Does green credit policy promote the formation of new quality productivity in resource-based enterprises? Evidence from China

Does green credit policy promote the formation of new quality productivity in resource-based enterprises? 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Evidence from China Ting LI, Wen ZHONG, Zhiqing YAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5891402/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 The green credit policy is designed to foster sustainable and high-quality development within enterprises. However, it currently lacks focus on the development of new productive forces in resource-based enterprises, particularly concerning the systemic effects of financing constraints, technological changes, and awareness of green transformation. These areas warrant further investigation. This study leverages a quasi-natural experiment based on the original Green Credit Guidelines by the China Banking Regulatory Commission, initiated in 2012. Using data from resource-based enterprises listed in China’s A-share market from 2008 to 2022, a difference-in-difference approach assesses the impact of green credit policies on the emergence of new quality production capacities within these firms. The research indicates that green credit policies can effectively integrate environmental regulation with financial resource allocation, substantially enhancing total factor productivity and fostering new quality productivity within resource-based enterprises. Mechanism analysis reveals that these policies mitigate financing constraints, stimulate technological advancements, and strengthen green transformation awareness, thereby boosting total productivity and quality of production. Heterogeneity analysis points out that the influence of green credit policies is more pronounced in less marketed regions compared to highly marketed ones, and it is more significant in state-owned enterprises than in non-state-owned enterprises. Additionally, throughout various enterprise lifecycles—decline, maturity, and growth—the need to bolster primary responsibilities and differentiate requirements for policy implementation becomes evident. Business and commerce/Economics Business and commerce/Finance Social science/Development studies Social science/Economics Social science/Finance new quality productive forces green credit policy financing constraints technological change green transformation awareness DID fixed effects model resource-based enterprise Figures Figure 1 Figure 2 Figure 3 1. Introduction In September 2023, during an inspection tour in Heilongjiang, General Secretary Xi Jinping introduced the scientific concept and strategic task of “accelerating the formation of new quality productivity.” This innovative approach to productivity is characterized by significant technological breakthroughs, novel factor configurations, and comprehensive industrial transformations. Resource-based enterprises, particularly those involved in mineral extraction and primary processing, are notorious for their substantial negative externalities, which include surface subsidence, water system destruction, land desertification, and atmospheric pollution. Their reliance on traditional, extensive operational methods hampers the enhancement of resource extraction efficiency and total factor productivity. Presently, China hosts approximately 67,000 resource-based enterprises, affecting over 3.75 million hectares of land through excavation, subsidence, or occupation. These enterprises annually contribute around 795 million tons of waste rock and discharge 6.004 billion cubic meters of mine water, inflicting damage to 22 billion cubic meters of groundwater resources [1]. It is evident that transforming the developmental strategies of these enterprises and hastening the emergence of new productive forces is crucial and urgent for achieving high-quality development. The development of new productive capacities in resource-based enterprises requires a balanced approach that avoids over-reliance on market forces or purely governmental directives. Green credit policy serves as a middle ground, integrating financial mechanisms with environmental strategies. This hybrid approach mitigates the rigidity associated with administrative control and enhances the market’s regulatory influence. Since the release of the “Green Credit Guidelines” by the China Banking Regulatory Commission in 2012 and the subsequent “Green Finance Evaluation Scheme for Banking Financial Institutions” by the People’s Bank of China in 2021, there has been continuous refinement in green credit management and evaluation standards. These policies are designed to bolster green industries by directing essential financial resources toward their development. Simultaneously, they aim to curb financial support for enterprises characterized by high pollution, energy consumption, and emissions – commonly referred to as the “Three Highs” – through raised loan thresholds. Consequently, these policies reallocate financial resources to promote environmentally sustainable practices. The critical questions remain: How do these policies impact resource-based enterprises, and are they effective in encouraging the formation of new, environmentally-friendly productive capacities within these sectors? 2. Literature review Reviewing the literature related to this research topic reveals that both domestic and international scholars primarily focus on three dimensions: Firstly, the effectiveness of the implementation of green credit policies. Some scholars, examining from the perspective of banks, have found that green credit policies explicitly guide banks to strictly control credit for projects categorized as "three highs" (high pollution, high energy consumption, high emissions), while providing low-interest rate credit support for green projects. This inevitably leads to a structural adjustment in the flow of credit resources, thereby promoting the development of green, low-carbon, and circular industries [ 3 – 5 ] . This policy not only reduces the risk for banks but also regulates the impact of bank liquidity on profitability [ 6 , 7 ] . Hence, it is evident that commercial banks recognize the necessity of implementing green credit policies. Additionally, some scholars have investigated from the perspective of enterprises and found that green credit policies can promote adjustments in corporate decision-making and enhancement of technological protection levels, thereby improving corporate performance [ 8 – 10 ] . Specifically, through the aspects of financing constraints and investment efficiency, these policies enhance the economic, social, and governance performance evaluations of enterprises [ 11 ] , ultimately fostering technological innovation within firms. Further research by Berikhanovna et al [ 12 ] . found that green credit policies significantly promote technological innovation in large enterprises but do not have a significant effect on technological innovation in small and medium-sized enterprises. This indicates a considerable disparity in existing research conclusions and a lack of in-depth studies on the heterogeneity of implementation effects across different industries. Secondly, attention is given to the influencing factors of productivity development