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Growing global concern over greenhouse gas emissions has led to a demand for understanding and addressing carbon emissions, with China being one of the main contributors to global carbon emissions, committed to reach the carbon peak by 2030. As a result, much previous research has delved into the drivers of carbon emissions in China; however, few studies have included new energy factors in the extended STIRPAT model when analysing the data, employed more advanced visualisation techniques such as force-directed diagrams, and explored factors outside of the industrial and energy sectors in determining China's ability to reach their environmental goals. In this study, we use the extended STIRPAT model to analyse a more diverse range of drivers for carbon emissions in China and discuss methods to reach peak carbon emissions through the implementation of environmental policies. Using data from China's 14th Five-Year Plan and Vision 2035 to set up two simulation scenarios, we predict China's carbon emissions, introducing ridge regression to ensure validity, and employing big data and visualisation techniques to aid in interpreting results. Our findings suggest that China needs to implement more stringent environmental policies to meet its commitment to reach peak carbon emissions by 2030, revealing that factors such as per capita arable land area, per capita GDP, the proportion of people living in extreme poverty, the level of tourism development, the use of fossil fuels, and new energy technologies have a significant impact on China's carbon emissions. As such, we can recommend more stringent policies relating to the agricultural, energy, and tourism sectors to help China achieve their goal of carbon peak by 2030. [ABSTRACT FROM AUTHOR]

It is of great scientific value to study the spatial differences and influencing factors of carbon emission intensity (CEI) in urban agglomerations (UAs), and it also has reference significance for China in formulating energy-saving and emission-reduction policies to achieve the target of carbon neutrality. Taking 165 prefecture-level cities in 19 UAs in China from 2007 to 2019 as the research object, this study investigated the spatial differences of CEI in UAs using exploratory spatial data analysis and explored the influencing factors of CEI via Geodetector. The results showed the following: (1) The CEI of the UAs showed a downward trend. (2) The CEI of the UAs has typical spatial agglomeration characteristics, where the North comprises mainly high-high and low-high types, whereas the South is primarily high-low and low-low types. (3) The influencing factors of CEI have undergone a transformation from industrial structure to population urbanization. [ABSTRACT FROM AUTHOR]