Climate Risks, Supply Chain Spillovers, and ESG Performance: Evidence from China

Climate Risks, Supply Chain Spillovers, and ESG Performance: Evidence from China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Climate Risks, Supply Chain Spillovers, and ESG Performance: Evidence from China Yulin Zheng, Dingwen Wu, Hong Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7331780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study utilizes supplier and customer information disclosed by China A-share listed companies from 2009 to 2023 to identify inter-firm relationships across the supply chain. Utilizing machine learning techniques for textual analysis of annual reports, we construct firm-level climate risk metrics. Our analysis disclosures a positive relationship between climate risk exposure in downstream enterprises and the ESG performance of midstream manufacturers. In contrast, the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric pattern in supply chain spillovers of climate risk. Mechanism analysis demonstrates that while downstream firms exert a significant negative moderating effect on midstream ESG performance through trade credit financing, this is outweighed by the dominant positive moderating effects arising from environmental awareness and green technology spillovers. Consequently, downstream climate risk exhibits a net positive effect on the ESG performance of midstream manufacturers. This research provides novel empirical evidence and a fresh perspective on the factors influencing corporate ESG performance. Business and commerce/Business and management Social science/Business and management Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Social science/Environmental studies Business and commerce/Finance Social science/Finance Figures Figure 1 1. Introduction Recent years have witnessed accelerating global climate change, exceeding expectations in speed, progression, and severity, prompting widespread concern among governments worldwide. The China Climate Bulletin (2024) indicates that China's average temperature in 2024 was 1.01°C higher than the 1991–2020 average, marking the highest level since 1951. The increasing frequency and intensity of extreme weather events, coupled with abrupt asset value fluctuations triggered by transition risks, present direct and severe challenges to corporate operations. Substantial research at the micro level has documented the significant impact of firm-level climate risk on financial outcomes (Shi and Liu, 2025; Ma et al., 2025) and corporate decision-making (Li et al., 2024; Su et al., 2024).The rise of Corporate Social Responsibility (CSR) since the 1970s marked a profound shift in the business value paradigm. Enterprises began emphasizing a balance between economic objectives and legal, environmental, and ethical responsibilities, responding to the rights and interests of six core stakeholder groups, including shareholders, employees, and customers (Fineman and Clarke, 1996; Miao et al., 2012; Di and Kostovetsky, 2014). As sustainability concepts deepened, the scope of corporate environmental responsibility expanded, with the identification, management, and disclosure of climate change risks becoming a core component. Against this backdrop, some scholars have begun examining the spillover effects (horizontal spillovers) of firm-specific climate-related factors on other firms within the same industry (Li et al., 2023). However, with the increasing refinement and complexity of corporate division of labor systems, attention is shifting towards the vertical spillover effects of climate factors along the supply chain network. Freeman's (1984) stakeholder theory provides a theoretical foundation for quantifying the spillover effects of climate factors on related firms within the supply chain network: Upstream and downstream entities, acting as external stakeholders, can significantly influence corporate decisions through expressions of opinion and indirect pressure (Gosman et al., 2004; Johnson et al., 2010; Kim and Wemmerlöv, 2015; Krolikowski and Yuan, 2017). Yet, the impact of climate risk on supply chain partners extends beyond direct operational or financial shocks. As research advances, evidence has emerged indicating that a firm's ESG performance itself can generate significant spillovers through the influence of external stakeholders, profoundly driving the green transformation of enterprises within industrial and supply chains (Lian et al., 2022; Yan et al., 2024).Crucially, a strong intrinsic link has been established between the climate risk a firm faces and its ESG performance (Tian and Zhao, 2025). This leads to a key inference: If climate risk can propagate vertically along the supply chain, and climate risk levels profoundly shape a firm's ESG performance, then is this vertically transmitted firm-specific climate risk a key channel influencing the ESG performance of other firms within the supply chain? Regrettably, existing literature lacks systematic empirical evidence and in-depth exploration regarding the existence and magnitude of this potential spillover mechanism: "Firm-level climate risk→Vertical Transmission→ESG performance of other supply chain firms". Addressing this gap, this study utilizes supplier and customer information disclosed by China A-share listed companies from 2009 to 2023 to map upstream-downstream supply chain relationships. We construct firm-level climate risk metrics by applying machine learning for textual analysis of annual reports. We employ the quarterly data from the Sino-Securities Comprehensive ESG Rating, averaged to measure the annual comprehensive ESG rating of firms. Empirically, climate risk faced by downstream enterprises transmits readily to midstream manufacturers, significantly enhancing their ESG performance. Conversely, the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric pattern in the supply chain spillovers of climate risk. This research contributes to the literature in three primary ways. First, it uncovers the influence of climate risk on firms from a supply chain perspective—a currently neglected area. While Tian and Zhao (2025) found that firm climate risk significantly promotes ESG performance, no prior study has explored the impact of firm climate risk on stakeholders through the vertical lens of the supply chain network. Particularly given the escalating climate crisis, investigating this gap confirms that supply chain climate risk transmission is a significant external driver of corporate ESG performance, enhancing our comprehensive understanding between the climate risk and ESG. Second, while extensive research confirms the significant impact of ESG on firms (Lian et al., 2022; Yan et al., 2024), this study extends the inquiry to the root cause—climate risk. It provides a novel perspective for understanding the complex mechanisms of ESG performance transmission between firms, facilitating a deeper comprehension of inter-firm dynamics within supply chains and their implications for sustainable development. Finally, the paper delves into the heterogeneous effects of climate risk across different ESG dimensions and industries, offering new explanations for variations in corporate ESG performance under diverse contexts. This research thus provides a vital empirical foundation for managing climate risk within supply chains. 2. Hypothesis development 2.1 Increased Climate Risk in Customer Firms Drive ESG Performance Improvement in Midstream Supply Chain Enterprises According to the stakeholder theory, firms within the supply chain network, acting as significant external stakeholders, influence the decisions and operations of other networked firms to protect their own interests. Downstream customers, as crucial external stakeholders, differ from internal stakeholders who directly impact resource allocation and key decisions. While they typically lack direct influence over their suppliers' actions, downstream customers can still exert significant indirect influence on corporate policies and economic outcomes through channels such as expressing opinions (Gosman et al., 2004; Johnson et al., 2010; Kim and Wemmerlöv, 2015; Krolikowski and Yuan, 2017). As the critical link directly interfacing with end consumers, downstream operations are vital for supply chain completion, rendering them particularly sensitive to climate risks. Against the backdrop of frequent climate events, supply chain risks stemming from end-market operational vulnerability exhibit significant spillover effects (Lian et al., 2022; Yan et al., 2024). We posit that when downstream firms face heightened climate risk, they inevitably exert influence on midstream manufacturers. This influence manifests in complex ways, affecting not only short-term operational adjustments but also extending to long-term strategic planning. Building on evidence that climate risk levels profoundly shape corporate ESG performance (Tian et al., 2025) and can propagate vertically along the supply chain, we posit that when downstream firms face climate risk, midstream manufacturers may undertake actions to enhance their ESG performance.The rationale is threefold: First, Escalating climate risks drive downstream firms to bolster their own green awareness in response to rising consumer demand for sustainable products and the imperative for operational sustainability. This shift in awareness motivates downstream firms to preferentially select midstream manufacturers demonstrating strong ESG credentials, thereby driving midstream enterprises to strive toward enhancing their ESG performance. Furthermore, observed losses suffered by downstream firms due to climate risk can heighten green awareness within midstream firms themselves (Cao et al., 2019; Li and Xiao, 2020), prompting increased focus on environmental issues, green technology innovation, and corporate social image—ultimately enhancing ESG performance. According to signaling theory, heightened green awareness among downstream firms transmits signals of environmental and social responsibility to midstream manufacturers. Perceiving this pressure, midstream firms are motivated to meet client demands, maintain business relationships, and preserve market competitiveness by actively responding. As ESG represents the internationally recognized framework for assessing non-financial performance and sustainability, proactive ESG enhancement becomes a key pathway for midstream firms to cultivate a positive public image and align with client expectations. Consequently, we propose that downstream climate risk promotes midstream ESG performance by elevating green awareness. Second, Based on Reputation Theory, a firm's reputation is a critical intangible asset whose impact diffuses across the supply chain. Downstream customers, occupying a pivotal position, may experience operational volatility and reputational damage due to climate risk. This damage can propagate upstream, potentially eroding the reputational capital of midstream manufacturers. To proactively safeguard and enhance their standing within the supply network and broader market, and to retain the trust of customers, investors, and other stakeholders, midstream firms possess a strong incentive to elevate their ESG performance. Moreover, downstream customers seeking to rehabilitate their own green reputation often explicitly incorporate environmental and social responsibility requirements into supplier contracts or agreements. This effectively transfers reputational management pressure downstream, creating binding constraints that systematically drive midstream ESG improvement and foster sustainability across the entire supply chain. Third, Information Asymmetry Theory also provides an explanation. The intensification of climate risk significantly exacerbates information asymmetry between firms and their stakeholders, a prominent concern in recent literature. Information asymmetry also exists between downstream customers and midstream manufacturers within the supply chain (Lee et al., 1997; Acemoglu et al., 2012). Facing climate risk, downstream customers seek to mitigate the negative consequences of this asymmetry. Beyond enhancing their own disclosures, they can demand greater transparency from midstream suppliers. Improving ESG performance is a recognized mechanism for enhancing transparency and reducing perceived risk. Therefore, when downstream climate risk increases, midstream firms have an impetus to boost their ESG performance as a means of alleviating information asymmetry concerns. Based on the above analysis, we propose: H1. Climate risk faced by downstream enterprises in the supply chain enhances the ESG performance of midstream manufacturers. H2. Climate risk faced by downstream enterprises enhances the ESG performance of midstream manufacturers by elevating green awareness. Technology spillovers constitute another crucial pathway through which downstream firms drive midstream ESG improvement. Downstream firms' climate risk management strategies and green technology innovation practices can provide substantial support for midstream ESG performance enhancement via this key mechanism. Specifically, when customer firms actively combat climate risk by increasing R&D investment in green technologies, they often accumulate advanced green technologies and management expertise (Fang and Hu, 2023). These innovations and experiences are transferred to midstream manufacturers through collaboration, including technology transfer, training, and joint R&D (Patatoukas, 2012). This spillover not only boosts the green innovation performance of midstream firms (Yan et al., 2024) but also aids them in reducing carbon emissions, optimizing environmental performance, enhancing social responsibility awareness, and improving governance structures, which thereby comprehensively promotes midstream ESG performance. Based on the above