracking slow environmental shifts and also for assessing progress and accomplishments regarding implemented policies toward heat wave mitigation and adaptation. Furthermore, this will reduce inconsistencies in varying database approaches used for climate monitoring across varied disciplines. Design actions aided by an Environmental data-driven design process is an innovative approach that combines environmental and technological design processes. Leveraging Information and Adaptive Communication Technologies tools for simulating the built environment through an environmentally data-driven approach ensures data interoperability between outdoor and indoor environments [ 149 ]. Progress in urban climate modeling involves coupling a meteorological model with a building-resolved local urban climate model and linking an urban climate model with a building energy simulation model [ 93 ]. This integrated methodology facilitates not only the design of buildings tailored to the local climate but also allows for a comprehensive understanding of the intricate interactions within the urban environment. Advancements in prediction modeling techniques forecasting vulnerability of urban areas, temperature rise, building energy demand and electricity demand can play a crucial role in informed decision-making and infrastructure preparedness for heat wave adaptation in urban areas [ 94 , 150 ].

Integrating community resilience and social equity into mitigation efforts at the local level to address the synergistic impacts of heat waves and UHI poses a considerable challenge. For example, a study in Winnipeg, Canada, revealed notable discrepancies between public and expert perceptions of heat waves and related risks, highlighting misconceptions about climate change effects and policy intervention responsibility. This underscores the imperative for enhanced risk communication tools and increased public engagement in knowledge-building practices [ 151 ]. Transitioning from national-level to municipal-level plans is crucial to facilitate localized actions toward community resilience in mitigating future heatwave events [ 152 , 153 ]. Local level initiatives to enhance community resilience can offer co-benefits for mitigating heat wave impacts in urban areas. For example, planting trees for shade, implementing cool roof programs, and adopting energy-efficient building designs help lower surface temperatures, reduce cooling energy demand, thereby mitigating UHI and reducing the severity of heat waves. Additionally, rectifying disparities and ensuring equitable access to cooling resources, green spaces, and protective infrastructure for vulnerable populations within the urban planning and infrastructure development processes can facilitate effective heat mitigation during heat waves.

Over the past three decades, scientific advancements have significantly enhanced our understanding of heatwave thresholds, characteristics, and their intricate relationship with UHI, particularly concerning land use, urban planning implications, and mitigation strategies. The convergence of UHI and heat waves amplifies ambient temperatures, leading to urban overheating, with densely urbanized areas bearing the brunt due to high pollution levels and heat-retaining building materials. Notably, low-income groups face heightened risks during heat waves, exacerbated by limited access to cooling, healthcare, and poorly constructed homes, underscoring the urgent need for policies addressing heat mitigation in urban and building design. Key findings from past studies emphasize several crucial points for reducing heatwave impacts in cities:

Integrating innovative green and blue infrastructure solutions into urban planning, addressing challenges such as the cooling capacity of plant species and water availability constraints.

Introducing a blend of compact, mid-rise structures featuring light-coloured facades, in conjunction with expansive parklands, to effectively mitigate rising air temperatures.

Harnessing cutting-edge cool and supercool materials, alongside sustainable architectural designs, to bolster heat mitigation efforts.

Enforcing stringent building regulations promoting energy optimization, especially during periods of heightened heat waves, while incorporating region-specific adaptive comfort standards.

Deploying advanced passive cooling and ventilation systems tailored to alleviate vulnerabilities in economically disadvantaged households.

Formulating robust policies and action plans emphasizing community engagement and equitable social participation to strengthen mitigation efforts against combined effects of heat waves and UHI.

Fostering seamless alignment between national-level action plans and localized urban planning strategies to enhance overall effectiveness in mitigating heat-related risks.

The review underscores the intricate relationship between heat waves and the urban built environment, highlighting the need for ongoing scientific innovation through interdisciplinary collaboration. By embracing diverse approaches, stakeholders can devise comprehensive strategies to mitigate heatwave impacts in urban areas while promoting sustainable development. Integration of expertise from architecture, urban design, environmental science, public health, and social sciences in urban planning is essential to prioritize heat mitigation and enhance community resilience against the compounded challenges posed by heat waves and UHI through tailored micro built environment designs. Collective efforts involving government, academia, non-profit organizations, and industry sectors drive innovation in resilient infrastructure, urban greening, and public health interventions. Empowering communities through inclusive decision-making strengthens their capacity to implement measures aimed at mitigating the negative consequences resulting from the synergies between heat waves and UHI.

Future research directions should focus on refining city-specific heatwave definitions, establishing occupant-centric thresholds, and exploring local moisture variations. Additionally, optimizing building geometry and thermal characteristics for both existing and new structures is imperative. A holistic understanding, with reduced uncertainties in predictions, can be achieved by developing integrated future scenarios that consider impacts and mitigation efforts across various scales. This entails examining future developments and interconnections between global, regional and local climate dynamics, socio-economic factors, demographics, and technological advancements to comprehensively assess the vulnerability and impacts of heat waves and UHI on health, energy consumption, and the environment.

Data availability

Not applicable.

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Joshi, K., Khan, A., Anand, P. et al. Understanding the synergy between heat waves and the built environment: a three-decade systematic review informing policies for mitigating urban heat island in cities. Sustain Earth Reviews 7 , 25 (2024). https://doi.org/10.1186/s42055-024-00094-7

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Environmental, geographical, and economic impacts of inbound tourism in china: a mixed-effects gravity model approach.

1. Introduction

2. literature review, 2.1. model development for tourism issues research, 2.2. tourism gravity modelling studies by chinese scholars, 3. methodology, 3.1. inter-provincial inbound tourism gravity model in china.