in resource-based enterprises. Some scholars, based on the rational allocation of production factors in economic growth theory, believe that capital substitution for labor, i.e., capital deepening, has a significant impact on the productivity development of enterprises [ 13 ] . Other scholars have pointed out that imperfect industrial policies, inadequate legal systems, and weak social awareness can hinder the green transformation of resource-based enterprises [ 14 ] , revealing barriers to the development of enterprise productivity. On the other hand, some scholars, using a panel threshold model for empirical testing, have found that digital transformation can free resource-based enterprises from time and space constraints, allowing them to leverage the comparative advantages of various production factors and achieve enhanced production efficiency [ 15 ] . More specific empirical findings indicate that digitization factors in strategic planning, marketing, and operations management significantly enhance the productivity development of resource-based enterprises [ 16 ] . Meanwhile, some scholars have argued that resource tax reform and higher executive compensation can significantly promote green innovation in resource-based enterprises [ 17 ] . Further research by other scholars has found that environmental tax reform drives productivity development in resource-based enterprises by encouraging technological innovation, restricting opportunistic behavior, and facilitating factor mobility [ 18 , 19 ] . Clearly, there is a lack of systematic research on these influencing factors, and the impact of green credit policies has not been considered. Thirdly, the focus is on the impact of green credit policies on corporate productivity development. Based on classical economic theory, some scholars have suggested that green credit policies reinforce the inherent risk aversion of commercial banks, increase the difficulty of obtaining loans for high-pollution enterprises, create a "penalty effect," causing enterprises to fail to secure sufficient funds and thus reduce green innovation. This may also induce "greenwashing" behaviors, ultimately suppressing productivity development [ 20 ] . In contrast, other scholars, examining the "Porter Hypothesis," believe that green financial policies promote green development by increasing the financing constraints and debt financing costs for high-pollution enterprises, thereby stimulating enterprise technological innovation and social responsibility [ 21 , 22 ] . Additionally, some other studies recognize the positive incentives green credit policies have on the productivity development of heavily polluting enterprises [ 23 , 24 ] . Clearly, existing studies have not reached a consensus, and since high-pollution enterprises represent an extreme type, further research is needed to explore and deepen the understanding of the impact on the majority of enterprises, which possess certain pollution levels but also serve as pillars of socio-economic development. In conclusion, previous research, whether focused on different subjects or utilizing varying theories, methods, and data, has not fully elucidated the theoretical "black box" of the relationship between green credit policies and the new productivity of resource-based enterprises. Delving into the specific pathways through which green credit policies influence the formation of new productivity in resource-based enterprises is also a crucial prerequisite for comprehensively evaluating the micro-effects of green finance. Resource-based enterprises possess certain environmental negative externalities and hold strategic importance in national resource development. The formation of their new productivity is an organizational behavior characterized by industry-specific and Chinese-specific traits. In exploring the mechanisms by which green credit policies affect the formation of new productivity in these enterprises, existing research lacks systematicity and specificity. Particularly, there is a notable absence of systematic examination of the pathways through which effects on their financing constraints, technological transformation, and green transition awareness contribute to the formation of new productivity. Considering that the "Guidelines" is China's first green credit policy document, this study takes the effective date of the "Guidelines" as the benchmark to design a quasi-natural experiment. By adopting the difference-in-differences method, the study aims to elucidate the mechanism through which the green credit policy influences the formation of new productivity in resource-based enterprises. The study also examines whether this effect is incentivizing or inhibiting and provides systematic insights to promote the comprehensive implementation and effective improvement of green credit policies. This is expected to accelerate the formation of new productivity and high-quality development of enterprises. Research on this issue effectively fills the gap in the theoretical study of green finance in the resource industry, making it of significant importance. Compared to previous literature, the marginal contributions and main innovations of this study are as follows: First, it discovers that green credit policies can effectively synergize the functionalities of environmental regulation and financial resource allocation, significantly promoting the total factor productivity (TFP) of resource-based enterprises and the formation of new productive capacities. Secondly, it reveals the partial mediating effects of financing constraints, technological transformation, and awareness of green transition in the functioning of green credit policies. Specifically, the implementation of these policies helps alleviate financing constraints, catalyze technological innovation, and enhance awareness of the green transition, thereby supporting the rise in TFP and the formation of new productive capacities in resource-based enterprises. Thirdly, it clarifies the heterogeneous impacts of green credit policies on the promotion of new productive capacities across different marketization levels, property rights, and lifecycle stages of resource-based enterprises. The effects are more pronounced in regions with lower levels of marketization, stronger for state-owned enterprises (SOEs) compared to non-SOEs, and decrease in order from declining, mature, to growing enterprises. Fourthly, it provides empirical evidence supporting the need for strengthened collaboration among commercial banks, resource-based enterprises, and governmental departments in the implementation of green credit policies to promote the formation of new productive capacities in resource-based enterprises, along with important policy implications for improvements. 