analysis, we propose: H3. Climate risk faced by downstream enterprises enhances the ESG performance of midstream manufacturers by promoting technology spillovers. Simultaneously, we contend that increased appropriation of trade credit by downstream firms facing climate risk can impede midstream ESG improvement. The financing substitution hypothesis highlights trade credit as an informal financing channel between firms (Hu and Wu, 2022). Downstream firms experiencing heightened operational pressure and uncertainty due to climate risk face exacerbated financing constraints, amplifying the negative effects (Naseer et al., 2025). To mitigate the adverse impact of reduced bank credit availability, they may increase demand for funds by delaying payments or extending payment terms, utilizing trade credit as a substitute financing source (Lu and Yang, 2011; Huang et al., 2016). Fabbri and Klapper (2016) argue that powerful downstream customers, especially when suppliers operate in competitive markets, can leverage the threat of switching suppliers to extract more trade credit. Midstream manufacturers, acting as suppliers, thus confront a dual challenge: operational instability stemming from downstream climate risk affecting sales channels, and more aggressive trade credit demands from powerful downstream customers. This situation elevates their financial risk, potentially forcing reductions in environmental, social, and governance (ESG) investments and negatively impacting ESG performance. Based on the above analysis, we propose: H4. Climate risk faced by downstream enterprises hinders the improvement of ESG performance in midstream manufacturers by increasing the appropriation of trade credit. In summary, downstream climate risk exerts a complex dual influence on midstream ESG performance within the supply chain. On one hand, downstream firms' response to climate risk elevates their own green awareness, the spillover of which incentivizes midstream ESG improvement. Downstream firms also provide crucial support for midstream ESG enhancement through technology spillovers. On the other hand, downstream firms seeking to alleviate their own climate risk burden may increase their appropriation of midstream trade credit financing. This capital diversion heightens midstream financial risk, constrains ESG investment capacity, and thereby negatively impacts ESG performance. Nevertheless, while the negative moderating effect of trade credit financing is significant, the positive moderating effects of green awareness and green technology spillovers dominate. Consequently, downstream climate risk exerts a net positive effect on the ESG performance of midstream manufacturers. The mechanism is illustrated in Fig. 1 . 2.2 Asymmetric Impact of Upstream and Downstream Climate Risk on Midstream ESG Performance Notably, existing research indicates that spillover effects within supply chains often exhibit significant asymmetry (Sun, 2025). We posit that the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric characteristic in the influence of upstream versus downstream climate risk on midstream ESG levels. Two primary reasons underpin this asymmetry:First, midstream manufacturers possess cost-passing ability. Negative effects stemming from upstream climate risk (e.g., increased costs) tend to be passed downstream through midstream price adjustments, rather than directly impacting the ESG performance of the midstream firms themselves. Competitive constraints in end markets limit downstream firms' cost-pass-through capacity. This constraint, amplified by demand elasticity, hinders upstream transmission of climate risk shocks through pricing. Second, ESG rating systems exhibit a systematic bias. Prevailing ESG rating frameworks demonstrate significant limitations in covering upstream supply chain risks (Berg et al., 2022). This institutional bias enables midstream firms to maintain high ESG ratings even when exposed to upstream high-carbon-emission risks, creating a "responsibility transfer gap". Based on the above analysis, we propose: H5. The effect of climate risk faced by upstream enterprises in the supply chain on enhancing the ESG performance of midstream manufacturers is insignificant. 3. Research design 3.1 Sample selection and data source The sample for this study comprises China A-share listed firms from 2009 to 2023. We utilize disclosed information on the top five suppliers and customers to identify supply chain relationships. Observations involving non-listed suppliers or customers were excluded, retaining only listed-firm observations structured as "downstream-midstream manufacturer-year" and "upstream-midstream manufacturer-year". We further excluded firms operating in the financial sector, those under special treatment or delisting warning status (ST/*ST), firms suspended or delisted during the sample period, and observations with substantial missing data.Supply chain data for listed companies were sourced from the Chinese Research Data Services Platform (CNRDS). Financial data were obtained from the CSMAR database. We winsorize all continuous variables at the 1st and 99th percentiles. 3.2 Variable constructions and definitions 3.2.1 Dependent variable:ESG Drawing on the metric construction approach of Yan et al. (2024), this study employs the annual Sino-Securities Comprehensive ESG Rating for regression analysis. The nine-tier rating scale ("AAA" to "C") is numerically assigned values from 9 to 1, respectively. Quarterly rating data are averaged to derive the annual ESG rating. 3.2.2 Key independent variable:corporate climate risk Choosing to use annual report texts instead of earnings conference calls is because domestic earnings conference calls reference the US earnings conference call system and are relatively underdeveloped. Combining them with voluntarily disclosed supply chain data of listed companies would lead to severe sample loss, unable to meet the sample size requirements for data analysis. Additionally, Chinese expression is more diverse than English. Simply translating the English dictionary constructed by Li et al. (2024) into Chinese to build a climate risk metric is not advisable. Therefore, this study draws on Du et al. (2023) who similarly base it on the corpus of China A-share listed companies' annual reports and machine learning methods, utilizing a seed word set to ultimately determine 98 words as the expanded lexicon to construct the climate risk metric. Specifically, this study uses the "Jieba" segmentation library in Python to process Chinese text, and removes stop words, to build the textual corpus of Chinese listed companies' annual reports. Then we count the occurrence frequency of the identified 98 words, and divide it by the total word frequency appearing in the report. The Chinese dictionary is detailed in Appendix B2, and its corresponding English dictionary can be found in Appendix B1. 3.3 Empirical model To verify whether climate risk of upstream and downstream firms in the supply chain affects midstream manufacturers' ESG, the baseline regression model constructed in this study is shown in Eq. ( 1 ): $$\:{ESG}_{m,t\:}={\alpha\:}_{0}+{\beta\:}_{1\:}{CR}_{i,t}^{}+\gamma\:{\text{Controls}}_{mi,t}+{Firm}_{mi,t}+{Year}_{t}+{ESG}_{i}+{\epsilon\:}_{\text{m,t}}\:$$ 1 Where m and t represent midstream manufacturer and year respectively; i takes values d or s, denoting downstream customer or upstream supplier firm in the supply chain. \(\:{ESG}_{m,t\:}\) represents the ESG level of midstream manufacturer m. In Eq. ( 1 ), \(\:{CR}_{i,t}^{}\) represents climate risk of downstream or upstream firms according to the different value of i. Drawing on Yan et al. (2024), \(\:{\text{Controls}}_{mi,t}\) includes control variables at the level of midstream and upstream/downstream firms. Drawing on Chen et al. (2024), this study also controls for firm fixed effects ( \(\:{Firm}_{mi,t}\) ), including customer-midstream manufacturer fixed effects ( \(\:{Firm}_{md,t}\) ) and supplier-midstream manufacturer fixed effects ( \(\:{Firm}_{ms,t}\) ). Controls for year fixed effects ( \(\:{Year}_{t}\) ). \(\:{\epsilon\:}_{\text{m,t}}\:\) is the error term. In Eq. ( 1 ), \(\:{ESG}_{i}\) (the ESG rating performance of downstream/upstream firms themselves) is also included to isolate the impact of their own ESG.Standard errors are clustered at the customer industry and supplier industry levels. Appendix A provides variable definitions. Table 1 reports statistics of main variables. The total sample size is 1,250. The ESG performance level of midstream manufacturers (ESG_m) ranges from 1.500 to 6.000, with a mean of 4.098. The mean and standard deviation of the main explanatory variable (CR) are 0.514 and 0.427 respectively. There exists a significant difference between the maximum and minimum values, indicating that the degree of climate risk varies across firms. Table 1 Descriptive statistics. VarName Obs Mean SD P50 Min Max ESG_m 1250 4.098 0.893 4.000 1.500 6.000 ESG_d 1250 4.772 0.888 5.000 2.750 6.750 CR 1250 0.514 0.427 0.382 0.047 2.141 C_Sales_ratio 1250 0.084 0.104 0.050 0.004 0.616 lnsize_m 1250 21.955 1.310 21.801 19.650 25.268 ROA_m 1250 0.037 0.059 0.039 -0.265 0.155 growth_m 1250 0.367 0.898 0.130 -0.546 6.621 lev_m 1250 0.403 0.211 0.395 0.045 0.947 tobinq_m 1250 2.021 1.399 1.525 0.846 9.020 top1_m 1250 0.355 0.149 0.321 0.100 0.728 indep_m 1250 0.364 0.049 0.333 0.286 0.556 INST_m 1250 0.442 0.250 0.460 0.004 0.904 lnsize_d 1250 25.058 2.508 24.663 20.636 31.001 ROA_d 1250 0.034 0.041 0.028 -0.100 0.178 growth_d 1250 0.130 0.422 0.032 -0.541 2.504 lev_d 1250 0.629 0.185 0.639 0.093 0.944 tobinq_d 1250 1.320 0.578 1.101 0.802 4.633 top1_d 1250 0.408 0.179 0.400 0.093 0.837 indep_d 1250 0.381 0.057 0.364 0.308 0.571 INST_d 1250 0.671 0.218 0.688 0.043 0.983 Note: See Appendix A for definitions of the variables. 4.Empirical test results and analysis 4.1 Baseline regression Table 2 reports the results of the baseline regression. Column (1) and Column (3) control only for fixed effects. Column (2) and Column (4) add fixed effects and control variables. From the results in Column (2), it can be seen that downstream firms' climate risk significantly enhances the ESG performance level of midstream manufacturers. A one-unit increase in downstream climate risk promotes an increase of 0.250% in the ESG performance level of midstream manufacturers. Simultaneously, from the results in Column (4), it can be seen that upstream firms' climate risk does not exhibit a significant spillover effect on the ESG level of midstream manufacturers. Climate risk from upstream and downstream in the supply chain exhibits an asymmetric characteristic in its impact on the ESG level of midstream manufacturers. Table 2 Baseline regression results. (1) (2) (3) (4) VARIABLES ESG_m ESG_m ESG_m ESG_m CR 0.253** 0.250*** -0.035 -0.191 (0.096) (0.084) (0.369) (0.446) ESG_d -0.028 (0.053) ESG_s -0.022 (0.058) C_Sales_ratio -0.548 (0.458) lnsize_m 0.078 0.181 (0.123) (0.164) ROA_m -0.818* 0.125 (0.427) (1.503) growth_m -0.001 -0.009 (0.030) (0.042) lev_m -0.364 -0.708 (0.439) (0.717) tobinq_m -0.077 0.013 (0.048) (0.062) top1_m -0.543 0.890 (0.969) (1.129) indep_m 1.776 0.001 (1.399) (0.010) INST_m 0.145 -0.001 (0.590) (0.005) lnsize_d 0.195 (0.145) ROA_d -0.233 (1.252) growth_d 0.128 (0.097) lev_d 0.504 (0.708) tobinq_d -0.026 (0.084) top1_d 0.520 (0.884) indep_d 0.670 (0.476) INST_d -0.104 (0.393) S_Sales_ratio 0.233 (0.565) lnsize_s 0.270 (0.220) ROA_s -0.262 (0.731) growth_s 0.099 (0.124) lev_s 0.248 (0.725) tobinq_s 0.021 (0.068) top1_s -0.529 (0.915) indep_s -0.016* (0.010) INST_s 0.003 (0.007) Constant 3.969*** -3.334 3.960*** -5.846 (0.049) (4.052) (0.096) (5.823) \(\:{\text{F}\text{i}\text{r}\text{m}}_{}\) YES YES YES YES \(\:{\text{Y}\text{e}\text{a}\text{r}}_{}\) YES YES YES YES \(\:\text{N}\) 1,250 1,250 736 736 \(\:{\text{R}}^{2}\) 0.750 0.763 0.736 0.749 Notes: The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. For columns (1) and (2), standard errors in parentheses are clustered at the downstream industry level. For columns (3) and (4), standard errors in parentheses are clustered at the upstream industry level. 4.2 Robustness tests 4.2.1 Instrumental variable regression To address potential endogeneity issues caused by reverse causality and omitted variables, this study employs a two-stage least squares (2SLS) instrumental variable approach. The mean climate risk of firms in the same province, same industry, and same year is selected as the instrumental variable (CR_iv). The rationale for adopting this instrumental variable is that downstream firms in the same city and same industry share similar environments and industry characteristics, and are generally positively correlated with the climate risk level of the downstream firm. Midstream manufacturers operate in different industries and environments, and their ESG is influenced by different factors, satisfying the exogeneity condition for the instrumental variable. The instrumental variable in this study passes the underidentification test and weak identification test, with results displayed in columns (1) and (2) of Table 3 . 4.2.2 Entropy balancing The entropy balancing method is adopted to minimize differences in control variables between the group with higher climate risk and the group with lower climate risk. Compared to propensity score matching which discards unmatched samples, the entropy balancing method maintains the full sample size without reduction, thereby better preserving sample information. Results in column (3) of Table 3 show that after entropy balancing, the coefficient of the impact of downstream customer firms' climate risk on the ESG performance level of midstream manufacturers remains significantly positive, confirming the robustness of the main conclusion. 