• Inbound Tourism Development: The primary indicator is the volume of inbound tourists, reflecting international tourism’s contribution to foreign exchange earnings. The model uses the count of inbound tourists as the dependent variable, represented by T , to encapsulate tourism flow dynamics.
• Economic Development: This study acknowledges the role of economic vitality in shaping regional attractiveness and infrastructure. It uses per capita real GDP, denoted by Y , as a key economic indicator.
• Geographical Location: Proximity to major entry points is crucial. The study quantifies geographical advantage by the distance from provincial capitals to Beijing, Shanghai, or Guangzhou, denoted by D .
• Tourism Resources: The quality and density of tourism attractions are operationalized through the prevalence of scenic spots rated 4A-grade and above, which signify competitive tourism assets, denoted by AA .
• Traffic Conditions: This study assesses the transportation network using road density, an essential component of the tourism value chain, denoted by TRA , reflecting the region’s accessibility.
• Tourism Services: Service capacity is gauged by the number of star-rated hotels, denoted by S, a surrogate for service quality and availability.
• Traffic Safety: Traffic safety, a significant factor for destination image, is measured by traffic accident mortality density, denoted by SS .
• Environmental Protection: The ecological variables include wastewater and solid waste discharge densities alongside urban green space, capturing the role of ecological stewardship in tourism, denoted by IW , IS , and GR , respectively.

3.2. Econometric Analysis

4. analysis results and discussion, 4.1. data and statistical testing, 4.1.1. data variables and description.

• Per capita real GDP (year): prices were adjusted from 1990 to reflect per capita GDP and the GDP deflator index, which is calculated using the per capita and capita GDP indexes from the ‘China Statistical Yearbook’.
• Geographical location variable (D): The shortest distance between each province’s capital and Beijing, Shanghai, and Guangzhou, respectively, is calculated, with Beijing’s, Shanghai’s, and Guangzhou’s D values set to zero. Distance information is obtained from the mileage query tool on the train ticket website ( http://search.huochepiao.com ).
• Tourism resources: this includes the number of 5A and 4A scenic spots in each province, as reported by the National Ministry of Culture and Tourism ( https://www.mct.gov.cn/ ) and on provincial culture and tourism departments’ official websites.
• Transportation and environmental factors: These include highway mileage, the number of traffic fatalities, total wastewater discharge, total industrial solid waste discharge, and green coverage area. For the years of the study, these figures have been obtained from the ‘China Statistical Yearbook’.
• Tourism facilities: the number of star-rated hotels is based on the ‘China Statistical Yearbook’ and ‘China Tourism Statistics Yearbook’.

4.1.2. Stationarity Test

4.1.3. correlation analysis, 4.2. model selection and estimation, 4.3. establishing the cluster-based portfolios, 4.4. heterogeneity test, 4.5. empirical results, 5. discussion.

• H1: Economic development, as indicated by the positive coefficient for LOG(Y), confirms that higher per capita GDP significantly increases inbound tourist numbers. This supports the hypothesis that economic factors positively impact tourism, aligning with findings from studies by Cortes-Jimenez and Pulina [ 1 ] and Zhou [ 2 ].
• H2: The negative coefficient for LOG(D) confirms that greater geographical distance from major urban centers significantly reduces tourist numbers, supporting the hypothesis that geographical proximity influences tourism. This is consistent with the work of Hanafiah et al. [ 23 ] and Guo [ 14 ], who also found that proximity to major cities enhances tourism activities.
• H3: Environmental factors such as green coverage (LOG(GR)) and reductions in industrial waste (LOG(IW)) positively affect inbound tourism. The positive coefficients for these variables confirm that environmental improvements enhance destination attractiveness, supporting the hypothesis that environmental factors positively influence tourism. This finding extends the work of Buckley [ 24 ] and Gössling [ 25 ], who highlighted the importance of environmental sustainability in tourism development.
• H4: The dual impact of environmental protection measures is observed. While improvements in environmental quality (e.g., increased green coverage) positively attract tourists, stricter regulations and conservation efforts can lead to increased operational costs and reduced availability of popular attractions. For instance, stringent fishing regulations in certain coastal areas have restricted traditional fishing tours, thereby decreased tourist activities and affected local tourism revenue. This confirms the hypothesis that environmental protection measures can have both positive and negative impacts on tourism. These findings are in line with the work of Buckley [ 24 ], who noted the potential trade-offs between environmental conservation and tourism development.

6. Conclusions

6.1. theoretical implications, 6.2. practical implications, 6.3. limitations and future research, 6.4. summary, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Zhu, B.; Wang, C.-C.; Hung, C.-Y. Environmental, Geographical, and Economic Impacts of Inbound Tourism in China: A Mixed-Effects Gravity Model Approach. Sustainability 2024 , 16 , 6671. https://doi.org/10.3390/su16156671

Zhu B, Wang C-C, Hung C-Y. Environmental, Geographical, and Economic Impacts of Inbound Tourism in China: A Mixed-Effects Gravity Model Approach. Sustainability . 2024; 16(15):6671. https://doi.org/10.3390/su16156671

Zhu, Bo, Chien-Chih Wang, and Che-Yu Hung. 2024. "Environmental, Geographical, and Economic Impacts of Inbound Tourism in China: A Mixed-Effects Gravity Model Approach" Sustainability 16, no. 15: 6671. https://doi.org/10.3390/su16156671

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• Published: 05 July 2024

Mitigating urban heat island through neighboring rural land cover

• Miao Yang 1 , 2 ,
• Chen Ren   ORCID: orcid.org/0000-0002-4691-1198 1 , 2 ,
• Haorui Wang 1 , 2 ,
• Junqi Wang   ORCID: orcid.org/0000-0001-8843-7781 1 , 2 ,
• Zhuangbo Feng 1 , 2 ,
• Prashant Kumar 1 , 3 , 4 ,
• Fariborz Haghighat 1 , 5 &
• Shi-Jie Cao   ORCID: orcid.org/0000-0001-9136-0949 1 , 2 , 3  

Nature Cities volume  1 ,  pages 522–532 ( 2024 ) Cite this article

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• Climate-change mitigation
• Environmental impact

