Pub Date : 2024-11-10DOI: 10.1016/j.scs.2024.105976
Jeongwoo Lee , Caryl Anne M. Barquilla , Kitae Park , Andy Hong
This study investigates the critical issue of how urban form characteristics influence PM2.5 concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM2.5 concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM2.5 levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.
{"title":"Urban form and seasonal PM2.5 dynamics: Enhancing air quality prediction using interpretable machine learning and IoT sensor data","authors":"Jeongwoo Lee , Caryl Anne M. Barquilla , Kitae Park , Andy Hong","doi":"10.1016/j.scs.2024.105976","DOIUrl":"10.1016/j.scs.2024.105976","url":null,"abstract":"<div><div>This study investigates the critical issue of how urban form characteristics influence PM<sub>2.5</sub> concentrations, a key concern for public health in densely populated cities. Traditional monitoring methods have faced data gaps and methodological limitations. To address this, we employed interpretable machine learning (ML) models with data from 1,069 Internet-of-Things (IoT) sensors across Seoul, South Korea (September 2020–August 2023). Over 80 urban form variables—including density, transportation, road design, building morphology, and land use—were analyzed using Recursive Feature Elimination to identify key factors affecting PM<sub>2.5</sub> concentrations within three buffer zones (300-m, 500-m, 1-km). The random forest model demonstrated the highest accuracy, with an R² of 95 % for autumn and 96 % for spring. Our findings show higher PM<sub>2.5</sub> levels in colder months, driven by road width and building density in autumn and traffic and industrial activity in winter. In summer, green spaces and meteorological conditions were primary factors, while spring air quality was notably impacted by localized traffic emissions around highways and bus stops. This study offers robust predictions and actionable insights for urban planning and air quality management. Future research could integrate additional environmental variables and expand sensor coverage to further refine predictive models.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105976"},"PeriodicalIF":10.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663179","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 : 2024-11-09DOI: 10.1016/j.scs.2024.105969
Kai Chen Goh , Tonni Agustiono Kurniawan , Nadzirah Zainordin , Ika Diyah Candra Arifah , Muhamad Azahar Abas , Md Asrul Nasid Masrom , Sulzakimin Mohamed , Roshartini Omar , Sui Lai Khoo , Hun Chuen Gui , Tien Choon Toh , Choo Wou Onn
This work explores the application of Industry 5.0 principles in smart cities development in Malaysia and China, focusing on digital transformation for sustainable urban development. The study presents case-studies from both the countries, highlighting the implementation strategies, challenges, and outcomes associated with integrating advanced technologies to enhance efficiency, climate resilience, and sustainability. This study aims to develop a data-driven methodology to address the absence of region-specific frameworks for sustainable smart cities (SSCs) and to evaluate their impacts. In Malaysia, the implementation of smart energy management systems that utilize IoT and AI has shown promise in reducing carbon footprints and maximizing resource efficiency. China's focus on smart water management using sensor networks and real-time data analytics provides insights into effective water conservation. Smart waste management systems have increased recycling rates by 20–30 %. Progress is crucial for the region's pursuit of SSCs to reach a global investment of USD 2.5 trillion by 2025. This work concludes by discussing the implications of this work in both countries to achieve sustainable urbanization through Industry 5.0 technologies. This work offers recommendations for policymakers, urban planners, and technologists to navigate the complexities of smart city development, while providing a roadmap to leverage digital transformation to achieve decarbonization goals in energy, waste and water sector by 2060.
