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Article

Impact of Climate Risk on the Financial Performance and Financial Policies of Enterprises

1
Airbus Noise Technology Centre, University of Southampton, University Road, Southampton SO17 1BJ, UK
2
School of Economics, Fujian Normal University, Fuzhou 350007, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(20), 14833; https://doi.org/10.3390/su152014833
Submission received: 20 July 2023 / Revised: 7 September 2023 / Accepted: 9 October 2023 / Published: 13 October 2023
(This article belongs to the Section Air, Climate Change and Sustainability)

Abstract

:
In this study, The Global Climate Risk Index, a global climate risk score, along with firm-level data are used to investigate how climate risk affects corporate financial performance and financial policy in various nations. To evaluate the impact of climate risk on various parts of a firm, this study uses the capabilities of SPSS (version: 28.0.1.1(15)) to perform correlation and regression analysis between climate risk indicators and the firm’s operational data. We obtained financial information from the Bloomberg database for companies in 37 different countries from 2017 to 2021, including return on assets, cash from operations, sales growth, short-term debt, long-term debt and short- and long-term debt. This is combined with a climate risk index to analyze the impact of climate risk on companies in different countries. The financial performance of a firm is found to be adversely, but not significantly, associated with climate risk in this study using correlation and regression analysis, whereas the long-term debt of the company is found to be favorably related to climate risk. From this investigation, the following findings can be derived. A lower returns on assets is the indicator of the comparatively poor financial performance of businesses in nations with increased climate risk. Businesses in nations with greater climate risk typically retain more long-term debt. Companies in nations with lower climate risk, on the other hand, typically retain less long-term debt. Lastly, this study contributes to the understanding of how climate risk affects different countries and how climate risk affects corporate financing strategies.

1. Introduction

Climate change is currently a global challenge that threatens the future viability of our planet. Climate change can bring catastrophes for humans and ecosystems: extreme weather, melting glaciers, rising sea levels, altered ecosystems, etc. The rate of climate change and extreme weather has accelerated in recent years. Despite the growing concern and awareness, it is difficult to predict climate change accurately with the available technological tools. However, scientific evidence suggests that the risks of climate change will continue to increase.
As climate-related risks are inherently more complex and persistent than most traditional business risks, and as the topic gains prominence, there are still many businesses that lack a clear understanding of the potential financial impacts caused by climate change and are unable to assess their scope and magnitude. While many businesses are reporting climate-related risk disclosures, only a few are currently planning forward-looking scenario analyses and applying them to their strategic vision of helping their businesses protect and create value. Companies therefore need to fully understand the relationship between their business operations and climate change. Trexler and Kosloff (2013) assert that businesses must adjust their risk management to account for the risks posed by climate change and that these adjustments must be strategic, local, and risk based [1]. The effects or threats of climate change are starting to be researched as they become a more prominent topic of discussion.
Corporate investments to combat climate change risks can be broadly divided into three dimensions: environmental, social and governance (ESG). According to the principles of responsible investment proposed by the United Nations, the meaning of ESG includes three dimensions: environment, society, and governance, which correspond to top-down environmental externalities, social externalities, and public aspects of corporate governance, respectively [2]. As the issue of climate risk has become increasingly serious, many companies have made ESG investments an important part of their investments, especially in many environmentally related industries such as new energy, heavy industry, and automotive manufacturing. According to Wang’s research (2019), ESG-related news can have a negative impact on stock returns in the short term [3]. There is a broad consensus that investors are primarily exposed to two types of climate risk when making investments: physical risk and transition risk. Physical risk refers to physical damage to investment targets due to changes in climate conditions, such as extreme heat, sea level rise, typhoons, etc. Transition risk is the financial risk associated with the transition to a low-carbon economy, such as regulatory risk or the risk of stranded assets due to emerging technological innovations. According to Liu (2021), some Asian financial markets have adopted and innovated new climate finance instruments to suit current financing practices and financial actors [4].
Companies can link climate change to financial theory by considering ESG factors and assessing the impact of financing and investment activities on the real economy, thus incorporating sustainability. According to Paseda and Okanya’s study (2020), some important theories of modern finance can also be applied to climate risk financing through the assessment of ESG factors [5]. Many argue that investment decisions made by financial institutions should be based not only on financial considerations but also on social and environmental objectives. There are two prevailing theories on this issue, namely the shareholder theory and the stakeholder theory, which will be described subsequently. Both theories require financial institutions to engage in a trade-off between external regulation and internal solutions. For this reason, it is necessary to study the impact of climate risk on the firm.
To study the impact of climate risk on companies, this study would like to examine how important indicators of companies are affected by climate risk. Since firms are profit-driven and adjust their financial policies to their own conditions, we ask the research question of whether climate risk in different regions has an impact on the financial performance of firms and financial policies. For the answer to this question, we created hypotheses by analyzing the existing literature and tried to test these hypotheses using regression analysis.
Much of the existing literature on climate risk remains at the level of different industries or at the regional level and does not go into the differences in the impacts of climate risk across countries. This study aims to both capture the local and regional variability of climate impacts and, at the same time, answer the question of climate change adaptation in an international context relevant to multinational enterprises. According to Zhu (2023), climate risk for companies has been shown to harm asset values, production capacity, and overall economic activity [6]. And climate risk has time-varying and differential impacts on the returns of stocks of both non-fossil and fossil fuel companies. At the same time, there are many impediments to the transition plans of many companies to transition to renewable energy sources in order to decarbonize. According to Choudhury (2023), of the many barriers to progress in renewable energy, financial barriers may be the most significant [7]. Financial institutions typically have more stringent requirements for such funding, and the financial sector’s exposure to renewable energy projects may lead to concerns about the continued solvency of these projects. When the unpredictability of cash inflows generated by these projects is taken into account, it will be difficult for renewable energy projects to gain support from banking institutions. This literature demonstrates that climate risk affects different types of firms in different ways, but the research is limited to within a specific country or region, and few studies have explored the impact of climate risk on firms in different countries, so this study will add to the existing literature in this regard. By establishing a regional climate risk indicator, the global climate risk index, we would like to investigate how climate risk affects business operations in various nations. We obtained the global climate risk index for the years 2017 to 2021 from the non-governmental organization Germanwatch to complete this study. We then compared these climate risk indicators with firm-level data from the Bloomberg database to examine how climate risk in various countries affects firms’ financial performance and financial policies. In this study, the impact of climate risk will be studied through correlation and regression analysis; there are many tools to support this analysis, such as SPSS, Python, etc., and in this study, SPSS is mainly used to process and analyze the collected data.
This study introduces country-level climate risk variables based on the analysis of firm-level data, providing a new literature for studying the impact of climate risk on firms in different regions. By studying the behavior of firms in regions with different levels of climate risk, this study provides some insights for firms to conduct international business. While the existing literature investigates the impact of corporate commitment to climate change action and carbon risk exposure on debt financing policies, this study introduces a new climate risk indicator from another perspective to study the impact of regional climate risk indicators on financing policies.

