# Acute vs Chronic Physical Climate Risk and Firm Value

Nicolas Baelen, Sylvain Marsat, Guillaume Pijourlet

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Nicolas Baelen, Sylvain Marsat, Guillaume Pijourlet. Acute vs Chronic Physical Climate Risk and Firm Value. Finance Contrôle Stratégie, 2024, 27 (4), 10.4000/14bd3. hal-05157466

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# Acute vs Chronic Physical Climate Risk and Firm Value

## Risques climatiques physiques aigus ou chroniques et valeur des entreprises

Nicolas Baelen<sup>a*</sup>, Sylvain Marsat<sup>b**</sup>, Guillaume Pijourlet<sup>b***</sup>

* nicolas.baelen@ube.fr, ** sylvain.marsat@uca.fr, *** guillaume.pijourlet@uca.fr

<sup>a</sup>Université Bourgogne Europe, IAE Dijon, CREGO, 21066 Dijon, France

<sup>b</sup>Université Clermont Auvergne, CleRMa, BP 10448, F-63000 Clermont-Ferrand, France

Post-print version of "Nicolas Baelen, Sylvain Marsat and Guillaume Pijourlet, "Acute vs Chronic Physical Climate Risk and Firm Value", *Finance Contrôle Stratégie*, 27-4 | 2024

URL: http://journals.openedition.org/fcs/13598

### Abstract

This paper investigates the effects of physical climate risk on firm valuation, introducing the distinction between acute and chronic physical risks. Since acute risks are more salient compared to chronic risks, we expect them to be more priced by investors. Drawing on an international dataset of 1,293 firms during the 2009-2020 period, we find that acute risk has a negative and significant direct association with firm value, while the effect of chronic risk is not significant on the 2050 horizon. Compared to chronic risk, the acute risk is shown to indirectly significantly impact more firm value by reducing sales growth and dividends.

**Keywords:** Climate Risk, Physical Risk, Acute Risk, Chronic Risk, Firm Value

### Résumé

Cet article étudie les effets des risques climatiques physiques sur la valeur des entreprises, en introduisant la distinction entre les risques physiques aigus et chroniques. Dans la mesure où les risques aigus sont plus saillants que les risques chroniques, nous postulons que ces dernières sont mieux pris en compte par les investisseurs. À partir d'un ensemble de données internationales portant sur 1 293 entreprises au cours de la période 2009-2020, nous constatons que le risque aigu a un effet significativement négatif sur la valeur des entreprises, tandis que l'effet du risque chronique n'est pas significatif à l'horizon 2050. Par ailleurs, comparativement aux risques chroniques, les risques aigus ont un impact indirect significatif plus important sur la valeur des entreprises du fait de la réduction de la croissance du chiffre d'affaires et des dividendes.

**Mots-clés:** Risque climatique, risque physique, risque aigu, risque chronique, valorisation

**JEL Classification:** G12, G32, M14, Q51, Q54
# 1. Introduction

Climate change is increasingly an economic issue, and firms have to adjust their strategy to deal with the related risks (Linnenluecke et al., 2016). Two hundred and fifteen of the biggest global companies report almost US$1 trillion at risk from climate impacts, with many likely to hit within the next five years (Bartlett & Coleman, 2019). In addition, while the economic impact of global warming is “unknowable” (Pindyck, 2013), investors increasingly consider climate change and now consider climate risk as a major risk for their portfolios (Ilhan et al., 2021; Krueger et al., 2020). Based on a survey of 439 institutional investors, Krueger et al. (2020) show that 21% of the respondents started incorporating climate risk into their investment strategy more than ten years ago, whereas 65% started doing it in the last five years.

Although most of the literature is focused on the influence of regulatory climate risk on firm value (Giglio et al., 2021), a nascent and growing stream of academic literature investigates more precisely the impact of physical risks (Bansal et al., 2016; Ginglinger & Moreau, 2023; Hong et al., 2019; Sautner et al., 2023). Physical risks are physical damage to assets and disruption of flow of goods caused by increased natural disasters attributed to climate change (IPCC, 2012). Investors require a risk premium to compensate for the risk of holding assets exposed to physical climate risks (Bansal et al., 2016). Some studies find evidence that several physical risks such as droughts (Hong et al., 2019; Huynh et al., 2020) or rising temperatures (Addoum et al., 2023; Bansal et al., 2016) are priced in stock markets. However, if the impact of physical risks is a real issue (Engle et al., 2020), studies on the financial impact of climate risk are often limited to examining the influence of very specific climate risks, such as droughts (Hong et al., 2019; Huynh et al., 2020), temperature changes (Bansal et al., 2016) or hurricanes (Lamb, 1995; Shelor et al., 1992). We aim to fill this gap by examining the impact of different types of firm-level physical climate risks on firm value.
In this paper, we use Carbon4 Finance CRIS data as an original forward-looking climate risk proxy to assess firm-related physical climate risk at the firm level. This measure proves to be significantly and positively correlated with other measures of physical risks (Hain et al., 2022) and also allows us to contribute to the existing literature by making the distinction between acute physical risk and chronic physical risk (Basel Committee on Banking Supervision, 2021). Acute risks are related to natural disasters (extreme drought, heatwaves, storms, floods), whereas chronic risks refer to long-term changes in climate patterns (increasing temperature, sea-level rise, rainfall patterns)<fnref n="1" />. In essence, acute risks are more salient to investors as their impact is more striking, while the gradual changes of chronic risks are less attention-grabbing (Ginglinger, 2020). In addition, acute risks are event-driven and more material for investors, but chronic risks such as sea level rise and long-term changes will only be tangible for companies in decades<fnref n="2" />. It is also possible that chronic risks may have only a negligible and difficult-to-measure impact on the operations of most companies. Therefore, it can be rational for a limited-horizon investor to be more sensitive to acute risks since these risks have already impacted and/or has a probability to concretely impact companies within this horizon. On the contrary, chronic risks may occur beyond this investment horizon, and long-term environmental risks are largely financially discounted from the investors' point of view. Thus, because of salience and investment horizon reasons, we expect acute climate risks to have a higher negative impact on firm valuation than chronic risks.

Although existing studies treat the impact of climate risks on firm value without disentangling them (Berkman et al., 2024; Krueger et al., 2020), our paper aims to complement the literature by capitalizing on the forward-looking measure CRIS of physical climate risk to unravel the impacts of acute and chronic risks (Ginglinger & Moreau, 2023). First, using a sample of 1,293

<footnote n="1">¹ https://www.oecd.org/corporate/ca/TCFD-Climate-Report.pdf</footnote>

<footnote n="2">² Idem</footnote>
firms from 24 countries over the 2009-2020 period, we show that physical risk has an overall
negative and significant effect on firm value. Second, we find that this effect is driven by acute
risks, whereas chronic risks have no significant relation to firm value. In other words, investors,
all else equal, seem to price only physical acute risks exposure. Based on these results, we
investigate the indirect effects of physical risk on firm value and show that sales growth and
dividends seem to be the negative indirect drivers of this relationship. By disentangling the
direct and indirect effects of chronic and acute risks, we reveal the economic significance of
physical risk on firm value and find that the effect is only significant in the most recent period.

We thus contribute to the existing literature on climate risk valuation by disentangling for the
first time chronic and acute physical risk effects and documenting their asymmetric effect on
firm value. We specifically shed light on the effect of acute physical risks on investors’ stock
valuation processes, whereas chronic risk is not considered. Acute physical climate risks may
trigger investors’ attention, while chronic risks are less attention-grabbing and largely affected
by discounting. We expect that these findings can be of interest to both managers and investors
who are concerned with the value of firms facing future physical climate risks. The remainder
of the paper is structured as follows: the next section reviews the related literature and presents
the hypotheses. The third section is dedicated to the data and the sample. We then present the
main empirical results and further analyses. The last section concludes the paper.

