Quantitative analysis of impact factors and scenario prediction of energy related carbon emissions at county level

Abstract

ABSTRACT The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.