2. Literature Review

2.1. Analysis of the Existing Literature

In order to explore the impact of climate risk on a company’s financial performance and financial policies, we need to analyze the existing literature. In this section, we focus on the classification of climate risk and the measures of climate risk used in this study. Based on this, we categorize the findings of the existing literature on the impact of climate risk on financial performance and debt structure. Finally, we explore the feasibility of using modern financial theory to analyze climate risk issues (Figure 1).

2.1.1. Climate Risk

The problem of climate change has received more public attention in recent years; however, it is challenging to predict future climate change with any degree of accuracy due to the numerous intricate uncertainties it contains. Ecosystem instability brought on by climate change can create uncertainty for businesses across a variety of industries. Additionally, uncertainty might affect businesses’ standard operating procedures and result in needless losses. Concern associated with climate change is a typical global security risk from the standpoint of risk management. The Intergovernmental Panel on Climate Change predicts that, given the current rate of increase in carbon emissions, global warming will increase from the current level of 1 degree Celsius above pre-industrial levels to an average of 1.5 degrees Celsius between 2030 and 2052 (IPCC, 2014) [8]. Intense consequences of rising global temperatures include, but are not limited to, droughts, floods, extinction of animals, and rising sea levels.
To mitigate the rising global temperature trend, many countries have introduced laws to limit the emission of greenhouse gases. In particular, the Paris Agreement (2015) has sounded the alarm for some countries regarding the current externalities of a high carbon economy (UNFCCC, 2015) [9]. Leaders from around the world have come together to agree to limit global temperature increases to less than 2 °C below pre-industrial levels, preferably to 1.5 °C. The Paris Agreement demonstrates a global commitment to mitigate climate change and has been ratified by 187 of the 197 countries since the convention was signed.
Physical, regulatory, and market risks are three major categories that can be used to categorize climate change risks (Sakhel, 2017) [10]. They may have a negative or positive impact on the business’s regular operations and raise concerns about its profitability.
Physical risk refers to the costs associated with the quality of physical damage and adaptation programs at the site of the funded project [11]. Physical risks usually come from extreme weather, such as droughts, tornadoes, floods, mudslides, etc., but physical risks also include changes in ecosystems such as rising temperatures, sea level rise, etc.
Regulatory risk is the risk of regulation modification because of climate change. To address the detrimental effects of business carbon emissions on climate change, new policies are continually being adopted as the public and lawmakers become more aware of it. From the standpoint of a corporation, these regulatory environment changes will result in financial risk since more stringent or extra mitigation strategies may result in higher operational and investment expenses [10].
Market risk is the risk brought on by shifting consumer preferences and adjustments to the financial markets because of climatic change. The risk associated with businesses creating new technologies and switching to a low-carbon economy is another aspect of market risk. Climate change will also have an impact on investors’ investments in companies that use fossil fuels and produce a lot of carbon [10]. There is a risk that these effects on customers and investors will ultimately cause financial losses for enterprises.
Nowadays, investors are increasingly taking climate risk into account when choosing stocks, which is one of the reasons why this study was conducted. New policies, regulations and technologies can change market conditions and, to some extent, can influence investment returns. An interesting question is how investors can measure climate risk in their investment process to achieve sustainable returns, as we will discuss in the next chapter.

2.1.2. Measurement of Climate Risk

Since the climate is a regional natural phenomenon, it is reasonable to measure climate risk by country. In this study, we will use the 2021 Global Climate Risk Index (CRI) to measure climate risk by country.
Germanwatch, a non-profit NGO, created and released the Global Climate Risk Index (CRI) [11]. The CRI offers a numerical assessment of the economic harm caused by extreme weather in a nation. The future occurrence of extreme weather is also predicted by this signal [12]. Since 2006, the CRI has been released yearly; the most recent edition was in 2021. There are two categories of CRI scores: annual scores and long-term scores. Data from two years prior to the annual edition are used to calculate annual scores. The 2021 version, for instance, includes an annual score based on information from 2019. The 2021 edition’s long-term scores, for instance, are based on data for the 20-year period ending two years before the edition’s year, from 2000 to 2019. This study uses annual scores as well as long-term scores for 2021 and the 5 years prior to it, meaning that climate data for 2019 and the 5 years prior to it are used.
The following two absolute and two relative indicators of climate-related risk serve as the foundation for the CRI. The number of fatalities and the total monetary losses in purchasing power parity (PPP) terms are the two absolute indicators. Losses per unit of gross domestic product and the number of fatalities per 100,000 people are the two relative metrics (GDP). The index score for a nation is equal to the average ranking of its four indicators, with each absolute indicator receiving a 1/6 weighting and each relative indicator receiving a 1/3 weighting. Therefore, the higher the appropriate ranking and the larger the apparent risk, the lower the index score. Since a lower index score implies a larger climate risk, we multiplied the index score in this study by a negative 1 to make a higher score imply a bigger risk and to make correlation analysis easier in the subsequent study.
With the international climate policy process having stalled in 2020 due to the 2019 coronavirus disease pandemic, there is an expectation that progress on long-term financing targets will be made in 2021 and 2022 and that adequate support for adaptation and development will be provided [12]. To complete this process, it is necessary to strengthen the implementation of climate change adaptation measures.

2.1.3. The Impact of Climate Risk on Financial Performance

Investors will consider climate risk while making investments given the global trend toward a low-carbon economy. Understanding and anticipating the effects of climate change threats on economic activity is becoming more crucial as global temperatures are predicted to continue to climb [13]. For instance, Sun et al. [14] study looked at how climate risk affected the financial success of mining firms. Three aspects—governance structure, environmental costs, and social responsibility performance—are the focus of current research on the variables impacting the financial success of mining firms. Empirical research on the risk of climate change has mostly been concentrated at the city and industry levels. This study uses firm-level data to rigorously assess how climate change risk affects corporate financial performance. According to their analysis, publicly traded mining corporations should abandon the status quo and acknowledge the significance of climate change concerns for future development. When preparing for future production operations, businesses should take the trend of climate change risk into consideration. Establishing a functional department to handle climate change is also required, as is incorporating climate change concerns into the organization’s risk management framework.
Corporate level climate risk is largely related to the carbon emissions from business operations. Previous studies have also examined the relationship between low or high carbon portfolios and investment performance from the perspective of carbon emissions. Midttun and Gjengedal [15], in their study, state that stocks with low carbon footprints generate higher abnormal returns than stocks with high carbon footprints and higher investment returns are generated when betting on low-carbon stocks than when betting on high-carbon stocks. The results of this study further suggest that investors who hold high-carbon stocks are not adequately compensated for their risk.
The role of banks in business operations is crucial, and there is already research on how natural disasters, the most prevalent type of climate risk, affect banks’ asset structures. In the near term following a natural disaster, banks reorganize their assets, boosting the amount of loans while decreasing their holdings of government bonds, according to Bos et al. [16]. According to their research, banks’ holdings of government bonds declined following a tragedy, while overall loans and real estate loans dramatically climbed. A number of heterogeneity analyses yield similar findings that are in line with earlier research: natural disaster-affected businesses and locals want more loans, and banks respond by selling more government securities to meet this demand. Through the lens of natural disasters, their findings enable us to assess the potential impact of climate change on bank balance sheets.
Previous research has also looked at how climate risk affects business performance and financing possibilities. Huang et al. [17] discovered that managers of large, publicly traded firms take climate risk financing into account when deciding how much money to allocate for a project. These occurrences include losses from storms, floods, and heat waves. Additionally, they discover that organizations with high climate risk tend to store more capital to strengthen their resilience against unforeseeable climate threats and that climate risk is positively correlated with profits volatility and adversely correlated with corporate profitability.

