Literature DB >> 33993452

On the driving factors of China's provincial carbon emission from the view of periods and groups.

Da Liu1,2, Runkun Cheng1,2, Xinran Li3,4, Mengmeng Zhao1,2.   

Abstract

China is the largest carbon emitter in the world. Understanding carbon emissions of China, especially at the provincial level, will help identify the critical factors behind carbon emissions and effectively implement carbon emission reduction measures. There are significant achievements in the study of carbon emissions of China's provinces. However, there is a gap for improvement in the study from periods and groups' perspectives using a decomposition-clustering method. This paper adopts the Logarithmic Mean Divisia Index (LMDI) to decompose each province's carbon emissions, introduces the elbow and K-means methods to cluster provinces based on the driving factors of decomposition, and analyzes the driving factors of carbon emissions from the view of groups and periods. By analyzing the carbon emissions data of 28 provinces in China from 1998 to 2018, a breakthrough has been found that economic activities and energy intensity were the main driving factors of carbon emissions. Some possible countermeasures, such as optimizing the industrial structure and the energy structure, significantly increasing clean energy consumption, would receive effective carbon emission reduction feedback. The results provide better decision-making support for emission reduction policies in China and contribute to global climate change issues.

Entities:  

Keywords:  Carbon emissions; Dynamic changes; K-means clustering; LMDI; Regional differences

Year:  2021        PMID: 33993452     DOI: 10.1007/s11356-021-14268-9

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  1 in total

1.  Exploration of Spatio-Temporal Characteristics of Carbon Emissions from Energy Consumption and Their Driving Factors: A Case Analysis of the Yangtze River Delta, China.

Authors:  Weiwu Wang; Huan Chen; Lizhong Wang; Xinyu Li; Danyi Mao; Shan Wang
Journal:  Int J Environ Res Public Health       Date:  2022-08-02       Impact factor: 4.614

  1 in total

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