Abstract
As a critical transmission channel of corporate risk contagion, supply chains serve as a vital pathway for the cross-firm propagation of climate physical risks, and clarifying such risk transmission mechanisms is essential for stabilizing corporate investment efficiency amid volatile market environments. This study constructs a novel supplier climate physical risk index by integrating data from the Top Five Supplier Procurement Information Table, Chinese Industrial and Commercial Enterprise Information, and the China Climate Physical Risk Dataset, and empirically examines how supplier-side climate physical risks shape corporate investment efficiency. The results indicate that the increase in supplier climate physical risks significantly reduces the investment efficiency of enterprises, and this conclusion still holds true after a series of robustness tests. Specifically, it affects the investment efficiency of enterprises by reducing their risk-taking ability and compressing their commercial credit financing channels. Enterprises with poor supply chain resilience, low credit availability, high proportion of fixed assets, and low R&D investment are more susceptible to the impact of supplier climate physical risks, resulting in a more significant decrease in investment efficiency. This study expands the theoretical boundary of Climate Physical Risk economics and supply chain financial governance, and provides empirical evidence and practical implications for Chinese enterprises to mitigate supplier Climate Physical Risk shocks and stabilize high-quality investment development.
Graphical Abstract
1 Introduction
The 64th session of the Subsidiary Body of the United Nations Framework Convention on Climate Change, held in Bonn, Germany from June 8 to 18, 2026, pointed out that frequent extreme weather disasters continue to amplify the substantial Climate Physical Risks faced by various industries, becoming a major common challenge for global sustainable development. Floods, droughts, and severe storms continue to disrupt the production and operation of manufacturing enterprises, while the closely interconnected upstream and downstream supply and demand networks allow climate shocks to propagate vertically along the industrial chain, resulting in spillover effects on downstream enterprise capital allocation and investment efficiency (). In this context, clarifying the spillover effects of climate physical risks along the supply chain on downstream enterprise investment efficiency has important practical and theoretical significance for enterprise resilience, industrial chain sustainability, and green policy formulation.
Investment efficiency is a key measure of the wisdom and rationality of enterprise investment decisions, especially in the context of intensified competition and accelerated globalization, its importance is becoming increasingly prominent (). However, in actual operation, the investment behavior of enterprises deviates from the optimal path, manifested as inefficient investment. This type of inefficient investment is mainly divided into two types: one is insufficient investment, that is, enterprises fail to seize investment project opportunities with positive net present value or return rates below the cost of capital, resulting in efficiency lower than the optimal state, thereby weakening the value of the enterprise; The second is overinvestment. Even when faced with investment projects with negative net present value or capital return below cost, companies still blindly invest, which will gradually erode the company’s future cash flow and create a dilemma of overinvestment (; Xue, 2025). Both of these inefficient investment situations will have a negative impact on the value of the enterprise and are specific manifestations of low investment efficiency. Prior literature has extensively examined climate related determinants of corporate investment. Existing evidence shows that climate risks promote corporate green investment (Wan et al., 2025), while managerial climate risk perception bias reduces investment efficiency (Zhao and Li, 2026). Furthermore, climate policy uncertainty hinders mergers and acquisitions and impairs investment efficiency for firms across various industries (Zhang et al., 2025; Zhang et al., 2026). Taken together, sufficient evidence has been established for the investment relevant consequences of climate risks.
Improving the investment efficiency of enterprises not only requires considering market competition and capital costs, but also dealing with complex external risk environments. As a core component of modern enterprise operations, the supply chain has been fully integrated into the global production and trading network. With the increasing complexity of the supply chain and cross-border collaboration, maintaining the stability and reliability of the supply chain is becoming increasingly important for improving enterprise investment and operational efficiency (; ). Enterprises face multiple external risks such as policy fluctuations, demand shocks, and supply disruptions. Among various external risks, climate physical risks have gradually become a prominent and under researched source of risk that affects the investment efficiency of enterprises (). The natural disasters, extreme weather events, and other issues brought about by climate change not only directly affect the production capacity of enterprises, but also transmit to the entire industrial chain and value chain by affecting the stability of various links in the supply chain (Su et al., 2025).
However, existing research mostly focuses on the direct impact of climate physical risks on the operation of enterprises, such as reducing total factor productivity, profit margins, increasing financing costs, default rates, etc., and pays less attention to their spillover effects on supply chain enterprises (; ; ). With the increasingly tight supply chain network, the impact of climate physical risks is not limited to the enterprises themselves, but also becomes a new external risk source for downstream enterprises through mechanisms such as supply chain breakage and raw material price fluctuations, affecting their strategic decision-making and long-term capital investment. Higher customer relevant climate risks improve suppliers ESG performance via risk linkage and incentive synergy mechanisms (Xu et al., 2026). Prior studies also show that greater customer climate risk disclosure pushes suppliers to hold more cash, reduces suppliers trade credit provision, and mitigates suppliers carbon green washing behavior when major customers suffer severe physical climate risks (; ; Zhong et al., 2026). Therefore, in-depth research on the external spillover effects of climate physical risks along the supply chain has important theoretical and practical significance for preventing systemic risks and improving investment efficiency of enterprises.
We selected Chinese listed companies as research samples based on two objective considerations: on the one hand, China’s industrial supply chain network is highly interconnected, and climate physical risks are prone to form large-scale cascading spillovers through upstream and downstream chains. In 2017, the middle reaches of the Yangtze River, Dongting Lake Basin, Songhua River and other areas in China suffered from serious floods and waterlogging disasters, resulting in the production lines of some factories being shut down, supply chains being interrupted, raw material supply being restricted, and finished product delivery being delayed, directly affecting the production plans and sales revenue of downstream enterprises. Meanwhile, found that in 2017, the indirect economic losses caused by the spread of flood disasters through the industrial chain in China amounted to 62.16 billion yuan, accounting for 29% of the direct economic losses (). The cross level and cross domain transmission effects of this risk have caused more complex external disturbances to the investment behavior of enterprises. The research conclusions based on the Chinese scenario can provide experience and reference for countries around the world to prevent climate shocks in the industrial chain. On the other hand, China provides a natural quasi experimental scenario for this study. By combining the Chinese supplier procurement information table, Chinese business enterprise information, and Chinese climate physical risk dataset, the transmission path of Climate Physical Risk supply chain can be accurately identified. By relying on a recognition framework constructed from multiple sources of micro data, a replicable research paradigm can be provided for countries with long chain manufacturing industries.
