Page 58 - 《环境工程技术学报》2023年第1期

Vol.13，No.1 环 境 工 程 技 术 学 报 第 13 卷，第 1 期
Jan.，2023 Journal of Environmental Engineering Technology 2023 年 1 月

杨青，彭若慧，刘星星，等.基于地理加权回归的省域碳排放影响因素研 究 [J].环境工程技术学报，2023，13（1）：54-62.
YANG Q,PENG R H,LIU X X,et al.Study on influencing factors of provincial carbon emission based on geographically weighted regression[J].Journal of
Environmental Engineering Technology，2023，13（1）：54-62.

基于地理加权回归的省域碳排放影响因素研究

杨青 ，彭若慧 ，刘星星 ，曹兰娟 1,2
1
1
1*
1.武汉理工大学安全科学与应急管理学院
2.浙大宁波理工学院机电与能源工程学院
摘要 碳减排已经成为新时代生态文明建设亟待解决的问题，碳排放量与地域空间位置密切相关，为更好地促进碳减排、碳达
峰，碳排放影响因素的区域性差异以及趋势分析已成为碳减排分析的焦点。通过地理加权回归方法研究我 国 3 个省（区、市）
0
2007—201 年的人口因素、能源消费、城镇化建设发展对碳排放量的影响，进而揭示碳排放量与区域社会经济发展的关系。结
7
果表明，碳排放量的空间聚集性较强，各影响因素的空间分布格局差异较大，其中电力消费总量和化石能源消费总量的增加对碳
排放量的正向影响作用最大，人口规模对碳排放量也有一定的正向促进作用，城市公共汽电车辆和主要建材消耗总量对碳排放
量的影响作用并不显著，均呈不稳定的正负相关关系。我国碳减排应调整能源消费结构，进一步提高清洁能源技术创新，将城镇
化建设与碳减排分阶段融合，加大绿色消费、绿色建筑和绿色出行的支持力度。
关键词 省域碳排放；空间自相关；地理加权回归；空间差异性
中图分类号：X822 文章编号：1674-991X（2023）01-0054-09 doi：10.12153/j.issn.1674-991X.20210523

Study on influencing factors of provincial carbon emission based on

geographically weighted regression
1
1
1*
YANG Qing , PENG Ruohui , LIU Xingxing , CAO Lanjuan 1,2
1.School of Safety Science and Emergency Management, Wuhan University of Technology
2.School of Mechatronics and Energy Engineering, Ningbo Tech University
Abstract Carbon emission reduction has become an urgent problem to be solved in the construction of ecological
civilization in the new era. Carbon emission is closely related to regional spatial location. In order to better promote
carbon peak and carbon emission reduction, regional differences and trend analysis of carbon emission influencing
factors have become the focus of carbon emission reduction analysis. Through the geographically weighted
regression method, the impact of population factors, energy consumption and urbanization construction and
development on the carbon emission in 30 provinces of China from 2007 to 2017 were studied, and then the
correlation between carbon emission and regional socioeconomic development was revealed. The results showed
that the spatial aggregation of carbon emissions was strong, and the spatial distribution patterns of various
influencing factors were quite different. Among them, the increase of total power consumption and total fossil
energy consumption had the greatest positive impact on carbon emissions, and population size also had a certain
positive role in promoting carbon emissions. The total consumption of urban public vehicles and main building
materials had no significant impact on carbon emissions, showing an unstable positive and negative correlation.
Some suggestions were provided for China's carbon emission reduction, including adjusting the energy consumption
structure, further improving clean energy technology innovation, integrating urbanization and carbon emission
reduction in stages, and increasing the support for green consumption, green building, and green travel.
Key words provincial carbon emission; spatial autocorrelation; geographically weighted regression; spatial
difference

收稿日期：2021-09-22
基金项目：国家社科基金重大项目（16ZDA045）；教育部人文社会科学研究项目（17YJAZH074）
作者简介：杨青（1962—），男，教授，博士，主要从事复杂系统智能管理研究，yangq@whut.edu.cn
* 通信作者：刘星星（1988—），男，副教授，博士，主要从事复杂系统智能管理、计算科学研究，liuxingxing@whut.edu.cn