Application of shared socioeconomic pathways at the subnational level: carbon emission forecasts in Qinghai Province, China, integrating socioeconomic models

Abstract

Forecasting carbon dioxide (CO₂) emissions is necessary to estimate their climate impact and the required mitigation efforts. However, existing scenario frameworks are not consistent and limit the comparability of projection studies. In this study, the “Stochastic Impacts by Regression on Population, Affluence and Technology” model was combined with the shared socioeconomic pathways (SSPs) defined by the Intergovernmental Panel on Climate Change. Model forecasts were calculated for each SSP from 2020 to 2060 in Qinghai Province, an underdeveloped, carbon-sink-rich region in China. Total CO₂ emissions in Qinghai Province consistently exhibited an upward then downward trend, with large differences between SSPs in the peak date and maximum value of total CO₂ emissions. Considering the current socioeconomic development status and total carbon emission trends in Qinghai Province, SSP1 and SSP2 were the most suitable pathways, providing balance between carbon emissions reduction and population well-being.

Keywords:

• shared socioeconomic pathways
• STIRPAT model
• CO₂ emission forecast
• underdeveloped region
• carbon sink-rich region

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The Fifth Review of Forest Resources in Qinghai Province in 2021.

2 National Development and Reform Commission, “Qinghai Province aims to build ‘four places’ and vigorously promotes green and low-carbon development,” https://www.ndrc.gov.cn/fggz/hjyzy/tdftzh/202203/t20220322_1320028.html, accessed 7 February 2023.

Additional information

Funding

This work was supported by the National Key R&D Program of China under Grant 2022YFC3800705; the Youth Innovation Promotion Association of the Chinese Academy of Sciences under Grant 2020424; the Graduate Research and Practice Projects of Minzu University of China under Grant BZKY2022007, and the National Natural Science Foundation of China under Grants 41801208, 41371529, and 41071353.