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Article

How Does the Power Generation Mix Affect the Market Value of US Energy Companies?

by
Silvia Bressan
Faculty of Economics and Management, Free University of Bozen, 39100 Bolzano, Italy
J. Risk Financial Manag. 2025, 18(8), 437; https://doi.org/10.3390/jrfm18080437
Submission received: 7 July 2025 / Revised: 27 July 2025 / Accepted: 4 August 2025 / Published: 6 August 2025
(This article belongs to the Special Issue Linkage Between Energy and Financial Markets)
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Abstract

To remain competitive in the decarbonization process of the economy worldwide, energy companies must preserve their market value to attract new investors and remain resilient throughout the transition to net zero. This article examines the market value of US energy companies during the period 2012–2024 in relation to their power generation mix. Panel regression analyses reveal that Tobin’s q and price-to-book ratios increase significantly for solar and wind power, while they experience moderate increases for natural gas power. In contrast, Tobin’s q and price-to-book ratios decline for nuclear and coal power. Furthermore, accounting-based profitability, measured by the return on assets (ROA), does not show significant variation with any type of power generation. The findings suggest that market investors prefer solar, wind, and natural gas power generation, thereby attributing greater value (that is, demanding lower risk compensation) to green companies compared to traditional ones. These insights provide guidance to executives, investors, and policy makers on how the power generation mix can influence strategic decisions in the energy sector.

1. Introduction

Superior shareholder value is key for strategic advantage and resilience during the energy transition. As power generation is one of the largest contributors to anthropogenic CO2 emissions, countries around the world are targeting the decarbonization of the energy sector, incentivizing the deployment of renewable energy technologies. Global energy investment is expected to exceed USD 3 trillion for the first time in 2024, with USD 2 trillion going to clean energy technologies and infrastructure. Investment in clean energy has accelerated since 2020, and spending on renewable power, grids, and storage is now higher than the total spending on oil, gas, and coal (International Energy Agency, 2024b). Production of green energy through sustainable, renewable sources is not only based on regulatory recommendations to combat climate change worldwide, but, more recently, it has become an imperative for companies to address energy supply shortages as a consequence of the war in Ukraine (Liao, 2023).
In this context, it is crucial for companies to boost their market values to secure stable equity funding. This funding is essential to maintain competitiveness and solvency on the path to a net zero economy. Accurate business valuation is key for energy companies, guiding decisions on capital allocation, risk management, and innovation. Technological advancement is vital in the energy sector to stay competitive and handle complex dynamics. Thus, reducing equity costs to enhance innovation performance is fundamental (Alkebsee et al., 2023; Chen et al., 2024). Therefore, determining whether renewable energy generation can increase corporate value is an important question for several stakeholders, including investors, regulators, and management. Few articles report evidence that renewable power generation improves financial dimensions in the energy sector (Dopierała et al., 2022; Dorigoni & Anzalone, 2024). However, the evidence is heterogeneous, related to measures for corporate performance (accounting or market-based metrics) and samples under examination. More frequently, the articles assess power generation in aggregate terms, but do not identify the different effects from the energy mix.
We expand this line of research and analyze, using panel regressions, a sample of US-traded energy companies during the period 2012–2024. We find that increasing renewable power generation (solar, wind, biomass, and hydroelectric) is positively correlated with the corporate market value measured by Tobin’s q and the price-to-book ratio. In statistical terms, the strongest impact is associated with solar and wind power. Furthermore, natural gas also has a positive effect, although smaller, on market values. In contrast, nuclear, petroleum oil, and coal power have a negative but only marginally significant influence on Tobin’s q and the price-to-book ratio. However, the accounting-based profitability measured by the return-on-assets (ROA) does not vary considerably with any type of energy source. In general, these findings suggest that market investors attribute a high value to solar, wind and natural gas sources of energy, according to theories of preferences for green investments (Gamel et al., 2016; Salm et al., 2016; Menyeh, 2021; Ansaram & Petitjean, 2024). However, such higher market valuations do not come with greater accounting-based profits. Therefore, the results suggest that the effects of power generation on company performance are nuanced and related to the issues of performance measurement and energy mix. This insight encourages recommendations to executives and policy makers.
The article is structured as follows. Section 2 reviews the literature most closely related to this article. Section 3 develops working hypotheses to test empirically. Section 4 presents the methodology. Section 5 summarizes and discusses the results. Section 6 concludes.

2. Literature

This article follows the stream of research that examines firm-specific determinants of the financial performance of energy companies. In general, the articles use both accounting indicators of performance, like the return on assets (ROA) and the return on equity (ROE), and market-based metrics like Tobin’s q, showing a significant correlation with corporate dimensions, such as leverage (Morina et al., 2021), firm growth (Morina et al., 2021), market capitalization (Tsai & Tung, 2017; Morina et al., 2021), and employee growth (Tsai & Tung, 2017).
The performance of the company is directly related to the costs of capital raising; therefore, some articles summarized by Steffen and Waidelich (2022) examine in more detail the funding costs of energy companies. Among these studies, Franc-Dabrowska et al. (2021) find that the weighted average cost of capital (WACC) of listed energy companies is positively correlated with company beta, market capitalization, and revenues, while negatively correlated with the age of the company and its effective tax rate. Finally, the risk dimension of company performance was also examined. For example, Sadorsky (2012b) develops and estimates a variable beta model to explain risk in the renewable energy sector, eventually showing that beta is negatively correlated with sales growth. In the nonrenewable energy sector instead, Bianconi and Yoshino (2014) use measures for stock returns and value at risk to show that both leverage and size are priced factors.
The growing attention among boards and policy makers on corporate social responsibility has spurred research to test whether environmental aspects affect the performance of energy companies. Pätäri et al. (2014) find that measures for concerns, as well as strength of concerns, on corporate social responsibility inside energy companies Granger-cause negative changes in their market capitalization. Sueyoshi and Goto (2009) observe that the environmental expenditures introduced by the US Clean Air Act had the effect of reducing the performance of utility firms, measured by ROA. P. Liu et al. (2022) use environmental, social, and governance (ESG) scores to assess the corporate social responsibility performance of Chinese energy companies, showing that profitability (ROA) varies significantly with different combinations of ESG ratings.
The pressure from regulators across sectors and businesses on the reduction of global greenhouse gas emissions (GHG) and the adoption of renewable energy sources and technologies poses the question of whether renewable power generation gives a competitive advantage. For example, Dorigoni and Anzalone (2024) show that the increasing share of renewable energy generation of European electric utilities is significantly and positively correlated with profitability (ROA) and market valuation (Tobin’s q). Dopierała et al. (2022) illustrate that the profitability of renewable energy producers in the Sea Baltic region followed an upward trend during the period 2011–2019, while fossil fuels producers exhibited the opposite negative trend. Morrone et al. (2022) indicate that the debt costs of international energy companies increase in their GHG emissions, while they decline as companies become more transparent about their environmental risks.1 Consistently, Kempa et al. (2021) confirm that renewable energy firms have a long-term cost advantage, while debt costs are likely to increase in the short term due to costly technologies alongside young and immature markets. Nevertheless, when comparing the costs of renewable and conventional power generation technologies in countries, Ram et al. (2018) and Timilsina (2021) report that the levelized electricity costs (LCOE) of renewables tend to be lower than the LCOE of fossil fuels and nuclear.
However, there is no consensus on whether energy companies that invest to increase their share of renewable energy generation are capable of effectively enjoying higher performance. Ruggiero and Lehkonen (2017) find that the volume of renewable energy produced by electric utilities negatively affects their profitability (ROA) and market value (Tobin’s q). Finally, Franc-Dabrowska et al. (2021) examine energy companies in the Baltic States and Central Europe in the years 2008–2017, showing that a large set of indicators of corporate profitability does not differ substantially between companies that use renewable energy sources and those that generate only fossil fuel-based energy. Similarly, the Altman Z-score (Altman, 1968), which approximates the risk of corporate bankruptcy, is not statistically different between firms.

