Research and analysis of urban-rural residential carbon emissions in China
Residential carbon emissions are an important component of anthropogenic carbon emissions. a significant increase in residential carbon emissions has become a reality under the global urbanization process.In this context, this paper built a feature combination value model based on NPP-VIIRS night light remote sensing data,and divided urban-rural areas through breakpoint analysis method and reference comparison method.Then,explored the characteristics and differences of residential carbon emissions and per capita residential carbon emissions in nine different levels of cities in 2019 from the perspective of urban-rural areas.The results indicate that the residential carbon emissions and per capita residential carbon emissions shows the spatial distribution characteristics of first tier cities>second tier cities>third tier cities. Among them, The residential carbon emissions in Beijing, Guangzhou, Nanjing, Taiyuan show a distribution pattern of urban >urban-rural fringe>rural.The residential carbon emissions in Shijiazhuang, Wuxi, Xiangyang, Zunyi,Huai’an show a distribution pattern of rural >urban-rural fringe >urban.The per capita residential carbon emissions of urban areas are relatively low, while the per capita residential carbon emissions of rural areas are relatively high,show a distribution pattern of rural >urban-rural fringe >urban.The results can help the Chinese government balance the needs of urban-rural development in different levels of cities, so as to formulate targeted carbon emission reduction policies and achieve low-carbon goals.
Authors
- Tian Zhao
- Chengzhi Xiang
- Ailin Liang (ORCID: https://orcid.org/0000-0003-1240-7185)
Institutions
- Nanjing University of Information Science and Technology (CN)
Publication Details
- Journal
- Sustainable Futures
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1016/j.sftr.2024.100287
- Citations
- 6
- Primary Topic
- Environmental Impact and Sustainability
- Type
- article
- Field-Weighted Citation Impact
- 1.23
Funders
- National Natural Science Foundation of China