Based on the above considerations, this article combines the “Top Five Supplier Procurement Information Table”, “China Business Enterprise Information”, and “China Climate Physical Risk Dataset” to construct the supplier’s 2010–2023 climate physical risk indicators. And based on this, the impact of supplier climate physical risks on enterprise investment efficiency was empirically tested, and its transmission mechanism was revealed. The empirical results show that the increase in supplier climate physical risk significantly reduces the investment efficiency of enterprises. For every one standard deviation increase in supplier climate physical risk, the investment efficiency of enterprises will decrease by 0.0023 units, accounting for about 5.897% of its mean and 4.694% of its standard deviation. Further research has shown that the impact of supplier climate physical risks on corporate investment efficiency is mainly concentrated in inhibiting the willingness of enterprises to invest and resulting in insufficient investment, while having no significant effect on promoting excessive investment by enterprises. Tracing the transmission mechanism of the spillover effect of supplier climate physical risks, this article finds that supplier climate physical risks mainly affect the investment efficiency of downstream enterprises through the following two mechanisms: first, reducing the risk bearing capacity of enterprises, thereby suppressing their investment willingness; The second is to compress the commercial credit financing of enterprises, restrict the ability of downstream enterprises to obtain funds, and thus affect their investment decisions. This article also examines how factors such as supply chain resilience, credit availability, and differences in investment structure of enterprises can regulate the impact of supplier climate physical risks on investment efficiency. Heterogeneity analysis shows that companies with stronger supply chain resilience and higher credit availability are better able to cope with Climate Physical Risks, and their investment efficiency is less affected. From the perspective of investment structure, enterprises with a high proportion of fixed assets usually rely more on long-term funds, making them more susceptible to the impact of climate physical risks, resulting in a significant decrease in investment efficiency. Enterprises with higher R&D investment demonstrate stronger response capabilities, and their investment efficiency is less affected by suppliers’ climate physical risks.
This article focuses on the dual perspectives of Climate Physical Risk and supply-chain spillover, and investigates the spillover effects of climate physical risk through the supply chain-specifically, the impact of supplier climate physical risk on corporate investment efficiency. Compared with existing literature, the possible marginal contributions of this article mainly include the following three points: firstly, this article extends the impact of Climate Physical Risk on corporate operational behavior to the supply chain level. The existing literature on Climate Physical Risk focuses on the impact of Climate Physical Risk on the operational performance of enterprises themselves (; ; ). However, this article constructs a supplier climate physical risk index to empirically test the external spillover effects of Climate Physical Risks, deepening the understanding of the externalities of Climate Physical Risks. Secondly, this article expands the relevant literature on the factors influencing corporate investment efficiency. The existing literature on the factors affecting corporate investment efficiency is quite abundant, including financial technology (), green credit policies (Zhang et al., 2022), digital technology innovation (), and so on. However, there are few studies that explore the factors that affect corporate investment efficiency from the perspective of Climate Physical Risk. This article provides a new perspective for the related research on the factors that affect investment efficiency. Thirdly, this article examines the specific transmission mechanism of Climate Physical Risk supply chain spillover effects. Existing research has analyzed the micro impacts of customer climate or environmental risks, such as ESG performance (Xu et al., 2026), cash holdings (), trade credit (), and carbon greenness (Zhong et al., 2026). Through in-depth analysis of the supply chain transmission mechanism of Climate Physical Risks, this study reveals how supplier climate physical risks can be suppressed by reducing enterprise risk-taking capacity and compressing commercial credit financing, thereby inhibiting downstream enterprises’ investment decisions. This provides policy guidance for policymakers and enterprise managers.
2 Development of the hypotheses
As a core component of enterprise production activities, the stability of the supply chain directly affects the investment decisions of the enterprise. Supply chain risks are complex and diverse, and the impact of different risks on supply chain node enterprises also varies. Among them, climate physical risks have become the main source of supply chain risks, and physical risks such as extreme rainfall, earthquakes, mudslides, and other natural disasters significantly affect the effective operation of the supply chain (Pankratz and Schiller, 2024). When a node enterprise in the supply chain suffers from climate physical risks, climate physical risks may generate spillover effects through the supply chain (Aldieri and Vinci, 2020; Su et al., 2025). When suppliers encounter raw material shortages, rising costs, and business losses due to climate physical risks, downstream enterprises’ production activities, especially investment decisions, are often restricted. The impact is mainly spread through two channels: firstly, unexpected extreme weather conditions directly damage the production behavior of suppliers, making it impossible to provide sufficient intermediate goods for downstream enterprises, resulting in downstream production contraction (Sun et al., 2025). Moreover, the increase in transaction costs caused by climate physical risks has also reduced the profit margins of downstream enterprises. When a company’s operations are affected, its investment decisions become more conservative, manifested as insufficient investment. Secondly, climate physical risks are not only reflected in direct supply chain disruptions, but can also be transmitted through financing channels. When suppliers face climate physical risks, they will change their funding allocation decisions, and companies will withdraw funds to increase their liquidity reserves to cope with the uncertainty of climate physical risks (). Commercial credit financing, as one of the important short-term funding sources for enterprises, the conservative capital allocation strategy of suppliers due to climate physical risks will compress the scale of commercial credit financing for enterprises. When corporate financing is constrained, companies choose to reduce long-term investments, leading to a decrease in investment efficiency. Based on the above theoretical analysis, we propose Hypothesis 1.
H1The increase in supplier climate physical risks leads to a decrease in investment efficiency for downstream enterprises.