Globally, the deteriorating Urban Heat Island (UHI) effect poses a significant threat to human health and undermines ecosystem stability. UHI mitigation strategies have been investigated and utilized extensively within cities by the provision of green, blue or gray infrastructures. However, urban land is precious and limited for these interventions, making it challenging to address this issue. Neighboring rural land cover may serve as a cooling source and have a great potential to mitigate UHI through processes such as heat absorption and circulation. This study aims to address the following questions: (1) what is the location of neighboring rural land cover to effectively mitigate UHI for the entire city and (2) what are the key parameters of the landscape. We investigated the quantitative and qualitative relationships between rural land cover and UHI, drawing on geographical and environmental data from 30 Chinese cities between 2000 and 2020. We found that the rural land cover extending outward from the urban boundary, approximately half of the equivalent diameter of city, had the most pronounced impact on UHI mitigation. The number and adjacency of landscape patches (a patch is a homogeneous and nonlinear basic unit of a landscape pattern, distinct from its surroundings) emerged as two key factors in mitigating UHI, with their individual potential to reduce UHI by up to 0.5 °C. The proposed recommendations were to avoid fragmentation and enhance shape complexity and distribution uniformity of patches. This work opens new avenues for addressing high-temperature urban catastrophes from a rural perspective, which may also promote coordinated development between urban and rural areas.

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Cities are the cradle of human civilization, ensuring human progress, scientific innovations and economic advancements. Despite the constructive development activities within cities, they have also created and intensified certain environmental challenges 1 , 2 . The Urban Heat Island (UHI) effect causing urban overheating is a prominent example of these concerns resulting from rising urbanization and anthropogenic activities 3 , 4 . It has seriously endangered human lives, well-being and ecosystems, ultimately leading to economic consequences 5 . In July 2023, the world experienced the hottest month on record, with widespread heatwaves across many countries 6 . Moreover, temperature extremes on land will increase even faster compared to the increase of global mean temperature (land and ocean) due to climate change from human activities 4 . Hence, holistically formulating an effective adaptation and mitigation strategy for the UHI effect has become a focal issue for sustainable urban development 7 , 8 .

The existing literature on mitigating the UHI effect is primarily focused on strategies that seek solutions within the city limits, such as the provision of green, blue or gray infrastructure including trees 9 , grass 10 , parks 11 , green walls 12 , green roofs 13 , lakes 14 and so on. These function as urban cooling sources/heat sinks, reducing the temperature in surrounding areas through processes such as heat absorption, evapotranspiration, convection and circulation 15 , 16 . However, urban land is precious and limited for those mitigation interventions. These interventions have limited capacity to reduce UHI intensity in a specific urban district 17 , 18 , making it challenging to sufficiently reduce the UHI at the city scale. Urban heat is not confined within the physical boundaries of a city. Instead, urban heat can diffuse to the neighboring rural areas, which have more natural land cover than the city, including trees, rivers, grassland, cropland and so on. 19 , 20 , 21 . This suggests that rural land cover may also serve as an element for absorbing urban heat, thereby harboring the significant potential for mitigating urban heat islands. This offers a unique opportunity to mitigate the UHI effect through the utilization of neighboring rural land cover (NRLC) to effectively tackle the UHI challenge.

The existing literature discusses the advantages and implications of NRLC in mitigating the UHI 22 , 23 . Yao et al. 24 reported that the effect of greening in rural areas was an important and widespread driver of diurnal surface urban heat island intensity variability, responsible for 22.5%. Two cities with relatively comparable urban configuration and population density may have different UHI intensities merely due to different surrounding rural land cover characteristics 25 . Given the limited number of investigations on UHI mitigation in rural areas, there remains a dearth of knowledge about the quantified impact of rural land cover on UHI. Specifically, there is lack of understanding regarding the influencing location and patterns of mitigating UHI through NRLC. It is significant to explore the impact of rural land cover patterns on mitigating UHI for enhancing the potential of their effective implementation.

The innovations of this study lie in: (1) systematically quantifying the spatial extent (location) of rural land cover that affect UHI along with physical mechanisms discussed and (2) identifying key factors of rural land cover and evaluating their potential in UHI mitigation. Both qualitatively and quantitatively associated relations are revealed between rural land cover and UHI mitigation based on Chinese cities, considering that China has undergone rapid urbanization in the past 30 years. These cities have vast characteristics such as diversity of geographic features, climatic conditions, urban forms, development and so on, which will facilitate the feasibility and validity of technology deployment. This study can significantly contribute to the development of UHI mitigation strategies and sustainable urban development.

Locations of rural land cover for UHI mitigation

The quantitative influence of rural land cover on the Urban Heat Island Intensity (UHII) was analyzed as shown in Fig. 1 , using the regression model depicted in Methods . Two important parameters of rural land cover were considered, that is, the distance from the urban boundary (locations) and land cover types. Here the urban areas were divided into five different rings, namely urban ladders UL i ( i  = 1–5). UL 1 represented the area of urban center, whereas UL 2–5 gradually expanded outwards ( i  = 5 corresponding to the urban boundary area), as shown in Fig. 2 . The urban boundary was taken as the baseline and was extended outwards to obtain four rings, that is, rural ladders RL j ( j  = 1–4), of different radii. The inner boundary of each RL was the urban boundary. The average equivalent diameter of the selected cities was 22 km. Rural land cover was classified into five types: woodland, cropland, impervious surface, water body and grassland (grassland was not analyzed independently; explanation in Methods ).

a – e , From the left to right, respectively: NRLC, woodland, cropland, impervious surface and water body. The horizontal coordinates represent the different urban ladders (UL i , i  = 1–5). The vertical coordinates represent the explanation degrees ( R 2 ) of different cover types to the surface UHI. f , The schematic representation of urban regions and rural land cover (the variation of color range standing for different urban regions). g , The specific locations of various urban and rural regions.

Source data

a , Urban ladders. b , Rural ladders.

