metadata: authors: 0: author: Yang,Jangho
1: author: Schoder,Christian
count: World
docna: 0: docna: Impact of Temperature Uncertainty on Firm
            Growth : A Grid-Level Analysis
docty: Policy Research Working Paper
theme: Structural Transformation and Economic Diversification,Mitigation,Financial Stability,Economic Policy,Green Growth,Economic Growth and Planning,Environment and Natural Resource Management,Finance,Climate change,Adaptation,Macroeconomic & Structural Policy Modelling
abstracts: cdata!: This study examines the impact of
            temperature uncertainty on firm fixed capital growth using a
            unique dataset that merges extensive firm-level financial
            data with detailed grid-level weather data. The analysis
            reveals a strong negative relationship between temperature
            uncertainty and fixed capital growth. Furthermore, the
            impact varies significantly across industries with differing
            levels of investment irreversibility and among countries
            with varying income levels. Firms in industries
            characterized by high investment irreversibility and those
            operating in higher-income countries experience more
            pronounced declines in fixed asset growth due to temperature uncertainty.
display_title: Impact of Temperature Uncertainty on
            Firm Growth : A Grid-Level Analysis
listing_relative_url: /research/2025/01/34443883/impact-temperature-uncertainty-firm-growth-grid-level-analysis
topicv3: Climate Change Impacts m1326159 1773,Determinants of Economic Growth m1327636 1078,Climate Change and Disaster Risk m1327652 1837,Firm Behavior and Jobs m1504204 2873
publishtoextweb_dt: 2025-01-08T00:00:00Z
dois: 10.1596/1813-9450-11020
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 36
author_keys: 0: author_key: NA
1: author_key: 1925719
model_extractions: Policy Research Working Paper 11020 Impact of Temperature Uncertainty on Firm Growth A Grid-Level Analysis _Jangho Yang Christian Schoder_ Development Economics Development Policy Team January 2025 A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.
Policy Research Working Paper 1120

# **Abstract**

This study examines the impact of temperature uncertainty on firm fixed capital growth using a unique dataset that merges extensive firm-level financial data with detailed gridlevel weather data. The analysis reveals a strong negative relationship between temperature uncertainty and fixed capital growth. Furthermore, the impact varies significantly across industries with differing levels of investment irreversibility and among countries with varying income levels. Firms in industries characterized by high investment irreversibility and those operating in higher-income countries experience more pronounced declines in fixed asset growth due to temperature uncertainty.

This paper is a product of the Development Policy Team, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at j634yang@uwaterloo.ca and cschoder@worldbank.org. A verified reproducibility package for this paper is available at http://reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R P<br>LI R<br>O E<br>O C<br>W R<br>H<br>S<br>P<br><!-- End of picture text --> _The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Impact of Temperature Uncertainty on Firm Growth: A Grid-Level Analysis

Jangho Yang<sup>∗1,2</sup> and Christian Schoder<sup>3</sup>

> 1 _Management Sciences, Faculty of Engineering, University of Waterloo_

> 2 _Institute for New Economic Thinking, University of Oxford_

> 3 _World Bank_

Keywords: temperature uncertainty, firms’ fixed asset growth, investment irreversibility, incomelevel heterogeneity JEL codes: Q54, D22, D25

> ∗We would like to acknowledge two research assistants at the University of Waterloo, Edward Jeong, Yuhan Zhang and Joanna Yang, who greatly helped to collect and process the data. All errors are our own. _Contacts:_ j634yang@uwaterloo.ca
# 1 Introduction

The effects of rising temperatures on economic activities have been extensively documented over the past few years. While quantifying the socioeconomic impacts accurately has proven difficult (Tubiello & Rosenzweig 2008, Piontek et al. 2021), and results often contain substantial uncertainty, sometimes leading to conflicting outcomes (Newell et al. 2021), there is a general consensus that rising temperatures have an overall adverse effect on economic activities, especially in terms of GDP levels (Newell et al. 2021).<sup>1</sup> What is less understood is temperature variability, defined as the change in daily temperatures over a given period, and its potential impact on economic activities.<sup>2</sup> Unlike the clear evidence of rising global temperatures, there is no general consensus on global trends in temperature variability (Huntingford et al. 2013, Olonscheck et al. 2021). Although some studies suggest a global increase (Alessandri & Mumtaz 2022), changes in temperature variability are highly region-specific, with recent evidence indicating that it may be increasing more rapidly in lower-income countries (Bathiany et al. 2018). However, the implications of this variability for economic activities remain unclear. Sectors like agriculture, where certain species are particularly vulnerable to increased temperature variability (Vasseur et al. 2014), may experience direct and measurable consequences, whereas the effects on other sectors, such as technology and services, are less predictable.

To examine the cross-industry, economy-wide effects of temperature changes, this paper focuses on _temperature uncertainty_ —the _unexpected_ fluctuations in temperature that deviate from expected patterns. Our main interest lies in understanding how location-specific temperature uncertainty, as part of the broader uncertainties faced by economic agents, influences overall economic behavior across industries. We focus on firm-level activities, particularly investment patterns (measured by fixed asset growth), and how the private sector, driven by profit motives, responds to climate change and its associated risks and uncertainties. To investigate the link between uncertainty and investment, we draw on Real Options Theory (ROT) (Myers 1977), which suggests that firms facing increased uncertainty are more likely to delay investment, reducing their overall investment levels. This effect is especially pronounced in sectors characterized by a higher degree of irreversible investments, such as infrastructure projects (Baldwin 1982).

Building on ROT, this study aims to evaluate whether increased temperature uncertainty is associated with slower firm growth across diverse geographical regions. Importantly, we investigate whether this adverse effect is more pronounced in sectors characterized by a higher proportion of irreversible assets. Additionally, recognizing the potential regional heterogeneity in temperature uncertainty across different income levels (Bathiany et al. 2018), we explore whether the negative relationship between temperature uncertainty and firms’ fixed asset growth varies according to country income levels.

> 1The most common approach to measuring the economic impact of climate change is to analyze the country- or region-level differences in its potentially adverse effects on GDP and income (Dell et al. 2009, Roson et al. 2016, Cruz & Rossi-Hansberg 2021), growth (Dell et al. 2012), productivity (Burke et al. 2015, Kalkuhl & Wenz 2020), and investment rates (Willner et al. 2021). Sector-specific impacts, especially on agriculture, have also been widely documented (Lobell & Asner 2003, Schlenker & Roberts 2009, Hatfield & Prueger 2015, Albert et al. 2021). For a meta-analysis of the existing economic activities and temperature relationship, see Newell et al. (2021), which discusses substantial uncertainty in the estimates of the impact and conflicting results.

