## 2 Related Work  
### Carbon-Peaking Implementation Pathway Prediction  
With the announcement of the carbon-peaking target, researchers have dedicated significant efforts to explore the feasibility and implementation approaches of the carbon-peaking goal. This topic has evolved into an increasingly attractive research hotspot within the academic community. Existing literature predominantly conducts analyses at a macro level, examining the influence of various factors on carbon emissions from the perspectives of the country [9], provinces [10], and regions [11]. Specific scenario frameworks are established based on economic development goals [12] to predict future carbon emission pathways [13]. The most frequently considered influencing factors include the population, GDP, energy intensity, urbanization level, industrial structure, energy structure, energy consumption, and investment [14].  
Regarding prediction methods, the STIRPAT method has garnered considerable interest as an effective analytical model [15]. STIRPAT originated from the IPAT model, initially proposed by [PERSON] et al. [16] to assess the impact of human economic activities on the natural environment. Subsequently, [PERSON] et al. [17] refined the IPAT framework and developed the STIRPAT model. Currently, the model is widely used to evaluate the impact of various influencing factors on the environment [18]. Recognizing the limitations of the original STIRPAT model in specific domains, most current studies have expanded the basic framework by introducing additional factors to enhance the model's flexibility and reliability. [PERSON] et al. [19] incorporated additional factors, such as economics, the population, and institutions, into the basic STIRPAT framework and studied the impact of fossil fuel consumption on the environment in 59 coal-consuming countries, 77 gas-consuming countries, and 96 oil-consuming countries from 2002 to 2016. Particularly, the adoption of an expanded STIRPAT model for predicting carbon emissions under carbon-peaking goals has become a current research focus [20]. [PERSON] et al. [21] explored the influences of 16 socioeconomic factors on carbon emissions in different cities and predicted the timing and peak levels of carbon emissions in Shandong Province, China. [PERSON] [22] used the STIRPAT-PLS framework and Monte Carlo simulations to establish a dynamic scenario simulation, observing the dynamic trajectory and possible values of the carbon intensity in 2030, as well as conducting a dynamic sensitivity analysis of the contributions of different influencing factors. Similarly, to study the impact of the transportation industry on carbon emissions, [PERSON] et al. [23] integrated transportation factors into the original factor set consisting of the environment, economy, population, and technology, expanding the basic structure of STIRPAT and examining the influences of these factors on carbon emissions.  
Moreover, a substantial body of literature has sought to apply the STIRPAT method within scenario frameworks to assess the feasibility and implementation pathways of carbon-peaking targets. For instance, [PERSON] et al. [24] utilized an expanded STIRPAT model to predict carbon emissions in Chinese provinces from 1996 to 2018 in three development scenarios: high speed, steady speed, and low speed. The research findings unveiled the influence of factors such as per capita GDP, energy intensity, industrial structure, energy structure, and the total population on carbon emissions and the timing for reaching the peak. The results also indicated that owing to different economic development conditions, various provinces could anticipate distinctly different carbon emission pathways.