Pub Date : 2026-01-15DOI: 10.1016/j.scs.2026.107155
Yaobin Liu , Sheng Hu , Shuoshuo Li , Weifeng Deng
Climate risks threaten the sustainable development of urban economies, societies, and ecosystems. Urban climate resilience (UCR) is difficult to comprehensively improve through single policies. Forest cities and smart cities are typical examples of nature-based and technology-driven solutions, which have been extensively studied for their environmental impacts. However, it remains uncertain whether dual pilot policies (DPP) for forest cities and smart cities can synergistically enhance UCR. Based on the social-ecological-technological systems (SETS) perspective, this paper employs the staggered DID models to examine the policy synergetic effects of DPP on UCR. Results show that DPP increases UCR by an average of 5.43%. DPP establishes comprehensive adaptation mechanisms, including expanding green spaces, developing digital infrastructure, and promoting green technological innovation. The policy effect is more pronounced in inland cities, small cities, and cities with lower climate risks. While boosting the local UCR, DPP also improves the average UCR by 27.00% in neighboring areas. Our research highlights the synergistic governance between ecological and technological systems. It provides empirical evidence for climate adaptation through green and smart transformations in rapidly urbanizing regions.
{"title":"Toward greener, smarter, and more resilient cities: Assessing the impact of dual pilot policies of forest city and smart city on urban climate resilience in China","authors":"Yaobin Liu , Sheng Hu , Shuoshuo Li , Weifeng Deng","doi":"10.1016/j.scs.2026.107155","DOIUrl":"10.1016/j.scs.2026.107155","url":null,"abstract":"<div><div>Climate risks threaten the sustainable development of urban economies, societies, and ecosystems. Urban climate resilience (UCR) is difficult to comprehensively improve through single policies. Forest cities and smart cities are typical examples of nature-based and technology-driven solutions, which have been extensively studied for their environmental impacts. However, it remains uncertain whether dual pilot policies (DPP) for forest cities and smart cities can synergistically enhance UCR. Based on the social-ecological-technological systems (SETS) perspective, this paper employs the staggered DID models to examine the policy synergetic effects of DPP on UCR. Results show that DPP increases UCR by an average of 5.43%. DPP establishes comprehensive adaptation mechanisms, including expanding green spaces, developing digital infrastructure, and promoting green technological innovation. The policy effect is more pronounced in inland cities, small cities, and cities with lower climate risks. While boosting the local UCR, DPP also improves the average UCR by 27.00% in neighboring areas. Our research highlights the synergistic governance between ecological and technological systems. It provides empirical evidence for climate adaptation through green and smart transformations in rapidly urbanizing regions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107155"},"PeriodicalIF":12.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107157
Junhao Wu , Yuanpeng Tang , Ling Ma , Dongfang Liang , Ioannis Brilakis , Svetlana Besklubova
Under the combined pressures of intensified extreme rainfall and accelerating impervious urban expansion, pluvial flooding has emerged as a dominant threat to urban safety and sustainability. Conventional flood-susceptibility models have faced challenges in handling highly sparse, long-tailed target distributions and in providing physical interpretability, which has limited the fine-scale delineation of flood-prone cells and the development of differentiated mitigation strategies. To address this issue, an integrated GeoAI-based framework was developed to systematically links urban surface characteristics with socio-hydrological processes for advancing flood-risk governance. The proposed framework synthesizes 25 natural and socio-economic variables to holistically capture flood-generation mechanisms across diverse city contexts. Through a two-stage feature distillation process, the ten most critical drivers shaping flood susceptibility in each city were identified. These drives underpin a novel zero-inflated convolutional self-attention network (ZI-Geo-CNN), which generated high-resolution susceptibility maps for six major Chinese cities with exceptional accuracy ( and . Post‑hoc analysis using Shapley Additive Explanations (SHAP) quantified each driver’s relative contribution, revealing universal controls alongside economy–infrastructure couplings. Based on shared and differentiated patterns of factor importance across cities, this study compared dominant patterns across cities and discussed several indicative adaptation directions. Overall, the framework breaks the accuracy–interpretability trade-off for sparse, long-tailed flood data and furnishes a replicable GeoAI workflow that can be applied consistently across cities through city-specific training, calibration, and interpretation, thereby providing an evidence-informed basis for resilient drainage planning under non-stationary climates.