2.1.4. The Impact of Climate Risk on Debt

In response to climate risks and increasingly extreme weather, much of the literature refers to the need for organizational resilience of companies to combat climate risks. For instance, Tschakert and Dietrich [18] indicate in their research that an organization’s capacity to systematically absorb and recover from the negative consequences of external environmental disturbances brought on by extreme weather constitutes organizational resistance to climate change. They conclude that climate risk resilience is gained through activities in low-risk areas, improved facilities and technology, and increased insurance coverage. However, all these approaches require a drain on the company’s capital or resources to achieve them, which also requires the company to make changes to its financial policies according to its own circumstances.
Companies may alter their financial strategies to become more resilient to climate risk, and one key financial strategy is debt structure. This is because climate risk may have a negative influence on corporate performance. The relationship between climate risk and corporate debt policy has been investigated in earlier studies. Lemma et al. [19] investigated how business promises to combat climate change and their exposure to carbon risk affects corporate debt financing practices. They created a model that connects a company’s commitment to climate change initiatives and carbon risk exposure with its debt financing strategy variable. The study’s findings demonstrate a significant positive relationship between a company’s commitment to addressing climate change and the maturity structure of its debt, supporting the claim that highly committed companies are more likely to experience lower default risk, better credit ratings, a positive corporate image, and lower agency and information asymmetry costs. Even after adjusting for carbon risk exposure, it is significant to note that companies with a higher commitment to combating climate change have a higher percentage of long-term debt in their debt financing structure.

2.1.5. Application of Modern Financial Theory

Climate risk finance is local, national, or transnational financing—from the public, private, and other sources of financing—that seeks to support mitigation and adaptation actions that address climate change risks. The financial sector uses environmental, social and governance (ESG) factors to assess the impact of financing and investment activities on the real economy, thereby incorporating sustainability. Through the assessment of ESG factors, climate change can be linked to financial theory. According to Paseda and Okanya’s study [5], some important theories of modern finance can also be applied to climate risk financing through the assessment of ESG factors, including utility theory, state preference theory, mean-variance portfolio theory, capital asset pricing model (CAPM) and its various extensions (including arbitrage pricing theory, APT), option pricing theory, agency theory, efficient market hypothesis, and the Modigliani–Miller (MM) theorem.
The problem that climate risk finance needs to address is how individuals and society allocate scarce resources through a price system based on the valuation of risky assets, and modern finance theory can help solve these problems. Utility theory provides the basis for rational decision-making, weighing choices in the face of risks, such as incurring carbon taxes and reputational damage, against the profitability of a firm’s production and operating decisions. Another trade-off may arise between industrial activities that create jobs at the cost of environmental warming and increased pollution [5]. In mainstream finance, the main descriptions of choice objects are national preference theory, mean-variance portfolio theory, capital asset pricing models, and option pricing theory. When choice theory is combined with choice objects, this integration yields models for assessing risky choices. When properly allocated, the efficient market hypothesis assumes that market prices provide useful signals to the economy to guide the efficient allocation of resources. Finally, Modigliani–Miller theory explains whether the method of financing affects the value of the firm. The answer to this question has far-reaching implications for the choice of a firm’s capital structure (debt–equity mix) and dividend policy. Some of the analytical frameworks used at the micro-firm level have been extended to the analysis of economic aggregates of firm capital structure, national debt policy and agency theory at the broad level of the firm and the economy.
Many people think that financial institutions should base their investment decisions not just on financial factors but also on social and environmental objectives. It is challenging to evaluate these recommendations without a greater grasp of the scope of the social responsibility financial institutions should take on in a world where environmental conservation is becoming a mainstream trend. A philosophical theory of corporate social responsibility must serve as the foundation for a systematic stance on this issue. The shareholder theory and the stakeholder theory are now the two most prevalent theories on this topic. According to the shareholder theory, financial agents should only work to increase shareholder wealth because they are only responsible to the shareholders [20]. Stakeholder theory, on the other hand, contends that they share similar duties with all parties involved, i.e., those who have an impact on or are affected by the agent’s decisions [21]. This suggests that financial organizations have duties to a variety of parties, including clients, creditors, local communities, and shareholders.
Although each theory has some merit, none of them can be fully applied to the current social climate. What is evident is that, as the recent financial crisis is an excellent illustration, businesses that seek to maximize profits in uncontrolled markets offer extremely detrimental risks to both the financial system and society. To manage global concerns like climate risk, a sustainable financial system is required. Sandberg, J. [22] offers a two-level sustainability paradigm. He contends that if there is agreement and dedication to the greater benefit of society, a social division of labor between the financial markets and the government can be reached. He discussed whether to promote internal remedies, such as a stronger emphasis on social responsibility and ESG issues in financial management, or external regulation, such as capital reserve requirements, proscribed bonus programs, or financial taxation. Without the backing of the other, neither external regulation nor internal remedies are viable in the long term. To establish a balance between external regulation and internal solutions, corporations must research how climate risk may affect their operations.

2.2. Innovation and Research Questions of This Study

In the existing literature, the impact of climate risk on various aspects of business has been presented. However, most of these studies stop at firm-level data, while the impact of climate risk is regional. Therefore, we would like to study the impact of climate risk on different regions of different countries with the help of a different regional climate risk indicator. While using regional climate indicators, firm-level data within these regions are collected to study the impact of different regions of climate risk on the firm’s operations level. During the study, the focus is on studying the financial performance of firms and controlling for firm characteristics, including assets, firm age, intangibles, etc. The purpose of this study is to enrich the study of regional climate risk in different countries and to further analyze the impact of climate risk on the operational level of firms in conjunction with firm-level data.
Based on the study of the impact of climate risk on corporate financial performance, this study also wants to further explore the impact of climate risk on corporate financing policies. There have been many studies in the existing literature on the relationship between climate risk and corporate debt structure, but the data used in these studies also mostly stay at the firm level. This study considers the possible impact of climate risk on corporate debt structure while examining the impact of different levels of climate risk on corporate financial performance in different countries. In this study, the impact of climate risk on corporate debt structure will be examined by controlling for variables related to corporate financial performance, and thus examining whether climate risk in different countries may have an impact on corporate debt structure.
The objectives of this study are to examine the impact of different climate risks on corporate financial performance in different countries and to examine the impact of climate risks on corporate financial policies. The existing literature can answer the impact of climate risk on the firm level, but there is little literature that delves into what impact climate risk in different regions of different countries can have on firm-level operations. Therefore, the research question of this study is: does climate risk in different regions affect the financial performance of firms? Does climate risk in different regions have an impact on the debt structure of firms?

