Under rapid urbanization, the urban heat island (UHI) problem impacts not only large cities, but also poses severe challenges to shrinking cities with rapidly declining population. In China, most shrinking cities are characterized by population loss alongside the expansion of built-up areas due to policy. Urban warming exacerbates the human settlement environment, with UHI intensifying due to urban expansion, while population loss simultaneously alleviates it. This raises a question: will the UHI problem be mitigated in shrinking cities? In this study, we analyze the spatiotemporal pattern evolution of surface urban heat island (SUHI) in Fushun and Tieling from 2000 to 2020 using Landsat series products. We combine landscape pattern indices and SUHI indicators, and perform correlation analyses of the factors influencing SUHI with multiscale geographically weighted regression (MGWR). The findings reveal that in Fushun, mining activities significantly impact SUHI, while in Tieling, extremely Land Surface Temperature (LST) zones are expanding and dispersing. SUHI patterns are notably shaped by subsurface conditions, and spatial configurations play key roles in regulating SUHI. However, population loss has not significantly influenced SUHI, even in shrinking cities. This study offers a new perspective for SUHI research and provides further insights into urban planning strategies.
{"title":"Study on the spatiotemporal pattern evolution of surface urban heat island in shrinking cities: Fushun and Tieling","authors":"Yanfei Wu , Junjie Qiu , Jiake Wang , Wenyuan Wu , Ting Wu , Hao Hou , Haiping Xia , Junfeng Xu","doi":"10.1016/j.scs.2024.105912","DOIUrl":"10.1016/j.scs.2024.105912","url":null,"abstract":"<div><div>Under rapid urbanization, the urban heat island (UHI) problem impacts not only large cities, but also poses severe challenges to shrinking cities with rapidly declining population. In China, most shrinking cities are characterized by population loss alongside the expansion of built-up areas due to policy. Urban warming exacerbates the human settlement environment, with UHI intensifying due to urban expansion, while population loss simultaneously alleviates it. This raises a question: will the UHI problem be mitigated in shrinking cities? In this study, we analyze the spatiotemporal pattern evolution of surface urban heat island (SUHI) in Fushun and Tieling from 2000 to 2020 using Landsat series products. We combine landscape pattern indices and SUHI indicators, and perform correlation analyses of the factors influencing SUHI with multiscale geographically weighted regression (MGWR). The findings reveal that in Fushun, mining activities significantly impact SUHI, while in Tieling, extremely Land Surface Temperature (LST) zones are expanding and dispersing. SUHI patterns are notably shaped by subsurface conditions, and spatial configurations play key roles in regulating SUHI. However, population loss has not significantly influenced SUHI, even in shrinking cities. This study offers a new perspective for SUHI research and provides further insights into urban planning strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105912"},"PeriodicalIF":10.5,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534088","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 : 2024-10-19DOI: 10.1016/j.scs.2024.105925
Li Wang , Baicheng Hu , Yuan Zhao , Kunlin Song , Jianmin Ma , Hong Gao , Tao Huang , Xiaoxuan Mao
Machine learning (ML) models have been extensively applied in air quality prediction. However, many of these models often failed to unveil complex mechanisms and regional spatiotemporal variations of composite air pollution. This brings uncertainties in using ML models for effective composite air pollution control. The present study developed a novel hybrid spatiotemporal model framework combining Graph Attention Network (GAT) and Gated Recurrent Unit (GRU), namely the GAT-GRU model, to foresee composite air pollutions with a focus on PM2.5 and O3. By extracting attention matrices for PM2.5O3 composite pollution and applying the Louvain algorithm, the framework established effective community network divisions for coordinated control of PM2.5O3 composite pollution. The framework was applied and tested in China's “2 + 26″ cities, a city cluster with most heavy PM2.5 and O3 pollution and precursor emission sources. The results demonstrate that the framework successfully captured spatiotemporal evolution of combined PM2.5 and O3 pollution. The attention matrix is autonomously generated during course of the model learning process with the aim to interpret the complex interactions among “2 + 26″ cities. The framework provides a new perspective for the interpretability of artificial intelligence models and offers a methodological support and scientific evidence for formulating regional pollution cooperative governance strategies.
