Provincial differences and model-based driver attribution of carbon emissions in China: A swarm intelligence–optimized random forest and scenario analysis

Assessing the long-term environmental implications of low-carbon development pathways requires methods that can capture nonlinear patterns and provincial differences. This study develops a swarm intelligence–optimized Random Forest model within an extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework for provincial carbon-emission driver attribution. Twelve indicators across five dimensions—population, economy, technology, industry, and energy—are incorporated. Six swarm intelligence algorithms optimize Random Forest hyperparameters, and Shapley additive explanations are used to interpret the model selected by the Arithmetic Optimization Algorithm (AOA). On the 2019–2022 temporal holdout, the selected model achieved a coefficient of determination of 0.9784 and a root mean squared logarithmic error of 0.1101. Total energy consumption and fossil energy consumption ranked first and second in mean absolute attribution values, while province-grouped attribution summaries showed differences in driver-attribution patterns across provinces. Three scenarios—business as usual, low carbon, and enhanced emission reduction—are used for conditional projections from 2023 to 2060 using a separate dynamic ridge-regression model developed within the same theoretical framework. Relative to 2022, national emissions in 2060 increase by 7.38% under the business-as-usual scenario but decline by 17.26% and 40.64% under the low-carbon and enhanced emission-reduction scenarios, respectively. All 30 provincial projections are below their 2022 levels under both emission-reduction scenarios by 2060, and the projected rates of change vary across provinces. By linking model-based driver attribution with conditional scenario projections, the framework provides a quantitative basis for comparing low-carbon pathways and provincial transition options.