3. Theoretical analysis and research assumptions 3.1 The role of green credit policy on the development of new productivity of resource-based enterprises The essence of green credit policy is to guide credit allocation under environmental constraints, involving two major functions: environmental regulation and financial resource allocation [ 25 , 26 ] . From the perspective of the environmental regulation function of green credit policy, based on signaling theory, financial institutions extend credit based on resource-based enterprises' environmental compliance and social responsibility levels. Consequently, enterprises face higher transaction costs due to expanded environmental information disclosure and social responsibility activities, which compel them to avoid the opportunity cost of "pseudo-disclosure," correct short-term behaviors, adapt to the regulatory requirements of financial institutions, and develop new qualitative productivity. Meanwhile, based on the "Porter Hypothesis," green credit policy can imply innovation for enterprises, incentivizing them to engage in technological and managerial transformations, actively contributing to the development of green technologies, processes, or products, and optimizing management methodologies. This not only offsets the "compliance costs" of environmental regulations but also generates a "transformation compensation" effect, promoting the formation of new qualitative productivity in enterprises. From the perspective of the financial resource allocation function of green credit policy, the policy internalizes the environmental negative externality costs of resource-based enterprises. By facilitating and reducing transaction costs for clean investments and complicating and increasing transaction costs for polluting investments, it dynamically adjusts the opportunity costs of environmental destruction by enterprises. This approach implements environmental governance starting from the production process and throughout its entirety, forming an effective path for financial resource allocation to guide the formation of new qualitative productivity in enterprises [ 27 ] . Consequently, the following hypothesis is proposed: H1a: The implementation of the green credit policy will promote the formation of new qualitative productivity of resource-based enterprises. However, the role of green credit policies may also result in counterproductive effects. Based on neoclassical economic theory, the environmental regulation function of green credit policies, under unchanged conditions of capital, labor, and technology in resource-based enterprises, leads to an increase in environmental governance costs, occupying production resources and crowding out funding for green technology development and green deep processing of products [ 28 ] . This worsens the balance sheet and restricts the capacity to develop new productive forces. The financial resource allocation function of green credit policies, when facing the substantial investment, long cycles, and high risks of green technological transformation in resource-based enterprises, raises the threshold for bank credit and increases the risk of funding gaps for enterprises. This challenge may be unbearable for enterprises with single financing channels, inhibiting actions to develop new productive forces. Thus, it proposes the following hypothesis: H1b: The implementation of green credit policy will inhibit the formation of new qualitative productivity of resource-based enterprises. 3.2 Green credit policy and financing constraints for resource-based enterprises Financing constraints are the main measure of the extent to which resource-based enterprises face limitations in external financing. With the implementation of green credit policies, the role of banks has become more prominent. Resource-based enterprises may not receive credit approval if their projects do not meet policy standards. Even if they receive credit, they may face temporary or permanent suspension of funding due to significant risks identified during process risk assessment and monitoring. This indicates that banks, inherently profit-seeking and risk-averse, have significantly raised the regulatory requirements for environmental and social risks, as well as the credit thresholds for enterprises. Consequently, the difficulty, cost, and social pressure for these enterprises to obtain bank loans have increased considerably [ 29 , 30 ] . Moreover, resource-based enterprises exhibit strong environmental negative externalities, making end-of-pipe treatment challenging, costly, and resource-intensive [ 31 , 32 ] . They also face the risk of environmental litigation and the withdrawal or termination of credit extensions by external creditors. Additionally, the development cycle for green technologies and new productive capacities is long, requires substantial investment, and carries high risks, further exacerbating financing constraints. Therefore, the following hypothesis is proposed: H2a: The green credit policy has increased the financing constraints of resource-based enterprises and inhibited the formation of new quality productivity of enterprises. Simultaneously, due to the enhancement of environmental information disclosure by resource-based enterprises under the green credit policy, the degree of information asymmetry has been significantly reduced. Based on the pecking order theory under an imperfect market, the degree of corporate financing constraints is positively correlated with the degree of information asymmetry. As corporate information becomes more transparent, financial fraud is curbed, reducing the information collection costs and investment risks for external investors. Furthermore, because most resource-based enterprises are state-owned and have implicit government guarantees, developing resources becomes "profitable," signaling a "greenwashing" image to society. This sends positive messages to external investors, allowing the enterprises to alleviate financing constraints through means with low restrictions and low financing costs, such as financing via current liabilities and trade credit. For example, they could settle payments through delayed payment or accounts payable, acquiring certain funds for developing new types of productivity such as introducing new talents and advancing digital and intelligent transformations. In other words, the green credit policy reduces the difficulty for resource-based enterprises to obtain funds through informal financing channels. This leads to the hypothesis: H2b: The green credit policy has alleviated the financing constraints of resource-based enterprises and promoted the formation of new quality productivity of enterprises. 3.3 Green Credit Policy and Technological Transformation of Resource-based Enterprises Technological innovation is the main avenue for enhancing the total factor productivity of resource-based enterprises. Policies such as the "Guidelines" explicitly instruct financial institutions to increase credit support and optimize processes for green and low-carbon development, thereby incentivizing the development of green products and services. This makes resource-based enterprises acutely aware that to alleviate financing constraints, enhance competitive advantages, and achieve high-quality development, they must align with government directives, social supervision, and bank preferences, sending positive signals by implementing technological changes and developing new productive capacities. Hence, the motivation for technological innovation within these enterprises continuously strengthens [ 33 ] . Existing research indicates that green credit policies lead to more active development of green technologies among heavily polluting enterprises [ 34 , 27 ] . Additionally, the fundamental purpose of policies like