Figure 1a shows that NRLC (considering the totality of five types) can achieve cooling for all ULs. Specially, NRLC had the largest explanation degree of nearly 30% on the surface UHII variance in UL 1 , due to the effect of heat island circulation and convection (Supplementary Information B ). The NRLC in RL 3 , RL 2 and RL 4 had the highest explanation degree on the surface UHII variance in UL 1,2,5 , UL 4 and UL 3 , respectively. Given that NRLC in RL 3 still had the second largest explanation degree to the surface UHII variance in UL 3,4 , the NRLC in RL 3 potentially had the greatest UHI mitigation capacity for the entire urban area compared with RL 1,2,4 .

Figure 1b–e shows that impervious surfaces had the most significant influence on surface UHII, followed by cropland, and finally woodland and water body. The explanation degree of impervious surface in RL 3 and RL 4 to surface UHII variance was compared, with the small difference being within 0.01. The UHI mitigation capacity of impervious surface in RL 3 and RL 4 was greater than that in RL 1 and RL 2 . Cropland and woodland in RL 1 presented a markedly lower explanation degree to UHI variance compared with other RLs. Cropland in RL 3 explained the greatest degree of the UHI variance in UL 3,5 and exhibited a slightly lower explanation degree compared with the maximum degree in UL 1,2,4 (that is, difference of 0.015, 0.005, 0.005 for UL 1 , UL 2 and UL 4 , respectively). Water body in RL 2 explained the UHI variations of all ULs to a higher extent than other RLs. Supplementary Fig. A2 shows the corresponding combinations UL i – RL j , that is, NRLC or land cover types in RL j have the largest explanation degree of surface UHII variance in UL i compared with other RLs. Taking NRLC as an example, the corresponding combinations were UL 1 –RL 3 , UL 2 –RL 3 , UL 3 –RL 4 , UL 4 –RL 2 , UL 5 –RL 3 .

Key parameters of rural land cover for UHI mitigation

This section aimed to rank the landscape-level parameters (LLPs, used for NRLC) with correlations to the UHI mitigation. These parameters were determined through the calculation of SHapley Additive exPlanations (SHAP) values and Pearson correlation analyses. In the previous section, the corresponding RL for each UL (rural land cover in RL explained most of the surface UHII variance in UL) was obtained. On the basis of it, Fig. 3 illustrates the ranking of the SHAP values for the LLPs in rural regions, which reflects the capability to mitigate the surface UHII of the corresponded urban areas (that is, UL i , i  = 1–5). The higher the SHAP value, the better the UHI mitigation. It can be noted that AI (aggregation index), COHESION (patch cohesion index), PR (patch richness) and DIVISION (Landscape Division Index) were almost at the top of the SHAP rankings. To sum up, the key LLPs for different corresponding combinations on UHI mitigation were: UL 1 –RL 3 (PR, DIVISION, NP (number of patches)); UL 2 –RL 3 (AI, IJI (interspersion & juxtaposition index), PR, NP); UL 3 –RL 4 (PR, LSI (landscape shape index), COHESION, NP, IJI); UL 4 –RL 2 (LSI, COHESION, IJI, PR); and UL 5 –RL 3 (COHESION, LSI, PR, NP) (more details can be found in Supplementary Table A1 ).

Columns from left to right represent UL 1 –RL 3 , UL 2 –RL 3 , UL 3 –RL 4 , UL 4 –RL 2 and UL 5 –RL 3 .

Supplementary Figs. A3 and A4 and Supplementary Table A1 show the extraction process and final results regarding the key parameters of four land cover types. The key parameters of woodland were mainly NP, IJI and AI. The key parameters of cropland were mainly CIRCLE_AM (circle index distribution) and IJI. The key parameters of impervious surface were mainly IJI, SPLIT (Splitting Index) and CIRCLE_AM. The key parameters for water body were mainly IJI, PD (patch density), CA (total (class) area), SHAPE_AM (shape index distribution) and CLUMPY (clumpiness index).

Impact of key parameters of rural land cover on UHI

This section aimed to elaborate the individual impact of the key LLPs on UHII for the aforementioned combinations (that is, UL 1 –RL 3 , UL 2 –RL 3 , UL 3 –RL 4 , UL 4 –RL 2 and UL 5 –RL 3 ), as presented in Fig. 4 . Most of the key landscape parameters and the surface UHII were close to a simple monotonic relationship. For instance, taking the urban region UL 1 (the hottest region) as an example (that is, UL 1 –RL 3 ; Fig. 4a ): (1) the value of surface UHII decreased as PR increased (PR, namely patch richness, indicates the total number of land cover types); (2) the value of surface UHII decreased as DIVISION increased (DIVISION, that is, landscape division index, indicates the probability that two randomly chosen pixels in the landscape are not situated in the same patch) and (3) the value of surface UHII decreased as NP decreased.

Accumulated local effects (ALE) plot is centered so that the mean effect is zero. The value of the ALE can be interpreted as the main effect of the feature at a certain value compared to the average prediction of the data, that is, the smaller the value, the more effective it is in UHI mitigation. Panels a– e correspond to the five combinations of urban and rural ladders, that is, UL 1 –RL 3 , UL 2 –RL 3 , UL 3 –RL 4 , UL 4 –RL 2 and UL 5 –RL 3 . The effects of key parameters of LLPs for each combination on the surface UHI are shown by subpanels under the panel of each combination, that is, subpanels (i)–(iii) for PR, DIVISION, NP in a ; subpanels (i)–(iv) for AI, IJI, PR, NP in b ; subpanels (i)–(v) for PR, LSI, COHESION, NP, IJI in c ; subpanels (i)–(iv) for LSI, COHESION, IJI, PR in d and subpanels (i)–(iv) for COHESION, LSI, PR, NP in e .

