Analyzing Climate Change Exposure and CEO Turnover: Evidence from U.S. Firms
School of Business, Indiana University Kokomo, Kokomo, IN 46902, USA
Int. J. Financial Stud. 2025, 13(3), 117; https://doi.org/10.3390/ijfs13030117
Submission received: 18 May 2025
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Revised: 23 June 2025
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Accepted: 27 June 2025
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Published: 1 July 2025
(This article belongs to the Special Issue Sustainable Investing and Financial Services)
Abstract
This work explores the link between CEO turnover patterns and firms’ climate change exposure in a data set of over two thousand U.S. publicly traded firms. The findings demonstrate that CEO turnover is negatively associated with measures of climate change exposure developed with machine learning based on the frequency of discussions linked to climate change in the firms’ earnings conference calls. The results further indicate that this significant negative relationship exists in the year after the CEO’s departure from the firm, not before their departure. CEO turnover scenarios differ in their impact on a firm’s climate change exposure and sentiment. The focus of a firm’s management and financial analysts covering the firm can shift away from the issues of climate change. The negative and significant relationship with firms’ climate change exposure is observed particularly for forced CEO departures in firings or resignations, as well as for outsider CEO replacements. No significant relationship is found for CEO departures due to retirement or for cases of internal CEO succession. The results provide insights for decision makers, investors and boards of directors trying to evaluate the role of CEO turnover in climate change exposure at firms.
1. Introduction
Issues of climate change and sustainability profoundly affect organizations around the world as they strive to employ responsible practices and sustainable business models. Firms, financial regulators, institutional investors, and other stakeholders drive transformation in the financial services sector with sustainable finance (Edmans & Kacperczyk, 2022; Kumar et al., 2025). Climate change in particular presents multifaceted physical and transition risks that strongly affect corporate operations and financial stability (Giglio et al., 2021; Pizzutilo et al., 2020; Wang et al., 2022; Aldoseri & Albaz, 2023; Sui, 2025; Qiu et al., 2025).
Recent studies suggest that turnover of chief executive officers (CEOs) and other high-profile employees may be associated with climate change exposure at businesses, particularly when the latter is manifested as poor environmental performance. For instance, Cornelli et al. (2024) report that a higher likelihood of a CEO being subjected to forced turnover exists when the firm has a lower ranking on environmental measures. This relationship underscores the growing importance of environmental stewardship in the evaluation of top executives. Shao et al. (2025) find that better performance on environmental, social, and governance (ESG) measures reduces the likelihood of CEO dismissal in China. Furthermore, Li (2023) demonstrates that higher measures of climate change exposure at firms are associated with larger turnover rates for the firms’ innovative employees. However, a gap in the literature still exists as the link between various potential scenarios of CEO turnover and firms’ climate change exposure has not been fully explored and little evidence exists on whether CEO turnover affects how firms view climate change.
One key challenge lies in estimating how climate change affects specific firms (Giglio et al., 2021). The multifaceted nature of climate change means that while some businesses experience climate-related costs, others may find opportunities (Sautner et al., 2023a). Recent advances in machine learning led to the creation of data sets that quantify the firm-level information presented in regular conference calls between firms’ management and the financial analysts covering the firms. Hassan et al. (2019, 2023, 2024) present methodologies that help to measure the share of the earnings call discussions linked to a specific topic. Sautner et al. (2023a) create measures that quantify how much attention was paid by the earnings call participants to climate change and capture both positive and negative sentiments related to climate change. Such measures have been used to evaluate real economic outcomes linked with net-zero transition, including job creation due to green technologies, as well as green patenting (Sautner et al., 2023a). Li (2023) applies these measures to find a link with turnover by inventors who worked for U.S. publicly traded firms.
Top management turnover is a complex issue that affects multiple aspects of firms’ operations, including climate change exposure. CEO turnover in particular has driven extensive academic research (Berns & Klarner, 2017). Earlier works explored the determinants of CEO turnover and linked it to firm performance (Murphy & Zimmerman, 1993; Huson et al., 2004). However, not all CEO turnover has the same effect on the firm, as only specific types of CEO departures such as forced ones have been linked to shifts in firms’ strategies (Karaevli, 2007; Barron et al., 2011; Zhang & Rajagopalan, 2010; Chulkov & Wang, 2024).
