西安市工业产业碳排放驱动因素分析及碳达峰情景预测

董瑞; DONG Rui

doi:10.48014/csdr.20240927001

西安市工业产业碳排放驱动因素分析及碳达峰情景预测

Analysis of Industrial Carbon Emission Driving Factors and Prediction of Carbon Peak in Xi' an

中国可持续发展评论 2024年第3卷第4期页码[120-133] 下载全文[3.5MB] [HTML]

摘要

西安市作为西部地区的核心城市和我国工业基础重要来源, 分析其工业产业碳排放驱动因素及碳达峰预测对我国可持续发展至关重要。本研究利用LMDI方法对西安市工业二氧化碳排放的影响因素进行分解, 然后采用岭回归法和STIRPAT模型分析四大驱动因素对碳排放的定量影响, 最后采用情景分析法预测了未来15年三种不同发展情景下的西安市工业产业碳排放情况和碳达峰时间。研究发现:

(1) 2013—2022年西安市工业碳排放总量呈负增长, 其中能源强度与能源结构负效应显著, 贡献率分别为376. 92%和210. 95%, 而经济发展和人口规模表现为碳排放增量的正效应, 贡献率分别为-288. 64%和-199. 24%。

(2) 2023—2040年, 西安市工业碳排放预计先上升后下降, 主要受人口规模影响, 弹性因子1. 735。

(3) 低碳和基准发展情景有助于西安市工业提前完成碳达峰目标, 但高碳模式在2040年都难以实现碳达峰。基准情景是西安市工业发展最佳模式, 预计2028年碳达峰, 峰值4941. 89万吨。

本研究可为开发西安市工业产业合理的碳达峰形成路径提供理论依据, 并帮助决策者制定相应的高质量发展路径。

Abstract

As a key region in the western region and an important source of China' s industrial foundation, analyzing the driving factors of industrial carbon emissions and predicting carbon peak in Xi' an is crucial for China' s sustainable development. . This study employed the Logarithmic Mean Divisia Index (LMDI) method to decompose the influencing factors of industrial carbon emissions in Xi’an. Then, the ridge regression method and the Stochastic Impacts by Regression on STIRPAT model was used to analyze the quantitative impact of four driving factors on industrial carbon emissions. Finally, scenario analysis was adopted to predict the carbon emissions and carbon peaking time of Xi' an' s industrial sector under three different development scenarios in the next 15 years. The study found that:

(1) From 2013 to 2022, the total industrial carbon emissions in Xi' an exhibited aa negative growth. energy intensity and energy structure exhibited significant negative effects, with contribution rates of 376. 92% and 210. 95%, respectively. In contrast, economic development and population size factors played a positive effects on carbon emission increments, with contribution rates of-288. 64% and-199. 24% respectively.

(2) From 2023 to 2040, the predicted total industrial carbon emissions in Xi' an primarily showed a trend of first rising and then declining, which primarily influenced by population size, with an elasticity factor of 1. 735.

(3) Both low-carbon and benchmark development scenarios could help Xi' an' s industrial sector achieve its carbon peaking target earlier, but a high-carbon model would struggle to reach carbon peaking even by 2040. The baseline scenario represented the optimal development model for Xi' an, with carbon peaking projected to occur in 2028, reaching a peak of 49. 4189 million tons.

This study provided a theoretical basis for developing a reasonable carbon peaking pathway for Xi' an' s industrial sector and assisted the government in formulating corresponding high-quality development paths.

DOI

10.48014/csdr.20240927001

文章类型

研究性论文

收稿日期

2024-09-27

接收日期

2024-10-25

出版日期

2024-12-28

关键词

工业碳排放, LMDI分解, STIRPAT模型, 碳达峰预测

Keywords

Industrial carbon emissions, LMDI decomposition method, STIRPAT Model, Carbon peak prediction

作者

董瑞^1,2

Author

DONG Rui^1,2

所在单位

1. 西安培华学院会计与金融学院, 西安 710199
2. 西安交通大学能源与动力工程学院, 西安 710049

Company

1. School of Accounting and Finance, Xi’an Peihua University, Xi’an 710199, China
2. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China

