Introduction

In recent years, extreme weather events such as extreme high temperature, low temperature, drought, and heavy precipitation due to climate change occur frequently (Dessaint and Matray, 2017). It not only poses a major challenge to the regular production and living of humans (Pinkse and Gasbarro, 2019), but also poses an urgent danger to the national economy and people’s livelihoods (Cui and Tang, 2024). According to the Global Risk Report published by the World Economic Forum in 2024, extreme weather events have become a prominent risk to the global community. For example, in 2022, persistent droughts in East Africa, record-breaking rains in Pakistan, and record-breaking high temperatures in China and Europe affected tens of millions of people, exacerbating food security concerns, driving large-scale population movements, and causing billions of dollars in economic losses and ecological damage. In addition, the United Nations Intergovernmental Panel on Climate Change (IPCC), in its latest scientific assessment of climate change in 2023, states that extreme weather events are expected to become more frequent and intense, putting the natural environment and populations in all regions of the globe at increasing risk. Therefore, how to effectively respond to the issue of climate change has become a real challenge that needs to be addressed now.

Scholarly research on climate change is extensive at both the macro and micro levels (Albitar et al., 2023; Tam et al., 2023). At the macroeconomic level, scholarly research has revealed the far-reaching impacts of climate change on the global economy and society (Cevik and Jalles, 2023; Dogru et al., 2019). Specifically, climate change not only poses a serious challenge to global food security but also directly threatens the livelihoods of billions of rural households that depend on agricultural production (Cui and Tang, 2024). In addition, the frequency of extreme weather events poses a significant threat to human health (Yu et al., 2019) and even leads to a significant increase in population migration (Jacobson et al., 2018; Maurel and Tuccio, 2016), which in turn triggers labor shortages and increased pressure on social welfare systems (Jacobson et al., 2018). These macroeconomic-level effects undoubtedly pose serious challenges to the stability and development of the global economy.

Moving from a broad perspective at the macroeconomic level to specific analyses at the micro level of firms, we can see that firms, as micro agents of economic activity, are also deeply affected by extreme weather (Dahlmann et al., 2019; Farrell, 2016). On the one hand, scholars have begun to focus on how extreme weather changes investors‘ investment behavior (Pankratz et al., 2023) and how such changes gradually affect firms’ cost structures and financial decisions (Han et al., 2023; Krueger et al., 2020; Vestrelli et al., 2024). On the other hand, extreme weather also directly affects the normal production and business activities of firms. The stability of the supply chain is severely impacted (Kim et al., 2023), and the ability of firms to cope with risk challenges is also greatly tested (Wang et al., 2023). In order to ensure business continuity and stability, firms have to take a series of countermeasures.

Against this backdrop, scholars have begun to explore in depth how firms can cope with the possible impacts of extreme weather on their business. In the face of this challenge, firms will adjust their strategic layout to adapt to the new market environment and opportunities brought about by climate change (Kim et al., 2023). At the same time, firms will also actively disclose climate risk information and enhance transparency and responsibility to win the trust of investors and consumers (Pankratz et al., 2023). In addition, firms will endeavor to improve their risk response capabilities and adaptability to maintain competitive advantage and market value during extreme weather events (Wang et al., 2023). Through these measures, businesses can increase their climate resilience and ensure robustness in the face of the challenges posed by climate change (Kim et al., 2022).

IT investments by firms are recognized as an important way to cope with weather risks. However, there is no systematic research to answer the question of how extreme weather facilitates or inhibits firms’ IT investment and construction. On the one hand, global climate risks are intensifying, and low-carbon transformation and development of firms have become a global consensus (He et al., 2020; Hou et al., 2024). Firms will increase their IT investments for digital and intelligent transformation. Extreme weather events have been studied to reduce workers’ working hours (Zhang and Shindell, 2021), jeopardize workers’ health (Bi et al., 2011), trigger regional population migration (Maurel and Tuccio, 2016), and result in insufficient supply of labor for firms as well as higher labor costs (Zhang and Shindell, 2021) consequences. To cope with these problems mentioned above, firms may invest more in information technology by introducing robots and automated systems to reduce their reliance on manual labor and upgrade their production lines with automation and intelligence (Pedersen et al., 2022). On the other hand, the physical climate risk associated with extreme weather not only directly affects the operational efficiency and profitability of firms but also may lead to damage or disruption of investment projects (Huang and Sugianto, 2024). Firms located in countries with higher climate risk have significantly lower revenue streams and higher cash flow volatility (Saura et al., 2023). In addition to this, extreme weather can cause physical damage to business assets and deprive firms of potential revenue (Mauelshagen et al., 2014). As a result, firms may also limit their investments in information technology due to reasons such as lack of cash flow and excessive economic stress.