{"title":"A hybrid spatiotemporal model combining graph attention network and gated recurrent unit for regional composite air pollution prediction and collaborative control","authors":"Li Wang , Baicheng Hu , Yuan Zhao , Kunlin Song , Jianmin Ma , Hong Gao , Tao Huang , Xiaoxuan Mao","doi":"10.1016/j.scs.2024.105925","DOIUrl":"10.1016/j.scs.2024.105925","url":null,"abstract":"<div><div>Machine learning (ML) models have been extensively applied in air quality prediction. However, many of these models often failed to unveil complex mechanisms and regional spatiotemporal variations of composite air pollution. This brings uncertainties in using ML models for effective composite air pollution control. The present study developed a novel hybrid spatiotemporal model framework combining Graph Attention Network (GAT) and Gated Recurrent Unit (GRU), namely the GAT-GRU model, to foresee composite air pollutions with a focus on PM<sub>2.5</sub> and O<sub>3</sub>. By extracting attention matrices for PM<sub>2.5<img></sub>O<sub>3</sub> composite pollution and applying the Louvain algorithm, the framework established effective community network divisions for coordinated control of PM<sub>2.5<img></sub>O<sub>3</sub> composite pollution. The framework was applied and tested in China's “2 + 26″ cities, a city cluster with most heavy PM<sub>2.5</sub> and O<sub>3</sub> pollution and precursor emission sources. The results demonstrate that the framework successfully captured spatiotemporal evolution of combined PM<sub>2.5</sub> and O<sub>3</sub> pollution. The attention matrix is autonomously generated during course of the model learning process with the aim to interpret the complex interactions among “2 + 26″ cities. The framework provides a new perspective for the interpretability of artificial intelligence models and offers a methodological support and scientific evidence for formulating regional pollution cooperative governance strategies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105925"},"PeriodicalIF":10.5,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142539064","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 : 2024-10-18DOI: 10.1016/j.scs.2024.105924
Yuxiao Tang , Yudi Tang
Underground consumer service spaces (UCSS) offer new solutions for urban residents’ daily needs, but existing studies on their distribution and driving forces are often fragmented and overshadowed by research on other underground spaces, lacking targeted analysis. This study examines UCSS in the central urban areas of seven representative Chinese megacities. Using spatial analysis methods like kernel density estimation, multi-distance spatial clustering, and geographical detectors, the spatial characteristics and driving forces of UCSS are analyzed alongside aboveground consumer service spaces (ACSS). Results show that both ACSS and UCSS exhibit multi-centered, concentric spatial patterns, though UCSS demonstrates higher spatial aggregation. Unlike other underground public spaces (UPS), UCSS relies more on service industry agglomeration and market factors, while other UPS are more influenced by surrounding development intensity. UCSS follows the core principles of central place theory but deviates from the market-driven patterns typical of ACSS. Socioeconomic conditions and transportation infrastructure form the foundational basis for UCSS distribution, while service industry agglomeration, market dependence, and land development intensity exert more direct influence. The commercial atmosphere and existing underground space development play critical roles in UCSS distribution. Two key spatial scales for understanding UCSS distribution are the strong influence zones of shopping malls and metro stations, and high-density urban areas.
{"title":"Analysis of the spatial characteristics and driving forces of underground consumer service space in Chinese megacities based on multi-source data","authors":"Yuxiao Tang , Yudi Tang","doi":"10.1016/j.scs.2024.105924","DOIUrl":"10.1016/j.scs.2024.105924","url":null,"abstract":"<div><div>Underground consumer service spaces (UCSS) offer new solutions for urban residents’ daily needs, but existing studies on their distribution and driving forces are often fragmented and overshadowed by research on other underground spaces, lacking targeted analysis. This study examines UCSS in the central urban areas of seven representative Chinese megacities. Using spatial analysis methods like kernel density estimation, multi-distance spatial clustering, and geographical detectors, the spatial characteristics and driving forces of UCSS are analyzed alongside aboveground consumer service spaces (ACSS). Results show that both ACSS and UCSS exhibit multi-centered, concentric spatial patterns, though UCSS demonstrates higher spatial aggregation. Unlike other underground public spaces (UPS), UCSS relies more on service industry agglomeration and market factors, while other UPS are more influenced by surrounding development intensity. UCSS follows the core principles of central place theory but deviates from the market-driven patterns typical of ACSS. Socioeconomic conditions and transportation infrastructure form the foundational basis for UCSS distribution, while service industry agglomeration, market dependence, and land development intensity exert more direct influence. The commercial atmosphere and existing underground space development play critical roles in UCSS distribution. Two key spatial scales for understanding UCSS distribution are the strong influence zones of shopping malls and metro stations, and high-density urban areas.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105924"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.scs.2024.105917
Zhifeng Wu , Yangfeng Zhou , Yin Ren
In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.
