A Study of the Factors Contributing to the Impact of Climate Risks on Corporate Performance in China’s Energy Sector

Abstract

As the climate crisis intensifies, corporate operations face unprecedented challenges from increasing climate risks, necessitating rigorous investigation into their resultant economic ramifications. This study employs text analysis and machine learning methods to construct climate risk perception indicators for a sample of China’s A-share listed energy sector firms (2014–2023). A two-way fixed effects panel model is then applied to study the impact of climate risk perception on corporate performance in the energy industry. The empirical results demonstrate that in China’s energy sector, a 1% rise in climate risk perception corresponds to a 0.104% decline in ROE, mediated through diminished financial flexibility (β = −0.075 **) and elevated R&D intensity (β = 0.649 ***). Moderating effect testing indicates that firms with higher levels of administrative spending effectively buffer against the adverse effects of heightened climate risk perception. Furthermore, this study shows that climate risk perception has more pronounced negative effects on corporate performance in state-owned enterprises (β = −0.113 **), heavily polluting enterprises (β = −0.131 *), carbon-intensive industries, and non-carbon trading pilot regions (β = −0.119 ***). These findings empirically demonstrate how climate risk perception reshapes corporate resource allocation and management, ultimately affecting performance. This study also proposes policy recommendations to enhance corporate climate risk responsiveness, promote technological innovation, accelerate the energy sector’s green transition, optimize corporate capital structure, and advance sustainable development goals.

1. Introduction

In recent years, extreme climate events have become increasingly frequent and intense globally, making climate change one of the most critical external threats to corporate sustainable development (World Meteorological Organization [1], TCFD [2]). Environmental disruptions have evolved from isolated incidents into persistent operational threats, manifested through natural disasters, resource scarcity, energy volatility, and regulatory uncertainty (Borozan & Pirgaip [3]). These compound risks significantly impact business continuity and financial performance, as evidenced by wildfire-induced sector shutdowns, drought-disrupted supply chains (Burke et al. [4]), and heatwave-driven power rationing (Liu & Feng [5]). The energy sector exhibits heightened vulnerability due to its dependence on natural resources and infrastructure, where climate impacts extend beyond physical asset damage to erode resilience through demand shifts, regulatory pressures, and supply chain disruptions—ultimately challenging long-term competitiveness (Schaeffer et al. [6]).

Climate risks may directly damage firms’ physical assets, but they can also undermine operational resilience and performance through mechanisms such as changes in demand structure, the tightening of environmental compliance policies, and supply chain disruptions. These effects pose systemic challenges to firms’ value creation and long-term competitiveness. In this context, how firms identify, perceive, and respond to climate risks is a critical factor influencing their survival and performance. For example, some research (Mugerman et al. [7]) demonstrates that making risks salient—through simple interventions such as warning symbols—can significantly alter investor behavior. This finding parallels the importance of climate risk perception, emphasizing its broader implications in financial and strategic decision-making.

Notably, the recent scholarship has increasingly identified climate risk perception as a critical mediating variable linking external climate disruptions with firms’ internal responses (Zhang & Bao [8]). However, there remains considerable divergence in the definition and measurement of this concept. Studies differ in their choice of perception subjects—such as investors, managers, or the public—and in data sources, including media reports, survey data, or online search behavior (Zhang et al. [9]). For instance, perception indicators based on public sentiment typically reflect investor expectations, while managerial discourse may better capture firms’ internal strategic responses. Therefore, clarifying the subject and method of measuring climate risk perception is of great theoretical and practical importance for identifying its impact on firm performance and constructing a systematic research framework (Zhang & Bao [8]). In this study, a climate risk perception index is constructed by calculating the proportion of climate-related keywords in the annual reports of China’s A-share listed energy companies relative to the total word count. This climate risk perception index serves as a proxy for executives’ climate risk awareness, since the intensity of disclosure reflects their strategic prioritization of such risks.

This study explores how climate risk perception influences corporate performance in China’s energy sector. Our objectives are as follows: Firstly, we aim to expand the perspective on how climate risk perception impacts corporate performance by focusing on the energy sector and incorporating climate risk perception into corporate activities. Secondly, we develop text-analytic indicators to quantify organizational climate risk awareness, providing empirical tools for sustainable transitions. Thirdly, we disentangle the direct and mediated effects of risk perception on performance, specifically testing financial resilience and R&D investment pathways. Fourthly, we identify heterogeneous risk perception patterns across ownership structures, contamination levels, industry subsectors, and carbon trading pilot regions, informing differentiated policy design and managerial interventions.

The remainder of this paper is structured as follows: Section 2 provides a review of the relevant literature; Section 3 presents the research hypotheses; Section 4 outlines the empirical research design, data and variable definitions; Section 5 discusses the empirical findings; and Section 6 concludes with key insights, policy recommendations, limitations, and future research.

2. Literature Review

Research on climate risks has primarily focused on the methods for quantifying and identifying climate-related risks, as well as their implications at both the macroeconomic and firm behavioral levels. This growing body of literature continues to enrich the theoretical framework of climate finance and provides strong support for empirical analysis and policy formulation.

In terms of measurement and identification, scholars have developed a variety of approaches in recent years that form a solid foundation for subsequent empirical studies. One stream of research assesses physical climate risks by using the historical frequency of extreme weather events, emphasizing the direct operational disruptions caused by natural disasters to firms’ production activities (Kruttli et al. [10], Addoum et al. [11], Hong et al. [12]). These studies typically rely on meteorological data and disaster records to evaluate the historical exposure of firms in specific regions, making them suitable for assessing the impact of environmental changes on infrastructure and supply chain stability (Li et al. [13]). Another line of work constructs exposure indicators based on carbon emission intensity to identify firms’ potential transition risks amid changing climate policies (Bolton & Kacperczyk [14]). Such approaches primarily reflect firms’ vulnerability to regulatory changes, particularly under tightening carbon regulations, where emission-intensive firms face greater compliance costs and market pressures. These indicators are often used as proxies for climate policy sensitivity or carbon pricing shocks (Bolton & Kacperczyk [15]).

Beyond objective exposure indicators, recent studies have increasingly emphasized climate risk perception—that is, the subjective assessment of climate risks by firms, managers, and investors—as a behavioral variable with greater predictive and explanatory power in actual decision-making processes (Gennaioli et al. [16]). However, conceptual ambiguity and methodological inconsistency remain prevalent in measuring this variable, especially regarding the definition of perception subjects and the selection of data sources. On the one hand, some studies focus on capturing investors’ expectations and emotional responses to climate risks using data such as the number of media reports, sentiment analysis of financial news, or internet search trends (Sautner et al. [17]). On the other hand, other studies adopt a managerial perspective, measuring internal awareness and response intentions by analyzing corporate annual reports, executive interviews, or ESG disclosures (Wang et al. [18]). These differences in subject orientation not only affect data collection pathways but also imply distinct transmission mechanisms in firm behavior and performance outcomes. Therefore, clearly identifying the perception subject (e.g., investors, managers, or the public) and data type (e.g., textual analysis, surveys, or search behavior) is crucial for improving the explanatory power of perception indicators and enhancing their policy relevance (Chen et al. [19]).

Some studies utilize managerial texts such as annual reports, earnings call transcripts, or survey data to assess executives’ depth of understanding and attitudinal stance toward climate issues (Krueger et al. [20]). Others employ media coverage counts and online search behavior (e.g., Google Trends) to evaluate public or investor attention to climate risks (Alok et al. [21]). These distinct data sources correspond to different behavioral expectations and transmission channels. For example, managerial perception is more likely to influence strategic adjustment and resource allocation, whereas investor perception is more reflective of market pricing preferences. As such, the construction of perception indicators—including their methods, data sources, and subject definitions—remains a key point of divergence and an active area of research.