{"title":"Urban flood susceptibility decoded: A GeoAI workflow for urban flood-prone area delineation and mitigation mechanism inference","authors":"Junhao Wu , Yuanpeng Tang , Ling Ma , Dongfang Liang , Ioannis Brilakis , Svetlana Besklubova","doi":"10.1016/j.scs.2026.107157","DOIUrl":"10.1016/j.scs.2026.107157","url":null,"abstract":"<div><div>Under the combined pressures of intensified extreme rainfall and accelerating impervious urban expansion, pluvial flooding has emerged as a dominant threat to urban safety and sustainability. Conventional flood-susceptibility models have faced challenges in handling highly sparse, long-tailed target distributions and in providing physical interpretability, which has limited the fine-scale delineation of flood-prone cells and the development of differentiated mitigation strategies. To address this issue, an integrated GeoAI-based framework was developed to systematically links urban surface characteristics with socio-hydrological processes for advancing flood-risk governance. The proposed framework synthesizes 25 natural and socio-economic variables to holistically capture flood-generation mechanisms across diverse city contexts. Through a two-stage feature distillation process, the ten most critical drivers shaping flood susceptibility in each city were identified. These drives underpin a novel zero-inflated convolutional self-attention network (ZI-Geo-CNN), which generated high-resolution susceptibility maps for six major Chinese cities with exceptional accuracy (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup><mo>></mo><mn>0.98</mn><mo>,</mo><mrow><mspace></mspace></mrow><mi>A</mi><mi>U</mi><mi>C</mi><mo>≈</mo><mn>1.00</mn><mo>,</mo></mrow></math></span> and <span><math><mrow><mi>S</mi><mi>M</mi><mi>A</mi><mi>P</mi><mi>E</mi><mo><</mo><mn>13</mn><mo>%</mo><mo>)</mo></mrow></math></span>. Post‑hoc analysis using Shapley Additive Explanations (SHAP) quantified each driver’s relative contribution, revealing universal controls alongside economy–infrastructure couplings. Based on shared and differentiated patterns of factor importance across cities, this study compared dominant patterns across cities and discussed several indicative adaptation directions. Overall, the framework breaks the accuracy–interpretability trade-off for sparse, long-tailed flood data and furnishes a replicable GeoAI workflow that can be applied consistently across cities through city-specific training, calibration, and interpretation, thereby providing an evidence-informed basis for resilient drainage planning under non-stationary climates.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107157"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107124
Bo Jiang , Hongtao Lei , Wenhua Li , Kai Xu , Yajie Liu , Tao Zhang
With rising energy demand and advances in energy conversion technologies, expansion planning for existing integrated energy systems is increasingly urgent, which is essential for improving efficiency and supply stability while reducing long-term costs. Additionally, the rising frequency of extreme disasters underscores the necessity of incorporating resilience alongside economic considerations in planning processes. To address these dual requirements of economic performance and resilience, this paper proposes a multi-objective two-stage stochastic programming model. In the first stage (planning stage), the model aims to minimize total costs while maximizing a standardized resilience index (RI) to determine the optimal expansion plan for the integrated energy system. In the second stage (operation stage), the model simulates both normal and fault modes to evaluate operational costs and RI values, feeding the results back to further improve the planning stage. Operational strategies aimed at either economic performance or resilience are developed for the two modes to effectively manage the model’s computational complexity. To efficiently solve the proposed multi-objective model, a diversity-enhanced evolutionary algorithm with a knowledge-guided offspring generation method (DeEA/K) is employed, yielding a uniformly distributed Pareto front. The experimental results demonstrate that the proposed method can achieve high-quality multi-objective expansion planning solutions, and the algorithm exhibits strong performance on mixed-integer optimization problems.