the "Guidelines," which serve as financial instruments of environmental policy, is not to force the closure or halting of resource-based enterprises but to compel them to overcome internal organizational inertia, implement technological changes, and realize the "compensation for change" effect [ 35 , 2 ] . This aligns with the development motivations and long-term goals of enterprise owners. Therefore, under the constraints of policies like the "Guidelines," banks should provide credit support for potential technological innovation projects of resource-based enterprises, particularly in the realm of green technology, processes, or products, following scientific risk assessments. This will in turn promote the improvement of resource utilization efficiency, the added value of enterprises, and the formation of new productive capacity [ 36 ] . Thus, the following hypothesis is proposed: H3a: The green credit policy will force the technological change of resource-based enterprises and promote the formation of new quality productivity of enterprises. However, the implementation of green credit policies may also have unintended consequences. Resource-based enterprises, which exhibit environmental negative externalities and some characteristics of "three-high" enterprises (high energy consumption, high pollution, and high emissions), face higher financing thresholds and costs due to the issuance of policies such as the "Guidelines." Consequently, these enterprises can only allocate their limited working capital to urgent production and operational needs, resulting in insufficient investment in technological transformation and the development of new qualitative productivity. Furthermore, the technological transformation and formation of new qualitative productivity in resource-based enterprises require significant investments over an extended period, characterized by longer return cycles and higher risks. The issuance of policies such as the "Guidelines" also leads risk-averse financial institutions to reduce their enthusiasm for investing in such long-term, high-risk projects that lack immediate economic benefits. Existing studies have suggested that the introduction of green credit policies has restricted corporate credit financing, particularly long-term borrowing, thereby negatively inhibiting the output of new technologies for enterprises [ 37 , 28 ] . Other research indicates that the green credit policy does not significantly enhance the quality of technological development [ 35 ] . Thus, we propose the following hypothesis: H3b: The green credit policy will hinder the technological change of resource-based enterprises and inhibit the formation of new quality productivity of enterprises. 3.4 Green credit policy and green transformation awareness of resource-based enterprises Green transition awareness refers to the concept wherein enterprises take sustainable development as their responsibility and shift towards low-carbon, zero-pollution, digital intelligent, and high-efficiency operational modes. After the introduction of the green credit policy, financial institutions can access more information about companies' environmental damage, governance, and green transition, thereby conducting more precise risk assessments for loan projects and corporate behaviors to reduce the default risk and environmental harm risk associated with corporate credit. Additionally, policies such as the "Guidelines" emphasize post-loan management by financial institutions, requiring banks to monitor the behavior of loan-receiving companies, and based on risk management, to reassess or adjust loan disbursements, thereby eliminating motivations and behaviors related to "greenwashing". Furthermore, market transmission mechanisms will be used to create new requirements from external creditors regarding companies' environmental and social responsibilities, as well as green transition performance. In response, resource-based enterprises will place greater emphasis on green, low-carbon, environmentally friendly, and new quality productivity development behaviors to gain the trust of banks and external creditors [ 38 , 39 ] . In other words, the punitive mechanisms of the green credit policy will effectively compel corporate managers to overcome the short-term "green evasion" mentality, induced by concerns over excessive sunk costs and compliance costs related to environmental regulations, thus enhancing their awareness of environmental and social responsibilities and green transition. This also strengthens the overall awareness of environmental, social responsibility, and green transition within enterprises, which can serve as an endogenous driving force for the formation of new quality productivity. Accordingly, we propose the following hypothesis: H4: The green credit policy can improve the awareness of green transformation of resource-based enterprises and promote the formation of new quality productivity of enterprises. Based on the aforementioned four types of hypotheses and their intrinsic logical relationships, a conceptual model can be established to elucidate the mechanism by which green credit policies impact the new productivity of resource-based enterprises, as illustrated in Fig. 1 . 4. Research Design 4.1 Model settings To systematically analyze and test the impact of the implementation of green credit policies on the new quality productivity of resource-based enterprises, based on the aforementioned theoretical analysis and research hypotheses, and considering that the Difference-in-Differences (DID) method effectively avoids endogeneity issues when analyzing the effects of macro policies on micro-individuals, and can also combine with fixed effects to mitigate omitted variable bias, inspired by previous studies [ 27 , 40 , 41 ] , the following DID model is constructed: \(\:{TFP}_{itd}={\alpha\:}_{0}\) + \(\:{\alpha\:}_{1}DID+{\alpha\:}_{2}{Controls}_{itd}+{\mu\:}_{i}+{}_{t}+{\rho\:}_{d}+{}_{itd}\) ⑴ In formula (1), TFP represents the dependent variable—Total Factor Productivity (TFP) of resource-based enterprises. The subscripts i , t and d represent the enterprise, year, and industry, respectively. DID denotes the explanatory variable, i.e., the Difference-in-Differences term Treat×Post, where Treat indicates whether it is a resource-based enterprise, and Post indicates whether the resource-based enterprise is affected by the green credit policy. Controls represent the control variables. \(\:\:{\mu\:}_{i}\) , \(\:{}_{t}\) and \(\:{\rho\:}_{d}\) represent enterprise, time, and industry fixed effects, respectively; and \(\:{}_{itd}\) denotes the random error term. 4.2 Variable Design 4.2.1 Explanatory variables To measure the performance of new productivity formation for resource-based enterprises, considering the characteristics of "three highs" and previous experiences, this study uses total factor productivity (TFP) as the explained variable, which is the core indicator of new productivity development. TFP not only reflects the average unit output of various production input factors under general conditions, indicating the overall efficiency of input and output, but is also related to technological progress and the combination of production factors. It embodies factors such as the technological level, management proficiency, institutional quality, and calculation errors, thus clearly observing the efficiency changes in the formation of new productivity in resource-based enterprises. Inspired by the research approach of Olley and Pakes [ 42 ] , the following production function model is established: \(\:{lnP}_{it}={\beta\:}_{0}+{\beta\:}_{1}{lnK}_{it}+{\beta\:}_{2}{lnL}_{it}+{\beta\:}_{3}{lnZ}_{it}+\sum\:Year+\sum\:industry+{}_{it}\) ⑵ In Eq. (2), P represents the output of the enterprise, measured by operating revenue and deflated using the producer price index. K represents the capital stock of the enterprise, measured by the net amount of fixed assets. L represents the end-of-period number of employees. Z represents the proxy variable, measured by the operating revenue minus the value added, where the latter includes the sum of depreciation, wages, net production taxes, and operating profit. According to the definition of Total Factor Productivity (TFP), the residuals from the estimation of the above production function are the TFP values. 