4.2.3 Replacing the measurement of climate risk and ESG: standardized both indices Following Ma et al. (2025), this study standardizes both the independent variable (CR_nor) and the dependent variable (ESG_nor). This method helps eliminate biases caused by factors such as text length and firm characteristics. Results are shown in column (4) of Table 3 . 4.2.4 Alternative proxy for ESG The Sino-Securities Comprehensive ESG Score indicator is replaced as the measure. Since the Sino-Securities score is quarterly data, this study takes the average to obtain the annual comprehensive score. Results are shown in column (5) of Table 3 . 4.2.5. Changing the regression model To further enhance the robustness of the conclusions, this study employs the following series of methods to test the robustness of the baseline regression model: Column (1) of Table 4 : Adds downstream industry-level fixed effects; Column (2): Changes to "Firm×Year" joint FE; Column (3): Clustered standard errors at the "downstream-midstream manufacturer" firm level; Column (4): Uses the "Industry×Firm" double cluster method; Column (5): Adds other control variables; Column (6): Changes the sample window period, excluding sample observations from the pandemic years 2020–2021; Column (7): Uses a manufacturing industry subsample for regression. The regression coefficients in columns (1) to (7) of Table 4 are all positive and significant, providing further evidence for the robustness of this study's main conclusions. Table 3 Robustness tests. (1) (2) (3) (4) (5) 2sls-iv entropy balancing standardized both indices Alternative proxy for ESG first second VARIABLES CR ESG_m ESG_m ESG_nor ESG_m2 CR_iv 0.847*** (0.087) CR 0.383*** 0.328*** 1.181*** (0.120) (0.096) (0.387) CR_nor 0.116*** (0.039) Constant -0.576 -1.072 46.559** (4.952) (0.901) (19.149) \(\:{\text{Controls}}_{}\) YES YES YES YES YES \(\:{\text{F}\text{i}\text{r}\text{m}}_{}\) YES YES YES YES YES \(\:{\text{Y}\text{e}\text{a}\text{r}}_{}\) YES YES YES YES YES \(\:\text{N}\) 1,250 1,250 1,250 1,250 1,250 \(\:{\text{R}}^{2}\) 0.056 0.761 0.763 0.788 Kleibergen–Paap rk LM 5.853** Kleibergen–Paap rk Wald F 94.783*** Note:These are the regression results of the 2sls-iv model,entropy balancing method, replacing the measurement of climate risk and ESG,standardized both indices and alternative proxy for ESG method.The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors in parentheses are clustered at the downstream industry level. Table 4 Other robustness tests. (1) (2) (3) (4) (5) (6) (7) Industry FE \(\:"\text{F}\text{i}\text{r}\text{m}\times\:\text{Y}\text{e}\text{a}\text{r}"\:\text{j}\text{o}\text{i}\text{n}\text{t}\:\text{F}\text{E}\) Clustered at firm level Double clustered Adding new control variables Adjust the Sample Window Manufacturing subsamples VARIABLES ESG_m ESG_m ESG_m ESG_m ESG_m ESG_m ESG_m CR 0.277*** 0.243*** 0.250** 0.277* 0.243** 0.200** 0.218* (0.085) (0.088) (0.115) (0.144) (0.100) (0.087) (0.112) Constant -3.562 2.445 -3.334 -3.562 -2.312 -1.950 -4.856 (4.138) (2.917) (3.924) (4.885) (4.776) (3.543) (4.588) dual_d -0.076 (0.095) dual_m -0.083 (0.161) ROE_m -0.132 (0.435) ROE_d -1.356 (0.986) audit_m 0.167 (0.424) audit_d -0.649*** (0.208) Controls YES YES YES YES YES YES YES \(\:{\text{F}\text{i}\text{r}\text{m}}_{}\) YES YES YES YES YES YES YES \(\:{\text{Y}\text{e}\text{a}\text{r}}_{}\) YES YES YES YES YES YES YES Industry FE YES NO NO NO NO NO NO \(\:\text{F}\text{i}\text{r}\text{m}\times\:\text{Y}\) ear NO YES NO NO NO NO NO \(\:\text{N}\) 1,250 1,250 1,250 1,250 1,163 1,052 735 \(\:{\text{R}}^{2}\) 0.765 0.756 0.763 0.765 0.769 0.769 0.775 Notes: Columns (1)-(2)、(5)-(7) standard errors in parentheses are clustered at downstream industry level. The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. 5. Further analysis 5.1 Mechanism analysis Drawing on Yan et al. (2024) to construct the green awareness indicator [i] (green) measuring a firm's focus on the environment, this study includes its interaction term with downstream climate risk (CR_green) in the regression model for empirical testing. Results in column (1) of Table 5 show that when downstream customers face climate risk, they promote the improvement of midstream manufacturers' ESG performance by enhancing corporate green awareness.Secondly, this study incorporates corporate environmental pollution into the evaluation system. Drawing on Yan et al. (2024), it adopts the non-radial SBM-ML index to measure corporate green total factor productivity [ii] (GTFP). This study decomposes green total factor productivity into two parts: green technology progress (GTC) and green technology efficiency change (GEC). GTC refers to the shift of the production frontier, representing the improvement of the firm's overall green technology level and reflecting its comprehensive performance in environmental protection. GEC refers to the change in the firm's position relative to the production frontier, representing the improvement in the efficiency of utilizing existing green technology resources and reflecting improvements in the firm's resource utilization. After adding the interaction terms (CR_GTC, CR_GEC) to the regression, column (2) indicates that downstream firms significantly enhance midstream ESG performance by promoting green technology progress in the midstream. Column (3) indicates that downstream firms do not enhance midstream ESG performance by promoting changes in midstream green technology efficiency. This finding suggests that in the promotion and application of green technologies, the improvement of technical efficiency has not yet played a key role in influencing the ESG performance of midstream manufacturers. Finally, this study uses the ratio of the sum of year-end accounts payable, notes payable, and advance receipts to total assets of downstream customer firms to measure trade credit financing capacity (credit). Substituting the interaction term of climate risk and trade credit financing capacity (CR_credit) into the model for regression, results in column (4) show that the more trade credit financing used by downstream firms, the more likely it is to hinder the improvement of midstream manufacturers' ESG. In summary, the transmission mechanism of downstream climate risk on midstream ESG performance exhibits a dual effect: On one hand, downstream firms strengthen green awareness due to climate risk and promote green technology spillovers to enhance midstream ESG performance; on the other hand, downstream firms may increase trade credit financing to cope with climate risk, exacerbating the financial risk of midstream manufacturers and inhibiting their ESG investment capacity. Although the negative moderating effect of trade credit financing is significant, the positive moderating effects of green awareness and green technology spillovers dominate, resulting in a net positive effect of downstream climate risk on the ESG performance of midstream manufacturers. Table 5 Mechanism analysis. (1) (2) (3) (4) green awareness green technology progress green technology efficiency change trade credit financing capacity VARIABLES ESG_m ESG_m ESG_m ESG_m CR -0.195 4.193** -0.642 0.567* (0.293) (2.009) (0.842) (0.305) CR_green 0.150* (0.089) CR_credit -2.094* (1.171) CR_GTC -4.001* (2.007) CR_GEC 0.925 (0.910) green -0.059 (0.067) credit -0.394 (0.953) GTC 2.657 (1.901) GEC -1.052 (2.155) Constant -3.085 -5.295 -2.703 -0.799 (4.102) (4.983) (4.679) (4.576) YES YES YES YES YES YES YES YES YES YES YES YES 1,250 1,016 1,016 767 0.764 0.779 0.778 0.795 Notes: The symbols *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively. Standard errors in parentheses are clustered at the downstream industry level. 5.2 Heterogeneity analysis This study analyzes the heterogeneity in the relationship between customer firms' climate risk and midstream manufacturers' ESG performance level. Heterogeneity analysis is conducted based on the performance of different ESG sub-dimensions. Results in columns (1)-(3) of Table 6 show the regression results for ESG sub-dimensions. It is found that downstream customers' climate risk has a significant enhancing effect on the environmental dimension of midstream manufacturers, while its enhancing effect on the social dimension and corporate governance dimension is not significant. In columns (4)-(5), drawing on Wang et al. (2020)'s definition of high-barrier industries [iii] , this study divides midstream firms into high-barrier industries and non-high-barrier industries. The results indicate that when the midstream is a non-high-barrier industry, downstream customers facing climate risk have a significant enhancing effect on midstream ESG levels. However, when the midstream is a high-barrier industry, this enhancing effect is not significant. This asymmetry may stem from two distinct mechanisms. First, downstream firms in non-high-barrier industries typically face fiercer market competition. To gain competitive advantage, they actively disseminate green awareness and technologies to midstream manufacturers through green procurement standards and technological cooperation, thereby enhancing midstream ESG performance. Second, downstream firms in non-high-barrier industries typically face fiercer market competition. To stand out in the competition, they more actively transmit their green awareness and green technologies to midstream manufacturers through methods like green procurement standards and technological cooperation, thereby promoting the improvement of midstream ESG levels. On the other hand, midstream manufacturers in high-barrier industries often face higher technological barriers and more complex technological systems, resulting in relatively weaker capabilities to absorb and apply new technologies. Even if downstream firms possess strong green awareness and advanced green technologies, midstream manufacturers may struggle to effectively absorb and apply these technologies due to their own technical limitations, making it difficult for the spillover effects of downstream green technologies to fully materialize. Additionally, midstream manufacturers in high-barrier industries may rely more on their own technological accumulation and R&D systems, with lower dependence on external technologies, which also weakens the role of downstream firms in enhancing their ESG levels. In columns (6)-(7), referring to Qi et al. (2018)'s delineation of heavily polluting industries [iv] , this study divides downstream firms into heavily polluting industries and non-heavily polluting industries. The results show that the climate risk of non-heavily polluting customer firms has a significant enhancing effect on midstream manufacturers' ESG, while the enhancing effect of heavily polluting customer firms on midstream manufacturers' ESG is not significant. Table 6 Regression results of heterogeneity. (1) (2) (3) (4) (5) (6) (7) High-Barrier=1 High-Barrier=0 Heavy Pollution=1 Heavy Pollution=0 VARIABLES E S G ESG_m ESG_m ESG_m ESG_m CR 0.434** 0.047 0.145 0.243 0.310** 0.055 0.344** (0.210) (0.230) (0.150) (0.144) (0.149) (0.160) (0.167) Constant -1.569 2.251 2.096 -10.820** 3.126 1.261 -1.484 (4.313) (6.743) (7.675) (5.114) (4.766) (7.577) (6.009) YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES YES 1,250 1,250 1,250 358 864 343 904 0.759 0.788 0.758 0.765 0.799 0.839 0.746 Note:The symbols *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively. Standard errors in parentheses are clustered at the downstream industry level. 6. Conclusion This study examines the impact of downstream customer firms' climate risk on midstream ESG performance using supply chain disclosure data from China A-share listed companies. One limitation is that this study does not include data on non-listed customers. However, listed companies are typically more standardized and transparent in information disclosure, corporate governance, and ESG practices, while non-listed companies may exhibit significant differences in these aspects. Future research could further expand data sources to incorporate more information from non-listed companies to more comprehensively examine the impact of downstream customer firms' climate risk on midstream manufacturers' ESG performance. This research yields several implications: First, optimize stakeholder management and cultivate a supply chain ESG ecosystem to better promote collaborative green transformation within the supply chain. Second, strengthen the exemplary role of high-barrier industries and non-heavily polluting industries. Policymakers should refine relevant standards or incentives, using firms in these industries as models, to drive enterprises within the supply chain network to jointly enhance ESG performance. Finally, leveraging the spillover effects of downstream firms' green awareness and green technologies is a key pathway to achieving green transformation and sustainable development in the supply chain. The government should encourage downstream firms to establish green supply chain management systems, transmitting green awareness and sustainable development concepts to midstream manufacturers by setting green procurement standards and supplier environmental conduct codes. Furthermore, policies should support downstream firms in engaging with midstream manufacturers through technology transfer, training, technological cooperation, and joint R&D to disseminate advanced green technologies. These measures not only provide important references for achieving sustainable development in the supply chain and enhancing new quality productive forces, but also offer new momentum and direction for the green transformation of China's entire economic system and its high-quality economic development. Declarations Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author Contribution Yulin Zheng: Conceptualization, Methodology, Software, Data curation, Visualization, Writing.Dingwen Wu: Supervision, Project administration, Conceptualization, Writing-review&editing. Hong Shen: Supervision, Project administration, Conceptualization, Writing-review&editing. Data Availability Data will be made available on request. References Acemoglu, D., Carvalho, V. M., Ozdaglar, A., & Tahbaz‐Salehi, A. (2012). The network origins of aggregate fluctuations. Econometrica , 80(5), 1977-2016. Berg, F., Kölbel, J. F., & Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. Review of Finance , 26(6), 1315-1344. Cao, J., Liang, H., & Zhan, X. (2019). Peer effects of corporate social responsibility. Management Science , 65(12), 5487-5503. Chen,W., & Fan, Y.Z. (2024). Enterprise supply chain risk perception and cooperative relationship stability. Management World , 40(11), 209–228. Di Giuli, A., & Kostovetsky, L. (2014). 