The climate physical risks of suppliers have worsened the business environment, forcing a decrease in the ability of enterprises to bear risks. Risk bearing capacity refers to the degree to which a company is willing to take risks in pursuit of future performance growth when facing external shocks. The risk-taking ability of an enterprise depends not only on its internal factors such as financial condition and strategic planning, but also on external environmental factors (). When faced with uncontrollable external environments, enterprises will choose to use limited resources to ensure the normal operation of existing businesses, rather than taking risks in expanding or innovating investments (Weisbach et al., 2004). However, venture capital is the source of enterprise development, and the improvement of risk-taking ability helps to enhance investment efficiency. Due to the uncertainty brought by climate physical risks, the risk bearing capacity of enterprises has been reduced. Although conservative investment strategies of enterprises can help reduce short-term risks, they have missed potential growth opportunities and reduced overall investment returns (). When facing changes in the external environment, investment decisions and business strategies of enterprises will be affected by the uncertainty of the operating environment. Uncertainty factors can prevent companies from accurately predicting their future development status. In such cases, companies typically adopt more conservative investment strategies to reduce potential risks and losses. At this point, companies tend to reduce their investment scale, postpone or reduce long-term and high-risk investment projects, resulting in insufficient investment and thus affecting investment efficiency (). Due to the lack of scientific and technological capabilities to fully control changes in the natural environment, supply chain disruptions caused by extreme weather and natural disasters have become an important source of business risk. With the increasing frequency of natural disasters caused by climate change, the stability of the supply chain that businesses rely on is significantly affected. The production behavior of suppliers is interrupted due to natural disasters, and the insufficient supply of intermediate goods directly affects the production plans and raw material procurement of downstream enterprises, resulting in an increase in supply chain risks. The instability of the supply chain has become an important external environmental risk that companies need to consider when making investment decisions. When suppliers face climate physical risks, business decision-makers find it more difficult to assess the future returns of investments, and may even postpone or reduce originally planned investment plans. This behavior leads to a decrease in investment efficiency for enterprises, especially in high-risk industries where underinvestment has become a common phenomenon (). Therefore, the climate physical risks of suppliers further reduce the investment efficiency of enterprises by compressing their risk-taking ability. Based on the above theoretical analysis, we propose Hypothesis 2.
H2The increase in supplier climate physical risks leads to a decrease in the risk-taking ability of enterprises, which in turn leads to a decrease in investment efficiency.
The investment efficiency of enterprises is influenced by various factors, among which financing ability is one of the key factors. Commercial credit financing is an important channel for enterprises to obtain short-term funds, especially for those with high funding needs or financial leverage. Commercial credit financing occupies an important position in their funding structure (). Commercial credit financing refers to the credit given by customers to suppliers, which should be recorded as accounts payable in accounting. However, the climate physical risks of suppliers may compress the financing channels for corporate commercial credit, and in the case of insufficient funds, corporate investment will be affected, manifested as insufficient investment. Firstly, when climate disasters occur in the region where the supplier is located, production interruptions and cost increases can lead to a deterioration of the supplier’s financial situation (). In this case, suppliers may reduce their credit support to downstream enterprises, reduce deferred payments and credit sales, thereby affecting the liquidity of downstream enterprises’ funds and directly affecting their investment behavior. When credit constraints intensify, companies often adopt more conservative investment strategies, thereby reducing investment scale and efficiency (). Secondly, the supply chain has a certain supervisory effect. The supply side of commercial credit will actively participate in the supervision of customer enterprises’ business behavior in order to safeguard their own rights and interests, and reduce the agency problem of enterprises. Due to the fact that suppliers and customers are usually in the same industry and have certain information advantages, they can more effectively regulate customers’ investment behavior. When there is a possibility of default by the enterprise, the supplier can constrain the investment behavior of the customer enterprise by stopping the supply of intermediate goods. Finally, the climate physical risks of suppliers may also have a negative impact on the relationship between enterprises and financial institutions. When evaluating the credit risk of enterprises, financial institutions usually consider the external risk factors they face. When suppliers reduce their financial transactions with downstream enterprises due to climate physical risks, it will affect the evaluation of the enterprise by financial institutions, lower its credit rating, thereby increasing borrowing costs or refusing credit support. This credit tightening directly affects the financing environment of enterprises, thereby affecting their investment efficiency. Enterprises may be unable to make necessary expansionary investments due to limited financing channels, thereby affecting their potential for capital appreciation (). Based on the above theoretical analysis, we propose Hypothesis 3.
H3The rising climate physical risks of suppliers have compressed the commercial credit financing channels of enterprises, leading to a decrease in investment efficiency.
3 Research design
3.1 Model building
To test the aforementioned assumptions, we construct a baseline regression model as follows:
Among them, represents the investment efficiency of enterprise i in year t, and the larger the value, the lower the investment efficiency of enterprise i; represents the weighted value of the climate physical risks experienced by the supplier of enterprise i in the tth year; is a series of enterprise level control variables; is the fixed effect of the industry; is the fixed effect f of the year; is the random interference term. If the estimated value of the is significantly greater than 0, it indicates that an increase in the supplier’s climate physical risks will lead to a decrease in the investment efficiency of the enterprise.
3.2 Variable definition
3.2.1 Explanatory variable
The core explanatory variable of this article is the supplier’s climate physical risk, and the damage caused by climate physical risk is sudden and irreversible. On the one hand, supply chain disruptions caused by extreme weather can directly reduce customer capacity utilization and increase enterprise costs; On the other hand, long-term climate pattern changes have forced customers to reassess their regional investment layouts, resulting in sunk cost losses. Clarifying the spillover effects of climate physical risks is of great practical significance for maintaining supply chain stability. In terms of exploring the measurement of climate physical risks, existing research generally focuses on diverse climate factors and natural disasters. The measurement of climate factors focuses on changes in climate environmental parameters such as temperature and rainfall, or fluctuations in climate related indices; For the measurement of natural disasters, it focuses on specific disaster events such as floods, heat waves, and droughts, as well as the economic and social losses they cause, in order to assess the level of climate physical risk exposure faced by economic entities. Drawing on the approach proposed by , based on data from 229 cities in China, and constructs a climate physical risk index to measure climate physical risks according to four types of climate events: extreme low temperature, extreme high temperature, extreme rainfall, and extreme drought.
The specific calculation steps for the indicators are as follows: Firstly, since the “Top Five Supplier Procurement Information Table” only includes the supplier’s enterprise name but not their location information, this article first matches the “Top Five Supplier Procurement Information Table” in the CSMAR database with the “Company Basic Information Table” in the database to obtain the initial addresses of the top five suppliers of the enterprise. The second step is to match the missing supplier basic information in the “Company Basic Information Table” with “Chinese Industrial and Commercial Enterprise Information” to obtain as much address information as possible for the supplier. Finally, for the missing supplier location information, it will be supplemented manually by querying Baidu and Tianyancha. The third step is to use supplier location information as a covariate and match it with the “China Climate Physical Risk Dataset” to obtain the climate physical risk level data of the top five suppliers of the enterprise. Step four, as each enterprise has multiple suppliers in the same year, this article calculates the weight based on the procurement amount and performs weighted processing to obtain the final supplier climate physical risk indicators. As shown in Equation 2: is the purchase amount of the supplier by enterprise in the year, and is the climate physical risks (including extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD)) that the supplier is exposed to in the year.