To examine our research issues, we focus on Chinese A-share listed companies. The reason for using a Chinese sample is threefold. (1) China has a variety of climate types, which provide rich natural experimental conditions for the study and help to comprehensively analyse the impacts of different types of extreme weather on corporate IT investment. (2) As the world’s second-largest economy, China’s rapid economic development has attracted the world’s attention. With the gradual transformation of China’s economy and the advancement of globalization, the position of Chinese enterprises in international competition has been rising, providing a rich source of cases and data for academic research. In addition, the openness and transparency of the financial information of China’s A-share listed companies, as well as the easy accessibility and high completeness of the data, facilitate data collection and analysis. Investors and researchers can easily access detailed financial data for more in-depth analyses and assessments. (3) As leaders in various industries, China A-share listed companies not only reflect market trends, but also have large market capitalization and active trading volume, making them ideal samples for researching the current status of China’s corporate development and future direction. In addition, the increased activity and optimization of the quality of listed companies in China’s A-share market in recent years also provide rich data support for this study. The study finds that the occurrence of extreme weather increases firms’ investment in IT. The main reason for this phenomenon is that extreme weather leads to a shortage of regular, low-skilled labor in the region where the firms are located, which in turn has a serious impact on their production operations. In particular, firms face an increased risk of production stoppages, as traditional labor and production models are unable to respond effectively to the challenges posed by extreme weather. This situation has forced firms to invest more in information technology, particularly in automation, teleoperation, and digital transformation, with the aim of increasing the efficiency and resilience of their operations and reducing their reliance on human resources. After a series of robustness tests, this conclusion still holds. Moreover, the impact of extreme weather on IT investment is particularly pronounced in tropical and subtropical monsoon climates. Firms in these regions face a higher frequency and intensity of extreme weather events and therefore have a more pressing need for IT investment. Firms with a lower risk-taking capacity are also more likely to make IT investments in the face of extreme weather events in order to reduce potential production disruptions and economic losses. This study further explores how extreme weather events affect companies’ value management through increased IT investment. IT investment is not just a means of managing weather risk; it can also have a profound impact on shareholder value by helping organizations improve their agility and long-term competitiveness. These are the key issues addressed in this study.

The marginal contributions of this study are as follows. Firstly, this study fills a research gap in this field by investigating for the first time the influence of extreme weather events on Chinese firms’ IT investments. While previous research has looked into the role of extreme weather in driving enterprise digitization and IT transformation, this study confirms how extreme weather encourages enterprise IT investment and expands on this line of research to provide new ideas for enterprises to cope with climate change. Secondly, this study confirms that extreme weather-induced IT investment enhancement can, in turn, improve organizational future performance, thereby filling the research gap on the link between extreme weather and organizational performance. At the same time, the importance of addressing extreme weather risks, enhancing opportunity management, improving business resilience, and achieving sustainable development is highlighted. Finally, considering the differences in the climatic characteristics of cities in different regions of China and the firms themselves, this study analyses the responses of firms in cities with different climate types and firms with different risk-taking capacities to extreme weather events. This study further enriches the research on the varying impacts of extreme weather on firms with different characteristics and provides theoretical support for firms’ coping strategies in different regional contexts.

We organize the remainder of this study as follows. Section 2 provides the background of extreme weather. Section 3 presents the hypotheses of the paper. Section 4 describes the sample, data, and methodology. Section 5 reports our main empirical results on extreme weather and firms’ IT investment. It also reports the main empirical results on the labor supply mechanism, and then summarizes the results of various robustness tests as well as the analysis of economic consequences. Section 6 concludes the paper.

Literature review

Extreme weather and macroeconomics

The increase and intensification of extreme weather events can have a significant impact on local energy supplies, food production, and people’s livelihoods. The influence of weather factors has long been an academic concern. For example, the negative impact of climate risk on China’s agricultural economic development has been found (Chen and Gong, 2021; Cui and Tang, 2024). Extreme weather events can also create a “thorn wheel effect” through inter-regional economic linkages, which can pose important challenges to the smooth operation of macroeconomic and financial markets in other regions and in general. Studies have revealed that real GDP, trade openness, energy use, and economic complexities have a positive and significant relationship with climate change crises in these economies (Zhang et al., 2021), whereas innovation intensity has a negative and significant relationship with climate change crises (Adedoyin et al., 2022).

Extreme weather and microenterprises

This study deals with firm-level impacts. The existing firm-level impacts are mainly categorized into micro-firm productivity and firm value. In enterprise productivity, extreme weather events do have some degree of impact on labor supply, especially in the case of low-skilled workers (Shayegh et al., 2021). Extreme temperature retards worker productivity in a manufacturing setting without climate control (Cai et al., 2018) and workers’ productivity decreases while absenteeism increases (Somanathan et al., 2021). In firm value, enterprises’ management of climate-related risks also enhances enterprise value. A positive relationship between climate risk disclosure and firm value has been found and may turn negative when concerns about climate change increase (Vestrelli et al., 2024). In addition, the measures taken by enterprises to address climate change can reflect their governance capacity and help them to gain market recognition or returns and maximize their corporate value (Aggarwal and Dow, 2012).

In the context of business financial performance, extreme temperatures have been studied as an event-driven acute physical risk that may negatively impact business performance. For example, it can have a negative impact in terms of direct asset losses and indirect supply chain disruptions (Lewis and Harvey, 2001). Studies on capital markets in China have also found that temperature, humidity, visibility, wind speed, and extreme weather have significant effects on firms listed on the A-share stock market (Huang et al., 2020; Lu and Chou, 2012). At the same time, the majority of investors believe that climate risks have significant financial implications for companies and call for greater disclosure (Krueger et al., 2020).

In the area of non-financial performance of enterprises, climate risk has become a major issue of concern for companies pursuing sustainable development strategies (Ikram et al., 2019). The transformational pressures and regulatory risks associated with climate risk will drive a revolution in economic and social technologies, policies, and laws. This will drive innovation in corporate green technologies and green patents (Flammer, 2021). Meanwhile, the study has focused on the environmental performance of companies. The results show that an increase in national climate risk will promote corporate carbon emissions (Ren et al., 2022).