Regarding the impact mechanisms of climate risks, the existing literature has explored both macroeconomic and corporate behavioral dimensions. At the macroeconomic level, a country or region’s climate vulnerability is often closely tied to its industrial structure, fiscal capacity, and policy responsiveness (Kling et al. [22], Battiston et al. [23]). Studies have shown that climate shocks can amplify income volatility in developing economies, suppress productivity growth, and increase the burden on public fiscal redistribution systems (Dell et al. [24]). Moreover, the capability of policymakers to manage and coordinate climate-related risks plays a vital role in mitigating systemic economic disruptions. Recent cross-country panel data studies have incorporated physical climate risk indicators into growth or fiscal models to reveal the long-term threats of climate change to macroeconomic resilience and sustainability (Kahn et al. [25]).

At the firm level, research has primarily examined how climate risks affect corporate financing, investment, and strategic decision-making, emphasizing market-driven behavioral responses to climate change (Arian & Naeem [26], Huang et al. [27], Khanra et al. [28]). Specifically, some studies have found that higher climate risk exposure is associated with tighter financing constraints, reflecting differentiated pricing mechanisms in credit markets for physical versus transition risks. Other studies suggest that firms may adopt proactive strategies—such as increasing green investment, reconfiguring supply chains, or enhancing disclosure transparency—to strengthen adaptability and build reputational capital (Serafeim [29]). In addition, some scholars have incorporated corporate climate risk behavior into ESG (environmental, social, and governance) performance frameworks, viewing climate governance as a critical component of firm valuation (Galbreath [30]).

While notable progress has been made in climate risk identification, mechanism analysis, and strategic responses, there is still a lack of systematic research on the mediating role of climate risk perception in corporate decision-making. This gap is particularly salient in the energy sector, which is highly sensitive to environmental changes. Key questions remain as to how energy firms perceive climate risks, how such perceptions vary across firms, and whether institutional and market environments moderate their performance outcomes.

This study contributes to the literature in two main ways. First, whereas most existing studies rely on the historical frequency of extreme weather events to measure climate risk and investigate resulting impacts (Addoum et al. [11]), this study shifts to a firm-level perspective by assessing companies’ perception of climate risk. It introduces a novel textual method using the frequency of climate-related keywords in annual reports to quantify firms’ climate risk perception. This approach avoids over-reliance on meteorological data and allows for a more direct evaluation of firms’ internal awareness, providing a new angle for micro-level risk identification and management. Second, while prior research has mainly focused on the macroeconomic or behavioral implications of climate risk, relatively few studies have examined its effect on corporate performance, a key indicator of resilience and long-term stability. Firm performance serves as a critical outcome for revealing the micro-level mechanisms of climate risk (Yin et al. [31]). Moreover, most firm-level studies emphasize external strategic adjustments (e.g., market responses and green transformation), while few explore how internal operations and resource allocation are shaped by climate risk perception and how these adjustments influence performance outcomes (Halm & Niskanen [32]).

3. Research Hypotheses

3.1. Climate Risk Perception and Corporate Performance

As the effects of climate change intensify, natural disasters increasingly disrupt corporate production and operations, posing unprecedented challenges to business stability and financial performance. Existing studies suggest that extreme weather events can directly damage production facilities and supply chain networks, thereby affecting output levels and resource allocation efficiency (Somanathan et al. [33]). Against this backdrop, a firm’s capacity to perceive climate risks emerges as a prerequisite for responding to external uncertainties and formulating adaptive strategies.

However, the existing literature tends to generalize the categorization of climate-change-induced risks, often failing to clearly distinguish between different types of climate events and their corresponding impact mechanisms (Dietz et al. [34]). According to the consensus in the climate science and disaster risk management field, natural risks driven by climate change can be broadly classified as slow-onset changes (e.g., rising average temperatures, altered precipitation patterns, sea-level rise) or rapid-onset shocks (e.g., extreme rainfall, floods, heatwaves, droughts, typhoons/hurricanes) (Kikstra et al. [35]). Slow-onset changes typically influence long-term operating environments and resource adaptation strategies, while rapid-onset shocks impose direct and immediate disruptions on firms’ short-term operational systems.

Moreover, some studies include geophysical disasters such as earthquakes and volcanic eruptions within corporate disaster preparedness frameworks. However, from a strict definitional standpoint, these events are not considered to be climate-related meteorological risks as they originate from tectonic disturbances rather than atmospheric changes. In this study, therefore, “climate risk” is specifically defined as risk stemming from extreme weather events and environmental trends related to the climate system, excluding non-climatic natural disasters such as earthquakes and volcanic eruptions.

Based on this definition, on the one hand, firms’ perception of climate risk is expected to significantly influence their anticipation of future environmental changes and, consequently, their responses in areas such as business planning, asset allocation, and emergency preparedness. According to dynamic capability theory, risk perception enables firms to identify emerging threats, reconfigure resource deployment, and enhance operational resilience (Zhang & Bao [8]). In addition, He and Ma [36], based on an empirical study of Chinese listed firms, found that foreign ownership inclusiveness enhances managerial attention to environmental risks and significantly improves firms’ ESG performance by strengthening their green innovation capabilities. This finding provides empirical support for the mechanism through which corporate risk perception influences strategic responses and performance outcomes. Zhou et al. [37] further demonstrate that China’s carbon emissions trading pilot policy significantly enhances ESG performance, primarily by incentivizing firms’ green innovation efforts and improving governance mechanisms. These findings provide empirical support for the mechanism through which climate risk perception or external policy pressure drives strategic responses and ESG performance outcomes.

Firms with low levels of climate risk perception are often inadequately prepared for climatic shocks, making them more vulnerable to production disruptions and asset losses. By contrast, firms with heightened climate risk perception are more likely to adopt proactive adaptation strategies in advance.

Nevertheless, such adaptation strategies—such as upgrading environmental technologies, purchasing carbon credits, and retrofitting production facilities—often entail substantial short-term expenditures, potentially resulting in tighter cash flows, increased operational costs, and compressed profit margins, thereby exacerbating financial strain and exerting a negative impact on short-term performance. This effect is particularly pronounced for traditional energy firms, which face mounting short-term financial burdens under the pressures of green transformation driven by carbon neutrality policies, further intensifying the risk of performance deterioration (Semieniuk et al. [38]).

On the other hand, climate events may permeate capital markets through information asymmetry mechanisms, amplifying informational uncertainty regarding corporate earnings trajectories and debt-servicing capacities; this elevates valuation volatility and constricts financial accessibility (Naseer et al. [39]). Studies have found that extreme rainfall events significantly reduce firms’ short-term valuations (Rao et al. [40]), further weakening their market performance and financing capacity.

In summary, although climate risk perception may contribute to greater corporate resilience in the long term, in the short term, the associated cost increases and cash flow pressures and worsening external financing environment are likely to exert a negative overall impact on corporate performance. Therefore, the following hypothesis is proposed:

H1. 

Climate risk perception has a negative effect on corporate performance.

3.2. Climate Risk Perception, Financial Flexibility, and Corporate Performance

Climate risk perception may lead to diminished financial flexibility, thereby having an adverse impact on corporate performance. In the context of climate-related research, financial flexibility is defined as a firm’s capacity not only to allocate internal financial resources to capitalize on opportunities or handle unforeseen events but also to respond to challenges stemming from climate change. As governments implement more climate-related regulations and policies, firms with higher climate risk perception are more likely to recognize these emerging risks early and take proactive preventive actions. Specifically, such firms may place greater emphasis on prudent capital allocation and increase investment in emission-reducing, energy-saving technologies and equipment upgrades (Ricci & Banterle [41], Hsiang & Jen [42], Wang et al. [43]), which could significantly impact their financial flexibility. Additionally, climate risks may disrupt supply chains—especially in terms of raw material availability—thereby threatening production continuity (Tenggren et al. [44]). Firms with stronger climate risk perception may respond by overstocking inventories or seeking alternative suppliers, which raises operational costs and further strains financial flexibility (Zhang & Chen [45]).

