Xiaosong Hou, Xiaoqi Wang, Shuiyuan Cheng, Chuanda Wang, Wei Wang
{"title":"High resolution CO2 emissions inventory and investigation of driving factors for China using an advanced dynamic estimation model","authors":"Xiaosong Hou, Xiaoqi Wang, Shuiyuan Cheng, Chuanda Wang, Wei Wang","doi":"10.1016/j.resconrec.2024.108109","DOIUrl":null,"url":null,"abstract":"<div><div>Developing a high-resolution CO<sub>2</sub> emissions inventory for China is challenging because of limited detailed parameter information in bottom-up approaches. This study integrated socioeconomic attributes, point emission data, industrial heat sources, and improved night-time light data to develop an advanced top-down dynamic CO<sub>2</sub> emissions estimation model. Using this model, a 0.01° resolution CO<sub>2</sub> emissions inventory for China from 2012 to 2022 was created. The results demonstrated that the model enhances spatial precision, distribution accuracy, and timeliness. Spatiotemporal dynamics help identify high emission periods and regions, and reflect the impact of geographical and social activities. The driver factor analysis indicated that GDP per capita, energy intensity, and carbon emissions intensity were the main drivers of changes in emissions. Each region should develop emission-reduction strategies based on the dynamic variations of these drivers. This study offers a reliable tool for carbon emissions inventory research, supporting accurate carbon emissions estimation and policy formulation.</div></div>","PeriodicalId":21153,"journal":{"name":"Resources Conservation and Recycling","volume":"215 ","pages":"Article 108109"},"PeriodicalIF":11.2000,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Resources Conservation and Recycling","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0921344924006992","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0
Abstract
Developing a high-resolution CO2 emissions inventory for China is challenging because of limited detailed parameter information in bottom-up approaches. This study integrated socioeconomic attributes, point emission data, industrial heat sources, and improved night-time light data to develop an advanced top-down dynamic CO2 emissions estimation model. Using this model, a 0.01° resolution CO2 emissions inventory for China from 2012 to 2022 was created. The results demonstrated that the model enhances spatial precision, distribution accuracy, and timeliness. Spatiotemporal dynamics help identify high emission periods and regions, and reflect the impact of geographical and social activities. The driver factor analysis indicated that GDP per capita, energy intensity, and carbon emissions intensity were the main drivers of changes in emissions. Each region should develop emission-reduction strategies based on the dynamic variations of these drivers. This study offers a reliable tool for carbon emissions inventory research, supporting accurate carbon emissions estimation and policy formulation.
期刊介绍:
The journal Resources, Conservation & Recycling welcomes contributions from research, which consider sustainable management and conservation of resources. The journal prioritizes understanding the transformation processes crucial for transitioning toward more sustainable production and consumption systems. It highlights technological, economic, institutional, and policy aspects related to specific resource management practices such as conservation, recycling, and resource substitution, as well as broader strategies like improving resource productivity and restructuring production and consumption patterns.
Contributions may address regional, national, or international scales and can range from individual resources or technologies to entire sectors or systems. Authors are encouraged to explore scientific and methodological issues alongside practical, environmental, and economic implications. However, manuscripts focusing solely on laboratory experiments without discussing their broader implications will not be considered for publication in the journal.