Pub Date : 2026-01-23DOI: 10.1016/j.scs.2026.107177
Bingyin Chen , Zhiquan Zhu , Wanxue Zhu , Xuemei Wang , Weiwen Wang
Amid accelerating global warming and intensifying urban heat islands, green roofs (GRs) are promoted as nature-based solutions for urban heat mitigation. However, their thermal performance in hot-humid subtropical climates remains underexplored, and observed warming phenomena are often overlooked. Here we use rooftop field experiments in Guangzhou, China, to compare GRs planted with Sedum spp. and Schefflera spp. against a bare roof during summer 2022 heatwaves. Combining high-frequency flux measurements, Bowen ratio energy-balance calculations and structural equation modelling, we analyse the mechanisms of roof–canopy thermal regulation. GRs reduced roof-surface temperature by 4–5 °C, associated with higher albedo (0.16–0.19 vs 0.10) and reduced net radiation (by 10–15 W m-2). Yet at 30 cm height, daytime canopy air temperature over GRs was 0.5–0.7 °C warmer than over the bare roof, with this warming intensifying by 0.06–0.15 °C per 1 °C increase in ambient temperature above 30 °C. We term this vertically non-uniform response the canopy heat-trapping effect, and show that it arises from reduced within-canopy wind speeds and diminished latent heat fluxes under heat stress, which limit the upward export of cool air. These findings challenge the common assumption that GRs provide uniformly cooling effects, and highlight the need to explicitly consider canopy ventilation and plant physiological responses when designing GRs to enhance thermal resilience in heat-vulnerable subtropical cities.
随着全球变暖的加速和城市热岛的加剧，绿色屋顶（GRs）被推广为基于自然的城市热缓解解决方案。然而，它们在亚热带湿热气候下的热性能仍未得到充分研究，观测到的变暖现象往往被忽视。在这里，我们在中国广州进行了屋顶田间试验，比较了在2022年夏季热浪期间种植景天属植物和舍弗勒属植物的GRs与光秃秃的屋顶。结合高频通量测量、波文比能量平衡计算和结构方程建模，分析了顶棚热调节的机理。GRs使屋顶表面温度降低4-5°C，反照率提高（0.16-0.19 vs 0.10），净辐射降低（10-15 W - m-2）。然而，在30厘米高度，GRs上方的日间冠层空气温度比光秃秃的屋顶高0.5-0.7°C，并且在30°C以上，环境温度每增加1°C，这种变暖就会加剧0.06-0.15°C。我们将这种垂直不均匀响应称为冠层吸热效应，并表明它是由于热应力下冠层内风速的降低和潜热通量的减少，这限制了冷空气的向上出口。这些研究结果挑战了GRs提供均匀冷却效果的普遍假设，并强调了在设计GRs以增强热脆弱的亚热带城市的热恢复能力时，需要明确考虑冠层通风和植物生理反应。
{"title":"Unveiling the canopy heat trapping effect in green roofs: Thermo-dynamic mechanisms during subtropical urban heatwaves","authors":"Bingyin Chen , Zhiquan Zhu , Wanxue Zhu , Xuemei Wang , Weiwen Wang","doi":"10.1016/j.scs.2026.107177","DOIUrl":"10.1016/j.scs.2026.107177","url":null,"abstract":"<div><div>Amid accelerating global warming and intensifying urban heat islands, green roofs (GRs) are promoted as nature-based solutions for urban heat mitigation. However, their thermal performance in hot-humid subtropical climates remains underexplored, and observed warming phenomena are often overlooked. Here we use rooftop field experiments in Guangzhou, China, to compare GRs planted with Sedum spp. and Schefflera spp. against a bare roof during summer 2022 heatwaves. Combining high-frequency flux measurements, Bowen ratio energy-balance calculations and structural equation modelling, we analyse the mechanisms of roof–canopy thermal regulation. GRs reduced roof-surface temperature by 4–5 °C, associated with higher albedo (0.16–0.19 vs 0.10) and reduced net radiation (by 10–15 W m<sup>-2</sup>). Yet at 30 cm height, daytime canopy air temperature over GRs was 0.5–0.7 °C warmer than over the bare roof, with this warming intensifying by 0.06–0.15 °C per 1 °C increase in ambient temperature above 30 °C. We term this vertically non-uniform response the canopy heat-trapping effect, and show that it arises from reduced within-canopy wind speeds and diminished latent heat fluxes under heat stress, which limit the upward export of cool air. These findings challenge the common assumption that GRs provide uniformly cooling effects, and highlight the need to explicitly consider canopy ventilation and plant physiological responses when designing GRs to enhance thermal resilience in heat-vulnerable subtropical cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107177"},"PeriodicalIF":12.0,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080598","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 : 2026-01-22DOI: 10.1016/j.scs.2026.107175
Haiyan Luo , Rui Zhou , Chunlin Li , Qun Ma , Xuening Fang , Yina Hu , Xinlin Lv , Zining Dong , Yujing Tian , Shubo Fang
