Abstract
In this paper, we study the relationship between Economic Policy Uncertainty (EPU) and carbon dioxide (CO2) emissions. Using an extensive dataset from 23 countries consisting of 6800 firm-year observations, we provide strong evidence that EPU increases firms’ CO2 emissions. Our main inference is robust when using alternative measures of CO2 emissions and EPU, alternative econometric specifications and samples, and several approaches to control for possible endogeneity. In a set of additional analyses, we first show that a board’s characteristics (i.e., board gender diversity and board independence) significantly moderate the studied relationship. Second, cross-country characteristics (i.e., government effectiveness, control of corruption, and democracy) seem important in the relationship between EPU and CO2 emissions. Our findings significantly contribute to the debate on firms’ ethical responsibility in managing climate change and CO2 emissions.
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The second physical component is mainly related to extreme weather events and long-term chronic changes of various climate variables (sea level, temperatures, etc.,). We do not consequently expect physical risk to intervene in the relationship between EPU and CO2 emissions as the interaction would mostly be through regulatory and business components.
The physical component of climate risk refers to extreme weather events and long-term chronic changes in various climate variables. Thus, we do not expect the physical component to intervene in the relationship between EPU and CO2 emissions. The interaction would mainly be through regulatory and business factors, as mentioned in the above discussion of these two components.
Dang et al. (2019) mobilize data from China which has a unique tax policy and quota system as highlighted by one of the referees. In order to ensure that this argument is valid in the context of our international sample, we conduct, in unreported tests, for the lack of space, similar analysis as the one from Dang et al. (2019) while using cross-country data. Our findings from these additional tests confirm the ones from Dang et al. (2019) and show that EPU significantly increases local corporate tax rates.
One counterargument would be that local governments could collect the revenue shortfall by recurring to penalties for the violation of environmental requirements. We believe this argument is not possible because of at least three reasons:
- First, our study’s sample includes firms from different industries with different environmental sensitivities that may not be fully ready to take into account the evolution of climate-related requirements. For instance, a recent study by MSCI analyzes the environmental disclosure for a panel of US firms and shows that only 28% of the analyzed companies disclose Scope 1 and 2 emissions. Firms’ investments and production processes are not yet fully ready for low-emissions strategies and adopting penalties for the violation of environmental requirements would likely harm the investments and penalize both firms and local governments’ revenues in the long term.
- Second, climate-related regulations are evolving worldwide with initiatives to set a clear framework for firms’ environmental responsibility still ongoing (e.g., Taxonomy, SFDR). Further, engagement in favor of fewer emissions may vary according to the countries’ political orientations. For instance, US president, Joe Biden reinstates the US into the Paris Climate Agreement hours after being sworn in as president while President Donald Trump had decided to formally withdraw from the same agreement during his mandate. This context of ongoing regulations, political differences in relation to the climate, and climate uncertainty makes it complicated for local governments to raise penalties for the violation of environmental requirements as the legal framework is still evolving in most countries.
- Third, it might be challenging for local governments to track any violations of environmental requirements because of the heterogeneity of CO2 emissions across firms, even in the same industry, and the absence of a common understanding on how to reliably quantify CO2 emissions. Assessing any violation of environmental requirements may be costly for local governments and would, sometimes, result in lawsuits that last over time.
We empirically explore the two above-mentioned underlying mechanisms, namely, local tax rate and corporate risk-taking. Our findings, that we report in Appendix 2 for the lack of space, provide support for our discussion on these underlying channels and confirm that EPU affects CO2 emissions through fiscal pressure and risk-taking. Please refer to Appendix 2 for more details.
Although the EPU index is available for 27 countries, we restrict our sample to 23 countries because we don’t have sufficient data on CO2 emissions for firms from India, Pakistan, and Croatia. The EPU index for Denmark was not available when we started our research and was only published in September 2021. Nevertheless, to ensure that our results are not driven by any sample selection issue, we run robustness checks with different sample selections and confirm that our findings are consistent through different sub-samples (please refer to “Alternative Sample Selection” Section and results from Table 6).
From the Refinitiv EIKON definition.
From the variable definition of Refinitiv EIKON for CO2 Equivalent Emissions Indirect, Scope 2.
From the variable definition of Refinitiv EIKON for CO2 Equivalent Emissions Indirect, Scope 3.
2-digit SIC code is used for industry classification.
The correlation matrix is provided in "Appendix 3". There is an expected positive correlation between CO2 emissions and firm size, leverage, investment, and tangibility at a 0.01 significance level. In contrast, profitability and carbon emissions are negatively correlated. Similarly, Tobin’s Q, ESG ratings, and financial slack are negatively correlated with carbon emissions at a 0.01 significance level. Moreover, as expected, board gender and cultural diversity are negatively correlated with CO2 emissions with a p-value less than 0.01. Finally, unexpectedly, board independence and CO2 emissions are positively correlated.