{"title":"Green space-building integration for Urban Heat Island mitigation: Insights from Beijing's fifth ring road district","authors":"Zhifeng Wu , Yangfeng Zhou , Yin Ren","doi":"10.1016/j.scs.2024.105917","DOIUrl":"10.1016/j.scs.2024.105917","url":null,"abstract":"<div><div>In this research, we delve into the complex arrangement of urban landscapes, where green spaces and buildings are not merely co-existing but are interwoven into a cohesive fabric that shapes the thermal environment. Our approach transcends the conventional methods of analysis, which typically isolate the roles of greenery or built environments. Instead, we adopt a synergistic perspective that recognizes the collective influence of these landscape constituents on the urban thermal pattern. Key insights are: (1) A linear decrease in average land surface temperature with increasing green space coverage is observed. However, substantial temperature variations (up to 8 °C) within the same coverage interval highlight the significant impact of built-up pattern on thermal conditions; (2) High Building Height and Floor Area Ratio, and low Building Coverage Ratio and Sky View Factor, are linked to cooler temperatures in areas with up to 50 % green space; (3) The study suggests that low-temperature areas can inform the adjustment of built-up patterns in high-temperature areas, offering a strategy for thermal environment optimization within specific green space coverage intervals. This research contributes insights into the integrated planning of green spaces and buildings, with implications for urban development and renewal initiatives aiming to enhance the urban thermal environment.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105917"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534092","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 : 2024-10-18DOI: 10.1016/j.scs.2024.105920
Hao Wu , Yi Yang , Wen Li
Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals1. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.
{"title":"Spatial optimization of land use and carbon storage prediction in urban agglomerations under climate change: Different scenarios and multiscale perspectives of CMIP6","authors":"Hao Wu , Yi Yang , Wen Li","doi":"10.1016/j.scs.2024.105920","DOIUrl":"10.1016/j.scs.2024.105920","url":null,"abstract":"<div><div>Land use/land cover (LULC) structure optimization can effectively increase carbon storage/carbon sequestration (CS) and help realize carbon neutrality goals<sup>1</sup>. Studying the spatial distributions of LULC and CS under climate change conditions is highly important for realizing sustainable development goals. This study is based on different climate change models, and the coordinated development of economic, water, carbon and ecological sustainability was considered to establish a comprehensive multiscale, multiscenario and multiobjective LULC optimization model. Then, different climate change scenarios were optimized, and regional CS values were predicted. The LULC simulation model provided satisfactory simulation results at different scales. Notably, the average accuracy exceeded 0.92. The optimized land expansion results exhibited heterogeneity. Forestland change accounted for the largest proportion of the total LULC change. After optimization, the CS values under the different scenarios were similar. The northwestern part of the study area served as the main carbon sink area. The aim of this study was to respond to future complex climate change by rationally planning the LULC structure, thus achieving the sustainable development of urban agglomerations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105920"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533633","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 : 2024-10-18DOI: 10.1016/j.scs.2024.105918
Jialin Du , Weihao Hu , Sen Zhang , Wen Liu , Zhenyuan Zhang , Daojuan Wang , Zhe Chen
The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.