Rapid urbanization and global warming have exacerbated the deterioration of the urban thermal environment and its adverse effects. Urban spatial pattern, as an important approach to regulating the urban thermal environment, has increasingly drawn academic attention for its influence on land surface temperature (LST). Although numerous studies in this field, quantitative research on the comprehensive effects and nonlinear interactions of 2D/3D building and green space morphology on LST remains scarce, particularly from urban functional zones (UFZs) perspective. Therefore, this research took Shanghai as a case study and utilized multi-source datasets, such as building outlines, green space distribution, and canopy height, to quantify the combined and interactive effects of building and green space morphology on LST across various UFZs, applying the XGBoost-SHAP model. The results indicated that (1) LST varied with UFZs. Biophysical parameters and building morphology accounted for a greater proportion of the effects on LST. The main factors influencing LST differed significantly across distinct UFZs, but showed some similar laws. (2) Within different UFZs, the influence of the dominant factors on LST showed significant spatial differences and threshold effects. Among them, BCR was the main warming factor, with thresholds of 24.64 %, 36.25 %, 23.22 %, and 33.64 % respectively in residential, commercial, public service, and industrial zones. Conversely, NDVI was the primary cooling factor with thresholds of 0.35, 0.23, 0.37, and 0.29, respectively. (3) The LST in different UFZs was affected by the interaction between building and green space morphology metrics. The synergistic interaction between high FAR and high BCR contributed to the reduction of LST in residential and commercial zones. In areas with high BCR, the cooling effects could be achieved in the commercial zone (when BH_SD was <15 m), in public service zone (when MTH was <3 m), and industrial zone (when NDVI and BSA was both greater than 0.2, and MBH exceeded 10 m). Our research findings can provide more targeted scientific evidence and decision support for the pattern optimization and planning design of buildings and green spaces aimed at improving the urban thermal environment.
{"title":"Quantifying the nonlinear interactions of 2D/3D building and green space morphology on land surface temperature across different urban functional zones","authors":"Haiyan Luo , Rui Zhou , Chunlin Li , Qun Ma , Xuening Fang , Yina Hu , Xinlin Lv , Zining Dong , Yujing Tian , Shubo Fang","doi":"10.1016/j.scs.2026.107175","DOIUrl":"10.1016/j.scs.2026.107175","url":null,"abstract":"<div><div>Rapid urbanization and global warming have exacerbated the deterioration of the urban thermal environment and its adverse effects. Urban spatial pattern, as an important approach to regulating the urban thermal environment, has increasingly drawn academic attention for its influence on land surface temperature (LST). Although numerous studies in this field, quantitative research on the comprehensive effects and nonlinear interactions of 2D/3D building and green space morphology on LST remains scarce, particularly from urban functional zones (UFZs) perspective. Therefore, this research took Shanghai as a case study and utilized multi-source datasets, such as building outlines, green space distribution, and canopy height, to quantify the combined and interactive effects of building and green space morphology on LST across various UFZs, applying the XGBoost-SHAP model. The results indicated that (1) LST varied with UFZs. Biophysical parameters and building morphology accounted for a greater proportion of the effects on LST. The main factors influencing LST differed significantly across distinct UFZs, but showed some similar laws. (2) Within different UFZs, the influence of the dominant factors on LST showed significant spatial differences and threshold effects. Among them, BCR was the main warming factor, with thresholds of 24.64 %, 36.25 %, 23.22 %, and 33.64 % respectively in residential, commercial, public service, and industrial zones. Conversely, NDVI was the primary cooling factor with thresholds of 0.35, 0.23, 0.37, and 0.29, respectively. (3) The LST in different UFZs was affected by the interaction between building and green space morphology metrics. The synergistic interaction between high FAR and high BCR contributed to the reduction of LST in residential and commercial zones. In areas with high BCR, the cooling effects could be achieved in the commercial zone (when BH_SD was <15 m), in public service zone (when MTH was <3 m), and industrial zone (when NDVI and BSA was both greater than 0.2, and MBH exceeded 10 m). Our research findings can provide more targeted scientific evidence and decision support for the pattern optimization and planning design of buildings and green spaces aimed at improving the urban thermal environment.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107175"},"PeriodicalIF":12.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080990","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 : 2026-01-22DOI: 10.1016/j.scs.2026.107174
Payal More , Dhaarna