{"title":"Co-optimization of expansion planning and dual-mode operations for regional integrated energy systems considering resilience enhancement under multiple uncertainties","authors":"Bo Jiang , Hongtao Lei , Wenhua Li , Kai Xu , Yajie Liu , Tao Zhang","doi":"10.1016/j.scs.2026.107124","DOIUrl":"10.1016/j.scs.2026.107124","url":null,"abstract":"<div><div>With rising energy demand and advances in energy conversion technologies, expansion planning for existing integrated energy systems is increasingly urgent, which is essential for improving efficiency and supply stability while reducing long-term costs. Additionally, the rising frequency of extreme disasters underscores the necessity of incorporating resilience alongside economic considerations in planning processes. To address these dual requirements of economic performance and resilience, this paper proposes a multi-objective two-stage stochastic programming model. In the first stage (planning stage), the model aims to minimize total costs while maximizing a standardized resilience index (RI) to determine the optimal expansion plan for the integrated energy system. In the second stage (operation stage), the model simulates both normal and fault modes to evaluate operational costs and RI values, feeding the results back to further improve the planning stage. Operational strategies aimed at either economic performance or resilience are developed for the two modes to effectively manage the model’s computational complexity. To efficiently solve the proposed multi-objective model, a diversity-enhanced evolutionary algorithm with a knowledge-guided offspring generation method (DeEA/K) is employed, yielding a uniformly distributed Pareto front. The experimental results demonstrate that the proposed method can achieve high-quality multi-objective expansion planning solutions, and the algorithm exhibits strong performance on mixed-integer optimization problems.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107124"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107159
Ke Lu , Jingfang Hu , Tingyu Shang , Yuan Xu
Despite explosive growth of integrated ride-hailing services (IRHS), the impact on long-term behavioral pattern has been little examined. This study intends to investigate travelers’ continuance behavioral intention towards IRHS, using a theoretical framework based on Expectation Confirmation Model (ECM). Moreover, four IRHS-specific feature variables are included, such as compatibility, hassle cost, convenience, and security. Further, this study introduces habit as moderating variable. Moreover, socio-demographic factors are considered as control variables, including gender, age, income, and educational level. With data collected from Nanjing, China, an empirical analysis is conducted using hybrid approach of Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The findings indicate that perceived usefulness, satisfaction, and expectation confirmation are key determinants. Noteworthily, perceived usefulness exhibits as more important than expectation confirmation. Further, it shows that all IRHS-specific features play crucial roles. Specifically, compatibility and hassle cost show stronger influence on expectation confirmation, while convenience and security affect more on perceived usefulness. Habit acts as a moderator within relationships between expectation confirmation and satisfaction, and satisfaction and continuance behavioral intention. Additionally, travelers’ continuance intention is negatively related to age and education level. These findings shed valuable insights for understanding the general pattern of travelers’ behavior, and add practical value for platforms and policymakers.
{"title":"To be integrated or not? Understanding continuance behavioral intention towards integrated ride-hailing services: Empirical evidence from Nanjing, China","authors":"Ke Lu , Jingfang Hu , Tingyu Shang , Yuan Xu","doi":"10.1016/j.scs.2026.107159","DOIUrl":"10.1016/j.scs.2026.107159","url":null,"abstract":"<div><div>Despite explosive growth of integrated ride-hailing services (IRHS), the impact on long-term behavioral pattern has been little examined. This study intends to investigate travelers’ continuance behavioral intention towards IRHS, using a theoretical framework based on Expectation Confirmation Model (ECM). Moreover, four IRHS-specific feature variables are included, such as compatibility, hassle cost, convenience, and security. Further, this study introduces habit as moderating variable. Moreover, socio-demographic factors are considered as control variables, including gender, age, income, and educational level. With data collected from Nanjing, China, an empirical analysis is conducted using hybrid approach of Partial Least Square Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN). The findings indicate that perceived usefulness, satisfaction, and expectation confirmation are key determinants. Noteworthily, perceived usefulness exhibits as more important than expectation confirmation. Further, it shows that all IRHS-specific features play crucial roles. Specifically, compatibility and hassle cost show stronger influence on expectation confirmation, while convenience and security affect more on perceived usefulness. Habit acts as a moderator within relationships between expectation confirmation and satisfaction, and satisfaction and continuance behavioral intention. Additionally, travelers’ continuance intention is negatively related to age and education level. These findings shed valuable insights for understanding the general pattern of travelers’ behavior, and add practical value for platforms and policymakers.