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Hu,Y., & Wu,W.F. (2022). Trade credit financing and structural issues of corporate debt in China. China Economic Quarterly , 22(1), 257–280. Huang, X. L., Deng, L., & Qu, Y. (2016). Monetary policy, trade credit and corporate investment behavior. Accounting Research , (2), 58–65+96. Johnson,W.C., Kang, J.K., & Yi,S., (2010).The Certification Role of Large Customers in the New Issues Market. Financial Management ,39(4),1425—1474. Kim, Y.H., & Wemmerlöv,U., (2015).Does a Supplier’s Operational Competence Translate into Financial Performance? An Empirical Analysis of Supplier-Customer Relationships. Decision Sciences, 46(1),101—134. Krolikowski,M., & Yuan, X.,(2017).Friend or Foe:Customer-supplier Relationships and Innovation. Journal of Business Research ,78,53 — 68. Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Li, Q., Shan, H., Tang, Y., & Yao, V. (2024). Corporate climate risk: Measurements and responses. 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International Journal of Production Economics , 140 (1), 18–27. Naseer, M. M., Guo, Y., & Zhu, X. (2025). When climate risk hits corporate value: The moderating role of financial constraints, flexibility, and innovation. Finance Research Letters , 74 , 106780. Patatoukas, P. N. (2012). Customer-base concentration: Implications for firm performance and capital markets: 2011 American accounting association competitive manuscript award winner. The accounting review , 87 (2), 363-392. Qi,S.Z.,Lin,S., & Cui.J.B. (2018). Can environmental rights trading markets induce green innovation? Evidence from green patents of Chinese listed companies. China Economic Review , 53(12), 129–143. Shi, Y., & Liu, J. (2025). How Climate Risk Affects Enterprise Liquidity: Configuration Effects Based on NCA and fsQCA. Sustainability , 17 (3), 1199. Su, Y., Tian, G. G., Li, H. C., & Ding, C. J. (2024). Climate risk and corporate energy strategies: Unveiling the Inverted-N relationship. Energy , 310 , 132968. Sun,W.J. (2025). Digitalization of chain leaders, asymmetric supply network spillovers and corporate green transformation. Finance & Economics , (05), 80–93. Tian, Y., & Zhao, M. (2025). Does managerial climate risk perception improve environmental, social and governance (ESG) performance? Evidence from China. International Review of Financial Analysis , 102 , 104000. Wan,P.B., Yang,M., & Chen.L. (2021). How environmental technical standards affect green transformation of manufacturing: Based on technological transformation perspective. China Industrial Economics , (9), 118–136. Wang,Y.C., Guo,X.M., & Yu,Y.M. (2020). Antitrust and competitive neutrality in debt market. Accounting Research, (7), 144–166. Xie,X.M., & Zhu,Q.W. (2021). How corporate green innovation practices resolve the "harmonious coexistence" dilemma? Management World , 37(1), 128–149, 9. Yan, B., Cheng, M., & Wang, N. (2024). ESG green spillovers, supply chain transmission and corporate green innovation. China Economic Review , 59(7), 72–91. Footnotes i Yan et al. (2024) based on key policy documents such as China's Five-Year Plans, the Environmental Protection Law, the Technical Guidelines for Corporate Environmental Behavior Evaluation, and the Green Manufacturing Standardization White Paper, combined with relevant research from Xie and Zhu (2021) and Wan et al. (2021), screened 113 keywords closely related to corporate green transformation from five dimensions: promotion and publicity, strategic concepts, technological innovation, pollution control and treatment, and environmental monitoring and management. By taking the natural logarithm after adding 1 to the frequency of each keyword in listed companies' annual reports, they obtained a quantitative measure of corporate green transformation. ii Yan et al. (2024) incorporated corporate environmental pollution into the evaluation system and adopted the non-radial SBM-ML index to measure corporate green total factor productivity. The measurement of input and output indicators for corporate green total factor productivity is as follows. Factor inputs: Labor input is proxied by the number of employees; Capital input is proxied by net fixed assets; Energy input is proxied by the city's industrial electricity consumption where the firm is located, converted based on the proportion of the firm's employees to the city's urban employed population. Desired output: Firm operating income is used as a proxy for desired output. Undesired output: The "industrial three wastes" emissions (industrial sulfur dioxide, industrial wastewater, and industrial soot and dust) are converted based on the proportion of the firm's employees to the city's urban employed population and used as a proxy for undesired output. iii Wang et al. (2020) defined high-barrier industries as: Mining; Petroleum Processing and Coking; Smelting and Pressing of Ferrous Metals; Smelting of Heavy Nonferrous Metals; Production and Supply of Electric Power, Gas, and Water; Railway Transport; Pipeline Transport; Water Transport; Air Transport; Communication Services; Finance; Insurance; Public Facility Services; Postal Services; and Culture and Media Industries. iv Qi et al. (2018), based on the industry classification standard issued by the China Securities Regulatory Commission (CSRC) in 2012, specifically selected the following industry codes for heavily polluting industries: B06, B07, B08, B09, B10, B11, B12, C17, C18, C19, C22, C25, C26, C27, C28, C29, C31, C33, D44. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":65725,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism Diagram\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7331780/v1/a29d5f7904bc783bd984a029.png"},{"id":104468047,"identity":"0e9d3d96-9726-4507-b7a1-7ee7693b19b3","added_by":"auto","created_at":"2026-03-12 06:42:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1408374,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7331780/v1/dc8470ba-deae-4fb9-8f2c-6205c8d58cb1.pdf"},{"id":93693006,"identity":"473e2568-b480-4ec3-a9dc-8fea9ec6afed","added_by":"auto","created_at":"2025-10-16 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Introduction","content":"\u003cp\u003eRecent years have witnessed accelerating global climate change, exceeding expectations in speed, progression, and severity, prompting widespread concern among governments worldwide. The China Climate Bulletin (2024) indicates that China's average temperature in 2024 was 1.01\u0026deg;C higher than the 1991\u0026ndash;2020 average, marking the highest level since 1951. The increasing frequency and intensity of extreme weather events, coupled with abrupt asset value fluctuations triggered by transition risks, present direct and severe challenges to corporate operations. Substantial research at the micro level has documented the significant impact of firm-level climate risk on financial outcomes (Shi and Liu, 2025; Ma et al., 2025) and corporate decision-making (Li et al., 2024; Su et al., 2024).The rise of Corporate Social Responsibility (CSR) since the 1970s marked a profound shift in the business value paradigm. Enterprises began emphasizing a balance between economic objectives and legal, environmental, and ethical responsibilities, responding to the rights and interests of six core stakeholder groups, including shareholders, employees, and customers (Fineman and Clarke, 1996; Miao et al., 2012; Di and Kostovetsky, 2014). As sustainability concepts deepened, the scope of corporate environmental responsibility expanded, with the identification, management, and disclosure of climate change risks becoming a core component. Against this backdrop, some scholars have begun examining the spillover effects (horizontal spillovers) of firm-specific climate-related factors on other firms within the same industry (Li et al., 2023). However, with the increasing refinement and complexity of corporate division of labor systems, attention is shifting towards the vertical spillover effects of climate factors along the supply chain network. Freeman's (1984) stakeholder theory provides a theoretical foundation for quantifying the spillover effects of climate factors on related firms within the supply chain network: Upstream and downstream entities, acting as external stakeholders, can significantly influence corporate decisions through expressions of opinion and indirect pressure (Gosman et al., 2004; Johnson et al., 2010; Kim and Wemmerl\u0026ouml;v, 2015; Krolikowski and Yuan, 2017).\u003c/p\u003e\u003cp\u003eYet, the impact of climate risk on supply chain partners extends beyond direct operational or financial shocks. As research advances, evidence has emerged indicating that a firm's ESG performance itself can generate significant spillovers through the influence of external stakeholders, profoundly driving the green transformation of enterprises within industrial and supply chains (Lian et al., 2022; Yan et al., 2024).Crucially, a strong intrinsic link has been established between the climate risk a firm faces and its ESG performance (Tian and Zhao, 2025). This leads to a key inference: If climate risk can propagate vertically along the supply chain, and climate risk levels profoundly shape a firm's ESG performance, then is this vertically transmitted firm-specific climate risk a key channel influencing the ESG performance of other firms within the supply chain? Regrettably, existing literature lacks systematic empirical evidence and in-depth exploration regarding the existence and magnitude of this potential spillover mechanism: \"Firm-level climate risk\u0026rarr;Vertical Transmission\u0026rarr;ESG performance of other supply chain firms\".\u003c/p\u003e\u003cp\u003eAddressing this gap, this study utilizes supplier and customer information disclosed by China A-share listed companies from 2009 to 2023 to map upstream-downstream supply chain relationships. We construct firm-level climate risk metrics by applying machine learning for textual analysis of annual reports. We employ the quarterly data from the Sino-Securities Comprehensive ESG Rating, averaged to measure the annual comprehensive ESG rating of firms. Empirically, climate risk faced by downstream enterprises transmits readily to midstream manufacturers, significantly enhancing their ESG performance. Conversely, the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric pattern in the supply chain spillovers of climate risk.\u003c/p\u003e\u003cp\u003eThis research contributes to the literature in three primary ways. First, it uncovers the influence of climate risk on firms from a supply chain perspective\u0026mdash;a currently neglected area. While Tian and Zhao (2025) found that firm climate risk significantly promotes ESG performance, no prior study has explored the impact of firm climate risk on stakeholders through the vertical lens of the supply chain network. Particularly given the escalating climate crisis, investigating this gap confirms that supply chain climate risk transmission is a significant external driver of corporate ESG performance, enhancing our comprehensive understanding between the climate risk and ESG. Second, while extensive research confirms the significant impact of ESG on firms (Lian et al., 2022; Yan et al., 2024), this study extends the inquiry to the root cause\u0026mdash;climate risk. It provides a novel perspective for understanding the complex mechanisms of ESG performance transmission between firms, facilitating a deeper comprehension of inter-firm dynamics within supply chains and their implications for sustainable development. Finally, the paper delves into the heterogeneous effects of climate risk across different ESG dimensions and industries, offering new explanations for variations in corporate ESG performance under diverse contexts. This research thus provides a vital empirical foundation for managing climate risk within supply chains.\u003c/p\u003e"},{"header":"2. Hypothesis development","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Increased Climate Risk in Customer Firms Drive ESG Performance Improvement in Midstream Supply Chain Enterprises\u003c/h2\u003e\u003cp\u003eAccording to the stakeholder theory, firms within the supply chain network, acting as significant external stakeholders, influence the decisions and operations of other networked firms to protect their own interests. Downstream customers, as crucial external stakeholders, differ from internal stakeholders who directly impact resource allocation and key decisions. While they typically lack direct influence over their suppliers' actions, downstream customers can still exert significant indirect influence on corporate policies and economic outcomes through channels such as expressing opinions (Gosman et al., 2004; Johnson et al., 2010; Kim and Wemmerl\u0026ouml;v, 2015; Krolikowski and Yuan, 2017). As the critical link directly interfacing with end consumers, downstream operations are vital for supply chain completion, rendering them particularly sensitive to climate risks. Against the backdrop of frequent climate events, supply chain risks stemming from end-market operational vulnerability exhibit significant spillover effects (Lian et al., 2022; Yan et al., 2024). We posit that when downstream firms face heightened climate risk, they inevitably exert influence on midstream manufacturers. This influence manifests in complex ways, affecting not only short-term operational adjustments but also extending to long-term strategic planning.