浏览量

228

下载量

186

参考文献

[1] KPMG. 世界能源统计年鉴2024[R/OL].(2024-08- 26).
https://kpmg.com/cn/zh/home/insights/2024/08/statisticalreview-of-world-energy—2024.html.
[2] Lu Y, Jiahua P. Disaggregation of carbon emission drivers in Kaya identity and its limitations with regard to policy implications[J]. Adv. Clim. Chang. Res. , 2013, 9(3): 210.
https://doi.org/10.3969/j.issn.1673-1719.2013.03.009.
[3] Fan F, Lei Y. Factor analysis of energy-related carbon emissions: A case study of Beijing[J]. J. Clean. Prod. , 2017, 163: S277-S283.
https://doi.org/10.1016/j.jclepro.2015.07.094.
[4] Su B, Ang BW. Multi-region comparisons of emission performance: Te structural decomposition analysis approach[ J]. Ecol. Ind. , 2016, 67: 78-87.
https://doi.org/10.1016/j.ecolind.2016.02.020.
[5] Ang BW. Decomposition analysis for policymaking in energy: Which is the preferred method[J]. Energy Policy, 2004, 32: 1131-1139.
https://doi.org/10.1016/S0301-4215(03)00076-4.
[6] 邓吉祥, 刘晓, 王铮. 中国碳排放的区域差异及演变特征分析与因素分解 [J]. 自然资源学报, 2014, 29(02): 189-200.
https://doi.org/10.11849/zrzyxb.2014.02.001
[7] Shen F, Abulizi A. Multidrivers of energy-related carbon emissions and its decoupling with economic growth in Northwest China[J]. Nature, 2024, 14: 7032.
https://doi.org/10.1038/s41598-024-57730-7.
[8] Jiang C P, Gong X J, Yang Y R, et al. Research on spatial and temporal diferences of carbon emissions and infuencing factors in eight economic regions of China based on LMDI model[J]. Nature, 2023, 13: 7965.
https://doi.org/10.1038/s41598-023-35181-w.
[9] Eleni K, Emmanouil H, Kostas B. Social and economic driving forces of recent CO2 emissions in three major BRICS economies[J]. Nature, 2024, 14: 8047.
https://doi.org/10.1038/s41598-024-58827-9.
[10] 姜克隽, 贺晨旻, 庄幸, 等. 我国能源活动CO2 排放在 2020—2022年之间达到峰值情景和可行性研究[J]. 气候变化研究进展, 2016, 12(03): 167-171.
https://doi.org/10.12006/j.issn.1673-1719.2015.200
[11] 王铮, 朱永彬, 刘昌新, 等. 最优增长路径下的中国碳排放估计 [J]. 地理学报, 2010, 65(12): 1559-1568.
https://doi.org/10.11821/xb201012011
[12] He X G, Zhang Y H. Infuence factors and environmental kuznets curve relink efect of Chinese industry’s carbon dioxide emission—Empirical research based on STIRPAT model with industrial dynamic panel data [J]. China Ind. Econ. , 2012, 01: 26-35.
[13] 黄蕊, 王铮, 丁冠群, 等. 基于STIRPAT 模型的江苏省能源消费碳排放影响因素分析及趋势预测[J]. 地理研究, 2016, 35(04): 781-789.
https://doi.org/10.11821/dlyj201604015
[14] 赵慈, 宋晓聪, 刘晓宇. 基于STIRPAT模型的浙江省碳排放峰值预测分析[J]. 生态经济, 2022, 38(06): 29-34.
[15] 姜克隽, 代春艳, 贺晨旻, 等. 2013年后中国大气雾霾治理对经济发展的影响分析———以京津冀地区为案例[J]. 中国科学院院刊, 2020, 35(06): 732-741.
https://doi.org/10.16418/j.issn.1000-3045.20200130001.
[16] Cao G X, Zhang L. Analysis of influencing factors and trend prediction of carbon emissions in key industries in Jiangsu Province under the carbon peak target. Yuejiang Acad. J. 2022, 14: 129-140+175.
[17] Shan et al.(2022)“City-level emission peak and drivers in China. Science Bulletin”,
https://doi.org/10.1016/j.scib.2022.08.024.
[18] 西安市统计局. 西安统计年鉴2023[EB/OL].(2024- 03-28).
http://tjj.xa.gov.cn/tjnj/2023/zk/indexch.html.
[19] Holdren J P, Ehrlich P R. Human population and the global environment: Population growth, rising per capita material consumption, and disruptive technologies have made civilization a global ecological force. Am. Sci. , 1974, 62(3): 282-292.
[20] York R, Rosa E A, Dietz T. STIRPAT, IPAT and Im- PACT: analytic tools for unpacking the driving forces of environmental impacts. Ecol. Econ. 2003.
https://doi.org/10.1016/S0921-8009(03)00188-5.
[21] 陈劭锋, 刘扬, 苏利阳. 发展低碳经济需跨越能源消费或碳排放的三大高峰[J]. 科技促进发展, 2011(07): 38-44.
[22] 张巍, 徐可欣, 李丹妮. “双碳”目标下陕西省工业碳减排路径模拟研究[J]. 西安理工大学学报, 2024, 4(3): 373-381.
http://kns.cnki.net/kcms/detail/61.1294.N.20240109.1656.002.html.
[23] 西安市人民政府关于印发扎实稳住经济若干政策措施的通知[J]. 西安市人民政府公报, 2022(07): 9-14.
[24] 西安印发国家碳达峰试点(西咸新区)实施方案[J]. 新西部, 2024(07): 207.
[25] 西安市人民政府. 西安市国民经济和社会发展第十四个五年规划和二○三五年远景目标纲要[R].(2021-03-22).
http://www.xa.gov.cn/ztzl/ztzl/lwlbzt/zcwj/60581326f8fd1c2073ff56af.html.
[26] 西安市住房和城乡建设局. 西安市“十四五”保障性租赁住房发展规划[R/OL].(2022-02-11).
https://zjj.xa.gov.cn/zw/zfxxgkml/ghjh/6374a572f8fd1c4c2129c56a.html.

引用本文

董瑞. 西安市工业产业碳排放驱动因素分析及碳达峰情景预测[J]. 中国可持续发展评论, 2024, 3(4): 120-133.

Citation

DONG Rui. Analysis of industrial carbon emission driving factors and prediction of carbon peak in Xi' an[J]. Chinese Sustainable Development Review, 2024, 3(4): 120-133.