制造业碳减排的脱钩效应及驱动机制——一个二维分析框架

俞洁; 张勇; 李清瑶

doi:10.13205/j.hjgc.202310019

制造业碳减排的脱钩效应及驱动机制——一个二维分析框架

doi: 10.13205/j.hjgc.202310019

中国计量大学质量经济研究所, 杭州 310018

基金项目:

浙江省科技计划软科学重点项目（2020C25030）；浙江省哲学社会科学规划课题（18MYZX09YB）；浙江省大学生科研创新活动计划资助项目（2022R409B048）

详细信息

作者简介:
俞洁(1997-),男,研究生,主要研究方向为碳排放模拟与评估。yujie66926@163.com

通讯作者:
张勇(1970-),男,教授,主要研究方向为低碳与可持续发展。zhfk@cjlu.edu.cn

计量
- 文章访问数: 88
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- PDF下载量: 4
- 被引次数: 0
出版历程
- 收稿日期: 2023-07-24
- 网络出版日期: 2023-12-26

DECOUPLING EFFECT AND DRIVING MECHANISM OF CARBON EMISSION REDUCTION IN MANUFACTURING INDUSTRY: A TWO-DIMENSIONAL ANALYSIS FRAMEWORK

Institute of Quality Economics, China Jiliang University, Hangzhou 310018, China

摘要

摘要: 基于2000—2020年中国制造业的数据，构建二维脱钩模型测度经济增长与碳排放的脱钩状况，并运用脱钩努力模型探究脱钩的驱动机制。结果显示：1）26个行业碳排放总量从2000年的176.09×10⁷ t增长至2020年的639.21×10⁷ t，21年间增长了3.63倍。2）26个行业共现18种二维脱钩状态，强脱钩次数占比从“十五”后半期的15.38%上升到“十三五”的40.77%，脱钩状态逐渐改善，但仍存在较大脱钩空间。3）劳动密集型行业更容易实现低经济水平-强脱钩，资本密集型行业更容易实现中经济水平-弱脱钩，技术密集型行业更容易实现低经济水平-弱脱钩。4）能源强度和产业结构的脱钩努力指数均值分别达0.686和0.031，成为推动制造业碳脱钩的关键，而能源结构的脱钩努力指数均值为-0.147，阻碍了碳脱钩进程。5）劳动密集型行业做出的脱钩努力最大，资本密集型行业次之，技术密集型行业最小。
- 制造业 /
- 碳排放 /
- 二维脱钩 /
- 驱动机制 /
- 双碳
Abstract: Based on data from China's manufacturing sector spanning from 2000 to 2020, a two-dimensional decoupling model was constructed to assess the decoupling status of economic growth and carbon emissions. Additionally, a decoupling effort model was applied to investigate the driving mechanisms of decoupling. The research results were as follows:1) the total carbon emissions across 26 industries increased from 176.09×10⁷ tons in 2000 to 639.21×10⁷ tons in 2020, marking a growth of 3.63 times over 21 years. 2) across the 26 industries, there were a total of 18 types of two-dimensional decoupling states. The proportion of strong decoupling increased from 15.38% in the latter half of the "10th Five-Year Plan" period, to 40.77% in the "13th Five-Year Plan" period. While the decoupling status gradually improved, significant room for decoupling remained. 3) labor-intensive industries were more likely to achieve low economic levels with strong decoupling, capital-intensive industries were more likely to achieve moderate economic levels with weak decoupling, and technology-intensive industries were more likely to achieve low economic levels with weak decoupling. 4) the average decoupling effort indices for energy intensity and industrial structure were 0.686 and 0.031, respectively, making them key drivers for carbon decoupling in the manufacturing sector. However, the average decoupling effort index for energy structure was -0.147, which hindered the decoupling process. 5) among the industries, labor-intensive industries made the greatest decoupling efforts, followed by capital-intensive industries, while technology-intensive industries made the smallest efforts.
- manufacturing /
- carbon emissions /
- two-dimensional decoupling /
- driving mechanism /
- carbon peaking and carbon neutrality

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