The urban thermal dynamics of a city comprise of Urban Heat Island Effect (UHI) and the Urban Cool Island Effect (UCI) that influence the local climate. This research employs a spatial and comparative approach to analyze thermal variations in Pune, India, by studying the conditions across three distinct years-2016, 2020, and 2024, to capture yearly variations influenced by changes in human activity and environmental dynamics, particularly during the 2020 period marked by the COVID-19 lockdown in India. To evaluate the urban thermal patterns, the research integrates four indices (i) Land Surface Temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), (iii) Normalized Difference Moisture Index (NDMI), and (iv) Normalized Difference Anthropogenic Impervious Surface Index (NDAISI). The spatial mapping has been done using Q-GIS, presenting diurnal and seasonal patterns for all indices. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 satellite data were utilized for April (summer) and November (winter) for the analysis. Statistical correlation techniques have been used to evaluate the interrelationship between LST with the other three indices. To capture the temporal disparity in heat retention and dissipation, it assesses interactions between these indices with diurnal and seasonal variations. Surface Urban Heat Island Intensity (SUHII) variation has been plotted using a Box and Whisker plot. The result suggests that Pune shows the UCI effect during the day, while the UHI effect during the night. During UCI (daytime), the results indicate a positive correlation between LST and NDAISI, whereas NDVI and NDMI exhibit strong negative correlations with LST, highlighting their cooling effects. The study highlights intra-urban thermal variation as an urban heat spread (UHS) effect. These findings provide valuable insights for urban planners and policymakers in developing heat action plans and climate-resilient urban strategies for Pune and similar cities.
{"title":"Urban heat & cool island: Investigating Pune’s (India) thermal dynamics using MODIS and Landsat 8–9 data","authors":"Payal More , Dhaarna","doi":"10.1016/j.scs.2026.107174","DOIUrl":"10.1016/j.scs.2026.107174","url":null,"abstract":"<div><div>The urban thermal dynamics of a city comprise of Urban Heat Island Effect (UHI) and the Urban Cool Island Effect (UCI) that influence the local climate. This research employs a spatial and comparative approach to analyze thermal variations in Pune, India, by studying the conditions across three distinct years-2016, 2020, and 2024, to capture yearly variations influenced by changes in human activity and environmental dynamics, particularly during the 2020 period marked by the COVID-19 lockdown in India. To evaluate the urban thermal patterns, the research integrates four indices (i) Land Surface Temperature (LST), (ii) Normalized Difference Vegetation Index (NDVI), (iii) Normalized Difference Moisture Index (NDMI), and (iv) Normalized Difference Anthropogenic Impervious Surface Index (NDAISI). The spatial mapping has been done using Q-GIS, presenting diurnal and seasonal patterns for all indices. Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8–9 satellite data were utilized for April (summer) and November (winter) for the analysis. Statistical correlation techniques have been used to evaluate the interrelationship between LST with the other three indices. To capture the temporal disparity in heat retention and dissipation, it assesses interactions between these indices with diurnal and seasonal variations. Surface Urban Heat Island Intensity (SUHII) variation has been plotted using a Box and Whisker plot. The result suggests that Pune shows the UCI effect during the day, while the UHI effect during the night. During UCI (daytime), the results indicate a positive correlation between LST and NDAISI, whereas NDVI and NDMI exhibit strong negative correlations with LST, highlighting their cooling effects. The study highlights intra-urban thermal variation as an urban heat spread (UHS) effect. These findings provide valuable insights for urban planners and policymakers in developing heat action plans and climate-resilient urban strategies for Pune and similar cities.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107174"},"PeriodicalIF":12.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080991","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 : 2026-01-22DOI: 10.1016/j.scs.2026.107173
Jiaxi Li , Zihan Liu , Zhenfeng Shao , Huyan Fu
Accelerating global urbanization has intensified the urban heat island (UHI) effect and exacerbated the fragmentation of blue-green landscapes (BGL) (i.e., water bodies and vegetation cover). While previous studies confirmed the mitigating impact of BGL on surface urban heat islands (SUHI), the dynamic influence mechanisms of their fragmentation on diurnal and seasonal SUHI intensity (SUHII) remain inadequately understood. Our study investigated Kunming, a representative plateau city, by integrating high-resolution land cover data with SUHII data. Using Pearson correlation analysis and generalized additive models, we systematically analyzed the diurnally differentiated impact mechanisms of six BGL fragmentation types (Intact, Interior, Dominant, Transitional, Patchy, and Rare) on the SUHII. The key findings: (1) BGL coverage reached 54.64 %, but the core area exhibited high fragmentation (Patchy + Transitional = 74.93 %). (2) SUHII showed spatial gradients (core > middle > expanded area), with stable nighttime values but significantly enhanced daytime intensity in summer. (3) Rare, Patchy, and Transitional types positively correlated with SUHII (r = 0.13 ∼ 0.60), while Dominant, Interior, and Intact types showed negative correlations (r = –0.03 ∼ –0.15). Crucially, their cooling effect demonstrated nonlinear enhancement when Intact/Interior exceeded 40 % coverage. Conversely, higher Patchy/Rare proportions exacerbated SUHII. (4) Patchy contributed most to diurnal SUHII (daytime: 56.47 %; nighttime: 51.59 %). The core and middle areas were dominated by highly fragmented types (Rare + Patchy > 68.45 %), while the Transitional type shaped the expanded area. This study reveals the impact mechanism of BGL fragmentation on the SUHI, informing UHI mitigation via landscape optimization.