## 2. Hypotheses

Physical climate risks may affect current and future economic growth due to physical damages,
increased operating costs, supply chain disruptions, and changes in revenues. As a result,
investors may require a risk premium to compensate for these risks (Bansal et al., 2016; Sautner
et al., 2023), either chronic or acute, and then decrease firm value. In addition, recent studies
show that investors’ perception of physical risk plays a major role in the valuation process.
Following this reasoning, physical risk due to future climate change may reduce the firm value
(Campiglio et al., 2023). However, empirical findings are so far not conclusive (Sautner et al., 2023). Sautner et al. (2023) use earnings conference calls to assess firm climate change exposure through a machine learning process. While regulatory risk is significantly linked with firm valuation, the physical dimension proves to be not clearly related to it. The measure used for physical climate change exposure, however, relies on conference calls and may be subject to bias that may be mitigated with an external assessment like Carbon4 CRIS methodology. Thus, we expect, all else being equal, the following hypothesis:

*H₁: Physical climate risk is negatively linked with firm value.*

Beyond this, we propose to investigate the differences in effects among physical risks. Climate hazards do not have the same financial costs, depending on direct asset destruction and long-term effects. Hence, we propose to unravel the impacts of acute and chronic risks. Two different perspectives may support the need to disentangle acute and chronic risks. On the one hand, the literature shows that individuals and economic actors use heuristics to assess risk. For instance, one heuristic is to consider the frequency of an event from its availability (Tversky & Kahneman, 1974). Dessaint & Matray (2017) document the importance of the salience of an event that affects that availability. Depending on the context, some events may easier come to mind. In summary, some factors can influence the investment decisions, such as proximity, media coverage, and past experiences that increase the salience of an event. In addition, the need to differentiate the type of physical risks may be explained by the frog-in-the-pan phenomenon. This hypothesis suggests that investors are more sensitive to abrupt and substantial price changes than slow and progressive price changes (Da et al., 2014). Investors are more likely to overreact to extreme events and underreact to less extreme events (Piccoli et al., 2017). Because acute risks may be more salient to investors and their impact are more striking and grab the investors' attention (Ginglinger, 2020), the type of physical risks should
be considered. When an acute risk occurs, investors are more likely to be attentive, resulting in
a market response. Conversely, chronic risks are more likely to be underestimated.

Besides, the investment horizon has to be considered in determining the effects of physical risks on firm value. Acute risks refer to event-driven physical climate risks. They may be more material for investors due to direct asset destruction, whereas chronic risks will only be tangible for firms in decades. Therefore, we observe a body of literature investigating the reactions of corporate managers and people's reactions after a natural disaster (Bernile et al., 2017; Brown et al., 2018; Cameron & Shah, 2015; Feng et al., 2022; Gao et al., 2020). We assume that chronic risks, such as long-term changes, may impact investors' decisions. However, investors have a limited investment horizon that should not exceed the time for chronic risks to impact companies. For instance, Addoum et al. (2023) find no significant relationship between temperature exposures and U.S. firms' sales. In summary, investors may be more sensitive to acute risk, given the probability of impacting firms within their investment horizon. In contrast, chronic risks can materialize beyond their investment horizon or would be highly discounted. Therefore, chronic risks refer to long-term risks that could be less valued by investors.

We then suppose that acute and chronic risks are priced differently by investors due to salience and investment horizon. We expect acute risks to have a higher negative impact on firm valuation than chronic risks. Therefore, we hypothesize:

*H₂: Acute risks have a higher negative impact on firm valuation than chronic risks.*

**3. Data and methodology**

**3.1. Climate risk measure**

To measure climate risk, we draw on Climate Risk Impact Screening (CRIS) data provided by
Carbon4 Finance, a leading data provider and independent agency in adaptation to climate
change. CRIS climate risk is related to location-specific climate hazards and industry-specific
vulnerabilities. The metrics is a combination of climate projections for specific geographic locations and vulnerability based on an issuer's sector. Carbon4 Finance developed climate risk measures for three intensity scenarios (low, medium, and high) and two time horizons (2050 and 2100). The CRIS methodology includes physical risk for each hazard, disentangling acute risks (heatwaves, droughts, extreme rainfall, and storms) and chronic risks (increases in average temperature, changes in rainfall patterns, and rise of sea level).

Risk ratings are attributed on a scale between 1 to 99, 1 being the lowest risk and 99 the highest. The aggregated multi-hazard is then based on the weighted geometric mean of all the risk ratings for each hazard. We note three main steps to perform climate risk analysis for a listed company. Carbon4 Finance first collects the geographical and sector distribution of the firm's operations. They then build the risk ratings for each hazard, time horizon, and scenario. Each hazard risk is calculated as an average of the risk rating of all business segments, weighted by the proportion of each segment in the overall activities of the firm. The last step is to build the aggregated risk rating, which represents a synthetic risk rating based on hazard risk ratings. The main interest of CRIS measures is that the ratings capture the increased physical risk in the future due to global warming compared to historical reference climate hazards. While there is still debate on the coherence of the methodologies used to assess physical risks, Hain et al. (2022) show that language-based models are largely not consistent, while this precise measure is significantly and positively correlated with the two other model-based approaches of Trucost and Southpole.

[Insert Table 1 about here]

Table 1 describes all variables used in our study. In our empirical estimation, we use three distinct proxies for climate risk. We first use PHYSICAL CLIMATE RISK, defined as CRIS global risk grade at a firm level. We also calculate CHRONIC (ACUTE) RISK as the mean of chronic (acute) risk ratings at a firm level. We combine the CRIS risk ratings for each physical
risk: acute (changes in the intensity or frequency of heatwaves, changes in rainfall extremes, changes in drought extremes, changes in the intensity or frequency of storms) and chronic (increase in average temperature, changes in rainfall patterns, sea level rise). Following Ginglinger & Moreau (2023), we consider the scores for the medium-intensity scenario and the 2050-time horizon<fnref n="3" />.

[Insert Table 2 about here]

Table 2 presents the physical risk ratings by industry for each climate hazard. Physical climate risk ratings are scaled from 0 (the lowest) to 100 (the highest) relative to peers in their industries. We observe that physical risks vary across industries. For instance, firms in polluting industries have high climate risks and associated sub-risks. The PHYSICAL CLIMATE RISK means are indeed 34.06 and 37.12 for firms in the Energy and Utilities sectors, respectively. The sector of utilities represents the sector with the highest physical climate risk, chronic and acute risks mean, whereas Financials and Telecommunications firms have unsurprisingly a mean climate risk under the global mean.

## 3.2. Firm value measure

We use Tobin's Q to measure firm value, calculated as (market value of equity + book value of assets - book value of equity - balance sheet deferred taxes)/book value of assets. Tobin's Q has the main advantage of being a forward-looking financial measure and is largely used in the literature (Aouadi & Marsat, 2018; Delmas et al., 2015). Thus, Tobin's Q represents an indicator of the market perception of a firm's long-term financial performance expectations that may well capture the potential effect of future climate change. We also use market-to-book (MB) as a robustness check.

<footnote n="3">³ We performed robustness checks for all scenarios and time horizons, results are not affected. These tables are available on request.</footnote>
### 3.3. Control variables

In line with previous studies (Berkman et al., 2024; Delmas et al., 2015; Huang et al., 2018; Matsumura et al., 2014), we control for firms' characteristics, including: return on assets (ROA), the natural logarithm of a firm's total assets (SIZE), firm leverage (LEV), sales growth (SG), research and development expenditures (R&D), dividends (DIV) and capex (CAPEX). We also include country-level macroeconomic control variables and control for GDP per capita (GDP) and GDP growth (GDP GROWTH) from the World Bank database. Moreover, we control for industry, year, and industry fixed-effects. We use the ICB classification of 11 sectors: Consumer discretionary, consumer staples, energy, financials, healthcare, industrials, information technology, materials, real estate, telecommunications, and utilities. We also define four regions: East Asia, Europa, Middle East, North Africa, and North America.

### 3.4. Sample

We collect climate ratings for all listed firms covered by the Carbon4 Finance database. Our sample is obviously limited by the availability of CRIS climate risk data. We match it with financial Worldscope data from the Refinitiv Thomson Reuters database for 2009-2020. After balancing unavailable observations of control variables, our sample entails 1,293 firms from 24 countries. Companies from the United States of America represent 36.14% of the sample, and the most represented industries are Industrials and Consumer Discretionary, respectively 19.29% and 16.52% of our sample. The remaining countries and sectors included in our sample are depicted in Table 3.