Figure 4a–e columns elaborate that some key LLPs belonged to more than one UL and had a similar relationship with the surface UHII of different ULs. For example, the surface UHII at UL 1,2,3,5 decreased with the decrease of NP, the surface UHII at UL 3,4,5 decreased with the increase of LSI and COHESION and the surface UHII at UL 2,3,4 decreased with the decrease of IJI. This finding provided an opportunity to achieve effective cooling of a large part of the urban area through simple regulation of the same landscape parameters. The influencing pattern of key parameters, which belong to more than or equal to three ULs and having the same influence pattern on different ULs, were recommended to be used for generating key strategies on UHI mitigation. The other key LLPs, which can only mitigate surface UHII of a particular UL or have different influencing mechanisms on the surface UHII of different ULs, were used for generating supplementary strategies for localized area of refined UHI mitigation. As shown in Fig. 5 , the influence patterns of NP, IJI, LSI and COHESION were considered in key strategies to mitigate UHI, that is, (1) decreasing the number of patches (NP); (2) decreasing the even distribution of adjacencies among patch types (IJI); (3) avoiding square patch shapes (LSI) and (4) increasing the connectedness of the patches (COHESION). AI, DIVISION and PR were selected in complementary strategies. Taking the combination of UL 2 –RL 3 as an example: (1) increasing the connectedness of the patches (AI) and (2) increasing the number of patch types (PR).

Strategies and suggestions for the key LLPs on UHI mitigation.

The impacts of key LCPs (landscape class parameters) for four cover types on surface UHII and the process of generating key strategies can be seen in Supplementary Figs. A5 – A12 . The influencing pattern of IJI, NP and AI for woodland were selected in key mitigation strategies on UHI. The influencing pattern of CIRCLE_AM and IJI for cropland; CIRCLE_AM, SPLIT and IJI for impervious surface; and NP, CA, CLUMPY, SHAPE_AM and IJI for water body were also selected in main strategies. All the main strategies are summarized in Table 1 .

The rural regions, with their rich natural land cover and simpler functional patterns, hold great potential for mitigating UHI 24 . This study aims to bridge this knowledge gap by investigating both quantitative and qualitative influence of NRLC on UHI mitigation in China from 2000 to 2020, as shown in Extended Data Figs. 1 and 2 . Results indicate that NRLC can possess the capacity to mitigate UHI for entire cities. Specifically, we discover that NRLC can contribute to urban cooling, with the most pronounced impact occurring within a 10–15 km radius from the urban boundary, which is closely interacted with the urban area. It further suggests that NRLC within this range can contribute up to 30% to the reduction of UHII in urban centers. The richness and density of landscape patches emerge as key factors in mitigating UHI, with the potential to reduce UHI by 0.5 °C through the modulation of key parameters. More suggestions are summarized in Table 1 .

Why do we need rural land cover types at specific locations to mitigate UHI? We explain this through urban physics, leveraging the concept of heat island circulation (Supplementary Information B ) and convection. Heat circulation holds paramount importance in urban ventilation and the exchange of energy between urban and rural environments. In this dynamic circulation process, air is warmed up within urban areas due to the effect of buoyancy force, creating a low-pressure zone near the ground. Subsequently the heated air will be transported to the rural regions via convection and diffusion, drawing cooler air from rural areas to continuously replenish the urban core. Our study reveals that RL 3 land cover can exert the most significant effect on UHI, with its circular radius encompassing a range of 10–15 km, which is approximately half of the city’s equivalent diameter, similar to the finding from Fan et al. 26

During the heat circulation cycles between different ladders (UL i and RL j ), the heat is absorbed by the rural landscapes at a different extent depending on the types and locations. Hence, well-designed landscape patches (including features such as the richness and density) should be promoted to realize self-cooling in rural regions.

The complexity and diversity of urban characteristics, including shape, development level, geographical location and climatic conditions, pose potential risks of introducing significantly deviated findings in this study. To address it, our research focuses exclusively on single-centered cities exceeding 200 km 2 , primarily characterized by plains interspersed with scattered terraces, hills and low mountains. This approach helps to reduce the impact of the cities’ shapes and geographical features under investigation. Furthermore, cities are categorized into five concentric rings (UL i ) based on their varying urban development intensities (UDI). This stratification enables a differentiated analysis of the impact of rural land cover on UHI effect across different urban development intensities. By doing so, we successfully group cities based on their urban development levels, thereby limiting and quantifying the influence of urban development on our findings. To validate the influence of climate, cities are grouped according to their climatic zones, and separate analyses are conducted. The mechanisms of rural land cover in UHI mitigation have differences between various climate zones. However, a significant impact of climate is not observed from the results. The overlap of key landscape parameters between different climate zones and China is basically higher than 0.7. Additionally, the majority of mitigation strategies identified in China are transferrable to different climate zones (Supplementary Information C ). Consequently, our findings still have relatively high generalizability and applicability in different cities. In the future, this study holds promise in offering valuable methodological and strategic guidance for refinement studies at the city scale and the development of context-specific policy formulations.

The heat island circulation can cross the physical urban boundaries to facilitate heat exchange between cities and rural areas to mitigate UHI. However, at the same time, due to the interaction and collision of energy and heat between cities and rural areas, it may lead to a large amount of pollutants flowing back into the city with the heat island circulation, causing pollution to the urban ecosystem. The local microcirculation between urban, suburban and urban–rural (buffer zone) areas can be improved by rationalizing the landscape patches of suburban and rural areas to provide spaces for heat exchange and pollutant filtration and sinking and to avoid pollutant refluxes. This study shows that different types and locations of rural landscapes may mitigate UHI to different degrees due to the different temperature gradients of the thermal cycles.

To sum up, rather than perceiving urbanization as an undesirable trend that opposes sustainable urban development, it is more constructive to embrace it as a continuous process. Unlike the intricate process of balancing urban development with sustainability, the regulation of rural land cover would yield numerous co-benefits for both urban and rural areas, including offering a nature-based solution without encroaching on urban land 3 , preserving rural landscape, boosting rural economy, assisting in mitigating the UHI and supporting ongoing urban prosperity and sustainability.