> 2The overall neglect of the impact of increased temperature variation is also observed in climate change research in ecology, as noted by Vasseur et al. (2014).

2
To do this, we employ a unique dataset that merges firm-level financial data with meteorological information. Our analysis hinges on the concept of location-specific weather uncertainty for firms, measured via grid-level temperature uncertainty. We utilize financial records from the Orbis database, covering 30 million firms across 64 countries, alongside daily surface temperature data on a 1x1 grid from the Berkeley Earth Foundation dataset (BERKEARTH). The merging of these two datasets creates an extensive grid-level dataset, covering around 4,000 economically meaningful grids with firm presence from 2000 to 2019. Grid-specific temperature uncertainty is measured based on the maximum daily temperatures over a year. We examine whether this grid-specific temperature uncertainty is associated with the growth of firms located in the grid.

Our analysis yields three key findings. First, there is a strong negative correlation between weather uncertainty and firm growth. Our results indicate that for every one-degree increase in temperature uncertainty, measured by the standard deviation of deviations from expected temperatures, firms’ growth rate of fixed assets decreases by 0.77 percentage points.

Second, the impact of weather uncertainty on firm growth varies across industries with different degrees of investment irreversibility. Industries with higher investment irreversibility experience a more pronounced negative impact on firm size growth. Doubling capital intensity intensifies the negative effect of temperature uncertainty on firm size growth by roughly 0.72 percentage points.

Third, the impact of weather uncertainty on firm growth varies across countries with different income levels. Higher-income countries are more adversely affected by weather uncertainties in terms of firm behavior. As income levels double, the negative impact of temperature uncertainty on fixed asset growth becomes more severe by 0.63 percentage points.

This paper is among the first to examine firms’ behavior in response to location-specific temperature uncertainty at the grid level. While climate-related uncertainty is often studied using indirect, non-weather measures like the Climate Change News Index (Engle et al. 2020) and in climate policy uncertainty literature (Gulen & Ion 2016), recent studies have increasingly focused on direct measures of weather variability, as highlighted by Kahn et al. (2021), Kotz et al. (2021), Donadelli et al. (2022), Sheng et al. (2022). Our approach builds on these recent efforts by emphasizing weather variability as a key factor influencing economic activity. In doing so, we also draw on the established tradition of using gridded output data, particularly Nordhaus (2006) and later studies employing grid-level or sub-national geographic data, such as Kalkuhl & Wenz (2020), Wenz et al. (2023), Gortan et al. (2024).

Our main contribution is the construction of grid-level economic data based on firm-level activities. Unlike regional GDP data, firm-level data shows how the private sector, driven by profit motives, responds to climate change and the risks it poses. This firm-level data enables us not only to capture the overall relationship between weather variability and economic activity but also to test important hypotheses, such as the moderating effects of investment irreversibility in this relationship—something that is difficult to achieve without detailed firm-level data. By doing so, we bridge the climate change impact literature, particularly studies using grid-level output data (Nordhaus 2006, Kalkuhl & Wenz 2020), with research on firms’ behavior under uncertainty (Myers 1977).

The paper is structured as follows: Section 2 reviews the real options theory. Section 3 discusses the main data sources, cleaning process, and key descriptive statistics. Section 4 outlines the empirical methodology and presents our findings. Section 5 concludes the paper.

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# 2 Uncertainty and investment

## 2.1 Real options theory

The primary research question we explore is how firms react to uncertainty associated with climate change by adjusting their investment behavior. We adopt a broader perspective by treating temperature uncertainty as a contributor to the overall uncertainty that firms face across various industries. This approach allows us to move beyond sector-specific impacts and focus on how unpredictable weather patterns generate broader uncertainty in firms’ strategic decision-making processes, affecting investment choices, operational planning, and risk management strategies in both direct and indirect ways.

In the firm-behavior theory, the relationship between uncertainty and a firm’s investment has been significantly shaped by the Real Options Theory (ROT), initially proposed by Myers (1977) and further developed by Abel (1983), Bernanke (1983), McDonald & Siegel (1986), Dixit et al. (1994), Abel & Eberly (1996). This theory redefines investment decision-making in the context of uncertainty. Unlike traditional accounting methods for investment decisions, such as the net present value rule—which states that firms should invest when the discounted present value of future cash flows exceeds the cost of the investment—ROT suggests that it might be optimal for firms to delay their investments until they have more information about their economic environment. The theory highlights that uncertainty is a critical factor in such decisions, suggesting that increased uncertainty raises the value of the option to wait thereby reducing the propensity to invest.

The issue of the investment–uncertainty relationship is particularly important in the context of irreversible investments (Baldwin 1982). Such assets, including infrastructure like gas pipelines, cannot be easily reversed or undone without significant costs or losses. Given the high costs associated with undoing such investments, the impact of uncertainty can be substantially more severe. This observation suggests that the relationship between uncertainty and investment may be more pronounced in sectors or firms with a higher proportion of irreversible assets (Pindyck 1990, Dixit et al. 1994).<sup>3</sup> In the broader context of the negative relationship between uncertainty and investment rates, we understand temperature uncertainty as a contributing factor of the overall uncertainty firms encounter in their decision-making. Sudden fluctuations in weather conditions can lead to unpredictable spikes in operational costs, particularly through volatile energy expenses. Additionally, weather-driven disruptions in market dynamics can trigger abrupt shifts in demand or create supply shortages, driving price instability and complicating firms’ ability to forecast future profitability. These unpredictable factors may increase the complexity of long-term strategic planning, indicating that temperature uncertainty could be an important component of the broader uncertainty influencing investment behavior.

## 2.2 Basic model

To establish the mathematical relationship between uncertainty and investment rates, we draw on McDonald & Siegel (1986), which derives the value of the investment option as a function of the project’s

> 3The investment–uncertainty relationship has been extensively tested both at the aggregate (Ferderer 1993, Caballero & Pindyck 1992, Ghosal & Loungani 2000, Huizinga 1993) and the firm levels (Leahy & Whited 1995, Bell & Campa 1997, Bulan 2005, Bulan et al. 2009, Bloom 2014). As surveyed in Leahy & Whited (1995) and Carruth et al. (2000), the general consensus is that increased uncertainty, at both the aggregate and disaggregate levels, leads to lower investment rates.

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volatility and cost.

In this model, the firm aims to maximize the present value of the project by choosing the optimal time to invest. This is framed as a “real options” problem, where the firm holds an option to invest, but once exercised, the decision becomes irreversible. Unlike financial options, real options are not financial derivatives and therefore cannot be traded.