4.2.2 Explanatory variables The explanatory variable involved in the model is the green credit difference-in-difference term (Treat×Post). Considering that the “Guideline” came into effect in February 2012, the experimental period is chosen as 2008 to 2022. For resource-based enterprises, the sample takes Treat as 1, otherwise Treat is 0; for sample years from 2012 onwards, Post is taken as 1, otherwise Post is 0. 4.2.3 Mediation variables To examine whether the green credit policy affects the new-quality productivity of resource-based enterprises through financing constraints, technological innovation, and green transition awareness, this study adopts the methodologies of existing research [ 43 , 44 ] . It utilizes the enterprise financing constraint index (SA, absolute value), enterprise research and development intensity (RD, logarithm of the sum of patent applications and granted patents plus one), and enterprise green transition focus (GF, comprehensive score of five indicators: establishment of environmental management systems, provision of environmental training, implementation of environmental actions, and acquisition of ISO9001 and ISO14001 certifications) as mediating variables. 4.2.4 Control variables To mitigate the potential influence of other factors on the dependent variable, a set of control variables is designed: (1) Firm size (Size): the natural logarithm of total assets at the end of the year. (2) Firm age (Age): the natural logarithm of the number of years since the firm's registration. (3) Leverage ratio (Lev): the ratio of total liabilities to total assets at the end of the year. (4) Return on assets (ROA): the ratio of net profit to the average balance of total assets. (5) Total asset growth rate (Growth): the ratio of the increase in total assets during the year to the total assets at the beginning of the year. (6) TOP1: the shareholding ratio of the largest shareholder. (7) Separation of ownership and control (Dual): the difference between the actual controller's controlling rights and ownership rights in the listed company. (8) Firm growth (TQ): the ratio of the sum of the market value of circulating shares, the value of preferred shares, and net liabilities to the book value of total assets. (9) Board size (Board): the natural logarithm of the total number of directors. 4.3 Sample selection and data sources The study selected A-share listed companies in China from 2008 to 2022 as the initial sample. Following the methodology of Ma Jie et al. [19], and based on the Chinese "Industry Classification for National Economic Activities" standard, we categorized companies in 15 industries, such as petroleum, coal, and non-ferrous metals, as resource-based enterprises. The remaining listed companies were assigned to the control group. We meticulously excluded three types of companies: listed financial enterprises, ST, *ST, and PT companies, as well as companies with severely incomplete financial and related research data. Ultimately, we obtained 958 companies with a total of 14,369 observations. Additionally, to mitigate the impact of extreme values on regression results, we winsorized the continuous variables at 1% and 99% quantiles. The financial data used in this study were sourced from the CSMAR database and the Wind database, while patent-related data were obtained from the National Intellectual Property Administration and the WIPO database. Indicators concerning corporate green transition focus were extracted from environmental information disclosed in the annual reports of listed companies. 5. Empirical Results 5.1 Descriptive statistics Descriptive statistical results of the various variables involved in the study are presented in Table 1 . As shown in Table 1 , firstly, there is a significant difference between the maximum and minimum values of TFP, indicating a substantial disparity in the development levels of new productivity among resource-based enterprises. Therefore, the effect of green credit policy on this matter requires further empirical investigation. Secondly, except for the large standard deviation of the shareholding ratio of the largest shareholder, the fluctuations of other control variables are within normal range, providing suitable conditions for further research. Table 1 Descriptive statistical results of variables Variables N Mean St.d Min Max TFP 14369 6.915 0.960 -1.352 11.418 DID 14369 0.172 0.377 0.000 1.000 RD 14040 0.457 0.980 0.000 7.548 GF 11702 0.918 0.912 0.000 5.384 SA 14369 3.821 0.285 2.120 4.670 SOE 14369 0.641 0.480 0.000 1.000 Size 14369 22.647 1.416 19.023 28.636 Lev 14369 0.482 0.189 0.014 1.056 ROA 14369 0.043 0.058 -0.644 0.517 Top 1 14369 35.498 15.258 3.390 89.986 Dual 14369 0.160 0.367 0.000 1.000 TQ 14369 1.889 1.377 0.641 31.400 area 14369 1.534 0.791 1.000 3.000 Growth 14369 0.183 1.045 -0.697 79.603 Board 14369 2.178 0.198 0.693 2.890 Age 14369 2.932 0.349 0.693 3.761 5.2 Correlation analysis The results of the correlation tests between the dependent variable and control variables, as well as among the control variables themselves, are shown in Table 2 . As evident from Table 2 , the correlation coefficients between variables are all significantly less than 0.7. The variance inflation factor (VIF) test results indicate that all VIF values are well below 10. According to the standards of statistics and econometrics, it can be concluded that there is no multicollinearity among the variables, and all variables can be effectively distinguished. Table 2 Table of correlation coefficients of variables TFP Size Lev ROA Top 1 Dual TQ Growth Board Age TFP 1 Size 0.069 1 Lev 0.419 0.436 1 ROA 0.069 -0.008 -0.343 1 Top 1 0.179 0.201 0.081 0.094 1 Dual -0.050 -0.067 -0.065 0.010 -0.115 1 TQ -0.257 -0.361 -0.340 0.262 -0.063 0.055 1 Growth 0.035 0.056 0.043 0.103 0.050 0.012 -0.013 1 Board 0.077 0.202 0.102 0.034 0.043 -0.147 -0.103 0.018 1 Age 0.228 0.263 0.063 -0.127 -0.174 -0.009 -0.065 -0.029 -0.050 1 5.3 Preliminary Regression Results To examine the impact of implementing green credit policies on the formation of new quality productivity in resource-based enterprises, total factor productivity (TFP) is used as the dependent variable to initiate the regression test of model (1), and the results are listed in Table 3 . In Table 3 , Column I include control variables and fixes individual, industry, and time effects; Column II includes control variables and fixes individual and industry effects without fixing time effects; Column III includes control variables and fixes time and industry effects without fixing individual effects; Column IV includes control variables and fixes individual and time effects without fixing industry effects. The DID coefficients are significantly positive at the 1% and 10% levels, indicating that, with the implementation of the green credit policy, the total factor productivity of resource-based enterprises has significantly increased, effectively promoting the formation of new quality productivity in these enterprises, thereby confirming hypothesis H1a. The fundamental reason for this is that the green credit policy has effectively coordinated the dual functions of environmental regulation and financial resource allocation: on one hand, it directs resource-based enterprises to disclose environmental information, thereby encouraging them to avoid the opportunity costs of "false information disclosure," correcting short-term behaviors, and simultaneously providing potential signals for technological transformation. This incentivizes enterprises to engage in research and development of green technologies, processes, or products, and optimization of management methods, thereby not only offsetting the "compliance costs" of environmental regulations but also producing a "transformation compensation" effect that promotes the formation of new quality productivity. On the other hand, it internalizes the environmental externality costs of resource-based enterprises by facilitating clean investments with low transaction costs and complicating polluting investments with high transaction costs, dynamically adjusting the opportunity costs of environmental destruction throughout the production process, forming an effective path of financial resource allocation that guides the formation of new quality productivity in enterprises. Table 3 Benchmark regression results TFP TFP TFP TFP Ⅰ Ⅱ Ⅲ Ⅳ DID 0.064*** (2.66) 0.024* (1.71) 0.063*** (2.6) 0.061*** (2.8) Control variables YES YES YES YES Fixed year YES YES YES YES Individual fixation YES YES NO YES Industry fixed YES YES YES NO R 2 0.887 0.886 0.654 0.883 N 14369 14369 14369 14369 Note: The figures in parentheses indicate the robust standard deviations clustered at the firm level; *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively. The same notations apply hereinafter. 5.4 Robustness test 5.4.1 Parallel trend test A prerequisite for applying the difference-in-differences (DID) method is to satisfy the parallel trends assumption, namely, that resource-based enterprises and non-resource-based enterprises exhibit similar trends in total factor productivity (TFP) prior to the policy implementation. To verify this hypothesis, the event study method was employed to examine the dynamic impact of the green credit policy on firms' TFP. The sample window period was restricted to three years before and after the policy issuance, with 2012 as the base period. A time dummy variable (Post) was constructed and interacted with the firm dummy variable (Treat), and the interaction term was included in Model (1) regression as the explanatory variable. The results are presented in Fig. 2 . As shown in Fig. 2 , the interaction term coefficient is not significant before the implementation of the green credit policy, indicating no significant difference in TFP between resource-based and non-resource-based enterprises prior to the policy implementation. However, the interaction term coefficients are significant in the post-implementation period, suggesting that the green credit policy has a distinct impact on the TFP of resource-based and non-resource-based enterprises, thereby satisfying the parallel trends assumption. 5.4.2 Placebo test To assess the potential influence of omitted variables and other disturbances on the regression results, Bootstrap technology was employed to randomly select samples, determine policy timing, and construct interaction terms to be included in model (1) regression. A distribution chart of the virtual regression coefficients for total factor productivity was obtained through 500 repeated experiments (Fig. 3 ). As indicated by Fig. 3 , the virtual regression coefficients from the repeated experiments are evidently concentrated around the zero point and approximately follow a normal distribution, which is significantly smaller than the benchmark regression's coefficient of 0.064. Clearly, this confirms that the likelihood of the virtual policy shock exerting a significantly positive or negative effect on the regression coefficient of total factor productivity for resource-based enterprises is small. Therefore, it can be inferred that the effect of the green credit policy is unlikely to be driven by other unobservable factors. 5.4.3 PSM-DID test To reduce the impact of endogeneity on the regression results, the propensity score matching method is employed to conduct the examination (Table 4 ). As shown in Table 4 , the standard deviation of the matched data decreases significantly, indicating that the data becomes more concentrated. The propensity score matching method reduces the selection bias of variables. Table 4 Comparison differences before and after variable matching Variables Sample matching Mean Standardization bias Deviation Reduction (%) t-value p-value Processing groups Control group Lev Unmatched 0.499 0.477 12.100 5.950 0.000 Matched 0.499 0.505 -3.500 71.3 -1.400 0.162 Board Unmatched 2.220 2.165 27.800 14.200 0.000 Matched 2.218 2.219 -0.700 97.5 -0.280 0.781 ROA Unmatched 0.044 0.043 1.500 0.770 0.442 Matched 0.044 0.045 -1.600 -7.5 -0.690 0.492 Growth Unmatched 0.192 0.181 1.100 0.530 0.594 Matched 0.192 0.176 1.600 -45.7 0.880 0.380 Size Unmatched 23.044 22.528 35.600 18.680 0.000 Matched 23.011 23.076 -4.500 87.5 -1.720 0.085 TQ Unmatched 1.594 1.978 -31.400 -14.200 0.000 Matched 1.600 1.667 -5.700 81.8 -3.020 0.003 Top 1 Unmatched 39.196 34.384 30.800 -7.230 0.000 Matched 38.919 38.877 0.300 99.1 -0.110 0.091 Age Unmatched 2.900 2.941 -11.700 -5.940 0.000 Matched 2.903 2.884 5.300 55.1 1.970 0.049 Dual Unmatched 0.120 0.172 -14.800 98.3 -7.230 0.000 Matched 0.121 0.120 0.300 0.110 0.910 After performing the PSM-DID test and subsequent regression analysis, the results are displayed in Table 5 . As shown in Table 5 , the regression outcome of the core explanatory variable DID on the total factor productivity of resource-based enterprises remains positive. Additionally, the coefficients of other control variables are consistent with expectations. This confirms that, after accounting for selection bias, the baseline regression results remain robust. Table 5 PSM-DID regression results TFP Coef. Std. Err. t P > t [95% Conf. Interval] DID 0.065 0.023 2.810 0.005 0.020 0.111 Size 0.405 0.014 28.520 0.000 0.377 0.433 Lev 0.099 0.060 1.640 0.100 -0.019 0.217 ROA 2.183 0.123 17.680 0.000 1.941 2.425 Top 1 0.001 0.001 1.040 0.297 -0.001 0.002 Dual 0.009 0.020 0.450 0.651 -0.030 0.048 TQ 0.021 0.009 2.400 0.016 0.004 0.038 Growth -0.025 0.006 -3.880 0.000 -0.038 -0.013 Board -0.042 0.046 -0.920 0.356 -0.132 0.047 Age 0.126 0.066 1.920 