However, most of these studies have overlooked the potential role of enterprise IT strategy and IT construction in coping with extreme weather risks (Chang et al., 2008; Islam and Welch, 2024). Enterprise IT systems are not only the core support for daily operations, but also a key tool for coping with external risks and enhancing resilience and adaptability. Although the construction and utilization of enterprise IT is considered to be an effective means of coping with external weather risks, research in this area has not received sufficient attention from scholars (Cao et al., 2023; Huang and Sugianto, 2024). Therefore, this study will explore the impact of extreme weather on enterprise IT strategy and IT construction from a new perspective. It will systematically analyse the specific role of extreme weather on the stability, security, flexibility, and innovation capability of IT systems, and explore how enterprises can respond to the challenges and opportunities by optimizing IT. The study will enrich the theory of climate risk and enterprise strategic management, and provide strategic recommendations and practical guidance for enterprises to enhance their ability to cope with extreme weather risks and maintain competitive advantages.

Research hypotheses

In recent years, with severe heat waves, widespread cold waves, intense rainfall, rare droughts, and other extreme weather and multiple disasters have been occurring frequently and concurrently. The negative impacts and risks posed by climate change are becoming increasingly evident (Guo et al., 2024). The impact of extreme weather on labor productivity and health is obvious. Firstly, extreme weather usually leads to a significant reduction in the productivity of workers. Studies have been conducted linking extreme heat to loss of labor time or labor productivity (Zhang and Shindell, 2021). For example, in hot weather, laborers are less motivated and work fewer hours. Secondly, extreme weather may also pose a threat to human health and even lead to an increase in the mortality rate of the population (Deschênes and Greenstone, 2011). At the same time, frequent weather-related disasters may trigger migration. A survey of 218 households in three districts in northwestern Cambodia showed that up to 45 per cent of households migrated, more than half of which were weather-related, which caused labor shortages and welfare problems (Jacobson et al., 2018). Overall, extreme weather can cause challenges in the labor supply. Businesses responding to labor shortages may rely more on technology and automation solutions to increase productivity and adapt to unstable human resource situations. Again, this is because IT investments have a substitution relationship with low-skilled labor (Zhang et al., 2021). Climate change adaptation strategies include innovation generation that involves highly technical measures. Extreme weather may therefore force companies to opt for IT investments and choose digitalization and information transformation development strategies. Drawing on the above argument, we propose and test the following hypothesis.

H1a: Extreme weather can improve the IT investment of businesses.

On the other hand, extreme weather can affect asset pricing (Hirshleifer and Shumway, 2003) and have a negative impact on the stock market. A study found a negative correlation between extreme weather and stock returns (He and Ma, 2021). A relatively early study found that the weather affects not only stock returns but also the trading behavior of investors (Chang et al., 2008). Many financial studies have concluded that when people feel good because of good weather, they are optimistic about their future prospects (Hirshleifer, 2001), while when the weather is worse, investors will reduce their investment trading activities due to the impact of negative emotions. It has also been shown that the negative impact of extreme weather on firm turnover, volatility, and liquidity is significant. As a result, listed companies will choose to invest cautiously in long-term strategies because of cash flow shortages and financing constraints (Lu and Chou, 2012). In other words, under the influence of extreme weather, companies may not have extra funds for IT investment and IT construction. Accordingly, we propose a second hypothesis:

H1b: Extreme weather can impair the IT investment of businesses.

Since extreme weather can have a huge impact at the business level, companies are also actively looking for countermeasures when it comes to extreme weather risks. A company’s response to climate change can reflect its corporate governance capabilities and help it to gain market recognition or returns and maximize value (Aggarwal and Dow, 2012). Current researchers have found that corporate management of climate-related risks also increases the value of the business. Weather factors also affect enterprise value (Wang et al., 2023), in which the degree of investor sentiment or risk preference is an important mechanism. The finding also suggests that CCR disclosures alter corporate behaviors and help curb climate change (Kim et al., 2022). Numerous studies have also shown that companies that have made IT investments tend to have higher company values (Pedersen et al., 2022). So, we suspect that organizations that invest more in IT will demonstrate greater corporate sustainability in the face of extreme weather events. They include resilience, adaptability and continuity. This raises the following question: “Under the influence of extreme weather, can enterprises take IT investment to increase enterprise value?” This study helps to clarify how climate risk is transmitted to enterprises.

H2: IT investment can improve business sustainability under extreme weather.

Data and methods

Sample selection

This study selects Chinese A-share listed companies from 2013 to 2022 as the research object. Considering the consistency and reliability of the sample as well as the availability of data, the validity of the measurement analysis is ensured. In this study, the raw data are processed as follows: (1) Considering the special characteristics of the financial industry and the differences in accounting treatment, the listed financial firms are excluded to maintain the consistency of the research sample. (2) Due to the differences in the limit of upward and downward movements, firms whose stock abbreviations are specially handled with the symbols “ST’” and “*ST” in stock short names to ensure the reliability of the sample data. (3) excluding listed firms with obvious abnormal financial data and serious missing data to ensure that the research sample data is available. Finally, an unbalanced panel is obtained, with 20,224 observations of 3330 companies.

The sources of data for this study are as follows: (1) The underlying data for the Climate Physical Risk Index (CPRI) and the other four types of extreme weather are derived from daily observations at meteorological stations across China. (2) The data on IT hardware investment and IT software investment for calculating the amount of IT information technology investment of firms are derived from the fixed asset details and intangible asset details in the notes to the financial statements. (3) The raw data for calculating enterprise-level variables and control variables are from the China Stock Market Accounting Research (CSMAR) database.

