From “Evidence Silos” to Computable Synergy: An Algorithmic Framework for Science-Policy Translation and Regionally Differentiated Governance Toward China’s Dual Carbon Goals - Yue, Zhang; Feng, Bin; Ali, Dayang Haszelinna Binti Abang; Salleh, Sazlina Md. (Journal of Cultural Analysis and Social Change, 2025)
Centering on China's “carbon peak-carbon neutrality” strategy, this study addresses the computable governance challenges of efficiently translating scientific knowledge into policy instruments and implementing regionally differentiated execution. Unlike existing research primarily discussing the institutional design and coordination dilemmas of the “1+N” policy system, this paper proposes a Computable Policy Synergy (CPS) framework that integrates “evidence generation—policy synthesis—regional adaptation—dynamic evaluation.” Methodologically, it constructs multi-source evidence pipelines for dual-carbon goals (statistically harmonized MRV data, sectoral technology scenarios, socio-economic-energy coupling indicators). Policy knowledge graphs and causal inference (synthetic control/difference-in-differences/instrumental variables) identify policy mechanisms, while a “policy sandbox” enables cross-sectoral tool synthesis and conflict detection. To characterize governance performance, this study designs two core indicators: the Cross-Sector Computable Synergy Index (CCI) and the Regional Scenario Transferability Index (RTI). The former measures the dynamic synergy among price-based, command-based, and information-based tools, while the latter evaluates policy transferability under varying factor endowments, industrial structures, and technological feasibility constraints. Using a 2015–2024 provincial-municipal panel sample and linking national carbon market (ETS) with non-ETS measures in key industries, empirical results demonstrate that compared to traditional sector-specific policy combinations, the toolkit generated by the CPS framework demonstrates significant advantages in reducing policy inefficiencies, enhancing the synergy between emission reduction intensity and total factor productivity, and achieving convergence in regional emission reduction costs without compromising equity. This study innovates by proposing an algorithmic policy synthesis and regional adaptation paradigm for dual-carbon governance, providing a reusable indicator system and evaluation process. It offers transferable technical pathways and institutional recommendations for deep science-policy coupling under complex objectives in developing economies.