The degradation of financial resilience attributable to climate risk awareness may exert adverse effects on corporate performance in two ways (Zhang & Bao [8]). Firstly, financial resilience can affect a firm’s investment decisions, causing executives to make poor decisions, which in turn reduces corporate performance. In addition, in the face of external shocks, firms may exhibit financial resilience by utilizing reserve funds to maintain production operations, affecting their asset flows and financing costs, which can negatively affect their performance. Based on the above analysis, the following hypothesis is proposed:

H2. 

Climate risk perception decreases corporate performance by eroding financial flexibility.

3.3. Climate Risk Perception, R&D Investment, and Corporate Performance

Climate risk perception may influence corporate performance by affecting firms’ R&D investment behavior. In order to address environmental risks and meet rising regulatory and market expectations, firms with higher climate risk perception tend to engage in technological innovation and introduce green product upgrades to enhance product quality and reduce emissions (Lv et al. [46]; Delistavrou et al. [47]; Fernando & Wah [48]). These actions reflect firms’ strategic intent to grow sustainably in the face of climate uncertainty.

However, although such development efforts are forward-looking and value-driven, the associated R&D investments often require significant financial resources, the outcomes of which are uncertain and difficult to realize within a predictable timeframe (Li & Bosworth [49]). When firms are unable to effectively manage the risks and costs linked to these investments—especially under volatile policy or market conditions—the resulting financial pressures may temporarily reduce operating efficiency or limit resource allocation, thereby adversely affecting performance (Bai et al. [50], Deng et al. [51]). Therefore, this study proposes the following hypothesis:

H3. 

Climate risk perception decreases corporate performance by increasing R&D investment.

4. Research Methodology

4.1. Sample Selection and Data Sources

In this study, companies in China’s A-share listed energy sector from 2014 to 2023 are examined as the research sample. The industry is categorized into five major subsectors: electric power and steam and hot water production and supply; coal mining and dressing; gas production and supply; petroleum and natural gas extraction; and petroleum processing, coking, and nuclear fuel processing. In this study, several data processing measures were implemented to ensure data quality. Financial sector companies were excluded. ST and *ST companies were removed from the sample, along with observations containing missing values. The climate risk perception index was winsorized at 2.5% to eliminate potential outlier effects. The climate risk perception data were extracted from corporate annual reports using text mining and machine learning techniques, while other data were sourced from the CSMAR and Wind databases.

4.2. Definition of Variables and Modeling

4.2.1. Definition of Variables

(1)

Dependent variable

The dependent variable in this study was corporate performance, measured by return on equity (ROE), calculated as the ratio of net income to average shareholders’ equity. Grounded in the DuPont Analysis framework, ROE comprehensively captures three fundamental dimensions of corporate performance: profitability (net profit margin), operational efficiency (asset turnover), and financial leverage (equity multiplier). ROE specifically quantifies shareholders’ capital productivity, directly reflecting value creation efficiency from an equity investor’s perspective and making it the core indicator of corporate performance from a shareholder perspective.

(2)

Independent variable

The key explanatory variable, climate risk perception (ctrp), was constructed through text analysis and machine learning methods following Loughran and McDonald’s framework (Loughran & McDonald [52]). Adapted for five subcategories of the energy sector, this index quantifies firms’ climate risk awareness over a ten-year period. Climate risk perception was calculated as the ratio of the total word frequency of the extended word set “climate risk” to the annual report’s total word count. The larger the value of this index, the greater the climate risk faced by the company.

Specifically, the construction of ctrp involved four key steps. First, we downloaded the annual reports of all A-share listed companies from 2014 to 2023 from CNINFO. Using Python’s 3.12 java PDFBox library, we systematically extracted and organized the complete textual content from these reports to establish the foundational corpus for feature word analysis. Second, a lexicon of “climate risk” in annual reports (98 keywords in total listed in

Table 1. Lexicon of “climate risk”.

Climate Risk Keywords

Lexicon

Energy saving, energy, clean, ecology, environment, transformation, solar energy, upgrading, recycling, utilization, nuclear power, wind power, natural gas, efficiency, fuel, efficiency, regeneration, emission reduction, environmental protection, green, low carbon, consumption reduction, fuel, water saving, photovoltaic, high efficiency, retrofit, fuel consumption, power consumption, energy consumption, wind power, photovoltaic, efficiency, intensification, disasters, earthquakes, typhoons, tsunamis, droughts and floods, extremes, harshness, waterlogging, high winds, dust, hurricanes, frost, floods, storms, mudslides, landslides, freezing, snow, droughts, floods, torrential rains, tornadoes, hail, floods, rain, snow, freezing, blizzards, freezing, drought, drought, heavy rains, flooding, severe cold, wind and sand, climate, weather, humidity, water temperatures, cooling, cold, temperature, rainfall, temperature, rainfall, rainy season, rainfall, precipitation, cloudy rain, rainy, extremely cold, winter, flood season, high humidity, water conditions, water level, light, water shortage, alpine, cold, cold wave, subsidence, groundwater, flood conditions, surface, water storage

) was developed based on the study of Loughran and Mcdonald [52]. Third, we processed the annual report text data using jieba for word segmentation in Python to generate the total word frequency of the annual reports. Finally, we extracted the total frequency (tf) of the occurrence of climate risk keywords from the word-split file. The word frequency percentage of climate risk keywords in the annual report was calculated and then multiplied by 100 to obtain the ctrp, as shown in Equation (1).

$$ctrp=tf\times 100$$

(1)

In this study, a time-series analysis of the climate risk perception index (CTRP) is conducted for four large-cap Chinese enterprises from 2014 to 2023, annotated with major domestic climate policy events (Figure 1). The findings reveal a distinct policy-driven pattern in corporate climate risk awareness. Overall, the CTRP demonstrates a consistent upward trend over the decade, indicating significantly heightened climate risk consciousness among Chinese firms.

The evolutionary process can be categorized into three key phases, each closely aligned with major policy milestones. (1) During the pre-Paris Agreement period in 2015, corporate risk perception exhibited notable fluctuations, reflecting uncertainty in climate policy direction. (2) The launch of the national carbon market in 2017 and the announcement of the Dual Carbon Goals in 2020 (peak emissions by 2030, carbon neutrality by 2060) triggered accelerated growth in the index. (3) Following the implementation of mandatory environmental disclosure regulations in 2021, the CTRP stabilized at elevated levels, suggesting a new equilibrium in risk internalization.

This trajectory demonstrates that Chinese enterprises have transitioned from passive policy responsiveness to proactive risk internalization, with particular sensitivity observed in energy-intensive sectors. The results provide empirical evidence that mandatory policy instruments play a role in shaping corporate climate risk perception, offering valuable insights into the policy–firm interaction mechanism within emerging economies’ climate governance frameworks.

(3)

Control variables

Following the words of Naseer et al. [39] and Hossain et al. [53], we incorporated a set of control variables: (1) firm size (size), which is the natural logarithm of total assets and indicates a firm’s resource reserves as well as its risk tolerance; (2) fixed asset ratio (far), calculated as net fixed assets divided by total assets, demonstrates the rigidity of a firm’s asset structure; (3) short-term debt reliance (sbd), defined as the sum of short-term borrowings and the current portion of long-term debt divided by total assets, serves as an indicator of firms’ liquidity risk; (4) firm age (age), determined by taking the natural logarithm of one plus the years since listing, denotes experience accumulation and path dependence within a firm; (5) growth performance (growth), reflecting the rate of revenue growth, is determined by the year-on-year operating income growth relative to the prior year’s total operating income; and (6) equity separation (top1), which is the percentage shareholding of the largest shareholder, highlights the differences in governance structure.