Variable selection

Explanatory variable

The explanatory variable is IT investment. In this study, IT investment is divided into three parts: IT hardware investment, IT software investment, and total IT investment. (1) The opening and closing average values of electronic equipment in the fixed asset details of the company’s annual report are used as the company’s IT hardware investment for the year. (2) The opening and closing average values of computer software or software royalties in the intangible asset details are used as the company’s IT software investment for the year. (3) The sum of IT hardware investment and IT software investment is used as the company’s total IT investment for the year. The measure of the explanatory variable (IT) is defined as the ratio of total IT investment to total assets at the end of the year, multiplied by 1000. It should be noted that individual companies have subdivided the electronic equipment into communication equipment and computer equipment, so they need to be combined in the calculation. For companies that do not disclose the data of IT investment in their annual reports, the amount of investment in IT is expressed as zero.

Core explanatory variables

The core explanatory variables in this study refer to the global climate physical risk dataset (Guo et al., 2024). The Climate Physical Risk Index (CRPI) is based on daily observations from weather stations situated around the globe. Four types of climatic events are considered in this study: extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD). The four extreme climate events were combined to form a composite climate physical risk index for each region.

The measurements are as follows: In order to define the level of “extremes” in each category, (1) based on the historical observation information from 1973 to 1992, the 10 per cent and 90 per cent quartiles of daily temperature, the 95 per cent quartile of rainfall and the 5 per cent quartile of humidity were used as the thresholds for extreme low temperatures, extreme high temperatures, extreme rainfall and extreme drought, respectively. (2) Based on the daily observation data of each weather station from 1993 to 2023, the total number of days above the corresponding thresholds in each of the four extreme weather categories was calculated. (3) The number of days in each extreme weather category in the region was averaged over all meteorological stations in the region to obtain the overall number of days of extreme weather events in the region. In order to maintain the comparability of the data, the four sub-indexes were standardized. (4) The standardized sub-index averages were used to represent the overall climate physical risk index (CPRI) for the region.

Intermediary variable

Based on the perspective of labor structure within a firm, this study divides the firm’s workforce into routine low-skilled labor (production, finance, and sales personnel) and non-routine high-skilled labor (technicians). As the work of routine low-skilled labor force is characterized by routine, repetitive, and low-skilled, it is more sensitive to changes in the external environment. The ratio of the number of productions, finance, and sales personnel to the total number of employees has been used to measure the supply of routine low-skilled labor in a firm. So, we will use this measure to measure the supply of routine low-skilled labor in firms.

Control variables

Based on previous studies (Hao et al., 2024; Lin et al., 2024), the company operating performance dimension and the corporate governance structure dimension are selected among the firm-level characteristic variables. Specifically, they include: firm size (Lnsize), corporate cash flow (Cflow), number of employees (Lnnumber), growth rate of operating income (Growth), age of the firm (Age), gearing ratio (Lev), two jobs (Captial), labor productivity (Labpord), and the proportion of the top ten shareholders’ shareholding (Top10). In addition, this study also controls for industry fixed effects, city fixed effects, and year fixed effects.

The definitions of the research variables employed are presented in Table 1.

Table 1 Variable definition table.
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Other variables

In addition to the variables used in the baseline regression model, other variables used in the robustness and heterogeneity tests in this study include: firms’ IT investment (ITT), Industrial sulfur dioxide emissions (LnS), Carbon dioxide emissions (LnC), Tobin’s Q (TQ), routine low-skilled labor in firms (Routine), climate type (CZ), and firm’s level of risk-taking ability (Risk). A table of variable definitions can be found in Appendix 1.

Empirical methods

To test the impact of extreme weather on firms’ IT investment (Hypothesis 1), we draw on previous research (Lin et al., 2024) and use a classic benchmark regression model allows us to explore the direct effect of extreme weather on firms’ IT investment. Firms’ IT investment (IT) is a continuous variable; thus, this study uses OLS estimation for empirical analysis. The model incorporates city fixed effects, industry fixed effects, and time fixed effects, allowing us to account for city-specific characteristics, industry-specific characteristics, as well as time-specific factors. The model to be tested is set as follows:

$${{\rm{IT}}}_{{\rm{i}},{\rm{t}}}={{\alpha}_{0}} +{\alpha }_{1}{{\rm{EWC}}}_{{\rm{i}},{\rm{t}}}+\sum {{\rm{Controls}}}_{{\rm{i}},{\rm{t}}}+{\sum}\,{\rm{Ind}}+\sum {\rm{City}}+\sum {\rm{Year}}+{\varepsilon }_{{\rm{i}},{\rm{t}}}$$
(1)

where subscript i represents the ith family listed company, t represents the year, and ε represents the residuals. In the model, the explanatory variable IT is the firm’s IT investment. The explanatory variable EWC is extreme weather conditions, measured as climate physical risk index (CPRI), extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD), respectively. Control represents the series of control variables. Meanwhile, industry, city, and year fixed effects are further controlled. The standard errors in Model (1) are clustered at the firm level.