This article looks to address the gap in the literature and explore how CEO turnover affects climate change exposure at U.S. firms. The empirical analysis is based on a novel data set that includes the measures of climate change exposure constructed with machine learning methods by Sautner et al. (2023b), as well as the information on specific types of CEO turnover and firms’ financial variables for 2369 publicly traded firms in the United States covering the fiscal years from 2002 to 2020. Analyzing these data helps to identify new and significant relationships between climate change exposure and patterns of CEO turnover. The findings contribute to the sustainable finance literature, as well as to the research on the role of CEO turnover.
2. Materials and Methods
In order to empirically explore the link between climate change exposure and CEO turnover, the data set construction began with climate exposure metrics developed by Sautner et al. (2023b). These metrics were derived using machine learning techniques applied to transcripts of corporate earnings conference calls, which serve as key forums for management and financial analysts to discuss both current operations and forward-looking issues facing firms (Hollander et al., 2010).
The methodology employed by Sautner et al. (2023a) builds on prior works by Hassan et al. (2019, 2024) and Jamilov et al. (2021), which quantify firm-level exposure to specific topics based on the proportion of related content in the corporate earnings call discussions. The resulting climate change exposure indicators reflect how both corporate executives and financial analysts perceive climate-related developments at a firm, encompassing physical risks, regulatory pressures, and technological innovations.
These indicators were constructed using keyword discovery algorithms, following the approach of King et al. (2017). Specifically, Sautner et al. (2023a) define exposure as the frequency of climate-related bigrams within a transcript, normalized by the total number of bigrams. Detailed documentation of the machine learning procedures used to generate these measures is provided in Sautner et al. (2023a).
In the current study, three separate firm-specific measures provided by Sautner et al. (2023b) were used—overall exposure and positive sentiments and negative sentiments regarding climate change. The exposure measure represented the occurrence or expectation of climate change events at a firm. It showed the share of discussion in the earnings call related to climate change. The positive and negative sentiment measures quantified the relative frequency of bigrams referring to climate change that were encountered in the same sentence as positive and negative words in the earnings calls, respectively (Loughran & McDonald, 2011). The positive and negative sentiment data had appropriate mathematical signs. Sautner et al. (2023a) provide extensive validation testing that ensures that the measures accurately represent the climate change discussions in the corporate earnings call transcripts. These climate change exposure and sentiment indicators are available for the fiscal years from 2002 to 2020.
The construction of the data set continued by merging the climate change exposure and sentiment measures for U.S. firms with the variables from two financial databases—ExecuComp for data on top executives and CompuStat for firm-level financial data. The methodology for constructing the data set followed those of earlier studies that used ExecuComp and CompuStat data (e.g., Barron et al., 2011; Jenter & Kanaan, 2015; Chulkov, 2024).
This article focused on the link between firms’ climate change exposure and CEO turnover, and it was crucial to establish turnover specifics clearly. Instances of CEO departure were identified when a new CEO appeared at a firm. This required the identification of CEOs for each firm in every fiscal year. Firms that had no clear identification of the CEO, including firms with co-CEOs or missing CEO information, were excluded from the data sample, following the method of Barron et al. (2011). Dummy variables were created to identify the fiscal year of each CEO turnover instance, as well as the fiscal years preceding and following the CEO turnover year. Dummy variables were also used to track CEO succession types based on the origin of replacement CEOs. If the incoming CEO appeared on the list of the firm’s executives in the fiscal year prior to turnover, such replacement was identified as an insider succession. Turnover instances when the incoming CEO was not employed at the firm in the prior fiscal year were identified as outsider successions.