[Insert Table 3 about here]

Table 4 presents the descriptive statistics of our variables. Our main physical risk measure, PHYSICAL CLIMATE RISK has an overall average of 30.47, while the average CHRONIC RISK and ACUTE RISK ratings are 23.95 and 25.41, respectively. For our sample, Tobin's Q
has a mean of 1.42. ROA has a mean of 5.82 percent, while the average sales growth is 5.52 percent. Average size, leverage, and R&D are respectively 9.72, 59.84 percent, and 2.04 percent of the assets. The average dividend (DIV) and capital expenditures (CAPEX) over total assets are 2.13 and 3.81. The macroeconomic control variables GDP and GDP GROWTH have a mean of 10.79 and 0.69, respectively.

[Insert Table 4 about here]

To gain insight into our research question, we conducted correlation tests between the variables used in our analysis. The correlation table is presented in Table 5. In line with H₁, we find a negative and significant correlation between climate risk measures and Tobin's q. The reported correlations are -0.138, -0.070, and -0.110 between Q and CLIMATE RISK CHRONIC RISK, and ACUTE RISK, respectively. We observe that correlations between control variables and climate risk proxies can be statistically significant but do not raise major multicollinearity issues.

[Insert Table 5 about here]

# 4. Results

In this section, we first examine the direct effect of physical climate risks on firm value. We then explore the transmission channels through which chronic and acute physical risks may affect firm value. We finally study how the evolution of climate risk perception may influence the impact of physical climate risk on firm value.

## 4.1. The direct effect of physical risk on firm value

To explore the relationship between physical climate risk and firm value, we employ a valuation model based on Tobin's, following existing literature (Berkman et al., 2024). This type of model has already been widely used in the literature to study numerous issues in corporate finance
(Benson & Davidson, 2009; Konijn et al., 2011; Oxelheim & Randøy, 2003; Vomberg et al., 2015; Xie et al., 2022). We then estimate the effect of climate risk on firm value using the following specification:

$$ \text{Firm Value}_{i,t} = \beta_0 + \beta_1 \text{Physical Climate Risk}_i + \sum_k \beta_k \text{CONTROLS}_{i,t-1}^k + \sum_t \alpha_t \text{Year}_t^t + \sum_p \gamma_p \text{Industry}_p^p + \sum_r \delta_r \text{Region}_r^r + \varepsilon_{i,t} $$

Where Firm Value$_{i,t}$ is our firm value measure (Tobin's Q or market-to-book ratio) for the firm i in year t. Climate Risk is our variable of interest, which can be measured by PHYSICAL CLIMATE RISK, CHRONIC RISK, and ACUTE RISK. Our model includes the control variables previously defined. Financial variables are winsorized at 1% and 99% to alleviate any potential effect of outliers. All control variables are one-year lagged to mitigate endogenous interdependence. We also include p industry, t year, and r region fixed effects.

[Insert Table 6 about here]

Table 6 depicts our estimations of the effect of physical risks on Tobin's Q (columns 1, 2, and 3) and Market-to-book (columns 4, 5, and 6). We find in Column (1) that the physical climate risk has a significant and negative coefficient of -0.010 (p-value <0.05). Consistent with our H₁, our findings reveal that investors value less firms that are vulnerable to increased physical risks due to future climate change. Besides, for all specifications, firm size is negatively related to Tobin's q and market-to-book. ROA, leverage, sales growth, research and development expenditures, and GDP growth are positively and significantly linked to firm value (p<0.01).

We analyze more precisely the impact of chronic and acute risks on Tobin's Q in Columns (2) and (3) in Table 6. In line with H₂, our results indicate that acute risks are negatively associated with Tobin's Q. The estimated effect of chronic risk on firm value is, however, non-significant.
Our results are robust using market-to-book as the dependent variable in Columns (4), (5), and (6). We checked collinearity among variables for our models and do not find any variance inflation factor exceeding 2, meaning that this issue is not a problem for our model (Table 7).

[Insert Table 7 about here]

Overall, our findings support $H_1$: Global physical risk significantly impacts firm value. These findings, compared to Sautner et al. (2023), highlight that the measure of physical risk may significantly impact the results. We expect our measure, however, to be the most correlated to other measures of physical risk, which is not the case of language-based methodologies (Hain et al., 2022). Moreover, regression results disentangling acute and chronic risks are in line with $H_2$. Our results show that the effect of physical risk on firm value is mostly driven by acute risks, which underlines the importance of differentiating acute and chronic risks when assessing the impact of physical risk on firm value.

The findings highlight that physical risks are considered by investors that, all else being equal, value companies exposed to these risks less. This study extends but differs from the literature by disentangling acute and chronic risks effects on firm value. We find evidence in line with the salience hypothesis, implying that investors are mainly affected by risks that are more extreme and attention-grabbing (Alok et al., 2020; Choi et al., 2020). Following Tversky & Kahneman (1974) and Dessaint & Matray (2017), salience may trigger an availability heuristic and overreaction that leads to a significant stock price decrease (Huynh & Xia, 2021). Our results also support the frog-in-the-pan hypothesis (Da et al., 2014), showing that investors are attentive to dramatic events due to acute risks and do not price frequent gradual changes due to chronic risks. Besides, investors may also value more risks that are more impactful in the short term, whereas chronic risks will only materialize in decades, implying that the negative cash-
flow impact will be largely discounted so that their impact will eventually be not significant for them.

## 4.2. Transmission channels of the effect of chronic and acute physical risks on firm value

We then investigate the transmission channels of the effect of chronic and acute physical risks on firm value. We first estimate the effect of both CHRONIC RISK and ACUTE RISK on firm's financial characteristics (ROA, LEV, SG, R&D, DIV and CAPEX). Table 8 displays our results. First of all, we note that both types of risk seem to have an impact on a number of firm value determinants. This may suggest that the absence of any significant effect of chronic risks on firm value is due to the difficulty investors have in taking these risks into account. More precisely, we find that physical risk is negatively and significantly related to ROA and leverage while it positively and significantly affects R&D and CAPEX. This effect is documented for both chronic and acute risks, even if CAPEX is significantly more positively impacted in the case of chronic risks. In addition, we investigate more precisely the indirect impact of physical climate risks on firm value by estimating the product of the effect of the physical risks on each mediator time the effect of the mediator on Tobin's Q. We show that chronic risks indirectly mitigate the value throughout the impact on ROA, but this effect may be counterbalanced by R&D and capex. Meanwhile, we find a clear difference in the indirect effects on two major dimensions: sales growth and dividends. Acute risks imply for firms both significantly less sales growth and lower dividends, which, in turn, negatively impact firm Tobin's Q<fnref n="4" />. These findings highlight that salience bias is not the only difference in the acute/chronic market valuation

<footnote n="4">⁴ Results with Market-to-Book are not displayed for the sake of brevity but are similar.</footnote>
asymmetry but is also be rooted in real reasons linked to lower sales growth and lower dividends
that impact firm value.

[Insert Table 8 about here]

**4.3. The direct and indirect economic significance of physical climate risks**

To assess the direct, indirect, and total effects of climate risk, we examine the consequences of
a change in climate risk on firm value following the methodology used by Li et al. (2013). We
first calculate the change in climate variables from the 25th percentile to the 75th percentile. The
change in global physical risk (PHYSICAL CLIMATE RISK) from the 25th percentile to the
75th percentile from our sample is ΔPHYSICAL CLIMATE RISK = 9.310. Similarly, we
compute the change in chronic and acute physical risk (CHRONIC RISK and ACUTE RISK)
from the 25th percentile to the 75th percentile and find ΔCHRONIC RISK = 4.624 and ΔACUTE
RISK = 6.658, respectively.