{"title":"Regulation of blue-green landscape fragmentation on day-night surface urban heat island: A case study of Kunming","authors":"Jiaxi Li , Zihan Liu , Zhenfeng Shao , Huyan Fu","doi":"10.1016/j.scs.2026.107173","DOIUrl":"10.1016/j.scs.2026.107173","url":null,"abstract":"<div><div>Accelerating global urbanization has intensified the urban heat island (UHI) effect and exacerbated the fragmentation of blue-green landscapes (BGL) (i.e., water bodies and vegetation cover). While previous studies confirmed the mitigating impact of BGL on surface urban heat islands (SUHI), the dynamic influence mechanisms of their fragmentation on diurnal and seasonal SUHI intensity (SUHII) remain inadequately understood. Our study investigated Kunming, a representative plateau city, by integrating high-resolution land cover data with SUHII data. Using Pearson correlation analysis and generalized additive models, we systematically analyzed the diurnally differentiated impact mechanisms of six BGL fragmentation types (Intact, Interior, Dominant, Transitional, Patchy, and Rare) on the SUHII. The key findings: (1) BGL coverage reached 54.64 %, but the core area exhibited high fragmentation (Patchy + Transitional = 74.93 %). (2) SUHII showed spatial gradients (core > middle > expanded area), with stable nighttime values but significantly enhanced daytime intensity in summer. (3) Rare, Patchy, and Transitional types positively correlated with SUHII (<em>r</em> = 0.13 ∼ 0.60), while Dominant, Interior, and Intact types showed negative correlations (<em>r</em> = –0.03 ∼ –0.15). Crucially, their cooling effect demonstrated nonlinear enhancement when Intact/Interior exceeded 40 % coverage. Conversely, higher Patchy/Rare proportions exacerbated SUHII. (4) Patchy contributed most to diurnal SUHII (daytime: 56.47 %; nighttime: 51.59 %). The core and middle areas were dominated by highly fragmented types (Rare + Patchy > 68.45 %), while the Transitional type shaped the expanded area. This study reveals the impact mechanism of BGL fragmentation on the SUHI, informing UHI mitigation via landscape optimization.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107173"},"PeriodicalIF":12.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080596","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 : 2026-01-21DOI: 10.1016/j.scs.2026.107170
Jun Zhao , Fei Guo , Mingxuan Luo , Hongchi Zhang
The relationship between urban spatial structure and thermal environment has been extensively investigated, with substantial progress in geographic coverage and the number of case cities. However, cross-city analyses of nonlinear diurnal thermal environment responses to built-up environment indicators in temperate and cold coastal cities require further case-based evidence. And the translation of scientific mechanism into actionable planning parameters remains insufficiently explored. In response, this study takes four Bohai Rim coastal cities as cases and employs the urban block as the analysis unit to develop a novel multi-objective optimization framework. By coupling an adaptive Non-dominated Sorting Genetic Algorithm III (NSGA-III) with interpretable eXtreme Gradient Boosting (XGBoost), the developed framework quantifies the drivers of both diurnal land surface temperature (LST) and its diurnal temperature amplitude (DTA), as well as the interactions among key parameters. Based on this, it derives feasible regulatory ranges for key urban planning indices, thereby translating thermal response mechanisms into actionable planning controls. The results indicate pronounced threshold effects in the influence of individual parameters on LST. The main findings are as follows: (1) Aspect ratio (AR), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) exert prominent effects on the DTA; (2) Socio-demographic and other factors exhibit strong interactions with nighttime LST; (3) The planning-support model indicates that urban form by vertical compactness (e.g., high floor area ratio (0.5–4.04), medium-to-high average height (> 12 m)), low building density (0.13–0.26), and open type effectively balance diurnal LST. This study bridges mechanistic analysis with planning practice, offering a scalable decision-support framework for climate-adaptive urban design.