\u003c/p\u003e\u003cp\u003eBuilding on evidence that climate risk levels profoundly shape corporate ESG performance (Tian et al., 2025) and can propagate vertically along the supply chain, we posit that when downstream firms face climate risk, midstream manufacturers may undertake actions to enhance their ESG performance.The rationale is threefold:\u003c/p\u003e\u003cp\u003eFirst, Escalating climate risks drive downstream firms to bolster their own green awareness in response to rising consumer demand for sustainable products and the imperative for operational sustainability. This shift in awareness motivates downstream firms to preferentially select midstream manufacturers demonstrating strong ESG credentials, thereby driving midstream enterprises to strive toward enhancing their ESG performance. Furthermore, observed losses suffered by downstream firms due to climate risk can heighten green awareness within midstream firms themselves (Cao et al., 2019; Li and Xiao, 2020), prompting increased focus on environmental issues, green technology innovation, and corporate social image\u0026mdash;ultimately enhancing ESG performance. According to signaling theory, heightened green awareness among downstream firms transmits signals of environmental and social responsibility to midstream manufacturers. Perceiving this pressure, midstream firms are motivated to meet client demands, maintain business relationships, and preserve market competitiveness by actively responding. As ESG represents the internationally recognized framework for assessing non-financial performance and sustainability, proactive ESG enhancement becomes a key pathway for midstream firms to cultivate a positive public image and align with client expectations. Consequently, we propose that downstream climate risk promotes midstream ESG performance by elevating green awareness.\u003c/p\u003e\u003cp\u003eSecond, Based on Reputation Theory, a firm's reputation is a critical intangible asset whose impact diffuses across the supply chain. Downstream customers, occupying a pivotal position, may experience operational volatility and reputational damage due to climate risk. This damage can propagate upstream, potentially eroding the reputational capital of midstream manufacturers. To proactively safeguard and enhance their standing within the supply network and broader market, and to retain the trust of customers, investors, and other stakeholders, midstream firms possess a strong incentive to elevate their ESG performance. Moreover, downstream customers seeking to rehabilitate their own green reputation often explicitly incorporate environmental and social responsibility requirements into supplier contracts or agreements. This effectively transfers reputational management pressure downstream, creating binding constraints that systematically drive midstream ESG improvement and foster sustainability across the entire supply chain.\u003c/p\u003e\u003cp\u003eThird, Information Asymmetry Theory also provides an explanation. The intensification of climate risk significantly exacerbates information asymmetry between firms and their stakeholders, a prominent concern in recent literature. Information asymmetry also exists between downstream customers and midstream manufacturers within the supply chain (Lee et al., 1997; Acemoglu et al., 2012). Facing climate risk, downstream customers seek to mitigate the negative consequences of this asymmetry. Beyond enhancing their own disclosures, they can demand greater transparency from midstream suppliers. Improving ESG performance is a recognized mechanism for enhancing transparency and reducing perceived risk. Therefore, when downstream climate risk increases, midstream firms have an impetus to boost their ESG performance as a means of alleviating information asymmetry concerns.\u003c/p\u003e\u003cp\u003eBased on the above analysis, we propose:\u003c/p\u003e\u003cp\u003eH1. Climate risk faced by downstream enterprises in the supply chain enhances the ESG performance of midstream manufacturers.\u003c/p\u003e\u003cp\u003eH2. Climate risk faced by downstream enterprises enhances the ESG performance of midstream manufacturers by elevating green awareness.\u003c/p\u003e\u003cp\u003eTechnology spillovers constitute another crucial pathway through which downstream firms drive midstream ESG improvement. Downstream firms' climate risk management strategies and green technology innovation practices can provide substantial support for midstream ESG performance enhancement via this key mechanism. Specifically, when customer firms actively combat climate risk by increasing R\u0026amp;D investment in green technologies, they often accumulate advanced green technologies and management expertise (Fang and Hu, 2023). These innovations and experiences are transferred to midstream manufacturers through collaboration, including technology transfer, training, and joint R\u0026amp;D (Patatoukas, 2012). This spillover not only boosts the green innovation performance of midstream firms (Yan et al., 2024) but also aids them in reducing carbon emissions, optimizing environmental performance, enhancing social responsibility awareness, and improving governance structures, which thereby comprehensively promotes midstream ESG performance.\u003c/p\u003e\u003cp\u003eBased on the above analysis, we propose:\u003c/p\u003e\u003cp\u003eH3. Climate risk faced by downstream enterprises enhances the ESG performance of midstream manufacturers by promoting technology spillovers.\u003c/p\u003e\u003cp\u003eSimultaneously, we contend that increased appropriation of trade credit by downstream firms facing climate risk can impede midstream ESG improvement. The financing substitution hypothesis highlights trade credit as an informal financing channel between firms (Hu and Wu, 2022). Downstream firms experiencing heightened operational pressure and uncertainty due to climate risk face exacerbated financing constraints, amplifying the negative effects (Naseer et al., 2025). To mitigate the adverse impact of reduced bank credit availability, they may increase demand for funds by delaying payments or extending payment terms, utilizing trade credit as a substitute financing source (Lu and Yang, 2011; Huang et al., 2016). Fabbri and Klapper (2016) argue that powerful downstream customers, especially when suppliers operate in competitive markets, can leverage the threat of switching suppliers to extract more trade credit. Midstream manufacturers, acting as suppliers, thus confront a dual challenge: operational instability stemming from downstream climate risk affecting sales channels, and more aggressive trade credit demands from powerful downstream customers. This situation elevates their financial risk, potentially forcing reductions in environmental, social, and governance (ESG) investments and negatively impacting ESG performance.\u003c/p\u003e\u003cp\u003eBased on the above analysis, we propose:\u003c/p\u003e\u003cp\u003eH4. Climate risk faced by downstream enterprises hinders the improvement of ESG performance in midstream manufacturers by increasing the appropriation of trade credit.\u003c/p\u003e\u003cp\u003eIn summary, downstream climate risk exerts a complex dual influence on midstream ESG performance within the supply chain. On one hand, downstream firms' response to climate risk elevates their own green awareness, the spillover of which incentivizes midstream ESG improvement. Downstream firms also provide crucial support for midstream ESG enhancement through technology spillovers. On the other hand, downstream firms seeking to alleviate their own climate risk burden may increase their appropriation of midstream trade credit financing. This capital diversion heightens midstream financial risk, constrains ESG investment capacity, and thereby negatively impacts ESG performance. Nevertheless, while the negative moderating effect of trade credit financing is significant, the positive moderating effects of green awareness and green technology spillovers dominate. Consequently, downstream climate risk exerts a net positive effect on the ESG performance of midstream manufacturers. The mechanism is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Asymmetric Impact of Upstream and Downstream Climate Risk on Midstream ESG Performance\u003c/h2\u003e\u003cp\u003eNotably, existing research indicates that spillover effects within supply chains often exhibit significant asymmetry (Sun, 2025). We posit that the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric characteristic in the influence of upstream versus downstream climate risk on midstream ESG levels. Two primary reasons underpin this asymmetry:First, midstream manufacturers possess cost-passing ability. Negative effects stemming from upstream climate risk (e.g., increased costs) tend to be passed downstream through midstream price adjustments, rather than directly impacting the ESG performance of the midstream firms themselves. Competitive constraints in end markets limit downstream firms' cost-pass-through capacity. This constraint, amplified by demand elasticity, hinders upstream transmission of climate risk shocks through pricing. Second, ESG rating systems exhibit a systematic bias. Prevailing ESG rating frameworks demonstrate significant limitations in covering upstream supply chain risks (Berg et al., 2022). This institutional bias enables midstream firms to maintain high ESG ratings even when exposed to upstream high-carbon-emission risks, creating a \"responsibility transfer gap\".\u003c/p\u003e\u003cp\u003eBased on the above analysis, we propose:\u003c/p\u003e\u003cp\u003eH5. The effect of climate risk faced by upstream enterprises in the supply chain on enhancing the ESG performance of midstream manufacturers is insignificant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Research design","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Sample selection and data source\u003c/h2\u003e\u003cp\u003eThe sample for this study comprises China A-share listed firms from 2009 to 2023. We utilize disclosed information on the top five suppliers and customers to identify supply chain relationships. Observations involving non-listed suppliers or customers were excluded, retaining only listed-firm observations structured as \"downstream-midstream manufacturer-year\" and \"upstream-midstream manufacturer-year\". We further excluded firms operating in the financial sector, those under special treatment or delisting warning status (ST/*ST), firms suspended or delisted during the sample period, and observations with substantial missing data.Supply chain data for listed companies were sourced from the Chinese Research Data Services Platform (CNRDS). Financial data were obtained from the CSMAR database. We winsorize all continuous variables at the 1st and 99th percentiles.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Variable constructions and definitions\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Dependent variable:ESG\u003c/h2\u003e\u003cp\u003eDrawing on the metric construction approach of Yan et al. (2024), this study employs the annual Sino-Securities Comprehensive ESG Rating for regression analysis. The nine-tier rating scale (\"AAA\" to \"C\") is numerically assigned values from 9 to 1, respectively. Quarterly rating data are averaged to derive the annual ESG rating.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Key independent variable:corporate climate risk\u003c/h2\u003e\u003cp\u003eChoosing to use annual report texts instead of earnings conference calls is because domestic earnings conference calls reference the US earnings conference call system and are relatively underdeveloped. Combining them with voluntarily disclosed supply chain data of listed companies would lead to severe sample loss, unable to meet the sample size requirements for data analysis. Additionally, Chinese expression is more diverse than English. Simply translating the English dictionary constructed by Li et al. (2024) into Chinese to build a climate risk metric is not advisable. Therefore, this study draws on Du et al. (2023) who similarly base it on the corpus of China A-share listed companies' annual reports and machine learning methods, utilizing a seed word set to ultimately determine 98 words as the expanded lexicon to construct the climate risk metric. Specifically, this study uses the \"Jieba\" segmentation library in Python to process Chinese text, and removes stop words, to build the textual corpus of Chinese listed companies' annual reports. Then we count the occurrence frequency of the identified 98 words, and divide it by the total word frequency appearing in the report. The Chinese dictionary is detailed in Appendix B2, and its corresponding English dictionary can be found in Appendix B1.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Empirical model\u003c/h2\u003e\u003cp\u003eTo verify whether climate risk of upstream and downstream firms in the supply chain affects midstream manufacturers' ESG, the baseline regression model constructed in this study is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{ESG}_{m,t\\:}={\\alpha\\:}_{0}+{\\beta\\:}_{1\\:}{CR}_{i,t}^{}+\\gamma\\:{\\text{Controls}}_{mi,t}+{Firm}_{mi,t}+{Year}_{t}+{ESG}_{i}+{\\epsilon\\:}_{\\text{m,t}}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere m and t represent midstream manufacturer and year respectively; i takes values d or s, denoting downstream customer or upstream supplier firm in the supply chain. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ESG}_{m,t\\:}\\)\u003c/span\u003e\u003c/span\u003erepresents the ESG level of midstream manufacturer m. In Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{CR}_{i,t}^{}\\)\u003c/span\u003e\u003c/span\u003erepresents climate risk of downstream or upstream firms according to the different value of i. Drawing on Yan et al. (2024), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Controls}}_{mi,t}\\)\u003c/span\u003e\u003c/span\u003e includes control variables at the level of midstream and upstream/downstream firms. Drawing on Chen et al. (2024), this study also controls for firm fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Firm}_{mi,t}\\)\u003c/span\u003e\u003c/span\u003e), including customer-midstream manufacturer fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Firm}_{md,t}\\)\u003c/span\u003e\u003c/span\u003e) and supplier-midstream manufacturer fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Firm}_{ms,t}\\)\u003c/span\u003e\u003c/span\u003e). Controls for year fixed effects (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Year}_{t}\\)\u003c/span\u003e\u003c/span\u003e). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{\\text{m,t}}\\:\\)\u003c/span\u003e\u003c/span\u003eis the error term. In Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ESG}_{i}\\)\u003c/span\u003e\u003c/span\u003e (the ESG rating performance of downstream/upstream firms themselves) is also included to isolate the impact of their own ESG.Standard errors are clustered at the customer industry and supplier industry levels. Appendix A provides variable definitions.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e reports statistics of main variables. The total sample size is 1,250. The ESG performance level of midstream manufacturers (ESG_m) ranges from 1.500 to 6.000, with a mean of 4.098. The mean and standard deviation of the main explanatory variable (CR) are 0.514 and 0.427 respectively. There exists a significant difference between the maximum and minimum values, indicating that the degree of climate risk varies across firms.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVarName\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eObs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP50\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.098\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eESG_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.772\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.888\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.750\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.141\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC_Sales_ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnsize_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e25.268\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.155\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egrowth_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.621\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elev_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.395\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etobinq_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e9.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etop1_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.728\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindep_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.556\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINST_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.904\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnsize_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.508\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egrowth_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elev_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.629\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etobinq_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.802\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etop1_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.408\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindep_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINST_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: See Appendix A for definitions of the variables.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4.Empirical test results and analysis","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Baseline regression\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the results of the baseline regression. Column (1) and Column (3) control only for fixed effects. Column (2) and Column (4) add fixed effects and control variables. From the results in Column (2), it can be seen that downstream firms' climate risk significantly enhances the ESG performance level of midstream manufacturers. A one-unit increase in downstream climate risk promotes an increase of 0.250% in the ESG performance level of midstream manufacturers. Simultaneously, from the results in Column (4), it can be seen that upstream firms' climate risk does not exhibit a significant spillover effect on the ESG level of midstream manufacturers. Climate risk from upstream and downstream in the supply chain exhibits an asymmetric characteristic in its impact on the ESG level of midstream manufacturers.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline regression results.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.253**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.250***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.191\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.369)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e-0.548\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.458)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnsize_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.078\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.123)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.164)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.818*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elev_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.708\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.439)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.717)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etobinq_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etop1_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.543\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.890\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.969)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(1.129)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.097)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elev_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.708)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etobinq_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.026\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.084)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etop1_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.884)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindep_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.670\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.476)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINST_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS_Sales_ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.565)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elnsize_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.270\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.220)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROA_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.731)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egrowth_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.124)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elev_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.725)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etobinq_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etop1_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.529\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.915)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindep_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.016*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eINST_s\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.007)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.969***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-3.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.960***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-5.846\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.049)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(4.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(5.823)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{F}\\text{i}\\text{r}\\text{m}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}\\text{e}\\text{a}\\text{r}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e736\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.749\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. For columns (1) and (2), standard errors in parentheses are clustered at the downstream industry level. For columns (3) and (4), standard errors in parentheses are clustered at the upstream industry level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Robustness tests\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1 Instrumental variable regression\u003c/h2\u003e\u003cp\u003eTo address potential endogeneity issues caused by reverse causality and omitted variables, this study employs a two-stage least squares (2SLS) instrumental variable approach. The mean climate risk of firms in the same province, same industry, and same year is selected as the instrumental variable (CR_iv). The rationale for adopting this instrumental variable is that downstream firms in the same city and same industry share similar environments and industry characteristics, and are generally positively correlated with the climate risk level of the downstream firm. Midstream manufacturers operate in different industries and environments, and their ESG is influenced by different factors, satisfying the exogeneity condition for the instrumental variable. The instrumental variable in this study passes the underidentification test and weak identification test, with results displayed in columns (1) and (2) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2 Entropy balancing\u003c/h2\u003e\u003cp\u003eThe entropy balancing method is adopted to minimize differences in control variables between the group with higher climate risk and the group with lower climate risk. Compared to propensity score matching which discards unmatched samples, the entropy balancing method maintains the full sample size without reduction, thereby better preserving sample information. Results in column (3) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that after entropy balancing, the coefficient of the impact of downstream customer firms' climate risk on the ESG performance level of midstream manufacturers remains significantly positive, confirming the robustness of the main conclusion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e4.2.3 Replacing the measurement of climate risk and ESG: standardized both indices\u003c/h2\u003e\u003cp\u003eFollowing Ma et al. (2025), this study standardizes both the independent variable (CR_nor) and the dependent variable (ESG_nor). This method helps eliminate biases caused by factors such as text length and firm characteristics. Results are shown in column (4) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e4.2.4 Alternative proxy for ESG\u003c/h2\u003e\u003cp\u003eThe Sino-Securities Comprehensive ESG Score indicator is replaced as the measure. Since the Sino-Securities score is quarterly data, this study takes the average to obtain the annual comprehensive score. Results are shown in column (5) of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\u003ch2\u003e4.2.5. Changing the regression model\u003c/h2\u003e\u003cp\u003eTo further enhance the robustness of the conclusions, this study employs the following series of methods to test the robustness of the baseline regression model: Column (1) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: Adds downstream industry-level fixed effects; Column (2): Changes to \"Firm\u0026times;Year\" joint FE; Column (3): Clustered standard errors at the \"downstream-midstream manufacturer\" firm level; Column (4): Uses the \"Industry\u0026times;Firm\" double cluster method; Column (5): Adds other control variables; Column (6): Changes the sample window period, excluding sample observations from the pandemic years 2020\u0026ndash;2021; Column (7): Uses a manufacturing industry subsample for regression. The regression coefficients in columns (1) to (7) of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e are all positive and significant, providing further evidence for the robustness of this study's main conclusions.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRobustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e2sls-iv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eentropy balancing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003estandardized both indices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAlternative proxy for ESG\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003efirst\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003esecond\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG_nor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eESG_m2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR_iv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.847***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.383***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.328***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.181***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.120)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.096)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.387)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR_nor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.116***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.576\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-1.