Global Value Chain Embedding and Low-Carbon Innovation Transition: A Sustainable Development Perspective on Carbon-Intensive Industries

The innovation-driven transformation of carbon-intensive industries is central to global climate governance and sustainable industrial specialization. Integrating Global Value Chain (GVC) embedding, external environmental dynamics, and industrial innovation, this study utilizes micro-to-macro extracted panel data from China’s carbon-intensive industries from 2007 to 2023 to construct a double debiased machine learning (DDML) model. We identify a robust innovation-promoting effect driven by both GVC participation and the GVC Domestic Content Ratio (DCR). Employing a causal mediation framework, we reveal that GVC embedding indirectly drives innovation by reshaping four functional dimensions: upgrading intermediate export quality, stimulating technology market activity, internalizing climate policy uncertainty, and aligning environmental violation disclosure. Furthermore, structural heterogeneity tests demonstrate that mature resource-based cities and backward-linked industries benefit substantially from learning-by-doing effects. In contrast, forward-linked industries and non-mature cities face an elevated risk of a positioning paradox and comparative advantage lock-in. This study provides rigorous empirical evidence for advancing SDG 9 in developing economies seeking to reconcile deeper global economic integration with carbon neutrality objectives.