3. Hypotheses

In this article, we analyze the market valuations of listed energy companies in the US. For current shareholders as well as potential new investors, market values give a clear indication of the performance of the business, indicating whether the current or future investment is worthwhile. Our main question is whether renewable power generation has a positive influence on the market value of energy companies. In fact, the industry is undergoing a significant transition towards renewable sources; therefore, grasping how the company equity valuation reacts to this process becomes vital for stakeholders.
Our argument is that renewable energy is of greater interest to investors, to the point that they apply a lower discount (i.e., a higher valuation) to companies that produce increasing shares of renewable energy compared to businesses relying on traditional nonrenewable power generation. This argument is consistent with the insights from the previous literature that investors exhibit a preference for investments in renewable energy (Hofman & Huisman, 2012; Leete et al., 2013; Gamel et al., 2016; Salm et al., 2016; Menyeh, 2021; Ansaram & Petitjean, 2024). To formally verify this argument, we employ two indicators of market value. The first is Tobin’s q, which is an index used to compare the market value of a company with the replacement value of its assets. Analysts use it to establish whether a company is over- or under-valued by the market, thereby driving investment decisions (Tobin & Brainard, 1976). This quantity has been examined in a few previous articles to assess the performance of energy companies (Tsai & Tung, 2017; Morina et al., 2021; Dorigoni & Anzalone, 2024). We extend this evidence by also computing and testing effects on the price-to-book ratio, i.e., the ratio of market value of equity capital to book value of equity capital. Traditionally, the price-to-book ratio was interpreted as indicating the expected return on equity (Wolfe & Zeff, 1963). Scholars have frequently used this quantity to model corporate growth; see, for instance, Nezlobin et al. (2016), Penman (1996), and Brief and Lawson (2014). Moreover, analysts regard the price-to-book ratio as a “margin of safety, a comparison of price to liquidation value” (Bodie et al., 2013). Therefore, testing both Tobin’s q and the price-to-book ratio seems to be a comprehensive way to examine market valuation effects. Moreover, although the previous literature mainly uses aggregate quantities for the generation of renewable/nonrenewable power (Dorigoni & Anzalone, 2024), we use separate measures for the type of power generation in our analysis. Specifically, renewable energy generation includes solar, wind, biomass, and hydroelectric power. Nonrenewable energy generation includes natural gas, nuclear, petroleum oil, and coal. This approach provides a more nuanced understanding of the impact of the power generation mix on company valuation. To summarize, the following two working hypotheses will be tested:
H1. 
Renewable power generation (solar, wind, biomass, and hydroelectric) is positively correlated with company market valuation (Tobin’s q and price-to-book).
H2. 
Nonrenewable power generation (natural gas, nuclear, petroleum oil, and coal) is negatively correlated with company market valuation (Tobin’s q and price-to-book).
The literature demonstrates that power generation is often correlated with accounting-based corporate performance. To verify whether the company’s profitability is associated with renewable energy generation, we use ROA (Sueyoshi & Goto, 2009; Ruggiero & Lehkonen, 2017; Morina et al., 2021; P. Liu et al., 2022). The argument posits that while renewable energy generation improves market values (HP1), it also improves cash flows, thus reflecting greater ROA. Thus, the working hypothesis is as follows:
H3. 
Renewable energy generation (solar, wind, biomass and hydroelectric) is positively correlated with company return on assets (ROA).