{"title":"Driving factors of summer diurnal land surface temperature in built-up blocks and planning support tool: A case from four Bohai Rim cities","authors":"Jun Zhao , Fei Guo , Mingxuan Luo , Hongchi Zhang","doi":"10.1016/j.scs.2026.107170","DOIUrl":"10.1016/j.scs.2026.107170","url":null,"abstract":"<div><div>The relationship between urban spatial structure and thermal environment has been extensively investigated, with substantial progress in geographic coverage and the number of case cities. However, cross-city analyses of nonlinear diurnal thermal environment responses to built-up environment indicators in temperate and cold coastal cities require further case-based evidence. And the translation of scientific mechanism into actionable planning parameters remains insufficiently explored. In response, this study takes four Bohai Rim coastal cities as cases and employs the urban block as the analysis unit to develop a novel multi-objective optimization framework. By coupling an adaptive Non-dominated Sorting Genetic Algorithm III (NSGA-III) with interpretable eXtreme Gradient Boosting (XGBoost), the developed framework quantifies the drivers of both diurnal land surface temperature (LST) and its diurnal temperature amplitude (DTA), as well as the interactions among key parameters. Based on this, it derives feasible regulatory ranges for key urban planning indices, thereby translating thermal response mechanisms into actionable planning controls. The results indicate pronounced threshold effects in the influence of individual parameters on LST. The main findings are as follows: (1) Aspect ratio (AR), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI) exert prominent effects on the DTA; (2) Socio-demographic and other factors exhibit strong interactions with nighttime LST; (3) The planning-support model indicates that urban form by vertical compactness (e.g., high floor area ratio (0.5–4.04), medium-to-high average height (> 12 m)), low building density (0.13–0.26), and open type effectively balance diurnal LST. This study bridges mechanistic analysis with planning practice, offering a scalable decision-support framework for climate-adaptive urban design.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107170"},"PeriodicalIF":12.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080996","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 : 2026-01-21DOI: 10.1016/j.scs.2026.107169
Sanghoon Kim, Min Kyu Sim
Recent advances in vehicle-to-everything (V2X) technology position electric-vehicle (EV) batteries as mobile energy-storage systems (ESSs), promising a new paradigm for power supply flexibility. However, prevailing centralized V2X operation scenarios and dynamic compensation schemes offer limited economic incentives for EV owners, hindering large-scale adoption. This study proposes a decentralized V2X scenario that treats EV owners as independent economic agents. It introduces a predetermined discharge tariff set at 70% of the contemporaneous time-of-use (ToU) electricity price, thereby offering clear, ex-ante profit expectations for all participants. Participations in V2X transactions are modeled as a time-dependent Poisson process. Within the proposed framework, we formulate an optimal ESS scheduling problem that minimizes the sum of electricity costs and ESS degradation costs for a commercial building equipped with photovoltaic (PV) generation, stationary ESS, and vehicle-to-building (V2B) interfaces. To solve this problem, we develop a world models-based reinforcement learning framework that performs multi-horizon forecasting of PV output and building load, using these forecasts as states to learn an optimal control policy. Compared with benchmark strategies, the proposed approach achieves a total cost reduction of up to 9.65% and attains near-global performance—within 1.57% of the optimality gap from the ideal strategy derived from perfect foresight data.