Empirical results

Summary statistics

Table 2 presents the summary statistics of the variables used in our analysis, including the additional dependent variables used in robustness checks. The maximum value of IT investment (IT) of firms during the sample period is 244.5637, the mean value is 7.3960, and the standard deviation is 12.1037. This indicates that there is a significant difference in the extent of IT investment among different firms. The climate physical risk index (CPRI) has a maximum value of 102.6909, a mean value of 27.2679, and a standard deviation of 6.8228, indicating that extreme weather events in China are relatively frequent and vary greatly in intensity. For example, the average frequency of extreme low-temperature events (LTD) is about 16 days per year, with a maximum of about 82 days, which means that extreme low temperatures are experienced for about a one-fourth of the year. The average annual frequency of extreme heat events, extreme precipitation events, and extreme drought events is about 52 days, 22 days, and 19 days, respectively, with a maximum of 112 days, 306 days, and 87 days, respectively. These data imply that the frequency of extreme weather events is not low and requires sufficient attention. Other statistical results are largely consistent with prior studies (Wang et al., 2023).

Table 2 Summary statistics.
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Baseline results

In order to avoid the effect of multicollinearity among variables, this study did the variance inflation factor (VIF) test for each variable before doing the regression analysis. The results show that the average VIF is 1.47, which is much smaller than the empirical threshold of 10, basically excluding the problem of multicollinearity in the regression model. In this study, unbalanced panel data were used for the analysis, and after the Hausman test was performed on the sample data, the regression was carried out using a fixed-effects model in order to avoid the disturbing effect of unobservable variables in the model on the regression results. In this study, the benchmark model was estimated with the industry-, city-, and year-­fixed effects, and the standard errors were clustered at the firm level. The specifications are detailed in Table 3. It presents the baseline regression estimates of the extreme weather impact on firms’ IT investment. The regression coefficient for extreme weather on firms’ IT investments remained consistent regardless of the inclusion of control variables, as shown in Columns (2), (4), (6), (8) and (10) report the results estimated based on the full sample for climate physical risk index (CPRI), extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD), respectively. Columns (1), (3), (5), (7), and (9) include only industry, city, and year fixed effects and no controls. The results from all model specifications consistently show significant and positive coefficients on CPRI, LTD, HTD, ERD, EDD, suggesting a positive impact of the extreme weather on firm IT investment.

Table 3 Impact of Extreme Weather on IT Investment for Businesses.
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Without any firm- level controls, the results in Column (1) of Table 3 show that, following the occurrence of extreme weather, IT investment raised by 4.4 percentage points. After including control variables in Columns (2), both the economic and statistical significance of the coefficients on CPRI persists. The results in Columns (3)-(10) show a similar pattern. For instance, in Column (3), the coefficient on LTD suggests that IT investment raised by 5.3 percentage points, relative to its sample mean of 7.3960.

Overall, the positive effect of the Overall extreme weather indicator and 4 sub-indicators is established to IT investment. This result is consistent with the hypothesis that the extreme weather will be positively associated with the IT investment of businesses.

Robustness test

In order to enhance the robustness of the benchmark regression and to ensure the reliability of the predicted results, this study conducted robustness tests in several dimensions, including the way the replacement variable was measured, the exclusion of outliers, and the use of the instrumental variable method.

Replacement measurement of firms’ IT investments

In order to overcome the bias that the measurement bias of the dependent variable may cause to the empirical results, this study uses the replacement of the explanatory variables for the robustness test. Here, the corporate IT investment metric is recalculated using the sum of corporate IT hardware investment and IT software investment, denoted as ITT. The regression results after adjusting the corporate IT investment metric are shown in Column (1) of Table 4. Not surprisingly, the regression coefficient of CPRI on ITT is 0.004 and is significant at the 1% level. This shows that after replacing the measure of the explanatory variables, extreme weather still significantly strengthens firms’ IT investment, which is consistent with the regression results in Table 3 and proves the robustness of the core findings.

Table 4 Robustness test.
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Excluding the effects of outliers

Macro weather variables may be biased in their collection and measurement. In order to mitigate the possible impact of such bias on the findings of the study, this study re-runs the parameter estimation after applying a 5% tailoring to the sample data for each variable, and the corresponding results are presented in Column (2) of Table 4. The results show that the robustness test is significant at the 5% level and the sign of the regression coefficients is positive as expected after estimating the sample data.

Endogeneity test results for regression models

Since extreme weather, as an exogenous variable, is not affected by firms’ IT investments, reverse causality is unlikely to exist. However, the model may still have the endogeneity problem caused by the omitted variable bias; therefore, this study utilizes the instrumental variable method to mitigate the endogeneity problem of the model. In this study, ‘industrial sulfur dioxide emissions’ and ‘carbon dioxide emissions’ are selected as instrumental variables for extreme weather. Sulfur dioxide can form sulfate aerosols that reflect solar radiation, thus lowering the atmospheric temperature and indirectly altering the atmospheric circulation pattern, while elevated carbon dioxide concentration leads to global warming and directly exacerbates extreme weather events. Sulfur dioxide emissions and carbon dioxide emissions are related to extreme weather, but cannot directly affect corporate IT investment, satisfying the instrumental variable hypothesis. Column (1) in Table 5 presents the results of the first-stage regression. There is a significant correlation between industrial sulfur dioxide emissions and carbon dioxide emissions, and extreme weather, and the F-value in the first stage is greater than 10, indicating a reasonable choice of instrumental variables. Column (2) presents the IV estimation results, and the results of the over-identification test support the original hypothesis that all instrumental variables are exogenous, indicating that there is no over-identification problem in the model. The results show that after considering the endogeneity problem, there is still a significant positive impact of extreme weather on corporate IT investment, indicating that the regression results are robust.