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107159"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Against the background of sustainable development, the effective management of urban carbon emissions has become a critical governance issue. However, under the urban hierarchical framework, the tasks of controlling carbon emissions and pursuing sustainable development are significantly complex. Some categories of emissions often exhibit characteristics of nonhierarchical emissions (NHEs) that are detached from the urban hierarchy. For the innovation, different from ordinary hierarchy-based research, the purpose of NHEs is to uncover the spatial mechanism of carbon emissions that are not constrained by administrative or economic hierarchies. Based on prefecture-level city data from four selected years in China (2010, 2015, 2019, and 2022), this study employs a spatial error model (SEM) and local spatial autocorrelation analysis (LISA) to capture the geographical distribution of NHE across the country. Furthermore, a core distance metric is employed to investigate the spatial distribution patterns of NHEs and their relationships with regional core cities. The study finds that the NHEs related to consumption are more strongly associated with a significant polarization effect. These cities are located in the area around the core cities, where population agglomeration leads to unique spatial patterns in specific categories of carbon emissions. In contrast, production-oriented NHEs are in the inner land. These categories of emissions often exhibit NHEs due to differences in resource endowments among cities. These findings provide a foundation for the localized governance of carbon emissions in noncore cities and introduce a place-based research perspective into carbon emission management.
{"title":"Beyond urban hierarchy: unveiling the spatial patterns of nonhierarchical carbon emissions","authors":"Jianyu Li, Mingxing Hu, Shumin Wang, Ziye Liu, Jingyue Huang","doi":"10.1016/j.scs.2026.107160","DOIUrl":"10.1016/j.scs.2026.107160","url":null,"abstract":"<div><div>Against the background of sustainable development, the effective management of urban carbon emissions has become a critical governance issue. However, under the urban hierarchical framework, the tasks of controlling carbon emissions and pursuing sustainable development are significantly complex. Some categories of emissions often exhibit characteristics of nonhierarchical emissions (NHEs) that are detached from the urban hierarchy. For the innovation, different from ordinary hierarchy-based research, the purpose of NHEs is to uncover the spatial mechanism of carbon emissions that are not constrained by administrative or economic hierarchies. Based on prefecture-level city data from four selected years in China (2010, 2015, 2019, and 2022), this study employs a spatial error model (SEM) and local spatial autocorrelation analysis (LISA) to capture the geographical distribution of NHE across the country. Furthermore, a core distance metric is employed to investigate the spatial distribution patterns of NHEs and their relationships with regional core cities. The study finds that the NHEs related to consumption are more strongly associated with a significant polarization effect. These cities are located in the area around the core cities, where population agglomeration leads to unique spatial patterns in specific categories of carbon emissions. In contrast, production-oriented NHEs are in the inner land. These categories of emissions often exhibit NHEs due to differences in resource endowments among cities. These findings provide a foundation for the localized governance of carbon emissions in noncore cities and introduce a place-based research perspective into carbon emission management.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107160"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107158
Javed Mallick , Hoang Thi Hang , Alok Das , Sayanti Poddar , Chander Kumar Singh
Urban flooding has become a major challenge in fast-growing cities worldwide, disrupting mobility, damaging infrastructure, and increasing social vulnerability. In Bengaluru, where rapid urban expansion and inadequate drainage exacerbate monsoon floods, this study develops an integrated and data-driven framework to map flood susceptibility and exposure between 2020 and 2025. The main objective is to generate a scientifically validated, high-resolution, and explainable flood-risk assessment model combining radar-based detection, machine learning, and social-infrastructure exposure analytics. Multi-temporal Sentinel-1 dual-polarisation (VV and VH) SAR data were used to detect flood events through backscatter change analysis, ensuring accurate mapping even under cloudy conditions. An ensemble of five algorithms-Random Forest, Histogram-based Gradient Boosting, XGBoost, Quadratic Discriminant Analysis, and Gaussian Naïve Bayes-was trained and optimized to capture complex spatial relationships between floods and controlling parameters. Explainable AI (SHAP, permutation, ablation) to identify key drivers for transparent planning. Probability calibration and reliability curves to check and correct forecast bias infrastructure and demographic overlays to quantify who and what is exposed. Results show that the ensemble delivered AUC = 0.985 with accuracy 78–83%, precision 0.78–0.83, recall 0.79–0.83, and F1 ≈ 0.80 across six independent flood events; calibration improved reliability with well-aligned predicted vs observed probabilities. Spatially, 32.63 km² were mapped as Very High and 61.73 km² as High susceptibility, with stable hotspots in Electronic City, Bommanahalli, Mahadevapura, and Bellandur-Varthur. Road exposure was dominated by local streets (62,402 segments) and tertiary roads (3505 segments), indicating neighbourhood-scale disruption; female and schedule caste (SC) populations showed disproportional exposure in central/eastern wards, while schedule tribe (ST) exposure was lower but non-negligible on the periphery. This analysis enable ward-wise prioritisation of drain upgrades, permeable retrofits, safe-route planning, and targeted protection of at-risk groups, aligning with the Sendai Framework and sustainable development goals (SDG)-11/13.