Firm size (size) is likely to positively influence ROE, as larger firms benefit from diversified financing channels and enhanced risk resilience. The fixed asset ratio (far) may exert a negative effect, given that asset-intensive firms face amplified physical exposure to climate risks, necessitating isolation of its direct impacts on profitability. Short-term debt reliance (sbd) could reduce ROE due to refinancing pressures under abrupt climate policy shifts, potentially forcing investment cuts and operational disruptions. Firm age (age) presents a dual mechanism: while mature firms leverage contingency experience to stabilize ROE, technological lock-in effects may hinder climate adaptation, offsetting their advantages. Growth performance (growth) is expected to enhance ROE as high-growth firms actively invest in climate-resilient technologies. Lastly, equity concentration (top1) may drive policy-aligned climate risk management, potentially boosting ROE.

(4)

Mediating variables

In accordance with the proposed research hypothesis, this study outlined two key mediating variables: financial flexibility (ff) and R&D investment (rd). Following the work of Bagh et al. [54], financial flexibility (ff) was measured by the sum of cash flexibility and debt flexibility. Cash flexibility measures a firm’s ability to utilize internal funds, expressed as the ratio of cash and cash equivalents to total assets. Debt flexibility, on the other hand, assesses a firm’s access to external financial resources and is defined as one minus the firm’s debt ratio. This indicator directly reflects a company’s ability to retain funds for risk mitigation and demonstrates strong correlation with cash flow pressures arising from perceived climate risks. Following the work of Fu [55], the ratio of R&D investment to operating revenue was used to measure R&D investment. This variable can systematically reveal the internal logic of the impact of climate risk perception on corporate performance through innovation resource allocation. The definitions of the main variables in this study are shown in

Table 2. Definition of the main independent variables.

Variable Type	Variable Name	Variable Symbol	Definition	Expected Effect
Explained variable	corporate performance	ROE	The ratio of net income to average shareholders’ equity
Control variables	firm size	size	Logarithmic value of total enterprise assets	+
	fixed asset ratio	far	The ratio of net fixed assets to total assets	$-$
	short-term debt reliance	sbd	The ratio of short-term borrowings and current portion of long-term debt to total assets	$-$
	firm age	age	The natural logarithm of one plus years since listing	+/$-$
	growth performance	growth	Percentage change in operating income relative to prior year	+
	equity separation	top1	The percentage shareholding of the largest shareholder	+
Mediating variables	financial flexibility	ff	The sum of cash flexibility and debt flexibility	+
	R&D investment	rd	The ratio of R&D investment to operating revenue	$-$

Note: Data sources are CSMAR and Wind databases.

.

4.2.2. Descriptive Analysis

The descriptive statistics results of each variable are listed in

Table 3. Results of descriptive statistics for the main variables.

VarName	Obs	Mean	SD	Min	Median	Max	sign Test
ROE	800	0.0592	0.156	−3.44	0.06	0.39	p = 0.0155
ctrp	800	0.5569	0.289	0.01	0.54	1.05	p = 0.0736
size	800	24.0287	1.508	20.47	23.86	28.64	p = 0.0073
far	800	0.4654	0.178	0.08	0.45	0.95	p = 0.0642
sbd	800	0.1150	0.081	0.00	0.10	0.45	p = 0.0000
growth	800	0.0752	0.177	−0.31	0.05	2.79	p = 0.0000
top1	800	45.5023	16.778	10.45	47.82	89.99	p = 0.0001
age	800	3.0348	0.267	2.20	3.09	3.40	p = 0.0000
ff	800	0.5728	0.217	0.09	0.56	1.50	p = 0.0000
rd	800	0.6375	1.004	0.00	0.13	6.31	p = 0.0000

. As shown in Table 3, the mean value of the dependent variable (ROE) is 0.059, indicating moderate profitability across the sample. The wide dispersion between the maximum (0.39) and minimum (−3.445) ROE values indicates substantial performance variation, with some energy firms reporting significant losses. The independent variable ctrp has a mean of 0.557 and median of 0.542, with these closely aligned values suggesting a relatively concentrated data distribution. This pattern indicates that most enterprises’ climate risk perceptions fall within a similar range. However, the standard deviation of 0.289 indicates that, despite the overall similarity in climate risk perception among enterprises, there are significant differences in their actual exposure levels. The average enterprise size is 24.029, the mean fixed asset ratio is 0.465, and the average equity separation is 45.502%. The large standard deviation for corporate mediating variables indicates significant differences in financial flexibility and R&D investment among listed companies.

As shown in Table 3, the mean value of ctrp is slightly higher than the median, indicating that there are a small number of high-value observations, which may be related to the highly concentrated shareholding structure of a few firms. size is right-skewed, which may be caused by the extreme values of some large firms, as is growth, suggesting that a small number of firms have abnormally high growth rates. ff and rd are also right-skewed, which may reflect the aggressive financial strategies or intense R&D behaviors of some firms. The differences between the mean and median of ROE and far are small, indicating a symmetric distribution of the data.

Furthermore, following the work of Rosenbaum [56], we performed a sign test to determine whether the median of the data is equal to the mean. Based on the data in the table, the p-values for all variables are below the 0.05 significance level except for ctrp (p = 0.0736), indicating that the median is significantly different from the mean in most cases. The close proximity between the median and mean of ctrp suggests a relatively symmetrical distribution of this variable.

4.2.3. Model Specification

To examine the relationship between climate risk perception and business performance, our empirical analysis features a two-way fixed effects model to account for unobserved heterogeneity across firms and over time, as shown in Equation (2).

$${ROE}_{it}={\alpha }_0+\beta {ctrp}_{it}+X_{it}^{\prime }\theta +{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(2)

where $i$ and $t$ denote the firm and year, respectively; ${ROE}_{it}$ is the level of corporate performance; ${ctrp}_{it}$ denotes the climate risk perception; $X_{it}$ is a vector of control variables; ${\mu }_i$ and ${\lambda }_t$ denote the individual fixed effect and year fixed effect of enterprises, respectively; and ${\epsilon }_{it}$ is the random error term.

To investigate the mechanism through which climate risk perception affects corporate performance, the following model is specified (Zhang et al. [57]), as presented in Equations (3) and (4).

$${fd}_{it}={\alpha }_0+\beta {ctrp}_{it}+X_{it}^{\prime }\theta +{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(3)

$${rd}_{it}={\alpha }_0+\beta {ctrp}_{it}+X_{it}^{\prime }\theta +{\mu }_i+{\lambda }_t+{\epsilon }_{it}$$

(4)

where ${fd}_{it}$ denotes the mediating variable financial elasticity, ${rd}_{it}$ denotes the mediating variable R&D investment, and the remaining control variables and fixed effects are consistent with those in Equation (2).

5. Empirical Results and Analysis

5.1. Benchmark Regression

Table 4. Benchmark regression.