{"title":"Expediting decarbonization in energy, waste, and water sector through digitalization in sustainable smart cities (SSC): Case-studies in Malaysia and China based on Industry 5.0 paradigm","authors":"Kai Chen Goh , Tonni Agustiono Kurniawan , Nadzirah Zainordin , Ika Diyah Candra Arifah , Muhamad Azahar Abas , Md Asrul Nasid Masrom , Sulzakimin Mohamed , Roshartini Omar , Sui Lai Khoo , Hun Chuen Gui , Tien Choon Toh , Choo Wou Onn","doi":"10.1016/j.scs.2024.105969","DOIUrl":"10.1016/j.scs.2024.105969","url":null,"abstract":"<div><div>This work explores the application of Industry 5.0 principles in smart cities development in Malaysia and China, focusing on digital transformation for sustainable urban development. The study presents case-studies from both the countries, highlighting the implementation strategies, challenges, and outcomes associated with integrating advanced technologies to enhance efficiency, climate resilience, and sustainability. This study aims to develop a data-driven methodology to address the absence of region-specific frameworks for sustainable smart cities (SSCs) and to evaluate their impacts. In Malaysia, the implementation of smart energy management systems that utilize IoT and AI has shown promise in reducing carbon footprints and maximizing resource efficiency. China's focus on smart water management using sensor networks and real-time data analytics provides insights into effective water conservation. Smart waste management systems have increased recycling rates by 20–30 %. Progress is crucial for the region's pursuit of SSCs to reach a global investment of USD 2.5 trillion by 2025. This work concludes by discussing the implications of this work in both countries to achieve sustainable urbanization through Industry 5.0 technologies. This work offers recommendations for policymakers, urban planners, and technologists to navigate the complexities of smart city development, while providing a roadmap to leverage digital transformation to achieve decarbonization goals in energy, waste and water sector by 2060.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105969"},"PeriodicalIF":10.5,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142662823","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 : 2024-11-09DOI: 10.1016/j.scs.2024.105977
Zihao Ma, Pingdan Zhang
Progress made in environmental protection may negatively affect regional socioeconomic development, reducing or impairing the ability of local economic systems to defend against external shocks, culminating in weakened economic resilience. Implementing carbon abatement programs without risking economic resilience is therefore an urgent problem for the international community, especially for its emerging national economies. Digital infrastructure construction (DIC), as a driving force of technological progress and structural transformation, may offer a viable solution to that problem. Here, we used county-level data for China, the biggest emerging economy in the world, to investigate whether and how Smart City Pilot policy (SCP, a policy close to DIC) has lowered regional carbon emissions and influenced economic resilience. We find that the SCP could significantly promote carbon abatement goals, and this result is robust under several tests. Further, chain-based mediating effect analysis revealed that the SCP's beneficial impact could have arisen by first promoting innovation and then productivity, and public concern acting as a stressor that pressures officials to engage in environmental governance. Furthermore, our results show the SCP is capable of bolstering regional economic resilience, and could even offset or negate the adverse effects of strict environmental regulation on that resilience. However, the positive effects of the SCP on carbon abatement in China's developed counties, or ones located in a resources-based city, were not significant. Altogether, our empirical results highlight how DIC can serve as a way to help achieve sustainable development, but more studies on its contributing role are clearly needed.
{"title":"Understanding how digital infrastructure construction could promote sustainable development","authors":"Zihao Ma, Pingdan Zhang","doi":"10.1016/j.scs.2024.105977","DOIUrl":"10.1016/j.scs.2024.105977","url":null,"abstract":"<div><div>Progress made in environmental protection may negatively affect regional socioeconomic development, reducing or impairing the ability of local economic systems to defend against external shocks, culminating in weakened economic resilience. Implementing carbon abatement programs without risking economic resilience is therefore an urgent problem for the international community, especially for its emerging national economies. Digital infrastructure construction (DIC), as a driving force of technological progress and structural transformation, may offer a viable solution to that problem. Here, we used county-level data for China, the biggest emerging economy in the world, to investigate whether and how Smart City Pilot policy (SCP, a policy close to DIC) has lowered regional carbon emissions and influenced economic resilience. We find that the SCP could significantly promote carbon abatement goals, and this result is robust under several tests. Further, chain-based mediating effect analysis revealed that the SCP's beneficial impact could have arisen by first promoting innovation and then productivity, and public concern acting as a stressor that pressures officials to engage in environmental governance. Furthermore, our results show the SCP is capable of bolstering regional economic resilience, and could even offset or negate the adverse effects of strict environmental regulation on that resilience. However, the positive effects of the SCP on carbon abatement in China's developed counties, or ones located in a resources-based city, were not significant. Altogether, our empirical results highlight how DIC can serve as a way to help achieve sustainable development, but more studies on its contributing role are clearly needed.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105977"},"PeriodicalIF":10.5,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663177","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 : 2024-11-08DOI: 10.1016/j.scs.2024.105959