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.559**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(4.952)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.901)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(19.149)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Controls}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{F}\\text{i}\\text{r}\\text{m}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}\\text{e}\\text{a}\\text{r}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.788\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen\u0026ndash;Paap rk LM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.853**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKleibergen\u0026ndash;Paap rk Wald F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.783***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote:These are the regression results of the 2sls-iv model,entropy balancing method, replacing the measurement of climate risk and ESG,standardized both indices and alternative proxy for ESG method.The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors in parentheses are clustered at the downstream industry level.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOther robustness tests.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(5)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(6)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(7)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\u0026quot;\\text{F}\\text{i}\\text{r}\\text{m}\\times\\:\\text{Y}\\text{e}\\text{a}\\text{r}\u0026quot;\\:\\text{j}\\text{o}\\text{i}\\text{n}\\text{t}\\:\\text{F}\\text{E}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClustered at firm level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDouble clustered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdding new control variables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdjust the Sample Window\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eManufacturing subsamples\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eESG_m\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.277***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.243***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.250**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.277*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.243**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.200**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.218*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.085)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.088)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.144)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.087)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.112)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-1.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-4.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(4.138)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2.917)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3.924)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(4.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(4.776)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(3.543)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(4.588)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edual_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.095)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edual_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.161)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROE_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.435)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROE_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.986)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaudit_m\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.424)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eaudit_d\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.649***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.208)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControls\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{F}\\text{i}\\text{r}\\text{m}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{Y}\\text{e}\\text{a}\\text{r}}_{}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry FE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{F}\\text{i}\\text{r}\\text{m}\\times\\:\\text{Y}\\)\u003c/span\u003e\u003c/span\u003eear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{N}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e735\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{R}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.763\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.765\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNotes: Columns (1)-(2)、(5)-(7) standard errors in parentheses are clustered at downstream industry level. The symbols *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"5. Further analysis","content":"\u003cp\u003e\u003cstrong\u003e5.1 Mechanism analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrawing on Yan et al. (2024) to construct the green awareness indicator\u003csup\u003e[i]\u003c/sup\u003e (green) measuring a firm\u0026apos;s focus on the environment, this study includes its interaction term with downstream climate risk (CR_green) in the regression model for empirical testing. Results in column (1) of Table 5 show that when downstream customers face climate risk, they promote the improvement of midstream manufacturers\u0026apos; ESG performance by enhancing corporate green awareness.Secondly, this study incorporates corporate environmental pollution into the evaluation system. Drawing on Yan et al. (2024), it adopts the non-radial SBM-ML index to measure corporate green total factor productivity\u003csup\u003e[ii]\u003c/sup\u003e (GTFP). This study decomposes green total factor productivity into two parts: green technology progress (GTC) and green technology efficiency change (GEC). GTC refers to the shift of the production frontier, representing the improvement of the firm\u0026apos;s overall green technology level and reflecting its comprehensive performance in environmental protection. GEC refers to the change in the firm\u0026apos;s position relative to the production frontier, representing the improvement in the efficiency of utilizing existing green technology resources and reflecting improvements in the firm\u0026apos;s resource utilization. After adding the interaction terms (CR_GTC, CR_GEC) to the regression, column (2) indicates that downstream firms significantly enhance midstream ESG performance by promoting green technology progress in the midstream. Column (3) indicates that downstream firms do not enhance midstream ESG performance by promoting changes in midstream green technology efficiency. This finding suggests that in the promotion and application of green technologies, the improvement of technical efficiency has not yet played a key role in influencing the ESG performance of midstream manufacturers.\u003c/p\u003e\n\u003cp\u003eFinally, this study uses the ratio of the sum of year-end accounts payable, notes payable, and advance receipts to total assets of downstream customer firms to measure trade credit financing capacity (credit). Substituting the interaction term of climate risk and trade credit financing capacity (CR_credit) into the model for regression, results in column (4) show that the more trade credit financing used by downstream firms, the more likely it is to hinder the improvement of midstream manufacturers\u0026apos; ESG.\u003c/p\u003e\n\u003cp\u003eIn summary, the transmission mechanism of downstream climate risk on midstream ESG performance exhibits a dual effect: On one hand, downstream firms strengthen green awareness due to climate risk and promote green technology spillovers to enhance midstream ESG performance; on the other hand, downstream firms may increase trade credit financing to cope with climate risk, exacerbating the financial risk of midstream manufacturers and inhibiting their ESG investment capacity. Although the negative moderating effect of trade credit financing is significant, the positive moderating effects of green awareness and green technology spillovers dominate, resulting in a net positive effect of downstream climate risk on the ESG performance of midstream manufacturers.\u003c/p\u003e\n\u003cp\u003eTable 5 Mechanism analysis.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003egreen awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003egreen technology progress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003egreen technology efficiency change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003etrade credit financing capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4.193**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-0.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.567*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(2.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(0.842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.305)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCR_green\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.089)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCR_credit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-2.094*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(1.171)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCR_GTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e-4.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(2.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eCR_GEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003egreen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003ecredit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.394\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(0.953)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(1.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eGEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-1.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(2.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e-3.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e-5.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e-2.703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e-0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(4.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e(4.983)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e(4.679)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e(4.576)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cimg width=\"61\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cimg width=\"39\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cimg width=\"36\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cimg width=\"9\" height=\"17\" src=\"data:image/png;base64,R0lGODlhDgAZAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAOAA4AhAAAAAAAAAAAOgAAZgA6OgA6kABmtjoAADqQ22YAAGa2/5A6AJCQZpDb/7ZmALb//9uQOtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwVUYLUEgQGcAEQKzemQCpqyKEQcw4NKeW28iF3vBDFQEjQAT0csRCSl07LmFAVi02YEMME9simnK4AAQ8SAo4AxDG+lAUL7/AZY2w70qduzBtoADj0hADs=\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e1,016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cimg width=\"15\" height=\"17\" src=\"data:image/png;base64,R0lGODlhFwAZAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAwAVABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADo6ZjqQtjqQ22YAAGY6AGaQ22a222a2/5A6AJC225Db/7ZmALZmOrb//9uQOtu2Ztu2kNv///+2Zv/bkP/btv/b2///tv//2wECAwV5ICCOJMlNRiA0ZTt6D4J5khC5+JjZcOD/BwiuQsCIJAGHaLMIJFqWA2VUSY40hsF0FN2KqkpRR2GjSkvg0VgryrABlXC6HSiIFT/VDW4FXAxFOVR5AgyCJGkbZHuHcxkBgY19PU+SYQCPbzlIlUd1Ri49Pp1YAZokIQA7\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 24px;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNotes: The symbols *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively. Standard errors in parentheses are clustered at the downstream industry level.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Heterogeneity analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study analyzes the heterogeneity in the relationship between customer firms\u0026apos; climate risk and midstream manufacturers\u0026apos; ESG performance level. Heterogeneity analysis is conducted based on the performance of different ESG sub-dimensions. Results in columns (1)-(3) of Table 6 show the regression results for ESG sub-dimensions. It is found that downstream customers\u0026apos; climate risk has a significant enhancing effect on the environmental dimension of midstream manufacturers, while its enhancing effect on the social dimension and corporate governance dimension is not significant.