[Insert Table 9 about here]

Table 9 presents the economic significance of climate risk on firm value. Following Li et al. (2013), we decompose the total effects into direct and indirect effects of physical risk. Row (1) presents the coefficients from the regressions (see Table 8 for acute and chronic risks). Row (2) displays the product of the Row (1) coefficients from indirect effect regressions and the change in physical risks (ΔPHYSICAL CLIMATE RISK, ΔCHRONIC RISK, and ΔACUTE RISK). Row (3) reports the coefficients from the direct effect presented in Table 6. Row (4) represents the product of the Row (2) and Row (3) coefficients. The sum of indirect effects is the sum of coefficients in Row (4). The direct effect is the product of the direct effect in Table 6 and the percentile change in physical climate risks. The total effect is calculated as the sum of the indirect and direct effects.
Our findings show that when global physical risk is increased from the 25th percentile to the 75th percentile, the direct effect is to decrease Tobin's q by -0,092. The indirect effect is to reduce Tobin's q by -0,018. The total effect of PHYSICAL CLIMATE RISK is to decrease Tobin's q by -0,110. Given Tobin's q mean of 1.422, the total effect of physical risk is significant from an economic point of view. While the indirect effects are quite similar for acute and chronic risks (respectively -0.019 and -0.020), their total effect is largely different (-0.017 for chronic risks versus -0.087 for acute risks), confirming the asymmetry between these two risks for investors. Moreover, interestingly, we find that the negative economic effect is partly mitigated by the indirect effect on corporate investment (R&D and CAPEX). While the sum of the indirect effects of PHYSICAL CLIMATE RISK is negative, the indirect effect on R&D (CAPEX) is 0.021 (0.007). Hence, firms facing physical climate risks may be incited to increase their R&D and capital budgeting to prevent and adapt, which has an indirect positive effect on firm value.

**4.4. Physical climate risk and time**

Flammer (2013) documents that the sensitivity of stakeholders towards the environment has changed significantly over time. As a matter of fact, she pinpoints, for example, that environmental regulation, media attention to environmental news, or the rise of shareholders' proposals related to environmental issues has increased as time passes. In our sample, we may then expect more attention to the impacts of physical climate risk in the most recent period of our sample.

[Insert Table 10 about here]

We split the sample according to the year 2015, which is both the middle of the period and the year of the Paris COP 21 that attracted particular attention to climate and environmental issues. Interestingly, we do not find any significant effect of any physical climate risk on the firm value
before 2015. However, after 2015, the effect is in line with our baseline results, i.e., for both
overall physical and acute climate risk but not significant for chronic risks. We then find that
the effect is only significant for 2015-20, which means that investors' perception of physical
risks has changed substantially over time, being more sensitive to physical risks in the recent
period. Moreover, the database distinguishes three climate change intensities (low, medium,
and high), we test the impact on firm value for all the possible scenarios (Table 11). Our findings
are robust for any 2050 horizon scenarios, since only acute risk has a negative and significant
impact on firm value.

[Insert Table 11 about here]

**5. Conclusion**

This study contributes to a growing literature on the impact of climate risk on firm value by examining the influence of acute and chronic physical risk related to climate change on firm value. Using a physical risk related to future climate change measure CRIS developed by Carbon4 Finance and based on an international dataset from 24 countries over the 2009-2020 period, we show that physical risk significantly decreases firm value as measured by Tobin's Q and Market-to-Book. More importantly, we uncover that this effect is driven by acute risks, while the direct effects of chronic risks on firm value remain nonsignificant.

Our findings pinpoint the importance of not taking physical risks as a monolithic concept and
the need to differentiate between acute and chronic risks. In fact, investors perceive acute and
chronic risks differently, and this has implications for the valuation of the firm. Additional tests
reveal that this difference is also related to the more material impact of acute risk on sales
growth and dividends. Furthermore, we document the direct, indirect, and total effects of
physical risks on firm value. Thus, we shed light on the economic significance of physical risk
in firm value and the need to consider physical risks as a significant factor in firm valuation,
especially in the most recent period. Finally, while acute risk is related to firm value, whatever
the horizon, chronic risks only materialize for long terms horizon forecasts (2100).

This paper takes additional steps toward an improved understanding of the influence of physical
climate risk on firm value. We hope that these findings may interest investors and corporate
managers, providing a better understanding of the type of climate risk that has an impact on a
company. Additionally, one of the conclusions that can be drawn from this study is that a
positive indirect effect on corporate investment mitigates the negative effect of physical risk on
firm value. Studying corporate managers' policies to alleviate the potential impacts of physical
chronic and acute risks and their impact on firm value calls for future research.
<table><caption>Table 1 - Variables definition and source</caption><thead><tr><th>Variable</th><th>Definition</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td>PHYSICAL CLIMATE RISK</td><td>CRIS Climate Risk</td><td>CRIS global risk grade at a firm level for median scenario and 2050 time-horizon.</td><td>Carbon4 Finance</td></tr><tr><td>CHRONIC RISK</td><td>Physical Chronic Risk</td><td>The mean of chronic risk ratings at a firm level defined by the CRIS methodology (increase temperature risk, changes in rainfall patterns, and sea-level rise risk) for median scenario and 2050 time-horizon.</td><td>Carbon4 Finance</td></tr><tr><td>ACUTE RISK</td><td>Physical Acute Risk</td><td>The mean of acute risk ratings at a firm level defined by the CRIS methodology (heatwave risk, changes drought risk, rainfall extremes risk, and storms risk) for median scenario and 2050 time-horizon.</td><td>Carbon4 Finance</td></tr><tr><td>Q</td><td>Tobin's Q</td><td>(Market value of equity + book value of assets - book value of equity - balance sheet deferred taxes)/book value of assets</td><td>Worldscope</td></tr><tr><td>SIZE</td><td>Firm size</td><td>Ln (book value of total assets)</td><td>Worldscope</td></tr><tr><td>ROA</td><td>Return on assets</td><td>EBITDA/book value of assets</td><td>Worldscope</td></tr><tr><td>SG</td><td>Firm sales growth</td><td>(Sales in year t / sales in year (t-1)) -1</td><td>Worldscope</td></tr><tr><td>RD</td><td>R&D expenses</td><td>R&D expenditures / total sales</td><td>Worldscope</td></tr><tr><td>LEV</td><td>Firm leverage</td><td>Total debt / Total assets</td><td>Worldscope</td></tr><tr><td>DIV</td><td>Dividend</td><td>Cash dividends scaled by total assets</td><td>Worldscope</td></tr><tr><td>CAPEX</td><td>Capital expenditure</td><td>Capital expenditure divided by total assets</td><td>Worldscope</td></tr><tr><td>GDP</td><td>GDP per capita</td><td>Natural logarithm of GDP per capita</td><td>World Bank database</td></tr><tr><td>GDP GROWTH</td><td>GDP growth</td><td>Annual percentage change in GDP</td><td>World Bank database</td></tr></tbody></table>
<table><caption>Table 2 - Physical risk distribution by industry</caption><thead><tr><th>ICB Industry</th><th>PHYSICAL CLIMATE RISK</th><th>CHRONIC RISK</th><th>ACUTE RISK</th></tr></thead><tbody><tr><td>Basic Materials</td><td>32.520</td><td>25.467</td><td>27.431</td></tr><tr><td>Consumer Discretionary</td><td>31.018</td><td>23.894</td><td>25.671</td></tr><tr><td>Consumer Staples</td><td>31.251</td><td>24.745</td><td>26.485</td></tr><tr><td>Energy</td><td>34.062</td><td>28.799</td><td>28.332</td></tr><tr><td>Financials</td><td>27.512</td><td>21.936</td><td>22.688</td></tr><tr><td>Health Care</td><td>30.167</td><td>23.839</td><td>25.825</td></tr><tr><td>Industrials</td><td>31.213</td><td>24.019</td><td>26.141</td></tr><tr><td>Real Estate</td><td>24.284</td><td>18.129</td><td>19.611</td></tr><tr><td>Technology</td><td>29.981</td><td>23.796</td><td>25.092</td></tr><tr><td>Telecommunications</td><td>26.428</td><td>21.191</td><td>21.762</td></tr><tr><td>Utilities</td><td>37.119</td><td>29.847</td><td>30.424</td></tr><tr><td>Total</td><td>30.467</td><td>23.952</td><td>25.410</td></tr></tbody></table>