Hang Yu , Fu Xiao , Hanbei Zhang , Wei Liao , Yanxue Li
The ever-increasingly severe weather events have elevated the quest for resilience in distribution grids. Cold load pickup (CLPU), a common occurrence in buildings with thermostatically controlled loads (TCLs), generates a significant peak power demand when loads restart. With widespread TCLs distribution, the restoration speed and power level could be impacted by the conventional grid restoration scheme due to limited distribution generator (DG) capability and power supply paths. In this context, this paper proposes a two-stage coordinated restoration scheme based on the novel hybrid AC/DC distribution grid, encompassing the grid configuration level, information interaction level, and designed restoration flow. The typical delayed exponential model is used to characterize CLPU properties during extended outages. In the 1st stage, the contained coordinated restoration strategy decides the optimal load restoration sequence with CLPU concerned. Then, the grid loss optimization is carried out in stage 2 to generate the proper power reference for DGs and voltage source converters (VSCs) of hybrid grids. In case studies, four types of heterogeneous buildings with varied CLPU characteristics are deployed in the analyzed grid. It is verified that the proposed scheme could make effective aggregation and dispatching for multiple DGs, achieving an additional 11.3 h of total load support, a 16.5 % increase of DG utilization and an 11.7 % enhancement of the resilience index compared to the conventional restoration scheme. Furthermore, this scheme demonstrates adaptability for resilience improvement under varied temperatures and fault locations.
{"title":"A two-stage coordinated restoration scheme of hybrid AC/DC distribution grid considering cold load pickup and resilience enhancement","authors":"Hang Yu , Fu Xiao , Hanbei Zhang , Wei Liao , Yanxue Li","doi":"10.1016/j.scs.2024.105959","DOIUrl":"10.1016/j.scs.2024.105959","url":null,"abstract":"<div><div>The ever-increasingly severe weather events have elevated the quest for resilience in distribution grids. Cold load pickup (CLPU), a common occurrence in buildings with thermostatically controlled loads (TCLs), generates a significant peak power demand when loads restart. With widespread TCLs distribution, the restoration speed and power level could be impacted by the conventional grid restoration scheme due to limited distribution generator (DG) capability and power supply paths. In this context, this paper proposes a two-stage coordinated restoration scheme based on the novel hybrid AC/DC distribution grid, encompassing the grid configuration level, information interaction level, and designed restoration flow. The typical delayed exponential model is used to characterize CLPU properties during extended outages. In the 1st stage, the contained coordinated restoration strategy decides the optimal load restoration sequence with CLPU concerned. Then, the grid loss optimization is carried out in stage 2 to generate the proper power reference for DGs and voltage source converters (VSCs) of hybrid grids. In case studies, four types of heterogeneous buildings with varied CLPU characteristics are deployed in the analyzed grid. It is verified that the proposed scheme could make effective aggregation and dispatching for multiple DGs, achieving an additional 11.3 h of total load support, a 16.5 % increase of DG utilization and an 11.7 % enhancement of the resilience index compared to the conventional restoration scheme. Furthermore, this scheme demonstrates adaptability for resilience improvement under varied temperatures and fault locations.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105959"},"PeriodicalIF":10.5,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663126","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 : 2024-11-07DOI: 10.1016/j.scs.2024.105945
Yuxin Yan, Wenchen Jian, Boya Wang, Zhicheng Liu
The impact of urban green infrastructure (UGI) on land surface temperature (LST) has been widely discussed as a strategy to improve urban thermal environments. However, most studies have focused primarily on daytime conditions. Due to the limitations of polar-orbiting satellites like the Landsat series, the influence of UGI on LST over a 24-hour cycle remains unclear. To assess the relative influence and interdependence of UGI characteristics on LST across four different grid scales over the diurnal cycle, this study, conducted within Beijing's Fifth Ring Road during the summer, utilized a random forest (RF) regression model. The results indicate that: (1) UGI's impact on LST follows two distinct patterns: daytime (morning and afternoon) and non-daytime (night and dawn), which indicates the intensity of sunlight as a key driving factor; (2) tree landscape pattern indices are the most significant factors affecting LST; (3) during non-daytime periods (night and dawn), cropland's cooling effect is equally important as that of trees. These findings are valuable for prioritizing and strategically placing different types of UGI in urban planning. However, as this study focuses on a specific area, future research should include comparative studies in cities with different climatic conditions.