\u003c/p\u003e\n\u003cp\u003eIn columns (4)-(5), drawing on Wang et al. (2020)\u0026apos;s definition of high-barrier industries\u003csup\u003e[iii]\u003c/sup\u003e, this study divides midstream firms into high-barrier industries and non-high-barrier industries. The results indicate that when the midstream is a non-high-barrier industry, downstream customers facing climate risk have a significant enhancing effect on midstream ESG levels. However, when the midstream is a high-barrier industry, this enhancing effect is not significant. This asymmetry may stem from two distinct mechanisms. First, downstream firms in non-high-barrier industries typically face fiercer market competition. To gain competitive advantage, they actively disseminate green awareness and technologies to midstream manufacturers through green procurement standards and technological cooperation, thereby enhancing midstream ESG performance. Second, downstream firms in non-high-barrier industries typically face fiercer market competition. To stand out in the competition, they more actively transmit their green awareness and green technologies to midstream manufacturers through methods like green procurement standards and technological cooperation, thereby promoting the improvement of midstream ESG levels. On the other hand, midstream manufacturers in high-barrier industries often face higher technological barriers and more complex technological systems, resulting in relatively weaker capabilities to absorb and apply new technologies. Even if downstream firms possess strong green awareness and advanced green technologies, midstream manufacturers may struggle to effectively absorb and apply these technologies due to their own technical limitations, making it difficult for the spillover effects of downstream green technologies to fully materialize. Additionally, midstream manufacturers in high-barrier industries may rely more on their own technological accumulation and R\u0026amp;D systems, with lower dependence on external technologies, which also weakens the role of downstream firms in enhancing their ESG levels.\u003c/p\u003e\n\u003cp\u003eIn columns (6)-(7), referring to Qi et al. (2018)\u0026apos;s delineation of heavily polluting industries\u003csup\u003e[iv]\u003c/sup\u003e, this study divides downstream firms into heavily polluting industries and non-heavily polluting industries. The results show that the climate risk of non-heavily polluting customer firms has a significant enhancing effect on midstream manufacturers\u0026apos; ESG, while the enhancing effect of heavily polluting customer firms on midstream manufacturers\u0026apos; ESG is not significant.\u003c/p\u003e\n\u003cp\u003eTable 6 Regression results of heterogeneity.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eHigh-Barrier=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHigh-Barrier=0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eHeavy Pollution=1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eHeavy Pollution=0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eESG_m\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.434**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.310**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.344**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(0.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(0.144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(0.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e-1.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e-10.820**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e-1.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(4.313)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(6.743)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e(7.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e(5.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(4.766)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e(7.577)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e(6.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cimg width=\"61\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cimg width=\"39\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cimg width=\"36\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cimg width=\"9\" height=\"17\" src=\"data:image/png;base64,R0lGODlhDgAZAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAABgAOAA4AhAAAAAAAAAAAOgAAZgA6OgA6kABmtjoAADqQ22YAAGa2/5A6AJCQZpDb/7ZmALb//9uQOtv///+2Zv/bkP//tv//2wECAwECAwECAwECAwECAwECAwECAwECAwECAwECAwVUYLUEgQGcAEQKzemQCpqyKEQcw4NKeW28iF3vBDFQEjQAT0csRCSl07LmFAVi02YEMME9simnK4AAQ8SAo4AxDG+lAUL7/AZY2w70qduzBtoADj0hADs=\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1,250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cimg width=\"15\" height=\"17\" src=\"data:image/png;base64,R0lGODlhFwAZAHcAMSH+GlNvZnR3YXJlOiBNaWNyb3NvZnQgT2ZmaWNlACH5BAEAAAAALAAAAwAVABEAhAAAAAAAAAAAOgAAZgA6kABmtjoAADo6ZjqQtjqQ22YAAGY6AGaQ22a222a2/5A6AJC225Db/7ZmALZmOrb//9uQOtu2Ztu2kNv///+2Zv/bkP/btv/b2///tv//2wECAwV5ICCOJMlNRiA0ZTt6D4J5khC5+JjZcOD/BwiuQsCIJAGHaLMIJFqWA2VUSY40hsF0FN2KqkpRR2GjSkvg0VgryrABlXC6HSiIFT/VDW4FXAxFOVR5AgyCJGkbZHuHcxkBgY19PU+SYQCPbzlIlUd1Ri49Pp1YAZokIQA7\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0.758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:The symbols *, **, and *** indicate significance at the 10 %, 5 %, and 1 % levels, respectively. Standard errors in parentheses are clustered at the downstream industry level.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eThis study examines the impact of downstream customer firms' climate risk on midstream ESG performance using supply chain disclosure data from China A-share listed companies. One limitation is that this study does not include data on non-listed customers. However, listed companies are typically more standardized and transparent in information disclosure, corporate governance, and ESG practices, while non-listed companies may exhibit significant differences in these aspects. Future research could further expand data sources to incorporate more information from non-listed companies to more comprehensively examine the impact of downstream customer firms' climate risk on midstream manufacturers' ESG performance.\u003c/p\u003e\u003cp\u003eThis research yields several implications: First, optimize stakeholder management and cultivate a supply chain ESG ecosystem to better promote collaborative green transformation within the supply chain. Second, strengthen the exemplary role of high-barrier industries and non-heavily polluting industries. Policymakers should refine relevant standards or incentives, using firms in these industries as models, to drive enterprises within the supply chain network to jointly enhance ESG performance. Finally, leveraging the spillover effects of downstream firms' green awareness and green technologies is a key pathway to achieving green transformation and sustainable development in the supply chain. The government should encourage downstream firms to establish green supply chain management systems, transmitting green awareness and sustainable development concepts to midstream manufacturers by setting green procurement standards and supplier environmental conduct codes. Furthermore, policies should support downstream firms in engaging with midstream manufacturers through technology transfer, training, technological cooperation, and joint R\u0026amp;D to disseminate advanced green technologies. These measures not only provide important references for achieving sustainable development in the supply chain and enhancing new quality productive forces, but also offer new momentum and direction for the green transformation of China's entire economic system and its high-quality economic development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eEthical approval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInformed consent\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYulin Zheng: Conceptualization, Methodology, Software, Data curation, Visualization, Writing.Dingwen Wu: Supervision, Project administration, Conceptualization, Writing-review\u0026amp;editing. Hong Shen: Supervision, Project administration, Conceptualization, Writing-review\u0026amp;editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D., Carvalho, V. M., Ozdaglar, A., \u0026amp; Tahbaz‐Salehi, A. (2012). The network origins of aggregate fluctuations. \u003cem\u003eEconometrica\u003c/em\u003e, 80(5), 1977-2016.\u003c/li\u003e\n\u003cli\u003eBerg, F., K\u0026ouml;lbel, J. F., \u0026amp; Rigobon, R. (2022). Aggregate confusion: The divergence of ESG ratings. \u003cem\u003eReview of Finance\u003c/em\u003e, 26(6), 1315-1344.\u003c/li\u003e\n\u003cli\u003eCao, J., Liang, H., \u0026amp; Zhan, X. (2019). Peer effects of corporate social responsibility. \u003cem\u003eManagement Science\u003c/em\u003e, 65(12), 5487-5503.\u003c/li\u003e\n\u003cli\u003eChen,W., \u0026amp; Fan, Y.Z. (2024). 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ESG green spillovers, supply chain transmission and corporate green innovation.\u003cem\u003e China Economic Review\u003c/em\u003e, 59(7), 72\u0026ndash;91.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003cp\u003e\u003csup\u003ei\u0026nbsp;\u003c/sup\u003eYan et al. (2024) based on key policy documents such as China\u0026apos;s Five-Year Plans, the Environmental Protection Law, the Technical Guidelines for Corporate Environmental Behavior Evaluation, and the Green Manufacturing Standardization White Paper, combined with relevant research from Xie and Zhu (2021) and Wan et al. (2021), screened 113 keywords closely related to corporate green transformation from five dimensions: promotion and publicity, strategic concepts, technological innovation, pollution control and treatment, and environmental monitoring and management. By taking the natural logarithm after adding 1 to the frequency of each keyword in listed companies\u0026apos; annual reports, they obtained a quantitative measure of corporate green transformation.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eii\u003c/sup\u003e Yan et al. (2024) incorporated corporate environmental pollution into the evaluation system and adopted the non-radial SBM-ML index to measure corporate green total factor productivity. The measurement of input and output indicators for corporate green total factor productivity is as follows.\u003c/p\u003e\n\u003cp\u003eFactor inputs: Labor input is proxied by the number of employees; Capital input is proxied by net fixed assets; Energy input is proxied by the city\u0026apos;s industrial electricity consumption where the firm is located, converted based on the proportion of the firm\u0026apos;s employees to the city\u0026apos;s urban employed population.\u003c/p\u003e\n\u003cp\u003eDesired output: Firm operating income is used as a proxy for desired output.\u003c/p\u003e\n\u003cp\u003eUndesired output: The \u0026quot;industrial three wastes\u0026quot; emissions (industrial sulfur dioxide, industrial wastewater, and industrial soot and dust) are converted based on the proportion of the firm\u0026apos;s employees to the city\u0026apos;s urban employed population and used as a proxy for undesired output.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eiii\u0026nbsp;\u003c/sup\u003eWang et al. (2020) defined high-barrier industries as: Mining; Petroleum Processing and Coking; Smelting and Pressing of Ferrous Metals; Smelting of Heavy Nonferrous Metals; Production and Supply of Electric Power, Gas, and Water; Railway Transport; Pipeline Transport; Water Transport; Air Transport; Communication Services; Finance; Insurance; Public Facility Services; Postal Services; and Culture and Media Industries.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eiv\u003c/sup\u003e Qi et al. (2018), based on the industry classification standard issued by the China Securities Regulatory Commission (CSRC) in 2012, specifically selected the following industry codes for heavily polluting industries: B06, B07, B08, B09, B10, B11, B12, C17, C18, C19, C22, C25, C26, C27, C28, C29, C31, C33, D44.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7331780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7331780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study utilizes supplier and customer information disclosed by China A-share listed companies from 2009 to 2023 to identify inter-firm relationships across the supply chain. Utilizing machine learning techniques for textual analysis of annual reports, we construct firm-level climate risk metrics. Our analysis disclosures a positive relationship between climate risk exposure in downstream enterprises and the ESG performance of midstream manufacturers. In contrast, the impact of upstream climate risk on midstream ESG performance is relatively limited, revealing an asymmetric pattern in supply chain spillovers of climate risk. Mechanism analysis demonstrates that while downstream firms exert a significant negative moderating effect on midstream ESG performance through trade credit financing, this is outweighed by the dominant positive moderating effects arising from environmental awareness and green technology spillovers. Consequently, downstream climate risk exhibits a net positive effect on the ESG performance of midstream manufacturers. This research provides novel empirical evidence and a fresh perspective on the factors influencing corporate ESG performance.\u003c/p\u003e","manuscriptTitle":"Climate Risks, Supply Chain Spillovers, and ESG Performance: Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 14:19:55","doi":"10.21203/rs.3.rs-7331780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32311ebd-b3d4-4d38-a633-62883a56705f","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56368189,"name":"Business and commerce/Business and management"},{"id":56368190,"name":"Social science/Business and management"},{"id":56368191,"name":"Business and commerce/Economics"},{"id":56368192,"name":"Social science/Economics"},{"id":56368193,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":56368194,"name":"Social science/Environmental studies"},{"id":56368195,"name":"Business and commerce/Finance"},{"id":56368196,"name":"Social science/Finance"}],"tags":[],"updatedAt":"2026-03-12T06:41:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-16 14:19:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7331780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7331780","identity":"rs-7331780","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}