基于核密度估计矿业碳排放动态演变及因素研究
Research on the Dynamic Evolution and Factors of Mining Carbon Emissions Based on Kernel Density Estimation
摘要: 作为工业的重要组成部分，矿业在实现中国2030年“碳达峰”和2060年“碳中和”目标的过程中面临巨大的减排压力。矿业活动强度和技术创新对碳排放有显著影响，但矿业规模结构和能源结构对碳排放的具体影响尚未得到充分研究。为此，本文旨在探讨矿业结构与碳排放之间的关系。我们使用核密度估计方法分析了中国及其东部、中部和西部地区的矿业碳排放强度，并采用Panel-VAR模型分析了影响矿业碳排放强度的因素。研究结果表明，中国及东部、中部地区的矿业碳排放强度均呈现不同程度的下降趋势，其中经济发达的东部地区的降幅最为显著。全国范围内，大中型矿山比例、单位矿山平均产矿量以及能源消费结构是影响矿业碳排放强度的关键因素，但这些因素在不同地区的影响程度存在差异。
Abstract: As an important part of industry, the mining industry faces huge pressure to reduce emissions in the process of achieving China’s “carbon peak” in 2030 and “carbon neutrality” in 2060. The intensity of mining activities and technological innovation have a significant impact on carbon emissions, but the specific impact of mining scale structure and energy structure on carbon emissions has not been fully studied. To this end, this paper aims to explore the relationship between mining structure and carbon emissions. We used the kernel density estimation method to analyze the mining carbon emission intensity of China and its eastern, central and western regions, and used the Panel-VAR model to analyze the factors affecting the mining carbon emission intensity. Research results show that the carbon emission intensity of mining in China and the eastern and central regions has shown a downward trend to varying degrees, with the most significant decline in the economically developed eastern region. Nationwide, the proportion of large and medium-sized mines, average mineral production per unit mine, and energy consumption structure are key factors affecting the carbon emission intensity of the mining industry, but the impact of these factors varies in different regions.
文章引用：易婷婷, 王兴悦, 董延佳. 基于核密度估计矿业碳排放动态演变及因素研究[J]. 统计学与应用, 2025, 14(1): 215-225. https://doi.org/10.12677/sa.2025.141021