The data sample also included variables designed to identify the various types of CEO turnover. These variables followed the data creation process outlined in Chulkov and Barron (2024). For the instances of CEO turnover in the data set, news articles about CEO turnover were examined in order to confirm the reasons for each departure, as suggested by Parrino (1997). Five categories of CEO turnover cases were established. These categories included CEOs who (1) resigned from the firm, (2) were fired, (3) retired, (4) left their position because of illness or death, and (5) left their position but took on a new role within the firm. Note that turnover cases that involved interim CEOs who were in the position for less than two fiscal years were excluded from the data sample, because the interim CEO departure reasons were not consistent with the turnover categories listed above.
The climate change exposure and sentiment variables were integrated with the CEO turnover data and firm-level financial data to construct the final data set. This data set comprised 2369 firms and 5080 executives, yielding a total of 24,989 firm-year observations. Summary statistics for the sample are provided in Table 1.
The dependent variables for the several empirical models presented below included measures of climate change exposure, as well as positive and negative sentiments on climate change based on the firms’ earnings call transcripts. These measures came from Sautner et al. (2023b) and represented the relative share of climate change-related discussion in the firms’ earnings call transcripts. The independent variables identified the fiscal years of CEO turnover, as well as the specific turnover types, including insider and outsider CEO succession, and the various reasons for CEO departure. CEO departures were observed 8.7 percent of the time, and this may be further classified into the departures replaced by an outsider, at 3.5 percent, and the departures replaced by an insider, at 5.2 percent, as well as the shares of departures in each of the five categories of resignation, firing, illness, retirement, and change of duty.
Control variables included the logarithm of CEO tenure, as well as a CEO gender dummy variable that equaled 1 for female CEOs and 0 otherwise. Only 3.5 percent of the CEOs in the data sample were female. Financial control variables included the firms’ ratio of the market to book value, firm size calculated as the logarithm of the firms’ assets, and leverage calculated as the logarithm of the ratio of debt to equity. All the empirical specifications also included controls for the industry and the fiscal year. Appendix A, Table A1 outlines all the variable definitions and lists the data sources.
3. Results
The analysis starts by exploring how climate change exposure and sentiments at firms are connected with CEO turnover overall. Three separate regression models are estimated. The dependent variable utilized in Model (1) is the Sautner et al. (2023b) measure of climate change exposure, while Model (2) focuses on the positive climate change sentiments in the firms’ earnings call transcripts and Model (3) explores the negative sentiments related to climate change. The models show the impacts of the timing of CEO turnover. As the timing of CEO departures within a fiscal year may vary widely, the models incorporate dummy variables that identify the years before and after the CEO’s departure year in order to present a clear contrast. Control variables include CEO tenure and gender and the financial measures of firm size, the market-to-book value ratio, and leverage. All the empirical models also include controls for industry and fiscal year. Table 2 presents the results.
The results of Model (1) demonstrate that climate change exposure at a firm is reduced in the fiscal year after CEO turnover as the coefficient is significant at the one-percent level. Model (2) presents evidence that positive sentiments in the context of climate change are less likely to be observed in the earnings call transcripts in the fiscal year after CEO turnover, significant at the five-percent level. Meanwhile, the results of Model (3) identify that negative sentiments are also less likely after CEO departure, significant at the one-percent level. Note that the Sautner et al. (2023b) data for negative sentiments have negative signs, so the positive and significant coefficient in Model (3) identifies a reduction in negative sentiments.
Earlier studies on the connection between environmental concerns and CEO turnover typically used the managerial turnover variable as the dependent one (e.g., Li, 2023; Cornelli et al., 2024). These studies did not explore the impact of CEO turnover timing. The specifications of Models (1)–(3) include variables that identify the fiscal years before and after CEO turnover at a firm. The results demonstrate that the significance in the relationship between climate change exposure measures and CEO turnover is observed in the year after the turnover and not the year before.
Coefficients for control variables in Table 2 indicate that firms led by female CEOs are associated with greater focus on climate change, as well as greater positive sentiments on climate change, as captured by the earnings call transcripts. Larger firms are also linked to greater climate change exposure, as well as greater positive and negative sentiments toward climate change. Meanwhile, firms with higher ratios of market value to the book value of assets have a negative association with climate change exposure and sentiments.
