全生命周期视角下汽车产业碳排放影响因素的研究——以吉林省为例

doi:10.3969/j.issn.2097-0706.2024.08.003

摘要/Abstract

摘要：

为实现中国的“双碳”目标，降低汽车产业碳排放成为一项重要举措。研究影响汽车产业碳排放的主要因素对制定低碳发展策略具有重要意义。为分析2012—2021年吉林省汽车产业总体碳排放及其驱动因素的变化，采用向量自回归检验（VAR）模型探究汽车产业的碳排放影响因素。研究发现，有4个影响吉林省汽车产业碳排放产生的主要因素，按影响程度由大到小依次为居民消费水平、家用汽车保有量、交通运输业碳排放、汽车制造碳排放。基于分析结果，提出相应建议促进吉林省汽车产业实现碳减排：基于清洁智能生产，进行节能技术升级；给予购车补贴，建设充电设施；建设公共交通，优化线路布局；回收废旧部件，形成循环经济等。

关键词: “双碳”目标, 汽车全生命周期, 碳排放, VAR模型, 影响因素, 脉冲分析

Abstract:

In order to achieve China's "double carbon" target， reducing the carbon emissions from automobile industry has become an important measure， and studying the main factors affecting the carbon emissions is of great significance for formulating low-carbon development strategies. To analyzed the changes of the total carbon emissions from automobile industry in Jilin Province from 2012 to 2021， a Vector Auto Regression（VAR） model is applied in exploring the driving factors for the emissions. It is found out that there are four main factors impacting the carbon emissions from automobile industry in Jilin Province， and the degree of impacts is as follows in descending order： the level of household consumption， the retain number of family cars， the carbon emissions from transportation industry and the carbon emissions of automobile manufacturing. Based on the analytic results above， following measures are proposed to promote the realization of emission reduction of automobile industry in Jilin Province： taking intelligent and clean technologies and new energy-saving measures in vehicle production； providing subsidies for car purchase and building charging facilities； developing public transportation and optimizing route layout； recycling waste parts and forming a cycling economy.

Key words: "dual carbon" target, whole life cycle of the vehicle, carbon emission, VAR model, influencing factor, impulse analysis

中图分类号:

TK01

李菲菲, 王舒泓, 崔金栋. 全生命周期视角下汽车产业碳排放影响因素的研究——以吉林省为例[J]. 综合智慧能源, 2024, 46(8): 20-27.

LI Feifei, WANG Shuhong, CUI Jindong. Study on influencing factors of automobile carbon emissions from the perspective of whole life cycle： A case study of Jilin Province[J]. Integrated Intelligent Energy, 2024, 46(8): 20-27.

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变量	系数	标准误差	T统计量	概率
lnV_P	0.357 7	0.090 2	3.966 2	0.000 3
lnL_C	0.388 4	0.172 3	2.256 1	0.030 4
lnT_E	0.225 6	0.047 6	4.739 7	0
lnR_T	0.327 1	0.071 9	4.551 4	0.000 1
C₀	-4.654 8	0.520 9	-8.936 3	0
拟合度R²			0.988 1
F统计量下的P值			0