3.2.2 Explained variable
The core explanatory variable of this article is the efficiency of corporate investment. Drawing on the practices of , , estimate the investment efficiency of enterprises using Equation 3 (; ).
Among them, is the actual new investment of the enterprise, measured as [cash paid for the purchase and construction of fixed assets, intangible assets, and other long-term assets + net cash paid for acquiring subsidiaries and other operating units - net cash received from the disposal of fixed assets, intangible assets, and other long-term assets - net cash received from the disposal of subsidiaries and other operating units - (depreciation of fixed assets + amortization of intangible assets + amortization of long-term deferred expenses)]/total assets at the beginning of the year; Growth is the opportunity for enterprise growth, measured by TobinQ value; Lev is the financial leverage ratio, measured by the asset liability ratio; Cash is the cash flow status of an enterprise, measured by dividing the net cash flow generated from operating activities by total assets; Age is the year the company went public; Size is the size of the enterprise, measured by taking the logarithm of its assets; Ret is the stock return rate of the enterprise, measured by the annual individual stock return rate considering cash dividends reinvestment; and represent the fixed effects of year and industry, respectively. Regression analysis of Equation 1 with lagged explanatory variables yields the residual value , which measures the difference between the actual new investment and the expected new investment of the enterprise. If , it indicates overinvestment; otherwise, it indicates underinvestment. Take the absolute value of residual to measure the degree of inefficient investment of the enterprise. The larger the value, the lower the investment efficiency of the enterprise.
3.2.3 Control variable
This article incorporates a series of control variables based on existing research (Su et al., 2025; ). This includes: enterprise size, which is measured by the natural logarithm of total assets; Debt to asset ratio is measured by dividing the year-end total liabilities by the year-end total assets; The cash flow ratio is measured by dividing the net cash flow generated from operating activities by total assets; Return on Assets measured as net profit/average balance of total assets; Revenue growth measured as the current year’s revenue divided by the previous year’s revenue −1; Receivables measured as net accounts receivable/total assets; Management Fee Ratio specifically measured as management expenses/operating income; Asset Turnover Ratio specifically measured as operating revenue/total assets; Shareholding ratio of the largest shareholder is the number of shares held by the largest shareholder divided by the total number of shares. The specific definition is shown in Table 1.
TABLE 1
| Variable type | Variable name | Variable symbol | Definition |
|---|---|---|---|
| Control variables | Company size | Size | Natural logarithm of total assets |
| Debt to asset ratio | Lev | Total year-end liabilities divided by total year-end assets | |
| Cash flow ratio | Cashflow | Net cash flows from operating activities divided by total assets | |
| Return on assets | ROA | Net profit/average balance of total assets | |
| Revenue growth | Growth | Current year’s operating income/previous year’s operating income −1 | |
| Receivables | Rec | Net accounts receivable/total assets | |
| Management fee ratio | Mfee | Management expenses/operating income | |
| Asset turnover ratio | ATO | Operating revenue/total assets | |
| Shareholding ratio of the largest shareholder | Top1 | Number of shares held by the largest shareholder/total number of shares |
Control variable definition.
3.3 Sample selection and data sources
This article uses Chinese A-share listed companies from 2010 to 2023 as the initial research sample to examine the impact of supplier climate physical risks on downstream enterprise investment efficiency. In the empirical part, the following three sets of data were mainly used: the first set is the “Top Five Supplier Procurement Information Table” in the Guotai An database, which records the supplier names, procurement quantities, and other data of some listed companies in China. The second set is “China Industrial and Commercial Enterprise Information Data (2000-2024)”, sourced from the State Administration for Industry and Commerce (SAIC) and other publicly available information. This article mainly uses the registered regional data of industrial and commercial enterprises to locate the location of listed company suppliers. The third set is the “China Climate Physical Risk Dataset (excluding data from Hong Kong, Macau, and Taiwan) (1993-2023)”, which is sourced from the website of the Energy Finance Professional Committee of the International Energy Transition Society (IEFN) and records the climate physical risk index of 229 cities in 31 provinces of China. In addition, the labor employment, financial, and production and operation data of listed companies are sourced from the Guotai An database and CNRDS database.
To ensure the accuracy of empirical regression, the original samples were processed as follows: 1. ST, ST *, and delisted samples were excluded. 2. Exclude samples from the financial industry. 3. Exclude samples with missing or significantly erroneous main variables. 4. The missing data of control variables are supplemented using linear interpolation. 5. Perform 1% bilateral truncation on continuous variables to eliminate the influence of extreme values on empirical regression. After processing the above samples, a total of 5085 annual samples of listed companies were obtained, providing rich data support for this study.
4 Empirical results and analysis
4.1 Descriptive statistics
The descriptive statistics of the main variables are shown in Table 2. The results show that the mean investment efficiency is 0.039 and the standard deviation is 0.049, indicating significant differences in investment efficiency among different enterprises. The mean of supplier climate physical risk is 0.274, the standard deviation is 0.051, the minimum value is 0.159, and the maximum value is 0.438, indicating that there are also some differences in the supplier climate physical risk indicators of the sample enterprises. In addition, the descriptive statistics of other variables are within a reasonable range and will not be further elaborated.
TABLE 2
| | Obs | Mean | SD | Min | Max |
|---|---|---|---|---|---|
| Absinveff | 5,085 | 0.039 | 0.049 | 0.000 | 0.326 |
| Scpr | 5,085 | 0.274 | 0.051 | 0.159 | 0.438 |
| Size | 5,085 | 22.230 | 1.263 | 19.573 | 25.876 |
| Lev | 5,085 | 0.460 | 0.213 | 0.061 | 0.951 |
| ROA | 5,085 | 0.020 | 0.070 | −0.349 | 0.171 |
| Growth | 5,085 | 0.544 | 1.770 | −0.798 | 14.288 |
| Rec | 5,085 | 0.110 | 0.102 | 0.000 | 0.447 |
| Mfee | 5,085 | 0.098 | 0.089 | 0.008 | 0.567 |
| ATO | 5,085 | 0.577 | 0.448 | 0.058 | 2.645 |
| Top1 | 5,085 | 0.332 | 0.150 | 0.079 | 0.738 |
Descriptive statistics.