The key behavioral assumption of the real options theory is that the firm’s objective is to maximize the present value of the project by choosing the _optimal time to invest_ . More specifically, a firm deciding when to invest weighs the immediate benefits of investing against the potential value gained by postponing the investment. Let _V_ denote the present value of expected net cash flows from the project, and _F_ represent the cost of investment. The firm’s decision rule can be formally expressed as: where _C_<sup>∗</sup> represents the critical threshold of the benefit-to-cost ratio at which immediate investment becomes optimal. This threshold is determined by the elasticity _ε_ , which measures how sensitive the option value is to changes in the ratio _V /F_ . A higher elasticity indicates that fluctuations in the ratio have a greater influence on the investment decision.

Let us assume that both the project value _V_ and the cost _F_ follow geometric Brownian motions. The stochastic differential equations governing their behavior are: where _αv_ and _αf_ are the expected growth rates of _V_ and _F_ , _σv_ and _σf_ represent their volatilities, and _dWv_ and _dWf_ are increments of Wiener processes. The two processes may be correlated, with correlation coefficient _ρvf_ .<sup>4</sup> Denoting the combined volatility _σ_<sup>2</sup> as _σ_<sup>2</sup> = _σv_<sup>2+</sup><sup>_σ_2</sup> _f_<sup>−2</sup><sup>_ρvf σvσf_, the key mathematical result from</sup> (McDonald & Siegel 1986) is the derivation of the elasticity in terms of the volatilities: The critical threshold _C_<sup>∗</sup> is then determined using the elasticity _ε_ , with the investment trigger point _V_ = _V_<sup>∗</sup> = _C_<sup>∗</sup> _F_ being derived from the value-matching and smooth-pasting conditions such that<sup>5</sup>

> 4Introducing additional sources of volatilities, such as weather variability, is mathematically straightforward and does not alter the core implications of the model; it only adds complexity to the correlation structure. To maintain focus on the key results of real options theory, we rely on the original model by McDonald & Siegel (1986).

> 5The value-matching condition, _X_ ( _V_ ∗) = _V_ ∗ − _F_ , and the smooth-pasting condition, _XV_ ( _V_ ∗) = 1, are used to determine the critical threshold for investment. Given the solution _X_ ( _V_ ) = _AV_<sup>_ε_</sup> , the value-matching condition becomes _A_ ( _V_<sup>∗</sup> )<sup>_ε_</sup> = _V_<sup>∗</sup> − _F_ , and the smooth-pasting condition yields _Aε_ ( _V_<sup>∗</sup> )<sup>_ε_−1</sup> = 1. Solving these two equations simultaneously determines the constants and the expression of the critical threshold _C_<sup>∗</sup> in terms of the elasticity _ε_ .

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This result shows that _C_<sup>∗</sup> is an increasing function of _ε_ , which itself depends on the combined volatility _σ_<sup>2</sup> and the growth rates _αv_ and _αf_ .

The option value _X_ ( _V ,F_ ) can be explicitly written as (McDonald & Siegel 1986): This expression demonstrates the dependence of the option value _X_ on the project’s value _V_ , the investment cost _F_ , the critical threshold _C_<sup>∗</sup> , and the elasticity _ε_ . The relationship between volatility and the critical threshold _C_<sup>∗</sup> can be further examined by differentiating _C_<sup>∗</sup> with respect to _σ_ : since _dσ_<sup>_<u>dε</u>>_0 and</sup><sup>_ε >_1.Therefore, both the value of the investment option</sup><sup>_X_and the critical threshold</sup> _C_<sup>∗</sup> are increasing functions of the variance _σ_<sup>2</sup> : This proportionality demonstrates that as uncertainty (volatility) increases, the value of the option to delay investment rises, similar to financial options where greater volatility expands the range of possible outcomes. This flexibility allows firms to respond more effectively to future changes, such as shifts in prices or market trends. Firms can capitalize on upside opportunities by exercising the option to invest or expand, while avoiding losses by deferring in unfavorable conditions. This asymmetry, which favors the investor, means that higher uncertainty or a broader range of potential outcomes benefits the firm by increasing the value of waiting.

The following sections outline the data sources and empirical strategies used to test the real options hypotheses, focusing on temperature uncertainty at the grid level to explore the potential negative relationship between temperature uncertainty and firms’ investment decisions.

# 3 Data

## 3.1 Data source

Firm-level data. We employ Orbis as our principal firm-level data source, which includes comprehensive financial statements for organizations on a global scale. Administered by Bureau van Dijk, the Orbis database integrates information from a variety of origins, including official company registries and regulatory filings. Its extensive coverage, particularly concerning balance sheet items and profit 6
and loss accounts for private enterprises, positions Orbis as one of the most robust datasets available for analyzing financial performance at the firm level.<sup>67</sup> The Orbis database provided by the Wharton Research Data Services (WRDS) includes latitude and longitude information at the firm level, enabling the precise determination of each firm’s geographic location. This aspect is essential for correlating firms with their respective regional weather data. However, not all firms in our dataset have this geographical data. Almost 20% of firms in our sample do not have the geographical information. To address this, we employed the Google Maps API, using available country, region, and city data for these firms. The methodology for this process is detailed in Appendix A.

For our study, we extracted firm-level information from 2000 to 2019, focusing on medium, large, and very large companies. The primary variables extracted included the firm’s location, fixed assets, and the NACE code for sector identification. While other financial variables were sparsely populated, fixed asset data was more consistently available. Consequently, we used fixed asset growth as a proxy for firm size growth.

Grid-level weather data. In our study, we utilize the temperature gridded data for global domains, which is derived from the Berkeley Earth Foundation dataset (BERKEARTH).<sup>8</sup> This dataset is sourced from the Climate Data Store, as part of the Copernicus Climate Change Service, managed by the European Centre for Medium-Range Weather Forecasts on behalf of the European Union. The BERKEARTH dataset has comprehensive coverage of global temperature changes, encompassing both land and ocean regions and providing historical temperature data spanning over two centuries. The methodology employed in the processing of this dataset is detailed in Rohde & Hausfather (2020).

The BERKEARTH dataset offers data in three distinct grid formats: 1 × 1, 2 × 2, and 3 × 3 degrees. Each ‘1 degree’ in this context refers to a geographical measure, where 1<sup>◦</sup> of latitude corresponds approximately to 111 kilometers (69 miles) on the Earth’s surface. Similarly, 1<sup>◦</sup> of longitude varies in distance, being wider near the equator and converging towards the poles due to the Earth’s spherical shape. For our analysis, we specifically employ the 1 × 1-degree grid format, which provides the most granular weather information. The dataset encompasses three primary variables: mean, maximum, and minimum daily temperatures. In our research, we focus on utilizing the maximum temperature readings.

Income-level data. For the country-level income data, we use the World Bank’s income data.<sup>9</sup> This data is used to investigate whether there are distinct dynamics in the impact of temperature uncertainty on firm behavior across different income levels.<sup>10</sup>

> 6However, Orbis does not provide comprehensive coverage for firms in North America, so the two major countries, the US and Canada, are not fully represented in our sample.