2.3. Hypotheses

Based on the available literature, we can know that climate risk has a negative impact on a company’s operations. Climate risk can affect the performance of firms in three ways: physical, regulatory, and market [10]. And these impacts can vary depending on the firm’s industry. Therefore, we make the following hypothesis for the research question of this study.
H1. 
Climate risk is significantly and negatively related to the financial returns of firms.
Regarding the impact of climate risk on corporate financial policies, existing studies have illustrated that climate risk can have an impact on financial policies. Lemma et al. [19] illustrate in their study that firms with higher investment in climate change actions, even after controlling for carbon risk exposure, have a higher proportion of long-term debt in their debt financing structure higher. Therefore, for the research questions in this study, we make the following hypotheses.
H2. 
Climate risk is significantly and positively related to firms’ long-term debt.
H3. 
Climate risk is significantly and negatively related to short-term debt.

3. Research Methodology

3.1. Methodological Choice and Data Analysis Technique

In the process of data analysis, the raw data needs to be processed first to remove invalid values from the raw data. For this step, we relied on the SPSS function of identifying outlier cases and removing them from the data set after identifying them. After processing the data set, we planned to first perform a Pearson correlation analysis on the variables to confirm that there was a significant correlation between the variables before performing the regression analysis. After examining the variables using Pearson correlation analysis, the next step is to use regression analysis to specifically analyze the effect of climate risk on each variable.
Data regression analysis’s goal and significance is to fit a set of influencing variables and results to an equation that can then be used to forecast additional occurrences with a similar structure. Regression analysis, as used in statistics, is a technique for figuring out the quantitative relationship between two or more variables that are dependent on one another. The types of regression analysis include linear regression and nonlinear regression based on the type of relationship between the independent and dependent variables, univariate regression and multiple regression based on the number of variables involved, simple regression and multiple regression based on the number of dependent variables, and univariate regression and multiple regression based on the number of variables involved.
Some modern regression analyses include carbon emissions as a variable. Midttun and Gjengedal [15] show that low-carbon portfolios outperform high-carbon portfolios, while high-carbon-emitting stocks lag the market. They find that investors whose investment strategies focus on high-carbon stocks are not fully compensated for their risk.
Financial performance is a subjective measure of how well a company utilizes the assets in its primary business model and generates revenue. The term is also used as a general measure of a company’s overall financial health over a period of time. Financial performance evaluation includes a comprehensive evaluation of an enterprise’s operating conditions, management level, competitive advantages and disadvantages in the market, and future development potential. It also includes the financial position of the enterprise, profitability, efficiency, stability, and growth ability. The basic evaluation indicators of financial performance include return on net assets, cash from operations, net profit margin, and other indicators. This study takes the enterprise’s return on assets and cash from operating operations as the two indicators in the data analysis of the enterprise’s financial performance.
Economic performance refers to the achievement (or non-achievement) of economic policy objectives. Economic policy objectives are usually defined first so that economic performance can be evaluated against those objectives. A crucial aspect of fiscal policy is a company’s debt composition. Current liabilities, often known as short-term debt, refer to debt that the corporation anticipates paying off within a year. Debt with a maturity of more than a year is referred to as long-term debt and is typically handled differently from short-term debt. For firms, the adoption of debt policy is considered a major decision that affects the value of the firm. In this study, debt policy (short-term debt, long-term debt and short-term and long-term debt) is chosen as an indicator of the economic performance of the company and the impact of climate risk on it is analyzed.
In this study, the data obtained is a combination of country-level and firm-level data, which are obtained from continuous observation of different subjects at different times, and therefore the data used in this study are panel data. In the choice of regression model, we chose linear regression to study the relationship between the dependent variable and multiple independent variables. We build regression models based on equations and subsequently investigate the causal relationship between climate risk and firms’ economic performance and economic decisions by controlling for variables, respectively. We estimate the impact of climate risk on financial performance and financial policies using the following specification:
F i n a n c i a l   p e r f o r m a n c e R O A = β 0 + β 1 C l i m a t e   R i s k i t + β 2 T o t a l   A s s e t s i t + β 3 T o t a l   I n t a n g i b l e   A s s e t s i t + β 4 S a l e s ( 5   y e a r s   a v e r a g e   g r o w t h ) i t + μ i t + ε i t C F O = β 0 + β 1 C l i m a t e   R i s k i t + β 2 T o t a l   A s s e t s i t + β 3 T o t a l   I n t a n g i b l e   A s s e t s i t + β 4 S a l e s ( 5   y e a r s   a v e r a g e   g r o w t h ) i t + μ i t + ε i t
F i n a n c i a l   p o l i c i e s S h o r t - T e r m   D e b t = β 0 + β 1 C l i m a t e   R i s k i t + β 2 T o t a l   A s s e t s i t + β 3 T o t a l   I n t a n g i b l e   A s s e t s i t + β 4 S a l e s   ( 5   y e a r s   a v e r a g e   g r o w t h ) i t + μ i t + ε i t L o n g - T e r m   D e b t = β 0 + β 1 β 1 C l i m a t e   R i s k i t + β 2 T o t a l   A s s e t s i t + β 3 T o t a l   I n t a n g i b l e   A s s e t s i t + β 4 S a l e s   ( 5   y e a r s   a v e r a g e   g r o w t h ) i t + μ i t + ε i t S h o r t -   a n d   L o n g - T e r m   D e b t = β 0 + β 1 β 1 C l i m a t e   R i s k i t + β 2 T o t a l   A s s e t s i t + β 3 T o t a l   I n t a n g i b l e   A s s e t s i t + β 4 S a l e s   ( 5 y e a r s   a v e r a g e   g r o w t h ) i t + μ i t + ε i t
In this model, the financial performance of the firm is measured by the dependent variables return on assets (ROA) and cash from operations (CFO). According to Sun et al. [14], ROA plays a guiding role in evaluating the financial performance of companies. The higher the firm’s return on assets, the more efficient the firm’s assets, the more profitable the company is, and the better the financial performance of the company, and vice versa. Meanwhile, cash flow from operations (CFO) refers to the amount of money a company brings in from its ongoing, regular business activities. According to Huang et al. [17], the higher the cash flow from operations, the more cash a firm generates over time than is needed to pay its current liabilities, and the better the financial performance of the firm. In the equation, the subscript i denotes the region and t denotes the period. μ denotes the unobserved effect, and ε is the regression residual.
Before conducting the analysis, the panel data model must be determined. There are usually three types of panel data models, namely mixed estimation without individual effects model, the variable intercept model with individual effects, and the variable coefficient model with individual effects, the latter two of which are further categorized into fixed-effects and random-effects models according to the different forms of individual effects. The model setting will determine the reliability of the estimation results, so it is usually necessary to use two tools, namely the F-test and the Hausman test. The F-test is used to determine the fixed-effect model. The original hypothesis is that the individual effect is not significant, so if the original hypothesis is rejected, then the fixed-effect model is chosen; otherwise, the mixed estimation model is chosen, and Hausman test is used to determine whether it is a fixed-effect model or a random-effect model. The original hypothesis is that it is random-effect; if the original hypothesis is rejected, then it indicates that the individual effect is related to the explanatory variables, and the fixed-effect model is chosen. If the original hypothesis is rejected, it indicates that the individual effects are correlated with the explanatory variables and a fixed-effects model is chosen. Usually, the F-test is conducted first, and if the F-test rejects the original hypothesis, then the Hausman test is conducted to determine whether it is a fixed-effect model or a random-effect model.
After the F-test, the F-test values for ROA, CFO, Short-Term Debt, Long-Term Debt, and Short- and Long-Term Debt are 2.839 (0.024), 28.186 (<0.001), 120.782 (<0.001), 167.631 (<0.001), and 719.735 (<0.001), respectively. Thus, at a 5% level of significance, all five tests rejected the original hypothesis of mixed estimation model, and hence the fixed-effect model will be chosen in this experiment.
According to Huang et al. [17], businesses in areas where extreme weather events are anticipated to occur will increase financial slack resources given the impact of predicted climate risks (i.e., decreased firm performance and greater earnings volatility). While weather extremes possess the potential to be affected by climate change, weather extremes and climate change should be considered separately, and climate change and weather-related extreme impacts are two different things. But they can also influence corporate debt policy. As a result, businesses may take on less short-term debt due to increased cash flow volatility and the associated liquidation risk. Businesses in places with extreme weather may prefer long-term debt to avoid capital limits because it can cause liquidity shocks. In order to assess the firm’s financial strategy, short-term debt, long-term debt, and short-term and long-term debt were selected as dependent variables.
Also, according to Huang et al. [17], firm-level characteristics such as firm size, intangible assets, and sales growth have an impact on the extent to which a firm receives the impact of climate risk, so we select total assets, total intangible assets, and sales growth over a five-year period as control variables.
In examining the impact of climate risk on corporate financial performance, we exclude short-term debt, long-term debt, and short-term and long-term debt from the model. Similarly, we exclude ROA and CFO from the model when examining the impact of climate risk on corporate financial policies.
First, we study the impact of climate risk by using the annual climate risk index as an indicator of climate risk. In the robustness test, we test the robustness by changing the climate risk indicator to analyze the impact of climate risk again, and in the second analysis, we use the long-term climate risk index as an indicator of climate risk. To support the robustness of the model, we tested the residual distribution of the dependent variable in both analyses.

