[Multi-dimensional Analysis of the Synergistic Effect of Pollution Reduction and Carbon Reduction in Tianjin Based on the STIRPAT Model].
Huan Jing Ke Xue
; 44(3): 1277-1286, 2023 Mar 08.
Article
em Zh
| MEDLINE
| ID: mdl-36922189
ABSTRACT
Based on the STIRPAT model, this study quantitatively analyzed the synergistic effect of pollution reduction and carbon reduction in Tianjin from three dimensionstotal emission, emission reduction, and synergy coefficient. The results showed that the main emission sources of air pollutants and greenhouse gases in Tianjin were industrial sources, and the Pearson correlation coefficient of air pollutants and greenhouse gases was 0.984. The total population, urbanization rate, gross regional product, energy intensity, and carbon dioxide emission intensity were important factors affecting the synergistic effect of pollution reduction and carbon reduction in Tianjin. In 2011 and 2012, Tianjin's air pollutants and greenhouse gas emissions increased synergistically, and the synergistic effect coefficients were 0.18 and 0.17, respectively. From 2013 to 2014 and from 2018 to 2023, the air pollutant emission reduction and greenhouse gas emission increased, the synergistic effect coefficient was less than 0, and the pollution reduction and carbon reduction had no synergistic effect. In 2015-2017 and 2024-2060, air pollutants and greenhouse gas emissions were predicted to be reduced at the same time, with a synergistic effect coefficient ranging from 2.74 to 8.76. Tianjin had the conditions to enter the synergistic stage of pollution reduction and carbon reduction in 2024. The most important things for Tianjin to do to promote the synergy of pollution reduction and carbon reduction were to strictly control the total amount of greenhouse gas emissions, continue to promote the reduction in energy intensity and carbon dioxide emission intensity, and reasonably control the total population, urbanization rate, and regional GDP.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Prognostic_studies
Idioma:
Zh
Ano de publicação:
2023
Tipo de documento:
Article