{"title":"Integrating ensemble machine learning and SAR-based geospatial modelling for inclusive and equitable urban flood resilience","authors":"Javed Mallick , Hoang Thi Hang , Alok Das , Sayanti Poddar , Chander Kumar Singh","doi":"10.1016/j.scs.2026.107158","DOIUrl":"10.1016/j.scs.2026.107158","url":null,"abstract":"<div><div>Urban flooding has become a major challenge in fast-growing cities worldwide, disrupting mobility, damaging infrastructure, and increasing social vulnerability. In Bengaluru, where rapid urban expansion and inadequate drainage exacerbate monsoon floods, this study develops an integrated and data-driven framework to map flood susceptibility and exposure between 2020 and 2025. The main objective is to generate a scientifically validated, high-resolution, and explainable flood-risk assessment model combining radar-based detection, machine learning, and social-infrastructure exposure analytics. Multi-temporal Sentinel-1 dual-polarisation (VV and VH) SAR data were used to detect flood events through backscatter change analysis, ensuring accurate mapping even under cloudy conditions. An ensemble of five algorithms-Random Forest, Histogram-based Gradient Boosting, XGBoost, Quadratic Discriminant Analysis, and Gaussian Naïve Bayes-was trained and optimized to capture complex spatial relationships between floods and controlling parameters. Explainable AI (SHAP, permutation, ablation) to identify key drivers for transparent planning. Probability calibration and reliability curves to check and correct forecast bias infrastructure and demographic overlays to quantify who and what is exposed. Results show that the ensemble delivered AUC = 0.985 with accuracy 78–83%, precision 0.78–0.83, recall 0.79–0.83, and F1 ≈ 0.80 across six independent flood events; calibration improved reliability with well-aligned predicted vs observed probabilities. Spatially, 32.63 km² were mapped as Very High and 61.73 km² as High susceptibility, with stable hotspots in Electronic City, Bommanahalli, Mahadevapura, and Bellandur-Varthur. Road exposure was dominated by local streets (62,402 segments) and tertiary roads (3505 segments), indicating neighbourhood-scale disruption; female and schedule caste (SC) populations showed disproportional exposure in central/eastern wards, while schedule tribe (ST) exposure was lower but non-negligible on the periphery. This analysis enable ward-wise prioritisation of drain upgrades, permeable retrofits, safe-route planning, and targeted protection of at-risk groups, aligning with the Sendai Framework and sustainable development goals (SDG)-11/13.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107158"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107162
Xi Wang , Hua Shang , Xiaofei Lv , Sai Yuan
The synergistic governance effects of multi-objective environmental regulations are crucial for achieving coordinated economic and ecological development. Nevertheless, existing investigations have primarily focused on their singular effects on either the environment or the economy, while their synergistic governance effects require further validation. Utilizing urban data from China spanning 2007 to 2023, we construct a policy synergies variable for pollution reduction and low-carbon (PPCR) based on the Key Air Quality Control Zone Policy (KACP) and the Low-Carbon City Pilot Policy (LCCP). Then, we employ the Difference-in-Differences (DID) model to investigate the composite effects of PPCR on urban economic resilience (EOR) and environmental performance (PCR). The findings indicate that PPCR significantly enhances both EOR and PCR, with this conclusion demonstrating robustness. Meanwhile, the mechanism analysis reveals that the primary channels fostering EOR are the high-skilled talent siphoning and labor productivity-driven effects. Energy structure optimization and circular economy initiatives serve as significant pathways for PPCR to enhance PCR. Furthermore, the enabling role of PPCR is even stronger in cities with non-resource-based economies and strong public-oriented environmental regulations (PER).