3.2. Description of Data and Sources

In this study, we chose the Climate Risk Index (CRI) as an indicator of climate risk to study the impact of climate risk on firms, which is an annual climate risk score for different countries around the world published by the non-governmental organization Germanwatch and has been used as a measure of climate risk in much of the existing literature. To examine the impact of climate risk on companies in different countries, we obtained company-level data from the Bloomberg database for 37 different countries. These data include the return of assets, cash from operations, sales growth, short-term debt, long-term debt, and short- and long-term debt.
The severity of a country’s potential climate risk because of climate change is determined using the CRI score in this study. The literature on climate risk studies has made extensive use of the CRI score [17]. The CRI score will be employed in this study as an indicator of climate risk for the period of 2017 to 2021. The indicator of climate risk in this study will be the annual score. Return on assets (ROA) and cash from operations (CFO), two indices of financial performance, are not considered control variables when examining the effect of climate risk on financial performance. To assess how climate risk affects financial policy, three key measures of debt are used: short-term debt, long-term debt, and short-term and long-term debt. Similarly, when researching how climate risk affects financial policies, these three indicators are not considered as control variables.
In our study, we control for firm characteristics, including assets, intangible assets, and average growth over 5 years. Based on previous studies, we include the firm’s total assets, total intangible assets, and average growth over 5 years as control variables and exclude the effect of these factors when studying the impact of climate risk on the firm’s financial performance and financial policies [17].
In selecting the sample, the method we chose was restricted simple random sampling. The sample is randomly selected within a certain range by controlling for firm-level factors such as total assets, total intangible assets, and 5-year average growth. When selecting data from the Bloomberg database terminal, we selected companies whose total assets did not exceed $2,000,000,000, whose total intangible assets did not exceed $200,000,000, and whose average growth did not exceed 200% in five years. In the selection process, companies in the financial and utility industries were excluded because they differ from other industries and are heavily regulated [23].
In order to ensure that the data collected reflect differences between countries, the sample was drawn by selecting functioning companies in as many different countries as possible. These collected data can be considered as time series data, and the data we obtained include the values of CRI scores for 37 different countries over the last 20 years. Also, the data for the companies in the selected sample are time-series data, which represent the economic performance of these companies under different CRI score years (Table 1).

4. Data Analysis and Research Findings

The purpose of this section is to describe in detail the various steps in the data analysis process and the results obtained. This is followed by an analysis of the results of the data analysis and a summary of the research findings. This chapter is the main part of the study and presents the findings of the study as well as the key data analysis steps. The data analysis techniques used are analyzed step-by-step in this chapter, followed by a presentation of the results of the data analysis. We will then attempt to interpret the results obtained from the data analysis and test the hypotheses previously formulated. This is followed by a discussion of the positioning of the results obtained in the existing literature and a robustness test of the analysis results. In the end, we present some analysis of the issues that arose during this study.
In this study, we selected ROA and CFO as variables to measure the financial performance of the firm and selected short-term debt, long-term debt, and short-term and long-term debt as variables to measure the financial policy of the firm. In examining the impact of climate risk on corporate financial performance, we exclude short-term debt, long-term debt, and short-term and long-term debt from the model. Similarly, we exclude ROA and CFO from the model when examining the impact of climate risk on corporate financial policies. The total assets, total intangible assets, and average growth over 5 years are used as control variables in the model.

4.1. Data Analysis Process

First, before performing the data analysis, we needed to match the company-level data with the CRI scores from Germanwatch. We matched the company-level data obtained from the Bloomberg database, sorted by the country in which the company is located, with the obtained CRI scores for 2017 to 2021. The data with the CRI scores were then imported into SPSS ready to start the analysis process.
After importing the data into SPSS, we first used SPSS’s own outlier detection function to filter out the outliers from the data set and then excluded these outliers from the test set. There were 435 data before the outliers were excluded, and 404 data remained after the outliers were excluded.