This study aims to explore the impacts of neighboring rural land cover (locations and landscape types) on urban heat island (UHI) mitigation. The method logic contains three main scenarios: (1) investigating the influence of rural land cover in different locations on UHI at the urban scale; (2) extracting the key rural landscape parameters on UHI mitigation and (3) identifying the impact of individual key landscape parameters on UHI and proposing key mitigation strategies. The applied research framework is shown in Extended Data Fig. 1 . First, 30 Chinese cities are selected as case studies. The data of the UHI intensity (UHII) and rural land cover for these cities are collected. Second, urban areas are divided into urban ladders (UL i ) based on UDI 27 , and rural areas are divided into Rural Ladders (RL j ) with different distances from the urban boundary 28 . The UHII values of different UL i are calculated and the land cover types of different RL j are categorized. Third, regression models are used to analyze the impact of different rural land cover from varying distances to the urban boundary 29 . Then, SHapley Additive exPlanations (SHAP) is employed to rank the key landscape parameters of rural land cover, including landscape-level parameters (LLPs) and landscape-class parameters (LCPs) 30 . Finally, accumulated local effects (ALE) plots are used to reveal the impact of individual key landscape parameters on UHII 31 .

Case studies

On the basis of the China Urban Statistical Yearbook 2020 ( http://www.stats.gov.cn ), 30 monocentric cities in China are selected with an urban area of more than 200 km 2 for investigation 32 . Documents have reported that the urban shape substantially influences the UHI; monocentric cities are more likely to experience the severe UHI phenomena 33 . As shown in Extended Data Fig. 2 , except Urumqi, all the cities are evenly distributed in China’s monsoon climate zone, representing a high level of urbanization. The landform characteristics of the sample cities can basically be categorized as dominated by plains, with scattered terraces, hills and low mountains in the city. Climatic differences in different regions of the country have negligible effects on the results, with sufficient data demonstrated in Supplementary Information C .

Data collection of UHII and rural land cover

Land cover data for 2000, 2005 and 2010 are obtained through Landsat 5 TM (Thematic Mapper) and for 2015 and 2020 through Landsat 8 OLI (Operational Land Imager). According to the common reference system of remote sensing monitoring in China (National Land Use/Cover Classification System for Remote Sensing Monitoring), the rural land cover was divided into five types: impervious surface, woodland, grassland, cropland and water body 34 . Neighboring rural land cover (NRLC) includes the totality of all land cover, that is, impervious surface, woodland, grassland, cropland and water body. When analyzing landscape types independently, only four land cover types (impervious surface, woodland, cropland and water body) are chosen and grassland is excluded. Grassland has limited latent heat and low heat absorption efficiency 35 , 36 and is not a common land cover type in rural area neighboring cities of China. The training sample points are equally distributed throughout the sample region obtained from high-resolution photos in Google Earth Pro. Landsat 5/8 Level 2, Collection 2, Tier 1 dataset and the Random Forest (RF) algorithm model are employed for land classification. The quality of categorization is assessed through Kappa values, which are all greater than 0.90.

This study incorporates 18 LLPs such as total area (TA), contagion (CONTAG) and Shannon’s evenness index (SHEI) and 22 LCPs such as the largest patch index (LPI), edge density (ED) and intersectionality and juxtaposition Index (IJI) 37 . LLPs are indicators for the landscape as a whole (NRLC); LCPs are indicators for individual landscape types (impervious surface, cropland, water body, woodland). The selected LLPs and LCPs are listed in Supplementary Table A2 . With the support of ArcGIS 10.2, the ArcGrid raster format images from 2000 to 2020 are imported in Fragstats 3.4. Background values of landscape types are filtered using the Class properties file. Finally, the LLPs and LCPs are selected and calculated.

The surface UHII is calculated based on remotely sensed land surface temperature (LST) data. LST is considered to be strongly connected to near-ground temperature and is commonly employed to investigate the geographic and temporal features of the UHI impact 38 . The LST dataset for the selected cities incorporates synthetic temperature data from MOD11A1 V6.1/LST_Day_1km in July and August. Due to its wide bandwidth, this dataset is well suited to regional-scale cross-sectional data analysis and modeling. Previous research has demonstrated the reliability of MODIS LST results, with errors typically within 1 K (ref. 39 ). The monthly average of the daily LST values is used to calculate the average summer LST for the selected cities in 2000, 2005, 2010, 2015 and 2020.

This study selects a reference line obtained by offsetting 20 km from the urban boundary as the baseline to calculate the surface UHII 40 . A ring equal to the urban area is obtained. The LST of this ring is considered to be unaffected by the UHI footprint and used to calculate the surface UHI. In this approach, the non-urban area used for calculating the surface UHII does not overlap with the RL area, to minimize the impact of UHII variation resulting from the temperature change of RL j .

where \({\mathrm{surface}}\;{\mathrm{UHII}}_{{\mathrm{UL}}_{i}}\) is the surface UHII of UL i in °C; \({\mathrm{LST}}_{{\mathrm{UL}}_{i}}\) is the average LST for UL i in June to August, °C; and \({\mathrm{LST}}_{\mathrm{rural}}\) is the average LST of the ring from June to August, °C.

Demarcation of UL i and RL j

To investigate the impact of rural land cover on UHI mitigation of different urban regions (at a city scale), both urban and rural areas are demarcated for the sake of cross analysis. The cities are divided into five UL i and the selected rural areas are divided into four RL j .

