Impact of intelligence on the carbon emissions of energy consumption in the mining industry based on the expanded STIRPAT model

被引:10
|
作者
Wei, Lili [1 ,2 ]
Feng, Xiwen [1 ]
Liu, Peng [3 ]
Wang, Naikun [4 ]
机构
[1] Shandong Univ Sci & Technol, Natl Demonstrat Ctr Expt Min Engn Educ, Coll Energy & Min Engn, Minist Mine Disaster Prevent & Control Jointly Bui, Qingdao 266590, Peoples R China
[2] Shandong Jianzhu Univ, Sch Business, Jinan 250101, Peoples R China
[3] Univ Jinan, Sch Business, Jinan 250101, Peoples R China
[4] Shandong Jianzhu Univ, Jinan 250101, Peoples R China
关键词
Mining; Intelligence; STIRPAT model; Carbon emissions intensity; Scenario analysis method; Grey prediction model; PANEL-DATA ANALYSIS; CO2; EMISSIONS; METAL INDUSTRIES; DRIVING FACTORS; CHINA; PERFORMANCE; EFFICIENCY; DECOMPOSITION; URBANIZATION; POPULATION;
D O I
10.1016/j.oregeorev.2023.105504
中图分类号
P5 [地质学];
学科分类号
0709 ; 081803 ;
摘要
The intelligent construction of the mining industry has become an emerging trend in its future development plans. As a traditional high energy consumption industry, its energy conservation and emissions reduction have become particularly urgent concerns. Therefore, it is of great significance to study the impact of intelligence on the carbon emissions from energy consumption in the mining industry. This paper first uses dynamic indirect energy emissions factors to build an accounting model for carbon emissions from energy consumption in the mining industry and calculates the related carbon emissions from 2001 to 2020 by sector. Then, an expandable stochastic environmental impact assessment (STIRPAT) model is established to analyze the impact of intellectualization on the carbon emissions intensity of China's mining industry. At the same time, many other important factors are included in the model, and the model is fitted using ridge regression. The results show that the order of influence, from large to small, is as follows: energy intensity, industrial output value, per capita output value, the proportion of output value of large and medium-sized enterprises, per capita stock of fixed assets, average enterprise output value, R & D intensity and proportion of indirect energy. Each 1% increase in the above factors will increase the corresponding carbon emissions intensity by 0.2005%, -0.1271%, -0.1207%, 0.7467%, -0.0595%, -0.09813%, 0.1143% and -0.1419%, respectively. Then, the prediction values of the independent variables are obtained by using the scenario analysis method and grey prediction model, and 12 scenarios are set up to predict the carbon emissions intensity of the mining industry energy consumption from 2021 to 2035 by using the expanded STIRPAT model. By analyzing the prediction results, we conclude that Scenario 8, Scenario 9, Scenario 10, Scenario 11 and Scenario 12 can achieve the emissions reduction goals of the mining industry in 2025 and 2030; the optimal path of emissions reduction is given for various scenarios. Finally, a development strategy for emissions reduction in China's mining industry is proposed.
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页数:21
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