4. Data and Model

The data for this analysis are obtained from S&P Global Capital IQ.2 We focus on energy companies listed in the United States during 2012–2014, and the sample includes a total of 390 firm-year observations. To measure the company market value, we use Tobin’s q, which is an important and widely accepted measure of corporate performance in both the finance (Lee & Tompkins, 1999) and managerial literature (Westerman et al., 2020). Empirical studies on the performance of energy companies have by far used Tobin’s q; see, for instance, Salas-Fumás et al. (2016), Tsai and Tung (2017), Kim et al. (2018), Westerman et al. (2020), Morina et al. (2021), and Dorigoni and Anzalone (2024). Moreover, Tobin’s q was also employed by theoretical research developing models to explain performance in the energy sector (Lin & Huang, 2011; Saltari & Travaglini, 2011; Salas-Fumás et al., 2016). As we explained above, computing Tobin’s q of a company is a method for estimating a stock’s fair value, since it measures the company market value against the replacement cost of its assets. In our analysis, the variable Q is the company’s Tobin’s q computed as total book value debt plus market value of common stock (including the effect of any convertible subsidiary equity) divided by the total book value assets. A value of Q above (below) one indicates that the company is overvalued (undervalued) by the market.
Our second metric for corporate equity market value is the price-to-book ratio (PB), i.e., the share price divided by the book value per share. As mentioned above, the price-to-book ratio is typically used by market participants to assess the market’s perception of a particular stock’s value, and is frequently used to develop investment strategies and portfolio composition (Anderson & Garcia-Feijoo, 2006; Braouezec, 2009). In general, a PB greater (lower) than one means that the stock price is trading at a premium (discount) to the company’s book value. Finally, the accounting-based performance is measured by the return on assets (ROA), i.e., the ratio of net income to total book value assets. This metric has been used frequently to analyze the performance of energy companies (Sueyoshi & Goto, 2009; Westerman et al., 2020; Ruggiero & Lehkonen, 2017; Gong et al., 2021; Morina et al., 2021; P. Liu et al., 2022). Table 1 defines all variables in the analysis, while Table 2 reports descriptive statistics. The average Q is slightly lower than one. This is typical for utilities and energy firms, due to the high capital intensity this business typically requires (Dorigoni & Anzalone, 2024). PB, instead, is slightly higher than 2, indicating that companies are, on average, overvalued.
The estimation period is 2012–2024. See Table 1 for the definitions of the variables.
Data on power generation fall under the Fossil Fuel and Energy (FFE) data within the S&P Capital IQ Trucost Environmental section. S&P Capital IQ analysts follow the process below to assemble the dataset. If a company has disclosed the absolute figures of the generation data, these are captured and included in the library (in GWh). In cases where the company has disclosed no total generation data, the analyst searches for information on plant capacity and directly updates plant capacity (MW) in the Trucost model, which provides an approximation of the total generation by fuel type (in GWh) in the back end. In general, the data for this procedure are sourced from publicly available company disclosures, such as annual reports, sustainability reports, or the Carbon Disclosure Project.
As we consult the energy mix, we distinguish renewable/nonrenewable power generation. Renewable energy is energy derived from natural sources that are replenished at a higher rate than they are consumed (United Nations, 2025). We use the total amount (thousands of GWh) of the following renewable energy types: solar (SOLAR), wind (WIND), biomass (BIOM), and hydro-electric (HY DR). Instead, nonrenewable energy sources encompass the total amount (thousands of GWh) generated by natural gas (NGAS), nuclear (NUCL), petroleum oil (PETR), and coal (COAL). Figure 1 and Figure 2 represent the averages of power generation. In the subsectors, the larger amounts of renewable power generated correspond to wind and hydroelectric, reaching higher levels in the subsectors of “electric utilities” and “renewable energy”. Concerning nonrenewable energy, the power generated from natural gas, nuclear and coal is larger than the power generation from petroleum oil. The nonrenewable power generation is greater in the subsectors of “electric utilities” and “independent power producers and energy traders”. Table 3 computes the pairwise correlation among the variables that we use in the regression analysis. The numbers show that Tobin’s q is related to power generation. In fact, Q has a positive and significant correlation with SOLAR (equal to 0.1439) and WIND (equal to 0.2481), while a negative correlation with COAL (equal to −0.2130). PB follows a similar correlation pattern with power generation, although the significance is slightly smaller. Notice, however, that some firms may have a zero value corresponding to certain types of power generation. It could also be the case that a firm has some (but not all) missing figures for power generation if the analyst does not have sufficient information to elaborate generation data by fuel type. This means that not all firms in the sample cover the entire power generation mix. This also explains why the sample size among the following regressions may differ.
Thousands of GWh of renewable power generation (solar, wind, biomass, and hydroelectric) of energy subsectors in the US during 2012–2024 are shown. The definitions of the variables are reported in Table 1.
Thousands of GWh of nonrenewable power generation (natural gas, nuclear, petroleum oil, and coal) of energy subsectors in the US during 2012–2024 are shown. The definitions of the variables are reported in Table 1.
To test the working hypothesis HP1 formally, the following equation estimates the effect of renewable power generation on the market value of company j in year t:
Market valuev,j,t = α1 + β1Renewable Power Generationre,j,t + γ1Controlsj,t + ϵ1,j,t.
The subscript v stays alternatively for Tobin’s q (Q) and the price-to-book ratio (PB). The subscript re stays alternatively for SOLAR, WIND, BIOM, and HYDR. We control for company-specific aspects that affect company valuation (Westerman et al., 2020). SIZE is the company size measured by the natural logarithm of total assets. LEV is the approximate leverage calculated by computing the ratio of the total book value debt to total book value equity. CAPEXP is capital expenditures normalized by total revenues. We also include a control (OIL) for the average annual crude oil price that accounts for the relationship between oil prices and market performance in the energy sector (Henriques & Sadorsky, 2008; Sadorsky, 2012a; Kocaarslan & Soytas, 2019; Geng et al., 2021). The average annual crude oil price (in US dollars per barrel) is taken from Statista (2025). The terms α and ϵ are, respectively, a constant and the estimation error. The standard errors are robust to heteroskedasticity in the residuals. We also test the model in (1) by year-fixed effects to the set of controls, beyond clustering standard errors by firm, to control for potential autocorrelation in the residuals. In fact, a few recent articles point out that in observational accounting studies such as our analysis, there are a few modeling issues that complicate the researcher’s selection of the most suitable methods. In particular, some studies prefer to use standard pooled ordinary least squares regressions due to concerns related to the use and interpretation of fixed effects (DeHaan, 2021; Breuer & DeHaan, 2024; Jennings et al., 2024) or the clustering of standard errors (Abadie et al., 2023) for the examination of data with group variation. However, to give an exhaustive overview, our tables include alternative specifications. The validity of the working hypothesis HP1 implies that the coefficient β1 is positive in the regressions for Q and PB, indicating that market valuations increase with the amount of renewable power generation.
To test the working hypothesis HP2, instead, we modify the above equation by regressing the market value of company j in year t on nonrenewable power generation:
Market valuev,j,t = α2 + β2Nonrenewable Power Generationnre,j,t + γ2Controlsj,t + ϵ2,j,t.
In this case, the subscript nre stays for alternatively NGAS, NUCL, PETR, and COAL. The set of controls is the same as in Equation (1). The finding of a negative sign on β2 is interpreted as evidence favoring the validity of HP2, i.e., nonrenewable power generation is inversely associated with market values.
Finally, to test HP3, we estimate the regression of the return-on-assets (ROA) on renewable power generation:
ROAj,t = α3 + β3Renewable Power Generationre,j,t + γ3Controlsj,t + ϵ3,j,t.
The variables in (3) have the same interpretation as in Equation (1). When β3 is positive, it means that renewable energy generation has a positive impact on the profitability of the company.3