Variables	(1)	(2)	(3)
	ROE	ROE	ROE
ctrp	−0.038 *	−0.129 ***	−0.104 **
	(−1.893)	(−2.807)	(−2.103)
size	0.007 *	0.027	0.038 *
	(1.787)	(1.604)	(1.774)
far	0.044	0.229 ***	0.219 ***
	(1.391)	(3.153)	(3.006)
sbd	−0.365 ***	−0.381 ***	−0.393 ***
	(−5.325)	(−3.610)	(−3.651)
age	−0.031	0.000	0.000
	(−1.530)	(.)	(.)
growth	0.072 **	0.060 *	0.058 *
	(2.307)	(1.880)	(1.757)
top1	0.000	−0.000	−0.000
	(1.111)	(−0.003)	(−0.188)
_cons	−0.003	−0.590	−0.827 *
	(−0.025)	(−1.489)	(−1.676)
Year FE	NO	NO	YES
Firm FE	NO	YES	YES
N	800	800	800
R2	0.063	0.041	0.055

Notes: Parentheses contain the T-values of the statistics. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. (.) for age indicates that its T-statistic could not be estimated due to its time-invariant nature causing perfect collinearity with the firm fixed effects in Columns (2) and (3).

presents the baseline regression equation used in this study. Column (1) indicates that the regression coefficient of ctrp is significantly negative at the 5% significance level when controlling for the control variables but not for fixed effects. Specifically, for every 1 percentage point increase in ctrp, the average return on equity (ROE) of enterprises will decrease by 0.038 percentage points. Column (2) shows the results of adding control variables and controlling for firm fixed effects. In this column, the regression coefficient of ctrp is significantly negative at the 1% significance level. With each 1 percentage point rise in ctrp, corporates’ average ROE is projected to fall by 0.129 percentage points. Column (3), which accounts for both firm- and time-fixed effects, shows that the coefficient of ctrp stays significantly negative. More specifically, a 1 percentage point rise in ctrp corresponds to a 0.104 percentage point drop in average ROE. The regression results consistently demonstrate a statistically significant negative coefficient for our core explanatory variable across all model specifications. This suggests that climate risk perception has a negative impact on corporate performance, regardless of whether control variables are included or whether the regression controls for both individual and firm fixed effects. Thus, research hypothesis H1 is supported.

The possible reasons for the above observations are that firms recognizing climate risk may face problems such as damage to production facilities and disruptions in the supply chain, leading to a decline in operational efficiency. Meanwhile, firms in this situation tend to take defensive measures to reduce potential losses, but these actions often lead to short-term cost spikes. Furthermore, the overestimation of climate-related damage may lead to excessive capital allocation toward low-return adaptation projects. This resource misallocation potentially overshadows core business innovation investments, with particularly severe consequences in capital-intensive industries where capital-intensive features amplify the negative impact of biased decision-making. Alternatively, financial institutions may raise lending rates or reduce loan amounts for firms with higher climate risk perceptions. This can lead to higher financing costs for these firms, thereby resulting in lower corporate performance. Finally, firms may actively manage the short-term costs of climate risk, bearing certain present costs to avoid potential future climate losses. However, the benefits of risk mitigation are often delayed and difficult to quantify. This intertemporal cost–benefit asymmetry is directly reflected in financial statements as a decline in performance in the current period.

The regression results in column (3) of Table 4 show that firm size has a significantly positive coefficient of 0.038 on ROE, significant at the 10% level, indicating a weak positive relationship, as larger firms tend to perform better. The coefficient for far is 0.219, positive and significant at the 1% level, suggesting a strong positive relationship between the ratio of net fixed assets to total assets and ROE. The estimated coefficient for sbd is −0.393, significantly negative at the 1% level, indicating a strong negative relationship between the short-term borrowing ratio and ROE. growth has a positive coefficient of 0.058, which is significant at the 10% level, pointing to a weak positive relationship between growth rates and ROE. Lastly, the regression coefficient of the proportion of shares held by the first shareholder is non-significant and close to zero.

5.2. Endogeneity Test

To further mitigate the endogeneity issue between climate risk perception and corporate performance, we used the mean climate risk perception of other enterprises in the same industry in the current year as an instrumental variable (IV). The two-stage least squares method_(2SLS) was used to carry out a rigorous test. The results of the weak instrument test show that the F-value is well above 10, which fully meets the test requirements and strongly confirms the validity of the selected instrumental variable. The results of the 2SLS regression are shown in

Table 5. Endogeneity test results.

Variables	(1)	(2)	(3)	(4)
	First-Stage Regression	Second-Stage Regression	First-Stage Regression	Second-Stage Regression
ctrp		−0.379 *		−1.286 *
		(−1.892)		(−1.682)
IV1	0.754 ***
	(6.917)
IV2			−0.006 **
			(−2.428)
controls	YES	YES	YES	YES
_cons	−1.543 ***	−1.175 **	−1.254 ***	−3.050 **
	(−4.243)	(−2.096)	(−2.716)	(−2.306)
Year FE	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
N	800	800	621	621
R2	0.408		0.352

Notes: Parentheses contain the T-values of the statistics. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

. Column (1) shows that the instrumental variable is highly positively correlated with the independent variable in the first stage, while column (2) indicates that the coefficient of climate risk perception remains significantly negative in the second stage, consistent with the baseline regression results. This further supports hypothesis H1.

We also selected physical climate risk (pcr) as an instrumental variable for the endogeneity analysis. pcr is the number of natural disasters such as floods, earthquakes, extreme temperatures, and storms that occur within the prefecture. Natural disasters constitute plausibly exogenous environmental shocks whose incidence is predominantly driven by geological and climatic determinants. Their occurrence satisfies the exclusion restriction for instrumental variables, as no theoretically grounded channel exists through which disaster frequency would directly influence firm-level financial performance. Moreover, the regression results of the first stage show that the number of natural disasters has a significant negative impact on the core explanatory variable (ctrp), indicating that there is a strong correlation between the instrumental variable and endogenous variables. The second-stage results show that after controlling for endogeneity, the coefficient of ctrp is −1.286, indicating that the negative impact of climate risk perception on the dependent variable is still significant, further supporting the robustness of the benchmark findings.

5.3. Robustness Test

To further verify the reliability of the regression results, in this study, two methods are employed for robustness testing: variables’ replacements and changing the sample cycle.

5.3.1. Variables’ Replacements

Previous studies have used various indicators to measure corporate performance. In robustness checks, we also used the financial indicator return on assets (ROAs) as the dependent variable and performed estimations using Equation (2). This substitution aligns with prior studies, in which asset-based profitability metrics were shown to better capture operational efficiency for leveraged firms. Columns (1)–(3) of

Table 6. Test results of replacing variables.

Variables	Replacement Variable			Replacement Variable
	(1)	(2)	(3)	(4)	(5)	(6)
	ROAs	ROAs	ROAs	EVA	EVA	EVA
ctrp	−0.009 *	−0.022 **	−0.027 **	−0.007	−0.038 **	−0.037 **
	(−1.825)	(−2.092)	(−2.322)	(−1.016)	(−2.380)	(−2.193)
controls	YES	YES	YES	YES	YES	YES
_cons	0.033	−0.085	0.015	−0.043	−0.234 *	−0.214
	(1.167)	(−0.928)	(0.133)	(−1.043)	(−1.714)	(−1.260)
Year FE	NO	NO	YES	NO	NO	YES
Firm FE	NO	YES	YES	NO	YES	YES
N	800	800	800	800	800	800
R2	0.124	0.045	0.055	0.104	0.050	0.063

Notes: Parentheses contain the T-values of the statistics. ** and * indicate significance at the 5% and 10% levels, respectively.

show that the negative relationship between climate risk perception and ROAs persists. The absolute effect size is smaller than that for ROE (β = −0.027 vs. β = −0.104, p < 0.05) when controlling for both firm- and time-fixed effects. This finding supports hypothesis H1, which posits that climate risk perception reduces corporate performance. By redefining and measuring the core variables while keeping the control variables unchanged, the results are consistent with the hypothesized expectations, thereby demonstrating the robustness of the conclusions drawn above.

In this study, the dependent variable was also replaced with the firm’s financial indicator EVA. Columns (4)–(6) of Table 6 present the regression results after substituting the original dependent variable with EVA using Equation (2). After controlling for year and firm fixed effects, the regression coefficient of ctrp remains statistically significant at the 5% level (β = −0.037), with a negative sign that aligns with hypothesis H1, positing that heightened climate risk perception reduces firm performance. Furthermore, robustness checks—in which core variables were redefined while retaining all other controls—yield consistent results, reinforcing the validity of the conclusions.