> 7See Lepore & Fernando (2023) for an analysis of physical risks from climate changes at the firm level using Orbis data. 8Similar studies using the grid-level weather data include Nordhaus (2006) and those studies using data from the Climate Research Unit of the University of East Anglia (Harris et al. 2014), such as Auffhammer et al. (2013), Kalkuhl & Wenz (2020).

> 9 `https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-2023`

> 10Some economies, such as Malta, Réunion, Taiwan, and Hong Kong, do not have their income information recorded in the World Bank income data. As a result, they are excluded from the regression analysis that involves income level. However, they are included in the regression analysis when income level is not a factor.

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Data merging. A key aspect of our research methodology is the merging of the Orbis and BERKEARTH datasets using latitude and longitude information. This common element allows us to associate firm-level financial performance data from Orbis with the corresponding weather data from BERKEARTH, both mapped to the same geographic region.

Given that the weather data is gridded with one degree of latitude and longitude, we first assign a unique index to each grid. For example, Index 1 is assigned to the upper leftmost grid (longitude −180 to −179 and latitude −90 to −89). Subsequently, we assign the same index to the location of each firm. For instance, a firm located at longitude −179 _._ 3 and latitude −89 _._ 3 would also be assigned Index 1.

These indices are then used to merge the two datasets. As a result, the final firm-level data includes annual observations of the firm, its location, sector, and fixed assets, as well as the mean, variance and maximum of the temperature for the gridded location of the firm.

# 3.2 Data pre-processing

We carry out several cleaning procedures to enhance the quality and consistency of the data. First, we exclude firms with non-positive assets from our dataset. This exclusion is based on the accounting principle that fixed assets should always be non-negative. While it is technically possible to have zero fixed assets, these cases are excluded to enable the calculation of the growth rate of fixed assets. Second, to avoid potential double counting, our analysis is limited to unconsolidated data. We select U1 and U2 as the consolidation codes in Orbis data.<sup>11</sup> This choice ensures that the data reflects each firm’s independent operations, preventing distortion from inter-company transactions or accounts. This is crucial for examining the impact of location-specific factors, such as weather, on firms’ behavior, which would be limited with consolidated data. Finally, We also focus on countries with more than 500 observations and more than single observation for country-year-grid group.

All nominal variables (fixed & total assets) are deflated using the GDP deflator at the country level, sourced from World Bank data.<sup>12</sup>

# 3.3 Construction of variables

Firm-level variable From Orbis data, we construct the variable that indicates the growth rate of the firm’s size. We use the fixed assets as the main size variable. For each firm _i_ in country _c_ and year _t_ , we define the size growth as the log return on the fixed asset _Ki,t_ : where _κ_ is the nominal fixed capital and _pc,t_<sup>_k_is the deflator of country</sup><sup>_c_at time</sup><sup>_t_.</sup> Furthermore, to construct the proxy for investment irreversibility, we use the ratio of tangible fixed assets to total assets, following Gulen & Ion (2016), who argues that companies with significant tangible fixed capital investment often undertake projects requiring substantial initial expenditures, and thus

> 11U1 represents the unconsolidated record without the accompanying consolidated record, while U2 represents the unconsolidated record with the accompanying consolidated record.

> 12 `https://data.worldbank.org/indicator/NY.GDP.DEFL.KD.ZG`

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their investments tend to be less irreversible. We call this variable the capital intensity (CapIntensity).

Weather variables In our study, we calculate the weather variables for each year and grid using daily temperatures.<sup>13</sup> Denoting the daily temperature for each grid by _Ti_ , the mean and maximum are _N_<sup><u>1</u></sup> � _Ni_ =1<sup>_Ti_and max(</sup><sup>_Ti_), respectively, for each grid</sup><sup>_i_, with</sup><sup>_N_= 365.</sup> To construct temperature uncertainty, we aggregate all temperature observations for each unique index (spatial grid) across multiple years and fit a smoothing spline<sup>14</sup> to capture the overall seasonal temperature pattern for each index. This spline represents the assumed constant expectation of firms regarding temperature levels and seasonality over time. For each year _t_ within a given index, we calculate the residuals _Rdt_ by comparing the actual daily temperature _Tdt_ to the predicted values from the common spline _T_<sup>ˆ</sup> _t_ for day _d_ in year _t_ : Figure 1 presents an example spline estimate for grid index 52259 (longitude: -1.5, latitude: 63.5) covering the period from January 1, 2000, to December 31, 2019. The smoothing spline is estimated over the full 365 days, using 20 years of daily observations to predict the temperature for each day.

Temperature uncertainty is defined as the standard deviation of these residuals, calculated annually to quantify the variability in temperature: This standard deviation represents the degree to which the actual temperature deviates from firms’ constant expectations, capturing yearly variations in weather conditions. This assumption of constant expectations is reasonable for several reasons as firms typically base their planning and operational strategies on long-term climate averages and historical weather patterns, which tend to remain relatively stable over short time horizons.

# 3.4 Grid-level aggregation

In our study, the primary unit of observation is the grid, aligning with how weather data is measured and recorded. For the firm-level data, we aggregate this data at the grid level.

We implement two distinct levels of aggregation in our analysis:

> 13For the daily temperature measurement, three variables are available from the BERKEARTH dataset at the Climate Data Store: minimum, mean, and maximum daily temperatures. We do not consider the minimum temperature in this study. Between the mean and maximum daily temperatures, there is a difference in data availability. The mean temperature data is accessible only up to June 2018, whereas the maximum temperature data extends through 2019. Due to this disparity in data coverage, we have chosen the maximum temperature variable as our primary variable for analysis. Both the mean and maximum daily temperature variables are highly correlated.

> 14The smoothing spline is fitted using the `smooth.spline()` function from the base R package. The default method for estimating the tuning parameter ( _λ_ ) uses generalized cross-validation (GCV), which balances the trade-off between smoothness and fidelity to the data.

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<!-- Start of picture text --> Common index−wide spline 52259<br>20<br>10<br>0<br>−10<br>0 100 200 300<br>Days since start of year<br>Temperature<br><!-- End of picture text --> Figure 1: An example of a common index-wide spline estimate This figure presents a common indexwide spline estimate for grid index 52259 (longitude: -1.5, latitude: 63.5) over the period from January 1, 2000, to December 31, 2019. The blue points represent the original daily temperature data, and the red line shows the spline fit, which is estimated across all 365 days using 20 years of observations. The x-axis shows the days since the start of the year, while the y-axis represents the predicted temperature.

