Social credit System pilots and corporate renewable energy technology Innovation: Insights from machine learning

Achieving net-zero emissions requires transformative innovation in renewable energy technologies (RETI), but firms face persistent institutional and financial obstacles that hinder technological advancement. To address this challenge, we draw on institutional theory to examine whether China's social credit system (SCS) pilots, as a credit-based governance reform, can incentivize corporate RETI through regulatory and reputational channels. A Bidirectional Encoder Representations from Transformers (BERT) model constructs corporate RETI indicators from patents, while causal effects are estimated using a double machine learning (DML) framework complemented by a difference-in-differences (DID) design. Results indicate that the SCS enhances corporate RETI by easing financing constraints, improving credibility, and fostering credit-based interactions. This effect is more pronounced among firms receiving green subsidies, suggesting institutional complementarity between formal incentives and credit-based discipline. The findings offer new evidence regarding how credit-based governance structures shape the micro foundations of energy transitions.