The subsequent empirical analysis introduces variation in the reasons for a CEO’s departure from their position. Table 3 explores the impacts of various specific turnover types—resignation, firing, retirement, illness, and cases when the CEO changed duties but remained with the firm. Model (4) replicates the analysis of Model (1) to provide a comparison point. Model (5) presents the impact of the specific CEO turnover types.
The results demonstrate significant differences in how various CEO departure scenarios relate to climate change exposure. CEO turnover due to a firing or resignation is negatively associated with climate change exposure at a firm in the year after turnover. Meanwhile, a significant relationship is not observed for the cases of CEO retirement, illness, or change of duties. No significant results are observed for the fiscal years before CEO turnover. CEO firings or resignations are typically seen as examples of unplanned or forced turnover, while retirements and changes of duty are part of a planned succession process (Barron et al., 2011; Chulkov & Barron, 2021). The results of Table 3 show that firms with such forced CEO departures are significantly less likely to devote attention to climate change in their earnings calls. The focus of such firms seems to be on other business issues in the fiscal year after the CEO’s departure.
The analysis proceeds by examining the influence of the origin of incoming CEOs. Successors who were employed by the same firm during the fiscal year preceding the leadership transition are categorized as internal appointments, whereas those who were not part of the firm’s top management team are classified as external hires. The empirical findings are summarized in Table 4.
Model (6) replicates the baseline specification from Model (1) to establish a reference point. Model (7) extends this framework by incorporating dummy variables that distinguish between internal and external CEO transitions. The results show that although CEO turnover is generally associated with a statistically significant decline in climate change exposure in the subsequent fiscal year, this effect is primarily attributable to external CEO replacements. In contrast, internal successions do not exhibit a significant association with changes in climate-related discourse at firms, suggesting that such transitions do not divert management or financial analyst attention away from climate issues toward other organizational concerns.
4. Discussion
The empirical findings yield several key insights. First, CEO turnover is associated with a statistically significant decline in climate change exposure, as reflected in the frequency of climate-related discussions during corporate earnings calls. This effect is observed in the fiscal year following the leadership change, rather than preceding it. Further analysis reveals a reduction in both positive and negative climate-related sentiment post-turnover, suggesting a shift in managerial and financial analyst attention away from climate-related issues. This complements earlier studies (e.g., Li, 2023; Cornelli et al., 2024; Shao et al., 2025), which primarily examined ESG performance as a determinant of CEO dismissal.
Second, the nature of the CEO departure plays an important role. The decline in climate change exposure is predominantly driven by forced CEO departures, including dismissals and resignations. In contrast, voluntary exits, such as retirements, health-related departures, or transitions to other roles within a firm, do not exhibit a significant effect on firms’ climate change exposure. These findings align with prior research indicating that involuntary CEO changes are often linked to broader organizational disruptions (Barron et al., 2011; Kalmanovich-Cohen et al., 2018).
Third, the results of this study confirm that the negative impact of turnover on climate change exposure is concentrated among firms that appoint external successors. Internal CEO replacements, typically part of planned successions, do not lead to a significant decline in climate-related discourse. This suggests that internal appointments may help to maintain continuity in strategic priorities, including sustainability-related concerns.
This study contributes to the literature in several ways. It introduces a unique data set that integrates machine learning-derived measures of climate change exposure with detailed information on CEO turnover types and successor origins. The findings offer new evidence on how leadership transitions influence corporate attention to climate issues, thereby enriching both the sustainable finance literature and the research on executive turnover. Notably, the results highlight that CEO changes, particularly those involving forced departures or external hires, can redirect managerial and financial analyst focus away from climate-related topics.
5. Conclusions
This article explores the link between firms’ climate change exposure and patterns of CEO turnover, using a data set comprising 2369 U.S. firms. The analysis leverages novel measures of climate change exposure and sentiment developed by Sautner et al. (2023b) through machine learning applied to transcripts of corporate earnings calls. These data are complemented by a detailed classification of CEO departure reasons based on the framework of Chulkov and Barron (2024).