5. Results and Discussion

Table 4, columns (1) to (4), report results for model (1). The sign of SOLAR, WIND, BIOM, and HY DR is always positive, indicating that increasing renewable power generation leads to higher Q. However, only the coefficient (i.e., β1 in Equation (1)) for solar and wind power is statistically significant, even controlling for fixed effects (columns (5) to (8)). Regarding nonrenewable power generation, Table 5 shows that NUCL, PETR, and COAL all have negative but not significant effects on Tobin’s q. In contrast, NGAS has a positive coefficient, but its significance disappears when including the fixed effects. The results for PB in Table 6 and Table 7 illustrate similar behavior: The price-to-book ratio is positively (negatively) correlated with renewable (nonrenewable) power generation, with solar and wind power having the strongest positive impact. The R-squared in the regressions for PB tends to be slightly higher than in the regressions for Q. However, the models have relatively good explanatory power with respect to both dependent variables.
We verify whether ESG ratings play a role in determining the most significant findings of the previous regressions. In fact, the literature provides heterogeneous evidence on the relationship between ESG ratings and financial performance of energy companies (Yoon et al., 2018; Behl et al., 2022; Naseer et al., 2024; Verma & Shroff, 2025), as it remains not well established that energy companies with good-quality ESG will also outperform. Despite the different approaches in the calculation of ESG ratings, corporate policies related to power generation directly affect the environmental pillar more than the other dimensions of social and governance of energy companies (see, for example, Zhao et al. (2018)). Therefore, we estimate the models in (1) by adding a control for the environmental score of the company (E) and also its interaction with the different power sources among SOLAR, WIND, and NGAS, which in previous estimates were found to have the strongest effects. Environmental scores are obtained from S&P Capital IQ and are available from 2013. The variable E ranges from 0 to 100, and a higher number indicates that the company has good performance on environmental dimensions. Table 8 illustrates that the market performance of energy companies does not vary with environmental scores. The sign of E does not point in a unique direction and is never significant. Its interaction terms with power sources are negative but almost never significant. Instead, the coefficients of SOLAR, WIND, and NGAS are positive and statistically relevant, as in the previous Table 4, Table 5, Table 6 and Table 7. This indicates that environmental ratings do not influence our main finding of a positive association between power generation and market valuations.
Finally, in Table 9, the equations for ROA do not find any statistically significant impact of renewable power generation on accounting-based corporate profitability. Thus, HP3 does not seem to provide a plausible explanation, as the sign of renewable power generation is—opposite to our prediction—negative in most of the models, although it is never significant. In Appendix A, Table A3 shows that nonrenewable power generation is also significantly related to ROA.
In general, the findings illustrate that HP1 is valid for wind and solar power generation. Thus, all four types of renewable energy relate to higher market values, but only wind and solar energy play a considerable role. The standard nonrenewable energy sources (nuclear, petroleum oil, and coal) are negatively correlated with market values, according to HP2. Natural gas, instead, impacts market values in a positive way, although the effect is relatively small. Natural gas is a cleaner source of power than coal, petroleum, and other fossil-fuel counterparts. Burning natural gas for energy results in fewer emissions of nearly all types of air pollutants and carbon dioxide (CO2) emissions than burning coal or petroleum products to produce an equal amount of energy. Evidence in the literature shows that natural gas consumption has a negative impact on CO2 emissions (Dong et al., 2017; Li & Su, 2017; Ummalla & Samal, 2019), suggesting that natural gas could be an important complementary transition fuel to support the renewable energy transition (Safari et al., 2019).
We obtain results in line with previous literature showing that the market valuation (Tobin’s q) of energy companies increases in the generation of renewable power (Dorigoni & Anzalone, 2024). However, we contribute to this prior evidence that shows that this result also holds for the price-to-book ratio, which market investors and the finance literature regard as a key metric for investment decisions. In addition, expanding the previous literature using aggregate power amounts, our approach allows a deeper and nuanced understanding of how the market assessment of equity value incorporates the energy mix, since we identified that solar and wind generation have stronger impacts. In fact, energy authorities in this field underscore the urgent need for the timely integration of solar and wind capacity to achieve global decarbonization goals, as these technologies are projected to contribute significantly to meeting the growing demand for electricity by 2030 (International Energy Agency, 2024a). Consequently, research in the fields of environmental sciences has focused efforts to illustrate how the combined effects of wind and solar power would reduce CO2 emissions and limit environmental impacts (Thomaidis et al., 2016; Carnevale et al., 2016; L. Liu et al., 2020; Hassan et al., 2023; Boubii et al., 2024).
Our analysis has delivered outcomes consistent also with previous results that accounting-based profitability (ROA) is not influenced by renewable energy generation (Hulshof & Mulder, 2020; Zhang et al., 2020). In general, our interpretation is that investors in the equity market are attracted to cleaner sources of energy. Assuming that investors establish company value with discounted cash flow methods, they have to use a discount rate that reflects their investment preferences. This discount rate indicates, in economic terms, the compensation required by investors to incur the risks related to the holding of company stock (Pratt, 2003). The results suggest that lower discount rates would be attached to wind and solar energy, eventually pushing upward the equity value of companies that are capable of increasing the power generated from these sources. However, this positive effect vanishes with respect to ROA-based accounting performance, that is, a metric that captures benefits in the short term (Gavetti et al., 2012; Ben-Oz & Greve, 2015), while it does not reveal whether potential advantages would be more appreciable over longer periods.

