Exploration of Dual-Carbon Target Pathways Based on Machine Learning Stacking Model and Policy Simulation—A Case Study in Northeast China

Xuezhi Ren, Jianya Zhao, Shu Wang, Chunpeng Zhang, Hongzhen Zhang () and Nan Wei ()
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Xuezhi Ren: College of New Energy and Environment, Jilin University, Changchun 130021, China
Jianya Zhao: Jinan University & University of Birmingham Joint Institution, Jinan University, Guangzhou 511443, China
Shu Wang: Jinan University & University of Birmingham Joint Institution, Jinan University, Guangzhou 511443, China
Chunpeng Zhang: College of New Energy and Environment, Jilin University, Changchun 130021, China
Hongzhen Zhang: Chinese Academy of Environmental Planning, Beijing 100041, China
Nan Wei: Chinese Academy of Environmental Planning, Beijing 100041, China

Land, 2025, vol. 14, issue 4, 1-31

Abstract: Northeast China, a traditional heavy industrial base, faces significant carbon emissions challenges. This study analyzes the drivers of carbon emissions in 35 cities from 2000–2022, utilizing a machine-learning approach based on a stacking model. A stacking model, integrating random forest and eXtreme Gradient Boosting (XGBoost) as base learners and a support vector machine (SVM) as the meta-model, outperformed individual algorithms, achieving a coefficient of determination (R 2 ) of 0.82. Compared to traditional methods, the stacking model significantly improves prediction accuracy and stability by combining the strengths of multiple algorithms. The Shapley additive explanations (SHAP) analysis identified key drivers: total energy consumption, urbanization rate, electricity consumption, and population positively influenced emissions, while sulfur dioxide (SO 2 ) emissions, smoke dust emissions, average temperature, and average humidity showed negative correlations. Notably, green coverage exhibited a complex, slightly positive relationship with emissions. Monte Carlo simulations of three scenarios (Baseline Scenario (BS), Aggressive De-coal Scenario (ADS), and Climate Resilience Scenario (CRS)) the projected carbon peak by 2030 under the ADS, with the lowest emissions fluctuation (standard deviation of 5) and the largest carbon emissions reduction (17.5–24.6%). The Baseline and Climate Resilience scenarios indicated a peak around 2039–2040. These findings suggest the important role of de-coalization. Targeted policy recommendations emphasize accelerating energy transition, promoting low-carbon industrial transformation, fostering green urbanization, and enhancing carbon sequestration to support Northeast China’s sustainable development and the achievement of dual-carbon goals.

Keywords: Northeast China; carbon emissions; machine learning; stacking model; SHAP-values (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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