# Predicting the Risk of High Carbon Emissions from Financial Ratios

Yang Yiwen, Hsu Shu-Han, Po-Keng Cheng

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Yang Yiwen, Hsu Shu-Han, Po-Keng Cheng. Predicting the Risk of High Carbon Emissions from Financial Ratios. 2026. hal-05480723

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Predicting the Risk of High Carbon Emissions from Financial Ratios

Yiwen Yang

Associate Professor, Ph.D.

Department of Civic Education and Leadership, College of Education

National Taiwan Normal University, Taipei 10610, Taiwan

Tel: +886-2-77491859

Email: yiwen@hawaii.edu

Shu-Han Hsu

Assistant Professor, Ph.D.

School of Accounting and Finance

National Taipei University of Business, Taipei 100025, Taiwan

Tel: +886-2-2322-6481

Email: hsushuhan0102@gmail.com

Po-Keng Cheng*

Assistant Professor, Ph.D.

Department of Finance and Cooperative Management, College of Business

National Taipei University, New Taipei City 237303, Taiwan

Tel: +886-2-86741111 ext. 67723

Email: ansd39@gmail.com

<footnote>* Corresponding author</footnote>
# Predicting the Risk of High Carbon Emissions from Financial Ratios

## Abstract

Firm-level carbon emissions, while directly measurable, are often reported with delays or incomplete information, particularly for firms not subject to mandatory disclosure. Understanding and predicting the risks of high carbon emissions is therefore essential for financial analysis in economies pursuing sustainability. This study investigates the relationship between carbon emissions and financial ratios using firm-level data from the Taiwan Economic Journal for the period 2005 to 2020. The empirical findings reveal that financial ratios can serve as effective predictors of high carbon emission risk, with clear industry-specific differences. While the relevant predictors differ across industries, our results suggest that the receivables-to-sales ratio may serve as a useful early-warning financial indicator for identifying firms at greater risk of high carbon emissions. Overall, the study develops a cost-effective and forward-looking early-warning mechanism that enables governments, investors, and companies to manage carbon-related risks by leveraging readily available financial information.

**Keywords:** early-warning system; risk of high carbon emission firms; financial ratios; logit model; probit model; Bayesian kernel machine regression model.

**JEL codes:** Q50, D22, G32

**Ethical statement:** This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. We have read and understood your journal's policies, and we believe that neither the manuscript nor the study violates any of these. There are no conflicts of interest to declare.
# 1. Introduction

The urgency of adopting sustainable business practices has increased due to growing environmental concerns in today's rapidly changing world (Kirkwood & Walton, 2014; Kraus et al., 2020; Marcon et al., 2017). Carbon emissions stand out as one of the foremost challenges, contributing to climate change, and they pose substantial risks to companies and investors (Roncoroni et al., 2021; Trink et al., 2020). Investors increasingly recognize the financial risks associated with climate change and carbon emissions (Bolton & Kacperczyk, 2021; Monasterolo, 2020). These risks manifest as declining asset values due to regulatory changes or market shifts toward renewable energy and increased insurance liabilities resulting from climate-related disasters (Habiba et al., 2022; Mittnik et al., 2020; Yuping et al., 2021). Moreover, investment portfolios that are perceived to be environmentally unsustainable or incompatible with climate goals face reputational risk.

In Taiwan, these challenges have gained heightened relevance under the recent enactment of the Climate Change Mitigation Law and the establishment of the Voluntary Carbon Trading Platform, which together mark a major step toward achieving the nation's 2050 net-zero target.<fnref n="1" /> As societies strive toward a greener future, access to accurate and comprehensive firm-level carbon emission information has become increasingly vital for both policymakers and financial markets. However, such data remain limited, as disclosure is often delayed or incomplete, particularly among firms not subject to mandatory reporting. Consequently, developing predictive approaches to assess high carbon emission risks has become essential for financial analysis and sustainable investment decisions.

This study is motivated by bankruptcy prediction research, which demonstrates that financial ratios possess predictive power for signaling corporate failure (Ohlson, 1980; Platt & Platt, 1990). Drawing on this perspective, the study extends this predictive framework to the environmental domain by examining whether financial ratios can serve as early indicators of high carbon emission risk. Traditionally, financial analysis has emphasized metrics such as profitability, liquidity, and solvency. Yet, financial ratios may also provide early signals of environmental performance. For instance, a higher current ratio (current assets to current liabilities) suggests greater liquidity and

<footnote n="1">¹ Taiwan enacted the Climate Change Mitigation Law on February 15, 2023, establishing the legal basis for its 2050 net-zero target. Under this legal framework, the government launched the Voluntary Carbon Trading Platform in Kaohsiung on August 7, 2023, marking the beginning of the country's first voluntary carbon market.</footnote>
the potential capacity to invest in efficiency-enhancing technologies, whereas a higher fixed asset
ratio may indicate energy-intensive production structures that contribute to higher emissions.
These insights imply that financial characteristics could reflect firms’ carbon emission tendencies.
To empirically investigate this hypothesis, the study employs logit, probit, and Bayesian Kernel
Machine Regression (BKMR) models to analyze the relationships between various financial
metrics, such as liquidity, profitability, and leverage ratios, and firms’ carbon emission behavior
across industries over time. The models also help identify the financial characteristics that
distinguish high-emission firms, offering insights for sustainable financial management and
emission mitigation strategies.

Several studies have demonstrated that carbon emissions influence company performance and
financial ratios (Lewandowski, 2017; Pelea & Santhia, 2022). Conversely, financial ratios can also
influence carbon emissions, raising potential endogeneity concerns. To address this issue and
account for the lagged effects of financial ratios on carbon emission risk, this study employs one-
period lagged financial ratios as explanatory variables. Additionally, the study focuses on
investigating the risk, or probability, of high carbon emissions in firms, further mitigating
endogeneity concerns. Companies are initially classified into two groups, high-emission and low-
emission firms, based on their carbon emissions. The study then applies logit, probit, and BKMR
models, utilizing lagged financial ratios to construct an early-warning system for identifying firms
at risk of high carbon emissions. Using Taiwan as the study context and firm-level data on carbon
emissions and corresponding financial ratios from the *Taiwan Economic Journal* (TEJ) for the
period 2005 to 2020, the empirical results indicate that the financial characteristics associated with
high carbon emissions vary across industries. While the relevant predictors differ across industries,
we find that the receivables-to-sales ratio may serve as a useful early-warning financial indicator
for identifying firms at greater risk of high carbon emissions.

Importantly, these risks persist even in policy environments where carbon pricing instruments
are already in place. While carbon pricing policies are increasingly adopted to internalize the
environmental costs of emissions, they do not fully eliminate heterogeneity in firms’ emission
trajectories. Differences in financial capacity and adjustment constraints imply that some firms
and industries remain at higher risk of persistently high emissions even under existing regulations.
Given the multifaceted risks associated with carbon emissions, understanding and predicting these
risks through financial ratios is crucial. This study contributes to the literature by demonstrating
that financial ratios can function as a cost-effective early-warning mechanism for identifying firms
that are likely to exhibit high carbon emissions within their respective industries. While prior
research has primarily examined the effects of carbon emissions on financial performance, this
study shifts the perspective to evaluating the predictive capacity of financial ratios in the context
of carbon emissions. By utilizing financial data routinely disclosed in firms’ financial statements,
the proposed framework enables governments, investors, and corporations to assess carbon
emission risks without additional data collection. Moreover, it complements direct measurement
by providing forward-looking insights that help policymakers, regulators, and market participants
manage carbon-related risks. Ultimately, this framework supports the transition toward
sustainability by facilitating timely identification and mitigation of high carbon emission risks.

The present paper is organized as follows. Section 2 presents a review of the literature regarding environmental monitoring and early-warning systems and the role of financial ratios in environmental challenges. Section 3 briefly develops a conceptual model linking financial ratios to carbon emissions. Section 4 presents the data source and variables. Section 5 introduces the empirical methodology of the study. Section 6 presents the obtained estimates, representing the study's results. Section 7 discusses the results and provides policy implications. Section 8 presents a summary of the study.

## 2. Literature Review

Although many firms have concurrently attained economic and technological progress, they
face criticism for their negative social and environmental effects (Reverte, 2009; Gallego-Álvarez
et al., 2015). Thus, firms have a growing need to engage in environmental governance, assume
social responsibility, and prioritize social welfare (Liu et al., 2024). Reducing carbon dioxide
emissions is arguably the most critical action to address climate change and achieve sustainable
development. For this reason, national governments have established emission reduction policies
and have monitored firms' carbon dioxide emissions. Moreover, capital providers and managers
have increasingly focused on balancing financial performance with carbon management strategies
in corporations.

Environmental monitoring and early-warning systems play a pivotal role in providing timely
alerts to predict and prevent the deterioration of environmental quality caused by industrial
activities (Chun et al., 2023; Gruiz, 2017; Su & Yu, 2020; Sun et al., 2018). Ho et al. (2015)
developed a prediction model for carbon dioxide emissions in Malaysia's manufacturing and construction sectors, employing multiple linear regression with gross domestic product, electricity production and consumption, foreign direct investment income, and net inflow as predictors. Sun et al. (2018) constructed a logistic regression early-warning model by using 60 listed companies from China's strategic emerging industries to analyze the influence of industrial activities on the ecological environment. Su and Yu (2020) constructed a dynamic early-warning model for China's regional atmospheric environmental carrying capacity, employing the cloud model and Markov chain and using daily monitoring data of atmospheric pollutants. Ren et al. (2023) proposed a system for monitoring and providing early warnings for atmospheric carbon emissions, using big data and spectral measurement in simulation experiments. Chun et al. (2023) used the support vector machine, an artificial intelligence method, as the primary model for providing early warnings about carbon price volatility risk. This model captures the multiscale structural information of the carbon market and accurately reflects its true risk state. These studies suggest that early-warning models can offer valuable insights for environmental management, enabling effective decisions on environmental problems and the development of strategies to mitigate the effects of climate change and environmental quality deterioration.