Note: This table reports the mean of CRIS climate risk rating and each climate hazard risk developed by Carbon4 by sector. We use the GICS structure of 11 sectors: Consumer Discretionary, consumer staples, energy, financials, health care, industrials, information technology, materials, real estate, telecommunications, and utilities.
<table><caption>Table 3 - Sample distribution</caption><thead><tr><th>Country</th><th>Freq.</th><th>Percent</th><th>Cumul.</th><th>ICB Industry</th><th>Freq.</th><th>Percent</th><th>Cumul.</th></tr></thead><tbody><tr><td>Australia</td><td>372</td><td>2.88</td><td>2.88</td><td>Basic Materials</td><td>881</td><td>6.82</td><td>6.82</td></tr><tr><td>Austria</td><td>55</td><td>0.43</td><td>3.30</td><td>Consumer Discretionary</td><td>2,135</td><td>16.52</td><td>23.34</td></tr><tr><td>Belgium</td><td>90</td><td>0.70</td><td>4.00</td><td>Consumer Staples</td><td>876</td><td>6.78</td><td>30.12</td></tr><tr><td>Canada</td><td>469</td><td>3.63</td><td>7.63</td><td>Energy</td><td>569</td><td>4.40</td><td>34.53</td></tr><tr><td>Denmark</td><td>146</td><td>1.13</td><td>8.76</td><td>Financials</td><td>2,001</td><td>15.49</td><td>50.01</td></tr><tr><td>Finland</td><td>105</td><td>0.81</td><td>9.57</td><td>Health Care</td><td>1,217</td><td>9.42</td><td>59.53</td></tr><tr><td>France</td><td>671</td><td>5.19</td><td>14.77</td><td>Industrials</td><td>2,492</td><td>19.29</td><td>78.72</td></tr><tr><td>Germany</td><td>527</td><td>4.08</td><td>18.85</td><td>Real Estate</td><td>548</td><td>4.24</td><td>82.96</td></tr><tr><td>Hong Kong</td><td>363</td><td>2.81</td><td>21.65</td><td>Technology</td><td>1,067</td><td>8.26</td><td>91.22</td></tr><tr><td>Ireland</td><td>43</td><td>0.33</td><td>21.99</td><td>Telecommunications</td><td>462</td><td>3.58</td><td>94.79</td></tr><tr><td>Israel</td><td>87</td><td>0.67</td><td>22.66</td><td>Utilities</td><td>673</td><td>5.21</td><td>100.00</td></tr><tr><td>Italy</td><td>131</td><td>1.01</td><td>23.67</td><td>Total</td><td>12,921</td><td>100.00</td><td></td></tr><tr><td>Japan</td><td>3,250</td><td>25.15</td><td>48.83</td><td></td><td></td><td></td><td></td></tr><tr><td>Luxembourg</td><td>12</td><td>0.09</td><td>48.92</td><td></td><td></td><td></td><td></td></tr><tr><td>Netherlands</td><td>115</td><td>0.89</td><td>49.81</td><td></td><td></td><td></td><td></td></tr><tr><td>New Zealand</td><td>13</td><td>0.10</td><td>49.91</td><td></td><td></td><td></td><td></td></tr><tr><td>Norway</td><td>75</td><td>0.58</td><td>50.49</td><td></td><td></td><td></td><td></td></tr><tr><td>Portugal</td><td>33</td><td>0.26</td><td>50.75</td><td></td><td></td><td></td><td></td></tr><tr><td>Singapore</td><td>155</td><td>1.20</td><td>51.95</td><td></td><td></td><td></td><td></td></tr><tr><td>Spain</td><td>199</td><td>1.54</td><td>53.49</td><td></td><td></td><td></td><td></td></tr><tr><td>Sweden</td><td>216</td><td>1.67</td><td>55.16</td><td></td><td></td><td></td><td></td></tr><tr><td>Switzerland</td><td>313</td><td>2.42</td><td>57.58</td><td></td><td></td><td></td><td></td></tr><tr><td>United Kingdom</td><td>811</td><td>6.28</td><td>63.86</td><td></td><td></td><td></td><td></td></tr><tr><td>United States</td><td>4,670</td><td>36.14</td><td>100.00</td><td></td><td></td><td></td><td></td></tr><tr><td>Total</td><td>12,921</td><td>100.00</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Note: These tables show our sample's industry and country distributions from 2009 to 2020. We use the ICB structure of 11 sectors: Consumer Discretionary, Consumer Staples, Energy, Financials, Health Care, Industrials, Information Technology, Materials, Real Estate, Telecommunications, and Utilities.
<table><caption>Table 4 - Descriptive statistics</caption><thead><tr><th>Variables</th><th>Mean</th><th>S.D.</th><th>P25</th><th>P75</th><th>N</th></tr></thead><tbody><tr><td>Q</td><td>1.424</td><td>1.256</td><td>0.680</td><td>1.738</td><td>12,921</td></tr><tr><td>MB</td><td>2.861</td><td>3.908</td><td>1.120</td><td>3.320</td><td>12,921</td></tr><tr><td>PHYSICAL CLIMATE RISK</td><td>30.467</td><td>6.375</td><td>26.133</td><td>35.415</td><td>12,921</td></tr><tr><td>CHRONIC RISK</td><td>23.951</td><td>4.156</td><td>21.579</td><td>26.203</td><td>12,921</td></tr><tr><td>ACUTE RISK</td><td>25.410</td><td>4.816</td><td>22.313</td><td>28.971</td><td>12,921</td></tr><tr><td>ROA</td><td>5.824</td><td>6.160</td><td>1.930</td><td>8.600</td><td>12,921</td></tr><tr><td>SIZE</td><td>9.723</td><td>1.487</td><td>8.834</td><td>10.764</td><td>12,921</td></tr><tr><td>LEV</td><td>59.842</td><td>22.112</td><td>44.719</td><td>76.028</td><td>12,921</td></tr><tr><td>SG</td><td>5.516</td><td>15.513</td><td>-1.820</td><td>10.660</td><td>12,921</td></tr><tr><td>R&D</td><td>2.037</td><td>3.495</td><td>0.000</td><td>2.619</td><td>12,921</td></tr><tr><td>DIV</td><td>2.131</td><td>2.618</td><td>0.400</td><td>2.834</td><td>12,921</td></tr><tr><td>CAPEX</td><td>3.813</td><td>3.513</td><td>1.234</td><td>5.432</td><td>12,921</td></tr><tr><td>GDP</td><td>10.786</td><td>0.156</td><td>10.704</td><td>10.873</td><td>12,921</td></tr><tr><td>GDP GROWTH</td><td>0.691</td><td>2.208</td><td>0.487</td><td>1.716</td><td>12,921</td></tr></tbody></table>

Note: Table 4 presents the descriptive statistics of all variables. This table includes the mean, the standard deviation, the number of observations, and the Q1 and Q3 of the variables. The sample consists of 12,921 observations. All variables are defined in Table 1.
<table><caption>Table 5 - Correlation matrix</caption><thead><tr><th></th><th>Q</th><th>MB</th><th>PHYSICAL CLIMATE RISK</th><th>CHRONIC RISK</th><th>ACUTE RISK</th><th>ROA</th><th>SIZE</th><th>LEV</th><th>SG</th><th>R&D</th><th>DIV</th><th>CAPEX</th><th>GDP</th><th>GDP GROWTH</th></tr></thead><tbody><tr><td>Q</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>MB</td><td>0.579***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>PHYSICAL CLIMATE RISK</td><td>-0.138***</td><td>-0.155***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CHRONIC RISK</td><td>-0.070***</td><td>-0.077***</td><td>0.760***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ACUTE RISK</td><td>-0.110***</td><td>-0.142***</td><td>0.944***</td><td>0.730***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>ROA</td><td>0.601***</td><td>0.339***</td><td>-0.148***</td><td>-0.072***</td><td>-0.134***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SIZE</td><td>-0.186***</td><td>-0.039***</td><td>0.004</td><td>0.045***</td><td>0.082***</td><td>-0.068***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>LEV</td><td>-0.314***</td><td>-0.016</td><td>-0.195***</td><td>-0.142***</td><td>-0.183***</td><td>-0.308***</td><td>0.228***</td><td>1</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SG</td><td>0.217***</td><td>0.143***</td><td>-0.103***</td><td>-0.077***</td><td>-0.103***</td><td>0.191***</td><td>-0.115***</td><td>-0.084***</td><td>1</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>R&D</td><td>0.415***</td><td>0.252***</td><td>0.011</td><td>0.033***</td><td>0.057***</td><td>0.154***</td><td>-0.023**</td><td>-0.279***</td><td>0.097***</td><td>1</td><td></td><td></td><td></td><td></td></tr><tr><td>DIV</td><td>0.468***</td><td>0.330***</td><td>-0.112***</td><td>-0.015</td><td>-0.116***</td><td>0.522***</td><td>-0.055***</td><td>-0.173***</td><td>-0.046***</td><td>0.084***</td><td>1</td><td></td><td></td><td></td></tr><tr><td>CAPEX</td><td>0.131***</td><td>0.039***</td><td>0.182***</td><td>0.249***</td><td>0.179***</td><td>0.099***</td><td>-0.037***</td><td>-0.191***</td><td>0.036***</td><td>-0.026**</td><td>0.129***</td><td>1</td><td></td><td></td></tr><tr><td>GDP</td><td>0.185***</td><td>0.124***</td><td>-0.141***</td><td>-0.090***</td><td>-0.144***</td><td>0.089***</td><td>-0.060***</td><td>-0.011</td><td>0.029***</td><td>0.084***</td><td>0.081***</td><td>0.003</td><td>1</td><td></td></tr><tr><td>GDP GROWTH</td><td>0.034***</td><td>0.025**</td><td>0.030***</td><td>0.024**</td><td>0.013</td><td>0.096***</td><td>-0.036***</td><td>-0.019*</td><td>0.204***</td><td>-0.030***</td><td>0.003</td><td>0.023**</td><td>0.099***</td><td>1</td></tr></tbody></table>