0.055 -0.003 0.255 Fixed year YES YES YES YES YES YES Individual fixation YES YES YES YES YES YES Industry fixed YES YES YES YES YES YES 5.4.4 Other robustness tests To further validate the robustness of the aforementioned regression model and results, multiple strategies were employed. First, the method of measuring the total factor productivity (TFP) of enterprises was changed. Specifically, the LP method was used to measure the TFP of resource-based enterprises and was re-incorporated into Model (2) for regression; the results are shown in Column I of Table 6 . These results are essentially consistent with the previous baseline regression results, confirming the robustness of the model. Second, the regression method was changed. The ordinary least squares (OLS) method was replaced with the generalized least squares (GLS) method for regression, and the results are shown in Column II of Table 6 . The DID regression coefficient is positive at the 1% significance level and is also similar to the baseline regression results, indicating that the research results are robust. Third, the sample range was adjusted. Given that the sample window period (2008–2022) spans a significant amount of time and is susceptible to major events, such as the global financial crisis in 2008 and the sudden outbreak of COVID-19 at the end of 2019, which could impact the production and operation as well as the implementation of green development strategies of resource-based enterprises, the window period was changed to three years before and after the issuance of the "Guideline." The samples were re-selected for regression, with the results shown in Column III of Table 6 . The DID regression coefficient is 0.039 and passes the 5% significance test, verifying that the research conclusions are relatively robust. Fourth, the impact of the "Environmental Protection Law" was excluded. As the new "Environmental Protection Law" issued in 2015 stipulates measures for enterprises to reduce emissions and pollution, which may affect the TFP of resource-based enterprises, a dummy variable for 2015 and subsequent years was added for regression. The results are shown in Column IV of Table 6 . After adding the dummy variable, the green credit DID coefficient and significance remain consistent with the baseline regression coefficient, ruling out the effect of the "Environmental Protection Law" policy on TFP, reflecting the robustness of the research results. Fifth, provincial-year fixed effects were added. Considering that the TFP of resource-based enterprises may be influenced by other environmental protection policies, and most environmental policies in China are implemented by administrative divisions, provincial-year fixed effects were further included in the regression model to control for the potential influence of environmental policies on the regression results. The test results shown in Column V of Table 6 , clearly indicate that the results remain robust. Table 6 Other robustness test results TFP-LP Ⅰ TFP(GLS) Ⅱ Replace the sample Ⅲ Exclusion of the Environmental Protection Act Ⅳ Fixed provinces – year Ⅴ DID 0.025*** (1.79) 0.064*** (2.61) 0.039** (2.45) 0.064*** (4.23) 0.064*** (4.26) Control variables YES YES YES YES YES Fixed effect YES YES YES YES YES R 2 0.927 0.928 0.887 0.888 N 14325 6704 14369 14369 6. Further test the analysis 6.1 Mechanism deepening test To test the aforementioned hypothesis and clarify whether the implementation of green credit policy impacts the formation of new quality productivity in resource-based enterprises through financing constraints, technological innovation, and green transition awareness, a stepwise method for mediation effect testing is adopted. First, the following regression model is established: \(\:{M}_{itd}={\beta\:}_{0}\) + \(\:{\beta\:}_{1}DID+{\beta\:}_{2}{Controls}_{it}+{\mu\:}_{i}+{}_{t}+{\rho\:}_{d}+{}_{itd}\) ⑶ \(\:{TFP}_{itd}={\theta\:}_{0}\) + \(\:{\theta\:}_{1}DID+{\theta\:}_{2}{M}_{it}+{\theta\:}_{3}{Controls}_{it}+{\mu\:}_{i}+{}_{t}+{\rho\:}_{d}+{}_{itd}\) ⑷ In Equations (3) and (4), M represents the mediating variable, while the definitions of other variables remain consistent with Model (1). Then proceed with the actual test step by step: Perform regression on Model (1); if \(\:{\alpha\:}_{1}\) is not significant, the testing stops. If \(\:{\alpha\:}_{1}\) is significant, proceed with tests for Models (3) and (4). If both \(\:{\beta\:}_{1}\) and \(\:{\theta\:}_{2}\) are significant, mediation effects exist. Furthermore, if \(\:{\theta\:}_{1}\) is also significant, it indicates partial mediation effects; if \(\:{\theta\:}_{1}\) is not significant, it indicates full mediation effects. If either \(\:{\beta\:}_{1}\) or \(\:{\theta\:}_{2}\) , or both, are not significant, a Bootstrap test is necessary. In Table 7 , Columns Ⅰ, Ⅲ, and Ⅴ present the regression results for Model (3), while Columns Ⅱ, Ⅳ, and Ⅵ present the regression results for Model (4). Table 7 Mechanism analysis results Variables Financing constraints Technological change Green Transition Awareness SA TFP RD TFP GF TFP Ⅰ Ⅱ Ⅲ Ⅳ Ⅴ Ⅵ DID 0.024*** (6.66) 0.043*** (2.61) 0.110** (4.00) 0.045*** (2.77) 0.070** (3.50) 0.032** (1.96) M 0.040 (0.46) 0.009* (1.77) 0.012* (1.65) Control variables YES YES YES YES YES YES Individual effects YES YES YES YES YES YES Time effect YES YES YES YES YES YES Industry effect YES YES YES YES YES YES N 14369 14369 14040 14040 14310 14310 R 2 0.949 0.873 0.672 0.872 0.540 0.874 6.1.1 The test of financing constraints In Table 7 , the coefficient for the interaction term (DID) in Column I is 0.024, which is significantly positive at the 1% level, indicating that the green credit policy has reduced the financing constraints for resource-based enterprises. The interaction term (DID) coefficient in Column II is 0.043 and is also significant at the 1% level. However, the coefficient for the mediating variable (M) is 0.040 and not significant. A subsequent Bootstrap test showed that the product of the coefficients \(\:{\beta\:}_{1}\) and \(\:{\theta\:}_{2}\) is significantly different from zero at the 99% confidence interval. Additionally, \(\:{\theta\:}_{1}\) 、 \(\:{\beta\:}_{1}\:\) and \(\:{\theta\:}_{2}\) are of the same sign, confirming that the financing constraint variable plays a partial mediating role in the effect of green credit policy on the total factor productivity of resource-based enterprises. Hypothesis H2b is thereby verified. The fundamental reason is that the implementation of the green credit policy has strengthened the environmental information disclosure by resource-based enterprises, alleviated the degree of information asymmetry, reduced instances of financial fraud, and decreased the risk exposure for external investors and stakeholders. This is particularly beneficial for resource-based enterprises, which largely possess state-owned attributes and implicit government guarantees, find resource development profitable, and signal their "greenwashing" to society. All these factors bring favorable news to external investors, making it easier for resource-based enterprises to obtain financing through commercial credit and equity. This allows them to maintain normal production and operations while securing more external investments and cooperation opportunities, facilitating the introduction of new talents, digital transformation, and ultimately enhancing total factor productivity and the formation of new quality productivity. 