Financial ratios serve to assess a firm's debt repayment capacity and to evaluate its business and managerial success (Barnes, 1987). They can also reveal the environmental link with firm financial performance. A firm's financial performance is strongly correlated with its environmental behavior (Sariannidis et al., 2013). Firms with poor environmental profiles or high environmental risks generally endure high capital costs (Chava, 2014; Sharfman & Fernando, 2008) and exhibit low financial performance (Busch et al., 2022; Dowell et al., 2008; Ganda & Milondzo, 2018; Konar & Cohen, 2001; Stefan & Paul, 2008; Matsumura et al., 2014). Several studies have indicated a positive association between environmental performance and financial performance (Al-Tuwaijri et al., 2004; Bragdon & Marlin, 1972; Christmann, 2004; King & Lenox, 2001; Konar & Cohen, 2001; Li et al., 2024; Naseer et al., 2023; Stanwick & Stanwick, 1998; Wagner, 2010; Yilmaz, 2021), some studies have indicated a negative association (Blacconiere & Patten, 1994; Cordeiro & Sarkis, 1997; Henriques & Sadorsky, 1996), and some studies document a mixed association (De la Fuente et al., 2022; Lahouel et al., 2020; Ozkan et al., 2022; Tatsuo, 2010; Trumpp and Guenther, 2017). In addition, several studies have suggested a nonlinear relationship between carbon emissions and financial performance (Elsayed & Paton, 2009; García-Sánchez &
Prado-Lorenzo, 2012; Ghosh et al., 2023; Hoffmann & Busch, 2008; Misani & Pogutz, 2015), and several studies have observed the association to be nonsignificant (King & Lenox, 2002; Toms, 2002). In response to conflicting evidence regarding the relationship between environmental and financial performance, several researchers have suggested considering different environmental performance measures, financial performance measures, sample geographies, sizes, environmental risk, periods, and model specifications (Misani & Pogutz, 2015; Semenova & Hassel, 2016; Walls et al., 2011; Wang, 2023).

The present study adopts carbon emissions as a proxy for environmental performance, given the critical role of carbon management in determining climate change action policies and corporate strategies. The selected financial performance variables are mostly derived from the broad class of financial ratios found to be significant explanatory variables in past financial early-warning models (Altman, 1968; Altman et al., 1977; Beaver, 1966; Edmister, 1972; Gombola et al., 1987; Hampton, 1983; Kahya & Theodossiou, 1999; Lo, 1986; Ohlson, 1980; Theodossiou, 1993; Theodossiou et al., 1996; Zmijewski, 1984). They cover various categories, such as liquidity, profitability, management efficiency, operating leverage, and financial leverage. Several studies have primarily focused on the influence of environmental factors, such as carbon performance and carbon disclosure, on financial performance (Adu et al., 2023; Gerged et al., 2021; Ghosh et al., 2023; Lewandowski, 2017; Misani & Pogutz, 2015). Variation in financial performance across industries and firms may influence carbon emissions, highlighting the potential to accurately predict a firm's carbon emissions risk based on its financial performance.

### 3. Economic Framework of Analysis

This section develops a simple economic framework to illustrate the mechanisms through which firms' financial conditions affect abatement decisions and carbon emissions. The framework is intended to provide economic intuition for the empirical analysis. Consider a firm i operating in period t. Production requires energy input $E_{it}$, which generates carbon emissions as a by-product. Firms can actively control emissions through abatement efforts or clean technology investments, denoted by $a_{it}$, in response to regulatory requirements or carbon-related economic incentives. Firm-level carbon emissions $C_{it}$ are given by:

$$C_{it} = E_{it} \kappa_{it} g(a_{it}) \quad (1)$$
where $\kappa_{it}$ represents the baseline emissions intensity determined by inherited technology and energy structure, and $g(a_{it})$ is a decreasing and convex function satisfying $g'(a_{it}) < 0, g''(a_{it}) > 0$. These assumptions imply that greater abatement effort reduces emissions, but at an increasing marginal cost. In this framework, corporate carbon control is achieved through the choice of $a_{it}$, which directly affects emissions intensity.

Firms maximize profits by choosing energy input and abatement effort. The firm's objective function is given by:

$$ \max_{E_{it}, a_{it}} \Pi_{it} = P_t F(K_{it}, L_{it}, E_{it}) - w_t L_{it} - r_t K_{it} - p_t^E E_{it} - p_t^C C_{it} - \Gamma(a_{it}) \quad (2) $$

where $P_t$ denotes the output price, $w_t$ the wage rate, $r_t$ the cost of capital, $p_t^E$ the energy price, and $p_t^C$ the (explicit or implicit) price of carbon emissions. The term $\Gamma(a_{it})$ captures the cost of abatement investment, with $\Gamma'(a_{it}) > 0, \Gamma''(a_{it}) > 0$. The carbon price $p_t^C$ reflects regulatory policies (e.g., carbon taxes or emissions trading systems) as well as internal carbon pricing, investor pressure, and supply-chain requirements.<fnref n="2" /> Substituting the emissions function into the objective yields:

$$ \Pi_{it} = P_t F(K_{it}, L_{it}, E_{it}) - w_t L_{it} - r_t K_{it} - p_t^E E_{it} - p_t^C E_{it} \kappa_{it} g(a_{it}) - \Gamma(a_{it}) \quad (3) $$

The first-order condition with respect to abatement effort is:

$$ \frac{\partial \Pi_{it}}{\partial a_{it}} = -p_t^C E_{it} \kappa_{it} g'(a_{it}) - \Gamma'(a_{it}) = 0 \quad (4) $$

Rearranging terms gives:

$$ \Gamma'(a_{it}^*) = -p_t^C E_{it} \kappa_{it} g'(a_{it}^*) \quad (5) $$

which equates the marginal cost of abatement to the marginal benefit from reducing carbon costs. This condition implies that firms have a clear economic incentive to control emissions, and that optimal abatement effort increases with the carbon price and the scale of energy use.

<footnote n="2">² Even in regulated environments, firms differ substantially in their ability to respond to carbon pricing due to heterogeneity in financial conditions. As a result, carbon pricing alone does not eliminate the risk of persistently high emissions, underscoring the value of an early-warning framework that can identify firms and industries requiring closer monitoring.</footnote>
Although firms may wish to increase abatement effort, doing so requires upfront financial resources. We therefore assume that abatement investment is subject to a financing constraint, $\Gamma(a_{it}) \le \Psi(FR_{it})$, where $FR_{it}$ is a vector of firm-level financial ratios and $\Psi(\cdot)$ represents the firm's available financing capacity. Financial ratios thus act as state variables that determine the feasibility of emissions control. We assume that financing capacity responds monotonically to financial conditions, such that higher profitability and greater liquidity relax financing constraints, whereas higher financial leverage tightens them. When the financing constraint is binding, optimal abatement effort satisfies $\Gamma(a_{it}^*) = \Psi(FR_{it})$. Applying implicit differentiation with respect to a financial ratio $FR_k$ yields $\Gamma'(a_{it}^*) \cdot \frac{\partial a_{it}^*}{\partial FR_k} = \frac{\partial \Psi}{\partial FR_k}$. Since $\Gamma'(a_{it}^*) > 0$, the sign of $\partial a_{it}^* / \partial FR_k$ is determined by the sign of $\partial \Psi / \partial FR_k$. That is, financial ratios that relax financing constraints increase optimal abatement effort, whereas those that tighten constraints reduce it. The marginal effect of a financial ratio on emissions follows from the chain rule, $\frac{\partial c_{it}}{\partial FR_k} = \frac{\partial c_{it}}{\partial a_{it}} \cdot \frac{\partial a_{it}}{\partial FR_k}$. Given $\frac{\partial c_{it}}{\partial a_{it}} = E_{it} \kappa_{it} g'(a_{it}) < 0$, it follows that financial ratios which expand firms' financing capacity are associated with lower carbon emissions through higher abatement effort, whereas those that tighten financing capacity are associated with higher emissions.

In practice, carbon emissions are often observed with reporting delays and substantial measurement noise, particularly for firms not subject to mandatory disclosure. Accordingly, rather than modeling the exact level of emissions, the empirical analysis focuses on the risk that a firm belongs to the high-emission group within its industry. The framework developed above provides the economic rationale for this approach. Financial ratios capture firms' financing capacity and working-capital conditions, which shape the feasibility of abatement investment and, in turn, influence emissions outcomes. Firms facing tighter financial constraints are less able to undertake emission-reduction efforts and are therefore more likely to remain in the upper tail of the emissions distribution. This mechanism implies that financial ratios contain forward-looking information about firms' future emission behavior, making them particularly informative for assessing the likelihood of high carbon emissions.

# 4. Data and Variable Definitions

To predict the risk of high carbon emissions by using financial ratios, this study collected data related to carbon emissions and company finances from TEJ. The data encompass 339 companies
across 17 industries, covering the period from 2005 to 2020.<fnref n="3" /> The 17 industries were cement, food, plastic, textile, chemical, glass and ceramic, paper and pulp, iron and steel, rubber, semiconductor, computer and peripheral equipment, optoelectronic, communications and internet, electronic parts and components, building material and construction, shipping and transportation, and gas and electricity. These 17 industries are selected due to their propensity for high carbon emissions.

To determine whether a company belongs to the high carbon emission group within its industry, this study first calculated each company's carbon emissions relative to its revenue. For each industry and year, the 70th percentile of carbon emissions per revenue was identified to distinguish high emitters from low emitters, as it captures firms whose emission intensity lies in the upper tail of the industry distribution while maintaining sufficient sample size for statistical inference.<fnref n="4" /> A company in a given year was classified as having high carbon emissions if its emissions per revenue exceeded the 70th percentile threshold within its industry, thereby ranking among the top 30 percent of emitters. Conversely, companies below this threshold were classified as having low carbon emissions.

This study constructed industry-relative financial ratios to measure the relative position of an individual company within the distribution of all companies in its industry, referencing Platt and Platt (1990). An industry-relative financial ratio is defined as a firm's financial ratio relative to the mean value of that ratio in the firm's industry at a point in time, as follows:

$$ \textit{Industry - Relative Financial Ratio}_{i,t} = \frac{\textit{Firm i's Financial Ratio in year t}}{(\textit{Mean Financial Ratio in Industry j in year t}) \times 100} \quad (6) $$

where *Firm i's Financial Ratio in year t* represents the individual company's financial ratio in year *t*, *Mean Financial Ratio in Industry j in year t* represents the average ratio in industry *j*, which is firm *i*'s industry, in year *t*. According to Platt and Platt (1990), 100 is used to adjust percentage ratios to scalar values that are greater than 1. To comprehensively consider financial ratios, this study referred to existing studies listed in Table 1 and included seven financial ratios. These seven ratios are related to financial aspects such as liquidity, profitability, and leverage. The rationales

<footnote n="3">³ We categorized 339 companies into 17 industries on the basis of the “Taiwan Stock Exchange Corporation Key Points for Classifying and Adjusting Categories of Industries of Listed Companies.”</footnote>

<footnote n="4">⁴ Compared with median-based thresholds, it more effectively captures firms exhibiting notably high emission intensity, while higher cutoffs such as the 80th or 90th percentiles would reduce the number of observations and hinder cross-industry comparability.</footnote>
for these ratios are briefly described in the following paragraphs.