6. Conclusions

The benefits of renewable energy resources have attracted a great deal of attention from academics and professionals, especially after the Kyoto Protocol. This topic is important for valuation studies, since capturing the economic value of the benefits from renewable energy generation is currently at the center of management decisions and policy making in the energy sector. This study has analyzed the relationship between power generation and the valuation of company equity of US energy companies during the period 2012–2024. Panel data regressions have shown that the market value of energy companies increases with renewable power generation, especially solar and wind power. In addition, market values also increase with the generation of power from natural gas. In contrast, nonrenewable energy sources (i.e., coal, nuclear, and petroleum oil) are negatively correlated with market valuations. The findings are in line with the hypothesis that market investors attribute a valuation premium (i.e., a lower risk compensation) to cleaner sources of energy compared to traditional sources. However, we also find that the company accounting-based profitability measured by the return-on-assets (ROA) does not vary significantly with power generation. That is, green energy sources have a greater impact on investors’ valuation models, while, at the same time, they do not positively influence the (short-term) cash flows factored into the contemporaneous ROA.
These findings are relevant for investors, stakeholders, and analysts in their assessment of the benefits of green energy, suggesting directions for policy making and future research. In fact, testing the energy mix provides insight into the advantages and challenges of various energy sources. Thus, different types of energy impact the environment in various ways and have different consequences for the competitiveness of the company. However, there is no clear evidence in the existing literature that green energy generation provides an effective advantage to energy companies (Hall et al., 2014). Within the framework of this debate, our results have shown that market values vary depending on the type of energy source generated by companies. However, the findings also point out that this relationship is affected by the measurement of company performance, which is an issue that both executives and asset managers should take into account.
Furthermore, research efforts could be made to explain more in depth the mechanism that drives market values to factor in the power generation mix of energy companies. For example, our results based on panel regressions hint at a more detailed examination of behaviors over time. Therefore, additional tests could include, for instance, dynamic panel models or the usage of alternative financial metrics that would allow more in-depth identification of whether advantages for corporate valuation develop over the short/long horizon (Dobbs & Koller, 2005; Martynov & Shafti, 2016; Wibbens & Siggelkow, 2020). Finally, the empirical outcomes and the related arguments would gain robustness by examining measures of expected returns, for example, based on the skewness of the stock return (Boyer et al., 2010). In fact, Discounted Cash Flow (DCF) methods are based on the assumption that investors discount the future cash flows according to their subjective preferences captured by their future expected returns (see, for example, Damodaran (2007)). Therefore, an interesting suggestion for future research would be to verify whether investors’ discount rates also reflect their preferences concerning the company power generation mix, with an ultimate impact on the corporate equity value.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to proprietary restrictions.

Conflicts of Interest

The author declares no conflicts of interest.

Appendix A

Table A1. Effect of power generation on the value of company equity.
Table A1. Effect of power generation on the value of company equity.
(1)
Q		(2)
Q		(3)
PB		(4)
PB
SOLAR0.0239 *** 0.0956 ***
(0.006) (0.027)
WIND 0.0051 *** 0.0221 ***
(0.001) (0.007)
COAL−0.0004−0.0008−0.0055 ***−0.0060 ***
(0.000)(0.001)(0.002)(0.002)
SIZE−0.0659 ***−0.0545 ***−0.1773 ***−0.1260 **
(0.017)(0.019)(0.058)(0.063)
CAPEXP0.3128 ***0.3203 ***0.38310.7146
(0.104)(0.122)(0.397)(0.474)
LEV0.0426 ***0.0364 **0.7878 ***0.7146 ***
(0.015)(0.016)(0.107)(0.151)
OIL−0.0006−0.0002−0.0019−0.0019
(0.000)(0.001)(0.002)(0.002)
Constant1.9356 ***1.7507 ***3.9337 ***3.0863 ***
(0.275)(0.295)(0.898)(0.952)
Observations214198218192
R-squared0.2510.2630.4270.366
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1): Estimates from panel regression models for Q according to Equation (1), including both the regressors SOLAR and COAL. Columns (2): Estimates from panel regression models for Q according to Equation (1), including both the regressors WIND and COAL. Columns (3): Estimates from panel regression models for PB according to Equation (2), including both the regressors SOLAR and COAL. Columns (4): Estimates from panel regression models for PB according to Equation (2), including both the regressors WIND and COAL. Robust standard errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table A2. Effect of power generation on the price-earnings ratio.
Table A2. Effect of power generation on the price-earnings ratio.
(1)(2)(3)(4)(5)(6)(7)(8)
PEPEPEPEPEPEPEPE
SOLAR0.0544 ***
(0.004)
WIND 0.0020 *
(0.001)
BIOM −0.0088
(0.033)
HY DR 0.0085
(0.008)
NGAS 0.0901 **
(0.000)
NUCL −0.0001
(0.000)
PETR −0.0005
(0.003)
COAL 0.0009
(0.001)
SIZE−0.0556 ***−0.0506 **−0.0717 *−0.0506 **−0.0142−0.00650.0075−0.0151
(0.017)(0.020)(0.041)(0.022)(0.014)(0.028)(0.011)(0.014)
LEV0.0398 **0.0513 **0.04830.0835 ***0.0159−0.00660.00920.0130
(0.020)(0.024)(0.040)(0.031)(0.016)(0.018)(0.020)(0.019)
OIL0.00000.00010.0007−0.0003−0.0006−0.00060.0002−0.0007
(0.001)(0.001)(0.001)(0.001)(0.000)(0.001)(0.000)(0.000)
Constant1.1411 ***1.0686 ***1.4107 **1.0119 ***0.5121 **0.40300.07330.5008 **
(0.278)(0.324)(0.637)(0.365)(0.236)(0.527)(0.197)(0.230)
Observations33523999212299170193239
R-squared0.0610.0610.0830.1100.0120.0120.0030.025
The estimation period is 2012–2024. The price-earnings ratio (PE) is the ratio of stock price to earnings in the last 12 months. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for the price-earnings ratio according to Equation (1). Columns (5)–(8): Estimates from panel regression models for the price-earnings ratio according to Equation (2). Robust standard errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table A3. Effect of nonrenewable power generation on the return-on-assets.
Table A3. Effect of nonrenewable power generation on the return-on-assets.
(1)(2)(3)(4)(5)(6)(7)(8)
ROAROAROAROAROAROAROAROA
NGAS0.0015 0.0016
(0.004) (0.004)
NUCL −0.0050 ** −0.0050 **
(0.002) (0.002)
PETR 0.0344 −0.0082
(0.045) (0.074)
COAL −0.0047 −0.0065
(0.004) (0.005)
SIZE−0.2108 **−0.0690−0.4165 ***−0.1234−0.2174−0.0226−0.4672 ***−0.1428
(0.087)(0.110)(0.097)(0.084)(0.161)(0.211)(0.141)(0.176)
CAPEXP−0.3539−0.52090.3277−0.4275−0.4279−0.18470.7190−0.2813
(0.334)(0.692)(0.682)(0.687)(0.255)(0.669)(0.555)(0.744)
LEV0.1356−0.28950.09700.16130.1529−0.17800.18210.2104
(0.103)(0.248)(0.155)(0.143)(0.152)(0.111)(0.184)(0.192)
OIL0.0033−0.0017−0.00020.0032
(0.003)(0.003)(0.003)(0.003)
Constant6.1178 ***4.8980 ***9.9209 ***4.8132 ***6.3557 **3.662710.8166 ***5.1478 *
(1.505)(1.765)(1.681)(1.384)(2.537)(3.211)(2.541)(2.661)
Fixed effectsNoNoNoNoYesYesYesYes
Observations344187211264344187211264
R-squared0.0440.0630.0900.0390.0690.1450.1570.080
Estimates from panel regression models for ROA on nonrenewable power generation and control variables. The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. In columns (1)–(4), the robust standard errors are in parentheses. In columns (5)–(8), the standard errors are clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

