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| Corporate Governance Level and Corporate Climate Risk Perception Bias: A Dual Correction Perspective Based on Climate Misjudgment |
| WANG Da, TIAN Hao, ZHOU Yingxue
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| School of Economics, Jilin University; School of Economics, Changchun University |
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Abstract In the context of intensifying climate risks and China's continuous efforts toward its “dual-carbon” goals (carbon peaking and carbon neutrality), improving firms' accuracy in assessing climate risk and strengthening climate governance outcomes have become a central question in climate finance and corporate governance research. Prior work largely examines downstream outcomes, such as environmental disclosure, carbon performance, or green investment, to understand how governance structures and climate policies shape corporate behavior, but it has paid comparatively less attention to a more fundamental element of climate governance: whether decision-makers' subjective perceptions of climate risk align with the firm's objective risk exposure. When firms systematically misperceive climate risk, strengthened external regulation and improved internal governance may still fail to translate into effective climate action. Accordingly, identifying the drivers of Climate Risk Perception Bias and evaluating feasible corrective channels is important both theoretically and practically for improving corporate climate-governance effectiveness and enhancing the stability of the climate-finance system.
Building on this premise, we examine how corporate governance quality shapes firms' Climate Risk Perception Bias. Drawing on polycentric collaborative governance, upper echelons theory, and the dynamic capabilities view, we develop a “government-firm dual-correction” framework that integrates internal governance mechanisms with the external policy environment. The core premise is that Climate Risk Perception Bias is not merely an information-deficit problem. Rather, it emerges from the joint influence of governance complexity, managers' cognitive capacity, and external institutional signals. Therefore, correcting this bias requires firms to recalibrate internal cognitive anchors and strengthen resource-reconfiguration (adaptation) capabilities, while governments provide stable and credible external constraints and incentives through sustained policy attention and institutional investment.
Methodologically, we move beyond the conventional single-data-source measurement paradigm and integrate machine learning, textual analysis, and panel econometric techniques to systematically quantify firms' Climate Risk Perception Bias. First, to measure objective climate risk, we use disaster-monitoring records from the National Meteorological Information Center and incorporate regional economic resilience and population vulnerability to construct a dynamic, comparable regional climate-risk index via a LightGBM model. Second, to capture subjective risk perception, we develop a climate-risk dictionary tailored to China's institutional context and apply it to firms' annual reports to quantify decision-makers' attention to climate risk. We then define Climate Risk Perception Bias as the systematic deviation between a firm's perceived climate risk and its objectively measured exposure. In our empirical design, we estimate two-way fixed-effects models and test for nonlinearity by including a quadratic term of governance quality. We further address potential endogeneity using instrumental variable estimation and system GMM.
We examine Chinese A-share listed firms on the Shanghai and Shenzhen exchanges from 2007 to 2022. We merge firm-level governance, financial, and macroeconomic data from CSMAR and WIND with annual-report texts and meteorological disaster records to construct an integrated dataset that links micro-level corporate behavior to the broader institutional environment. We construct a composite measure of corporate governance quality using principal component analysis (PCA) on multidimensional proxies capturing managerial incentives, board monitoring, and ownership structure, thereby providing a comprehensive assessment of firms' overall governance quality.
Our empirical results indicate a statistically significant inverted U-shaped relationship between corporate governance quality and Climate Risk Perception Bias. Specifically, in the early stage of governance improvement, the rapid accumulation of governance instruments and the rising complexity of information processing can increase managers' cognitive load, thereby amplifying firms' misperceptions of climate risk. Once governance quality surpasses a critical threshold, firms enhance their ability to identify and respond to climate risk through organizational learning and strategic allocation of slack resources, which significantly attenuates Climate Risk Perception Bias. This evidence challenges the prevailing linear premise in the literature that better governance monotonically improves risk management, and instead highlights the stage-dependent and nonlinear role of corporate governance in shaping climate-risk cognition.
Mechanism analyses further suggest that disclosure quality and commercial credit constitute two key channels through which corporate governance shapes Climate Risk Perception Bias. In the early phase of governance improvement, strategically ambiguous disclosure and contractions in commercial credit may reinforce managerial short-termism, thereby exacerbating misperceptions of climate risk. By contrast, at mature governance stages, high-quality and forward-looking climate disclosures, together with improvements in the supply-chain credit environment, enhance cognitive transparency and financial flexibility, effectively recalibrating managers' subjective assessments of climate risk. We also find that government-firm collaborative governance exerts a pronounced amplifying effect in correcting climate-risk misperceptions. Coordinated increases in local governments' policy focus on climate issues and ecological investment can substantially boost the efficiency of corporate governance mechanisms in mitigating Climate Risk Perception Bias, through enhanced credibility of regulatory signals and lower transition costs.
Based on these findings, we derive three policy implications. First, regulators should explicitly target the correction of Climate Risk Perception Bias by further strengthening climate-risk disclosure requirements and improving the comparability, forward-looking content, and verifiability of disclosures. Second, differentiated yet stable local green-regulation arrangements can help reduce the noise introduced by institutional complexity and policy uncertainty in firms' climate-risk cognition. Third, firms should strengthen climate-risk-oriented executive selection and incentive schemes within corporate governance to reduce the likelihood of climate-risk misperceptions at their cognitive source.
This study makes three primary contributions. First, adopting a cognition-based perspective, we position Climate Risk Perception Bias as a key mediating mechanism linking corporate governance to climate-governance performance, thereby extending the analytical frontier at the intersection of climate finance and corporate governance. Second, we integrate meteorological disaster data with text-mining techniques to develop a replicable firm-level measurement framework for Climate Risk Perception Bias. Third, through the lens of government-firm collaborative governance, we systematically document the complementarities between regulatory instruments and corporate governance mechanisms in correcting climate-risk cognition.
Future research can extend this work in three directions. First, a dynamic perspective could model how the governance threshold endogenously adjusts as the policy environment evolves. Second, incorporating industry heterogeneity and cross-country samples would allow comparative tests of how Climate Risk Perception Bias arises and is corrected across institutional contexts. Third, future studies could unpack governance components to examine the differential roles of institutional investor types, board structures, and incentive schemes in shaping climate-risk cognition.
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Received: 14 May 2025
Published: 27 February 2026
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