[Table 1 goes here]

**Ratio 1: Fixed Assets to Total Assets**

In general, a company with a higher proportion of fixed assets typically has more extensive operating activities than does a company with a lower proportion of fixed assets. Therefore, a higher ratio of fixed assets to total assets could indicate higher carbon emissions due to more intensive operating activities. We expect a positive correlation between Ratio 1 and carbon emissions.

**Ratio 2: Inventory to Sales**

High inventory levels typically signal more extensive operating activities. Additionally, a company with higher inventory may have fewer funds available to invest in technologies that reduce carbon emissions compared with a company with lower inventory levels. Therefore, we expect a positive correlation between Ratio 2 and carbon emissions.

**Ratio 3: Net Income to Total Assets**

A more-profitable company may have more funds available to invest in technology to reduce carbon emissions compared with a less-profitable company. Therefore, we expect a negative correlation between Ratio 3 and carbon emissions.

**Ratio 4: Total Debt to Total Assets**

A company with high carbon emissions may have difficulty borrowing from banks, potentially resulting in a low total debt to total assets ratio. However, a company with higher total debt may incur higher interest expenses and have fewer funds available to invest in carbon-reduction technologies compared with a company with lower total debt. Hence, the correlation between Ratio 4 and carbon emissions is undetermined.

**Ratio 5: Current Assets to Current Liabilities**

A company with high carbon emissions may have difficulty borrowing money from banks, resulting in a high financial ratio of current assets to current liabilities due to a smaller denominator. We expect a positive correlation between Ratio 5 and carbon emissions.

**Ratio 6: Receivables to Sales**
Accounts receivable reflect firms' working-capital conditions and the timing of cash inflows. On the one hand, higher receivables may constrain liquidity and limit firms' ability to invest in emission-reduction technologies. On the other hand, receivables can also provide short-term financing flexibility that supports abatement activities. Accordingly, the relationship between the receivables-to-sales ratio and carbon emissions is theoretically ambiguous and may vary across industries.

## Ratio 7: Cash to Total Assets

A company holding more cash may have additional funds available for investment in technologies that reduce carbon emissions compared with a company holding less cash. Therefore, we expect a negative correlation between Ratio 7 and carbon emissions.

Descriptive statistics are presented in Table 2. The gas and electricity industry had the highest carbon emissions per revenue, followed by the building material and construction industry and the cement industry. The number of observations in the 17 industries ranges from 6 to 231.

[Table 2 goes here]

# 5. Methodology

## 5.1 Logit and Probit Models

The logit and probit models are appropriate for modeling scenarios involving two alternative outcomes (Hoetker, 2007). To investigate the financial features of companies at risk of high carbon emissions, this study constructed the following logit and probit models:

$$y_{i,t}^* = \alpha x_{i,t-1} + \varepsilon_{i,t} \quad (7)$$

A given company's propensity to emit high carbon emissions in year t, $y_{i,t}^*$, is modeled as being linearly related to a vector of observable variables, $x_{i,t-1}$, which includes a set of financial ratios, ratio 1: fixed assets to total assets, ratio 2: inventory to sales, ratio 3: net income to total assets, ratio 4: total debt to total assets, ratio 5: current assets to current liabilities, ratio 6: receivables to sales, and ratio 7: cash to total assets. To address potential endogeneity and account for the lagged effects of financial ratios on carbon emission risk, this study employs one-period lagged financial ratios as explanatory variables. Lastly, $\varepsilon_{i,t}$ represents the error term.
The propensity to emit high carbon emissions, $y_{i,t}^*$, is not directly observable; only the actual carbon emissions, denoted as $y_{i,t}$, can be observed. We assign $y_{i,t}$ a value of 1 when the company is classified as a high carbon emitter, that is, if its carbon emissions per unit of revenue (carbon intensity) fall within the top 30 percent of the industry distribution in year $t$; otherwise $y_{i,t} = 0$. The probability that a company is classified as a high carbon emitter is given by Equation (3)

$$P(y_{i,t} = 1 | x_{i,t-1}) = \begin{cases} \frac{\exp(x'_{i,t-1}\beta)}{1 + \exp(x'_{i,t-1}\beta)} & \text{for Logit} \\ \Phi(x'_{i,t-1}\beta) & \text{for Probit} \end{cases} \quad (8)$$

where $\beta$ denotes the vector of parameters to be estimated, and $x_{i,t-1}$ represents the lagged company-specific covariates. A positive coefficient indicates that the likelihood of being a high carbon emitter increases with the corresponding explanatory variable. $\Phi(\cdot)$ denotes the cumulative distribution function for the standard normal distribution.

Additionally, the logarithm of the carbon emissions-to-sales ratio for the previous period (ln carbon$_{i,t-1}$) is included to capture the persistence of carbon emission intensity over time, while the logarithm of sales revenue for the current period (ln sales$_{i,t}$) is incorporated as a proxy for company size. Both variables are included as control variables in the Logit and Probit models to account for potential dynamic and scale effects on the likelihood of high carbon emissions.

The study initially pooled data from all companies to examine the relationship between financial ratios and the risk of high carbon emissions. In this analysis, we further included industry and year fixed effects to control for unobservable factors specific to each industry and time shocks. To account for industry-specific characteristics, we then analyzed the probability of high carbon emissions within individual industries. In this industry-level analysis, we controlled only for unobservable factors related to time-specific shocks. We also compute the pseudo R² (McFadden R²) to evaluate the explanatory performance of the Logit and Probit models, respectively. Higher values of McFadden R² indicate a better in-sample model fit. The pseudo R² is obtained using the pscl package in R.

## 5.2 BKMR

Logit and probit models capture only the linear relationships between carbon emissions and
financial ratios, but their relations could be nonlinear. Therefore, we utilize the BKMR model proposed by Bobb et al. (2015) to investigate the nonlinear relationships between carbon emissions and financial ratios in this study. BKMR represents a method for estimating the health effects of multipollutant mixtures in a flexible and nonparametric manner. BKMR employs a kernel function to model the exposure–response relationship based on the mixture components and used a Bayesian approach to infer the parameters and uncertainties.

In the BKMR model, for each subject $i = 1, ..., n$, $Y_i = h(z_i) + x_i^T \beta + \varepsilon_i$, where $Y_i$ is a health endpoint and could be continuous or binary, $h(\cdot)$ is a flexible function of the predictor variables $z_i$, $x_i$ contains a set of potential confounders assumed to have a linear relationship with the outcome, and $\varepsilon_i \stackrel{\text{i.i.d.}}{\sim} N(0, \sigma^2)$. $h(\cdot)$ characterizes a high-dimensional exposure–response function that may incorporate nonlinearity and/or interaction among the mixture components and is represented by a kernel machine representation in BKMR model. Several choices exist for the kernel function used to represent $h(\cdot)$, with BKMR focusing on the Gaussian kernel. The Gaussian kernel flexibly captures a wide range of underlying functional forms for $h(\cdot)$ and can accommodate nonlinear and non-additive effects of the multivariate exposure. $h(z) = \sum_{i=1}^n K(z_i, z)\alpha_i$ for some set of coefficients $\{\alpha_i\}_{i=1}^n$, where $K(z, z') = \exp\{-\sum_{m=1}^M r_m(z_m - z_m')^2\}$, $z, z'$ represent vectors of predictors for two different individuals, and $r_m \ge 0$ denotes the tuning parameter that control the smoothness of $h(\cdot)$ as a function of the exposure $z_m$ ($r_m = 0$ indicates that no association exists between the $m^{th}$ exposure and the outcome). Intuitively, the kernel function shrinks the estimated health effects of two individuals with similar exposure profiles toward each other.

We examined the univariate relationship between each financial ratio and carbon emissions. A positive slope in the plot of the univariate predictor–response function indicates a positive correlation between the financial ratio and carbon emissions. Conversely, a negative slope indicates a negative correlation. This study utilizes the R packages *bkmr* and *bkmrhat* to implement the BKMR model, with the number of iterations of the Markov chain Monte Carlo sampler equaling 10,000 and the number of parallel chains equaling 100.<fnref n="5" />

# 6. Empirical Results

<footnote n="5">⁵ Due to the limit of computation power, the number of parallel chains is equal to 10 for “All” (whole sample).</footnote>
This study included 17 industries (cement, food, plastic, textile, chemical, glass and ceramic, paper and pulp, iron and steel, rubber, semiconductor, computer and peripheral equipment, optoelectronic, communications and internet, electronic parts and components, building material and construction, shipping and transportation, and gas and electricity) in the total sample and applied logit, probit, and BKMR models. All models included the one-period lag of seven financial ratios as explanatory variables, while controlling for ln carbon$_{i,t-1}$ and ln sales$_{i,t}$ and yearly fixed effects. For the model with all 17 industries, industry fixed effects were also included. However, due to sample size requirements, results are reported for only 9 industries, each with more than 67 observations (i.e., industries such as cement, textile, glass and ceramic, paper and pulp, iron and steel, rubber, communications and internet, and gas and electricity are excluded). Furthermore, the potential multicollinearity problem was tested by computing the variance inflation factors for logit and probit models in all models considered in this study. The variance inflation factors in all models were less than 10, alleviating multicollinearity concerns in all models.

Table 3 presents the estimation results for the Logit, Probit, and BKMR models. The Logit and Probit estimates are largely consistent across most specifications, except for the cases including all industries, the semiconductor industry, and the electronic parts and components industry. For these three cases, the preferred model was selected based on the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) (Chen & Tsurumi, 2010). That is, the smaller the AIC or BIC, the better the model. Therefore, the results were analyzed on the basis of the selected models,<fnref n="6" /> and their relationships between financial ratios and the risk of high carbon emissions among all industries based on logit and probit results are further summarized in Table 4.

[Table 3 and Table 4 go here]

## 6.1 Logit and Probit Results

### 6.1.1 Early-warning financial ratios for high carbon emissions across all industries

The results for all (17) industries provide a general idea of the relationships between financial ratios and the risk of high carbon emissions (Table 4). The results demonstrate that Ratio 2

<footnote n="6">⁶ The Logit model was selected for the specifications including all industries, the semiconductor industry, and the electronic parts and components industry.</footnote>
(inventory to sales), Ratio 3 (net income to total assets), Ratio 5 (current assets to current liabilities), Ratio 6 (receivables to sales), and Ratio 7 (cash to total assets) are significantly correlated with high carbon emissions. Among them, Ratios 3 and 5 are positively correlated, whereas Ratios 2, 6, and 7 are inversely correlated.

