Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities
Chinese cities contributes a large amount of CO2 emissions. Reducing CO2 emissions through urban governance is an important issue. Despite the increasing attention paid on the CO2 emission prediction, few studies consider the collective and complex influence of governance element system. To predict...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023039002 |
| _version_ | 1797811067712176128 |
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| author | He Zhang Jingyi Peng Rui Wang Mengxiao Zhang Chang Gao Yang Yu |
| author_facet | He Zhang Jingyi Peng Rui Wang Mengxiao Zhang Chang Gao Yang Yu |
| author_sort | He Zhang |
| collection | DOAJ |
| description | Chinese cities contributes a large amount of CO2 emissions. Reducing CO2 emissions through urban governance is an important issue. Despite the increasing attention paid on the CO2 emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO2 emissions through comprehensive urban governance elements, this paper use the random forest model through the data from 1903 Chinese county-level cities in 2010, 2012 and 2015, and establish a CO2 forecasting platform based on the effects of urban governance elements. The results are as follows: (1) The municipal utility facilities element, the economic development & industrial structure element, and the city size &structure and road traffic facilities elements are crucial for residential, industrial and transportation CO2 emissions, respectively; (2) Governance elements have nonlinear relationship with CO2 emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO2 emissions more accurately than can other predictive models. These findings can be used to conducts the CO2 scenario simulation and help government formulate active governance measurements. |
| first_indexed | 2024-03-13T07:18:10Z |
| format | Article |
| id | doaj.art-7d3c4ba08a4648fbb8819cdec9ca577f |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T07:18:10Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-7d3c4ba08a4648fbb8819cdec9ca577f2023-06-05T04:12:58ZengElsevierHeliyon2405-84402023-06-0196e16693Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese citiesHe Zhang0Jingyi Peng1Rui Wang2Mengxiao Zhang3Chang Gao4Yang Yu5Tianjin University, Tianjin, ChinaTianjin University, Tianjin, ChinaTianjin University, Tianjin, China; Corresponding author.Tsinghua Tongheng Urban Planning & Design Institute, Beijing, ChinaTianjin University Research Institute of Architectural Design & Urban Planning, Tianjin, ChinaTianjin University, Tianjin, ChinaChinese cities contributes a large amount of CO2 emissions. Reducing CO2 emissions through urban governance is an important issue. Despite the increasing attention paid on the CO2 emission prediction, few studies consider the collective and complex influence of governance element system. To predict and regulate CO2 emissions through comprehensive urban governance elements, this paper use the random forest model through the data from 1903 Chinese county-level cities in 2010, 2012 and 2015, and establish a CO2 forecasting platform based on the effects of urban governance elements. The results are as follows: (1) The municipal utility facilities element, the economic development & industrial structure element, and the city size &structure and road traffic facilities elements are crucial for residential, industrial and transportation CO2 emissions, respectively; (2) Governance elements have nonlinear relationship with CO2 emissions and most of the relations present obvious threshold effects; (3) Random forest can be used to predict CO2 emissions more accurately than can other predictive models. These findings can be used to conducts the CO2 scenario simulation and help government formulate active governance measurements.http://www.sciencedirect.com/science/article/pii/S2405844023039002CO2 emissionsUrban governance elementsRandom forestForecasting |
| spellingShingle | He Zhang Jingyi Peng Rui Wang Mengxiao Zhang Chang Gao Yang Yu Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities Heliyon CO2 emissions Urban governance elements Random forest Forecasting |
| title | Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities |
| title_full | Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities |
| title_fullStr | Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities |
| title_full_unstemmed | Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities |
| title_short | Use of random forest based on the effects of urban governance elements to forecast CO2 emissions in Chinese cities |
| title_sort | use of random forest based on the effects of urban governance elements to forecast co2 emissions in chinese cities |
| topic | CO2 emissions Urban governance elements Random forest Forecasting |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023039002 |
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