The results indicate that CEO turnover at a firm is negatively associated with climate change exposure in the year after the CEO’s departure. Earlier studies on the connection between ESG and CEO turnover typically used the managerial turnover variable as the dependent (Li, 2023; Cornelli et al., 2024; Shao et al., 2025) and suggested that lower environmental measures contribute to CEO dismissal. Focusing on the climate change exposure measures and using more detailed information on CEO turnover timing in this study leads to the conclusion that the attention of the management and financial analysts covering the firms shifts away from climate change issues after CEO turnover.
The negative and significant relationship between climate change exposure and CEO turnover is driven by instances of forced CEO departures, as well as by outsider CEO replacements. Firms are more likely to shift their focus away from climate change towards other pressing issues after such CEO turnover scenarios. These findings are consistent with studies that show that forced CEO departures, as well as outsider successions, are more likely to be associated with significant economic changes at firms (Murphy & Zimmerman, 1993; Barron et al., 2011; Kalmanovich-Cohen et al., 2018; Chulkov & Barron, 2021).
The limitations of this study originate in the data set used. Data on climate change exposure created by Sautner et al. (2023b) are only available for publicly traded firms as they come from the analysis of earnings conference calls. These machine learning-based measures of climate change exposure have been constructed for 34 countries. The data on CEO turnover scenarios based on Chulkov and Barron (2024) are available only for U.S. firms. Extending the analysis to other major economies may help to evaluate the generality of the conclusions. This remains a direction for future research.
CEO turnover represents a critical juncture for firms, with implications that extend beyond financial performance to include shifts in strategic focus. The observed decline in climate-related exposure at U.S. firms following such transitions underscores the importance of succession planning in sustaining corporate engagement with environmental issues. These insights are particularly relevant for investors, boards of directors, and policymakers concerned with the intersection of corporate governance and sustainability.
Funding
This research was supported by Grant-in-Aid of Faculty Research and Creative Activity funding from the Indiana University Kokomo Office of Academic Affairs.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Two publicly available databases—ExecuComp and CompuStat—provided data for this study. Data on climate change exposure and sentiment were sourced from Sautner et al. (2023b). Definitions of the data variables and sources are listed in Appendix A, Table A1.
Conflicts of Interest
The author declares no conflicts of interest.
Appendix A
Table A1.
Variable definitions and data sources.
| Variable | Definition | Data Source |
|---|---|---|
| Climate change exposure | Relative frequency of climate change discussion in the transcripts of firms’ earnings calls | Sautner et al. (2023b) |
| Climate change positive sentiment | Relative frequency of climate change discussion next to positive words in earnings call transcripts | Sautner et al. (2023b) |
| Climate change negative sentiment | Relative frequency of climate change discussion next to negative words in earnings call transcripts | Sautner et al. (2023b) |
| Fiscal year of CEO turnover | Dummy variable with 1 when CEO departs from the position in the fiscal year | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to resignation | Dummy variable with 1 when CEO departs from the position due to a resignation in the fiscal year | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to firing | Dummy variable with 1 when CEO departs from the position due to a firing in the fiscal year | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to retirement | Dummy variable with 1 when CEO departs from the position due to a retirement in the fiscal year | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to illness | Dummy variable with 1 when CEO departs from the position due to illness or death in the fiscal year | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to change of duty | Dummy variable with 1 when CEO departs from the position but stays with the firm | Chulkov and Barron (2024) |
| Fiscal year of CEO turnover due to insider replacement | Dummy variable with 1 if the outgoing CEO departs and the incoming CEO was employed at the firm for at least one fiscal year | ExecuComp, author calculations |
| Fiscal year of CEO turnover due to outsider replacement | Dummy variable with 1 if the outgoing CEO departs and the incoming CEO was not employed at the firm for at least one fiscal year | ExecuComp, author calculations |
| CEO tenure | Natural logarithm of the number of years a CEO stays in position | ExecuComp |
| Female CEO | Dummy variable with 1 if the CEO is female | ExecuComp |
| Market-to-book value ratio | Ratio of firms’ market value to book value of assets | CompuStat |
| Firm size | Natural logarithm of firms’ total assets | CompuStat |
| Leverage | Natural logarithm of the ratio of debt to equity | CompuStat |
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Table 1.