These findings reveal two distinct patterns in the relationships between financial characteristics and the risk of high carbon emissions. First, the positive correlations for Ratio 3 and Ratio 5 suggest that both profitability and liquidity constraints are associated with higher carbon emissions. The positive association between Ratio 5 and high carbon emissions supports the notion that firms facing difficulties in obtaining external financing may maintain relatively high levels of current assets compared with current liabilities. Banks may be less willing to extend credit to high-emission firms in order to mitigate reputational risks under growing environmental scrutiny. Likewise, the positive relationship between Ratio 3 and carbon emissions indicates that more profitable firms may operate in energy-intensive industries or production processes that inherently generate higher emissions, despite their greater financial capacity to invest in cleaner technologies.

Second, the negative correlations observed for Ratio 2 (inventory to sales), Ratio 6 (receivables to sales), and Ratio 7 (cash to total assets) indicate that firms with more efficient working-capital management and greater cash reserves tend to have lower carbon emissions. The negative coefficients for Ratios 2 and 6 suggest that firms maintaining lower levels of inventories and receivables manage production and sales more efficiently. Effective inventory control and tighter credit management may reflect leaner operations, shorter production cycles, and faster cash conversion, all of which contribute to lower energy consumption and reduced carbon emissions. From an operational efficiency perspective, a smaller proportion of inventory and receivables relative to sales implies that less capital is tied up in working assets, improving liquidity, and allowing firms to allocate more resources to energy-efficient production or carbon-reduction technologies. The negative coefficient for Ratio 7 is also consistent with expectations, as firms with higher cash holdings have greater financial flexibility to invest in emission-reduction technologies or upgrade equipment.

Overall, these results indicate that financial structure, profitability, and liquidity jointly influence a firm's carbon emission behavior, although the directions of association vary across
financial indicators.

## 6.1.2 Early-warning financial ratios for high carbon emissions by industry

In addition to examining the overall relationships between financial ratios and the risk of high carbon emissions, this study further investigates these relationships across specific industries to capture industry-specific characteristics. The empirical results summarized in Table 4 reveal substantial heterogeneity across sectors.

For Ratio 1 (fixed assets to total assets), the relationship differs across industries. Positive and significant associations are observed in the plastic and semiconductor industries, suggesting that higher capital intensity is linked to greater carbon emissions. This result is consistent with the fact that production processes in these sectors are highly equipment- and energy-intensive, where greater use of machinery and production facilities naturally leads to higher emissions. In contrast, a negative and significant association is found in the chemical industry. This may reflect the industry's ongoing investment in process optimization and cleaner production technologies. Firms with a higher share of fixed assets may have upgraded or replaced older, less efficient facilities with more advanced and energy-efficient systems, thereby reducing emission intensity. No significant relationship is detected for the remaining industries, indicating that capital intensity is not a primary determinant of carbon emissions in those sectors.

In the case of Ratio 2 (inventory to sales), higher inventories are generally expected to be associated with higher carbon emissions because they typically reflect more extensive operating activities. However, a negative and significant relationship is found in the plastic industry. This unexpected result may indicate that firms in this sector benefit from economies of scale in production and energy use. Companies with higher inventory levels often anticipate stronger sales and thus have greater financial capacity to invest in cleaner technologies, which ultimately helps reduce their carbon emissions. For Ratio 3 (net income to total assets), a positive and significant relationship is found only in the semiconductor industry, indicating that more profitable firms in this sector tend to have higher carbon emissions. This result may reflect that profitability in the semiconductor industry is closely tied to energy-intensive manufacturing processes and high-capacity equipment utilization, where production expansion often leads to greater energy consumption and emissions.

For Ratio 4 (total debt to total assets), the results show a positive relationship in the plastic
and chemical industries and a negative one in the semiconductor industry. The positive associations suggest that firms with higher leverage may have limited capacity to invest in carbon-reduction technologies due to heavier debt burdens. Conversely, the negative coefficient in the semiconductor industry implies that more highly leveraged firms may be subject to greater scrutiny from lenders and investors, encouraging stricter emission control. Regarding Ratio 5 (current assets to current liabilities), a positive and significant relationship is found only in the chemical industry, indicating that firms with higher current ratios are more likely to exhibit higher carbon emissions. This finding aligns with our expectation that borrowing constraints limit access to external financing for high-emission firms, leading them to rely more heavily on internal liquidity.

For Ratio 6 (receivables to sales), a negative and significant relationship is found between the receivables-to-sales ratio and high carbon emissions in the plastic, chemical, optoelectronic, and building materials and construction industries. As discussed in Section 4, the relationship between receivables and carbon emissions is theoretically ambiguous, reflecting the dual role of receivables in shaping firms’ liquidity and operational scale. One plausible explanation for the negative association observed in these industries is that higher receivables may reflect stronger sales activity and larger production volumes rather than liquidity stress. Firms operating at larger scales may benefit from economies of scale and have greater capacity to invest in cleaner production processes or emission-reduction technologies. As a result, despite higher receivables, emission intensity may decline. In contrast, the positive coefficient for the electronic parts and components industry may indicate that higher levels of receivables may experience tighter liquidity constraints, limiting its ability to invest in low-carbon technologies or emission-reduction initiatives. Lastly, for Ratio 7 (cash to total assets), no significant relationships are found for any industry. This result suggests that cash holdings do not play a decisive role in explaining the likelihood of high carbon emissions at the industry level. The absence of significance implies that the effect of liquidity on emission behavior may be heterogeneous across firms and offset within industries.

Regarding the relationship between sales and carbon emissions, sales did not have a significant impact on the risk of high carbon emissions in most industries, with the exception of the chemical, semiconductor, and electronic parts and components industries. In the electronic parts and components industry, the positive relationship between sales and carbon emissions may be attributed to the absence of economies of scale. Conversely, in the chemical and semiconductor industries, the negative impact of sales on emissions could be explained by the efficiencies gained
as companies scale up. Larger firms may achieve greater operational efficiency, reducing emissions per unit of production through improved transportation logistics and bulk sourcing of eco-friendly materials, which in turn lowers their overall carbon footprint. Finally, the positive and significant coefficient for lagged carbon emissions across all industries indicates strong persistence in emission behavior. Firms that emitted more carbon in the previous period tend to maintain higher emission levels in the current period, reflecting technological inertia and adjustment costs in energy use.

## 6.2 BKMR Model

The BKMR model captures the nonlinear relationships between explanatory variables and response variables. The BKMR results, presented in Table 3, indicate no detection of nonlinear relationships within any industry, indicating that linear regression models may be more precise for estimating the relationships between financial ratios and the risk of high carbon emissions.<fnref n="7" /> In addition, the BKMR model employs a Bayesian approach and does not depend on the traditional statistical framework for testing null hypotheses. Thus, 95% confidence intervals are interpreted as credible intervals, and common discussions on statistical power should be avoided (Bellavia, 2021). As a robustness check, this study also examined the ratios that are significant in the logit and probit models against their results in the BKMR model. The BKMR results for the entire sample and each individual industry are displayed in Figures 1 and 2, respectively.

[Figures 1 and 2 go here]

The signs of the coefficients for the ratios in both the overall model, which includes all seventeen industries, and the individual industry models are generally consistent across the BKMR, logit, and probit estimations. Although not all significant coefficients identified in the logit and probit models are replicated in the BKMR analysis, the results show that within individual industries, the directional signs of the ratios estimated by the BKMR models are consistent with those of the significant coefficients in the logit and probit models. In other words, the BKMR results largely align with the regression outcomes obtained from the logit and probit models in

<footnote n="7">⁷ In Wu et al. (2020), the authors employed a logistic model, weighted quantile sum regression, and the BKMR model to investigate the relationships between exposure to nine compounds and obesity in children and adolescents. They emphasized the necessity of combining three different models, considering their various advantages and disadvantages when explaining their results.</footnote>
terms of the direction of the relationships.

# 7. Discussions and Policy Implications

This study established an early-warning mechanism for predicting the risk of high carbon emissions from financial ratios. In general, the results from linear (logit and probit models) and nonlinear (BKMR) regressions are consistent; however, a few subtle differences exist. Because the BKMR results revealed no nonlinear relationships within individual industries, and because the logit and probit models are advantageous due to their ease of application<fnref n="8" /> and generally yielded consistent results, this study identified the predictors from the discussed financial ratios on the basis of the logit and probit results.

## 7.1 Features of Companies with High Carbon Emissions Vary across Industries

This study demonstrates cross-industry heterogeneity in the financial characteristics that predict high carbon emission risk. For example, in the plastic industry, the features linked to high carbon emissions include Ratios 1, 2, 4, and 6, whereas in the optoelectronic, electronic parts and components, and building materials and construction industries, only Ratio 6 is significant. Notably, no significant features of high carbon emissions are identified in the food, computer and peripheral equipment, or shipping and transportation industries.<fnref n="9" /> These findings highlight the heterogeneity of emission drivers across sectors and suggest that effective carbon-reduction strategies should be tailored to the financial and operational structures of individual industries.

## 7.2 An Early-Warning Perspective on High Carbon Emission Risk

Due to the awareness of environmental consciousness, the media and the public pay attention to the behaviors of firms contributing to reducing carbon emissions, especially for industries with high carbon emissions associated with their production. In the case of Ratio 6 (receivables-to-sales), a significantly negative relationship is observed, suggesting that firms with higher receivables relative to sales tend to exhibit lower carbon emissions in the plastics, chemical, optoelectronic, and building materials and construction industries. Building on the economic framework, receivables may be informative about firms' short-term financing capacity, which can

<footnote n="8">⁸ Compared with computing the logit and probit results, computing the BKMR results is time-consuming.</footnote>

<footnote n="9">⁹ Considering that logistic and probit regressions may suffer from perfect or quasi-perfect separation, we additionally employ penalized logistic regression (Lasso) using the R package *glmnet*. The results confirm that certain financial ratios in the food and shipping and transportation industries are important determinants of the risk of high carbon emissions. The detailed results are available upon request.</footnote>
relax financing constraints on abatement investment. In the short run, greater working-capital flexibility may enable firms to invest in emission-reduction activities and respond to external pressures. Elevated receivables can be interpreted as a signal of strong sales and large-scale operations rather than financial distress. Larger firms may exploit economies of scale and allocate more resources toward cleaner technologies, leading to lower emission intensity despite higher receivables. As a result, Ratio 6 may serve as a useful early-warning financial indicator for higher emission risk.