1. Country, Grid, Year Level Aggregation: At this level, the unit of observation is a unique combination of country, grid, and year. For example, one such unit could be Spain, a specific grid index, and the year 2015. It allows for an examination of how weather patterns in a specific country, grid and year affect the firms located there. It is important to note that some grids are shared by multiple countries. In our dataset, roughly 5% of the total grids span across multiple countries. Despite this overlap, we retain the country-level grouping because the behavior of firms can vary significantly based on the country, even if they experience similar weather patterns.

2. Industry, Grid, Year Level Aggregation: The second level of aggregation employs the industry classification, using the NACE industry code (Statistical Classification of Economic Activities in the European Community). We use the highest level of aggregation (level 1), which includes 21 sectors. Unlike the country-grid level aggregation, where grids are nested within the country, the industry-grid level aggregation leads to a unique combination of industries and grids, resulting in a higher number of observations (316,324). For example, in the geographical grid ‘16191,’ we observe multiple different industries each year, such as agriculture, manufacturing, etc. This level of aggregation allows us to examine industry-level heterogeneity, especially investment irreversibility (Baldwin 1982), and its influence on the relationship between temperature uncertainty and firm size growth.

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For each group defined by these aggregation methods, we use the mean value of the variables.

# 3.5 Descriptive statistics

The final dataset contains 23,437,477 firm-level observations from 64 countries. In total, there are 3,882 economically meaningful grids where firms from our Orbis dataset are present. Table 1 presents the summary statistics for the key weather and economic variables based on grid-level observations.

The mean temperature uncertainty across all grids during the sample period from 2001 to 2019 is 2.09<sup>◦</sup> C. The average temperature (MeanT) and maximum temperature (MaxT) are 16.20<sup>◦</sup> C and 30.54<sup>◦</sup> C, respectively. The mean fixed asset growth (SizeGrowth) is -1.45%.

Further detailed descriptions can be found in the appendices, where we provide comprehensive country-level summary statistics. Table 7 in Appendix B presents these country-level statistics using country-specific firm-level observations. Table 8 in Appendix B shows the number of grids for each country for each year.

||Mean|SD|Min|Q25|Q50|Q75|Max|
|---|---|---|---|---|---|---|---|
|# of frms|302.89|1253.10|2.00|5.25|22.58|136.08|26070.63|
|Uncertainty|2.09|0.52|0.71|1.72|2.06|2.43|4.55|
|MeanT|16.20|9.94|-9.87|8.05|15.22|24.02|35.46|
|MaxT|30.54|5.97|11.96|26.42|30.51|34.73|49.27|
|SizeGrowth|-1.45|23.20|-733.03|-10.47|-0.10|7.23|277.01|
|Capintensity|0.36|0.14|0.00|0.27|0.34|0.43|0.97|
|Income|13532.81|14080.59|1061.59|5016.33|7471.71|11146.79|66870.01|

Table 1: Summary statistics. This table presents key aggregate statistics at the grid level, including the number of firms per grid, temperature uncertainty (Uncertainty) average temperature (MeanT), maximum temperature (MaxT), income, growth rate of fixed assets (SizeGrowth), and the ratio of tangible fixed assets to total assets (CapIntensity). Observations are made at the grid level using the mean values of these variables over time, resulting in 3,882 observations. For each variable, key summary statistics (mean, standard deviation, minimum, 25th percentile, median, 75th percentile, and maximum) are presented.

Time series of weather variables. Figure 2 presents time series plots for three key weather variables (temperature uncertainty, mean and maximum) across all the grids considered in our study. The black line in each plot represents the annual mean value of the weather variable, accompanied by a grey shaded area representing the 95% interval for each year. Since the 95% interval is wide and the overall trend in the variables is unclear, an inset plot without the 95% interval is included.

Consistent with the existing evidence (IPCC 2023), our data shows an observable increasing trend in both the mean and maximum temperature values. However, the temperature uncertainty does not display a clear global pattern, aligning with findings from other studies on temperature variability (the standard deviation of temperature measures) (Huntingford et al. 2013, Olonscheck et al. 2021).<sup>15</sup>

> 15Figures 5, 6, and 7 in Appendix B show the country-specific time trends of these weather variables.

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<!-- Start of picture text --> 30.5<br>2.4 30 15.00<br>30.0<br>2.2 14.75 35<br>3 14.50 29.5<br>2.0 20 14.25 29.0<br>30<br>2 10 25<br>20<br>2000 2005 2010 2015 2000 2005 2010 2015 2000 2005 2010 2015<br>Mean Max<br>Uncertainty<br><!-- End of picture text --> Figure 2: Time series of temperature variables. Each plot illustrates the annual trajectory of temperature variables across all grids, with separate plots for the temperature uncertainty (left) , mean (middle), and max (right). The black line in each plot denotes the annual mean value of the respective temperature variable, accompanied by a grey shaded area that represents the 95% interval. Additionally, an inset plot highlights the mean values without the 95% interval, emphasizing the overall trend in these mean values over the years.

It is worthwhile to note that the trends and patterns identified in Figure 2 do not necessarily represent regional or global averages of the weather variables. The analysis is specifically based on the grids where firms from the Orbis dataset are located. Therefore, the findings are reflective of the weather variable characteristics in these particular economic areas, rather than providing a broader, generalized view of regional or global weather trends.

Scatterplots of temperature uncertainty and asset growth. To demonstrate the overall relationship between temperature uncertainty and fixed asset growth, we present a scatterplot of these variables in Figure 3. The plot includes all country-grid-year observations; each dot represents the mean value of fixed asset growth and the temperature uncertainty for a specific country, grid, and year. The result shows that there is a strong negative relationship with a slope of -4.77, as shown by the grey linear line.

Figure 8 in Appendix B displays the cross-sectional (grid-level) relationship for each year, confirming a negative relationship.

# 4 Empirical method and results

In this section, we explore the potential negative correlation between temperature uncertainty and firms’ annual fixed asset growth, structured into three parts. First, we assess the overall relationship at the grid level. Next, we examine industry-level differences by analyzing how the impact of temperature uncertainty varies with sectoral investment irreversibility, measured by the ratio of tangible fixed assets to total assets. Finally, we investigate how income levels might influence this relationship.

Table 2 presents the correlation matrix for all variables in our regression models. Temperature uncertainty is negatively correlated with firm size growth, with a coefficient of -0.09, indicating that higher uncertainty is associated with lower fixed asset growth. Temperature uncertainty also shows a negative correlation with both mean temperature (-0.59) and maximum temperature (-0.28), reflecting regional patterns where warmer areas tend to exhibit less temperature uncertainty. As expected, the 12
<!-- Start of picture text --> Slope = −4.77<br>200<br>100<br>0<br>−100<br>−200<br>1 2 3 4 5<br>Temperature uncertainty<br>Fixed asset growth (%)<br><!-- End of picture text --> Figure 3: Scatter plot of temperature uncertainty and fixed asset growth. This scatter plot illustrates the relationship between the temperature uncertainty and fixed asset growth. Each dot represents a grid-level-year observation. For visualization purposes, only observations with size growth between -200% and 200% are included.

mean and maximum temperatures are strongly positively correlated (0.82), suggesting that regions with higher average temperatures also experience higher maximum temperatures.

