Zhen Tan

This study examines the association between corporate green investment and subsequent debt financing cost and the moderating role of prior green institutional investor attention. Using 40,060 firm-year observations from 4633 non-financial Chinese A-share firms over investment years 2011–2024, firm and year fixed-effects models relate investment in year t to financing cost in year t + 1, with attention and pre-investment financing cost measured in year t − 1. Green investment is associated with lower subsequent financing cost, and this association becomes more negative as prior attention increases. At the 25th and 75th percentiles of attention, a one-percentage-point increase in green investment relative to total assets is associated with financing-cost reductions of 2.75 and 3.62 basis points, respectively. The negative interaction persists after ownership and selected liability-composition controls, within positive-investment observations, and under alternative clustered and wild-cluster bootstrap inference. Evidence is stronger for financing-cost measures scaled by total liabilities than for the measure based on average core interest-bearing debt. Period comparisons indicate a stronger negative investment association but weaker attention moderation after 2019, including when 2020 is excluded. Differences across firm characteristics and environmental-exposure groups are less conclusive after multiple-testing adjustment. The findings associate the financing relevance of environmental expenditure with prior specialized investor attention, with the strength of the evidence varying across accounting measures and periods.

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.