Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data
China has become the world's largest emitter of carbon dioxide, putting significant pressure on the government to reduce emissions. This study analyzes the driving factors of carbon emissions in 281 prefecture-level cities in China from 2003 to 2019, based on carbon emission data matched with t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S240584402405117X |
| _version_ | 1797220300087099392 |
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| author | Qingsheng Lei Hongwei Yu Zixiang Lin |
| author_facet | Qingsheng Lei Hongwei Yu Zixiang Lin |
| author_sort | Qingsheng Lei |
| collection | DOAJ |
| description | China has become the world's largest emitter of carbon dioxide, putting significant pressure on the government to reduce emissions. This study analyzes the driving factors of carbon emissions in 281 prefecture-level cities in China from 2003 to 2019, based on carbon emission data matched with the locations of thermal power stations and nighttime light data. Firstly, we compare the accuracy of multivariate linear regression and random forest models, finding that the random forest regression yields superior results. Then, we rank the impact of various factors using the random forest method, revealing that population, economic development, and industrialization are the top three influencing factors. The interaction between population and economic development explains 68.5% of carbon emissions, with regional variations in the ranking of influencing factors. The main policy implications of this study are as follows: firstly, there is no need to overly concern about the impact of population growth on carbon emissions, and policies regarding fertility can be adjusted flexibly; secondly, controlling urbanization to a certain extent is conducive to achieving efficient low-carbon cities; thirdly, during the process of industrialization, carbon emissions inevitably increase, and it is advisable to accelerate industrialization to reach a turning point as soon as possible. |
| first_indexed | 2024-04-24T12:47:21Z |
| format | Article |
| id | doaj.art-e4e31270678944ac9c816e442bfae2e6 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T12:47:21Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-e4e31270678944ac9c816e442bfae2e62024-04-07T04:35:56ZengElsevierHeliyon2405-84402024-04-01107e29086Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing dataQingsheng Lei0Hongwei Yu1Zixiang Lin2State Grid Hubei Electric Power Co., Ltd. Economic and Technological Research Institute, Wuhan, 430077, PR ChinaInstitute of Quality Development Strategy, Wuhan University, Wuhan, 430072, PR ChinaInstitute of Quality Development Strategy, Wuhan University, Wuhan, 430072, PR China; Corresponding author.China has become the world's largest emitter of carbon dioxide, putting significant pressure on the government to reduce emissions. This study analyzes the driving factors of carbon emissions in 281 prefecture-level cities in China from 2003 to 2019, based on carbon emission data matched with the locations of thermal power stations and nighttime light data. Firstly, we compare the accuracy of multivariate linear regression and random forest models, finding that the random forest regression yields superior results. Then, we rank the impact of various factors using the random forest method, revealing that population, economic development, and industrialization are the top three influencing factors. The interaction between population and economic development explains 68.5% of carbon emissions, with regional variations in the ranking of influencing factors. The main policy implications of this study are as follows: firstly, there is no need to overly concern about the impact of population growth on carbon emissions, and policies regarding fertility can be adjusted flexibly; secondly, controlling urbanization to a certain extent is conducive to achieving efficient low-carbon cities; thirdly, during the process of industrialization, carbon emissions inevitably increase, and it is advisable to accelerate industrialization to reach a turning point as soon as possible.http://www.sciencedirect.com/science/article/pii/S240584402405117XCO2 emissionsRandom forestEnvironmental impactEmission reduction strategies |
| spellingShingle | Qingsheng Lei Hongwei Yu Zixiang Lin Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data Heliyon CO2 emissions Random forest Environmental impact Emission reduction strategies |
| title | Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data |
| title_full | Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data |
| title_fullStr | Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data |
| title_full_unstemmed | Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data |
| title_short | Understanding China's CO2 emission drivers: Insights from random forest analysis and remote sensing data |
| title_sort | understanding china s co2 emission drivers insights from random forest analysis and remote sensing data |
| topic | CO2 emissions Random forest Environmental impact Emission reduction strategies |
| url | http://www.sciencedirect.com/science/article/pii/S240584402405117X |
| work_keys_str_mv | AT qingshenglei understandingchinasco2emissiondriversinsightsfromrandomforestanalysisandremotesensingdata AT hongweiyu understandingchinasco2emissiondriversinsightsfromrandomforestanalysisandremotesensingdata AT zixianglin understandingchinasco2emissiondriversinsightsfromrandomforestanalysisandremotesensingdata |