Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.

China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the...

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Autores principales:	Zhaohan Wang, Zijie Zhao, Chengxin Wang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f0624cb9af074c41a3db9f9e68ec365f
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spelling	oai:doaj.org-article:f0624cb9af074c41a3db9f9e68ec365f2021-11-25T06:23:36ZRandom forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.1932-620310.1371/journal.pone.0252337https://doaj.org/article/f0624cb9af074c41a3db9f9e68ec365f2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252337https://doaj.org/toc/1932-6203China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.Zhaohan WangZijie ZhaoChengxin WangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252337 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zhaohan Wang Zijie Zhao Chengxin Wang Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
description	China became the country with the largest global carbon emissions in 2007. Cities are regional population and economic centers and are the main sources of carbon emissions. However, factors influencing carbon emissions from cities can vary with geographic location and the development history of the cities, rendering it difficult to explicitly quantify the influence of individual factors on carbon emissions. In this study, random forest (RF) machine learning algorithms were applied to analyze the relationships between factors and carbon emissions in cities using real-world data from Chinese cities. Seventy-three cities in three urban agglomerations within the Yangtze River Economic Belt were evaluated with respect to urban carbon emissions using data from regional energy balance tables for the years 2000, 2007, 2012, and 2017. The RF algorithm was then used to select 16 prototypical cities based on 10 influencing factors that affect urban carbon emissions while considering five primary factors: population, industry, technology levels, consumption, and openness to the outside world. Subsequently, 18 consecutive years of data from 2000 to 2017 were used to construct RFs to investigate the temporal predictability of carbon emission variation in the 16 cities based on regional differences. Results indicated that the RF approach is a practical tool to study the connection between various influencing factors and carbon emissions in the Yangtze River Economic Belt from different perspectives. Furthermore, regional differences among the primary carbon emission influencing factors for each city were clearly observed and were related to urban population characteristics, urbanization level, industrial structures, and degree of openness to the outside world. These factors variably affected different cities, but the results indicate that regional emission reductions have achieved positive results, with overall simulation trends shifting from underestimation to overestimation of emissions.
format	article
author	Zhaohan Wang Zijie Zhao Chengxin Wang
author_facet	Zhaohan Wang Zijie Zhao Chengxin Wang
author_sort	Zhaohan Wang
title	Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
title_short	Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
title_full	Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
title_fullStr	Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
title_full_unstemmed	Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.
title_sort	random forest analysis of factors affecting urban carbon emissions in cities within the yangtze river economic belt.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f0624cb9af074c41a3db9f9e68ec365f
work_keys_str_mv	AT zhaohanwang randomforestanalysisoffactorsaffectingurbancarbonemissionsincitieswithintheyangtzerivereconomicbelt AT zijiezhao randomforestanalysisoffactorsaffectingurbancarbonemissionsincitieswithintheyangtzerivereconomicbelt AT chengxinwang randomforestanalysisoffactorsaffectingurbancarbonemissionsincitieswithintheyangtzerivereconomicbelt
_version_	1718413836877299712

Random forest analysis of factors affecting urban carbon emissions in cities within the Yangtze River Economic Belt.

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