{"title":"基于多种交通方式碳排放的可持续交通空间洞察:中国乡镇案例研究","authors":"","doi":"10.1016/j.cities.2024.105405","DOIUrl":null,"url":null,"abstract":"<div><p>Understanding the CO<sub>2</sub> emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO<sub>2</sub> emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO<sub>2</sub> emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and Spatial Error Model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (−0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO<sub>2</sub> emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO<sub>2</sub> emissions.</p></div>","PeriodicalId":48405,"journal":{"name":"Cities","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Spatial insights for sustainable transportation based on carbon emissions from multiple transport modes: A township-level case study in China\",\"authors\":\"\",\"doi\":\"10.1016/j.cities.2024.105405\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Understanding the CO<sub>2</sub> emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO<sub>2</sub> emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO<sub>2</sub> emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and Spatial Error Model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (−0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO<sub>2</sub> emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO<sub>2</sub> emissions.</p></div>\",\"PeriodicalId\":48405,\"journal\":{\"name\":\"Cities\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cities\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S026427512400619X\",\"RegionNum\":1,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"URBAN STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cities","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026427512400619X","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"URBAN STUDIES","Score":null,"Total":0}
引用次数: 0
摘要
了解旅客多种出行方式的二氧化碳排放量和影响因素,可以为节能减排提供方向,对发展可持续城市具有重要意义。以往的研究主要关注交通部门或单种交通方式的二氧化碳排放量。这忽略了交通系统内部排放的变化。因此,本研究重点关注粤港澳大湾区城镇的多种交通方式(即小汽车、地铁、公交车和自行车)。本研究结合空间计量经济模型(即 Moran's I 指数和空间误差模型)和机器学习模型(即随机森林和 SHAP 模型),提出了基于比率(即每种模式的二氧化碳排放量除以总排放量)和关键因素的城市交通排放结构空间自相关性探索框架。此外,还研究了不同空间尺度下比率的空间自相关性。结果表明,每种交通方式的比率都具有高度的空间依赖性,四种比率的莫兰 I 指数分别为 0.883、0.886、0.706 和 0.776。此外,地铁与汽车的比率呈现负空间相关性(-0.798),地铁与自行车的比率呈现正相关性(0.570)。人口密度、道路长度和土地利用多样性是二氧化碳排放比率的主要驱动因素,对各种交通方式的影响也不尽相同。此外,随着空间尺度从乡镇扩展到不同的城市,比率的空间自相关性降低。这项研究可为优化城市交通战略和减少二氧化碳排放提供政策启示。
Spatial insights for sustainable transportation based on carbon emissions from multiple transport modes: A township-level case study in China
Understanding the CO2 emissions and influencing factors of travelers' multiple modes can provide direction for energy conservation and emission reduction, which is of great significance for developing sustainable cities. Previous studies focused on the CO2 emissions of the transportation sector or individual modes. Which has overlooked the variations of emissions within the transport system. Hence, this study focuses on multiple modes (i.e., car, subway, bus, and bike) in the township in the Guangdong-Hong Kong-Macao Greater Bay Area. This study proposes a framework for exploring the spatial autocorrelation of urban transport emission structure based on ratios (i.e., CO2 emissions from each mode divided by total emissions) and key factors by combining spatial econometric model (i.e., Moran's I index and Spatial Error Model) and machine learning model (i.e., Random Forest and SHAP model). In addition, the spatial autocorrelation of ratios at different spatial scales is investigated. The results indicate the high spatial dependence in the ratios from each transport mode and Moran's I indices for four ratios are 0.883, 0.886, 0.706, and 0.776, respectively. In addition, subway and car ratios exhibit a negative spatial correlation (−0.798), and subway and bike show a positive correlation (0.570). Population density, road length, and land use diversity are the key drivers of CO2 emission ratios and have different effects on various transport modes. Furthermore, as the spatial scales expand from townships to distinct and city, the spatial autocorrelation of the ratios decreases. This study could provide policy implications for optimizing urban transport strategies and reducing CO2 emissions.
期刊介绍:
Cities offers a comprehensive range of articles on all aspects of urban policy. It provides an international and interdisciplinary platform for the exchange of ideas and information between urban planners and policy makers from national and local government, non-government organizations, academia and consultancy. The primary aims of the journal are to analyse and assess past and present urban development and management as a reflection of effective, ineffective and non-existent planning policies; and the promotion of the implementation of appropriate urban policies in both the developed and the developing world.