4.1.1. Application of Statistical Analysis

Pearson’s correlation coefficient is a statistic commonly used to measure the strength of linear correlation between two variables. It can help us understand the degree of correlation between variables and has important applications in statistical analysis, machine learning, and data mining. We can use the frequency distribution to make a preliminary understanding and judgment of the data to be analyzed. After that, we can analyze the strength of linear correlation between the variables through Pearson correlation and carry out the subsequent regression analysis on this basis.
Therefore, we performed statistical analysis of each variable using SPSS, followed by Pearson correlation analysis, and then regression analysis after confirming that each variable was associated with climate risk. The table below shows the results of the statistical analysis and Pearson correlation analysis.
According to the results in Table 2, we can learn that ROA, CFO, short-term debt, long-term debt, short-term and long-term debt, and sales in our study are not significantly correlated with climate risk indicators. ROA and short-term debt are negatively correlated with climate risk indicators, and long-term debt and short-term and long-term debt are significantly and negatively correlated with climate risk indicators. We speculate that the correlation is not significant because of the sample selection, which was randomly selected by hand, and the sample size was not large enough for the correlation to be significant. We confirm that the target variables are correlated with climate risk through a Pearson correlation study, and we will then use regression analysis to analyze the extent to which each variable receives climate risk effects. As can be seen from Figure 2, Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8, Figure 9, Figure 10 and Figure 11 the frequency distributions of the variables in this study largely conform to normal distributions for the most part.

4.1.2. Regression Analysis

Before conducting a regression analysis, it is important to first specify the measures for which the regression analysis is to be performed. In this study, we intend to use the return of assets (hereafter referred to as ROA) and cash from operations (hereafter referred to as CFO) as indicators of the firm’s financial performance. Therefore, what we need to achieve in the regression analysis is to analyze the impact of climate risk on the ROA as well as the CFO. The remaining variables, such as sales growth, total assets, and total intangible assets, are used as control variables. Below are the results of the regression analysis on the effect of climate risk on the financial performance of the firm.
From the data in Table 3, we can see that the coefficients of the effects of climate risk on ROA and CFO are both negative, and the confidence levels of the effects of climate risk on ROA and CFO are 0.263 and 0.606, respectively, which are both greater than 0.05. This indicates that climate risk does not have a significant effect on both the return of assets and cash from operations of the firm negative impact. With a 95% confidence interval between −0.044 and 0.012, the ROA-annual climate risk coefficient is −0.017 (p > 0.05). With a 95% confidence interval between −51.611 and 88.434, the CFO’s coefficient for annual climate risk is 18.411 (p > 0.05). The adjusted R² of the regression models for the two indicators are 0.281 and 0.212, respectively, indicating that climate risk, sales growth, total assets, and total intangible assets can explain 28.1% and 21.2% of the changes in the return of assets and cash from operations. After performing the F-test, the F-values of the regression models for the two indicators were 2.839 (0.024) and 28.186 (<0.001), respectively, which passed the F-test, indicating that at least one of the variables in the model affects the return of assets and cash from operations. From Figure 12 and Figure 13, it can be learned that the residual distribution of the regression models from the two indicators basically conforms to the normal distribution, indicating that the detection of the two regression models is reliable.
To detect the presence of multicollinearity among variables, we used variance inflation factor (VIF) analysis. As can be seen from Table 4, the VIF values of all variables are less than 5, so there is no multicollinearity problem in the model. To detect the absence of autocorrelation between the variables, we used the Durbin Watson test, which yielded results of 1.176 and 1.591, respectively, which are not very close to 2. We speculate that the reason for this is that this data set contains not only time series data but also cross-sectional data, so these scores are acceptable.
The results of the two indicators show that climate risk has a significant negative impact on the return of assets and cash from operations. The design of this study is to measure the firm’s financial performance through ROA and CFO. While the correlation coefficient between ROA and annual climate risk is −0.017, which falls within the 95% confidence interval, the correlation is not significant. The correlation coefficient between CFO and annual climate risk is 18.411, which does not fall within the 95% confidence interval. Thus, we can reject Hypothesis 1, that climate risk has a significant and negative impact on the financial performance of firms. However, since the sample in this study was randomly selected by hand and the sample size was not large enough, the correlation was not significant, so further research is needed with an expanded sample size.
Having examined the impact of climate risk on the financial performance of firms, we intend to further examine the impact of climate risk on the financial policies of firms. In this study, we intend to use short-term debt, long-term debt, and short- and long-term debt as indicators of corporate financial policy. To study the effect of climate risk on these indicators, we constructed regression analysis models using these indicators as dependent variables, climate risk as independent variables, and sales growth, total assets, and total intangible assets as control variables, respectively. The following are the results obtained from the regression analysis.
From the contents of Table 5, we can see that the correlation coefficient between climate risk and short-term debt is negative, the correlation coefficient between long-term debt and short-term and long-term debt is positive, and the confidence level of the effect between climate risk and long-term debt is less than 0.05, while the other two are greater than 0.05. From this, we can infer that climate risk shows a significant positive relationship with the company’s long-term debt. The correlation between annual climate risk and short-term debt is −32.927 (p > 0.05), with a 95% confidence interval ranging from −78.128 to 12.274. The coefficient of the long-term debt with annual climate risk is 166.129 (p < 0.05), with a 95% confidence interval of between 30.025 and 302.232. The correlation between annual climate risk and short- and long-term debt is 0.073 (p < 0.05), with a 95% confidence range ranging from −0.081 to 2.736. The adjusted R² of the three regression models for short-term debt, long-term debt, and short-term and long-term debt are 0.543, 0.623, and 0.877, respectively, which indicates that climate risk, total assets, total intangible assets, and sales growth can explain 54.3%, 62.3%, and 87.7% of the variation in the company’s short-term debt, long-term debt, and short-term and long-term debt. Subsequently, after F-testing the model, the F-values of the three indicators are 120.782 (<0.001), 167.631 (<0.001), and 719.735 (<0.001), respectively. The confidence levels of the F-test are less than 0.001, which proves that the model is significant, i.e., at least one variable in the model affects short-term debt, long-term debt, and short-term and long-term debt. From Figure 14, Figure 15 and Figure 16 we can learn that the residual distributions of all three regression models are roughly in line with the normal distribution, which can prove that the regression models are significant.
To detect the presence of multicollinearity among variables, we used variance inflation factor (VIF) analysis. As can be seen from Table 6, the VIF values of all variables are less than 5, so there is no multicollinearity problem in the model. To detect the absence of autocorrelation between the variables, we used the Durbin Watson test, which yielded results of 0.947, 0.652, and 0.780, respectively, which are not very close to 2. We speculate that the reason for this is that this data set contains not only time series data but also cross-sectional data, so these scores are acceptable.
In this study, we use short-term debt, long-term debt, and short- and long-term debt to show the financial policy of the company. From the results of the regression analysis, it is evident that climate risk has a significant effect on part of the financial policy of the firm, while the correlation coefficient between long-term debt and annual climate risk is 166.129, which falls within the 95% confidence interval. Therefore, we cannot reject Hypothesis 2. Because the correlation between short-term debt and climate risk falls outside the 95% confidence interval and the correlation is not significant, we will reject Hypothesis 3. That is, climate risk can have an impact on firms’ financial policies, where an increase in climate risk motivates firms to increase their long-term debt holdings.
At this point, the following conclusions can be drawn for our proposed hypothesis (Table 7).
The impact of climate risk on various aspects of business has been presented in the existing literature [2,16,19,24]. However, most of these studies are stuck with firm-level data, while the impact of climate risk is regional. Therefore, the climate indicator used in this study is the climate risk index, which is a climate risk indicator published by a non-governmental organization that is settled annually according to a country’s classification. Therefore, the results of the regression analysis should also be elevated to the national level. The results of the regression analysis show that climate risk is negatively related to the financial performance of companies, but not significantly. In the regression analysis, we used sales growth, total assets, and total intangible assets to control for the size of the firm, as well as the characteristics of growth. We can conclude that firms in countries with higher climate risk do not perform as well financially as firms in countries with lower climate risk, given the same size and growth profile.
Regarding the study on the effect of climate risk on the financial performance of firms, this study obtains the conclusion that climate risk is negatively related to the financial returns of firms. This conclusion is consistent with the findings of Huang et al. [17], but the correlation in this study was not significant. However, in the study by Sun et al. [14], the authors conclude that climate risk has both positive and negative effects on the financial performance of firms, which is inconsistent with this study’s findings. We believe the reason for the discrepancy is that their study mainly investigated the impact of climate risk on different sectors of the coal mining industry, considering the diversity of functions among different sectors in the study. Existing studies [6,25] have confirmed that climate risk affects different industries differently, but this study focuses on the financial performance of firms in a broad sense and does not take industry differences into account, which we believe can be considered in subsequent studies.
Regarding the study on the impact of climate risk on firms’ financial policies, this study obtained the conclusion that climate risk is negatively related to firms’ short-term debt, but not significantly. Climate risk is positively and significantly related to firms’ long-term debt. This finding is consistent with the findings of Huang et al. (2018) [17]. Existing studies [14] have demonstrated the impact of climate risk on companies within a certain region, and after introducing data from different countries to be analyzed in this study, we can conclude that climate risk in different countries can also have an impact on companies. Therefore, it can be concluded that companies in countries with higher climate risk tend to hold more long-term debt. Conversely, in countries with lower climate risk, there is a tendency to reduce holdings of long-term debt. To have a more comprehensive study of the impact of climate risk, we believe that further variables measuring firms’ financial policies, such as cash holdings, and cash dividends, can be added in subsequent studies.
Now to review the research question of this study: does climate risk in different regions affect the financial performance and debt structure of firms with impact? Having tested Hypotheses 1, 2, and 3, we can affirm that climate risk has an impact on the long-term debt of firms. This means that climate risk, as a threat to the development of a sustainable economy, needs to be brought to the attention of various financial institutions. Investment decisions made by financial institutions should be based not only on financial considerations but also on social and environmental objectives. Both stakeholder theory and shareholder theory need to be considered in the existing economic system. To achieve sustainable development, financial markets and governments need to balance external regulation and internal solutions. Neither can achieve sustainable development without the support of the other.
We believe there are two reasons why climate risk influences corporate financial performance and financial policy. First, there are better climate policies in nations with more climate risk. Businesses in nations with more climate risk are more vulnerable to regulatory impacts, which can further affect the financial success of the organization. Additionally, because of the increased risk from climate change, businesses must pay closer attention to how climate change will affect them in the near future and decide whether to cut their dangerous short-term debt in favor of more stable long-term debt and short- and long-term debt. The second argument is that to tackle climate risk, businesses must have climate risk resilience or the capacity to deal with unforeseeable climate threats. The organization will decide to enhance its cash holdings as a risk mitigation strategy to lessen the impact of unidentified threats on the business. Growing a company’s cash position could be detrimental to its financial health. Additionally, businesses typically choose stable long-term debt over unstable short-term debt when increasing their cash reserves.