Summary statistics for the data set.
| Variable | Mean | Median | Standard Deviation | Min | Max |
|---|---|---|---|---|---|
| Climate change exposure measure | 0.0009 | 0.0003 | 0.002 | 0 | 0.043 |
| Climate change positive sentiment | 0.0004 | 0.0001 | 0.001 | 0 | 0.022 |
| Climate change negative sentiment | −0.0002 | 0 | 0.0005 | −0.013 | 0 |
| Fiscal year of CEO turnover | 0.087 | 0 | 0.282 | 0 | 1 |
| Fiscal year of CEO turnover in resignation | 0.022 | 0 | 0.147 | 0 | 1 |
| Fiscal year of CEO turnover in firing | 0.007 | 0 | 0.086 | 0 | 1 |
| Fiscal year of CEO turnover in retirement | 0.044 | 0 | 0.204 | 0 | 1 |
| Fiscal year of CEO turnover due to illness | 0.002 | 0 | 0.048 | 0 | 1 |
| Fiscal year of CEO turnover in change of duty | 0.012 | 0 | 0.107 | 0 | 1 |
| Fiscal year of CEO turnover insider replacement | 0.052 | 0 | 0.223 | 0 | 1 |
| Fiscal year of CEO turnover outsider replacement | 0.035 | 0 | 0.184 | 0 | 1 |
| CEO tenure | 2.008 | 2.079 | 0.709 | 0 | 3.367 |
| Female CEO | 0.035 | 0 | 0.185 | 0 | 1 |
| Market to book value | 1.345 | 0.931 | 1.471 | 0 | 27.457 |
| Firm size | 8.07 | 7.96 | 1.758 | 1.383 | 15.186 |
| Leverage | 0.014 | 0.007 | 0.675 | −75.576 | 53.042 |
Table 2.
Firms’ exposure to climate change and CEO turnover.
| Climate Change Exposure Model (1) | Positive Sentiment Model (2) | Negative Sentiment Model (3) | |
|---|---|---|---|
| Fiscal Year After CEO Turnover | −0.000128 ** (0.0000404) | −0.0000516 * (0.0000218) | 0.0000285 ** (0.00000919) |
| Fiscal Year Before CEO Turnover | −0.0000403 (0.0000396) | −0.0000284 (0.0000213) | 0.00000543 (0.00000899) |
| CEO Tenure | 0.000000323 (0.0000173) | 0.00000126 (0.00000936) | −0.000000358 (0.00000394) |
| Female CEO | 0.000171 ** (0.0000596) | 0.0000918 ** (0.0000321) | 0.0000118 (0.0000135) |
| Market-to-Book Value Ratio | −0.0000350 ** (0.00000767) | −0.0000136 ** (0.00000414) | 0.0000124 ** (0.00000174) |
| Firm Size | 0.0000283 ** (0.00000746) | 0.0000140 ** (0.00000402) | −0.00000432 * (0.00000170) |
| Leverage | −0.0000329 * (0.0000163) | −0.00000433 (0.00000878) | 0.00000422 (0.00000370) |
| Industry Controls | Included | Included | Included |
| Year Controls | Included | Included | Included |
| R2 | 0.456 | 0.254 | 0.333 |
| Observations | 24,989 | 24,989 | 24,989 |
Values in parentheses represent standard errors. Statistical significance is shown by * and ** at the levels of 5 percent and 1 percent, respectively.
Table 3.
Firms’ exposure to climate change and types of CEO turnover.