In summary, the empirical findings provide evidence that the financial characteristics associated with high carbon emissions vary across industries. While the relevant predictors differ across sectors, Ratio 6 (receivables to sales) may serve as a useful early-warning financial indicator for identifying firms at greater risk of high carbon emissions. This insight offers practical guidance not only for investors in making informed portfolio decisions but also for managers and policymakers responsible for overseeing firms across different sectors. Managers and regulatory authorities may monitor Ratio 6 to identify potential emission risks and to implement timely mitigation strategies. Furthermore, because financial ratios are routinely disclosed in financial statements, this measure represents a low-cost and accessible approach for monitoring emission risks. Overall, the early-warning mechanism developed in this study provides a cost-effective and scalable tool for predicting the likelihood of high carbon emissions based on financial information.

## 8. Conclusion

To successfully achieve net-zero carbon emissions by 2050, establishing an early-warning system and preventing firms from emitting carbon emissions are necessary. This study sheds light on the crucial intersection between financial metrics and environmental sustainability by examining the relationship between carbon emissions and financial ratios across industries with high carbon emissions. Leveraging comprehensive data spanning 2005 to 2020 sourced from TEJ, the empirical analysis unveils significant insights. Specifically, the receivables-to-sales ratio is identified as a promising predictor of high carbon emission risk.

This study provides a relatively low-cost early-warning method to quickly identify firms that may become high carbon emission firms within their respective industries through readily available financial indicators. The method can enable firms to propose corresponding adjustment strategies. By proactively addressing carbon emission risks, firms can not only reduce operational
costs and regulatory compliance burdens but also enhance brand reputation and competitiveness in an increasingly carbon-conscious market. In addition, governments or supervisors could maintain control over firms' and overall carbon emissions and implement corresponding policies. For investors, they can better assess the sustainability of investment portfolios and allocate resources more efficiently to mitigate environmental effects.

This study presents a significant advancement in connecting financial ratios with environmental sustainability. Through rigorous analysis and modeling, the findings demonstrate the potential of financial metrics to serve as indicators of carbon emission intensity, offering valuable implications for investors, businesses, and policymakers. While the study is focused on Taiwanese companies, the relationship between carbon emissions and financial ratios may potentially exhibit similar patterns in other economies, particularly in regions where sustainability and environmental regulations are increasingly prioritized. For example, high-emission industries in both the United States and Taiwan share several commonalities, particularly in sectors such as steel, cement, and petrochemicals, which are characterized by high energy consumption and process-related emissions (US EPA, 2024; Chou et al., 2019). Both countries are investing in technologies such as carbon capture, utilization, and storage (CCUS) to mitigate these emissions and are aligning with international climate initiatives, including the Task Force on Climate-related Financial Disclosures (TCFD), to better manage and disclose climate-related risks. Despite these ongoing efforts, both the United States and Taiwan continue to face significant challenges in reducing emissions, especially in industries where mitigating process-related emissions is inherently complex and where the implementation of new technologies remains costly. These industries are vital to their respective economies, reflecting the global nature of industrial emissions and the shared challenges in addressing climate change.

Nonetheless, variations in market structures, regulatory frameworks, and industry compositions may influence the extent and nature of these relationships in different contexts. While the study offers valuable insights, further research is needed to explore the generalizability of these findings across diverse economic environments. Additionally, the scope of this study is limited to identifying the risk of firms being high carbon emitters, without investigating the magnitude of these emissions, which represents a promising direction for future research.
**Conflict of Interest Statement**

Authors declare that they do not have financial or non-financial interests that are directly or indirectly related to this work submitted for publication.

**Data Availability Statement**

Data are contained within the article.
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<figure><img src="image_3.png" /><figcaption>Figure 1. BKMR results across all industries</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>z
Food industry</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>z
Plastic industry</figcaption></figure>

<caption>Figure 2. BKMR results by individual industries</caption>
<figure><img src="image_6.png" /></figure>

Chemical industry

<figure><img src="image_7.png" /></figure>

Semiconductor industry

<caption>Figure 2. BKMR results by individual industries (cont'd)</caption>
<figure><img src="image_8.png" /><figcaption>z

Computer and peripheral equipment industry</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>z

Optoelectronic industry</figcaption></figure>

<caption>Figure 2. BKMR results by individual industries (cont'd)</caption>
<figure><img src="image_10.png" /><figcaption>Electronic parts and components industry</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Building material and construction industry</figcaption></figure>

<caption>Figure 2. BKMR results by individual industries (cont'd)</caption>
<figure><img src="image_12.png" /></figure>