5.3.2. Changing the Sample Cycle

Between 2020 and 2022, the country faced severe challenges due to the COVID-19 pandemic, which introduced unprecedented uncertainty into economic development and business operations. A critical question warranting further investigation is whether our core findings remain robust to the exogenous shock caused by this crisis. To verify this, we reran Equation (2) and excluded sample data from the 2020–2022 epidemic. The results in

Table 7. Changing sample period test results.

Variables	(1)	(2)	(3)
	ROE	ROE	ROE
ctrp	−0.009	−0.056 **	−0.059 **
	(−0.713)	(−2.028)	(−1.993)
controls	YES	YES	YES
_cons	−0.080	−0.154	−0.077
	(−1.159)	(−0.677)	(−0.273)
Year FE	NO	NO	YES
Firm FE	NO	YES	YES
N	560	560	560
R2	0.110	0.064	0.074

Notes: Parentheses contain the T-values of the statistics. ** indicates significance at the 5% levels.

show that the regression coefficients remain significantly negative across subperiods. This indicates that the negative impact of climate risk perception on the performance of energy firms is not attributable to the COVID-19 pandemic, thereby confirming the robustness and reliability of our findings.

5.4. Mechanism Analysis

To explore research hypotheses H2 and H3, in this study, the roles of firms’ financial resilience (ff) and R&D investment (rd) in the relationship between climate risk perception and firm performance were tested, and the results are shown in

Table 8. Results of the analysis of mechanisms through financial flexibility.

Variables	(1)	(2)	(3)
	ff	ff	ROE
ctrp	−0.116 ***	−0.075 **
	(−3.352)	(−2.399)
ff			Positive #
controls	NO	YES
_cons	0.599 ***	3.221 ***
	(32.144)	(10.251)
Year FE	YES	YES
Firm FE	YES	YES
N	800	800
R2	0.027	0.234

Notes: Parentheses contain the T-values of the statistics. *** and ** indicate significance at the 1% and 5% levels, respectively. ^#: According to Naseer et al. [58], financial flexibility has a positive impact on corporate performance.

.

5.4.1. Reduced Financial Flexibility

Climate risk perception might enable enterprises to anticipate potential climate change impacts earlier, thereby facilitating the proactive adoption of preventive measures. However, this strategic advantage could lead to excessive resource allocation toward risk mitigation. For instance, substantial investments in green transformation initiatives (e.g., purchasing carbon emission rights and upgrading production equipment) might create short-term financial pressures by straining liquidity and reducing fiscal flexibility. Furthermore, a high level of risk perception might trigger negative market expectations, leading to an increase in financing costs and a subsequent reduction in financial flexibility.

Table 8 presents the regression results from the financial flexibility channel analysis. The results indicate that financial flexibility has a significantly negative impact on corporate performance. This finding suggests that climate risk perception adversely affects corporate performance by reducing financial flexibility, thereby providing strong support for hypothesis H2. The negative impact of financial flexibility on corporate performance may manifest through several pathways. First, a decline in financial flexibility can force firms to cut back on non-core businesses or innovation investments, leading to a loss of the market share. Second, an excessive focus on climate adaptation investments could overshadow productive investments, leading to delayed equipment updates and reduced R&D investment. These actions can create imbalances in resource allocation, causing a sharp increase in operating costs, a decline in production efficiency, and sluggish market responsiveness. The opportunity cost losses from these misallocations can further drag down overall performance, creating the following negative cycle: strengthening climate resilience → rising operating costs → weakening core business competitiveness. Finally, reduced financial flexibility may increase financing costs and elevate debt burdens, thereby compressing profit margins and diminishing corporate performance.

5.4.2. Increased R&D Intensity

With the increasing global focus on climate change, governments are likely to introduce more stringent environmental regulations and standards, requiring firms to reduce carbon emissions and enhance energy efficiency. These policies exert external pressure on enterprises, prompting them to increase investment in innovation and R&D to develop new technologies and products that meet environmental requirements. Meanwhile, the increasing consumer demand for environmentally friendly products, coupled with heightened attention on corporate environmental performance from the media and social organizations, has driven firms to boost their innovation and R&D investment in areas such as green technologies and clean energy. By increasing R&D investment, enterprises can make early inroads into emerging fields and seize market opportunities.

Table 9. Results of the analysis of mechanisms through R&D investment.

Variables	(1)	(2)	(3)
	rd	rd	ROE
ctrp	0.709 ***	0.649 ***
	(3.719)	(3.337)
rd			Negative #
controls	NO	YES
_cons	0.057	−0.594
	(0.550)	(−0.305)
Year FE	YES	YES
Firm FE	YES	YES
N	800	800
R2	0.252	0.262

Notes: Parentheses contain the T-values of the statistics. *** indicates significance at the 1% levels. ^#: According to Karlilar & Tarzibashi [59], R&D investment has a negative impact on corporate performance.

presents the regression results when R&D investment is used as a mediating variable. Column (2) of Table 9 indicates that in the regression of the climate risk perception index on R&D investment, the regression coefficient of ctrp is significantly positive at the 1% level. This indicates that increased R&D investment mediates the negative effect of climate risk perception on corporate performance. Specifically, climate risk perception negatively impacts corporate performance through the channel of heightened R&D investment. This may be attributed to the fact that the transformation of the energy industry to becoming low carbon involves collaborative innovation across multiple domains, including power generation technology innovation, energy storage system upgrades, and carbon capture and storage. These R&D activities are characterized by high capital intensity, long development cycles, and strong path dependence.

When compelled to rapidly reallocate resources to comply with climate policy, firms face three strategic trade-offs. First, cash flow generation in traditional energy firms is highly dependent on fossil fuel prices. During the low-carbon transition, these firms must sustain existing operations while funding the R&D of emerging technologies, leading to a reduced working capital turnover efficiency. Second, clean energy technology pathways have yet to fully converge, with hydrogen, photovoltaic, and wind power potentially substituting one another. To hedge risks, firms often pursue concurrent R&D avenues across multiple technologies, resulting in fragmented resource allocation. Third, traditional energy firms’ technical capabilities are predominantly concentrated in fossil fuels. Their R&D teams’ knowledge structures and experimental equipment configurations differ significantly from those required for new clean technologies, imposing substantial conversion costs for new R&D activities. In summary, as firms’ climate risk perception increases and they ramp up R&D investment, performance declines across these three dimensions.

5.4.3. Further Investigation

In order to verify whether firms increase their green or R&D investment when climate risk perception increases, we randomly selected five firms and analyzed the trends in three indicators: firms’ climate risk perception, R&D investment (rd), and number of green patents (green invention). The results are shown in Figure 2, and we find that the trends in these indicators are basically consistent, indicating that firms do increase their green and R&D investment when their climate risk perception increases.

Specifically, we find that both R&D investment (rd) and the number of green patents (green invention) show an increasing trend as climate risk perception increases. This suggests that firms do increase green and R&D investments when climate risk perception increases. The trends in R&D investment (rd) and the number of green patents (green invention) are basically consistent, which further validates this hypothesis that enterprises increase green investment and R&D investment when climate risk perception is enhanced.

Our results align with the dynamic capability theory and real options theory. The dynamic capabilities theory suggests that climate risk drives firms to reconfigure resources toward green technologies and sustainable practices, enhancing adaptive capacity despite near-term costs. Concurrently, the real options theory frames such investments as strategic premiums that ensure flexibility against future policy shocks. These dual mechanisms explain the observed trade-offs: short-term costs reflect capability-building and option premiums, while long-term resilience emerges through these options and reduced regulatory risks. Our empirical tests validate these pathways.

5.5. Moderating Effect Test

Existing studies have found a positive correlation between prior administrative expenses and current operating profits. Investments in management-related expenditures, such as employee training and technological development, have been shown to enhance firm performance. To further explore potential interaction effects and examine the role of administrative expenses in the relationship between climate risk perception and firm performance, an interaction term (crtp × mgmt) between climate risk perception (crtp) and the management expense ratio (mgmt) is introduced in this study. This allows us to assess whether internal managerial capabilities moderate the impact of climate risk perception on firm performance. The results are shown in

Table 10. Regression results: moderating effect of management expense ratio.

