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

doi:10.12153/j.issn.1674-991X.20210523

基于地理加权回归的省域碳排放影响因素研究

doi: 10.12153/j.issn.1674-991X.20210523

杨青^1,,
彭若慧¹,
刘星星^1,,
曹兰娟^{1, 2}

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

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）

详细信息

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

中图分类号: X822
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- HTML全文浏览量: 175
- PDF下载量: 76
- 被引次数: 0
出版历程
- 收稿日期: 2021-09-22

Study on influencing factors of provincial carbon emission based on geographically weighted regression

1.
School of Safety Science and Emergency Management, Wuhan University of Technology
2.
School of Mechatronics and Energy Engineering, Ningbo Tech University

摘要

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30个省（区、市）2007—2017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。
- 省域碳排放 /
- 空间自相关 /
- 地理加权回归 /
- 空间差异性
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.
- provincial carbon emission /
- spatial autocorrelation /
- geographically weighted regression /
- spatial difference

HTML全文

图 1 碳排放量空间分布结果

Figure 1. Spatial distribution results of carbon emission

下载: 全尺寸图片幻灯片

图 2 碳排放量局部空间自相关结果

Figure 2. Local spatial autocorrelation results of carbon emission

下载: 全尺寸图片幻灯片

图 3 人口规模回归系数

Figure 3. Regression coefficient of population size

下载: 全尺寸图片幻灯片

图 4 电力消费总量回归系数

Figure 4. Regression coefficient of total power consumption

下载: 全尺寸图片幻灯片

图 5 化石能源消费总量回归系数

Figure 5. Regression coefficient of total fossil energy consumption

下载: 全尺寸图片幻灯片

图 6 城市公共汽电车辆回归系数

Figure 6. Regression coefficient of urban buses and trolley buses

下载: 全尺寸图片幻灯片

图 7 主要建材消耗总量回归系数

Figure 7. Regression coefficient of total consumption of main building materials

下载: 全尺寸图片幻灯片

表 1 GWR模型的估计结果

Table 1. Estimation results of GWR Model

指标 2007年 2012年 2017年

R² 0.99 0.98 0.98
调整R² 0.98 0.98 0.97
残差平方和 1.09 3.51 4.17
带宽 431.09 431.09 57.41
AICc 557.39 592.47 598.59

下载: 导出CSV

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