Shipping and transportation industry

<caption>Figure 2. BKMR results by individual industries (cont'd)</caption>
<table><caption>Table 1 Financial ratios</caption><thead><tr><td>Variables</td><td>Computation</td><td>Proxy</td><td>Used in</td></tr></thead><tbody><tr><td>Ratio 1</td><td>Fixed Assets/Total Assets</td><td>Operating Leverage</td><td>Theodossiou (1993), Kahya and Theodossiou (1999)</td></tr><tr><td>Ratio 2</td><td>Inventory/Sales</td><td>Management Efficiency</td><td>Beaver (1966), Edmister (1972), Theodossiou (1993), Theodossiou et al. (1996), Kahya and Theodossiou (1999)</td></tr><tr><td>Ratio 3</td><td>Net Income/Total Assets</td><td>Profitability</td><td>Beaver (1966), Ohlson (1980), Zmijewski (1984), Lo (1986), Gombola et al. (1987), Kahya and Theodossiou (1999)</td></tr><tr><td>Ratio 4</td><td>Total Debt/Total Assets</td><td>Financial Leverage</td><td>Ohlson (1980), Zmijewski (1984), Gombola et al. (1987), Theodossiou et al. (1996)</td></tr><tr><td>Ratio 5</td><td>Current Assets/Current Liabilities</td><td>Liquidity</td><td>Beaver (1966), Altman et al. (1977), Gombola et al. (1966), Zmijewski (1984)</td></tr><tr><td>Ratio 6</td><td>Receivables/Sales</td><td>Liquidity</td><td>Hampton (1983)</td></tr><tr><td>Ratio 7</td><td>Cash/Total Assets</td><td>Liquidity</td><td>Beaver (1966), Altman (1968), Gombola et al. (1987)</td></tr></tbody></table>
<table><caption>Table 2 Descriptive statistics</caption><thead><tr><th rowspan="2">Industry</th><th rowspan="2">Stats</th><th rowspan="2">CO2_Rev</th><th rowspan="2">CO2_high</th><th colspan="2">Ratio 1</th><th colspan="2">Ratio 3</th><th colspan="2">Ratio 5</th><th colspan="2">Ratio 7</th></tr><tr><th>Fixed Assets/Total Assets</th><th>Inventory/Sales</th><th>Net Income/Total Assets</th><th>Total Debt/Total Assets</th><th>Current Assets/Current Liabilities</th><th>Receivables/Sales</th><th>Cash/Total Assets</th></tr></thead><tbody><tr><td rowspan="4">Cement</td><td>N</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td><td>36</td></tr><tr><td>Mean</td><td>0.281</td><td>0.556</td><td>0.01</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.283</td><td>0.504</td><td>0.005</td><td>0.007</td><td>0.013</td><td>0.004</td><td>0.006</td><td>0.003</td><td>0.005</td><td>0.005</td></tr><tr><td>Min</td><td>0.002</td><td>0</td><td>0.001</td><td>0.002</td><td>-0.016</td><td>0.002</td><td>0.003</td><td>0.005</td><td>0.002</td><td>0.002</td></tr><tr><td></td><td>Max</td><td>1.112</td><td>1</td><td>0.019</td><td>0.028</td><td>0.044</td><td>0.016</td><td>0.025</td><td>0.017</td><td>0.022</td><td></td></tr><tr><td rowspan="4">Food</td><td>N</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td><td>131</td></tr><tr><td>Mean</td><td>0.020</td><td>0.519</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.040</td><td>0.502</td><td>0.004</td><td>0.008</td><td>0.014</td><td>0.004</td><td>0.006</td><td>0.006</td><td>0.007</td><td>0.007</td></tr><tr><td>Min</td><td>0.001</td><td>0</td><td>0.001</td><td>0.003</td><td>-0.020</td><td>0.001</td><td>0.003</td><td>0.003</td><td>0.000</td><td>0.000</td></tr><tr><td></td><td>Max</td><td>0.321</td><td>1</td><td>0.018</td><td>0.041</td><td>0.068</td><td>0.019</td><td>0.030</td><td>0.036</td><td>0.033</td><td></td></tr><tr><td rowspan="4">Plastic</td><td>N</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td><td>86</td></tr><tr><td>Mean</td><td>0.117</td><td>0.535</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.210</td><td>0.502</td><td>0.002</td><td>0.004</td><td>0.026</td><td>0.004</td><td>0.003</td><td>0.003</td><td>0.007</td><td>0.007</td></tr><tr><td>Min</td><td>0.005</td><td>0</td><td>0.005</td><td>0.004</td><td>-0.160</td><td>0.003</td><td>0.004</td><td>0.005</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>1.202</td><td>1</td><td>0.016</td><td>0.032</td><td>0.072</td><td>0.019</td><td>0.018</td><td>0.015</td><td>0.030</td><td></td></tr><tr><td rowspan="4">Textile</td><td>N</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td><td>81</td></tr><tr><td>Mean</td><td>0.069</td><td>0.556</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.067</td><td>0.5</td><td>0.003</td><td>0.004</td><td>0.026</td><td>0.004</td><td>0.006</td><td>0.003</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>0.001</td><td>0</td><td>0.004</td><td>0.004</td><td>-0.056</td><td>0.002</td><td>0.005</td><td>0.005</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>0.328</td><td>1</td><td>0.016</td><td>0.021</td><td>0.143</td><td>0.019</td><td>0.036</td><td>0.016</td><td>0.033</td><td></td></tr><tr><td rowspan="4">Chemical</td><td>N</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td><td>169</td></tr><tr><td>Mean</td><td>0.022</td><td>0.521</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.030</td><td>0.501</td><td>0.004</td><td>0.005</td><td>0.011</td><td>0.004</td><td>0.015</td><td>0.004</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>0.000</td><td>0</td><td>0.001</td><td>0.001</td><td>-0.040</td><td>0.002</td><td>0.002</td><td>0.003</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>0.273</td><td>1</td><td>0.022</td><td>0.024</td><td>0.063</td><td>0.023</td><td>0.090</td><td>0.021</td><td>0.029</td><td></td></tr></tbody></table>
<table><caption>Table 2 Descriptive statistics (cont'd)</caption><thead><tr><th rowspan="2">Industry</th><th rowspan="2">Stats</th><th rowspan="2">CO2_Rev</th><th rowspan="2">CO2_high</th><th colspan="2">Ratio 1</th><th colspan="2">Ratio 3</th><th colspan="2">Ratio 5</th><th colspan="2">Ratio 7</th></tr><tr><th>Fixed Assets/Total Assets</th><th>Inventory/Sales</th><th>Net Income/Total Assets</th><th>Total Debt/Total Assets</th><th>Current Assets/Current Liabilities</th><th>Receivables/Sal es</th><th>Cash/Total Assets</th></tr></thead><tbody><tr><td rowspan="4">Glass and ceramic</td><td>N</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td><td>6</td></tr><tr><td>Mean</td><td>0.065</td><td>0.5</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.005</td><td>0.548</td><td>0.002</td><td>0.005</td><td>0.009</td><td>0.001</td><td>0.003</td><td>0.001</td><td>0.005</td><td>0.005</td></tr><tr><td>Min</td><td>0.057</td><td>0</td><td>0.008</td><td>0.005</td><td>-0.004</td><td>0.009</td><td>0.007</td><td>0.009</td><td>0.004</td><td>0.004</td></tr><tr><td></td><td>Max</td><td>0.072</td><td>1</td><td>0.012</td><td>0.015</td><td>0.024</td><td>0.011</td><td>0.013</td><td>0.011</td><td>0.016</td><td>0.016</td></tr><tr><td rowspan="4">Paper and pulp</td><td>N</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td><td>21</td></tr><tr><td>Mean</td><td>0.117</td><td>0.524</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.045</td><td>0.512</td><td>0.001</td><td>0.003</td><td>0.018</td><td>0.002</td><td>0.003</td><td>0.003</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>0.046</td><td>0</td><td>0.008</td><td>0.006</td><td>-0.043</td><td>0.006</td><td>0.006</td><td>0.006</td><td>0.003</td><td>0.003</td></tr><tr><td></td><td>Max</td><td>0.185</td><td>1</td><td>0.011</td><td>0.013</td><td>0.052</td><td>0.012</td><td>0.016</td><td>0.017</td><td>0.024</td><td>0.024</td></tr><tr><td rowspan="4">Iron and steel</td><td>N</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td><td>76</td></tr><tr><td>Mean</td><td>0.059</td><td>0.526</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.081</td><td>0.503</td><td>0.004</td><td>0.006</td><td>0.034</td><td>0.004</td><td>0.008</td><td>0.007</td><td>0.009</td><td>0.009</td></tr><tr><td>Min</td><td>0.001</td><td>0</td><td>0.003</td><td>0.002</td><td>-0.128</td><td>0.002</td><td>0.002</td><td>0.002</td><td>0.000</td><td>0.000</td></tr><tr><td></td><td>Max</td><td>0.335</td><td>1</td><td>0.019</td><td>0.028</td><td>0.177</td><td>0.016</td><td>0.037</td><td>0.027</td><td>0.041</td><td>0.041</td></tr><tr><td rowspan="4">Rubber</td><td>N</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td><td>26</td></tr><tr><td>Mean</td><td>0.042</td><td>0.577</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.029</td><td>0.504</td><td>0.003</td><td>0.008</td><td>0.006</td><td>0.001</td><td>0.003</td><td>0.003</td><td>0.003</td><td>0.003</td></tr><tr><td>Min</td><td>0.006</td><td>0</td><td>0.006</td><td>0.003</td><td>0.002</td><td>0.007</td><td>0.007</td><td>0.006</td><td>0.006</td><td>0.006</td></tr><tr><td></td><td>Max</td><td>0.092</td><td>1</td><td>0.016</td><td>0.028</td><td>0.026</td><td>0.011</td><td>0.018</td><td>0.014</td><td>0.019</td><td>0.019</td></tr><tr><td rowspan="4">Semiconductor</td><td>N</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td><td>266</td></tr><tr><td>Mean</td><td>0.032</td><td>0.511</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.062</td><td>0.501</td><td>0.007</td><td>0.006</td><td>0.065</td><td>0.005</td><td>0.009</td><td>0.004</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>3.47E-05</td><td>0</td><td>0.000</td><td>0.000</td><td>-0.755</td><td>0.001</td><td>0.001</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td></td><td>Max</td><td>4.50E-01</td><td>1</td><td>0.024</td><td>0.042</td><td>0.194</td><td>0.025</td><td>0.060</td><td>0.024</td><td>0.032</td><td>0.032</td></tr></tbody></table>
<table><caption>Table 2 Descriptive statistics (cont'd)</caption><thead><tr><th rowspan="2">Industry</th><th rowspan="2">Stats</th><th rowspan="2">CO2_Rev</th><th rowspan="2">CO2_high</th><th colspan="2">Ratio 1</th><th colspan="2">Ratio 3</th><th colspan="2">Ratio 5</th><th colspan="2">Ratio 7</th></tr><tr><th>Fixed Assets/Total Assets</th><th>Inventory/Sales</th><th>Net Income/Total Assets</th><th>Total Debt/Total Assets</th><th>Current Assets/Current Liabilities</th><th>Receivables/Sales</th><th>Cash/Total Assets</th></tr></thead><tbody><tr><td rowspan="4">Computer and peripheral equipment</td><td>N</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td><td>196</td></tr><tr><td>Mean</td><td>0.004</td><td>0.515</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.006</td><td>0.501</td><td>0.006</td><td>0.004</td><td>0.023</td><td>0.004</td><td>0.007</td><td>0.003</td><td>0.004</td><td>0.004</td></tr><tr><td>Min</td><td>0.000</td><td>0</td><td>0.000</td><td>0.003</td><td>-0.122</td><td>0.001</td><td>0.003</td><td>0.003</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>0.037</td><td>1</td><td>0.032</td><td>0.026</td><td>0.079</td><td>0.018</td><td>0.052</td><td>0.024</td><td>0.023</td><td>0.023</td></tr><tr><td rowspan="4">Optoelectronic</td><td>N</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td><td>155</td></tr><tr><td>Mean</td><td>0.020</td><td>0.523</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.027</td><td>0.501</td><td>0.005</td><td>0.006</td><td>0.083</td><td>0.003</td><td>0.005</td><td>0.005</td><td>0.005</td><td>0.005</td></tr><tr><td>Min</td><td>0.000</td><td>0</td><td>0.002</td><td>0.002</td><td>-0.345</td><td>0.002</td><td>0.002</td><td>0.001</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>0.153</td><td>1</td><td>0.026</td><td>0.029</td><td>0.322</td><td>0.018</td><td>0.038</td><td>0.029</td><td>0.029</td><td>0.029</td></tr><tr><td rowspan="4">Communications and internet</td><td>N</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td><td>117</td></tr><tr><td>Mean</td><td>0.006</td><td>0.504</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.006</td><td>0.502</td><td>0.007</td><td>0.005</td><td>0.093</td><td>0.004</td><td>0.006</td><td>0.003</td><td>0.005</td><td>0.005</td></tr><tr><td>Min</td><td>9.91E-05</td><td>0</td><td>0.000</td><td>0.001</td><td>-0.752</td><td>0.001</td><td>0.003</td><td>0.004</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>2.36E-02</td><td>1</td><td>0.031</td><td>0.022</td><td>0.243</td><td>0.018</td><td>0.051</td><td>0.018</td><td>0.022</td><td>0.022</td></tr><tr><td rowspan="4">Electronic parts and components</td><td>N</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td><td>231</td></tr><tr><td>Mean</td><td>0.019</td><td>0.506</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.021</td><td>0.501</td><td>0.005</td><td>0.004</td><td>0.016</td><td>0.003</td><td>0.006</td><td>0.002</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>0.000</td><td>0</td><td>0.003</td><td>0.004</td><td>-0.122</td><td>0.002</td><td>0.003</td><td>0.003</td><td>0.001</td><td>0.001</td></tr><tr><td></td><td>Max</td><td>0.088</td><td>1</td><td>0.033</td><td>0.026</td><td>0.068</td><td>0.016</td><td>0.041</td><td>0.018</td><td>0.037</td><td>0.037</td></tr><tr><td rowspan="4">Building material and construction</td><td>N</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td><td>108</td></tr><tr><td>Mean</td><td>1.704</td><td>0.528</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>11.107</td><td>0.502</td><td>0.013</td><td>0.034</td><td>0.019</td><td>0.002</td><td>0.028</td><td>0.027</td><td>0.009</td><td>0.009</td></tr><tr><td>Min</td><td>8.13E-06</td><td>0</td><td>0.000</td><td>0.000</td><td>-0.082</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td></td><td>Max</td><td>9.93E+01</td><td>1</td><td>0.057</td><td>0.199</td><td>0.097</td><td>0.015</td><td>0.184</td><td>0.153</td><td>0.038</td><td>0.038</td></tr></tbody></table>
<table><caption>Table 2 Descriptive statistics (cont'd)</caption><thead><tr><th rowspan="2">Industry</th><th rowspan="2">Stats</th><th rowspan="2">CO2_Rev</th><th rowspan="2">CO2_high</th><th colspan="2">Ratio 1</th><th colspan="2">Ratio 3</th><th colspan="2">Ratio 5</th><th colspan="2">Ratio 7</th></tr><tr><th>Fixed Assets/Total Assets</th><th>Inventory/Sales</th><th>Net Income/Total Assets</th><th>Total Debt/Total Assets</th><th>Current Assets/Current Liabilities</th><th>Receivables/Sales</th><th>Cash/Total Assets</th></tr></thead><tbody><tr><td rowspan="5">Shipping and transportation</td><td>N</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td><td>82</td></tr><tr><td>Mean</td><td>0.115</td><td>0.537</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>0.141</td><td>0.502</td><td>0.005</td><td>0.018</td><td>0.030</td><td>0.003</td><td>0.005</td><td>0.007</td><td>0.008</td><td>0.008</td></tr><tr><td>Min</td><td>0.0001</td><td>0</td><td>0.000</td><td>0.000</td><td>-0.126</td><td>0.003</td><td>0.002</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Max</td><td>0.517</td><td>1</td><td>0.020</td><td>0.065</td><td>0.074</td><td>0.016</td><td>0.029</td><td>0.036</td><td>0.032</td><td>0.032</td></tr><tr><td rowspan="5">Gas and electricity</td><td>N</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td><td>13</td></tr><tr><td>Mean</td><td>4.016</td><td>0.615</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>5.468</td><td>0.506</td><td>0.007</td><td>0.008</td><td>0.013</td><td>0.002</td><td>0.004</td><td>0.005</td><td>0.003</td><td>0.003</td></tr><tr><td>Min</td><td>0.001</td><td>0</td><td>0.001</td><td>0.001</td><td>-0.005</td><td>0.007</td><td>0.003</td><td>0.001</td><td>0.006</td><td>0.006</td></tr><tr><td>Max</td><td>15.121</td><td>1</td><td>0.018</td><td>0.023</td><td>0.035</td><td>0.014</td><td>0.017</td><td>0.019</td><td>0.015</td><td>0.015</td></tr><tr><td rowspan="5">Total</td><td>N</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td><td>1800</td></tr><tr><td>Mean</td><td>0.169</td><td>0.522</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td><td>0.010</td></tr><tr><td>SD</td><td>2.795</td><td>0.500</td><td>0.006</td><td>0.010</td><td>0.045</td><td>0.004</td><td>0.010</td><td>0.008</td><td>0.006</td><td>0.006</td></tr><tr><td>Min</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td><td>-0.755</td><td>0.001</td><td>0.000</td><td>0.000</td><td>0.000</td><td>0.000</td></tr><tr><td>Max</td><td>99.333</td><td>1.000</td><td>0.057</td><td>0.199</td><td>0.322</td><td>0.025</td><td>0.184</td><td>0.153</td><td>0.041</td><td>0.041</td></tr></tbody></table>
<table><caption>Table 3 Results for logit, probit, and BKMR models</caption><thead><tr><th rowspan="3">Industry</th><th colspan="5">All</th><th colspan="5">Food industry</th><th colspan="5">Plastic industry</th></tr><tr><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th></tr><tr><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th></tr></thead><tbody><tr><td>Intercept</td><td>14.18***</td><td>-</td><td>6.39***</td><td>-</td><td></td><td>17.23</td><td>-</td><td>9.77</td><td>-</td><td></td><td>-9.46</td><td>-</td><td>-5.67</td><td>-</td><td></td></tr><tr><td>L_Ratio 1</td><td>23.38</td><td>1.91</td><td>24.18**</td><td>4.03</td><td>+</td><td>148.62</td><td>7.96</td><td>75.35</td><td>7.02</td><td>+</td><td>892.83*</td><td>40.45</td><td>526.50*</td><td>40.36</td><td>+</td></tr><tr><td>L_Ratio 2</td><td>-46.93***</td><td>-3.83</td><td>-17.24*</td><td>-2.87</td><td></td><td>31.63</td><td>1.69</td><td>7.36</td><td>0.69</td><td></td><td>-1821.68**</td><td>-82.41</td><td>-1069.92**</td><td>-81.85</td><td>-</td></tr><tr><td>L_Ratio 3</td><td>4.28*</td><td>0.35</td><td>2.05*</td><td>0.34</td><td></td><td>67.52</td><td>3.62</td><td>34.09</td><td>3.18</td><td></td><td>-56.24</td><td>-2.55</td><td>-34.12</td><td>-2.62</td><td></td></tr><tr><td>L_Ratio 4</td><td>42.90</td><td>3.50</td><td>15.99</td><td>2.66</td><td></td><td>161.28</td><td>8.64</td><td>110.36</td><td>10.28</td><td></td><td>974.44*</td><td>44.15</td><td>571.19*</td><td>43.78</td><td>+</td></tr><tr><td>L_Ratio 5</td><td>29.07*</td><td>2.37</td><td>16.51*</td><td>2.75</td><td>+</td><td>226.39</td><td>12.12</td><td>139.65</td><td>13.01</td><td>+</td><td>277.46</td><td>12.58</td><td>172.23</td><td>13.21</td><td>+</td></tr><tr><td>L_Ratio 6</td><td>-151.25***</td><td>-12.35</td><td>-77.45***</td><td>-12.90</td><td></td><td>-18.96</td><td>-1.02</td><td>-15.32</td><td>-1.43</td><td></td><td>-740.05*</td><td>-33.54</td><td>-442.35*</td><td>-33.92</td><td>-</td></tr><tr><td>L_Ratio 7</td><td>-34.89*</td><td>-2.85</td><td>-11.73</td><td>-1.95</td><td>-</td><td>-0.09</td><td>-0.00</td><td>-3.62</td><td>-0.34</td><td></td><td>391.33</td><td>17.74</td><td>222.53</td><td>17.07</td><td>+</td></tr><tr><td>L_In Carbon</td><td>2.82***</td><td>6.49</td><td>1.28***</td><td>5.87</td><td></td><td>6.27***</td><td>22.33</td><td>3.60***</td><td>22.53</td><td></td><td>7.87**</td><td>5.63</td><td>4.62**</td><td>5.53</td><td></td></tr><tr><td>In Sales</td><td>-0.19**</td><td>-0.00</td><td>-0.10**</td><td>-0.00</td><td></td><td>-0.66</td><td>-0.00</td><td>-0.38</td><td>-0.00</td><td></td><td>1.09</td><td>0.00</td><td>0.65</td><td>0.00</td><td></td></tr><tr><td>Year FE</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td>Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td></tr><tr><td>Industry FE</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td>Yes</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td></tr><tr><td>Obs.</td><td colspan="2">1463</td><td colspan="2">1463</td><td></td><td colspan="2">109</td><td colspan="2">109</td><td colspan="2">-</td><td colspan="2">72</td><td colspan="2">72</td></tr><tr><td>McFadden R<sup>2</sup></td><td colspan="2">0.572</td><td colspan="2">0.539</td><td></td><td colspan="2">0.720</td><td colspan="2">0.723</td><td colspan="2">-0.765</td><td colspan="2">0.770</td><td colspan="2">0.770</td></tr><tr><td>Log-likelihood</td><td colspan="2">-380.438</td><td colspan="2">-409.378</td><td></td><td colspan="2">-18.698</td><td colspan="2">-18.525</td><td colspan="2">-10.409</td><td colspan="2">-10.211</td><td colspan="2">-10.211</td></tr><tr><td>AIC</td><td colspan="2">814.877</td><td colspan="2">872.756</td><td></td><td colspan="2">59.396</td><td colspan="2">59.049</td><td colspan="2">42.817</td><td colspan="2">42.422</td><td colspan="2">42.422</td></tr><tr><td>BIC</td><td colspan="2">957.659</td><td colspan="2">1015.538</td><td></td><td colspan="2">89.001</td><td colspan="2">88.654</td><td colspan="2">67.861</td><td colspan="2">67.466</td><td colspan="2">67.466</td></tr></tbody></table>