4.2 Baseline regression
Table 3 reports the benchmark regression results of the impact of supplier climate physical risks on enterprise investment efficiency. In column (1), only core explanatory variables, industry fixed effects, and year fixed effects were included, and the estimated coefficient of Cpri was 0.045, which passed the significance test at the 1% level. Indicating that an increase in climate physical risks for suppliers will lead to a decrease in investment efficiency for enterprises. Furthermore, after adding a series of enterprise level control variables to column (2), the coefficient of cpri remains significantly positive, indicating the robustness of the above conclusion. In column (2), the coefficient of the supplier’s climate physical risk is 0.045, indicating that for every one standard deviation increase in climate physical risk, the investment efficiency of the enterprise will change by 0.0023 units, accounting for approximately 5.897% of its mean and 4.694% of its standard deviation, all other conditions remaining constant. This result validates the research hypothesis that supplier climate physical risks have supply chain spillover effects and have a significant negative impact on firm investment efficiency.
TABLE 3
| | (1) Absinveff | (2) Absinveff |
|---|---|---|
| Cpri | 0.045*** (0.016) | 0.045*** (0.016) |
| Size | | −0.005*** (0.001) |
| Lev | | 0.010* (0.005) |
| ROA | | 0.064*** (0.013) |
| Growth | | 0.000 (0.000) |
| Rec | | −0.028*** (0.008) |
| Mfee | | 0.035*** (0.013) |
| ATO | | −0.007*** (0.002) |
| Top1 | | −0.011* (0.006) |
| Constant | 0.041*** (0.009) | 0.133*** (0.020) |
| Industry FE | YES | YES |
| Time FE | YES | YES |
| N | 5,085 | 5,085 |
| R2 | 0.071 | 0.098 |
Baseline regression results.
The robust standard error of enterprise level clustering is reported in parentheses; ***, **, and * indicate significance levels 1%, 5%, and 10%. Same as in the following tables.
4.3 Robustness test
4.3.1 PSM method
The relationship between supplier climate physical risks and enterprise investment efficiency may be influenced by sample selection, leading to sample selection bias. For example, certain types of enterprises are more inclined to choose to cooperate with suppliers with low climate physical risks, and this choice may be related to the investment efficiency of the enterprise, leading to biased estimation results. This article adopts the propensity score matching method to solve this problem. Firstly, the sample is divided into high-risk and low-risk groups based on the median of the supplier’s climate physical risk. The control variable is used as the observable feature of the enterprise for matching, so that these two groups have similarity in the control variable. This method can effectively eliminate bias caused by sample selection, ensuring more accurate estimation of the causal relationship between supplier climate physical risks and enterprise investment efficiency.
The first column of Table 4 shows the empirical results, which are the regression results after 1:3 nearest neighbor matching. Figure 1 shows the diagnosis of PSM covariate balance. The climate and physical risks of suppliers have a significant positive impact on the investment efficiency of enterprises, with a coefficient of 0.059, passing the 1% significance level test. Indicating that in the matching samples, an increase in climate physical risks will significantly improve the absolute value of enterprise investment efficiency, that is, a decrease in investment efficiency. The results ensured the robustness of the benchmark regression results in this article.
TABLE 4
| | 1: 3 nearest neighbor matching | Heteroscedastic instrumental variable |
|---|---|---|
| | (1)Absinveff | (2)Absinveff |
| Cpri | 0.059*** (0.018) | 6.903** (2.699) |
| Constant | 0.137*** (0.023) | −1.749** (0.751) |
| Controls | YES | YES |
| Industry FE | YES | YES |
| Time FE | YES | YES |
| Phase 1 F-value | | 38.97 |
| N | 3,967 | 5,085 |
| R2 | 0.090 | 0.139 |
Robustness testing—PSM and Heteroscedastic instrumental variable.
FIGURE 1
4.3.2 Heteroscedastic instrumental variable
Due to the potential endogeneity issues caused by bidirectional causal relationships or omitted variables, this article draws on the research of and to construct instrumental variables through heteroscedasticity for endogeneity treatment (; ). The second column of Table 4 reports the empirical results of two-stage least squares estimation using heteroscedastic instrumental variables. The F-value in the first stage was 38.97, which passed the weak instrumental variable test, indicating the validity of the instrumental variable. In the second stage regression results, the coefficient of Cpri remained significantly positive, ensuring the robustness of the conclusions in this paper.
4.3.3 Replace the dependent variable
In addition to referring to the model proposed by , this article also follows the practices of and to calculate the investment efficiency of enterprises using Equations 4, 5.
Among them, represents new investment, which is consistent with the model proposed by . is the growth rate of the enterprise’s operating income, and is the virtual variable of the enterprise’s operating income growth rate. If the enterprise’s operating income growth rate is less than 0, it is set to 1; otherwise, it is set to 0. Using the above two equations for estimation, the absolute value of the residual obtained represents the investment efficiency of the listed company, denoted as Absinvest 2 and Absinvest 3, respectively. The newly calculated investment efficiency data was regressed, and the results of the regression after replacing the dependent variable are shown in columns (1)–(2) of Table 5. The coefficient of Cpri remains significantly positive, consistent with the conclusion of the benchmark regression, ensuring the robustness of the research findings in this paper.
TABLE 5
| | Replace the dependent variable | Re select sample range | Control omission and control variables | |||
|---|---|---|---|---|---|---|
| | (1) Absinveff2 | (2) Absinveff3 | (3) Absinveff | (4) Absinveff | (5) Absinveff | (6) Absinveff |
| Cpri | 0.039** (0.017) | 0.029* (0.017) | 0.046*** (0.017) | 0.048*** (0.017) | 0.048*** (0.017) | 0.039** (0.020) |
| Constant | 0.101*** (0.017) | 0.100*** (0.017) | 0.134*** (0.021) | 0.143*** (0.020) | 0.101*** (0.035) | −0.189*** (0.065) |
| Controls | YES | YES | YES | YES | YES | YES |
| Individual FE | NO | NO | NO | NO | NO | YES |
| Firm FE | YES | YES | YES | YES | YES | YES |
| Time FE | YES | YES | YES | YES | YES | YES |
| Industry-year FE | NO | NO | NO | YES | NO | NO |
| Macroeconomic control variables | NO | NO | NO | NO | YES | NO |
| N | 4,854 | 4,854 | 4,829 | 5,062 | 5,085 | 4,623 |
| R2 | 0.071 | 0.076 | 0.094 | 0.145 | 0.099 | 0.364 |
Robustness testing—Replace the dependent variable, Re select sample range and Control omission and control variables.