{"title":"The compounding effects of pollution reduction and low-carbon policy synergies on urban economic resilience and environmental performance","authors":"Xi Wang , Hua Shang , Xiaofei Lv , Sai Yuan","doi":"10.1016/j.scs.2026.107162","DOIUrl":"10.1016/j.scs.2026.107162","url":null,"abstract":"<div><div>The synergistic governance effects of multi-objective environmental regulations are crucial for achieving coordinated economic and ecological development. Nevertheless, existing investigations have primarily focused on their singular effects on either the environment or the economy, while their synergistic governance effects require further validation. Utilizing urban data from China spanning 2007 to 2023, we construct a policy synergies variable for pollution reduction and low-carbon (<em>PPCR</em>) based on the Key Air Quality Control Zone Policy (<em>KACP</em>) and the Low-Carbon City Pilot Policy (<em>LCCP</em>). Then, we employ the Difference-in-Differences (<em>DID</em>) model to investigate the composite effects of <em>PPCR</em> on urban economic resilience (<em>EOR</em>) and environmental performance <em>(PCR</em>). The findings indicate that <em>PPCR</em> significantly enhances both <em>EOR</em> and <em>PCR</em>, with this conclusion demonstrating robustness. Meanwhile, the mechanism analysis reveals that the primary channels fostering <em>EOR</em> are the high-skilled talent siphoning and labor productivity-driven effects. Energy structure optimization and circular economy initiatives serve as significant pathways for <em>PPCR</em> to enhance <em>PCR</em>. Furthermore, the enabling role of <em>PPCR</em> is even stronger in cities with non-resource-based economies and strong public-oriented environmental regulations (<em>PER</em>).</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107162"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-14DOI: 10.1016/j.scs.2026.107161
Aihemaiti Namaiti , Suiping Zeng , Weijie He , Xiang Liu , Jian Zeng
Understanding the spatiotemporal heterogeneity of diurnal urban heat exposure is crucial for addressing urban heat governance challenges. However, most studies focus on either urban form or function, lacking an integrated perspective to fully capture heat exposure heterogeneity. This study, using Nanchang, a typical Chinese “furnace city,” as a case study, proposes a novel form-function coupling framework. Through K-means clustering’s flexibility and local adaptability, it divided the study area into 12 homogeneous form-function clusters. By integrating high-resolution ECOSTRESS LST and mobile signaling data, it assessed diurnal heat exposure levels and their heterogeneity with precision. The High-Risk Contribution Index (HCI) was introduced to quantify each cluster’s contribution to high heat exposure risk. Results showed that the 12 clusters, based on form-function coupling, exhibited distinct heat exposure patterns, effectively capturing urban heat exposure heterogeneity. Kruskal-Wallis H tests and post hoc multiple comparisons confirmed highly significant differences in heat exposure among clusters across all time points (H values 3713.242–4367.439, p<0.001), with 71.21 %–80.30 % of pairwise comparisons showing significant differences (p<0.05). Three contribution patterns emerged: (1) consistently high contribution (clusters 8, 9, 10, 11, 12; average HCI >2; high-density commercial and residential zones), requiring priority intervention; (2) consistently low contribution (clusters 1, 2, 3, 4; average HCI <0.6; ecological zones), needing protection to leverage their “cool source” role; and (3) diurnal variation (clusters 6, 7; daytime HCI >1, nighttime <1; influenced by industrial activity timing), requiring flexible interventions based on production schedules. These findings provide a replicable paradigm for precise, dynamic, and localized urban heat exposure governance and offer a theoretical-methodological framework for similar cities, enhancing the scientific rigor and practicality of heat governance strategies.