<table><thead><tr><th rowspan="3">Industry</th><th colspan="5">Chemical industry</th><th colspan="5">Semiconductor industry</th><th colspan="5">Computer and peripheral equipment industry</th></tr><tr><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th></tr><tr><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th></tr></thead><tbody><tr><td>Intercept</td><td>55.06***</td><td>-</td><td>29.51***</td><td>-</td><td></td><td>37.64***</td><td>-</td><td>22.08***</td><td>-</td><td></td><td>25.01***</td><td>-</td><td>12.30**</td><td>-</td><td></td></tr><tr><td>L_Ratio 1</td><td>-278.20**</td><td>-13.37</td><td>-159.66**</td><td>-14.00</td><td></td><td>303.59**</td><td>16.21</td><td>185.95***</td><td>19.06</td><td>+</td><td>-36.57</td><td>-2.42</td><td>0.18</td><td>0.02</td><td></td></tr><tr><td>L_Ratio 2</td><td>144.54</td><td>6.95</td><td>73.98</td><td>6.49</td><td>+</td><td>58.11</td><td>3.10</td><td>22.01</td><td>2.26</td><td></td><td>-5.86</td><td>-0.39</td><td>6.95</td><td>0.87</td><td></td></tr><tr><td>L_Ratio 3</td><td>-7.48</td><td>-0.36</td><td>-6.68</td><td>-0.59</td><td></td><td>24.70***</td><td>1.32</td><td>13.95***</td><td>1.43</td><td></td><td>0.80</td><td>0.05</td><td>-0.58</td><td>-0.07</td><td></td></tr><tr><td>L_Ratio 4</td><td>582.21**</td><td>28.00</td><td>299.12**</td><td>26.23</td><td>+</td><td>-212.20*</td><td>-11.33</td><td>-139.87**</td><td>-14.34</td><td>-</td><td>-22.03</td><td>-1.46</td><td>-4.55</td><td>-0.57</td><td>-</td></tr><tr><td>L_Ratio 5</td><td>528.64*</td><td>25.42</td><td>264.52*</td><td>23.20</td><td></td><td>-33.34</td><td>-1.78</td><td>-40.99</td><td>-4.20</td><td></td><td>-16.16</td><td>-1.07</td><td>1.54</td><td>0.19</td><td></td></tr><tr><td>L_Ratio 6</td><td>-465.87**</td><td>-22.40</td><td>-263.33***</td><td>-23.09</td><td>-</td><td>-181.42</td><td>-9.69</td><td>-126.19*</td><td>-12.94</td><td>-</td><td>-39.75</td><td>-2.63</td><td>-41.56</td><td>-5.18</td><td>-</td></tr><tr><td>L_Ratio 7</td><td>-219.22</td><td>-10.54</td><td>-115.45</td><td>-10.12</td><td>-</td><td>88.03</td><td>4.70</td><td>56.12</td><td>5.75</td><td></td><td>-61.51</td><td>-4.07</td><td>-21.16</td><td>-2.64</td><td></td></tr><tr><td>L_In Carbon</td><td>4.41***</td><td>13.60</td><td>2.39***</td><td>13.40</td><td></td><td>5.40***</td><td>9.60</td><td>2.69***</td><td>9.69</td><td></td><td>3.27***</td><td>70.49</td><td>1.73***</td><td>70.26</td><td></td></tr><tr><td>In Sales</td><td>-3.19***</td><td>-0.00</td><td>-1.67***</td><td>-0.00</td><td></td><td>-1.38***</td><td>-0.00</td><td>-0.85***</td><td>-0.00</td><td></td><td>-0.28</td><td>-0.00</td><td>-0.10</td><td>-0.00</td><td></td></tr><tr><td>Year FE</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td>Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td></tr><tr><td>Industry FE</td><td colspan="2">-</td><td colspan="2">-</td><td>-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td></tr><tr><td>Obs.</td><td colspan="2">142</td><td colspan="2">142</td><td></td><td colspan="2">218</td><td colspan="2">218</td><td colspan="2">159</td><td colspan="2">159</td><td colspan="2">159</td></tr><tr><td>McFadden R<sup>2</sup></td><td colspan="2">0.740</td><td colspan="2">0.740</td><td></td><td colspan="2">0.722</td><td colspan="2">0.708</td><td colspan="2">0.624</td><td colspan="2">0.616</td><td colspan="2">0.616</td></tr><tr><td>Log-likelihood</td><td colspan="2">-22.679</td><td colspan="2">-22.635</td><td></td><td colspan="2">-37.670</td><td colspan="2">-39.574</td><td colspan="2">-34.562</td><td colspan="2">-35.215</td><td colspan="2">-35.215</td></tr><tr><td>AIC</td><td colspan="2">67.358</td><td colspan="2">67.269</td><td></td><td colspan="2">97.340</td><td colspan="2">101.148</td><td colspan="2">91.124</td><td colspan="2">92.430</td><td colspan="2">92.430</td></tr><tr><td>BIC</td><td colspan="2">99.872</td><td colspan="2">99.784</td><td></td><td colspan="2">134.569</td><td colspan="2">138.378</td><td colspan="2">124.882</td><td colspan="2">126.188</td><td colspan="2">126.188</td></tr></tbody></table>