5. Conclusions

Climate risk is now a global threat to businesses around the world. Climate risk can affect companies in different industries in different ways, including physical, regulatory, and reputational aspects. This study investigates the impact of climate risk on financial performance and financial policies by introducing the global climate risk index as an indicator of climate risk in order to provide more evidence for climate risk research and to provide some inspiration for companies to develop their business for climate risk.
During the data analysis of this study, we first demonstrated through Pearson correlation analysis that there is a correlation between climate risk indicators and the target variables, followed by a regression analysis of the variables of climate risk indicators and firm financial performance, which yielded results with impact coefficients falling within the 95% confidence interval. However, the correlation between corporate financial performance and climate risk is not significant, so we rejected our hypothesis that climate risk is negatively related to firm financial performance. Similarly, we performed regression analysis on the variables of climate risk indicators and corporate financial policies, and the resulting impact coefficients fell within the 95% confidence interval, so we accepted our hypothesis that climate risk is positively correlated with long-term debt. This conclusion is consistent with the findings of Huang et al. (2018) [17]. Because the correlation between short-term debt and climate risk falls outside the 95% confidence interval and the correlation is not significant, we will reject our hypothesis that climate risk is negatively correlated with corporate short-term debt.
This study introduces country-level climate risk variables based on the analysis of firm-level data, providing new literature to study the impact of climate risk on firms in different regions. By introducing the global climate risk index, this study provides a new basis for the study of the internationalization impact of climate risk and offers some insights for companies to conduct international business. While the existing literature investigates the impact of corporate commitment to climate change action and carbon risk exposure on debt financing policies, this study introduces a new climate risk indicator from another perspective to examine the impact of climate risk on financing policies. On this basis, the introduced country-level climate risk indicators can also reveal the impact of climate risk on corporate financing policies in different regions.
Measures to mitigate and adapt to climate change may open up new opportunities for businesses, such as improving resource efficiency and cost savings, adopting and utilizing low-emission energy sources, developing new products and services, and increasing the resilience and adaptability of supply chains. Climate-related opportunities depend on the regions, markets and industries in which businesses operate. In the context of climate change, scenario analysis can help organizations explore the impacts, magnitude of change, and timing of change in economic modules under different future climate scenarios. In general, scenario analysis can be used to assess the potential resilience of an organization’s strategic plans for various scenarios and to help identify responses to strategic and operational risks; it can also identify potential opportunities that can be uncovered through adjustments to strategic and financial plans and can help respond to disclosures required by stakeholders.
For multinational companies, they can reduce the impact of climate on their operations by operating in areas with relatively low climate risk. They can conduct business in regions with relatively stable climate environments or less stringent climate regulations. At the same time, they need to restructure their finances to increase cash flow holdings in advance of predictable climate risks.
Non-multinational companies can detect and prevent climate risks by setting up climate risk management departments to maintain their “organizational resilience” to the risks posed by climate change. At the same time, new environmentally friendly businesses can be developed to gain policy advantages. On this basis, they need to adapt their financial structures to the level of climate risk in their own regions. For companies in regions with higher climate risk, they need to increase their holdings of long-term debt and, depending on the circumstances, reduce their holdings of short-term debt as appropriate, and they need to increase their cash holdings to increase their resilience to risk. For companies in areas with lower climate risk, they can reduce their long-term debt holdings and reduce their short-term debt and cash holdings as appropriate.