{"title":"Exploring diurnal spatiotemporal heterogeneity in urban heat exposure: A novel perspective from urban form-function coupling","authors":"Aihemaiti Namaiti , Suiping Zeng , Weijie He , Xiang Liu , Jian Zeng","doi":"10.1016/j.scs.2026.107161","DOIUrl":"10.1016/j.scs.2026.107161","url":null,"abstract":"<div><div>Understanding the spatiotemporal heterogeneity of diurnal urban heat exposure is crucial for addressing urban heat governance challenges. However, most studies focus on either urban form or function, lacking an integrated perspective to fully capture heat exposure heterogeneity. This study, using Nanchang, a typical Chinese “furnace city,” as a case study, proposes a novel form-function coupling framework. Through K-means clustering’s flexibility and local adaptability, it divided the study area into 12 homogeneous form-function clusters. By integrating high-resolution ECOSTRESS LST and mobile signaling data, it assessed diurnal heat exposure levels and their heterogeneity with precision. The High-Risk Contribution Index (HCI) was introduced to quantify each cluster’s contribution to high heat exposure risk. Results showed that the 12 clusters, based on form-function coupling, exhibited distinct heat exposure patterns, effectively capturing urban heat exposure heterogeneity. Kruskal-Wallis H tests and post hoc multiple comparisons confirmed highly significant differences in heat exposure among clusters across all time points (H values 3713.242–4367.439, p<0.001), with 71.21 %–80.30 % of pairwise comparisons showing significant differences (p<0.05). Three contribution patterns emerged: (1) consistently high contribution (clusters 8, 9, 10, 11, 12; average HCI >2; high-density commercial and residential zones), requiring priority intervention; (2) consistently low contribution (clusters 1, 2, 3, 4; average HCI <0.6; ecological zones), needing protection to leverage their “cool source” role; and (3) diurnal variation (clusters 6, 7; daytime HCI >1, nighttime <1; influenced by industrial activity timing), requiring flexible interventions based on production schedules. These findings provide a replicable paradigm for precise, dynamic, and localized urban heat exposure governance and offer a theoretical-methodological framework for similar cities, enhancing the scientific rigor and practicality of heat governance strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107161"},"PeriodicalIF":12.0,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146025731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.scs.2026.107156
Liang Tang , Runyu Shao , Xinran Zhou , Yali Zhang , Ziyi Chen , Long Yang , Hui Li
Under the dual pressures of global climate change and rapid urbanization, the thermal environment of high-density megacities has become increasingly complex, with intensified heat risks and spatial heterogeneity. Taking Guangzhou as a case study, this research integrates multi-temporal remote sensing data to construct a cold-source–heat-sink network and proposes an analytical paradigm of “network framework–spatial dynamics–targeted implementation” to uncover the spatiotemporal evolution and ventilation-coupling mechanisms of urban thermal systems. From 2004 to 2023, Guangzhou exhibited a three-stage thermal evolution pathway—“aggregation–fragmentation–reconstruction.” Cold sources first contracted and later re-expanded, shifting from fragmented patches to renewed agglomeration, while core heat sinks continuously enlarged and merged northward, intensifying the urban heat island effect. Circuit-based modeling revealed a 38% decline in source–sink corridors and an increase in ventilation pinch points from 11 to 23, forming high-resistance bottlenecks that weakened cold–heat coupling across urban transition zones. Topological diagnostics further showed that the thermal network evolved from a “multi-core–high-connectivity” configuration to a “centralized–vulnerable” structure, followed by a stage of “localized recovery–structural rebuilding.” The identified three-stage trajectory highlights the coupled reorganization of cold/heat sources and ventilation corridors, offering a dynamic perspective on the mechanisms underlying urban heat risk formation. This study advances the theoretical understanding of cold–heat interaction networks, demonstrates the synergistic value of combining circuit theory with topological metrics, and proposes a four-tier coordinated regulation strategy—cold-source preservation, heat-sink mitigation, corridor optimization, and node restoration—to support refined thermal governance and resilience enhancement in megacities.