Note: This table reports the correlation coefficients among variables of interest from 2009 to 2020. All variables are defined in Table 1. ***, **, * denote statistical significance at the 1, 5, and 10% levels.
<table><caption>Table 6 - Physical risks and firm value</caption><thead><tr><th></th><th>(1)<br>Q</th><th>(2)<br>Q</th><th>(3)<br>Q</th><th>(4)<br>MB</th><th>(5)<br>MB</th><th>(6)<br>MB</th></tr></thead><tbody><tr><td>PHYSICAL<br>CLIMATE RISK</td><td>-0.009**<br>(-2.12)</td><td></td><td></td><td>-0.040**<br>(-2.56)</td><td></td><td></td></tr><tr><td>CHRONIC RISK</td><td></td><td>-0.008<br>(-1.43)</td><td></td><td></td><td>-0.024<br>(-1.21)</td><td></td></tr><tr><td>ACUTE RISK</td><td></td><td></td><td>-0.010**<br>(-2.00)</td><td></td><td></td><td>-0.061***<br>(-3.51)</td></tr><tr><td>ROA</td><td>0.035***<br>(28.90)</td><td>0.035***<br>(28.93)</td><td>0.035***<br>(28.90)</td><td>0.056***<br>(8.86)</td><td>0.057***<br>(8.92)</td><td>0.056***<br>(8.82)</td></tr><tr><td>SIZE</td><td>-0.132***<br>(-12.66)</td><td>-0.131***<br>(-12.58)</td><td>-0.130***<br>(-12.48)</td><td>-0.105***<br>(-2.58)</td><td>-0.102**<br>(-2.51)</td><td>-0.092**<br>(-2.27)</td></tr><tr><td>LEV</td><td>0.004***<br>(6.16)</td><td>0.004***<br>(6.14)</td><td>0.004***<br>(6.15)</td><td>0.019***<br>(7.15)</td><td>0.019***<br>(7.14)</td><td>0.019***<br>(7.11)</td></tr><tr><td>SG</td><td>0.003***<br>(8.05)</td><td>0.003***<br>(8.06)</td><td>0.003***<br>(8.04)</td><td>0.009***<br>(5.12)</td><td>0.009***<br>(5.16)</td><td>0.009***<br>(5.09)</td></tr><tr><td>R&D</td><td>0.0532***<br>(11.80)</td><td>0.0531***<br>(11.77)</td><td>0.0534***<br>(11.83)</td><td>0.215***<br>(11.41)</td><td>0.213***<br>(11.34)</td><td>0.217***<br>(11.55)</td></tr><tr><td>DIV</td><td>0.074***<br>(21.26)</td><td>0.075***<br>(21.38)</td><td>0.074***<br>(21.21)</td><td>0.243***<br>(14.18)</td><td>0.246***<br>(14.36)</td><td>0.241***<br>(14.03)</td></tr><tr><td>CAPEX</td><td>0.013***<br>(4.66)</td><td>0.013***<br>(4.66)</td><td>0.013***<br>(4.64)</td><td>0.037***<br>(2.84)</td><td>0.037***<br>(2.79)</td><td>0.037***<br>(2.82)</td></tr><tr><td>GDP</td><td>0.239**<br>(2.01)</td><td>0.249**<br>(2.11)</td><td>0.234**<br>(1.97)</td><td>0.250<br>(0.59)</td><td>0.293<br>(0.70)</td><td>0.199<br>(0.47)</td></tr><tr><td>GDP GROWTH</td><td>0.013***<br>(3.16)</td><td>0.013***<br>(3.13)</td><td>0.013***<br>(3.16)</td><td>0.057***<br>(2.59)</td><td>0.056**<br>(2.57)</td><td>0.057***<br>(2.61)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Constant</td><td>-0.997<br>(-0.77)</td><td>-1.261<br>(-0.98)</td><td>-1.025<br>(-0.79)</td><td>-1.909<br>(-0.41)</td><td>-3.269<br>(-0.71)</td><td>-1.124<br>(-0.24)</td></tr><tr><td>Observations</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td></tr><tr><td>R<sup>2</sup></td><td>0.525</td><td>0.525</td><td>0.524</td><td>0.233</td><td>0.232</td><td>0.234</td></tr></tbody></table>

Note: Table 6 presents the results of OLS regressions for Tobin's Q and Market-to-Book on physical climate risks (PHYSICAL CLIMATE RISK, CHRONIC RISK and ACUTE RISK) at 2050 time horizon, and other selected control variables. All variables are defined in Table 1. All regressors are one-year lagged to account for endogenous interdependence. All variables are winsorized at the 1% and 99% level. The models include year, industry, and region fixed-effects. Our sample spans from 2009 to 2020. t-statistics are reported in parenthesis. ***, **, * denote statistical significance at the 1, 5, and 10% levels.
<table><caption>Table 7 - Variance Inflation Factors (VIF) from Table 6 regressions</caption><thead><tr><th>Variables</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>PHYSICAL</td><td>1.16</td><td></td><td></td><td>1.16</td><td></td><td></td></tr><tr><td>CLIMATE RISK</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CHRONIC RISK</td><td></td><td>1.12</td><td></td><td></td><td>1.12</td><td></td></tr><tr><td>ACUTE RISK</td><td></td><td></td><td>1.16</td><td></td><td></td><td>1.16</td></tr><tr><td>ROA</td><td>1.57</td><td>1.55</td><td>1.56</td><td>1.57</td><td>1.55</td><td>1.56</td></tr><tr><td>LEV</td><td>1.36</td><td>1.31</td><td>1.35</td><td>1.36</td><td>1.31</td><td>1.35</td></tr><tr><td>SIZE</td><td>1.08</td><td>1.08</td><td>1.09</td><td>1.08</td><td>1.08</td><td>1.09</td></tr><tr><td>R&D</td><td>1.11</td><td>1.11</td><td>1.11</td><td>1.11</td><td>1.11</td><td>1.11</td></tr><tr><td>SG</td><td>1.08</td><td>1.08</td><td>1.08</td><td>1.08</td><td>1.08</td><td>1.08</td></tr><tr><td>GDP</td><td>1.05</td><td>1.04</td><td>1.05</td><td>1.05</td><td>1.04</td><td>1.05</td></tr><tr><td>GDP GROWTH</td><td>1.02</td><td>1.02</td><td>1.02</td><td>1.02</td><td>1.02</td><td>1.02</td></tr><tr><td>DIV</td><td>1.43</td><td>1.42</td><td>1.44</td><td>1.43</td><td>1.42</td><td>1.44</td></tr><tr><td>CAPEX</td><td>1.09</td><td>1.12</td><td>1.09</td><td>1.09</td><td>1.12</td><td>1.09</td></tr><tr><td>MEAN VIF</td><td>1.19</td><td>1.19</td><td>1.20</td><td>1.19</td><td>1.19</td><td>1.20</td></tr></tbody></table>

