Carbon Kuznets curve in China: Nighttime light analysis in prefecture-level cities
Analysing the relationship between economic growth and carbon emissions may help China to determine how to reduce carbon emissions. This study examined 221 Chinese prefecture-level cities using data from 2003 to 2018 to investigate the possible inverted U-shaped relationship between carbon emissions and economic growth, based on per capita nighttime light brightness. This revealed a significant inverted U-shaped relationship between per capita nighttime and carbon emissions with a per capita GDP of 139.8 thousand Yuan corresponding to the turning point. For China to achieve its carbon emissions peak before 2030, the per capita GDP growth rate would need to be at least 6.6 %. Therefore, for help prefecture-level cities to achieve peak carbon, they must strive towards high-quality economic growth and accelerate transformation of economic growth modes to improve low-carbon pilot policies, and such projects should be implemented in more cities. Efforts to achieve peak should be accelerated first in eastern cities and later in western cities.
Authors
- Xiaoqi Zheng (ORCID: https://orcid.org/0000-0002-7534-2633)
- Xiangbo Xu (ORCID: https://orcid.org/0000-0003-4984-7950)
- Ran Yu (ORCID: https://orcid.org/0000-0003-3625-6502)
- Sheng Zhang (ORCID: https://orcid.org/0000-0001-8885-119X)
- J Wang (ORCID: https://orcid.org/0009-0008-5134-8708)
Institutions
- Chinese Academy of Sciences (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Institute of Geographic Sciences and Natural Resources Research (CN)
- Renmin University of China (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e36312
- Citations
- 4
- Primary Topic
- Impact of Light on Environment and Health
- Type
- article
- Field-Weighted Citation Impact
- 1.13
Funders
- National Natural Science Foundation of China
- National Aerospace Science Foundation of China
- Jiangsu Provincial Special Program of Clinical Medical Science