{"title":"Topological and source–sink integrated analysis of urban thermal environment networks in a megacity: Longitudinal insights from Guangzhou","authors":"Liang Tang , Runyu Shao , Xinran Zhou , Yali Zhang , Ziyi Chen , Long Yang , Hui Li","doi":"10.1016/j.scs.2026.107156","DOIUrl":"10.1016/j.scs.2026.107156","url":null,"abstract":"<div><div>Under the dual pressures of global climate change and rapid urbanization, the thermal environment of high-density megacities has become increasingly complex, with intensified heat risks and spatial heterogeneity. Taking Guangzhou as a case study, this research integrates multi-temporal remote sensing data to construct a cold-source–heat-sink network and proposes an analytical paradigm of “network framework–spatial dynamics–targeted implementation” to uncover the spatiotemporal evolution and ventilation-coupling mechanisms of urban thermal systems. From 2004 to 2023, Guangzhou exhibited a three-stage thermal evolution pathway—“aggregation–fragmentation–reconstruction.” Cold sources first contracted and later re-expanded, shifting from fragmented patches to renewed agglomeration, while core heat sinks continuously enlarged and merged northward, intensifying the urban heat island effect. Circuit-based modeling revealed a 38% decline in source–sink corridors and an increase in ventilation pinch points from 11 to 23, forming high-resistance bottlenecks that weakened cold–heat coupling across urban transition zones. Topological diagnostics further showed that the thermal network evolved from a “multi-core–high-connectivity” configuration to a “centralized–vulnerable” structure, followed by a stage of “localized recovery–structural rebuilding.” The identified three-stage trajectory highlights the coupled reorganization of cold/heat sources and ventilation corridors, offering a dynamic perspective on the mechanisms underlying urban heat risk formation. This study advances the theoretical understanding of cold–heat interaction networks, demonstrates the synergistic value of combining circuit theory with topological metrics, and proposes a four-tier coordinated regulation strategy—cold-source preservation, heat-sink mitigation, corridor optimization, and node restoration—to support refined thermal governance and resilience enhancement in megacities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107156"},"PeriodicalIF":12.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1016/j.scs.2026.107154
Shengnan Li , Pu Wang , Qi Liu , Ling Liu
While existing works have extensively documented vehicle emission patterns, the carbon footprint of short-distance vehicle trips (SDTs) remains critically understudied. Here, we employ large-scale License Plate Recognition data from Changsha, China to systematically analyze the emission patterns, influential factors and emission reduction potentials of SDTs. Our analysis indicates that SDTs account for 27.31 % of urban vehicle trips, and the associated CO2 emissions exhibit spatial agglomerations at specific urban areas. By leveraging an interpretable machine learning framework, we identify the land use, demographic and socioeconomic characteristics that exhibit a strong correlation with the volume of SDTs. This study emphasizes the potential to mitigate emissions induced by SDTs. It suggests that with the enhancement of public’s environmental awareness and the promotion of new energy vehicles, daily CO2 emissions caused by SDTs could reduce 172 tons, which are equivalent to 1.23 % of the total CO2 emissions of all small vehicles, providing valuable insights for developing sustainable urban transport.
{"title":"Reducing CO2 emissions from short-distance vehicle trips: A pathway to sustainable urban transport","authors":"Shengnan Li , Pu Wang , Qi Liu , Ling Liu","doi":"10.1016/j.scs.2026.107154","DOIUrl":"10.1016/j.scs.2026.107154","url":null,"abstract":"<div><div>While existing works have extensively documented vehicle emission patterns, the carbon footprint of short-distance vehicle trips (SDTs) remains critically understudied. Here, we employ large-scale License Plate Recognition data from Changsha, China to systematically analyze the emission patterns, influential factors and emission reduction potentials of SDTs. Our analysis indicates that SDTs account for 27.31 % of urban vehicle trips, and the associated CO<sub>2</sub> emissions exhibit spatial agglomerations at specific urban areas. By leveraging an interpretable machine learning framework, we identify the land use, demographic and socioeconomic characteristics that exhibit a strong correlation with the volume of SDTs. This study emphasizes the potential to mitigate emissions induced by SDTs. It suggests that with the enhancement of public’s environmental awareness and the promotion of new energy vehicles, daily CO<sub>2</sub> emissions caused by SDTs could reduce 172 tons, which are equivalent to 1.23 % of the total CO<sub>2</sub> emissions of all small vehicles, providing valuable insights for developing sustainable urban transport.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107154"},"PeriodicalIF":12.0,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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