{"title":"Decentralized off-grid vehicle-to-building (V2B) operation: A reinforcement learning approach for optimal charge–discharge control of energy storage systems","authors":"Sanghoon Kim, Min Kyu Sim","doi":"10.1016/j.scs.2026.107169","DOIUrl":"10.1016/j.scs.2026.107169","url":null,"abstract":"<div><div>Recent advances in vehicle-to-everything (V2X) technology position electric-vehicle (EV) batteries as mobile energy-storage systems (ESSs), promising a new paradigm for power supply flexibility. However, prevailing centralized V2X operation scenarios and dynamic compensation schemes offer limited economic incentives for EV owners, hindering large-scale adoption. This study proposes a decentralized V2X scenario that treats EV owners as independent economic agents. It introduces a predetermined discharge tariff set at 70% of the contemporaneous time-of-use (ToU) electricity price, thereby offering clear, ex-ante profit expectations for all participants. Participations in V2X transactions are modeled as a time-dependent Poisson process. Within the proposed framework, we formulate an optimal ESS scheduling problem that minimizes the sum of electricity costs and ESS degradation costs for a commercial building equipped with photovoltaic (PV) generation, stationary ESS, and vehicle-to-building (V2B) interfaces. To solve this problem, we develop a world models-based reinforcement learning framework that performs multi-horizon forecasting of PV output and building load, using these forecasts as states to learn an optimal control policy. Compared with benchmark strategies, the proposed approach achieves a total cost reduction of up to 9.65% and attains near-global performance—within 1.57% of the optimality gap from the ideal strategy derived from perfect foresight data.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107169"},"PeriodicalIF":12.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146006826","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 : 2026-01-21DOI: 10.1016/j.scs.2026.107168
Zhaoping Wu , Xuandi Wang , Yandong Tan , Xu Wu , Kai Fang
Urbanization has driven global urban transition, making urban energy scaling critical for effective energy management. Yet, most studies focused on static scaling between energy use and city size, overlooking their dynamics and typological differences. This study analyzed energy scaling across 324 Chinese cities from 2005 to 2021, exploring both its temporal evolution and variations across seven city types. Using the XGBoost-SHAP model, we further identified key drivers of per capita energy use. Results show that: (1) Urban energy use follows a stable linear scaling (95CI%: 0.83–1.04) across all cities; (2) Service-based and high-tech cities display near-linear scaling (β ∼ 1), while light-industry, pre-industry, and agriculture cities present sublinear scaling (β ∼ 0.85), and energy and heavy-industry cities show the strongest scale effects (β ∼ 2/3); (3) Scaling exponent fell by 12% and 27% in service-based and energy cities, showing inverted U-shapes in high-tech and agriculture cities, and U-shapes in pre- and light-industry cities over time; (4) Per capita GDP and road length emerged as the crucial determinants of energy intensity across all city types; (5) Scaling models predict service-based cities will experience the most substantial increases in both population (24%) and energy use (26%). These findings suggest differentiated policy pathways: service-based and high-tech cities should enhance energy infrastructure and adopt clean energy to offset linear scaling pressures; energy and heavy-industry cities should use scale effects to reduce energy intensity through innovation and low-carbon transition; and agriculture and pre-industry cities need to optimize infrastructure to ensure balanced regional development.