The urban development intensity indicator (UDI i ), which typically exhibits a linear correlation with UHI 41 , is used for the division of urban areas. Different UL i correspond to different UDI i with certain intervals. Each city is subdivided into five UL i to maximize the segmentation of the metropolitan area and prevent the UL i from becoming excessively small and fragmented 42 . There are significant differences in surface UHII between the different UL i shown in Supplementary Table A4 . The city clustering algorithm (CCA) is utilized in this study to define city boundaries 43 . Initially, a city map with a resolution of 3,000 m is generated using an UDI threshold higher than 25% (ref. 44 ). UDI is calculated as the proportion of total number of impervious grids within each 3,000 × 3,000 m pane 45 , as shown in equation ( 2 ). Subsequently, the urban area is identified using CCA with a clustering parameter of 3,000 m, corresponding to the spatial resolution of the initial urban map 45 . Therefore, two pixels in the city maps previously processed through UDI with a distance between pixel centers not exceeding 3,000 m (clustering parameter of CCA) is assigned to the same city. Then, the complete shape of an urban area is obtained and the periphery of the urban area is extracted as the urban boundary. Finally, UDI intervals within the urban area are further subdivided to better represent the changes in UHI along the UDI. With an UDI interval of 15%, five UL i of UL 1 (UDI = 85–100%, UL 2 (UDI = 70–85%), UL 3 (UDI = 55–70%), UL 4 (UDI = 40–55%) and UL 5 (UDI = 25–40%), were derived, as illustrated in Fig. 1 .

where i denotes the i th image element on the raster map; UHI i denotes the Urban Development Intensity value of the i th image element; S i denotes the total area of the i th image element; and S Impervious surface, i denotes the area of imperviousness in the i th image element.

The RL j outside the urban area is delineated to ascertain the extent of rural land cover that can exert the most significant impact on the UHI. In previous studies, the widely adopted approach for determining RL j radius is to make the RL j area equal to the urban area 46 or to employ a uniform RL j radius of 5 km or 10 km (refs. 47 , 48 ). However, when the RL j radius is too small, for instance, less than 1 km, it becomes challenging to reflect the true rural LST and there is also likelihood of it being influenced by the UHI footprint 49 . Therefore, in this study, we adopt a varying RL j radius methodology 50 . On the basis of the selection of the rural region in most UHI mitigation studies and the distances between urban boundaries of sample cities (approximately 40 km). We tentatively establish a maximum study area of 20 km from the urban boundaries for rural land cover. The rural area within this boundary is also in close proximity to the metropolitan area. The width of the RL j radius, denoted as D , is determined to change by one level at a distance of 5 km. This choice aligns with the 900-m resolution of rural land cover, and the observed land cover changes between different RL j are significant. Thus, the urban boundary is offset outward by four RL j , each with varying ring widths. As shown in Fig. 1 , the minimum and maximum radii for these RL j are set as 2.5 km and 5 km (RL 1 ), 5 km and 10 km (RL 2 ), 10 km and 15 km (RL 3 ) and 15 km and 20 km (RL 4 ), respectively. The specific RL j radius of each city is calculated by equation ( 3 ).

where D is the radius of the RL j ; D min is the minimum radius of the RL j ; D max is the maximum radius of the RL j ; S is the urban area of the sample cities; S min is the minimum metropolitan area among all sample cities; and S max is the maximum urban area among all sample cities.

Analytic method to determine rural land cover regions

After dividing urban and rural areas, the rural region (RL j ) to maximize the surface UHI mitigation for each urban area can be determined by R 2 . R 2 is a parameter reflecting the goodness of a regression model 51 . This statistic also indicates the percentage of variance in the dependent variable that is jointly explained by the independent variables. In other words, R 2 offers a measure of how effectively the variations in surface UHII can be explained by the NRLC in the regression model 52 . The greater the extent to which rural land cover elucidates variations in the UHI, the more pronounced its influence on UHI dynamics and its efficacy in mitigating UHI effects. On the basis of this, seven machine learning models (Lasso regression, Ridge regression, ElasticNet regression, Random Forest regression, Support Vector regression, K-Nearest Neighbors regression and Multilayer Perceptron regression) are used to train the dataset (independent variable: LLPs and LCPs, dependent variable: surface UHII) in this study. These models are selected because they are significantly different in training methods and recognized for their ability to train high-dimensional data with various capabilities 53 . To achieve better training results, a tenfold cross validation is used to tune the model parameters. To prevent model overfitting, the dataset is divided into training (70%) and test (30%) sets. Random seeds may significantly affect the model training outcomes 54 . To ensure the robustness of the results, random seeds controlling the division of the training and test sets are not defined, and the model output parameters are averaged over multiple training sessions 55 . The average values of model output parameters reach equilibrium when the number of trainings reaches 100. The R 2 of each regression model in this study is the average value obtained after training 100 times. The non-parametric test is also used to test for significant differences in the training results of different regression models, showing significant variability between the models. In this context, a single example will yield seven distinct R 2 values derived from seven separate regression models. The highest among these seven R 2 values is extracted to measure the degree to which rural land cover accounts for variations in UHI changes, termed as the ‘explanation degree’, as depicted in equation ( 4 ). The next interpretability analyses are carried out based on the model corresponding to the largest R 2 (explanation degree).

where R 2 denotes the extent to which the independent variables in a regression model explain changes in the dependent variable and k represents seven regression models.

By comparing the explanation degree ( R 2 ) of different rural regions (RL j ), the corresponded rural region (RL j ) to maximize the surface UHI mitigation for each urban area (UL i , i  = 1–5) are obtained, and together make up the combinations (RL j –UL i ).

In this study, three linear regression models (including Lasso regression, Ridge regression and ElasticNet regression) and four nonlinear regression models, including Random Forest regression, Support Vector regression, K-Nearest Neighbors regression and Multilayer Perceptron regression, are used for correlation analyses, which are described as follows. Supplementary Fig. A1 shows the distribution of R 2 for these regression models, and the appropriate model is determined by comparing the R 2 values.