参考文献

[1] Li, W., Yang, G., Li, X., Sun, T. and Wang, J. (2019) Cluster Analysis of the Relationship between Carbon Dioxide Emissions and Economic Growth. Journal of Cleaner Production, 225, 459-471. [Google Scholar] [CrossRef] 
[2] Chontanawat, J. (2019) Driving Forces of Energy-Related CO2 Emissions Based on Expanded IPAT Decomposition Analysis: Evidence from ASEAN and Four Selected Countries. Energies, 12, Article 764. [Google Scholar] [CrossRef] 
[3] Zhang, C., Su, B., Zhou, K. and Yang, S. (2019) Decomposition Analysis of China’s CO2 Emissions (2000-2016) and Scenario Analysis of Its Carbon Intensity Targets in 2020 and 2030. Science of The Total Environment, 668, 432-442. [Google Scholar] [CrossRef] [PubMed]
[4] Sadorsky, P. (2014) The Effect of Urbanization and Industrialization on Energy Use in Emerging Economies: Implications for Sustainable Development. The American Journal of Economics and Sociology, 73, 392-409. [Google Scholar] [CrossRef] 
[5] Du, Q., Pang, Q., Bao, T., Guo, X. and Deng, Y. (2021) Critical Factors Influencing Carbon Emissions of Prefabricated Building Supply Chains in China. Journal of Cleaner Production, 280, Article ID: 124398. [Google Scholar] [CrossRef] 
[6] Shen, Y., Lin, Y., Cui, S., Li, Y. and Zhai, X. (2022) Crucial Factors of the Built Environment for Mitigating Carbon Emissions. Science of the Total Environment, 806, Article ID: 150864. [Google Scholar] [CrossRef] [PubMed]
[7] Wang, M., Wang, Y., Wu, Y., Yue, X., Wang, M. and Hu, P. (2022) Identifying the Spatial Heterogeneity in the Effects of the Construction Land Scale on Carbon Emissions: Case Study of the Yangtze River Economic Belt, China. Environmental Research, 212, Article ID: 113397. [Google Scholar] [CrossRef] [PubMed]
[8] Chen, C. and Bi, L. (2022) Study on Spatio-Temporal Changes and Driving Factors of Carbon Emissions at the Building Operation Stage—A Case Study of China. Building and Environment, 219, Article ID: 109147. [Google Scholar] [CrossRef] 
[9] Xu, H., Li, Y., Zheng, Y. and Xu, X. (2022) Analysis of Spatial Associations in the Energy-Carbon Emission Efficiency of the Transportation Industry and Its Influencing Factors: Evidence from China. Environmental Impact Assessment Review, 97, Article ID: 106905. [Google Scholar] [CrossRef] 
[10] Wang, X., Fan, F., Liu, C., Han, Y., Liu, Q. and Wang, A. (2022) Regional Differences and Driving Factors Analysis of Carbon Emissions from Power Sector in China. Ecological Indicators, 142, Article ID: 109297. [Google Scholar] [CrossRef] 
[11] Kou, J. and Xu, X. (2022) Does Internet Infrastructure Improve or Reduce Carbon Emission Performance?—A Dual Perspective Based on Local Government Intervention and Market Segmentation. Journal of Cleaner Production, 379, Article ID: 134789. [Google Scholar] [CrossRef] 
[12] Yi, M., Liu, Y., Sheng, M.S. and Wen, L. (2022) Effects of Digital Economy on Carbon Emission Reduction: New Evidence from China. Energy Policy, 171, Article ID: 113271. [Google Scholar] [CrossRef] 
[13] Shao, S., Liu, J., Geng, Y., Miao, Z. and Yang, Y. (2016) Uncovering Driving Factors of Carbon Emissions from China’s Mining Sector. Applied Energy, 166, 220-238. [Google Scholar] [CrossRef] 
[14] Chen, W. and Yan, S. (2022) The Decoupling Relationship between CO2 Emissions and Economic Growth in the Chinese Mining Industry under the Context of Carbon Neutrality. Journal of Cleaner Production, 379, Article ID: 134692. [Google Scholar] [CrossRef] 
[15] Fang, P. (2023) Short Term Carbon Emission Prediction Method of Green Building Based on IPAT Model. International Journal of Global Energy Issues, 45, 1-13. [Google Scholar] [CrossRef] 
[16] Yang, S., Zhu, S., Deng, G. and Li, H. (2022) Study on Influencing Factors and Spatial Effects of Carbon Emissions Based on Logarithmic Mean Divisia Index Model: A Case Study of Hunan Province. Sustainability, 14, Article 15868. [Google Scholar] [CrossRef] 
[17] Wen, L. and Shao, H. (2019) Analysis of Influencing Factors of the Carbon Dioxide Emissions in China’s Commercial Department Based on the STIRPAT Model and Ridge Regression. Environmental Science and Pollution Research, 26, 27138-27147. [Google Scholar] [CrossRef] [PubMed]
[18] Liu, Y., Chang, X. and Huang, C. (2022) Research and Analysis on the Influencing Factors of China’s Carbon Emissions Based on a Panel Quantile Model. Sustainability, 14, Article 7791. [Google Scholar] [CrossRef] 
[19] Chen, J., Lin, Y., Wang, X., Mao, B. and Peng, L. (2022) Direct and Indirect Carbon Emission from Household Consumption Based on LMDI and SDA Model: A Decomposition and Comparison Analysis. Energies, 15, Article 5002. [Google Scholar] [CrossRef] 
[20] Xu, S., Zhang, L., Liu, Y., Zhang, W., He, Z., Long, R., et al. (2017) Determination of the Factors That Influence Increments in CO2 Emissions in Jiangsu, China Using the SDA Method. Journal of Cleaner Production, 142, 3061-3074. [Google Scholar] [CrossRef] 
[21] Gedikli, A., Erdoğan, S., Çevik, E.I., Çevik, E., Castanho, R.A. and Couto, G. (2022) Dynamic Relationship between International Tourism, Economic Growth and Environmental Pollution in the OECD Countries: Evidence from Panel VAR Model. Economic Research-Ekonomska Istraživanja, 35, 5907-5923. [Google Scholar] [CrossRef] 
[22] Yu, Y. and Qayyum, M. (2022) Dynamics between Carbon Emission, Imported Cultural Goods, Human Capital, Income, and Energy Consumption: Renewed Evidence from Panel VAR Approach. Environmental Science and Pollution Research, 29, 58360-58377. [Google Scholar] [CrossRef] [PubMed]
[23] Meng, X., Li, X., Hu, G., Zhang, Z., Zhang, H., Huang, C., et al. (2023) Toward Integrated Governance of Urban CO2 Emissions in China: Connecting the “Codes” of Global Drivers, Local Causes, and Indirect Influences from a Multi-Perspective Analysis. Cities, 134, Article ID: 104181. [Google Scholar] [CrossRef] 
[24] Pang, Q., Zhou, W., Zhao, T. and Zhang, L. (2021) Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone. Land, 10, Article 1130. [Google Scholar] [CrossRef] 
[25] Wang, B., Sun, Y. and Wang, Z. (2018) Agglomeration Effect of CO2 Emissions and Emissions Reduction Effect of Technology: A Spatial Econometric Perspective Based on China’s Province-Level Data. Journal of Cleaner Production, 204, 96-106. [Google Scholar] [CrossRef] 
[26] He, H., Pan, J., Lu, N., Chen, B. and Jiao, R. (2020) Short-term Load Probabilistic Forecasting Based on Quantile Regression Convolutional Neural Network and Epanechnikov Kernel Density Estimation. Energy Reports, 6, 1550-1556. [Google Scholar] [CrossRef] 

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