{"title":"A typology of energy scaling in Chinese cities and its implication for sustainable urban transitions","authors":"Zhaoping Wu , Xuandi Wang , Yandong Tan , Xu Wu , Kai Fang","doi":"10.1016/j.scs.2026.107168","DOIUrl":"10.1016/j.scs.2026.107168","url":null,"abstract":"<div><div>Urbanization has driven global urban transition, making urban energy scaling critical for effective energy management. Yet, most studies focused on static scaling between energy use and city size, overlooking their dynamics and typological differences. This study analyzed energy scaling across 324 Chinese cities from 2005 to 2021, exploring both its temporal evolution and variations across seven city types. Using the XGBoost-SHAP model, we further identified key drivers of per capita energy use. Results show that: (1) Urban energy use follows a stable linear scaling (95CI%: 0.83–1.04) across all cities; (2) Service-based and high-tech cities display near-linear scaling (<em>β</em> ∼ 1), while light-industry, pre-industry, and agriculture cities present sublinear scaling (β ∼ 0.85), and energy and heavy-industry cities show the strongest scale effects (β ∼ 2/3); (3) Scaling exponent fell by 12% and 27% in service-based and energy cities, showing inverted U-shapes in high-tech and agriculture cities, and U-shapes in pre- and light-industry cities over time; (4) Per capita GDP and road length emerged as the crucial determinants of energy intensity across all city types; (5) Scaling models predict service-based cities will experience the most substantial increases in both population (24%) and energy use (26%). These findings suggest differentiated policy pathways: service-based and high-tech cities should enhance energy infrastructure and adopt clean energy to offset linear scaling pressures; energy and heavy-industry cities should use scale effects to reduce energy intensity through innovation and low-carbon transition; and agriculture and pre-industry cities need to optimize infrastructure to ensure balanced regional development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107168"},"PeriodicalIF":12.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081085","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 : 2026-01-20DOI: 10.1016/j.scs.2026.107167
Tongtong Shang, Wei Wu, Pan Wu, Xiaoling Luo, Shan Han
The transportation sector is a significant source of global carbon emissions, and widespread adoption of New Energy Vehicles (NEVs) is critical for mitigation. Existing studies, however, rely predominantly on macro-level scenario analyses and life cycle assessments (LCA), often lacking integration with real-world vehicle operation data to quantify the actual emission reduction impacts of growing NEV fleets. This study analyzes over one million traffic monitoring records from Chongqing (2019-2024) to characterize spatiotemporal evolution of urban road carbon emissions. We extract micro-scale travel characteristics based on travel distance and develop a K-ME-XGBoost-LSTM hybrid model to illuminate nonlinear relationships between NEV growth and traffic emissions. The results indicate that road network mileage, NEV penetration rate, travel distance, and travel speed are the primary determinants of emission levels. A critical finding is that significant emission reductions materialize only after NEV penetration exceeds a threshold of approximately 10%. Moreover, the mitigating effect on NOₓ emissions becomes progressively stronger than that on CO as the NEV share increases. Furthermore, within urban core areas, the adoption of NEVs effectively reduces the demand for long-distance travel using fuel-powered vehicles, thereby driving system-wide declines in emissions. Projections for the next five years (2025-2029) indicate that this positive emission reduction trajectory is likely to continue across multiple districts.
{"title":"Quantifying emission reductions from new energy vehicle adoption via integrated macro-micro data analysis","authors":"Tongtong Shang, Wei Wu, Pan Wu, Xiaoling Luo, Shan Han","doi":"10.1016/j.scs.2026.107167","DOIUrl":"10.1016/j.scs.2026.107167","url":null,"abstract":"<div><div>The transportation sector is a significant source of global carbon emissions, and widespread adoption of New Energy Vehicles (NEVs) is critical for mitigation. Existing studies, however, rely predominantly on macro-level scenario analyses and life cycle assessments (LCA), often lacking integration with real-world vehicle operation data to quantify the actual emission reduction impacts of growing NEV fleets. This study analyzes over one million traffic monitoring records from Chongqing (2019-2024) to characterize spatiotemporal evolution of urban road carbon emissions. We extract micro-scale travel characteristics based on travel distance and develop a K-ME-XGBoost-LSTM hybrid model to illuminate nonlinear relationships between NEV growth and traffic emissions. The results indicate that road network mileage, NEV penetration rate, travel distance, and travel speed are the primary determinants of emission levels. A critical finding is that significant emission reductions materialize only after NEV penetration exceeds a threshold of approximately 10%. Moreover, the mitigating effect on NOₓ emissions becomes progressively stronger than that on CO as the NEV share increases. Furthermore, within urban core areas, the adoption of NEVs effectively reduces the demand for long-distance travel using fuel-powered vehicles, thereby driving system-wide declines in emissions. Projections for the next five years (2025-2029) indicate that this positive emission reduction trajectory is likely to continue across multiple districts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107167"},"PeriodicalIF":12.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080599","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 : 2026-01-15DOI: 10.1016/j.scs.2026.107165
Bo Wan, Ningjie Shen, Haijian Zhang, Qiang Sheng
Urbanization has led to frequent heatwaves and urban heat islands (UHIs). While the impact of building density and other factors on land surface temperature (LST) has been studied, the mechanisms of spatial configuration remain unclear. We conducted a comparative study across seven cities (Beijing, Hangzhou, Nanjing, Shanghai, Wuhan, Zhengzhou and Changsha), integrating summer LST data with multidimensional morphological indicators. We applied both linear and machine learning models, and used SHAP (Shapley Additive exPlanations) to interpret the contribution and thresholds of variables. The results show that incorporating spatial configuration significantly improves explanatory power, and machine learning outperforms linear models. High-intensity development generally increases LST, while vegetation and mixed land use reduce LST, both exhibiting threshold effects: tree canopy cover beyond approximately 15% shows diminishing marginal cooling effects, and proximity to water bodies within 300 meters creates significant cooling (cold island) effects. Spatial configuration has a critical and nonlinear influence on LST, offering an evidence base for multidimensional, synergistic heat island mitigation and urban planning decisions.
