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Research on statistical measure under double carbon target - Self-moving regression model of grey prediction based on
Sanglin Zhao1, Hao Deng1, Jackon Steve2
1School of Engineering Management, Hunan University of Finance and Economics, Changsha, Hunan, China.
Background:
At the 2020 UN General Assembly, China pledged to peak carbon emissions before 2030 and achieve carbon neutrality by 2060. However, the traditional social development model has led to increasing carbon emissions annually, highlighting the need to resolve the contradiction between development and carbon reduction. This study examines the relationship between carbon emissions, economy, population, and energy consumption in a specific region to support carbon peak and neutrality goals.
Methods:
A comprehensive indicator system was established, encompassing economic, population, energy consumption, and carbon emissions indicators. The study analyzed these factors during the 12th and 13th Five Year Plans, comparing total carbon emissions in 2010 and across the plans, and assessing trends. It also comprehensively analyzed the relationships and mutual influences among these factors. The study identified the main challenges in achieving carbon peak and neutrality. Using the Kaya model and various factor models, it calculated carbon peak times for three scenarios: baseline (2022), natural (2036), and ambitious (2021). These findings provide a basis for dual carbon path planning.
Result:
The research results indicate that carbon emissions are closely related to the economy, population, and energy consumption. The prediction shows that the future trend of carbon emissions is controllable. Suggestions for dual carbon path planning are proposed to provide empirical basis for policy formulation. Under the baseline scenario, the peak carbon emissions are expected to occur around 2022; Under natural circumstances, the peak carbon emissions will be postponed to 2036; In the ambitious scenario, the carbon peak time can be advanced to 2021.
Conclusion:
The research results are crucial for achieving carbon reduction targets and sustainable development and can be used to formulate targeted policies to promote regional development and support China's carbon neutrality commitments.
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