Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption.

Liyuan Fu, Qing Wang
Author Information
  1. Liyuan Fu: School of Economics, Liaoning University, Shenyang 110036, China.
  2. Qing Wang: School of Economics, Liaoning University, Shenyang 110036, China.

Abstract

Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005-2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China's "double carbon" target quickly and effectively.

Keywords

References

  1. Int J Environ Res Public Health. 2022 Jul 26;19(15): [PMID: 35897474]
  2. Environ Sci Pollut Res Int. 2021 Dec;28(45):64719-64738 [PMID: 34312759]
  3. Environ Sci Pollut Res Int. 2022 Jan;29(3):4641-4653 [PMID: 34414540]
  4. J Environ Manage. 2021 Dec 1;299:113572 [PMID: 34450298]
  5. J Environ Prot (Irvine, Calif). 2016 Dec;7(13):2081-2094 [PMID: 28845334]
  6. Sci Adv. 2022 Jan 28;8(4):eabl9526 [PMID: 35080980]
  7. Environ Sci Pollut Res Int. 2021 Aug;28(30):41242-41254 [PMID: 33779906]
  8. Int J Environ Res Public Health. 2022 Mar 02;19(5): [PMID: 35270588]
  9. J Environ Manage. 2022 Feb 15;304:114286 [PMID: 34915389]
  10. Environ Sci Pollut Res Int. 2022 Aug;29(36):54890-54901 [PMID: 35312920]
  11. J Environ Manage. 2022 Oct 15;320:115808 [PMID: 35947905]
  12. Int J Environ Res Public Health. 2022 Aug 12;19(16): [PMID: 36011605]
  13. Int J Environ Res Public Health. 2022 Aug 02;19(15): [PMID: 35954834]
  14. Int J Environ Res Public Health. 2022 Jul 30;19(15): [PMID: 35954683]
  15. Environ Sci Pollut Res Int. 2021 Jun;28(21):26948-26960 [PMID: 33496950]

MeSH Term

Carbon
Carbon Dioxide
China
Economic Development
Global Warming
Industry

Chemicals

Carbon Dioxide
Carbon

Word Cloud

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