Table 5 Endogeneity test.
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Mechanism test

The above findings suggest that extreme weather has a facilitating effect on companies making IT investments. So, what are the channels through which extreme weather enhances the role of IT investment in enterprises? To answer the above question, this study examines it from the perspective of Intra-firm labor supply.

Under the accumulation of negative effects of extreme weather events over a long period of time, routine low-skilled labor supply will decrease, which may hinder various aspects of economic and social development. On the one hand, the environment is the survival. People migrate in search of more opportunities for development and space for adaptation. On the other hand, extreme weather can also affect mental health. Extreme weather affects the level of Cortisol/Stress Hormone Cortisol in the body, affecting people’s sleep and daily life. This change reduces people’s sense of comfort and well-being and increases their mental tension, which leads to lower productivity. Therefore, this study adopts the mediation effect test using the three-step method (classical three-step method) and sets the following model based on model (1):

$$\begin{array}{l}{{\rm{Routine}}}_{{\rm{i}},{\rm{t}}}={\rm{\beta }}+{{\rm{\beta }}}_{1}{{\rm{EWC}}}_{{\rm{i}},{\rm{t}}}+\sum {{\rm{Controls}}}_{{\rm{i}},{\rm{t}}}+\sum {\rm{Ind}}\\\qquad\qquad\quad\,\,+\,\sum {\rm{City}}+\sum {\rm{Year}}+{{\rm{\varepsilon }}}_{{\rm{i}},{\rm{t}}}\end{array}$$
(2)
$$\begin{array}{l}{{\rm{IT}}}_{{\rm{i}},{\rm{t}}}={{\rm{\lambda }}}_{0}+{{{\lambda }}}_{1}{{\rm{EWC}}}_{{\rm{i}},{\rm{t}}}+{{{\lambda }}}_{2}{{\rm{Routine}}}_{{\rm{i}},{\rm{t}}}+\sum {{\rm{Controls}}}_{{\rm{i}},{\rm{t}}}\\\qquad\quad+\,\sum {\rm{Ind}}+\sum {\rm{City}}+\sum {\rm{Year}}+{{{\varepsilon }}}_{{\rm{i}},{\rm{t}}}\end{array}$$
(3)

The regression results are shown in Table 6. Column (1) shows that CPRI is significantly negatively correlated with Routine, i.e., firms located in cities with higher Extreme Risk Climate Index (ERCI) have lower levels of routine low-skilled labor supply. Column (2) shows that CPRI is significantly positively correlated with IT. Routine is significantly negatively correlated with IT after adding Routine. This indicates that the routine low-skilled labor supply level plays a partial mediating role in the relationship between extreme and abnormal weather and firms’ IT investment.

Table 6 Channel analysis of the role of extreme weather in impacting IT investment.
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Heterogeneity analysis

Climatic heterogeneity

Due to China’s vastness, geographic conditions, and regional climate differences, there are natural differences in how cities with different climatic characteristics react to extreme weather. By categorizing cities according to climate type, the impact of extreme weather events on corporate IT investment is explored separately. There are five climate types in China, namely temperate continental climate, highland mountain climate, tropical monsoon climate, temperate monsoon climate, and subtropical monsoon climate. The number of cities and municipalities belonging to the first two types of climates is relatively small, which is not convenient for separate regression, and setting dummy variables will increase too many variables, resulting in large bias in the estimation results. Therefore, this study only discusses the heterogeneous effects of weather extremes on corporate IT investment in temperate monsoon climate and tropical and subtropical monsoon climate.

The results are shown in Table 7, with different results for different indicators of heterogeneity. (1) Overall indicator (CPRI): From the overall indicator, there is a significant positive effect of extreme weather in both climates, and the coefficients for cities belonging to tropical and subtropical monsoon climates are larger than those for temperate monsoon climates, which suggests a greater degree of impact. That is, firms in cities with tropical and subtropical monsoon climates are more inclined to make IT investments in the presence of extreme weather compared to cities with temperate monsoon climates. This indicates that extreme weather has a greater overall impact on businesses in tropical and subtropical monsoon climates. (2) Extreme Low Temperature (LTD): In the heterogeneity analysis, the impact of extreme low temperature on IT investment in the face of extreme low temperature is positively significant for firms in cities with tropical and sub-tropical monsoon climates, as well as in cities with temperate monsoon climates. However, the coefficients for cities belonging to temperate monsoon climate are larger than those for cities with tropical and subtropical monsoon climate, indicating a greater impact. This is due to the fact that cities belonging to temperate monsoon climate have lower average annual temperatures, which is definitely compounded by the increase in the duration of extreme low temperatures. Therefore, organizations that are negatively affected by extremely low temperatures are more inclined to make IT investments. (3) Extreme High Temperature (HTD): Organizations in cities with temperate monsoon climates are more inclined to make IT investments in the face of extreme high temperatures; while organizations in cities with tropical and subtropical monsoon climates do not have a significant impact on IT investments in the face of extreme high temperatures. This is because the latter have higher average annual temperatures than the former, exacerbating the negative impact of extreme heat events. In turn, extreme heat can spike electricity demand, leading to electricity constraints in hot and hot areas. In order to proactively support the alleviation of power supply pressure during peak hours, the region requires industrial enterprises to stagger production and safeguard people’s livelihoods. Since enterprise IT construction and operations rely on power resources, it makes little sense for cities and enterprises in tropical and subtropical monsoon climates to make IT investments in extreme heat. In contrast, enterprises in cities with temperate monsoon climates are less affected by power outages and production stoppages in extreme heat and are more inclined to make IT investments. (4) Extreme Rainfall (ERD): Firms in cities with tropical and subtropical monsoon climates are more inclined to invest more in information technology when faced with extreme rainfall. These cities have high annual precipitation and intense monsoon rains, and extreme rainfall is prone to flooding, traffic paralysis, and supply chain disruptions, which can seriously affect business operations. Temperate monsoon climate zones have less annual precipitation, and extreme rainfall is rarer, which has less impact on business operations. According to the ‘push-pull theory’ of population migration, ecological degradation and frequent disasters are the push force for migration, while better employment opportunities and living environment are the pull force to attract migration. For arid cities, extreme rainfall can improve the environment by easing pressure on water supplies, increasing employment opportunities, improving soil structure, and contributing to agricultural and ecological restoration. As a result, laborers from tropical and subtropical monsoon climates are more willing to migrate when conditions in arid cities improve. Not only does it provide a resource advantage for firms to recruit new employees, but also the improved environmental conditions can enhance the motivation of the firms’ employees and improve their work quality and efficiency. Cities and firms in tropical and subtropical monsoon climates are more negatively affected. Consequently, organizations negatively affected by extreme rainfall are more inclined to make IT investments. (5) Extreme drought (EED): In the heterogeneity analysis, firms in cities with subtropical monsoon climates and temperate monsoon climates do not have a significant impact on IT investment when facing extreme drought. The other control variables also do not show significant difference.