{"title":"A multi-objective robust dispatch strategy for renewable energy microgrids considering multiple uncertainties","authors":"Jialin Du , Weihao Hu , Sen Zhang , Wen Liu , Zhenyuan Zhang , Daojuan Wang , Zhe Chen","doi":"10.1016/j.scs.2024.105918","DOIUrl":"10.1016/j.scs.2024.105918","url":null,"abstract":"<div><div>The demand for low-carbon transformations and the uncertainty of renewable energy sources and loads present significant challenges for the optimal dispatch of microgrid. This study proposed a multi-objective robust dispatch strategy to reduce the risks associated with the uncertainty of renewable energy source output and loads while promoting low-carbon and economical microgrid operation. The economic emission dispatch problem for a microgrid was formulated as a multi-objective robust dual-layer optimization model. Consequently, a high-dimensional adjustable linear polyhedral uncertainty set was proposed to describe the uncertainty of renewable energy sources and loads. This study transformed the original model into an easy-to-solve single-layer second-order cone programming optimal power flow optimization model by employing second-order cone relaxation and duality transformation. Thereafter, a synthetic membership function was proposed to determine the optimal compromise solution. To determine the charging and discharging statuses of the battery storage system and the electricity traded between the microgrid and the external power grid, a battery storage system control strategy based on time-of-use electricity prices and real-time power flow calculations was proposed. Simulations conducted on a modified IEEE-30 bus system demonstrated that the proposed strategy effectively reduced the economic costs and carbon emissions of the microgrid by 8.23 % and 2.43 %, respectively.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105918"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534203","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 : 2024-10-18DOI: 10.1016/j.scs.2024.105916
Chunguang Hu , Maomao Zhang , Gaoliu Huang , Zhuoqi Li , Yucheng Sun , Jianqing Zhao
China's rapid economic growth and urbanization have caused significant Land Cover Changes (LCC), worsened the Urban Heat Island (UHI) effect and reducing the Thermal Comfort (TC). Despite existing studies, there remains a gap in understanding the specific contributions of various LCC types to the TC, particularly in Qinhuai River Basin. This study addresses this gap by examining the LCC effects from 2013 to 2022 based on targeted metrics. We propose a novel TC classification model and introduce indices, including the Land Cover Contribution Index (LCI) and the Land Cover Classification Contribution Index (LCCI), to quantify the influence of different LCC types on the TC. Our findings reveal that farmland and woodland positively impact the TC, while the negative influence of impervious surfaces has intensified. The area of farmland in the most comfortable category has shown significant variability, while impermeable surfaces in uncomfortable and very uncomfortable categories have surged. Additionally, the Urban Water Body Contribution Index (U-WCI) consistently exceeded the Non-Urban Water Body Contribution Index (N-WCI), indicating an enhanced UHI effect within urban areas. This study concludes that changes in farmland and impervious surfaces are crucial for the TC and provides practical recommendations for land use planning against climate change.
{"title":"Tracking the impact of the land cover change on the spatial-temporal distribution of the thermal comfort: Insights from the Qinhuai River Basin, China","authors":"Chunguang Hu , Maomao Zhang , Gaoliu Huang , Zhuoqi Li , Yucheng Sun , Jianqing Zhao","doi":"10.1016/j.scs.2024.105916","DOIUrl":"10.1016/j.scs.2024.105916","url":null,"abstract":"<div><div>China's rapid economic growth and urbanization have caused significant Land Cover Changes (LCC), worsened the Urban Heat Island (UHI) effect and reducing the Thermal Comfort (TC). Despite existing studies, there remains a gap in understanding the specific contributions of various LCC types to the TC, particularly in Qinhuai River Basin. This study addresses this gap by examining the LCC effects from 2013 to 2022 based on targeted metrics. We propose a novel TC classification model and introduce indices, including the Land Cover Contribution Index (LCI) and the Land Cover Classification Contribution Index (LCCI), to quantify the influence of different LCC types on the TC. Our findings reveal that farmland and woodland positively impact the TC, while the negative influence of impervious surfaces has intensified. The area of farmland in the most comfortable category has shown significant variability, while impermeable surfaces in uncomfortable and very uncomfortable categories have surged. Additionally, the Urban Water Body Contribution Index (U-WCI) consistently exceeded the Non-Urban Water Body Contribution Index (N-WCI), indicating an enhanced UHI effect within urban areas. This study concludes that changes in farmland and impervious surfaces are crucial for the TC and provides practical recommendations for land use planning against climate change.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105916"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534205","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 : 2024-10-18DOI: 10.1016/j.scs.2024.105903
Haolin Yang , Weijun Gao , Siqi Xu , You Li , Xindong Wei , Yafei Wang
Urban power decarbonization is essential in the fight against climate change, yet current research often neglects the financial risks faced by investors and the shifting demands of consumers in liberalized electricity markets. This study addresses these gaps by proposing a modified Markowitz Mean-Variance Portfolio (MVP) theory, integrated with the Low Emissions Analysis Platform (LEAP), and a deep learning model. On this basis, an urban energy transition framework centered on Power Purchase Agreements (PPAs) is proposed and developed. The framework is validated considering a case study in Kitakyushu, Japan, highlighting its potential in accelerating power sector decarbonization and achieving net-zero emissions by 2038. Additionally, the internal rate of return (IRR) remains stable between 14.5 % and 19.6 % across seven other cities. While the framework reduces long-term cash flow volatility, its effectiveness hinges on industrial electrification efficiency and regional energy self-sufficiency. The findings indicate that relying solely on renewable energy for low-carbon transitions is unrealistic. Furthermore, green hydrogen could emerge as a viable alternative to fossil fuels, potentially replacing batteries for long-term energy storage. Future research should explore cross-regional energy trade and establish legal frameworks for long-term energy transactions to bolster urban energy transition resilience across diverse geographic and economic contexts.
