Fostering Urban Sustainability: A Spatio-Temporal and TextCNN-DID Evaluation of China’s Green Finance Pilots

This paper explores how green finance policies influence the sustainability and resilience of urban areas, particularly those operating under tight fiscal budgets. By treating China’s Green Finance Reform and Innovation Pilot Zone as a quasi-natural experiment, we examine a comprehensive panel of 299 prefecture-level cities between 2011 and 2024. The analytical framework integrates traditional difference-in-differences (DID) estimations with machine-learning-driven text analysis. To quantify governmental foresight regarding sustainability risks, the authors employ Word2Vec semantic expansion and NLP tokenization. Additionally, a TextCNN classifier is utilized to group cities by their digital-economy readiness, allowing for a nuanced assessment of heterogeneous policy impacts. The results demonstrate that implementing green finance pilots not only tangibly boosts sustainable productive capacities but also noticeably sharpens official textual focus on sustainability governance. Furthermore, mechanism analyses suggest that these positive outcomes are driven by structural industrial upgrades, enhanced technological expenditures, and the easing of local fiscal burdens. The study’s conclusions—reinforced by rigorous robustness checks like PSM-DID, CS-DID, and spatio-temporal mapping—indicate that green finance functions as a vital governance mechanism capable of stabilizing local finances and driving long-term, low-carbon transitions.