Variables	(1)
	ROE
crtp	−0.164 ***
	(−2.685)
mgmt	−2.234 ***
	(−4.555)
crtp × mgmt	1.778 **
	(2.047)
controls	YES
_cons	−0.739
	(−1.520)
N	800
R2	0.089

Notes: Parentheses contain the T-values of the statistics. *** and ** indicate significance at the 1% and 5% levels, respectively.

.

The regression results of Table 10 indicate that the interaction term crtp × mgmt is positive and statistically significant at the 5% level (β = 1.778). This suggests that firms with higher levels of administrative spending are more capable of mitigating the negative impact of climate risk perception on performance. This implies that in the context of uncertainty, firms with greater investments in internal governance, employee development, information systems, and strategic planning possess stronger risk management capabilities and can more efficiently allocate resources.

Furthermore, the results highlight the crucial moderating role of internal managerial capacity under environmental uncertainty. Compared to firms with lower management expense ratios, those with higher expenditures may have more robust decision-making and implementation mechanisms, enabling them to identify and respond to climate-related risks more promptly, thereby reducing potential operational disruptions. These findings underscore the importance of managers prioritizing internal management investments when formulating strategies to address climate change, especially in industries exposed to long-term and systemic environmental challenges, where such investments are vital for enhancing organizational resilience and sustaining firm performance.

5.6. Heterogeneity Test

5.6.1. Heterogeneity Analysis of Ownership Structures

When examining the influence of climate risk perception on corporate performance, enterprises with different ownership structures exhibit varying degrees of sensitivity. To explore this heterogeneity, in this study, we conducted an analysis distinguishing between state-owned enterprises (SOEs) and non-state-owned energy enterprises (NSOEs). According to the regression results for SOEs in column (1) of

Table 11. Heterogeneity test results of ownership structures.

Explained Variables	ROE	ROE	rd	rd
Sample	(1)	(2)	(3)	(4)
	SOEs	NSOEs	SOEs	NSOEs
ctrp	−0.113 **	0.028	0.627 **	0.625
	(−2.118)	(0.260)	(3.058)	(1.154)
controls	YES	YES	YES	YES
_cons	−0.892	0.159	−0.665	2.838
	(−1.548)	(0.225)	(−0.300)	(0.786)
Year FE	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES
N	724	76	724	76
R2	0.058	0.298	0.282	0.350

Notes: Parentheses contain the T-values of the statistics. ** indicates significance at the 5% levels, respectively.

, the estimated coefficient of ctrp is −0.113, which is significant at the 1% level. Conversely, the estimated coefficient for NSOEs is positive but not significant, indicating that SOEs are more vulnerable to climate risks.

This disparity may stem from the fact that among listed enterprises in China, NSOEs, despite their relatively late entry into the market, have demonstrated advantages in management, technological innovation, and social connections compared to SOEs. These strengths enable them to respond more effectively to the challenges posed by climate risks. Specifically, the flexibility in management and active participation in market competition provide NSOEs with enhanced capabilities in organizational structure optimization and innovative management practices. They can adjust strategies, reconfigure business layouts, and optimize resource allocation as needed and in accordance with policies, thereby strengthening their adaptability.

In contrast, SOEs, tasked with maintaining social stability and providing public goods, often possess relatively stable organizational structures but face greater challenges in implementing transformative changes. Consequently, they are more exposed to the risks of physical damage and face higher risks during the transition process. Furthermore, SOEs in the energy sector, which shoulder the critical mission of implementing national energy strategies and ensuring energy security, often face problems related to highly rigid policy implementation. When addressing climate risks, SOEs may prioritize achieving broader social goals over optimizing their own performance.

On the other hand, NSOEs in the energy sector enjoy greater flexibility in policy implementation and can selectively adopt policies based on their specific circumstances and market environments. Additionally, driven by the goal of profit maximization, these enterprises pay closer attention to market signals and economic benefits. Therefore, the impact of climate risk perception on the performance of SOEs is more pronounced compared to that for NSOEs.

Columns (3) and (4) present the regression results examining the impact of climate risk perception on R&D investment for SOEs and NSOEs, respectively. In column (3), the coefficient of 0.627 (significant at the 5% level) indicates that heightened climate risk perception significantly increases R&D investment in SOEs due to policy-driven mandates and subsidized long-term innovation. Column (4) shows that the impact is insignificant for NSOEs. This may reflect NSOEs’ market-driven logic: prioritizing short-term returns over uncertain R&D in climate technologies, favoring asset-light strategies while financing constraints further deter high-risk investments.

5.6.2. Heterogeneity Analysis of Contamination Levels

Considering the differences in energy types, we performed a heterogeneity analysis to determine whether firms are serious pollution sources. Group regressions were performed according to the intensity of the firms’ pollution (based on the classification of heavily polluting industries in the Environmental Statistics Yearbook), and regression analyses were performed again, with the results shown in columns (1)–(2) of

Table 12. Heterogeneity test results of contamination levels.

Variables	(1)	(2)
	Non-Heavily Polluting Enterprises	Heavily Polluting Enterprises
crtp	0.036	−0.131 **
	(0.454)	(−2.392)
controls	YES	YES
Year FE	YES	YES
Firm FE	YES	YES
_cons	0.905	−0.956 *
	(0.540)	(−1.832)
N	65	735
R2	0.408	0.060

Notes: Parentheses contain the T-values of the statistics. ** and * indicate significance at the 5% and 10% levels, respectively.

.

The impact of perceived climate risk on firm performance is more pronounced in heavily polluting industries, mainly because the multiple pressures of policy, economy, and industry characteristics faced by these industries are superimposed onto each other to form a unique risk transmission mechanism. First, the strictness of policy control directly determines the survival of enterprises. Heavily polluting firms, as the core target of environmental protection regulations, are strongly constrained in their operations by the performance grading system. Second, the high cost of environmental protection upgrades significantly amplifies the economic risks. The technological transformation of heavily polluting industries often requires huge capital investment, and the transformation of environmental protection facilities for highly polluting processes such as the iron and steel and cement industries can easily involve tens of millions or even hundreds of millions of US dollars. Industry characteristics further amplify the risk transmission effect. Heavily polluting industries are generally characterized by high energy consumption and a high level of emissions, and their production processes are directly linked to environmental pollution. In addition, the pressure of public opinion and risks to brand image should not be ignored. Poor environmental performance not only incurs administrative penalties but may also damage corporate reputation. In general, the impact of climate risk perception on corporate performance is significantly intensified in heavily polluting industries under the coupling of high policy pressure, economic costs, and industry characteristics. Strictly differentiated control policies are directly related to business survival, while high environmental investment pushes up operating costs, and industry ecology accelerates the spread of risk conduction. In contrast, the impact of climate risk on non-polluting industries is relatively limited due to looser policy constraints and lower environmental costs.

5.6.3. Heterogeneity Analysis Across Industries

Given the heterogeneous effects of transitional climate risks on carbon emission efficiency across enterprises categorized by energy types, a heterogeneity analysis was conducted. Following the previously defined classification of five major energy industries—the electricity and heat production and supply industry; coal mining and washing industry; gas production and supply industry; oil and gas extraction industry; and petroleum processing, coking, and nuclear fuel processing industry—we re-estimated the regression models. The results are presented in columns (1)–(5) of

Table 13. Heterogeneity test results of industry differences.