In addition to the current impact of EPU on CO2 emissions, and for further robustness, we test the lagging effect of EPU on firms’ CO2 levels. We got a similar positive significant impact of lagged EPU for first and second levels on firms’ CO2 emissions. We do not report the results due to the lack of space but they are available from the authors upon request.
As our model includes the natural logarithm of both the dependent variable (CO2/revenue) and the main interest variable (EPU), the coefficient of the interest variable implies the percentage change in CO2/revenue corresponds to a one percent change in the main interest variable.
We note that the total CO2 and CO2 equivalent emissions are in tonnes and the revenue is in US million dollars.
For the previous three months, EPUt = ln EPUm-3 + 2 × EPUm-2 + 3 × EPUm-16.
For the last six months, EPUt = ln EPUm-6 + 2 × EPUm-5 + 3 × EPUm-4 + 4 × EPUm-3 + 5 × EPUm-2 + 6 × EPUm-121.
Further, we distinguish between Scopes 1, 2, and 3 and test the effect of EPU on each of these scopes. Our findings show a significant positive impact of EPU on Scope 1 emissions but an insignificant impact on Scope 2 and 3 emissions. These results confirm our thoughts on the importance of Scopes 1, 2, and 3, respectively, as discussed in the variables’ definitions section.
We also use a 3-digit SIC code for industry classification that we do not report for the lack of space.
To ensure that simultaneous causality does not drive our findings, we conduct several regressions that use lagged control and interest variables. Our findings strongly support our inference from the main analysis and confirm that reverse causality does not drive the relationship between EPU and CO2 emissions. These analyses are unreported here due to the lack of space; the results are available from the authors upon request.
Contrary to the findings of Attig et al. (2021), we show a negative impact of polarization on EPU in the first-stage regression; our sample period is different from the sample period of Attig et al. (2021), which is 1991–2015. When we restrict our sample to 2004–2015, our findings support the positive effect of polarization on EPU in the first stage, similar to Attig et al. (2021). Either way, the second-stage regression results do not change, and we have a positive significant effect of fitted EPU on firms’ CO2 emissions in the second-stage regressions.
To ensure that the difference between EPU coefficients among the two sub-samples is statistically significant, we perform t-tests and report the findings in respective tables (Tables 9, 10). Further, in unreported tables for the lack of space, we also run the same analysis while using an interaction approach. The results from this additional analysis provide relatively similar findings and confirm the above discussion.
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Appendices
Appendix 1
See Table 11.
Appendix 2: Underlying mechanisms
In order to test the two underlying mechanisms that explain the relationship between EPU and CO2 emissions, namely, local tax rate and corporate risk-taking, we follow one of the referees’ recommendations by implementing an interaction approach as shown below:
- First, we use the local tax rate variable as provided by Thomson Reuters Datastream and create a dummy variable that is equal to one if the tax rate is above the yearly tax median and zero otherwise (HIGH Tax). We then create an interaction term for the policy uncertainty measure and the tax dummy (High tax*ln(EPU)). We finally include in our model from this additional analysis the EPU variable, the tax rate dummy variable (HIGH Tax), the interaction term (High tax*ln(EPU)), and similar control variables and fixed effects as those used in our main inference. The findings from this regression are reported in Column 1 from the Table below. First, we continue to find a positive relationship between EPU and CO2 emissions as documented in all our analyses even after controlling for taxes. Second, the coefficient on the tax dummy loads positive and statistically significant at the 5% significance level. Finally, the interaction term (ln(EPU)* High tax) loads positive and statistically significant confirming our expectation from the hypotheses discussion section that the high tax rate is likely to be an underlying channel through which EPU affects CO2 emissions.
- Second, we use a similar approach to empirically explore the role of firm risk-taking. To measure risk-taking, we use the annualized standard deviation of daily stock returns for each firm as in Bernile (2018). We then create a dummy variable that is equal to one if the firms’ risk-taking is above the yearly median of the sample and zero otherwise (High-risk taking). We next create an interaction term for the policy uncertainty measure and the risk-taking dummy (High-risk taking*ln(EPU)). We finally include in our model from this approach the EPU variable, the risk-taking dummy (High- risk taking), the interaction term (High-risk taking*ln(EPU)), and similar control variables and fixed effects as those used in our main inference. The findings from this regression are reported in Column 2 from the Table below. First, as in the previous regression, we continue to show a positive relationship between EPU and CO2 emissions. Second, although the coefficient on risk-taking loads positive, it is statistically insignificant. Finally, the interaction term (ln(EPU)*High-risk taking) loads positive and statistically significant which confirms our expectation from the hypotheses discussion section that risk-taking is likely to explain the positive relationship between EPU and CO2 emissions (See Table 12).
Appendix 3
See Table 13.
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Benlemlih, M., Yavaş, Ç.V. Economic Policy Uncertainty and Climate Change: Evidence from CO2 Emission. J Bus Ethics (2023). https://doi.org/10.1007/s10551-023-05389-x
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DOI: https://doi.org/10.1007/s10551-023-05389-x