The income variable shows negative correlations with the weather-related variables, implying that higher-income countries tend to have lower average temperatures, lower temperature uncertainty, and lower maximum temperatures.

Interestingly, capital intensity is positively correlated with temperature uncertainty (0.13), suggesting that more capital-intensive industries may be located in regions with greater temperature uncertainty. However, the slightly negative correlation between capital intensity and firm size growth (-0.08) suggests that higher capital intensity may slightly dampen growth in the presence of temperature uncertainty.

Table 2: Correlation Matrix

||SizeGrowth|Uncertainty|Mean|MaxT|LogCapintensity|
|---|---|---|---|---|---|
|SizeGrowth||||||
|Uncertainty|-0.09***|||||
|Mean|0.11***|-0.59***||||
|MaxT|0.07***|-0.28***|0.82***|||
|LogCapintensity|-0.08***|0.13***|-0.13***|-0.06***||
|LogIncome|0.02**|-0.03***|-0.19***|-0.32***|-0.10***|

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# 4.1 Regression method and specification

Baseline model The baseline regression model is structured as follows: SizeGrowth[ _i,c,t_ ] = _β_ 0 + _β_ 1Uncertainty[ _i,c,t_ ] + _β_ 2MeanT[ _i,c,t_ ] + _β_ 3MaxT[ _i,c,t_ ] + _δ_ [ _c_ ] + _γ_ [ _t_ ] + _ε_ [ _i,c,t_ ] _,_ (10) where the subscript _i,c,t_ denotes the grid (i), country (c), and year (t) groupings in our dataset. The variable SizeGrowth represents the growth in firm size (Eq. 7), while Uncertainty refers to the temperature uncertainty (Eq 9). The terms _δ_ and _γ_ represent fixed effects at the country and year levels, respectively, and _ε_ is an error term.

It is important to note that this regression model is relatively unconstrained. As outlined in Section 3, this approach stems from our objective to encompass a broad range of firms, we have focused primarily on location, size growth, and sector variables extracted from firm-level data.<sup>16</sup> Industry heterogeneity: investment irreversibility across industries In this part of our analysis, we employ a second-level aggregation approach that includes the industry group, as outlined in Section 3. It tests the main assumption of real option theory, which posits that firms with more irreversible assets (e.g., high capital intensity) might experience a more pronounced negative impact of uncertainty on their investment plans (Baldwin 1982).

The regression model used is as follows: where LogCapIntensity denotes the log capital intensity (Eq. 8) and _κ_ [ _j_ ] is the industry fixed effect. The coefficient of the interaction term between capital intensity and temperature uncertainty enables us to assess how the effects of temperature uncertainty on size growth vary across different levels of capital intensity.

Income-level heterogeneity Finally, we examine the income level heterogeneity and its impact on the relationship between temperature uncertainty and firm size. To explore this, we introduce an interaction between the income level indicator (log-transformed), denoted as LogIncome, and temperature uncertainty variables in our regression model. The model is formalized as follows: The inclusion of the interaction term between income level and temperature uncertainty variables allows us to examine the heterogenous impacts of temperature uncertainty on size growth across different income levels.

> 16As will be discussed in the discussion section (Section 5), integrating more firm-specific variables such as cash flow rate, capital intensity, and firm age is left to future research.

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# 4.2 Estimation results

Table 3: Weather uncertainty and fixed asset growth

|||Dep.|variable: Fi|xed assetgro|wth||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Uncertainty|−5.048<sup>∗∗∗</sup>|−0.831<sup>∗∗</sup>|−1.181<sup>∗∗∗</sup>|−0.768<sup>∗∗</sup>|−6.517<sup>∗∗∗</sup>|7.348<sup>∗∗</sup>|
||(0.268)|(0.357)|(0.370)|(0.360)|(0.342)|(3.337)|
|Mean|||−0.154<sup>∗∗∗</sup><br>(0.043)||||
|Maximum||||−0.064|0.236<sup>∗∗∗</sup>|−0.059|
|||||(0.048)|(0.021)|(0.048)|
|Capital Intensity|||||−2.029<sup>∗∗∗</sup><br>(0.393)||
|Income||||||−4.300<sup>∗∗∗</sup><br>(1.116)|
|Uncertainty:Capital Intensity|||||−1.038<sup>∗∗∗</sup><br>(0.179)||
|Uncertainty:Income||||||−0.903<sup>∗∗</sup><br>(0.366)|
|Constant|10.012<sup>∗∗∗</sup>|14.337<sup>∗∗∗</sup>|19.019<sup>∗∗∗</sup>|16.799<sup>∗∗∗</sup>|10.741<sup>∗∗∗</sup>|42.887<sup>∗∗∗</sup>|
||(0.604)|(2.210)|(2.559)|(2.873)|(1.224)|(9.652)|
|Year FE|No|Yes|Yes|Yes|Yes|Yes|
|Country FE|No|Yes|Yes|Yes|Yes|No|
|<br>Industry FE|No|No|No|No|No|Yes|
|Observations<br>|50,249|50,249|50,249|50,249|200,991|49,778|
|R<sup>2</sup><br>|0.007|0.138|0.138|0.138|0.063|0.140|
|Adjusted R<sup>2</sup>|0.007|0.136|0.137|0.136|0.063|0.138|

_Notes:_ This table presents the regression of fixed asset growth on weather uncertainty, measured by the standard deviation of deviations from expected temperatures. Models (1) through (4) progressively expand the baseline specification (Eq 10) by incorporating fixed effects (Model 2) and adding controls for mean and maximum temperatures (Models 3 and 4). Models 5 and 6 report the results for industry heterogeneity by capital intensity (Eq 11) and income-level heterogeneity (Eq 12), respectively. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

For the baseline regression results, we begin with the first four columns in Table 3, which summarizes the results from different specifications of the main model described in Equation 10. We consider four models, starting with the simplest specification that includes only temperature uncertainty as a predictor (Model 1), and gradually expanding it to test the robustness of the estimated coefficient as additional variables are included. Model 2 adds country and year fixed effects, while Models 3 and 4 incorporate maximum and mean temperatures, respectively. The results consistently show a negative relationship between location-specific temperature uncertainty and firm size growth across all 15
models.<sup>17</sup> Using Model 4 as the baseline, the coefficient of -0.768 suggests that for each one-unit increase in temperature uncertainty, measured by the standard deviation of deviations from expected temperatures, firms’ growth rates decrease by 0.768 percentage points. These findings align with recent literature on the detrimental effects of temperature uncertainty on economic activities (Kahn et al. 2021, Kotz et al. 2021, Donadelli et al. 2022, Sheng et al. 2022).