Note: This table reports the variance inflation factors from the models tested in Table 6. All variables are defined in Table 1.
<table><caption>Table 8 - The effects of chronic and acute physical risks on firm's financial characteristics</caption><thead><tr><th></th><th>(1)<br>ROA</th><th>(2)<br>LEV</th><th>(3)<br>SG</th><th>(4)<br>R&D</th><th>(5)<br>DIV</th><th>(6)<br>CAPEX</th></tr></thead><tbody><tr><td>CHRONIC RISK</td><td>-0.048***<br>(-3.19)</td><td>-0.324***<br>(-6.41)</td><td>-0.068<br>(-1.55)</td><td>0.074***<br>(9.23)</td><td>0.034***<br>(5.39)</td><td>0.095***<br>(11.19)</td></tr><tr><td>ACUTE RISK</td><td>-0.065***<br>(-4.82)</td><td>-0.235***<br>(-5.11)</td><td>-0.221***<br>(-5.56)</td><td>0.075***<br>(10.31)</td><td>-0.048***<br>(-8.29)</td><td>0.054***<br>(6.92)</td></tr><tr><td>β<sub>CHRONIC RISK</sub></td><td>0.017</td><td>-0.089*</td><td>0.153***</td><td>-0.001</td><td>0.082***</td><td>0.041***</td></tr><tr><td>- β<sub>ACUTE RISK</sub></td><td>(1.49)</td><td>(-1.79)</td><td>(3.53)</td><td>(-0.16)</td><td>(11.33)</td><td>(4.16)</td></tr><tr><td colspan="7">Indirect effects on Tobins' Q</td></tr><tr><td>CHRONIC RISK</td><td>-0.169***<br>(-3.44)</td><td>-0.097***<br>(-2.04)</td><td>-0.018<br>(-1.08)</td><td>0.394***<br>(-8.87)</td><td>0.254<br>(-5.17)</td><td>0.119***<br>(-5.67)</td></tr><tr><td>ACUTE RISK</td><td>-0.229***<br>(-4.87)</td><td>-0.084***<br>(-1.83)</td><td>-0.058***<br>(-5.05)</td><td>0.401***<br>(-9.89)</td><td>-0.357***<br>(-7.84)</td><td>0.067***<br>(-4.88)</td></tr><tr><td>Difference of indirect<br>effects</td><td>0.060<br>(-1.47)</td><td>-0.013<br>(-0.19)</td><td>0.040***<br>(3.26)</td><td>-0.007<br>(-0.17)</td><td>0.611**<br>(2.1)</td><td>0.052<br>(0.31)</td></tr></tbody></table>

Note: Table 8 presents the results of OLS regressions of firm characteristics (ROA, LEV, SG, R&D, DIV, and CAPEX) on physical climate risks at 2050 time horizon and other selected control variables. We tested the significance of the difference of the coefficients of CHRONIC RISK and ACUTE RISK. This table also reports the indirect effects on Tobin's Q. The indirect effects are computed as the product of the effect of the physical risks on the mediators time the effect of the mediator on Tobin's Q. Coefficients of indirect effects on Tobin's Q are multiplied by 100 for readability. All variables are defined in Table 1. All regressors are one-year lagged to account for endogenous interdependence. All variables are winsorized at the 1% and 99% levels. The models include control variables used in Table 6 and year, industry, and region fixed-effects. Our sample spans from 2009 to 2020. t-statistics are reported in parenthesis. ***, **, * denote statistical significance at the 1, 5, and 10% levels.
<table><caption>Table 9 - The economic significance of physical risks</caption><thead><tr><th></th><th>ROA</th><th>LEV</th><th>SG</th><th>R&D</th><th>DIV</th><th>CAPEX</th><th>Sum of indirect effects</th><th>Direct effects</th><th>Total effects</th></tr></thead><tbody><tr><td>PHYSICAL CLIMATE RISK</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(1)</td><td>-0.052</td><td>-0.141</td><td>-0.172</td><td>0.042</td><td>-0.027</td><td>0.061</td><td></td><td></td><td></td></tr><tr><td>(2) = (1) * ΔPHYSICAL CLIMATE RISK</td><td>-0.482</td><td>-1.313</td><td>-1.601</td><td>0.393</td><td>-0.251</td><td>0.564</td><td></td><td></td><td></td></tr><tr><td>(3)</td><td>0.035</td><td>0.004</td><td>0.003</td><td>0.053</td><td>0.074</td><td>0.013</td><td></td><td>-0.010</td><td></td></tr><tr><td>(4) = (2) * (3)</td><td>-0.017</td><td>-0.005</td><td>-0.005</td><td>0.021</td><td>-0.019</td><td>0.007</td><td>-0.018</td><td>-0.092</td><td>-0.110</td></tr><tr><td>CHRONIC RISK</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(1)</td><td>-0.048</td><td>-0.324</td><td>-0.068</td><td>0.074</td><td>0.034</td><td>0.095</td><td></td><td></td><td></td></tr><tr><td>(2) = (1) * ΔCHRONIC RISK</td><td>-0.222</td><td>-1.498</td><td>-0.314</td><td>0.342</td><td>0.157</td><td>0.439</td><td></td><td></td><td></td></tr><tr><td>(3)</td><td>0.035</td><td>0.004</td><td>0.003</td><td>0.053</td><td>0.074</td><td>0.013</td><td></td><td>-0.008</td><td></td></tr><tr><td>(4) = (2) * (3)</td><td>-0.008</td><td>-0.006</td><td>-0.001</td><td>0.018</td><td>0.012</td><td>0.005</td><td>-0.020</td><td>-0.036</td><td>-0.056</td></tr><tr><td>ACUTE RISK</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>(1)</td><td>-0.065</td><td>-0.235</td><td>-0.221</td><td>0.075</td><td>-0.048</td><td>0.054</td><td></td><td></td><td></td></tr><tr><td>(2) = (1) * ΔACUTE RISK</td><td>-0.435</td><td>-1.565</td><td>-1.471</td><td>0.498</td><td>-0.318</td><td>0.359</td><td></td><td></td><td></td></tr><tr><td>(3)</td><td>0.035</td><td>0.004</td><td>0.003</td><td>0.053</td><td>0.074</td><td>0.013</td><td></td><td>-0.010</td><td></td></tr><tr><td>(4) = (2) * (3)</td><td>-0.015</td><td>-0.006</td><td>-0.004</td><td>0.026</td><td>-0.024</td><td>0.004</td><td>-0.019</td><td>-0.069</td><td>-0.088</td></tr></tbody></table>