Author Contributions

Conceptualization, Z.F. and X.Z.; Data curation, Z.F. and X.Z.; Formal analysis, M.Z. and X.Z.; Investigation, M.Z.; Methodology, Z.F. and X.Z.; Visualization, Z.F.; Supervision, M.Z. and X.Z.; Project administration, X.Z.; Writing—original draft preparation, M.Z.; Writing—review and editing, M.Z. and Z.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by “National Finance” and “Mezzo Economics” teaching, and the research Fund of Guangfa Securities Social Charity Foundation for financial support (BJ2018017).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Key literature topics.
Figure 1. Key literature topics.
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Figure 2. Distribution of ROA.
Figure 2. Distribution of ROA.
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Figure 3. Distribution of CFO.
Figure 3. Distribution of CFO.
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Figure 4. Distribution of short-term debt.
Figure 4. Distribution of short-term debt.
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Figure 5. Distribution of long-term debt.
Figure 5. Distribution of long-term debt.
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Figure 6. Distribution of short- and long-term debt.
Figure 6. Distribution of short- and long-term debt.
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Figure 7. Distribution of total assets.
Figure 7. Distribution of total assets.
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Figure 8. Distribution of total intangible assets.
Figure 8. Distribution of total intangible assets.
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Figure 9. Distribution of sales growth.
Figure 9. Distribution of sales growth.
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Figure 10. Distribution of climate risk index annual.
Figure 10. Distribution of climate risk index annual.
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Figure 11. Distribution of climate risk index long-term.
Figure 11. Distribution of climate risk index long-term.
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Figure 12. ROA regression standard residual distribution.
Figure 12. ROA regression standard residual distribution.
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Figure 13. CFO regression standard residual distribution.
Figure 13. CFO regression standard residual distribution.
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Figure 14. Short-term debt regression standard residual distribution.
Figure 14. Short-term debt regression standard residual distribution.
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Figure 15. Long-term debt regression standard residual distribution.
Figure 15. Long-term debt regression standard residual distribution.
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Figure 16. Short- and long-term debt regression standard residual distribution.
Figure 16. Short- and long-term debt regression standard residual distribution.
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Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
nMinimumMaximumMeanStd. Deviation
Return on Assets431−38.318841.45955.4253038.2453643
Cash From Operations435−115,970.098207,4599651.84750223,138.71624
Short-Term Debt4350163,397.4078711.01245619,611.27849
Long-Term Debt4350405,217.48930,436.1431665,019.98609
Short- and Long-Term Debt4351.516940,682.42749,382.74031118,058.9699
Total Assets43531.4261,899,340.511159,275.9495320,122.631
Total Intangible Assets4350142,742.61314,616.8070526,018.15411
Sales—5 Year Average Growth435−13.1864111.63826.60708110.9374627
climate risk index (annual)435−125−5.5−61.899828.90067
Table 2. Pearson correlation.
Table 2. Pearson correlation.
CRI AnnualROACFOShort-Term DebtLong-Term DebtShort- and Long-Term DebtTotal AssetsTotal Intangible AssetsSales Growth
CRI annual1
ROA−0.0491
CFO0.0340.0691
Short-Term Debt−0.039−0.161 **0.383 **1
Long-Term Debt0.082−0.143 **0.498 **0.668 **1
Short- and Long-Term Debt0.048−0.149 **0.339 **0.736 **0.834 **1
Total Assets0.019−0.136 **0.427 **0.734 **0.772 **0.936 **1
Total Intangible Assets0.001−0.0490.294 **0.261 **0.345 **0.235 **0.257 **1
Sales Growth0.110 *0.088−0.02−0.104 *−0.118 *−0.093−0.074−0.122 *1
* At the p < 0.05 level, the correlation is significant. ** At the p < 0.01 level, the correlation is significant.
Table 3. Regression results on firm financial performance.
Table 3. Regression results on firm financial performance.
Regression Coefficients (Significance in Parentheses)
ROACFO
CRI annual−0.017(0.263)18.411 (0.606)
Total Assets−3.284 × 10−6 (0.013)0.027 (<0.001)
Total Intangible Assets−2.060 × 10−6 (0.900)0.178 (<0.001)
Sales growth0.063 (0.098)63.442 (0.505)
Adjusted R²0.2810.212
F2.839 (0.024)28.186 (<0.001)
Durbin-Watson test1.1761.591
Table 4. Variance inflation factor analysis for financial performance.
Table 4. Variance inflation factor analysis for financial performance.
Variance Inflation Factor (VIF) Analysis
ROACFO
CRI annual1.0131.013
Total Assets1.0721.073
Total Intangible Assets1.0831.083
Sales growth1.0311.030
Table 5. Regression results on firm financial policies.
Table 5. Regression results on firm financial policies.
Regression Coefficients (Significance in Parentheses)
Short-Term DebtLong-Term DebtShort- and Long-Term Debt
CRI annual−32.927 (0.153)166.129 (0.017)133.452 (0.064)
Total Assets0.044 (<0.001)0.148 (<0.001)0.345 (<0.001)
Total Intangible Assets0.055 (0.038)0.380 (<0.001)−0.037 (0.651)
Sales growth−65.817 (0.284)−318.358 (0.086)−306.365 (0.111)
Adjusted R²0.5430.6230.877
F120.782 (<0.001)167.631 (<0.001)719.735 (<0.001)
Durbin-Watson test0.9470.6520.780
Table 6. Variance inflation factor analysis for financial policies.
Table 6. Variance inflation factor analysis for financial policies.
Variance Inflation Factor (VIF) Analysis
Short-Term DebtLong-Term DebtShort- and Long-Term Debt
CRI annual1.0131.0131.013
Total Assets1.0731.0731.073
Total Intangible Assets1.0831.0831.083
Sales growth1.0301.0301.030
Table 7. Hypothesis testing results.
Table 7. Hypothesis testing results.
Hypotheses Results (Either Accepted or Not Accepted)Reference
H1Not AcceptedSun et al. (2020) [14]
H2AcceptedHuang et al. (2018) [17]
H3Not AcceptedHuang et al. (2018) [17]
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Zhang, X.; Zhang, M.; Fang, Z. Impact of Climate Risk on the Financial Performance and Financial Policies of Enterprises. Sustainability 2023, 15, 14833. https://doi.org/10.3390/su152014833

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Zhang X, Zhang M, Fang Z. Impact of Climate Risk on the Financial Performance and Financial Policies of Enterprises. Sustainability. 2023; 15(20):14833. https://doi.org/10.3390/su152014833

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Zhang, Xin, Mateng Zhang, and Zhong Fang. 2023. "Impact of Climate Risk on the Financial Performance and Financial Policies of Enterprises" Sustainability 15, no. 20: 14833. https://doi.org/10.3390/su152014833

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