Page 75 - 《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

2
张剑，刘景洋，董莉，等.中国能源消 费 CO 排放的影响因素及情景分 析 [J].环境工程技术学报，2023，13（1）：71-78.
ZHANG J,LIU J Y,DONG L,et al.Influencing factors and scenario analysis of China's CO emission of energy consumption[J].Journal of Environmental
2
Engineering Technology，2023，13（1）：71-78.
中国能源消 费 CO 排放的影响因素及情景分析
2

张剑，刘景洋 ，董莉，乔琦 *
*
国家环境保护生态工业重点实验室, 中国环境科学研究院
摘要 针对我 国 203 年碳达峰要求，立足当前经济和能源需求快速发展的现状，选 取 2000—202 年时间序列数据，采用
0
0
Tapi 脱钩模型，定量分析中国能源消 费 CO 排放量与经济增长的脱钩状况；建立扩展 的 STIRPA 模型，探讨中国能源消费
T
o
2
CO 排放的影响因素；运用情景分析法对基准情景（S0）、产业结构优化情景（S1）、能源结构优化情景（S2）、多要素优化情景
2
（S3） 种情景下 的 CO 排放量进行了预测。结果表明：中国能源消 费 CO 排放量与经济增长之间的脱钩状态总体以弱脱钩为
2
2
4
主。人口规模、能源消费结构、第二产业占比、城镇化率、人 均 GDP、第三产业占比、碳排放强度每变 动 1 % 时，分别引起能源
消费 CO 排放量的 2.857%、0.879%、0.836%、0.623%、(0.221+0.011ln A )%、0.241%、0.132 % 的变动。基准情景下中国在
2
1
203 年之前不能实现碳达峰，产业结构优化情景和能源结构优化情景下 在 203 年实现碳达峰，峰值分别 为 110.9 亿和
0
0
0
109.1 亿 t，多要素优化情景下可以 在 203 年之前实现碳达峰，峰值 为 105.0 亿 3 t。
0
8
关键词 能源消费；CO 排放；脱钩效应；影响因素；趋势预测
2
中图分类号：X24，X196 文章编号：1674-991X（2023）01-0071-08 doi：10.12153/j.issn.1674-991X.20210563
Influencing factors and scenario analysis of China's CO emission of
2
energy consumption
*
ZHANG Jian, LIU Jingyang , DONG Li, QIAO Qi *
State Environmental Protection Key Laboratory of Eco-Industry, Chinese Research Academy of Environmental Sciences
Abstract In view of China's action plan for peak carbon dioxide emission before 2030 and the current rapid
development of economic and energy demand, based on the time series data from 2000 to 2020, the Tapio
decoupling model was used to quantitatively analyze the decoupling status between CO emission of energy
2
consumption and economic growth in China. The expanded STIRPAT model was established, the influencing
factors on CO emission of energy consumption were analyzed, and the scenario analysis was used to predict CO
2 2
emission of China's energy consumption in the future under four different scenarios: baseline scenario (S0),
industrial structure optimization scenario (S1), energy structure optimization scenario (S2) and multi-factor
optimization scenario (S3). The results showed that: The decoupling between CO emission of energy consumption
2
and economic growth was generally dominated by weak decoupling. It was found that for 1% change in population,
energy consumption structure, proportion of the secondary industry, urbanization level, per-capita GDP, proportion
of the tertiary industry, and carbon emissions intensity, there was 2.857%, 0.879%, 0.836%, 0.623%, (0.221+
0.011ln A )%, 0.241%, and 0.132% change in CO emission, respectively. Under the baseline scenario, the carbon
1 2
dioxide peak could not be achieved before 2030. Under the industrial structure optimization scenario and the energy
structure optimization scenario, China would achieve the peak carbon dioxide emission in 2030, with peaks of
11.090 billion tons and 10.918 billion tons, respectively. Under the multi-factor optimization scenario, the carbon
dioxide peak could be achieved before 2030, and the peak would be 10.503 billion tons.
Key words energy consumption; CO emission; decoupling effect; influencing factors; trend prediction
2

收稿日期：2021-10-09
基金项目：国家重点研发计划项目（2018YFC1903601）
作者简介：张 剑 (1997—)，男，硕士研究生，主要从事低碳发展与循环经济研究，16013326@sdtbu.edu.cn
* 责任作者：1.刘景 洋 (1974—)，男，研究员，博士，主要从事循环经济及碳排放研究，liujy@craes.org.cn
2.乔 琦 (1963—)，女，研究员，博士，主要从事产业生态学、清洁生产与循环经济研究，qiaoqi@craes.org.cn