{"title":"Analyzing the impact of urban morphology on urban land surface temperature from the perspective of spatial configuration and explainable machine learning: A case study of seven cities","authors":"Bo Wan, Ningjie Shen, Haijian Zhang, Qiang Sheng","doi":"10.1016/j.scs.2026.107165","DOIUrl":"10.1016/j.scs.2026.107165","url":null,"abstract":"<div><div>Urbanization has led to frequent heatwaves and urban heat islands (UHIs). While the impact of building density and other factors on land surface temperature (LST) has been studied, the mechanisms of spatial configuration remain unclear. We conducted a comparative study across seven cities (Beijing, Hangzhou, Nanjing, Shanghai, Wuhan, Zhengzhou and Changsha), integrating summer LST data with multidimensional morphological indicators. We applied both linear and machine learning models, and used SHAP (Shapley Additive exPlanations) to interpret the contribution and thresholds of variables. The results show that incorporating spatial configuration significantly improves explanatory power, and machine learning outperforms linear models. High-intensity development generally increases LST, while vegetation and mixed land use reduce LST, both exhibiting threshold effects: tree canopy cover beyond approximately 15% shows diminishing marginal cooling effects, and proximity to water bodies within 300 meters creates significant cooling (cold island) effects. Spatial configuration has a critical and nonlinear influence on LST, offering an evidence base for multidimensional, synergistic heat island mitigation and urban planning decisions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"138 ","pages":"Article 107165"},"PeriodicalIF":12.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146080597","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}
The increasing frequency of extreme heat events poses serious challenges to public health and urban sustainability. Urban expansion is a key driver of extreme heat, yet the distinct mechanisms behind daytime and nighttime heat remain underexplored. This study proposes a multi-scale analytical framework to examine how 2D and 3D urban landscape changes influence extreme heat intensity (EHI), using both macro-scale (Spatial Difference-in-Differences) and finer-scale (Causal Forest) approaches. Two key findings emerge: 1) at the macro scale, urbanization significantly intensifies EHI, demonstrating its detrimental impact on thermal environments; 2) at the finer scale, heterogeneity analysis reveals that the landscape changes of building, impervious surface, cropland, and water bodies affect EHI in varied and localized ways. The results indicate the need for differentiated daytime and nighttime heat mitigation strategies, including enhancing blue-green infrastructure, optimizing urban landscape, and preserving cropland–water spatial balance to improve urban thermal resilience.
{"title":"Discovering the causal mechanism of day-night extreme heat driven by 2D and 3D urban landscape changes: a case study of Wuhan, China","authors":"Yingqiang Zhong , Shaochun Li , Xinmeng Zhou , Xun Liang , Qingfeng Guan","doi":"10.1016/j.scs.2026.107163","DOIUrl":"10.1016/j.scs.2026.107163","url":null,"abstract":"<div><div>The increasing frequency of extreme heat events poses serious challenges to public health and urban sustainability. Urban expansion is a key driver of extreme heat, yet the distinct mechanisms behind daytime and nighttime heat remain underexplored. This study proposes a multi-scale analytical framework to examine how 2D and 3D urban landscape changes influence extreme heat intensity (EHI), using both macro-scale (Spatial Difference-in-Differences) and finer-scale (Causal Forest) approaches. Two key findings emerge: 1) at the macro scale, urbanization significantly intensifies EHI, demonstrating its detrimental impact on thermal environments; 2) at the finer scale, heterogeneity analysis reveals that the landscape changes of building, impervious surface, cropland, and water bodies affect EHI in varied and localized ways. The results indicate the need for differentiated daytime and nighttime heat mitigation strategies, including enhancing blue-green infrastructure, optimizing urban landscape, and preserving cropland–water spatial balance to improve urban thermal resilience.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"137 ","pages":"Article 107163"},"PeriodicalIF":12.0,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039038","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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