Research on Decomposition of Influencing Factors and Prediction Methods of Carbon Emissions in Industrial Sector

doi:10.16628/j.cnki.2095-8188.2023.10.005

Abstract

Abstract:

The industrial sector is one of the main sources of global carbon dioxide emissions. Understanding the factors that affect carbon emissions in the industrial sector can help to develop the targeted optimization measures to reduce carbon emissions. Meanwhile, predicting carbon emissions can achieve to control and adjust future carbon emissions, and formulate the corresponding policy measures to guide and regulate the development of the industrial sector. Firstly, a calculation model of the industry's carbon emission contribution is coustructed through the logarithmic mean Divisia index (LMDI) decomposition method, and the contribution of various influencing factors to carbon emissions is analyzed. Further combining the analysis results of factor decomposition method, a stochastic impacts by regression on population, affluence and technology (STIRPAT) model is constructed to predict the carbon emissions of various industries in the industrial sector. Finally, a study is conducted on an industrial sector in a certain region of China, and the results verify the feasibility of the proposed model.

Key words: carbon emissions, carbon emission contribution, decomposition of influencing factors, logarithmic mean Divisia index(LMDI) decomposition method, stochastic impacts by regression on population, affluence and technology (STIRPAT) model

CLC Number:

TM73

CHU Linlin, ZONG Ming, LI Qinyu, CHEN Yanjun, ZHU Xia, LIU Yiting, GU Jie. Research on Decomposition of Influencing Factors and Prediction Methods of Carbon Emissions in Industrial Sector[J]. LOW VOLTAGE APPARATUS, 2023, 0(10): 28-35.

Figures/Tables 9

References 25

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年份	能源消费结构	能源强度	产业结构	经济发展	工业化率	人口
20112012	-227.80	-5 505.00	-440.50	3 509.70	-180.69	55.42
20122013	393.89	208.73	-668.02	3 362.80	-1 224.10	50.88
20132014	84.65	2 768.80	-6 025.70	2 191.50	-1 123.40	64.30
20142015	-8.80	877.75	-4 332.90	1 430.70	-1 713.70	50.21
20152016	-3.07	102.57	-2 245.60	1 946.70	-1 777.20	67.07
20162017	-12.40	-783.62	-1 773.90	2 643.60	-1 339.30	76.38
20172018	1.85	-3 040.60	-163.03	2 648.20	2.98	47.10
20182019	80.84	-1 812.50	-65.20	2 305.80	-254.77	51.79
20192020	-75.61	294.18	-241.41	1 213.60	-1 009.50	40.06

检验方法	检验值
R²检验	0.937 8
F检验	65.380 5
t检验	4.251 1*e^(-8)
误差方差估计值	0.001 828

项目	碳排放量/吨二氧化碳							RMSE
项目	2018年	2019年		2020年		2021年		RMSE
实际值	21 972.73		22 481.99		23 760.13		23 231.67	—
STIRPAT模型预测值	22 017.37		22 410.06		23 048.57		22 997.76	35.965
BP神经网络模型预测值	20 851.46		20 861.79		20 872.13		20 882.46	1 444
灰色预测模型预测值	23 177.67		23 445.88		23 715.57		23 986.75	377.54

年份	实际值					年份	预测值
年份	人口/ 万人	工业增加值/ 亿元		能源强度/ [吨标准煤· (亿元)^-1]		年份	人口/ 万人	工业增加值/ 亿元		能源强度/ [吨标准煤· (亿元)^-1]
2018	7 267.50		4 333.75		1.80	2022	9 601.83		5 315.71		1.57
2019	8 060.35		4 684.78		1.71	2023	10 306.33		5 581.92		1.49
2020	8 287.54		4 797.22		1.76	2024	11 088.30		5 870.23		1.41
2021	8 941.73		5 043.35		1.66	2025	11 908.83		6 174.25		1.33