| Climate Change Exposure Model (4) | Climate Change Exposure Model (5) | |
|---|---|---|
| Fiscal Year After CEO Turnover | −0.000128 ** (0.0000404) | |
| Fiscal Year Before CEO Turnover | −0.0000403 (0.0000396) | |
| Fiscal Year After CEO Turnover Due to Resignation | −0.000227 ** (0.0000805) | |
| Fiscal Year After CEO Turnover Due to Firing | −0.000287 * (0.000145) | |
| Fiscal Year After CEO Turnover Due to Retirement | −0.0000843 (0.0000584) | |
| Fiscal Year After CEO Turnover Due to Change of Duties | −0.0000245 (0.000106) | |
| Fiscal Year After CEO Turnover Due to Illness | −0.0000548 (0.000251) | |
| Fiscal Year Before CEO Turnover Due to Resignation | −0.000105 (0.0000805) | |
| Fiscal Year Before CEO Turnover Due to Firing | −0.000206 (0.000133) | |
| Fiscal Year Before CEO Turnover Due to Retirement | 0.0000576 (0.0000586) | |
| Fiscal Year Before CEO Turnover Due to Change of Duties | −0.0000106 (0.000108) | |
| Fiscal Year Before CEO Turnover Due to Illness | −0.000305 (0.000245) | |
| CEO Tenure | 0.000000323 (0.0000173) | 0.00000356 (0.0000208) |
| Female CEO | 0.000171 ** (0.0000596) | 0.000158 * (0.0000666) |
| Market-to-Book Value Ratio | −0.0000350 ** (0.00000767) | −0.0000422 ** (0.00000888) |
| Firm Size | 0.0000283 ** (0.00000746) | 0.0000232 ** (0.00000831) |
| Leverage | −0.0000329 * (0.0000163) | −0.0000322 (0.0000166) |
| Industry Controls | Included | Included |
| Year Controls | Included | Included |
| R2 | 0.456 | 0.461 |
| Observations | 24,989 | 21,118 |
Values in parentheses represent standard errors. Statistical significance is shown by * and ** at the levels of 5 percent and 1 percent, respectively. Note that Model (5) omits data from fiscal year 2002 due to collinearity.
Table 4.
Insider and outsider CEO successions and climate change exposure.
| Climate Change Exposure Model (6) | Climate Change Exposure Model (7) | |
|---|---|---|
| Fiscal Year After CEO Turnover | −0.000128 ** (0.0000404) | |
| Fiscal Year Before CEO Turnover | −0.0000403 (0.0000396) | |
| Fiscal Year After CEO Turnover, Outsider Replacement | −0.000191 ** (0.0000708) | |
| Fiscal Year After CEO Turnover, Insider Replacement | −0.0000913 (0.0000525) | |
| Fiscal Year Before CEO Turnover, Outsider Replacement | −0.0000927 (0.0000634) | |
| Fiscal Year Before CEO Turnover, Insider Replacement | 0.0000184 (0.0000539) | |
| CEO Tenure | 0.000000323 (0.0000173) | 0.00000177 (0.0000218) |
| Female CEO | 0.000171 ** (0.0000596) | 0.000158 * (0.0000666) |
| Market-to-Book Value Ratio | −0.0000350 ** (0.00000767) | −0.0000419 ** (0.00000889) |
| Firm Size | 0.0000283 ** (0.00000746) | 0.0000236 ** (0.00000830) |
| Leverage | −0.0000329 * (0.0000163) | −0.0000322 (0.0000166) |
| Industry Controls | Included | Included |
| Year Controls | Included | Included |
| R2 | 0.456 | 0.460 |
| Observations | 24,989 | 21,118 |
Values in parentheses represent standard errors. Statistical significance is shown by * and ** at the levels of 5 percent and 1 percent, respectively. Note that Model (7) omits data from fiscal year 2002 due to collinearity.
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Chulkov, D. Analyzing Climate Change Exposure and CEO Turnover: Evidence from U.S. Firms. Int. J. Financial Stud. 2025, 13, 117. https://doi.org/10.3390/ijfs13030117
AMA Style
Chulkov D. Analyzing Climate Change Exposure and CEO Turnover: Evidence from U.S. Firms. International Journal of Financial Studies. 2025; 13(3):117. https://doi.org/10.3390/ijfs13030117
Chicago/Turabian StyleChulkov, Dmitriy. 2025. "Analyzing Climate Change Exposure and CEO Turnover: Evidence from U.S. Firms" International Journal of Financial Studies 13, no. 3: 117. https://doi.org/10.3390/ijfs13030117
APA StyleChulkov, D. (2025). Analyzing Climate Change Exposure and CEO Turnover: Evidence from U.S. Firms. International Journal of Financial Studies, 13(3), 117. https://doi.org/10.3390/ijfs13030117
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