Analysis of Industrial Carbon Emission Driving Factors and Prediction of Carbon Peak in Xi' an

As a key region in the western region and an important source of China' s industrial foundation, analyzing the driving factors of industrial carbon emissions and predicting carbon peak in Xi' an is crucial for China' s sustainable development. . This study employed the Logarithmic Mean Divisia Index (LMDI) method to decompose the influencing factors of industrial carbon emissions in Xi’an. Then, the ridge regression method and the Stochastic Impacts by Regression on STIRPAT model was used to analyze the quantitative impact of four driving factors on industrial carbon emissions. Finally, scenario analysis was adopted to predict the carbon emissions and carbon peaking time of Xi' an' s industrial sector under three different development scenarios in the next 15 years. The study found that: (1) From 2013 to 2022, the total industrial carbon emissions in Xi' an exhibited aa negative growth. energy intensity and energy structure exhibited significant negative effects, with contribution rates of 376. 92% and 210. 95%, respectively. In contrast, economic development and population size factors played a positive effects on carbon emission increments, with contribution rates of-288. 64% and-199. 24% respectively. (2) From 2023 to 2040, the predicted total industrial carbon emissions in Xi' an primarily showed a trend of first rising and then declining, which primarily influenced by population size, with an elasticity factor of 1. 735. (3) Both low-carbon and benchmark development scenarios could help Xi' an' s industrial sector achieve its carbon peaking target earlier, but a high-carbon model would struggle to reach carbon peaking even by 2040. The baseline scenario represented the optimal development model for Xi' an, with carbon peaking projected to occur in 2028, reaching a peak of 49. 4189 million tons. This study provided a theoretical basis for developing a reasonable carbon peaking pathway for Xi' an' s industrial sector and assisted the government in formulating corresponding high-quality development paths.