4.3.4 Re select sample range
Because there may be systematic bias when using method to measure the investment efficiency of enterprises, and this model may lead to the inefficiency of the investment efficiency of all enterprises, thus affecting the reliability of the regression results. To avoid this problem, this article draws on the approach of Wang and Wang (2024) to sort the investment efficiency of the samples in ascending order, delete the top 5% of high-efficiency samples, and use the bottom 95% of samples for re regression (Wang and Wang, 2024). The empirical results are shown in column (3) of Table 5. The regression results indicate that after removing extreme values, the coefficient of Cpri is still significantly positive, supporting the reliability of the benchmark regression results.
4.3.5 Control omission and control variables
In order to ensure the stability of the benchmark regression results, this article added industry year fixed effects, macroeconomic control variables, and individual fixed effects to the benchmark model for robustness testing, in order to eliminate possible omitted variables and interference factors at different levels, thereby enhancing the reliability of the research conclusions. Firstly, considering that macroeconomic environments and policy changes in different years may also have significant impacts on corporate decision-making at the industry level. Therefore, this article adds industry year fixed effects to the benchmark regression model to eliminate the interference of different industries and years on the investment efficiency of enterprises. The empirical results are shown in column (4) of Table 5; Secondly, in order to further control the influence of macroeconomic factors, this article added macroeconomic control variables to the regression model. Including per capita GDP, urbanization rate, advanced industrial structure (GPD ratio of secondary and tertiary industries), the proportion of the sum of deposits and loans of financial institutions to GDP, and tax burden level (tax revenue/GDP). Controlling these variables helps to eliminate the interference of macroeconomic environment changes on corporate investment efficiency. The empirical results are shown in column (5) of Table 5; Finally, considering the heterogeneity at the individual level, the investment efficiency of a company will be influenced by factors such as internal management, culture, and strategy. Therefore, adding individual fixed effects can effectively control the potential impact of these internal factors on the regression results. The empirical results are shown in column (6) of Table 5. The consistency between the results and the benchmark regression results ensures the robustness of the basic conclusionshe influence of extreme values on empirical regression. After processing the above samples, a total of 5085 annual samples of listed companies were obtained, providing rich data support for this study.
5 Mechanism testing and heterogeneity testing
5.1 Mechanism verification
5.1.1 Reduce the risk bearing capacity of enterprises
The investment decisions of enterprises are not only influenced by their own resources and market demand, but also by changes in the external economic environment. Due to external environmental changes such as natural disasters and extreme weather caused by climate change, suppliers may experience production interruptions, cost increases, or delayed deliveries. These external risks are further transmitted to business decision-making. The climate physical risks of suppliers affect downstream enterprises’ expectations of future economic situations through changes in the external environment, thereby reducing their ability to bear risks. When faced with higher uncertainty, enterprises usually tend to reduce investment projects with higher risks and turn to low-risk, more stable return projects, resulting in a decrease in investment efficiency. Therefore, the climate physical risks of suppliers will be transmitted to downstream enterprises through the supply chain, reducing their risk bearing capacity and thus lowering their investment efficiency.
To verify the above mechanism, this article empirically tested the impact of supplier climate physical risks on the level of enterprise risk-taking. The level of enterprise risk-taking is measured by the fluctuation of annualized monthly returns (; ). The empirical results are shown in column (1) of Table 6. The results show that the coefficient of supplier climate physical risk on Enterprise risk-taking level (ERT) is negative (−0.315) and has passed the significance test at the 5% level, indicating that supplier climate physical risk significantly reduces the risk-taking level of enterprises. Therefore, the climate physical risks of suppliers have worsened the business environment, reduced the risk bearing capacity of enterprises, and led to insufficient investment. The results confirmed research hypothesis 2.
TABLE 6
| | (1) ERT | (2) AP | (3) NTC |
|---|---|---|---|
| Cpri | −0.315** (0.123) | −0.076** (0.032) | −0.061* (0.035) |
| Constant | −0.857*** (0.150) | 0.020 (0.059) | −0.018 (0.067) |
| Controls | YES | YES | YES |
| Industry FE | YES | YES | YES |
| Time FE | YES | YES | YES |
| N | 5,065 | 5,058 | 5,058 |
| R2 | 0.341 | 0.455 | 0.541 |
Mechanism testing.
5.1.2 Compressing commercial credit financing
Climate physical risks not only pose a threat to the production capacity and supply stability of suppliers, but may also affect the credit conditions of downstream enterprises, thereby affecting their financing efficiency. When suppliers face climate physical risks and find it difficult to maintain long-term partnerships or stable supply, they may adopt more conservative credit policies, reduce credit support for downstream enterprises, and thus lower their ability to obtain financing. Due to the impact on the commercial credit financing channels of downstream enterprises, the problem of insufficient liquidity of funds is exacerbated, which limits the investment ability of enterprises and leads to insufficient investment.
To verify the above mechanism, referring to the approach of Zhang et al. (2012), this article uses two indicators, accounts payable (AP) and net commercial credit (NTC), to measure the level of commercial credit financing of enterprises (Zhang et al., 2012). Accounts payable refers to the sum of accounts payable, notes payable, and accounts receivable of an enterprise, which have been standardized by total assets. Net commercial credit is measured by calculating (accounts payable + notes payable + prepayments) - (accounts receivable + notes receivable + prepayments) and standardizing it with total assets to assess a company’s level of commercial credit financing. The higher the level of commercial credit financing, the stronger the ability of downstream enterprises to obtain financing from upstream enterprises through credit methods. The empirical results are shown in Table 6. The coefficient of Cpri for AP is negative and passed the 5% level significance test, indicating that the supplier’s climate physical risks reduce the commercial credit financing obtained by enterprises through credit methods. In addition, the coefficient of Cpri for NTC is also negative and passed the 10% significance level test, indicating that the supplier’s climate physical risk also reduces the net commercial credit of the enterprise, that is, the overall commercial credit financing ability of the enterprise is reduced. Therefore, empirical results indicate that the increase in supplier climate physical risks significantly reduces a company’s accounts payable and net commercial credit, leading to a decline in the company’s financing capacity and subsequently affecting its investment efficiency. Verified research hypothesis 3.
