Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model

Fangming Jiang; Binjie Chen; Penghan Li; Jiawen Jiang; Qingyu Zhang; Jinnan Wang; Jinsong Deng

doi:10.1016/j.envres.2023.115775

Spatio-temporal evolution and influencing factors of synergizing the reduction of pollution and carbon emissions - Utilizing multi-source remote sensing data and GTWR model

Environ Res. 2023 Jul 15:229:115775. doi: 10.1016/j.envres.2023.115775. Epub 2023 Apr 6.

Authors

Fangming Jiang¹, Binjie Chen², Penghan Li³, Jiawen Jiang⁴, Qingyu Zhang⁵, Jinnan Wang⁶, Jinsong Deng⁷

Affiliations

¹ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Ecological Civilization Academy, Anji, 313300, China. Electronic address: 22014177@zju.edu.cn.
² Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo, 315211, China. Electronic address: chenbinjie@nbu.edu.cn.
³ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China. Electronic address: penghanli@zju.edu.cn.
⁴ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China. Electronic address: 22214177@zju.edu.cn.
⁵ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Ecological Civilization Academy, Anji, 313300, China. Electronic address: qy_zhang@zju.edu.cn.
⁶ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Chinese Academy of Environmental Planning, Beijing, 100012, China. Electronic address: wangjn1962@126.com.
⁷ College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China; Zhejiang Ecological Civilization Academy, Anji, 313300, China. Electronic address: jsong_deng@zju.edu.cn.

PMID: 37028541
DOI: 10.1016/j.envres.2023.115775

Abstract

Grasping current circumstances and influencing components of the synergistic degree regarding reducing pollution and carbon has been recognized as a crucial part of China in response to the protection of the environment and climate mitigation. With the introduction of remote sensing night-time light, CO₂ emissions at multi-scale have been estimated in this study. Accordingly, an upward trend of "CO₂-PM_2.5" synergistic reduction was discovered, which was indicated by an increase of 78.18% regarding the index constructed of 358 cities in China from 2014 to 2020. Additionally, it has been confirmed that the reduction in pollution and carbon emissions could coordinate with economic growth indirectly. Lastly, it has identified the spatial discrepancy of influencing factors and the results have emphasized the rebound effect of technological progress and industrial upgrades, whilst the development of clean energy can offset the increase in energy consumption thus contributing to the synergy of pollution and carbon reduction. Moreover, it has been highlighted that environmental background, industrial structure, and socio-economic characteristics of different cities should be considered comprehensively in order to better achieve the goals of "Beautiful China" and "Carbon Neutrality".

Keywords: Carbon mitigation; GTWR; Night-time light images; PM(2.5) concentration; Synergy degree.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
Air Pollution* / prevention & control
Carbon / analysis
Carbon Dioxide / analysis
China
Cities
Economic Development
Remote Sensing Technology

Substances

Air Pollutants
Carbon
Carbon Dioxide