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively; Ratio 1: Fixed Assets/Total Assets, Ratio 2: Inventory/Sales, Ratio 3: Net Income/Total Assets, Ratio 4: Total Debt/Total Assets, Ratio 5: Current Assets/Current Liabilities, Ratio 6: Receivables/Sales, Ratio 7: Cash/Total Assets; Year FE presents the year fixed effects. AME: Average marginal effect.
<table><caption>Table 3 Results for logit, probit, and BKMR models</caption><thead><tr><th rowspan="3">Industry</th><th colspan="4">Optoelectronic industry</th><th colspan="4">Electronic parts and components industry</th></tr><tr><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th></tr><tr><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th></tr></thead><tbody><tr><td>Intercept</td><td>31.35**</td><td>-</td><td>16.17**</td><td>-</td><td></td><td>-9.00</td><td>-</td><td>-5.01</td><td>-</td><td></td></tr><tr><td>L_Ratio 1</td><td>-174.00</td><td>-9.03</td><td>-85.18</td><td>-8.31</td><td></td><td>124.61</td><td>6.23</td><td>58.62</td><td>5.93</td><td></td></tr><tr><td>L_Ratio 2</td><td>-56.71</td><td>-2.94</td><td>-29.14</td><td>-2.84</td><td></td><td>-78.60</td><td>-3.93</td><td>-62.17</td><td>-6.29</td><td></td></tr><tr><td>L_Ratio 3</td><td>-4.18</td><td>-0.22</td><td>-1.88</td><td>-0.18</td><td></td><td>-48.43*</td><td>-2.42</td><td>-25.27*</td><td>-2.56</td><td></td></tr><tr><td>L_Ratio 4</td><td>-417.70</td><td>-21.68</td><td>-222.67</td><td>-21.73</td><td></td><td>224.23</td><td>11.21</td><td>148.94</td><td>15.07</td><td></td></tr><tr><td>L_Ratio 5</td><td>-534.51</td><td>-27.74</td><td>-278.44</td><td>-27.17</td><td>-</td><td>42.96</td><td>2.15</td><td>46.61</td><td>4.71</td><td></td></tr><tr><td>L_Ratio 6</td><td>-381.47**</td><td>-19.80</td><td>-202.01**</td><td>-19.71</td><td>-</td><td>416.05**</td><td>20.79</td><td>240.75***</td><td>24.35</td><td></td></tr><tr><td>L_Ratio 7</td><td>81.29</td><td>4.22</td><td>37.32</td><td>3.64</td><td></td><td>63.62</td><td>3.18</td><td>22.32</td><td>2.26</td><td></td></tr><tr><td>L_In Carbon</td><td>5.22***</td><td>14.72</td><td>2.71***</td><td>14.89</td><td></td><td>4.47***</td><td>15.17</td><td>2.31***</td><td>16.09</td><td></td></tr><tr><td>In Sales</td><td>-0.01</td><td>-0.00</td><td>0.01</td><td>0.00</td><td></td><td>0.86*</td><td>0.00</td><td>0.44**</td><td>0.00</td><td></td></tr><tr><td>Year FE</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td></tr><tr><td>Industry FE</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td></tr><tr><td>Obs.</td><td colspan="2">124</td><td colspan="2">124</td><td colspan="2">183</td><td colspan="2">183</td><td colspan="2">183</td></tr><tr><td>McFadden R<sup>2</sup></td><td colspan="2">0.728</td><td colspan="2">0.723</td><td colspan="2">0.726</td><td colspan="2">0.713</td><td colspan="2">0.713</td></tr><tr><td>Log-likelihood</td><td colspan="2">-20.996</td><td colspan="2">-21.364</td><td colspan="2">-30.471</td><td colspan="2">-31.872</td><td colspan="2">-31.872</td></tr><tr><td>AIC</td><td colspan="2">63.992</td><td colspan="2">64.728</td><td colspan="2">82.943</td><td colspan="2">85.743</td><td colspan="2">85.743</td></tr><tr><td>BIC</td><td colspan="2">95.015</td><td colspan="2">95.751</td><td colspan="2">118.247</td><td colspan="2">121.047</td><td colspan="2">121.047</td></tr></tbody></table>

<table><thead><tr><th rowspan="3">Industry</th><th colspan="4">Building material and construction industry</th><th colspan="4">Shipping and transportation industry</th></tr><tr><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th><th colspan="2">Logit</th><th colspan="2">Probit</th><th rowspan="2">BKMR</th></tr><tr><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th><th>Coef.</th><th>AME</th></tr></thead><tbody><tr><td>Intercept</td><td>19.27**</td><td>-</td><td>11.38***</td><td>-</td><td></td><td>7.55</td><td>-</td><td>6.54</td><td>-</td><td></td></tr><tr><td>L_Ratio 1</td><td>20.20</td><td>1.71</td><td>16.67</td><td>2.50</td><td></td><td>-446.53</td><td>-23.47</td><td>-256.25</td><td>-25.35</td><td></td></tr><tr><td>L_Ratio 2</td><td>-18.70</td><td>-1.58</td><td>-7.91</td><td>-1.19</td><td></td><td>-33865.32</td><td>-856.70</td><td>-10971.95</td><td>-792.10</td><td>-</td></tr><tr><td>L_Ratio 3</td><td>12.01</td><td>1.02</td><td>6.67</td><td>1.00</td><td></td><td>45.57</td><td>2.40</td><td>23.299</td><td>2.31</td><td></td></tr><tr><td>L_Ratio 4</td><td>197.41</td><td>16.69</td><td>110.77</td><td>16.60</td><td></td><td>-1393.99</td><td>-73.23</td><td>-630.630</td><td>-62.36</td><td></td></tr><tr><td>L_Ratio 5</td><td>47.56</td><td>4.02</td><td>24.84</td><td>3.72</td><td></td><td>-341.51</td><td>-17.95</td><td>-163.802</td><td>-16.21</td><td>-</td></tr><tr><td>L_Ratio 6</td><td>-121.22***</td><td>-10.25</td><td>-72.12***</td><td>-10.81</td><td>-</td><td>-295.41</td><td>-15.53</td><td>-163.988</td><td>-16.22</td><td>-</td></tr><tr><td>L_Ratio 7</td><td>-4.21</td><td>-0.36</td><td>0.82</td><td>0.12</td><td></td><td>239.31</td><td>12.58</td><td>97.683</td><td>9.66</td><td>+</td></tr><tr><td>L_In Carbon</td><td>1.38***</td><td>0.38</td><td>0.79***</td><td>0.46</td><td></td><td>1.70**</td><td>14.25</td><td>0.86**</td><td>10.76</td><td></td></tr><tr><td>In Sales</td><td>-0.25</td><td>-0.00</td><td>-0.16</td><td>-0.00</td><td></td><td>1.44</td><td>0.00</td><td>0.59</td><td>0.00</td><td></td></tr><tr><td>Year FE</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td><td colspan="2">Yes</td></tr><tr><td>Industry FE</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td><td colspan="2">-</td></tr><tr><td>Obs.</td><td colspan="2">86</td><td colspan="2">86</td><td colspan="2">67</td><td colspan="2">67</td><td colspan="2">67</td></tr><tr><td>McFadden R<sup>2</sup></td><td colspan="2">0.548</td><td colspan="2">0.552</td><td colspan="2">0.717</td><td colspan="2">0.708</td><td colspan="2">0.708</td></tr><tr><td>Log-likelihood</td><td colspan="2">-23.833</td><td colspan="2">-23.595</td><td colspan="2">-11.323</td><td colspan="2">-11.663</td><td colspan="2">-11.663</td></tr><tr><td>AIC</td><td colspan="2">69.666</td><td colspan="2">69.190</td><td colspan="2">44.646</td><td colspan="2">45.326</td><td colspan="2">45.326</td></tr><tr><td>BIC</td><td colspan="2">96.664</td><td colspan="2">96.188</td><td colspan="2">68.897</td><td colspan="2">69.577</td><td colspan="2">69.577</td></tr></tbody></table>

Note: *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively; Ratio 1: Fixed Assets/Total Assets, Ratio 2: Inventory/Sales, Ratio 3: Net Income/Total Assets, Ratio 4: Total Debt/Total Assets, Ratio 5: Current Assets/Current Liabilities, Ratio 6: Receivables/Sales, Ratio 7: Cash/Total Assets; Year FE presents the year fixed effects. AME: Average marginal effect.
<table><caption>Table 4 The relationships between financial ratios and the risk of high carbon emissions on the basis of results for logit and probit models</caption><thead><tr><th>Industry</th><th>Ratio 1</th><th>Ratio 2</th><th>Ratio 3</th><th>Ratio 4</th><th>Ratio 5</th><th>Ratio 6</th><th>Ratio 7</th></tr></thead><tbody><tr><td>All</td><td></td><td>-</td><td>+</td><td></td><td>+</td><td>-</td><td>-</td></tr><tr><td>Food industry</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Plastic industry</td><td>+</td><td>-</td><td></td><td>+</td><td></td><td>-</td><td></td></tr><tr><td>Chemical industry</td><td>-</td><td></td><td></td><td>+</td><td>+</td><td>-</td><td></td></tr><tr><td>Semiconductor industry</td><td>+</td><td></td><td>+</td><td>-</td><td></td><td></td><td></td></tr><tr><td>Computer and peripheral equipment industry</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Optoelectronic industry</td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td></tr><tr><td>Electronic parts and components industry</td><td></td><td></td><td></td><td></td><td></td><td>+</td><td></td></tr><tr><td>Building material and construction industry</td><td></td><td></td><td></td><td></td><td></td><td>-</td><td></td></tr><tr><td>Shipping and transportation industry</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: Ratio 1: Fixed Assets/Total Assets, Ratio 2: Inventory/Sales, Ratio 3: Net Income/Total Assets, Ratio 4: Total Debt/Total Assets, Ratio 5: Current Assets/Current Liabilities, Ratio 6: Receivables/Sales, Ratio 7: Cash/Total Assets. + reports positive between CO2 and financial ratio; - reports negative between CO2 and financial ratio.