Notes

1
This result also extends to other sectors, as confirmed by Kozak (2021), showing that non-financial companies of various sectors in the EU in the years 2018–2021 that reduced the intensity of their CO2 emissions could reduce their debt costs considerably.
2
https://www.spglobal.com/market-intelligence/en/solutions/products/sp-capital-iq-pro (accessed on 27 June 2025).
3
We run regressions for each type of energy separately to have larger samples for our estimation. In fact, by including two or more than one energy type simultaneously in the regressions, we ended up having only very small sub-samples to examine, because not all firms generate multiple types of power. However, to provide an example for the partial effects, in Table A1 of Appendix A we show that SOLAR and WIND have both positive significant coefficients in the regressions for Q and PB as we also control for COAL. In addition, we run the regressions in (1) and (2) for the trailing price-earnings (PE) ratio as another metric of equity valuation. This number represents the ratio between the price of the share and the earnings of the company in the last 12 months, showing how much investors are willing to spend for each dollar of the company’s profit. The results we obtained were in line with the results reported in Tables 6 and 7. However, in heavy-asset industries (such as the energy sector, see https://financestu.com/asset-intensive-industries/, accessed on 27 June 2025), analysts tend to prefer using book-value measures of equity rather than the PE ratio (https://www.analystinterview.com/article/the-price-book-ratio-explained, accessed on 27 June 2025). Therefore, we prefer to report in the main text the results for Q and PB, while the results for the price-earnings ratio can be found in Table A2 inside Appendix A.