Table 7 Heterogeneity analysis results - climate dimension.
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In summary, businesses in different climate zones react differently to different weather extremes. Therefore, this study can provide a practical reference for enterprises to decide whether or not to make IT investments.

Risk-taking levels heterogeneity

Firms with different levels of their own risk-taking capacity make very different corporate strategic decisions in the face of extreme business environments. Firms with high risk-taking capacity are able to effectively manage and control possible risks and reduce the negative impacts of risk. Firms with low risk-taking capacity are more vulnerable to unexpected risks due to limited preparedness. They need to improve their overall competitiveness and risk-resistance through measures in the areas of improved management, resource allocation, and strategic planning. So, is there an existential asymmetric effect of extreme weather on their IT investments? For this reason, this section further looks at the dimension of firms’ risk-taking ability to reveal the different performance of firms’ IT investment due to extreme weather in heterogeneous groups.

Based on the degree of firms’ risk-taking capacity level, this study divides the sample into a sample of firms with high risk-taking capacity level and a sample of firms with low risk-taking capacity level. The regression results are presented in columns (1) and (2) of Table 8, which shows that the estimated coefficients of extreme weather are insignificant among firms with high levels of risk-taking capacity. Among firms with low levels of risk-taking capacity, the estimated coefficient on firms’ IT investment is significantly positive, suggesting that the promotional effect of extreme weather is stronger for firms with low levels of risk-taking capacity.

Table 8 Heterogeneity analysis results - firm level.
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This is due to the fact that extreme weather can lead to depreciation of property and restriction of productive activities, reducing the return on assets of a business. This prompts firms to look for new investment opportunities and risk management strategies to improve returns. In addition, extreme weather events can increase a firm’s credit risk, leading to higher financing costs. In order to maintain financial stability, firms need to improve their risk-taking capacity and market competitiveness through IT investments. Those firms with high risk-taking capacity are more resilient to risk and have a natural advantage in gaining access to resources and market share. They are less negatively affected by extreme weather, which is not enough of an incentive to invest in IT. On the contrary, firms with low risk-taking capacity are more vulnerable to market fluctuations or unforeseen extreme weather risks. These firms tend to capture ‘creative destruction’ (Segal et al., 2015). This helps them to cope with climate change and seek possible returns and long-term sustainability. Therefore, such firms have a stronger willingness to make IT investments to improve their overall competitiveness and risk tolerance.

Further analysis

This study also examines the effect of IT investment on an enterprise’s market value under extreme climate. In Eq. 4, the explained variable is the market value of the enterprise, which is measured using the Tobin Q. The previous study found that extreme weather positively affects corporate IT investment. The prior study showed that corporate IT investment is positively correlated with firm value (Mithas and Rust, 2016). Then, the text further validates whether enterprise value can be increased by increasing enterprise IT investment in the context of extreme weather.

$$\begin{array}{l}{{\rm{TQ}}}_{{\rm{i}},{\rm{t}}}={{{\lambda }}}_{0}+{{{\lambda }}}_{1}{{\rm{EWC}}}_{{\rm{i}},{\rm{t}}}+{{{\lambda }}}_{2}{{\rm{IT}}}_{{\rm{i}},{\rm{t}}}+\sum {{\rm{Controls}}}_{{\rm{i}},{\rm{t}}}\\\qquad\qquad+\,\sum {\rm{Ind}}+\sum {\rm{City}}+\sum {\rm{Year}}+{{{\varepsilon }}}_{{\rm{i}},{\rm{t}}}\end{array}$$
(4)

Based on Eq. (4), we examine the impact of IT investment on an enterprise’s market value under extreme climate. Columns 1–2 of Table 9 present the results. In column (1), CPRI is significantly and positively related to firm value. After further consideration of the mediating effect, it is found that IT is significantly positively related to TQ after controlling for CPRI as shown in column (2) of Table 8. The above results suggest that firms are able to increase their firm value by making IT investments in the context of facing extreme weather.

