基于Geodetector和MGWR的贵州工业碳排放效率时空演化及影响因素分析

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中文摘要：探究工业碳排放效率的时空演化及影响因素对区域产业绿色发展具有重要意义.基于2010-2020年贵州省9个市州的面板数据,利用super-SBM模型与Malmquist指数对工业碳排放效率进行静态和动态分析,并采用探索性时空数据分析方法揭示时空交互特征;基于此结合地理探测器和多尺度地理加权回归模型研究其影响因素.结果表明:①贵州工业碳排放效率整体呈上升趋势,年均增长率为8.45％.②技术进步是贵州工业碳排放效率提升的主要内动力.③工业碳排放效率空间自相关的时间路径长度呈现由东部市州向中、西部增大的趋势;贵州各市州的工业碳排放效率随时间演变呈现出较强的空间依赖关系.④对外开放程度、城市化水平、能源消耗强度、产业结构、重工业水平、生产力水平6个因素是影响工业碳排放效率的主导因子,且影响显著性出现不同程度的提高;对外开放程度、能源消耗强度与工业碳排放效率存在负相关,其余主导因子与工业碳排放效率呈正相关.

外文标题：Spatio-temporal variation and influencing factors of industrial carbon emission efficiency in Guizhou based on Geodetector and multi-scale geographically weighted regression

外文摘要：Exploring the spatiotemporal evolution and influencing factors of industrial carbon emission effi-ciency is of great significance for the green development of regional industries.The purpose of this study is to analyze the industrial carbon emission efficiency and its influencing factors in Guizhou,and to provide theoret-ical basis and policy inspiration for achieving green and high-quality development.Based on the panel data of nine cities and prefectures in Guizhou Province from 2010 to 2020,the super-SBM model and Malmquist in-dex were used to calculate the static and dynamic industrial carbon emission efficiency,and the exploratory spatio-temporal data analysis method was used to reveal their spatio-temporal interaction characteristics.Then,the influence factors were studied by combining Geodetector and multi-scale geographically weighted regres-sion.The results show that:1)From 2010 to 2020,the industrial carbon emission efficiency of Guizhou shows an upward trend with an average annual growth rate of 8.45％.The values of the nine cities and prefectures show the significant spatial heterogeneity.2)The Malmquist index of industrial carbon emission efficiency from 2010 to 2020 is greater than 1 in all regions except Liupanshui,with an upward trend of productivity change.Technological progress is the main internal driving force for the improvement of industrial carbon emission efficiency in Guizhou.3)The time path length of spatial autocorrelation of industrial carbon emission efficiency shows a trend of increasing from the eastern region to the central and western regions.The time paths of Guiyang and Tongren move relatively short,constituting a relatively stable local spatial structure.The curvature analysis concludes that the industrial carbon emission efficiency of each region in Guizhou shows a strong spatial dependence with time evolution.4)The dominant factors affecting the spatial variation of indus-trial carbon emission efficiency include the degree of urbanization,energy consumption intensity,and industri-al structure.The q-values obtained from the interaction of the driving factors increase in varying degrees.The opening up,energy consumption intensity and heavy industry level are negatively correlated with industrial carbon emission efficiency.In order to improve industrial carbon emission efficiency,the government should concentrate on the key drivers of spatial and temporal variation in industrial carbon emission efficiency,as well as regional coordination and cooperation.

外文关键词：

industrial carbon emission efficiencysuper-SBM modelMalmquist indexexploratory spatio-temporal data analysisGeodetectormulti-scale geographically weighted regression model(MGWR)

作者：

尹剑、姜洪涛、焦露、张斌、丁乙、黄嘉瑜

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作者单位：

贵州财经大学西部现代化研究中心,贵州贵阳 550025

贵州财经大学应用经济学院,贵州贵阳 550025

同济大学经济与管理学院,上海 200092

关键词：

工业碳排放效率 super-SBM模型 Malmquist指数探索性时空数据分析地理探测器多尺度地理加权回归模型

基金：

贵州省哲学社会科学规划重点课题

项目编号：

21GZZD59

出版年：

2024

DOI：

10.13249/j.cnki.sgs.20221095

地理科学

中国科学院东北地理与农业生态研究所

地理科学

CSTPCDCSSCICHSSCD北大核心

影响因子：3.117

ISSN：1000-0690

年,卷(期)：2024.44(7)