Ranking method of key landscape parameters for rural land cover

On the basis of the obtained corresponding combinations (RL j –UL i ), the average marginal contribution of different landscape parameters to UHI changes is calculated by coupling the SHAP model with the best-fit machine learning model obtained in the previous section. SHAP belongs to the method of ex post interpretation. The basic idea is to calculate the marginal contribution of a feature when it is added to the model and then take the mean value, that is, the SHAP baseline value of the feature, considering the different marginal contributions in the case of all feature sequences. The SHAP values of LLPs and LCPs are sorted and accumulated, and when the accumulated amount reaches 80% of the total number, the parameters whose SHAP values are accumulated are chosen. Because these parameters may not be independent, considering all these parameters may affect or even restrict each other when applying. Therefore, the parameter screening is done by the correlation analysis. Most of the existing data analysis studies use 0.5 to 0.7 as the correlation value for high-dimensional parameter screening 56 , 57 , and we chose the middle value of 0.6 as the screening value for this study. In the set of data with correlation coefficients greater than 0.6, parameters with smaller SHAP values are eliminated. This is because the larger the SHAP value, the larger the effect of the parameter on the UHI within its own range of variation.

Influence of the key rural landscape parameters responding to UHI

ALE are used to recognize the influencing patterns of key landscape parameters on UHI mitigation. ALE is a global explanation technique that can describe how key parameters affect the prediction values from a machine learning model, which is a faster and unbiased alternative to partial dependence plots. In this study, ALE can examine the relationship between feature values (that is, landscape parameters) and target variables (that is, UHII). ALE averages and adds the difference in predictions throughout the key landscape parameters, thereby isolating the impacts of each feature value, which is at the cost of a greater number of observations and a nearly uniform distribution. Overall, the ALE model shows the main effects of individual predictor variables and their second-order interactions in black-box supervised learning models that are easy to understand. According to the interactive relationships between variables, ALE plots can be generated based on the fitted supervised learning model.

Data analysis

The data analysis process is shown in Supplementary Fig. A17 , which can be considered as a nesting of three loops. The first level of the loop is to train the LLPs and LCPs (independent variable) for a specific RL j and the UHII (dependent variable) for a particular UL i through seven machine learning models. The regression model with the largest R 2 is considered as the best trained. This regression model and the corresponding R 2 , that is, explanation degree, are the output terms of the first layer of the loop. The first loop is to obtain the best-trained model for the specified UL i and RL j (corresponding combination). The second level of the loop, based on the result of the first loop, can be used to compare the extent to which the land cover of different RL j affects the UHI of a specified UL i . At the end, to output the rural region that has the most significant effect on the UHI of a specified UL i . The first two layers of the cycle allow the first objective of this study to be achieved. The criterion for judging whether to continue analyzing the key parameters and patterns of the impact of rural land affects the UHI (the third level of the cycle) is whether there is a RL j of land cover that has a greater than 0° impact on the intensity of the heat island in that urban ladder. The third level of the cycle performs the previous steps once in each of the five UL i to obtain the region of rural land cover that has the most significant impact on the UHI of the respective UL i and the impact extent of rural land cover on the UHI, that is, explanation degree. If explanation degree is less than 0, it suggests that the UHI of this UL i is not affected by rural land cover. If explanation degree is greater than 0, the LLPs and LCPs of rural land cover are ranked from the largest to the smallest by SHAP value. Accumulation starts with the first SHAP value and stops, when the value reaches 80% of the total. The accumulated LLPs and LCPs are subjected to the correlation analysis, and parameters with lower SHAP rankings in the set of parameters with correlation coefficients greater than 0.6 are deleted, which aims to identify the key parameters of rural land cover affecting the UHI. Finally, ALE plots of these key parameters are plotted to explain the pattern of UHI response to the key parameters. At this point, the last question of this study is answered.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

Landsat 5/8 Level 2, Collection 2, Tier 1 dataset and MOD11A1 V6.1 product are available through Google Earth Engine platform at http://code.earthengine.google.com . The dataset produced and used in this study is available as Supplementary Information and via Zenodo at https://doi.org/10.5281/zenodo.10424322 (ref. 58 ).

Code availability

The code used to produce and analyze the data in this study is available via Zenodo at https://doi.org/10.5281/zenodo.10424322 (ref. 58 ).

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Acknowledgements

We disclose support for this work from National Natural Science Funds for Distinguished Young Scholar (grant number 52225005). We greatly appreciate the free access to the Landsat data provided by the United States Geological Survey (USGS) and MOD11A1 V6.1 product provided by the USGS and National Aeronautics and Space Administration (NASA). We thank the Google Earth Engine team for their excellent work to maintain the planetary-scale geospatial cloud platform.

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Miao Yang, Chen Ren, Haorui Wang, Junqi Wang, Zhuangbo Feng, Prashant Kumar, Fariborz Haghighat & Shi-Jie Cao

Jiangsu Province Engineering Research Center of Urban Heat and Pollution Control, Southeast University, Nanjing, China

Miao Yang, Chen Ren, Haorui Wang, Junqi Wang, Zhuangbo Feng & Shi-Jie Cao

Global Centre for Clean Air Research, School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey, UK

Prashant Kumar & Shi-Jie Cao

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M.Y.: writing–original draft, conceptualization, data curation, formal analysis, investigation, methodology, software, visualization. C.R.: writing–original draft, writing–review and editing, methodology. H.W.: writing–original draft, writing–review and editing, methodology, software. J.W.: writing–review and editing, methodology, project administration. Z.F.: writing–review and editing, methodology, conceptualization. P.K.: writing–review and editing, methodology. F.H.: writing–review and editing, methodology, conceptualization. S.-J.C.: writing–review and editing, conceptualization, funding acquisition, project administration, resources, supervision, visualization.

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Correspondence to Shi-Jie Cao .

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Extended data

Extended data fig. 1.

Framework of this work .

Extended Data Fig. 2

Locations of the 30 selected cities in the mainland China .

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Supplementary information.

Appendix A Figs. 1–17, Tables 1–4; Appendix B Figs. 1–2; Appendix C Figs. 1–16, Table 1, Discussions of variables, Statistics and Regressions.

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Yang, M., Ren, C., Wang, H. et al. Mitigating urban heat island through neighboring rural land cover. Nat Cities 1 , 522–532 (2024). https://doi.org/10.1038/s44284-024-00091-z

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