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产业结构调整能否提高中国全要素碳减排绩效?

Can Industrial Structural Adjustment Improve the Total-Factor Carbon Emission Performance in China?

机构信息

China Institute of Manufacturing Development, Nanjing University of Information Science & Technology, Nanjing 210044, China.

School of Management Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.

出版信息

Int J Environ Res Public Health. 2018 Oct 18;15(10):2291. doi: 10.3390/ijerph15102291.

DOI:10.3390/ijerph15102291
PMID:30340422
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6210780/
Abstract

How to improve the industrial total-factor carbon emission performance (TCPI), or total-factor carbon productivity, through industrial structural adjustment, is crucial to China's energy conservation and emission reduction and sustainable growth. In this paper, we use a dynamic spatial panel model to empirically analyze the effect of industrial structural adjustment on TCPI of 30 provinces in China from 2000 to 2015. The results show that most of the provinces with high TCPI are located in the eastern coastal areas, while the provinces with relatively low TCPI are to be found in the central and western regions. The spatial auto-correlation tests show that there are significant global spatial auto-correlation and local spatial agglomeration characteristics in TCPI. The regression results of the dynamic spatial panel models show that at the national level, the structure of industrialization, the industrial structure of heavy industrialization, the coal-based energy consumption structure and the endowment structure have significant negative effects on the improvement of TCPI. The expansion of industrial enterprise scale, on the other hand, is conducive to an improvement in TCPI while the effects of foreign direct investment (FDI) structure and ownership structure on TCPI are not significant. At the regional level, there are certain differences in the effects of different types of industrial structural adjustment on TCPI.

摘要

如何通过产业结构调整来提高工业全要素碳排放绩效(TCPI)或全要素碳生产力,对中国的节能减排和可持续增长至关重要。本文利用动态空间面板模型,实证分析了 2000-2015 年中国 30 个省份工业结构调整对 TCPI 的影响。结果表明,TCPI 较高的省份大多位于东部沿海地区,而 TCPI 相对较低的省份则分布在中、西部地区。空间自相关检验表明,TCPI 存在显著的全局空间自相关和局部空间集聚特征。动态空间面板模型的回归结果表明,在全国层面上,工业化结构、重工业化结构、煤炭能源消费结构和禀赋结构对 TCPI 的提高具有显著的负向影响。而工业企业规模的扩大则有利于 TCPI 的提高,外资结构和所有制结构对 TCPI 的影响不显著。在区域层面上,不同类型的产业结构调整对 TCPI 的影响存在一定差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/2043b9e39ff7/ijerph-15-02291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/85c913a29cfc/ijerph-15-02291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/a27e5ce13931/ijerph-15-02291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/a83bfc13758f/ijerph-15-02291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/2043b9e39ff7/ijerph-15-02291-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/85c913a29cfc/ijerph-15-02291-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/a27e5ce13931/ijerph-15-02291-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/a83bfc13758f/ijerph-15-02291-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1050/6210780/2043b9e39ff7/ijerph-15-02291-g004.jpg

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本文引用的文献

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Drivers Analysis of CO₂ Emissions from the Perspective of Carbon Density: The Case of Shandong Province, China.从碳密度角度分析中国山东省的二氧化碳排放驱动因素。
Int J Environ Res Public Health. 2018 Aug 16;15(8):1762. doi: 10.3390/ijerph15081762.
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Environmental Regulation, Foreign Direct Investment and Green Technological Progress-Evidence from Chinese Manufacturing Industries.环境规制、外国直接投资与绿色技术进步——来自中国制造业的证据。
Int J Environ Res Public Health. 2018 Jan 29;15(2):221. doi: 10.3390/ijerph15020221.
3
Chinese CO emission flows have reversed since the global financial crisis.
Nonlinear and Spatial Effects of Tourism on Carbon Emissions in China: A Spatial Econometric Approach.
中国旅游业碳排放的非线性和空间效应:一种空间计量经济学方法。
Int J Environ Res Public Health. 2019 Sep 11;16(18):3353. doi: 10.3390/ijerph16183353.
中国的二氧化碳排放量自全球金融危机以来已经出现逆转。
Nat Commun. 2017 Nov 23;8(1):1712. doi: 10.1038/s41467-017-01820-w.