Firm-Level Exposure to Climate Policy Uncertainty and Green Innovation: Nonlinear Evidence from Chinese Listed Firms

Climate policy uncertainty may prompt firms to prepare for regulation while delaying irreversible green investment. We examine whether firm-level exposure is nonlinearly associated with green innovation. Exposure combines a news-based provincial climate policy uncertainty index with transition exposure extracted from annual-report management discussion and analysis sections. Using 13,972 firm-year observations from Chinese A-share listed firms over 2008–2024, we estimate models with firm and year fixed effects. Green invention patent applications exhibit an inverted U-shaped association with exposure. The estimated turning point is 0.686, and 13.52% of observations exceed it; a formal U-shape test supports the pattern. R&D results are consistent with a partial internal adjustment channel, but the conditional indirect association is indistinguishable from zero in the upper exposure tail. Tighter financing constraints weaken the positive marginal association and bring forward its reversal. The curve is more pronounced among large firms, while evidence for initial asset tangibility depends on its measurement. Several robustness checks preserve the nonlinear pattern, but stricter fixed effects and province-level clustering weaken the evidence. These observational findings suggest that innovation responses depend on both policy exposure and firms’ capacity to adjust.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-09-25
DOI
https://doi.org/10.3390/su18199847
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Firm-Level Exposure to Climate Policy Uncertainty and Green Innovation: Nonlinear Evidence from Chinese Listed Firms

Yifan Li, Meijiao Wang
Sustainability
Energy, Environment, Economic Growth
article

Firm-Level Exposure to Climate Policy Uncertainty and Green Innovation: Nonlinear Evidence from Chinese Listed Firms

Yifan Li, Meijiao Wang
article en

Abstract

Climate policy uncertainty may prompt firms to prepare for regulation while delaying irreversible green investment. We examine whether firm-level exposure is nonlinearly associated with green innovation. Exposure combines a news-based provincial climate policy uncertainty index with transition exposure extracted from annual-report management discussion and analysis sections. Using 13,972 firm-year observations from Chinese A-share listed firms over 2008–2024, we estimate models with firm and year fixed effects. Green invention patent applications exhibit an inverted U-shaped association with exposure. The estimated turning point is 0.686, and 13.52% of observations exceed it; a formal U-shape test supports the pattern. R&D results are consistent with a partial internal adjustment channel, but the conditional indirect association is indistinguishable from zero in the upper exposure tail. Tighter financing constraints weaken the positive marginal association and bring forward its reversal. The curve is more pronounced among large firms, while evidence for initial asset tangibility depends on its measurement. Several robustness checks preserve the nonlinear pattern, but stricter fixed effects and province-level clustering weaken the evidence. These observational findings suggest that innovation responses depend on both policy exposure and firms’ capacity to adjust.

SustainabilityVol. 18(19)
University of Shanghai for Science and Technology (CN)
Climate action
Openalex Percentile: Top 5%
Energy, Environment, Economic Growth
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.