Table 9 Economic consequences test.
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Conclusion and policy implication

Conclusion

Enterprises’ adaptation to climate change is multidimensional. In this paper, we provide the first comprehensive empirical analysis of how enterprises are affected by extreme climate and how they manage to mitigate the impact from IT investment perspective. This paper verifies the impact of extreme weather on corporate IT investment against the background of extreme weather in China and a sample of A-share listed companies from 2013 to 2022. The results show that extreme weather increases corporate IT investment. The mechanism test finds that extreme weather plays a positive role in corporate IT investment through the path of internal labor supply, i.e., extreme weather reduces the supply of regular low-skilled labor within the firm and thus strengthens the firm’s emphasis on IT investment. This finding expands the channels through which extreme weather affects IT investment.

Furthermore, after distinguishing the types of regional climate zones in China, it is found that the facilitating effect of extreme weather mainly exists in enterprises in tropical and subtropical monsoon climates. After distinguishing the level of risk-taking ability of enterprises, it is found that enterprises with lower level of risk-taking ability are more inclined to make enterprise IT investments when facing extreme weather. This paper also finds that, for enterprises with lower level of risk-taking ability, extreme temperature shocks awaken their awareness of climate hazards, encourage them to take risks, and turn such events into opportunities to enhance value. The economic consequences analysis found that extreme weather boosts IT investment in firms, which can further increase firm value.

Policy implication

In determining the influence of extreme temperature on firms’ IT investment and market value, this study provides empirical evidence on the impact of extreme temperature on China’s capital market from the perspective of IT investment. The findings have the following policy implications.

Firstly, policymakers should attach great importance to extreme climate risks and create a favorable business environment for firms. When formulating industrial, fiscal, and tax policies, they should tailor them to the characteristics of firms and guide them to invest more in information technology, green technology research and development, and the transformation of the low-carbon economy. This will not only enhance the innovative capacity of firms but also strengthen global competitiveness. Policymakers should encourage firms to accelerate digital transformation, promote the application of technologies such as artificial intelligence, big data, and the Internet of Things, optimize the industrial structure, improve the efficiency of resource allocation, and enhance their ability to withstand climate risks. In addition, through measures such as tax incentives and financial subsidies, it should support firms’ investments in green technology, clean energy, and other areas, helping them to cope with the challenges of climate change and enhance their resilience.

Secondly, businesses should raise awareness of climate risk and establish a sound governance structure. Research shows that the supply of low-skilled labor is negatively correlated with extreme weather events. To ensure sustainable development, businesses need to improve digital infrastructure and other countermeasures to reduce their reliance on labor. That is, IT investment should be an important part of enterprise risk management, especially for risk weaker businesses, and awareness of the impacts of extreme weather should be strengthened to promote investment and innovation activities to cope with weather shocks. At the same time, companies should proactively disclose climate and environmental information to shareholders and stakeholders. Such a move not only enhances public awareness of extreme weather risks but also strengthens the public’s trust in the enterprise, which helps businesses achieve win-win climate and environmental risk management.

Finally, investors should pay more attention to the impact of corporate IT investments on the value enhancement of corporate R&D and innovation. This is because IT not only improves operational efficiency but also accelerates the R&D process of new products and services, creating a sustainable competitive advantage for companies. It can also play a role in optimizing resource allocation, enhancing emergency response capabilities, and strengthening supply chain management when dealing with the challenges posed by extreme weather, thereby effectively reducing the potential impact of climate risk on business operations. Investors should therefore fully recognize the critical role of information technology in the long-term sustainability of businesses. At the same time, extreme weather events may trigger negative market sentiment towards companies, leading to volatility in share prices. Investors should also accurately assess the market value of extreme weather and companies. Avoid the negative sentiment amplification effect of extreme weather leading to corporate valuation bias.

Limitations and future directions

The research in this paper has some limitations for future improvement. Firstly, due to the limitation of the research scope, this paper divides the labor force within the enterprise into ordinary low-skilled labor and high-skilled labor. This division criterion is universal in China, but may not be directly generalizable to other countries. Therefore, part of our conceptual logic may not be applicable to study firms in other countries. In the future, we will actively explore labor force classification criteria applicable to different countries and regions to ensure broader applicability of our findings.

Secondly, in analysing labor supply, this paper focuses mainly on the quantity and structure of the labor force. However, the constituent elements of labor supply include not only quantity and structure, but also factors such as labor intensity, working hour utilization, education level of the labor force, production work experience, and skills. Changes in other factors may cause fluctuations in the size of labor supply when the quantity and structure of the labor force remain unchanged. However, due to the difficulty in obtaining data on enterprise labor hours, this paper only uses the number and structure of the workforce as an indicator of labor supply, failing to fully consider the quality of labor supply. Therefore, if data availability is improved, future research could further explore the combined role of labor quantity and quality. Expand the dimensions of labor supply analysis and provide a more comprehensive and detailed examination of the relevant variables.

Thirdly, as global climate change increasingly affects business operations, future research could further explore the strategies and measures that firms in different countries and regions have adopted in response to extreme weather shocks. In particular, how these coping styles are affected by geographical, cultural, and policy environments. In-depth analyses of the coping mechanisms of transnational and cross-regional firms could support the construction of theoretical frameworks with more cross-cultural and cross-regional applicability, and provide broader and richer empirical data for empirical research.