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Academic Journal of Environment & Earth Science, 2022, 4(7); doi: 10.25236/AJEE.2022.040710.

Prediction of Carbon Emission Peak and Selection of Development Path in Gansu Province Based on the Constructed STIRPAT Model

Author(s)

Xuejie Yang, Yuchen Li, Xiaoping Zhang, Fusen Nan, Anle Yang

Corresponding Author:
Xiaoping Zhang
Affiliation(s)

Northwest Normal University, Lanzhou, China

Abstract

The ridge regression method was applied to analyze the factors affecting carbon emission in Gansu from 2000 to 2019 based on the estimated carbon emission data, constructed carbon emission prediction model and predicted the trend of carbon emissions of Gansu Province under six scenarios. The results indicated that the overall trend of carbon emissions in Gansu Province from 2000 to 2019 is growing; and the carbon emission in Gansu was significantly affected by the size of population, industrial structure, and urbanization rate; Gansu Province could achieve carbon peak in 2040 under six development models; The peak of carbon emission in Gansu Province is proportional to the economic growth rate and inversely proportional to the emission reduction efforts, the faster its economic development rate, the more sensitive the peak size is to the emission reduction effort, and the smaller the emission reduction effort, the more sensitive the peak size is to the economic development rate; Low peaks are generally peaked early, and high peaks are generally peaked late. Based on the above analysis, it is believed that the more suitable carbon peak development path for Gansu Province is the he medium economic growth with medium emission reduction model.

Keywords

Panel data, construction of STIRPAT model, Scenario analysis, Carbon peak prediction

Cite This Paper

Xuejie Yang, Yuchen Li, Xiaoping Zhang, Fusen Nan, Anle Yang. Prediction of Carbon Emission Peak and Selection of Development Path in Gansu Province Based on the Constructed STIRPAT Model. Academic Journal of Environment & Earth Science (2022) Vol. 4 Issue 7: 56-61. https://doi.org/10.25236/AJEE.2022.040710.

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