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Multi-Scenario Prediction Analysis of Carbon Peak Based on STIRPAT Model-Take South-to-North Water Diversion Central Route Provinces and Cities as an Example.
- Source :
- Land (2012); Nov2023, Vol. 12 Issue 11, p2035, 17p
- Publication Year :
- 2023
-
Abstract
- With the increase in energy demand, environmental issues such as carbon emissions are becoming more and more prominent. China will scale its intended nationally determined contributions by adopting more vigorous policies and measures. China aims to have CO<subscript>2</subscript> emissions peak before 2030 and achieve carbon neutrality before 2060. The current challenge and priority of China's high-quality development is to ensure a harmonious balance between the ecological environment and the economy. The South-to-North Water Diversion Project passes through Beijing, Tianjin, Henan, and Hebei, which were chosen as the study sites. The carbon emission data was from the China Carbon Emission Database 2000–2019. Decoupling modeling using statistical yearbook data from four provinces and municipalities. KMO and Bartlett's test used SPSS 27 software. The selection of indicators was based on relevance. Analyses were performed using the extended STIRPAT model and ridge regression. Moreover, projections of carbon peaks in the study area for 2020–2035 under different rates of change were simulated by the scenario analysis method. The results show that: (1) The decoupling analysis of the four provinces and cities from 2000-2019 gradually shifts to strong decoupling; (2) Resident population, energy structure, and secondary industry as a proportion of GDP significantly impact carbon emissions; (3) From 2000–2035, Beijing and Henan experienced carbon peaks. The peak time in Beijing was 96.836 million tons in 2010. The peak time in Henan was 654.1004 million tons in 2011; (4) There was no peak in Hebei from 2000–2035. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 2073445X
- Volume :
- 12
- Issue :
- 11
- Database :
- Complementary Index
- Journal :
- Land (2012)
- Publication Type :
- Academic Journal
- Accession number :
- 173862552
- Full Text :
- https://doi.org/10.3390/land12112035