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Jrfm 18 00437 g001
Figure 1. Renewable power generation (000 GWh) in energy subsectors.
Figure 1. Renewable power generation (000 GWh) in energy subsectors.
Jrfm 18 00437 g001
Jrfm 18 00437 g002
Figure 2. Nonrenewable power generation (000 GWh) in energy subsectors.
Figure 2. Nonrenewable power generation (000 GWh) in energy subsectors.
Jrfm 18 00437 g002
Table 1. Definition of variables.
Table 1. Definition of variables.
Variable TypeVariable NameDefinition
Dependent variablesQRatio of total book value debt plus market value of common stock
to total book value assets.
PBRatio of share price to book value per share.
ROARatio of total net income to total book value assets.
Renewable power generation (000 GWh)SOLARSolar power generated by the company.
WINDWind power generated by the company.
BIOMBiomass power generated by the company.
HY DRHydroelectric power generated by the company.
Nonrenewable power generation (000 GWh)NGASNatural gas power generated by the company.
NUCLNuclear power generated by the company.
PETRPetroleum (oil) power generated by the company.
COALCoal power generated by the company.
ControlsSIZELog of total assets.
LEVRatio of total book value debt to total book value equity.
CAPEXPRatio of total capital expenditures to total revenues.
OILAverage annual OPEC crude oil price (in US dollars per barrel).
EEnvironmental score of the company.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
MeanMinimumMaximumStandard Deviation
Dependent variables
Q0.95450.44761.63200.2101
PB2.10230.77485.98080.9653
ROA0.0269−0.02210.07340.0127
Renewable power generation (000 GWh):
SOLAR1.11080.000219.79862.2487
WIND5.47680.001862.83199.3200
BIOM0.42290.00013.45830.6354
HY DR3.42100.005438.85055.0827
Nonrenewable power generation (000 GWh):
NGAS19.53150.0056108.561527.4703
NUCL30.89031.2814182.843439.6494
PETR0.98330.000210.01791.8904
COAL20.10270.0271102.725320.8614
Controls:
SIZE16.979112.222019.01021.2531
LEV1.62350.52675.45291.0077
CAPEXP0.38450.02322.38740.3250
OIL (USD)70.463340.7600109.451121.7590
E (0–100)47.020210.000084.000016.5799
Table 3. Pairwise correlation coefficients.
Table 3. Pairwise correlation coefficients.
QPBROASOLARWINDBIOMHY DRNGASNUCLPETRCOALSIZELEVCAPEXPOILE
Q1.0000
PB0.6603 ***1.0000
ROA0.2383 ***0.2450 ***1.0000
SOLAR0.1439 **0.1023 *−0.08381.0000
WIND0.2481 ***0.1436 *−0.07320.6112 ***1.0000
BIOM−0.1963 *−0.1391−0.02580.5182 ***0.08101.0000
HY DR−0.1032−0.0115−0.05790.11700.0564−0.17371.0000
NGAS0.07540.1019−0.05530.5416 ***0.6202 ***0.02900.12171.0000
NUCL−0.1616 *−0.0778−0.1655 *0.1624 *−0.0035−0.1640−0.02690.2895 ***1.0000
PETR−0.03650.09470.0263−0.0100−0.10950.21970.4963 ***0.10020.2118 *1.0000
COAL−0.2130 ***−0.1215−0.09520.0489−0.0792−0.13300.6448 ***0.3491 ***−0.02590.11031.0000
SIZE−0.3219 ***−0.1996 ***0.1061 *0.3867 ***0.3045 ***0.2803 **0.3207 ***0.5096 ***0.5090 ***0.1366 *0.4117 ***1.0000
LEV0.2340 ***0.6313 ***0.0697−0.0256−0.0831−0.2136 *0.08000.0424−0.2093 **0.2088 **0.1664 **−0.1376 **1.0000
CAPEXP0.2149 ***0.2175 ***−0.2288 ***0.1397 **0.2164 ***0.1439−0.05700.1247 *0.02960.0017−0.2370 ***−0.2148 ***0.2164 ***1.0000
OIL−0.0985−0.1485 **0.03290.00730.04540.11810.05280.0252−0.0165−0.03150.01460.0368−0.0896−0.05081.0000
E−0.1911 **−0.1139 **−0.02420.10660.03770.1788 *0.1334 **0.1773 ***0.4416 ***0.08130.1612 **0.4237 ***0.0027−0.08140.08611.0000
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 4. Effect of renewable power generation on Tobin’s q.
Table 4. Effect of renewable power generation on Tobin’s q.
(1)(2)(3)(4)(5)(6)(7)(8)
QQQQQQQQ
SOLAR0.0277 *** 0.0244 ***
(0.006) (0.009)
WIND 0.0074 *** 0.0066 ***
(0.001) (0.002)
BIOM −0.0253 −0.0157
(0.023) (0.047)
HY DR 0.0002 0.0021
(0.002) (0.003)
SIZE−0.0817 ***−0.0544 ***−0.0211−0.0688 ***−0.0784 ***−0.0489 **−0.0224−0.0703 ***
(0.011)(0.011)(0.016)(0.011)(0.021)(0.021)(0.026)(0.023)
CAPEXP0.0788 *0.01610.21250.12290.10730.04270.24080.1470
(0.042)(0.036)(0.144)(0.077)(0.069)(0.053)(0.278)(0.131)
LEV0.0329 ***0.0463 ***0.0882 ***0.0426 ***0.0288 *0.0471 ***0.0890 ***0.0353 **
(0.011)(0.010)(0.015)(0.015)(0.016)(0.016)(0.020)(0.017)
OIL−0.0006−0.0003−0.0005−0.0004
(0.000)(0.000)(0.001)(0.000)
Constant2.2764 ***1.8105 ***1.1464 ***2.0395 ***1.7936 ***1.5161 ***1.0040 *1.8572 ***
(0.202)(0.192)(0.279)(0.210)(0.339)(0.379)(0.518)(0.423)
Foxed effectsNoNoNoNoYesYesYesYes
Observations390281113250390281113250
R-squared0.2410.2680.3390.2690.3310.4100.4050.337
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for Q according to Equation (1). Robust standard errors are in parentheses. Columns (5)–(8): Estimates from panel regression models for Q according to Equation (1) with inclusion of fixed effects and standard errors clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 5. Effect of nonrenewable power generation on Tobin’s q.
Table 5. Effect of nonrenewable power generation on Tobin’s q.
(1)(2)(3)(4)(5)(6)(7)(8)
QQQQQQQQ
NGAS0.0016 *** 0.0017
(0.000) (0.001)
NUCL −0.0008 ** −0.0008
(0.000) (0.001)
PETR −0.0042 −0.0028
(0.009) (0.023)
COAL −0.0004 0.0000
(0.000) (0.001)
SIZE−0.0731 ***0.0111−0.0984 ***−0.0487 ***−0.0741 ***0.0041−0.0961 ***−0.0459
(0.013)(0.017)(0.019)(0.017)(0.027)(0.041)(0.032)(0.032)
CAPEXP0.1838 ***0.5003 ***0.7076 ***0.4858 ***0.1969 *0.4983 *0.7236 ***0.4631 **
(0.064)(0.129)(0.119)(0.121)(0.104)(0.245)(0.202)(0.213)
LEV0.0359 ***0.03540.0686 ***0.0505 ***0.02910.02920.0685 ***0.0408 **
(0.014)(0.037)(0.017)(0.015)(0.018)(0.046)(0.023)(0.017)
OIL−0.0006−0.0006−0.0002−0.0006
(0.000)(0.000)(0.001)(0.000)
Constant2.0934 ***0.5313 *2.3279 ***1.5928 ***1.7057 ***0.44562.1061 ***1.2125 **
(0.229)(0.313)(0.331)(0.264)(0.417)(0.642)(0.574)(0.478)
Fixed effectsNoNoNoNoYesYesYesYes
Observations344187211264344187211264
R-squared0.2120.2230.2920.1800.3310.2830.3670.318
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for Q according to Equation (2). Robust standard errors are in parentheses. Columns (5)–(8): Estimates from panel regression models for Q according to Equation (2) with inclusion of fixed effects and standard errors clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 6. Effect of renewable power generation on the price-to-book ratio.
Table 6. Effect of renewable power generation on the price-to-book ratio.
(1)(2)(3)(4)(5)(6)(7)(8)
PBPBPBPBPBPBPBPB
SOLAR0.0885 *** 0.0833 **
(0.024) (0.035)
WIND 0.0247 *** 0.0226 ***
(0.006) (0.008)
BIOM 0.0371 0.0621
(0.061) (0.124)
HY DR 0.0079 0.0120