{"title":"Multi-scale effects of LCZ and urban green infrastructure on diurnal land surface temperature dynamics","authors":"Yuxin Yan, Wenchen Jian, Boya Wang, Zhicheng Liu","doi":"10.1016/j.scs.2024.105945","DOIUrl":"10.1016/j.scs.2024.105945","url":null,"abstract":"<div><div>The impact of urban green infrastructure (UGI) on land surface temperature (LST) has been widely discussed as a strategy to improve urban thermal environments. However, most studies have focused primarily on daytime conditions. Due to the limitations of polar-orbiting satellites like the Landsat series, the influence of UGI on LST over a 24-hour cycle remains unclear. To assess the relative influence and interdependence of UGI characteristics on LST across four different grid scales over the diurnal cycle, this study, conducted within Beijing's Fifth Ring Road during the summer, utilized a random forest (RF) regression model. The results indicate that: (1) UGI's impact on LST follows two distinct patterns: daytime (morning and afternoon) and non-daytime (night and dawn), which indicates the intensity of sunlight as a key driving factor; (2) tree landscape pattern indices are the most significant factors affecting LST; (3) during non-daytime periods (night and dawn), cropland's cooling effect is equally important as that of trees. These findings are valuable for prioritizing and strategically placing different types of UGI in urban planning. However, as this study focuses on a specific area, future research should include comparative studies in cities with different climatic conditions.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105945"},"PeriodicalIF":10.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663181","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 : 2024-11-06DOI: 10.1016/j.scs.2024.105966
Puju Cao , Zhao Liu , Huan Zhang , Lanye Wei
The long-term processes of urbanization and industrialization have led to the agglomeration of population and industry, fostering economic development while introducing opportunities and challenges for carbon reduction in transport. This paper integrates the Stochastic Impacts by Regression on Population, Affluence, and Technology Model with the Spatial Durbin Model to assess the effects of population agglomeration and industrial agglomeration on transport carbon dioxide emissions. The empirical results show that a 1% increase in population agglomeration decreases local transport carbon dioxide emissions by 1.7065% and generates a spillover effect of 1.0542% in surrounding areas. In contrast, industrial agglomeration increases regional transport carbon dioxide emissions by an average of 0.3308% without significant spillover effects. Furthermore, economic agglomeration exhibits an N-shaped relationship with transport carbon dioxide emissions, reflecting the dual influences of the "economic effect" and the "congestion effect". Mechanism analysis reveals that both types of agglomeration can modulate the impact of infrastructure development on transport carbon dioxide emissions, suggesting that effective infrastructure planning can help alleviate the negative environmental impacts. This study provides a spatial mode for understanding the synergistic effects of population planning, industrial development, and environmental improvement, offering significant reference value for policymakers in the decision-making related to low-carbon transport development.