5.2 Heterogeneity test
5.2.1 Heterogeneity of supply chain resilience
When facing the rising climate physical risks of suppliers, the investment efficiency of enterprises may be significantly affected by the resilience of the supply chain. Enterprises with strong supply chain resilience are better able to cope with production interruptions or supply chain disruptions caused by climate change. Therefore, they exhibit strong flexibility and adaptability in dealing with supplier climate physical risks, and can maintain high investment efficiency. In order to explore how differences in supply chain resilience can regulate the impact of supplier climate physical risks on enterprise investment efficiency, this paper selects two indicators, supply chain concentration and supply chain resilience, as proxy variables for supply chain resilience. Based on this, this article refers to the research of to calculate the supplier concentration index by summing the procurement proportions of the previous five major suppliers (). The sample is then divided into two sub samples, supply chain diversification and supply chain concentration, using the median for grouped regression. The regression results are shown in columns (1)–(2) of Table 7. Secondly, supply chain recovery capability measures a company’s ability to resume production after encountering supply chain disruptions or other external shocks. The stronger the company’s recovery capability, the better it can respond to unexpected events such as supply chain disruptions and maintain the continuity of production and investment. Based on this, this article draws on the research of and Zhang et al. (2024) to measure the supply chain recovery capability by the degree of deviation of production fluctuations from demand fluctuations (; Zhang and Gu, 2024). The samples are divided into strong recovery capability and weak recovery capability sub samples by median for grouped regression. The regression results are shown in columns (3)–(4) of Table 7. From the regression results in the table, it can be seen that in enterprises with centralized supply chains, the impact of supplier climate physical risks on investment efficiency is significant, indicating that centralized supply chains make enterprises more sensitive to external risks, thereby reducing investment efficiency. Similarly, in enterprises with weak supply chain recovery capabilities, the impact of supplier climate physical risks on investment efficiency is significant. Therefore, in companies with strong supply chain resilience, the impact of supplier climate physical risks on investment efficiency is relatively small.
TABLE 7
| | (1) Diversification | (2) Centralization | (3) Strong recovery ability | (4) Weak recovery ability |
|---|---|---|---|---|
| Cpri | 0.031 (0.020) | 0.059** (0.025) | 0.011 (0.022) | 0.079*** (0.023) |
| Constant | 0.098*** (0.027) | 0.140*** (0.027) | 0.145*** (0.032) | 0.124*** (0.028) |
| Controls | YES | YES | YES | YES |
| Industry FE | YES | YES | YES | YES |
| Time FE | YES | YES | YES | YES |
| N | 2,541 | 2,544 | 2,542 | 2,541 |
| R2 | 0.091 | 0.128 | 0.095 | 0.121 |
Supply chain resilience heterogeneity testing.
5.2.2 Heterogeneity of differences in credit availability
The availability of credit is a key factor affecting corporate investment behavior. Enterprises with high credit availability, abundant financing channels, and lower financing costs can better cope with production interruptions or supply chain fluctuations caused by climate physical risks, and maintain relatively stable investment behavior (). To further explore how differences in credit availability can regulate the impact of supplier climate physical risks on enterprise investment efficiency, this article measures the credit availability of the region where the enterprise is located through two indicators: marketization level and bank competitiveness. This article draws on the marketization level index proposed by Fan Gang to group the samples, dividing them into high marketization level and low marketization level sub samples for grouped regression. The regression results are shown in columns (1)–(2) of Table 8. In regions with strong competition among banks, the competition between banks promotes the relaxation of credit conditions, making it easier for enterprises to obtain loans with lower interest rates. This article follows the approach of Cai and Dong (2016) to construct the Herfindahl index using regional commercial bank data to measure the competitiveness of regional banks. The sample is divided into two sub samples, weak bank competitiveness and strong bank competitiveness, for grouped regression. The regression results are shown in columns (3)–(4) of Table 8. The empirical results show that in areas with low levels of marketization, supplier climate physical risks significantly affect the investment efficiency of enterprises. In areas with lower levels of marketization, the financing channels of enterprises are limited, leading to a more conservative approach when facing the transmission effect of supplier climate physical risks, resulting in a decrease in investment efficiency. Similarly, in regions with weak bank competition, the impact of supplier climate physical risks on corporate investment efficiency is significant. This indicates that in regions with weak bank competition, corporate credit conditions are poor, which increases the difficulty for companies to cope with supplier climate physical risks, thereby affecting their investment efficiency. Therefore, enterprises located in regions with abundant credit resources are less affected by supplier climate physical risks.
TABLE 8
| | (1) Diversification | (2) Centralization | (3) Strong recovery ability | (4) Weak recovery ability |
|---|---|---|---|---|
| Cpri | 0.031 (0.020) | 0.059** (0.025) | 0.011 (0.022) | 0.079*** (0.023) |
| Constant | 0.098*** (0.027) | 0.140*** (0.027) | 0.145*** (0.032) | 0.124*** (0.028) |
| Controls | YES | YES | YES | YES |
| Industry FE | YES | YES | YES | YES |
| Time FE | YES | YES | YES | YES |
| N | 2,541 | 2,544 | 2,542 | 2,541 |
| R2 | 0.091 | 0.128 | 0.095 | 0.121 |
Differences in credit availability heterogeneity testing.
5.2.3 Heterogeneity of differences in credit availability
The investment structure of a company reflects the way it allocates resources, especially the balance between long-term and short-term investments. Enterprises with a high proportion of fixed assets typically require long-term financial support, and these investments have longer return periods, making them particularly sensitive to external risks. When facing issues such as climate change or supply chain disruptions, enterprises with a high proportion of fixed assets face significant financial pressure, leading to a reduction in high-risk investments and a decrease in investment efficiency. To further explore how differences in corporate investment structure can regulate the impact of supplier climate physical risks on corporate investment efficiency, this article divides the sample into two sub samples, high fixed asset proportion and low fixed asset proportion, based on the median proportion of fixed assets in the enterprise, for grouping and regression. The empirical results are shown in columns (1)–(2) of Table 9. In addition, based on the median R&D investment, this article divides the sample into two sub samples: high R&D investment and low R&D investment for grouped regression. The empirical results are shown in columns (3)–(4) of Table 9. The empirical results show that for enterprises with a high proportion of fixed assets, the impact of supplier climate physical risks on investment efficiency is significant. This indicates that these enterprises, due to their reliance on long-term capital, may reduce high-risk investments when facing climate physical risks, thereby affecting investment efficiency. In enterprises with low R&D investment, the impact of supplier climate physical risks on investment efficiency is significant, indicating that enterprises with low R&D investment rely more on existing production capacity. Therefore, when facing climate physical risks, more conservative investment strategies may be adopted to reduce investment efficiency.