The regression results on the moderating effects of capital intensity in the impact of temperature uncertainty on firms’ asset growth (Eq. 11) are shown in the fifth column (Model 5) of Table 3. The negative interaction coefficient between temperature uncertainty and capital intensity indicates that the adverse impact of temperature uncertainty on firm growth intensifies as capital intensity increases, suggesting that capital-intensive firms are more susceptible to environmental fluctuations. Specifically, in industries where log capital intensity is higher by one unit—which corresponds to a 171.8% increase in actual capital intensity (since _e_<sup>1</sup> = 2 _._ 718)—the negative effect of temperature uncertainty on firm size growth is amplified by 1.038 percentage points. To illustrate this effect more intuitively, a 100% increase in capital intensity, which corresponds to a rise of approximately 0.693 (ln(2)) in log capital intensity, amplifies the negative impact of temperature uncertainty on firm size growth by about -0.718 percentage points (−1 _._ 038 × 0 _._ 693).

The regression results for income-level heterogeneity in the effect of temperature uncertainty on firms’ asset growth (Eq. 11) are presented in the sixth column (Model 5) of Table 3. The interaction coefficient between Uncertainty and LogIncome is consistently negative, indicating that the adverse effects of temperature uncertainty are more pronounced in higher-income countries. This finding aligns with Donadelli et al. (2022), who also observed stronger negative effects of temperature uncertainty in high-income countries. Our results show that the coefficient for the interaction term is −0 _._ 903. Since income is expressed in logarithmic terms, a 100% increase in income (doubling income) results in an increase of ln(2) ≈ 0 _._ 693 in the log income. Therefore, a 100% increase in income amplifies the negative effect of temperature uncertainty on firm size growth by approximately −0 _._ 625 percentage points (−0 _._ 903 × 0 _._ 693). This indicates that as income levels double, the detrimental impact of temperature uncertainty on fixed asset growth becomes more severe by 0.625 percentage points.

In summary, our analysis shows a strong negative correlation between temperature uncertainty and fixed capital growth. The severity of this impact varies significantly across industries with differing levels of investment irreversibility and across countries with different income levels. Firms in industries with higher investment irreversibility and those in high-income countries experience more pronounced negative effects on fixed asset growth due to temperature uncertainty.

# 4.3 Robustness checks

To ensure the robustness of our regression results, we performed additional checks by incorporating 1) a lagged dependent variable and 2) grid-level fixed effects into the main regression models.

> 17Although we observe a similar negative relationship when both maximum and mean temperatures are included in the model, we do not report this result due to potential multicollinearity caused by the high correlation between the two variables (as shown in Table 2).

16
Lagged dependent variable Including the lagged dependent variable controls for any underlying momentum or autocorrelation in firm growth. The results, summarized in Table 4, are generally consistent with the original regression. For instance, in Model 4, the coefficient for temperature uncertainty in the baseline regression (Eq 10) decreases slightly from -0.768 to -1.078, indicating that the negative effect of temperature uncertainty on firm growth becomes more pronounced when accounting for the autocorrelation in fixed asset growth. The results for industry heterogeneity by capital intensity and income levels remain largely unchanged, with the coefficients moving from -1.038 to -0.947 for industry differences and from -0.903 to -0.923 for income-level variations.

Table 4: Weather uncertainty and fixed asset growth: Lagged dependent variable

|||Dep.|variable: Fi|xed assetgro|wth||
|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Lagged Growth|0.049<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|0.024<sup>∗∗∗</sup>|−0.014<sup>∗∗∗</sup>|
||(0.004)|(0.004)|(0.004)|(0.004)|(0.002)|(0.004)|
|Uncertainty|−4.348<sup>∗∗∗</sup>|−0.700<sup>∗∗</sup>|−0.630<sup>∗</sup>|−1.078<sup>∗∗∗</sup>|−5.568<sup>∗∗∗</sup>|7.663<sup>∗∗</sup>|
||(0.269)|(0.356)|(0.359)|(0.369)|(0.351)|(3.405)|
|Maximum|||−0.070||0.203<sup>∗∗∗</sup>|−0.058|
||||(0.048)||(0.022)|(0.048)|
|Mean||||−0.169<sup>∗∗∗</sup><br>(0.043)|||
|Capital Intensity|||||−2.298<sup>∗∗∗</sup><br>(0.405)||
|Income||||||−5.174<sup>∗∗∗</sup><br>(1.168)|
|Uncertainty:Capital Intensity|||||−0.947<sup>∗∗∗</sup><br>(0.184)||
|Uncertainty:Income||||||−0.923<sup>∗∗</sup><br>(0.373)|
|Constant|8.372<sup>∗∗∗</sup>|19.164<sup>∗∗∗</sup>|21.834<sup>∗∗∗</sup>|24.536<sup>∗∗∗</sup>|14.348<sup>∗∗∗</sup>|53.936<sup>∗∗∗</sup>|
||(0.605)|(2.344)|(2.965)|(2.708)|(1.283)|(10.227)|
|Year FE|No|Yes|Yes|Yes|Yes||
|Country FE|No|Yes|Yes|Yes|No||
|Industry FE|No|No|No|No|Yes||
|Observations|46,922|46,922|46,922|46,922|183,756|46,507|
|R<sup>2</sup><br>|0.009|0.140|0.140|0.140|0.060|0.142|
|Adjusted R<sup>2</sup>|0.009|0.138|0.138|0.139|0.060|0.140|

_Notes:_ This table presents the regression of fixed asset growth on weather uncertainty, measured by the standard deviation of deviations from expected temperatures, with the inclusion of a lagged dependent variable. Models (1) through (4) progressively expand the baseline specification (Eq 10) by incorporating fixed effects (Model 2) and adding controls for mean and maximum temperatures (Models 3 and 4). Models 5 and 6 report the results for industry heterogeneity by capital intensity (Eq 11) and income-level heterogeneity (Eq 12), respectively. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

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Grid-level fixed effects Adding grid-level fixed effects controls for unobserved regional characteristics that might influence both temperature uncertainty and firm growth, as both are measured at the grid level. This approach helps account for spatial heterogeneity, such as differences in climate, infrastructure, or regional economic conditions, which could otherwise bias the results. The results are summarized in Table 5.