Variables	(1)	(2)	(3)	(4)	(5)
	Electric Power, Steam and Hot Water Production and Supply	Coal Mining and Dressing	Petroleum Processing, Coking, and Nuclear Fuel Processing Industries	Gas Production and Supply	Petroleum and Natural Gas Extraction
ctrp	−0.060	−0.102 **	−0.703 **	0.036	−0.599 *
	(−0.822)	(−2.433)	(−2.346)	(0.454)	(−1.994)
controls	YES	YES	YES	YES	YES
_cons	−1.031	−0.740	−0.988	0.905	−11.548
	(−1.425)	(−1.103)	(−0.938)	(0.540)	(−1.700)
Year FE	YES	YES	YES	YES	YES
Firm FE	YES	YES	YES	YES	YES
N	455	168	82	65	30
R2	0.124	0.718	0.409	0.408	0.775

Notes: Parentheses contain the T-values of the statistics. ** and * indicate significance at the 5% and 10% levels, respectively.

.

Columns (2), (3), and (5) show that for companies in the coal mining and washing industry, the petroleum processing, coking, and nuclear fuel processing industry, and the petroleum and natural gas extraction industry, climate risk perception has a negative impact on corporate performance. Among these, the impact is most significant in the petroleum processing, coking, and nuclear fuel processing industry.

Within the macro context of global commitments to mitigating climate risks and accelerating the transition of the global energy system, the petroleum processing, coking, and nuclear fuel processing industry was among the first sectors subjected to stringent environmental regulations and policy constraints. To meet emission reduction targets, firms in this sector must allocate substantial capital investments toward upgrading production technologies and deploying an advanced pollution control infrastructure. A representative example lies in the substitution of conventional wet quenching methods with dry quenching technology during coking processes, which demonstrates the potential to reduce carbon but entails significant upfront capital expenditures and ongoing operational and maintenance costs. Concurrently, amid growing market demand for clean energy products, the market share of traditional carbon-intensive petroleum derivatives faces intensifying pressure. Empirical evidence suggests that failure to promptly adapt to this structural shift through strategic product portfolio restructuring could precipitate more pronounced contractions in sales revenue compared to other energy subsectors, thereby exacerbating the adverse effects of climate risk perceptions on corporate financial performance.

5.6.4. Heterogeneity Analysis Across Carbon Trading Pilot Regions

To further explore how institutional context moderates the effect of climate risk perception, we conducted a heterogeneity analysis based on firms’ locations in relation to China’s carbon emissions trading pilot regions. A binary variable was used to differentiate firms inside (treat = 1) and outside (treat = 0) of these regulated zones. The results are presented in

Table 14. Heterogeneity analysis based on carbon trading pilot regions.

Variables	(1)	(2)
	Treat = 0	Treat = 1
	ROE	ROE
crtp	−0.119 ***	−0.084
	(−2.658)	(−0.860)
controls	YES	YES
_cons	−0.778 *	−1.693
	(−1.785)	(−1.358)
N	448	352
R2	0.113	0.105

Notes: Parentheses contain the T-values of the statistics. *** and * indicate significance at the 1% and 10% levels, respectively.

.

We found that the coefficient of crtp is significantly negative in non-pilot regions (β = −0.119, p < 0.01) but statistically insignificant in pilot regions. This indicates that firms in non-pilot, or non-regulated, regions are more vulnerable to performance fluctuations driven by climate risk perception. The absence of well-defined policy frameworks, institutional support, or incentive mechanisms in these areas may leave firms more exposed to the uncertainty and operational pressures arising from climate concerns.

By contrast, the established carbon trading mechanisms and policy incentives in pilot regions appear to buffer performance volatility. These institutional infrastructures provide clearer compliance expectations, incentivize proactive investments in green technologies, and reframe climate risks as strategic considerations rather than external shocks. Consequently, firms in pilot regions leverage policy scaffolding to mitigate economic disruptions, underscoring the critical role played by regulatory maturity in reconciling climate resilience with financial stability.

6. Conclusions and Policy Suggestions

6.1. Conclusions

This study utilizes text analysis and machine learning techniques to develop climate risk perception metrics for Chinese A-share listed energy companies. Using a bidirectional fixed-effects panel model analysis, we examine how climate risk perception influences corporate performance in the energy sector. The findings reveal that heightened climate risk perception negatively correlates with corporate performance, primarily through reduced financial adaptability and intensified research development efforts. The moderating effects analysis demonstrates that enterprises with a stronger administrative capacity can better mitigate the performance impacts of climate risk perception. The research further identifies differential effects across organizational and regional contexts, with climate risk perception exerting more pronounced negative impacts on corporate performance in SOEs, heavily polluting firms, carbon-intensive industries, and regions outside of carbon market pilot initiatives.

6.2. Policy Recommendations

Based on the aforementioned research findings, we propose the following suggestions:

First, considering the short-term negative impact of climate risk perception on the performance of energy firms, governments need to implement measures to mitigate such effects when introducing stricter climate-related policies. Sector-specific support policies, such as progressive tax deductions for clean technology adoption, innovation subsidies for renewable R&D, and green credit quota increments tied to decarbonization milestones, could offset transitional costs while preserving market competitiveness.

Second, to reduce the impact of climate risks on corporate performance, energy companies should integrate climate goals into their financial strategies, focusing on reliable green technologies and maintaining flexible funds. They can implement dynamic capital allocation mechanisms and risk control frameworks, such as liquidity buffers and scenario-based contingency planning, to secure funding adequacy, prevent market expansion constraints from capital shortages, and safeguard long-term profitability by prioritizing investments in scalable growth areas. This approach ensures that operational resilience aligns with climate requirements while preserving financial stability. Moreover, enterprises should adopt flexible resource allocation and cross-functional R&D coordination to mitigate climate adaptation transition costs.

Third, given the heterogeneity of the impacts of climate risk perception, especially for SOEs, policies should be tailored. For SOEs, policymakers should refine investment efficiency criteria to prioritize technology commercialization and measurable emission reductions over symbolic green investments. Introducing outcome-based evaluation standards and fostering public–private partnerships for technology diffusion can mitigate inefficiencies. For private firms, enhancing market incentives—such as raising carbon prices, expanding green procurement mandates, and boosting consumer demand for low-carbon products—is critical to improving the cost–benefit ratio of climate investments. Concurrently, lowering financing barriers would strengthen private firms’ capacity to align profitability with decarbonization goals.

Heavily polluting enterprises should establish institutionalized climate risk governance frameworks. This involves integrating real-time environmental compliance tracking with cross-departmental green innovation teams to align operational resilience with regulatory requirements. Carbon-intensive industries need to prioritize modular production upgrades to achieve a balance between emission reductions and cost efficiency. Meanwhile, firms located in non-carbon-trading zones are advised to proactively form cross-regional partnerships with institutions in pilot areas. This strategy facilitates carbon credit pre-certification and the joint development of green technologies. Additionally, these firms should establish internal contingency reserves to cushion against policy uncertainties.

6.3. Limitations and Future Research Directions

This study focuses mainly on the short-term impact of climate risk perception on corporate performance. It does not examine firms’ adaptive behaviors or dynamic adjustment paths in the medium to long term. Thus, it may underestimate the positive role of risk perception in enhancing corporate resilience and improving performance. Although theory indicates that firm characteristics such as size and regional climate vulnerability may influence the relationship between climate risk perception and performance, this study has not tested such heterogeneity mechanisms empirically. Lastly, the conclusions of this study are context-specific to China’s energy sector, where firms face intense regulatory pressures and public scrutiny. This may limit the findings’ applicability to less-regulated industries or regions.

Future research could extend the analysis to the long-term effects of climate risk perception by exploring how firms improve their adaptability and performance through strategic transformation, technological upgrades, and governance enhancement. It is also recommended that interaction terms be incorporated or subgroup regressions be conducted to examine the moderating roles of firm size and regional climate vulnerability in the mechanism of the impact of climate risk perception. Such efforts would provide a more comprehensive understanding of behavioral heterogeneity and outcome variation in firms’ responses to climate change.