TABLE 9
| | (1) High proportion of fixed assets | (2) Low proportion of fixed assets | (3) High R&D investment | (4) Low R&D investment |
|---|---|---|---|---|
| Cpri | 0.049** (0.022) | 0.036 (0.025) | 0.032 (0.026) | 0.062*** (0.028) |
| Constant | 0.140*** (0.028) | 0.130*** (0.027) | 0.116*** (0.035) | 0.134*** (0.046) |
| Controls | YES | YES | YES | YES |
| Industry FE | YES | YES | YES | YES |
| Time FE | YES | YES | YES | YES |
| N | 2,540 | 2,543 | 1,791 | 1,792 |
| R2 | 0.078 | 0.139 | 0.102 | 0.122 |
Differences in credit availability heterogeneity testing.
6 Further research: overinvestment or underinvestment
There are two manifestations of decreased investment efficiency in enterprises: overinvestment and underinvestment. Insufficient investment may lead to companies missing out on profitable projects, while overinvestment may waste resources on inefficient or unprofitable projects. This article refers to the practices of , to test the overinvestment and underinvestment of enterprises separately (; ). This article uses the absolute value of positive (negative) residuals based on model regression as a proxy variable for overinvestment (underinvestment) (). The group with residuals greater than 0 is defined as the overinvestment group, and the group with residuals less than 0 is defined as the underinvestment group. Two sub samples are used for empirical analysis. The empirical results are shown in Table 10. In column (1) of Table 10, the regression results of overinvestment show that Cpri has no significant impact on overinvestment, with a coefficient of −0.010, indicating that there is no significant relationship between supplier climate physical risk and corporate overinvestment. In column (2) of Table 10, the regression results for underinvestment show that the coefficient of Cpri is 0.065 and has passed the 1% significance level test, indicating that supplier climate physical risks have a significant positive impact on underinvestment in enterprises. This means that the increase in climate physical risks will lead to underinvestment in investment decisions by companies, thereby reducing investment efficiency. When enterprises face higher risks of supply chain instability, due to risk avoidance considerations, they will adopt conservative investment strategies, resulting in underinvestment rather than overinvestment.
TABLE 10
| | (1) Excessive investment | (2) Insufficient investment |
|---|---|---|
| Cpri | −0.010 (0.033) | 0.065*** (0.017) |
| Constant | 0.180*** (0.043) | 0.135*** (0.017) |
| Control | YES | YES |
| Industry FE | YES | YES |
| Time FE | YES | YES |
| N | 2,097 | 2,986 |
| R2 | 0.073 | 0.195 |
Further analysis: overinvestment or underinvestment.
7 Conclusion
With the increasing uncertainty of the global economy, the decision-making of enterprises in resource allocation and fund utilization has become increasingly complex. The climate physical risks of suppliers significantly affect the effective operation of the supply chain, which in turn affects the investment decisions of downstream enterprises and leads to a decrease in investment efficiency. Therefore, this article combines the “Top Five Supplier Procurement Information Table”, “China Business Enterprise Information”, and “China Climate Physical Risk Dataset” to construct supplier climate physical risk indicators. The main research conclusions are as follows: Firstly, the increase in climate physical risks of suppliers leads to a decrease in investment efficiency of downstream enterprises. And this conclusion still holds after conducting robustness tests on PSM method, heteroscedastic instrumental variables, changing the dependent variable, reselecting the sample range, and controlling for omitted control variables. Secondly, the climate physical risks of suppliers affect the investment efficiency of downstream enterprises through two main ways: firstly, by reducing the risk bearing capacity of enterprises, increasing external uncertainty, and thereby suppressing their investment willingness; The second is to compress the commercial credit financing channels of enterprises, reduce the financing ability of downstream enterprises, and restrict their investment behavior. Thirdly, companies with poor supply chain resilience are more susceptible to the impact of supplier climate physical risks, while companies with high supply chain concentration and weak supply chain recovery capabilities have a more significant decrease in investment efficiency; Enterprises with lower credit availability are more susceptible to the impact of supplier climate physical risks, and the effect of decreased investment efficiency is more significant in areas with low marketization levels and weak bank competition; Enterprises with a high proportion of fixed assets and low R&D investment are more susceptible to the impact of supplier climate physical risks, resulting in a more significant decrease in investment efficiency. Fourthly, the supply chain spillover effects of supplier climate physical risks mainly manifest in underinvestment rather than overinvestment.
In summary, this study not only reveals the important impact and mechanism of supplier climate physical risks on downstream enterprise investment efficiency, but also deeply analyzes the differences in this negative impact on different supply chain resilience, credit availability, and enterprise investment structures. The research conclusion deepens the understanding of the externalities of Climate Physical Risks, provides a new perspective for the study of factors affecting investment efficiency, and offers policy insights for policymakers and business managers. It has important theoretical and practical significance for preventing systemic risks and improving corporate investment efficiency. Meanwhile, this study has several limitations. First, this paper mainly examines the one way inhibitory effect of rising supplier climate physical risk on corporate investment efficiency, and does not investigate the potential two way interaction between them. Second, this research only focuses on climate physical risk, and does not analyze climate transition risk and climate related opportunities.
In view of the above limitations, future research can be expanded in two directions. On the one hand, scholars can explore the two way mechanism between supplier and downstream corporate climate physical risk. On the other hand, researchers can construct a unified analytical framework to comprehensively investigate climate physical risk, climate transition risk and climate related opportunities.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
C-ZD: Methodology, Investigation, Writing – original draft, Writing – review and editing. X-YL: Project administration, Investigation, Writing – original draft. C-CR: Methodology, Supervision, Writing – review and editing. M-MS: Investigation, Software, Writing – review and editing. Q-PG: Data curation, Validation, Writing – review and editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The author(s) declared that generative AI was not used in the creation of this manuscript.
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Summary
Keywords
climate risk, commercial credit, enterprise investment efficiency, physical risks, supply chain overflow
Citation
Deng C-Z, Lin X-Y, Rao C, Sun M-M and Guo Q-P (2026) Climate physical risks and corporate investment efficiency: evidence from supply chain spillover effects. Front. Environ. Sci. 14:1954810. doi: 10.3389/fenvs.2026.1954810
Received
31 July 2026
Revised
03 September 2026
Accepted
07 September 2026
Published
23 September 2026
Volume
14 - 2026
Edited by
Jin Hu, Fudan University, China
Reviewed by
Min Hong, Hunan Institute of Technology, China
Yikai Han, Central University of Finance and Economics, China
Mingyue Wang, Wuyi University, China
Updates
Copyright
© 2026 Deng, Lin, Rao, Sun and Guo.
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*Correspondence: Qiu-Ping Guo, qiupingguo@ynnu.edu.cn
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