{"title":"The impact of agglomeration on CO2 emissions in China's transport sector: A spatial econometric analysis","authors":"Puju Cao , Zhao Liu , Huan Zhang , Lanye Wei","doi":"10.1016/j.scs.2024.105966","DOIUrl":"10.1016/j.scs.2024.105966","url":null,"abstract":"<div><div>The long-term processes of urbanization and industrialization have led to the agglomeration of population and industry, fostering economic development while introducing opportunities and challenges for carbon reduction in transport. This paper integrates the Stochastic Impacts by Regression on Population, Affluence, and Technology Model with the Spatial Durbin Model to assess the effects of population agglomeration and industrial agglomeration on transport carbon dioxide emissions. The empirical results show that a 1% increase in population agglomeration decreases local transport carbon dioxide emissions by 1.7065% and generates a spillover effect of 1.0542% in surrounding areas. In contrast, industrial agglomeration increases regional transport carbon dioxide emissions by an average of 0.3308% without significant spillover effects. Furthermore, economic agglomeration exhibits an N-shaped relationship with transport carbon dioxide emissions, reflecting the dual influences of the \"economic effect\" and the \"congestion effect\". Mechanism analysis reveals that both types of agglomeration can modulate the impact of infrastructure development on transport carbon dioxide emissions, suggesting that effective infrastructure planning can help alleviate the negative environmental impacts. This study provides a spatial mode for understanding the synergistic effects of population planning, industrial development, and environmental improvement, offering significant reference value for policymakers in the decision-making related to low-carbon transport development.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105966"},"PeriodicalIF":10.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663118","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 : 2024-11-06DOI: 10.1016/j.scs.2024.105946
Ahmad Helmi , Viviana Bastidas , Kwadwo Oti-Sarpong , Jennifer Schooling
This study explores the leadership competencies required in practice by city planners and managers in smart city projects focusing on environmental urban sustainability. Although the literature notes that urban technologies and their capabilities can help address sustainability challenges in cities, there is a lack of studies exploring the competency requirements necessary to foster leadership capacity. This paper identifies leadership competencies within four real-world case studies in the urban built environment, guided by a socio-technical competency framework (DC2-CF). The selected case studies represent a diverse set of city planning purposes, geographic regions, various levels of spatial scale, and socio-technical elements of digital innovation. In these case studies, city managers exhibit specific competencies to develop digital innovation projects that uphold and advance urban sustainability. The study demonstrates the relevance and practical application of DC2-CF as a valuable tool to identify competency needs for local public, private, and community stakeholders throughout diverse stages of the urban digital innovation process. The findings suggest the complex relationship between competencies and project delivery, stressing variations in how they are utilised across various projects. Drawing from these key results, this paper provides practical recommendations for city professionals, guiding them in leading climate-friendly and sustainable urban digital innovation.
{"title":"Sustainable urban digital innovation: A socio-technical competency-based approach to evaluation","authors":"Ahmad Helmi , Viviana Bastidas , Kwadwo Oti-Sarpong , Jennifer Schooling","doi":"10.1016/j.scs.2024.105946","DOIUrl":"10.1016/j.scs.2024.105946","url":null,"abstract":"<div><div>This study explores the leadership competencies required in practice by city planners and managers in smart city projects focusing on environmental urban sustainability. Although the literature notes that urban technologies and their capabilities can help address sustainability challenges in cities, there is a lack of studies exploring the competency requirements necessary to foster leadership capacity. This paper identifies leadership competencies within four real-world case studies in the urban built environment, guided by a socio-technical competency framework (DC2-CF). The selected case studies represent a diverse set of city planning purposes, geographic regions, various levels of spatial scale, and socio-technical elements of digital innovation. In these case studies, city managers exhibit specific competencies to develop digital innovation projects that uphold and advance urban sustainability. The study demonstrates the relevance and practical application of DC2-CF as a valuable tool to identify competency needs for local public, private, and community stakeholders throughout diverse stages of the urban digital innovation process. The findings suggest the complex relationship between competencies and project delivery, stressing variations in how they are utilised across various projects. Drawing from these key results, this paper provides practical recommendations for city professionals, guiding them in leading climate-friendly and sustainable urban digital innovation.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105946"},"PeriodicalIF":10.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663170","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 : 2024-11-05DOI: 10.1016/j.scs.2024.105953
Kende Kocsis , József Kövendi , Balázs Bokor
The city of Budapest produces approximately 680–700 000 tonnes of municipal solid waste every year, of which <2/3 is being recycled or used for energetic purposes, the rest ends up in a landfill. To combat this environmental and logistical problem the installation of a second waste incineration plant has been proposed in the south of the city. The only cost associated with fuel consumption in the case of waste to energy powerplants (WtE plants) is the transport cost, as the city council provides economic support for waste disposal. Since the transportation has a huge influence on the cost of opertation, logistical optimisation of the transport routes promises a direct impact on cost savings. In this study the logistical optimisation of the Southern Budapest area was carried out using image processing and logic based algorithm programming. As a result the optimal transport of 230 000 tonnes of municipal solid waste (MSW) was solved resulting in a 4 835.2 km monthly travel distance reduction.This value can be translated to 7 823 €/month cost, 9 459.6 kg/month CO2 and 45.3 kg/month NOx emissions reduction in the urban areas.