{"title":"Urban-scale power decarbonization using a modified power purchase agreements framework based on Markowitz mean-variance theory","authors":"Haolin Yang , Weijun Gao , Siqi Xu , You Li , Xindong Wei , Yafei Wang","doi":"10.1016/j.scs.2024.105903","DOIUrl":"10.1016/j.scs.2024.105903","url":null,"abstract":"<div><div>Urban power decarbonization is essential in the fight against climate change, yet current research often neglects the financial risks faced by investors and the shifting demands of consumers in liberalized electricity markets. This study addresses these gaps by proposing a modified Markowitz Mean-Variance Portfolio (MVP) theory, integrated with the Low Emissions Analysis Platform (LEAP), and a deep learning model. On this basis, an urban energy transition framework centered on Power Purchase Agreements (PPAs) is proposed and developed. The framework is validated considering a case study in Kitakyushu, Japan, highlighting its potential in accelerating power sector decarbonization and achieving net-zero emissions by 2038. Additionally, the internal rate of return (IRR) remains stable between 14.5 % and 19.6 % across seven other cities. While the framework reduces long-term cash flow volatility, its effectiveness hinges on industrial electrification efficiency and regional energy self-sufficiency. The findings indicate that relying solely on renewable energy for low-carbon transitions is unrealistic. Furthermore, green hydrogen could emerge as a viable alternative to fossil fuels, potentially replacing batteries for long-term energy storage. Future research should explore cross-regional energy trade and establish legal frameworks for long-term energy transactions to bolster urban energy transition resilience across diverse geographic and economic contexts.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105903"},"PeriodicalIF":10.5,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534089","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 : 2024-10-17DOI: 10.1016/j.scs.2024.105904
Pingtao Yi , Ruxue Shi , Weiwei Li , Qiankun Dong
The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions.