Note: Table 9 reports the economic significance of physical risks on Tobin's q. We compute the change in climate variables from the 25th percentile to the 75th percentile of each climate variable (ΔPHYSICAL CLIMATE RISK = 9.310, ΔCHRONIC RISK = 4.624, ΔACUTE RISK = 6.658). Row (1) displays the coefficients from the regression results of the effects of physical risks on firm's financial characteristics. Row (2) presents the product of Row (1) and the change in physical risk variables from the 25th percentile to the 75th percentile. Row (3) reports the coefficients from the direct effect regression in Table 6. Row (4) presents the product of Row (2) and Row (3) and represents the indirect effect due to changes in physical risk. We then compute the sum of indirect effects. The direct effect is calculated as the result of the coefficient on physical risk in Table 6 and the percentile change. The total effect is the sum of indirect and direct effects.
<table><caption>Table 10 - Physical risk and firm value over time</caption><thead><tr><th></th><th>(1)<br>2009-2014<br>Q</th><th>(2)<br>2015-2020<br>Q</th><th>(3)<br>2009-2014<br>Q</th><th>(4)<br>2015-2020<br>Q</th><th>(5)<br>2009-2014<br>Q</th><th>(6)<br>2015-2020<br>Q</th></tr></thead><tbody><tr><td>PHYSICAL<br>CLIMATE RISK</td><td>-0.008<br>(-1.45)</td><td>-0.013**<br>(-2.27)</td><td></td><td></td><td></td><td></td></tr><tr><td>CHRONIC RISK</td><td></td><td></td><td>-0.006<br>(-0.93)</td><td>-0.011<br>(-1.55)</td><td></td><td></td></tr><tr><td>ACUTE RISK</td><td></td><td></td><td></td><td></td><td>-0.008<br>(-1.38)</td><td>-0.014**<br>(-2.14)</td></tr><tr><td>ROA</td><td>0.022***<br>(14.40)</td><td>0.027***<br>(15.86)</td><td>0.022***<br>(14.45)</td><td>0.027***<br>(15.90)</td><td>0.022***<br>(14.40)</td><td>0.027***<br>(15.86)</td></tr><tr><td>SIZE</td><td>-0.117***<br>(-8.95)</td><td>-0.164***<br>(-10.98)</td><td>-0.116***<br>(-8.86)</td><td>-0.163***<br>(-10.89)</td><td>-0.116***<br>(-8.82)</td><td>-0.161***<br>(-10.73)</td></tr><tr><td>LEV</td><td>-0.006***<br>(-6.76)</td><td>0.002***<br>(2.79)</td><td>-0.006***<br>(-6.78)</td><td>0.002***<br>(2.78)</td><td>-0.006***<br>(-6.77)</td><td>0.002***<br>(2.77)</td></tr><tr><td>SG</td><td>0.002***<br>(6.35)</td><td>0.002***<br>(5.48)</td><td>0.002***<br>(6.36)</td><td>0.002***<br>(5.50)</td><td>0.002***<br>(6.35)</td><td>0.002***<br>(5.48)</td></tr><tr><td>R&D</td><td>0.036***<br>(6.57)</td><td>0.085***<br>(12.77)</td><td>0.036***<br>(6.55)</td><td>0.085***<br>(12.77)</td><td>0.036***<br>(6.59)</td><td>0.085***<br>(12.81)</td></tr><tr><td>DIV</td><td>0.070***<br>(15.99)</td><td>0.060***<br>(11.84)</td><td>0.071***<br>(16.08)</td><td>0.061***<br>(11.99)</td><td>0.070***<br>(15.97)</td><td>0.060***<br>(11.79)</td></tr><tr><td>CAPEX</td><td>-0.003<br>(-0.99)</td><td>0.009**<br>(2.29)</td><td>-0.003<br>(-0.98)</td><td>0.009**<br>(2.25)</td><td>-0.003<br>(-1.01)</td><td>0.009**<br>(2.27)</td></tr><tr><td>GDP</td><td>1.116***<br>(8.67)</td><td>0.428***<br>(3.14)</td><td>1.118***<br>(8.68)</td><td>0.438***<br>(3.22)</td><td>1.113***<br>(8.64)</td><td>0.419***<br>(3.07)</td></tr><tr><td>GDP GROWTH</td><td>0.026***<br>(9.52)</td><td>0.010***<br>(3.81)</td><td>0.026***<br>(9.53)</td><td>0.009***<br>(3.79)</td><td>0.026***<br>(9.52)</td><td>0.010***<br>(3.84)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Constant</td><td>-9.631***<br>(-6.83)</td><td>-2.089<br>(-1.39)</td><td>-9.779***<br>(-6.96)</td><td>-2.411<br>(-1.62)</td><td>-9.652***<br>(-6.86)</td><td>-2.097<br>(-1.39)</td></tr><tr><td>Observations</td><td>6,131</td><td>6,790</td><td>6,131</td><td>6,790</td><td>6,131</td><td>6,790</td></tr><tr><td>R<sup>2</sup></td><td>0.480</td><td>0.527</td><td>0.480</td><td>0.527</td><td>0.480</td><td>0.527</td></tr></tbody></table>

Note: This table presents the results of OLS regressions for Tobin's Q on physical climate risks (PHYSICAL CLIMATE RISK, CHRONIC RISK and ACUTE RISK), and other selected control variables. We replicate the analysis in Table 6 on two subsamples over time. All variables are defined in Table 1. All regressors are one-year lagged to account for endogenous interdependence. All variables are winsorized at the 1% and 99% level. The models include industry and region fixed-effects. Our sample spans from 2009 to 2020. t-statistics are reported in parenthesis. ***, **, * denote statistical significance at the 1, 5, and 10% levels.
<table><caption>Table 11 – Acute and chronic physical risks and firm value by scenario</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>Low 2050<br>CHRONIC<br>Q</th><th>Low 2050<br>ACUTE<br>Q</th><th>Med 2050<br>CHRONIC<br>Q</th><th>Med 2050<br>ACUTE<br>Q</th><th>High 2050<br>CHRONIC<br>Q</th><th>High 2050<br>ACUTE<br>Q</th></tr></thead><tbody><tr><td>PHYSICAL_RISK</td><td>-0.008<br>(-1.37)</td><td>-0.014***<br>(-2.83)</td><td>-0.008<br>(-1.43)</td><td>-0.010**<br>(-2.00)</td><td>-0.008<br>(-1.41)</td><td>-0.008**<br>(-2.08)</td></tr><tr><td>ROA</td><td>0.035***<br>(28.93)</td><td>0.035***<br>(28.90)</td><td>0.035***<br>(28.93)</td><td>0.035***<br>(28.90)</td><td>0.035***<br>(28.93)</td><td>0.035***<br>(28.90)</td></tr><tr><td>SIZE</td><td>-0.131***<br>(-12.58)</td><td>-0.130***<br>(-12.50)</td><td>-0.131***<br>(-12.58)</td><td>-0.130***<br>(-12.48)</td><td>-0.131***<br>(-12.57)</td><td>-0.132***<br>(-12.67)</td></tr><tr><td>LEV</td><td>0.004***<br>(6.13)</td><td>0.004***<br>(6.10)</td><td>0.004***<br>(6.14)</td><td>0.004***<br>(6.15)</td><td>0.004***<br>(6.13)</td><td>0.004***<br>(6.16)</td></tr><tr><td>SG</td><td>0.003***<br>(8.06)</td><td>0.003***<br>(8.05)</td><td>0.003***<br>(8.06)</td><td>0.003***<br>(8.04)</td><td>0.003***<br>(8.06)</td><td>0.003***<br>(8.04)</td></tr><tr><td>R&D</td><td>0.053***<br>(11.77)</td><td>0.053***<br>(11.85)</td><td>0.053***<br>(11.77)</td><td>0.053***<br>(11.83)</td><td>0.053***<br>(11.78)</td><td>0.053***<br>(11.80)</td></tr><tr><td>DIV</td><td>0.075***<br>(21.37)</td><td>0.074***<br>(21.23)</td><td>0.075***<br>(21.38)</td><td>0.074***<br>(21.21)</td><td>0.075***<br>(21.37)</td><td>0.074***<br>(21.21)</td></tr><tr><td>CAPEX</td><td>0.013***<br>(4.66)</td><td>0.013***<br>(4.67)</td><td>0.013***<br>(4.66)</td><td>0.013***<br>(4.64)</td><td>0.013***<br>(4.65)</td><td>0.013***<br>(4.64)</td></tr><tr><td>GDP</td><td>0.243**<br>(2.05)</td><td>0.224*<br>(1.88)</td><td>0.249**<br>(2.11)</td><td>0.234**<br>(1.97)</td><td>0.242**<br>(2.04)</td><td>0.243**<br>(2.05)</td></tr><tr><td>GDP GROWTH</td><td>0.013***<br>(3.15)</td><td>0.013***<br>(3.18)</td><td>0.013***<br>(3.13)</td><td>0.013***<br>(3.16)</td><td>0.013***<br>(3.15)</td><td>0.013***<br>(3.14)</td></tr><tr><td>Industry FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Region FE</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Constant</td><td>-1.194<br>(-0.92)</td><td>-0.781<br>(-0.60)</td><td>-1.261<br>(-0.98)</td><td>-1.025<br>(-0.79)</td><td>-1.168<br>(-0.90)</td><td>-1.104<br>(-0.85)</td></tr><tr><td>Observations</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td><td>12,921</td></tr><tr><td>R2</td><td>0.525</td><td>0.525</td><td>0.525</td><td>0.524</td><td>0.525</td><td>0.524</td></tr></tbody></table>

Note: This table presents the results of OLS regressions for Tobin's Q on chronic and acute physical risks depending intensity, and other selected control variables. Carbon4 distinguishes 3 climate change intensities (low, medium, and high) at 2050 time horizon. All variables are defined in Table 1. All controls are one-year lagged to account for endogenous interdependence. All variables are winsorized at the 1% and 99% level. The models include year, industry, and region fixed-effects. Our sample spans from 2009 to 2020. t-statistics are reported in parenthesis. ***, **, * denote statistical significance at the 1, 5, and 10% levels.
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