(0.008) (0.009)
SIZE−0.1679 ***−0.0787 **0.0788−0.1691 ***−0.1617 **−0.06340.0683−0.1741 **
(0.037)(0.040)(0.068)(0.045)(0.066)(0.071)(0.116)(0.067)
CAPEXP0.07690.0812−0.7318−0.14860.11370.1318−0.7545−0.2037
(0.189)(0.204)(0.620)(0.356)(0.223)(0.280)(0.659)(0.413)
LEV0.6052 ***0.6360 ***0.8404 ***0.4953 ***0.6016 ***0.6481 ***0.8626 ***0.4957 ***
(0.073)(0.076)(0.088)(0.094)(0.100)(0.116)(0.118)(0.105)
OIL−0.0031 **−0.0033 *−0.0038−0.0031 *
(0.002)(0.002)(0.003)(0.002)
Constant4.1031 ***2.5529 ***−0.07044.4365 ***2.7560 **1.4480−0.72523.8532 ***
(0.661)(0.707)(1.047)(0.826)(1.041)(1.275)(2.010)(1.254)
Fixed effectsNoNoNoNoYesYesYesYes
Observations384275110246384275110246
R-squared0.4540.4900.6610.3970.4930.5570.7300.447
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for PB according to Equation (1). Robust standard errors are in parentheses. Columns (5)–(8): Estimates from panel regression models for PB according to Equation (1) with inclusion of fixed effects and standard errors clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 7. Effect of nonrenewable power generation on the price-to-book ratio.
Table 7. Effect of nonrenewable power generation on the price-to-book ratio.
(1)(2)(3)(4)(5)(6)(7)(8)
PBPBPBPBPBPBPBPB
NGAS0.5531 *** 0.5701
(0.194) (0.350)
NUCL 0.0562 0.0681
(0.197) (0.336)
PETR 1.3278 2.2825
(2.629) (4.723)
COAL −0.5317 *** −0.4647
(0.192) (0.367)
SIZE−18.4974 ***−2.5726−24.5918 ***−10.8236 *−19.1342 **−5.4859−23.2210 ***−10.2284
(5.075)(7.635)(5.445)(5.504)(8.725)(18.066)(8.438)(11.310)
CAPEXP50.4330 **122.5942 **160.7197 ***110.6148 **50.5320 *116.9055158.6296 *101.8664
(22.467)(53.291)(53.325)(50.042)(28.632)(102.373)(93.533)(94.128)
LEV64.8627 ***55.2558 ***76.0695 ***81.2503 ***63.9481 ***51.1242 *75.6428 ***79.3047 ***
(9.244)(20.799)(12.343)(10.947)(13.294)(27.797)(10.221)(10.769)
OIL−0.2563−0.2353−0.1396−0.1611
(0.156)(0.187)(0.193)(0.173)
Constant415.1760 ***129.5584478.1210 ***255.3142 ***308.3223 **100.2863398.3238 **148.7629
(88.157)(127.553)(88.337)(84.010)(136.534)(280.052)(148.200)(165.799)
Fixed effectsNoNoNoNoYesYesYesYes
Observations338185206258338185206258
R-squared0.4310.2340.3910.3760.4710.2750.4460.433
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for PB according to Equation (2). Robust standard errors are in parentheses. Columns (5)–(8): Estimates from panel regression models for PB according to Equation (2) with inclusion of fixed effects and standard errors clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 8. Effect of renewable power generation on market valuations—Control for the company environmental rating.
Table 8. Effect of renewable power generation on market valuations—Control for the company environmental rating.
(1)(2)(3)(4)(5)(6)
QQQPBPBPB
SOLAR0.0465 ** 0.1789 **
(0.022) (0.087)
E0.0004−0.00110.00020.0031−0.00080.0045
(0.001)(0.001)(0.001)(0.003)(0.004)(0.003)
SOLAR × E−0.0004 −0.0019
(0.000) (0.002)
WIND 0.0112 * 0.0424 *
(0.007) (0.030)
WIND × E −0.0001 −0.0004
(0.000) (0.001)
NGAS 0.0037 *** 0.0149 **
(0.001) (0.006)
NGAS × E −0.0000 −0.0002 *
(0.000) (0.000)
SIZE−0.1028 ***−0.0591 ***−0.0897 ***−0.2500 ***−0.1159 **−0.2601 ***
(0.014)(0.013)(0.015)(0.049)(0.057)(0.065)
CAPEXP0.1462 ***0.03200.2356 **0.20770.16670.6311 **
(0.056)(0.044)(0.091)(0.214)(0.243)(0.304)
LEV0.0247 **0.0392 ***0.0319 **0.5892 ***0.6186 ***0.6469 ***
(0.012)(0.011)(0.014)(0.076)(0.081)(0.094)
OIL−0.00010.00000.0002−0.0030 *−0.0031−0.0010
(0.000)(0.001)(0.000)(0.002)(0.002)(0.002)
Constant2.5994 ***1.9514 ***2.3208 ***5.3928 ***3.2891 ***5.1365 ***
(0.240)(0.235)(0.265)(0.811)(0.926)(1.095)
Observations346242311340236305
R-squared0.2890.3190.2510.4700.5050.439
Columns (1)–(3): Estimates from panel regression models for Q according to Equation (1). Columns (4)–(6): Estimates from panel regression models for PB according to Equation (2). The estimation period is 2013–2024. E is the company environmental score (on a scale of 0–100). The definition of all other variables is reported in Table 1. Robust standard errors are in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
Table 9. Effect of renewable power generation on the return-on-assets.
Table 9. Effect of renewable power generation on the return-on-assets.
(1)(2)(3)(4)(5)(6)(7)(8)
ROAROAROAROAROAROAROAROA
SOLAR−0.0315 −0.0192
(0.025) (0.035)
WIND −0.0013 0.0007
(0.007) (0.012)
BIOM 0.1326 −0.1163
(0.128) (0.219)
HY DR −0.0131 −0.0216
(0.011) (0.022)
SIZE−0.0817−0.05610.1236−0.0399−0.0934−0.06460.1301−0.0291
(0.060)(0.071)(0.093)(0.090)(0.125)(0.152)(0.204)(0.211)
CAPEXP−0.4930 *−0.5511 **−1.3488 **−0.7902 **−0.6300 **−0.6809 **−1.1654−0.9318
(0.288)(0.257)(0.643)(0.391)(0.235)(0.293)(1.032)(0.666)
LEV0.10000.1700 **0.4690 ***0.09810.11410.17330.4700 ***0.1440
(0.082)(0.070)(0.065)(0.086)(0.135)(0.167)(0.130)(0.204)
OIL0.00220.0058 **−0.00100.0044
(0.003)(0.003)(0.004)(0.003)
Constant4.0696 ***3.2218 **0.12643.2524 **4.5268 **4.05770.80423.9296
(1.073)(1.255)(1.553)(1.635)(1.972)(2.780)(3.418)(3.873)
Fixed effectsNoNoNoNoYesYesYesYes
Observations390281113250390281113250
R-squared0.0330.0610.2920.0350.0600.0970.3890.123
The estimation period is 2012–2024. The definitions of the variables are reported in Table 1. Columns (1)–(4): Estimates from panel regression models for ROA according to Equation (3). Robust standard errors are in parentheses. Columns (5)–(8): Estimates from panel regression models for ROA according to Equation (3) with inclusion of fixed effects and standard errors clustered at the company level. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.
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Bressan, S. How Does the Power Generation Mix Affect the Market Value of US Energy Companies? J. Risk Financial Manag. 2025, 18, 437. https://doi.org/10.3390/jrfm18080437

AMA Style

Bressan S. How Does the Power Generation Mix Affect the Market Value of US Energy Companies? Journal of Risk and Financial Management. 2025; 18(8):437. https://doi.org/10.3390/jrfm18080437

Chicago/Turabian Style

Bressan, Silvia. 2025. "How Does the Power Generation Mix Affect the Market Value of US Energy Companies?" Journal of Risk and Financial Management 18, no. 8: 437. https://doi.org/10.3390/jrfm18080437

APA Style

Bressan, S. (2025). How Does the Power Generation Mix Affect the Market Value of US Energy Companies? Journal of Risk and Financial Management, 18(8), 437. https://doi.org/10.3390/jrfm18080437

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