{"title":"Evaluation of the coordination-difference-driven sustainability of 12 urban agglomerations in China based on the dynamic probability weighting method","authors":"Pingtao Yi , Ruxue Shi , Weiwei Li , Qiankun Dong","doi":"10.1016/j.scs.2024.105904","DOIUrl":"10.1016/j.scs.2024.105904","url":null,"abstract":"<div><div>The sustainable development of urban agglomerations represents a significant driving force in national and global development. This study establishes an indicator system comprising factors associated with the economy, society, and environment, in accordance with the Triple Bottom Line, to assess the sustainability of 12 urban agglomerations in China. A novel framework is proposed, including a dynamic probability weighting method based on sufficient stochastic simulations and a coordination-difference-driven aggregation approach that considers the coordination degree and differences between evaluated objects. The evaluation revealed significant regional disparities in urban agglomeration sustainability from 2012 to 2021. The eastern region's Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei region, and Shandong Peninsula exhibit above-average sustainability performance. Conversely, the western region's Guangzhong, Guangxi Beibu Gulf, Chengyu, and Ningxia Yellow River regions exhibit below-average performance. Moreover, the growth rate of sustainability values for the 12 urban agglomerations followed a downward trajectory. Furthermore, the environmental dimension is the primary driver of sustainable development in urban agglomerations, while the economic dimension represents the main obstacle. These findings offer policymakers a scientific and practical framework to guide sustainability-related decisions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105904"},"PeriodicalIF":10.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534091","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 : 2024-10-17DOI: 10.1016/j.scs.2024.105910
Jinting Zhang , Kui Yang , Jingdong Wu , Ying Duan , Yanni Ma , Jingzhi Ren , Zenan Yang
Under the "dual carbon" goals, targeting issues such as the difficulty in changing the high-carbon economic development model in pilot cities and the inability of previous prediction models to meet current needs, this paper provides an in-depth analysis of carbon stocks and emissions in a peak pilot City spanning from 2000 to 2020. Utilizing the PLUS model, this study forecasts land use/cover data under diverse future scenarios, encompassing natural development (ND) as well as ecological protection (EP). Moreover, the Bi-LSTM deep learning model is developed using six influencing factors to simulate carbon emissions. The research also examined the spatiotemporal changes in carbon budget and balance. The findings of the study reveal several significant conclusions:(1) The PLUS model demonstrated high predictive accuracy in forecasting future land-use types, achieving an average overall accuracy exceeding 0.89 and a Kappa value of 0.8568; The Bi-LSTM model achieved the highest accuracy among all competing models, with an score reaching 0.864. (2) Under the EP scenario from 2020 to 2030, the rate of decline in carbon storage has slowed down ( of carbon storage have been avoided from disappearing), and land use efficiency has significantly improved. Due to the protection of ecological land, a certain carbon sink effect has been generated, resulting in lower regional carbon emissions compared to the ND scenario, emphasizing the importance and necessity of setting ecological red lines for carbon stock optimization. (3) Carbon payment areas are primarily concentrated in urban centers, and over time, these areas and carbon compensation zones each account half of the total area. (4) Under different scenarios, the carbon balance of built land has been partially mitigated, and the overall trend is developing favorably.
{"title":"Scenario simulation of carbon balance in carbon peak pilot cities under the background of the \"dual carbon\" goals","authors":"Jinting Zhang , Kui Yang , Jingdong Wu , Ying Duan , Yanni Ma , Jingzhi Ren , Zenan Yang","doi":"10.1016/j.scs.2024.105910","DOIUrl":"10.1016/j.scs.2024.105910","url":null,"abstract":"<div><div>Under the \"dual carbon\" goals, targeting issues such as the difficulty in changing the high-carbon economic development model in pilot cities and the inability of previous prediction models to meet current needs, this paper provides an in-depth analysis of carbon stocks and emissions in a peak pilot City spanning from 2000 to 2020. Utilizing the PLUS model, this study forecasts land use/cover data under diverse future scenarios, encompassing natural development (ND) as well as ecological protection (EP). Moreover, the Bi-LSTM deep learning model is developed using six influencing factors to simulate carbon emissions. The research also examined the spatiotemporal changes in carbon budget and balance. The findings of the study reveal several significant conclusions:(1) The PLUS model demonstrated high predictive accuracy in forecasting future land-use types, achieving an average overall accuracy exceeding 0.89 and a Kappa value of 0.8568; The Bi-LSTM model achieved the highest accuracy among all competing models, with an <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> score reaching 0.864. (2) Under the EP scenario from 2020 to 2030, the rate of decline in carbon storage has slowed down (<span><math><mrow><mn>6.44</mn><mspace></mspace><mo>×</mo><mspace></mspace><msup><mrow><mn>10</mn></mrow><mn>6</mn></msup><mspace></mspace><mi>t</mi></mrow></math></span> of carbon storage have been avoided from disappearing), and land use efficiency has significantly improved. Due to the protection of ecological land, a certain carbon sink effect has been generated, resulting in lower regional carbon emissions compared to the ND scenario, emphasizing the importance and necessity of setting ecological red lines for carbon stock optimization. (3) Carbon payment areas are primarily concentrated in urban centers, and over time, these areas and carbon compensation zones each account half of the total area. (4) Under different scenarios, the carbon balance of built land has been partially mitigated, and the overall trend is developing favorably.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"116 ","pages":"Article 105910"},"PeriodicalIF":10.5,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533634","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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