{"title":"Waste collection route optimisation for the second waste-to-energy plant in Budapest","authors":"Kende Kocsis , József Kövendi , Balázs Bokor","doi":"10.1016/j.scs.2024.105953","DOIUrl":"10.1016/j.scs.2024.105953","url":null,"abstract":"<div><div>The city of Budapest produces approximately 680–700 000 tonnes of municipal solid waste every year, of which <2/3 is being recycled or used for energetic purposes, the rest ends up in a landfill. To combat this environmental and logistical problem the installation of a second waste incineration plant has been proposed in the south of the city. The only cost associated with fuel consumption in the case of waste to energy powerplants (WtE plants) is the transport cost, as the city council provides economic support for waste disposal. Since the transportation has a huge influence on the cost of opertation, logistical optimisation of the transport routes promises a direct impact on cost savings. In this study the logistical optimisation of the Southern Budapest area was carried out using image processing and logic based algorithm programming. As a result the optimal transport of 230 000 tonnes of municipal solid waste (MSW) was solved resulting in a 4 835.2 km monthly travel distance reduction.This value can be translated to 7 823 €/month cost, 9 459.6 kg/month CO<sub>2</sub> and 45.3 kg/month NO<sub>x</sub> emissions reduction in the urban areas.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105953"},"PeriodicalIF":10.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663146","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 : 2024-11-05DOI: 10.1016/j.scs.2024.105958
Jeetendra Sahani , Prashant Kumar , Sisay E. Debele
As climate change intensifies, the frequency and intensity of heatwaves are rising to pose significant health risks. Population vulnerability, influenced by socioeconomic and demographic factors, is a widespread concern. We analysed heat vulnerability by demonstrating usefulness of principal component analysis on recent, localised census data at lower super output scale for vulnerability factors such as poverty, access to cooling facilities, age, and gender for a non-city yet highly heat risk vulnerable case study of Surrey, UK. Four major factors (poverty, elderly population, unemployed students, daily commute) were identified, creating a cumulative Heat Vulnerability Index, aiding in prioritising interventions and mapping vulnerable areas. Mapping revealed most areas had a moderate vulnerability level of 3 out of 6 for individual major factors, with cumulative scores ranging from 11 to 12 out of 20. The study emphasises the interconnectedness of vulnerability factors and highlights the applicability of the approach beyond Surrey. The demonstrated methodology provides a valuable template for vulnerability assessments in regions facing similar challenges and have its up-to-date effective heat action plan underlining the importance of tailored strategies for comprehensive heat risk management (e.g. cooling centres, transport aid, multilingual risk communication and home visits). Policymakers can utilise the insights gained to develop targeted measures for vulnerable populations and manage heat-related issues effectively on a global scale.
