The Green Paradox in Resource-Dependent Regions: Heterogeneous Impacts of Environmental Regulation on Carbon Lock-in Subtitle: Evidence from Double Machine Learning in China's Yellow River Basin

Abstract Resource-based cities in the Global South universally face a "dual dilemma" of sustaining economic growth while breaking path-dependent carbon lock-in. As a critical energy basin in China, the low-carbon transition of the Yellow River Basin is pivotal for achieving national carbon neutrality goals. However, debate persists regarding whether the "Yellow River Basin Ecological Protection and High-Quality Development Strategy" (implemented in 2019) effectively disrupts this high-carbon dependency. Existing studies often rely on linear models, ignoring the high-dimensional non-linear confounders typical of complex eco-economic systems, potentially masking the true "Green Paradox" effects. Treating this strategy as a quasi-natural experiment, this study employs a Double Machine Learning (DML) causal inference framework on a long-term panel dataset (2000–2023) covering 938 counties. This approach utilizes orthogonalization techniques to effectively eliminate the interference of 75 high-dimensional confounding variables. The findings reveal: (1) Aggregate Effect: The strategy significantly reduced carbon intensity on average (ATE = -0.058, p