The results indicate that incorporating grid fixed effects generally amplifies the impact of temperature uncertainty. For instance, the coefficient for temperature uncertainty in Model 1 decreases from -0.768 to -2.575. Similarly, the coefficients for the interaction terms between Uncertainty and CapIntensity and between Uncertainty and LogIncome change from -1.038 to -1.352 and from -0.903 to -1.569, respectively.

Table 5: Weather uncertainty and fixed asset growth with grid fixed effects

||Dep. varia|ble: Fixed as|setgrowth|
|---|---|---|---|
||(1)|(2)|(3)|
|Uncertainty|−2.575<sup>∗∗∗</sup>|−3.851<sup>∗∗∗</sup>|11.360<sup>∗∗∗</sup>|
||(0.517)|(0.464)|(3.907)|
|Maximum|−0.022|−0.294<sup>∗∗∗</sup>|−0.003|
||(0.114)|(0.079)|(0.114)|
|Capital Intensity||−1.815<sup>∗∗∗</sup><br>(0.412)||
|Income|||−2.840<sup>∗∗</sup>|
||||(1.258)|
|Uncertainty:Capital Intensity||−1.352<sup>∗∗∗</sup><br>(0.187)||
|Uncertainty:Income|||−1.569<sup>∗∗∗</sup>|
||||(0.431)|
|Constant|23.947|22.811|40.973|
||(24.165)|(15.595)|(26.101)|
|Year FE|Yes|Yes|Yes|
|Country FE|Yes|Yes|No|
|Industry FE|No|No|Yes|
|Grid FE|Yes|Yes|Yes|
|Observations|50,249|200,991|49,778|
|R<sup>2</sup>|0.234|0.111|0.236|
|Adjusted R<sup>2</sup>|0.169|0.095|0.172|

_Notes:_ This table regresses fixed asset growth on weather uncertainty variables, measured by the standard deviation temperature. Model (1) includes the weather uncertainty, Model (2) includes the weather uncertainty interacting with the income level, and Model (3) includes the weather uncertainty interacting with the capital intensity. All models incorporate the gridlevel fixed effects. Standard errors are shown in parentheses. Significance level:<sup>∗</sup> p _<_ 0.1;<sup>∗∗</sup> p _<_ 0.05;<sup>∗∗∗</sup> p _<_ 0.01.

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# 5 Discussion and conclusion

In this research, we present a unique dataset merging firm-level financial statements with weather data to explore how climate change uncertainty affects firm investment behavior. Using financial records from 64 countries from the Orbis database and daily surface temperature data on a 1x1 grid from BEKREARTH, we created a comprehensive grid-level dataset. This dataset integrates firms’ fixed asset growth with temperature uncertainty.

A key objective of this study is to provide grid-level evidence on the impact of temperature uncertainty on firms’ fixed asset growth. Our findings showed a strong negative association between temperature uncertainty and firm growth.

Several factors could explain this significant negative relationship. First, temperature uncertainty introduces additional complexity into firms’ operational and strategic planning, making it harder to forecast costs and returns on investment accurately. Firms might reduce or delay capital investments as they become uncertain about the economic viability of long-term projects, especially when faced with unpredictable climate conditions.

Moreover, temperature uncertainty may also raise firms’ operating costs, particularly in sectors dependent on stable climatic conditions, such as manufacturing, energy, or logistics. For instance, unpredictable fluctuations in temperature could affect energy consumption, maintenance costs, or supply chain efficiency, all of which contribute to a more cautious approach to capital expenditures. Firms may allocate fewer resources to expanding fixed assets if they expect higher variability in operational expenses due to environmental uncertainties.

We also documented significant heterogeneity in the impact of temperature uncertainty on firm growth across industries. Industries with higher levels of irreversible assets, as measured by the ratio of tangible fixed assets to total assets, tend to experience a more pronounced negative effect from temperature uncertainty. This result aligns with the pediction by ROT (Myers 1977, Baldwin 1982), which suggests that firms with more irreversible investments are more likely to delay or reduce their investment activities in response to uncertainty. To be more specific, firms in those sectors with high proportion of irreversible assets typically face high sunk costs, making it difficult to adjust or reverse investments once they are committed. As a result, the temperature uncertainty increases the risk of making suboptimal investment decisions, leading firms to adopt a more cautious stance. Second, capital-intensive industries often require longer planning horizons for investment projects, which can exacerbate the impact of uncertainty. The unpredictability of future environmental conditions makes it harder for firms to commit to long-term investments when they are unsure how future conditions might evolve.

Finally, our study revealed that the impact of temperature uncertainty on firm growth varies significantly across income levels. High-income countries are more adversely affected by weather uncertainties in terms of firms’ size growth. It is important to note that there is no general consensus on how income levels influence the impact of weather on economic activities, as different studies use varying metrics. For example, while Donadelli et al. (2022) focus on the growth rate of total factor productivity and find the negative effects of temperature variability more pronounced in high-income countries, Kotz et al. (2021) use GDP per capita growth and find that lower-income countries are more negatively affected.

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One possible explanation for the weaker link between temperature uncertainty and firm growth in low-income countries is the presence of more binding constraints, such as limited access to finance or inadequate infrastructure. In many lower-income regions, firms may already face significant barriers to growth that are unrelated to weather conditions. For example, financial constraints can severely limit a firm’s ability to invest, expand, or respond to new opportunities, making additional uncertainty from temperature fluctuations less impactful. Further, the stronger impact of temperature uncertainty on firm growth in high-income countries can be attributed to the greater complexity and interdependence of their economies. High-income countries tend to have more integrated and globalized supply chains, with firms relying on precise planning and forecasting for productivity, energy use, and logistics. Even relatively small disruptions, such as unexpected temperature fluctuations, can ripple through these systems and have far-reaching consequences. This heightened sensitivity to external shocks means that temperature uncertainty may pose a greater risk to firm growth in wealthier economies, where economic interdependencies and higher expectations for stability amplify the effects of weather-related unpredictability.

However, our research has limitations that should guide future studies. Our regression model, while extensive, lacks some key financial variables often used in testing and operationalizing real option theory (Leahy & Whited 1995, Bell & Campa 1997, Bulan 2005, Bulan et al. 2009, Bloom 2014). This limitation arose from our focus on achieving maximal coverage at the expense of excluding certain financial variables, particularly to include lower-income countries and assess the country-level heterogeneous impact of temperature uncertainty. Future research should aim for a more constrained standard investment model, incorporating key financial variables like cash flow rate.

Additionally, the time coverage of our data spans only 20 years, with many countries lacking full coverage. The limited time span and smaller numbers of observations in earlier years restrict the time series analysis, which is crucial for studying phenomena like climate change that unfold over long periods. Future research should use longer time horizons with firm-level or regional data to capture sufficient time series variations.

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