{"title":"Assessing demographic and socioeconomic susceptibilities to heatwaves in the Southeastern United Kingdom","authors":"Jeetendra Sahani , Prashant Kumar , Sisay E. Debele","doi":"10.1016/j.scs.2024.105958","DOIUrl":"10.1016/j.scs.2024.105958","url":null,"abstract":"<div><div>As climate change intensifies, the frequency and intensity of heatwaves are rising to pose significant health risks. Population vulnerability, influenced by socioeconomic and demographic factors, is a widespread concern. We analysed heat vulnerability by demonstrating usefulness of principal component analysis on recent, localised census data at lower super output scale for vulnerability factors such as poverty, access to cooling facilities, age, and gender for a non-city yet highly heat risk vulnerable case study of Surrey, UK. Four major factors (poverty, elderly population, unemployed students, daily commute) were identified, creating a cumulative Heat Vulnerability Index, aiding in prioritising interventions and mapping vulnerable areas. Mapping revealed most areas had a moderate vulnerability level of 3 out of 6 for individual major factors, with cumulative scores ranging from 11 to 12 out of 20. The study emphasises the interconnectedness of vulnerability factors and highlights the applicability of the approach beyond Surrey. The demonstrated methodology provides a valuable template for vulnerability assessments in regions facing similar challenges and have its up-to-date effective heat action plan underlining the importance of tailored strategies for comprehensive heat risk management (e.g. cooling centres, transport aid, multilingual risk communication and home visits). Policymakers can utilise the insights gained to develop targeted measures for vulnerable populations and manage heat-related issues effectively on a global scale.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105958"},"PeriodicalIF":10.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663121","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}
The factors influencing carbon emissions in the construction sector are numerous, and the relationships between these factors are complex. Previous studies on carbon peaking have often overlooked the dynamic changes between influencing factors and limited the number of variables to simplify the computation of predictive models. Based on the goal of carbon peaking, this study explores the relationships between internal factors within the construction industry and establishes a network of factor correlation. Furthermore, this network is embedded into an improved STIRPAT model, and a multi-factor dynamic correlation prediction model is constructed by incorporating scenario analysis. Taking Shaanxi Province, China, as a case for empirical analysis, the study explores carbon-peaking solutions for the building sector under different development scenarios. The findings indicate that carbon emissions in Shaanxi's building sector continuously increased during the study period, reaching 213 MtCO2 in 2020. Through factor screening, 12 driving factors were found to be significantly related to carbon emissions, all showing positive correlations, with the urbanization rate contributing the most to emissions. The dynamic association prediction model constructed had an accuracy of 0.996. Using this model, nine carbon emission scenarios were predicted, with optimizing the energy structure identified as the critical pathway, achieving a 5.01% reduction in emissions. A comprehensive strategy could achieve a 12.49% reduction and meet the carbon peaking target. Finally, the study proposes policy recommendations for the coordinated management of emissions reductions in cities and the construction industry, contributing to the development of sustainable cities and societies.
{"title":"Multi-factor dynamic correlation prediction and analysis of carbon peaking for building sector: A case study of Shaanxi province","authors":"Xue Zhang, Zengfeng Yan, Pingan Ni, Xia Yan, Fuming Lei, Yingjun Yue","doi":"10.1016/j.scs.2024.105960","DOIUrl":"10.1016/j.scs.2024.105960","url":null,"abstract":"<div><div>The factors influencing carbon emissions in the construction sector are numerous, and the relationships between these factors are complex. Previous studies on carbon peaking have often overlooked the dynamic changes between influencing factors and limited the number of variables to simplify the computation of predictive models. Based on the goal of carbon peaking, this study explores the relationships between internal factors within the construction industry and establishes a network of factor correlation. Furthermore, this network is embedded into an improved STIRPAT model, and a multi-factor dynamic correlation prediction model is constructed by incorporating scenario analysis. Taking Shaanxi Province, China, as a case for empirical analysis, the study explores carbon-peaking solutions for the building sector under different development scenarios. The findings indicate that carbon emissions in Shaanxi's building sector continuously increased during the study period, reaching 213 MtCO<sub>2</sub> in 2020. Through factor screening, 12 driving factors were found to be significantly related to carbon emissions, all showing positive correlations, with the urbanization rate contributing the most to emissions. The dynamic association prediction model constructed had an accuracy of 0.996. Using this model, nine carbon emission scenarios were predicted, with optimizing the energy structure identified as the critical pathway, achieving a 5.01% reduction in emissions. A comprehensive strategy could achieve a 12.49% reduction and meet the carbon peaking target. Finally, the study proposes policy recommendations for the coordinated management of emissions reductions in cities and the construction industry, contributing to the development of sustainable cities and societies.</div></div>","PeriodicalId":48659,"journal":{"name":"Sustainable Cities and Society","volume":"117 ","pages":"Article 105960"},"PeriodicalIF":10.5,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663172","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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