6.1.2 Technological change test The interaction term (DID) coefficient in Column III of Table 7 is 0.11, which is significantly positive at the 5% level, corroborating that the green credit policy compels technological innovation in resource-based enterprises. The interaction term (DID) coefficient in Column IV is 0.045 at the 1% significance level, and the coefficient of the mediating variable (M) is 0.009 at the 10% significance level, indicating that the technological innovation variable exerts partial mediation effects. The green credit policy enhances the total factor productivity by elevating the level of technological innovation in resource-based enterprises, promoting the formation of new-quality productivity. Thus, hypothesis H3a is confirmed. The fundamental reason lies in the fact that the implementation of the green credit policy has made resource-based enterprises acutely aware that to alleviate financing constraints, enhance competitive advantages, and achieve sustainable and high-quality development, they must align with government guidance, social oversight, and banking preferences, thereby emitting a favorable signal. This fundamentally forces enterprises to overcome internal organizational inertia, strengthen their motivation and actions towards technological innovation, and utilize the "innovation compensation" effect, thereby enhancing the efficiency and value-added of resources and promoting the formation of new-quality productivity. 6.1.3 Green Transformation Awareness Test The coefficient of the interactive term (DID) in Column V of Table 7 is 0.07, significantly positive at the 5% level, indicating that the implementation of green credit policies can promote internal reflection within resource-based enterprises and enhance the recognition of the importance of green, low-carbon, and environmental protection. The coefficient of the interactive term (DID) in Column VI is 0.032 at the 5% significance level, and the coefficient of the mediating variable (M) is 0.012 at the 10% significance level. This confirms that in the impact of green credit policies on the total factor productivity of resource-based enterprises, the variable of green transition awareness exerts a partial mediating effect, which is a positive shock effect. Thus, hypothesis H4 is verified. The fundamental reason lies in the implementation of green credit policies through measures such as risk assessment of pre-loan projects and corporate behaviors, post-loan risk management and adjustment, and market transmission mechanisms. These measures increase external creditors' requirements for enterprise environmental, social responsibility, and green transition performance, thereby compelling enterprise managers to overcome the short-term "green evasion" mentality due to concerns over high sunk costs and "compliance costs" of environmental regulation. This enhances the awareness of environmental and social responsibility and green transition, as well as strengthens the awareness of environmental and social responsibility and green transition among all employees, forming an endogenous driving force for the new quality productivity of resource-based enterprise development. 6.2 Heterogeneity analysis Based on model (1), a heterogeneity analysis was conducted. First, the heterogeneity analysis considered the degree of marketization in different regions. According to the marketization index, the locations of resource-based enterprises were categorized into high, medium, and low marketization regions. The specific marketization index was derived from the "China Provincial Marketization Index Report," and the econometric analysis results are shown in Table 8 . From Table 8 , it can be seen that the impact of green credit policies on the total factor productivity of resource-based enterprises varies significantly with the degree of marketization. Specifically, green credit policies have a significant and strong impact on enterprises in regions with low marketization, no significant impact on enterprises in regions with medium marketization, and a significant but weak impact on enterprises in regions with high marketization. The main reason for this is that economically developed regions with higher marketization have more comprehensive environmental regulations and diverse funding channels, thereby reducing the impact of green credit policies on resource-based enterprises. In contrast, resource-based enterprises in regions with lower marketization are more constrained by environmental, transportation, and funding limitations, making them more susceptible to the influence of green credit policies. The second aspect is the heterogeneity analysis considering the nature of property rights. Resource-based enterprises are categorized into state-owned and non-state-owned enterprises based on their ownership nature. The results of the quantitative analysis are shown in Table 8 . As can be seen from Table 8 , the implementation of green credit policies has a more significant impact on state-owned enterprises, indicating that state-owned enterprises respond more promptly and enforce green credit policies more rigorously compared to non-state-owned enterprises. The primary reason is that state-owned enterprises possess a higher national mission, social responsibility, and a complete mechanism for responding to national policies. They respond faster to policy changes and specific demands. Furthermore, they receive more attention and supervision from the government, the public, and stakeholders, which compels them to urgently convey positive signals to the outside world and actively promote the formation of new productive capacities. Thirdly, consider the heterogeneity analysis of the enterprise lifecycle stages. The lifecycle of resource-based enterprises often aligns with the exploitable cycle of their mineral resources. At different lifecycle stages, profitability, operational status, and innovation awareness exhibit distinct characteristics, thereby varying the impact of green credit policies. Inspired by the research of Tang Song et al. [45], enterprises are classified into growth, maturity, and decline stages for grouping regression analysis. As shown in Table 8 , the impact of green credit policies on the total factor productivity of resource-based enterprises in the decline, maturity, and growth stages decreases sequentially. The primary reason is that resource-based enterprises in the growth stage have weaker profitability and greater incentives for production expansion and revenue increase. Their pursuit of economic performance significantly surpasses that of environmental performance, resulting in a lesser impact from green credit policies. Resource-based enterprises in the maturity stage, with stronger profitability, stable production and cash flows, and a higher inertia for innovation and transformation, tend to place greater importance on social reputation and environmental policies, thus experiencing a moderate impact from green credit policies. In the decline stage, resource-based enterprises face dual constraints from the depletion of resources and environmental rehabilitation, leading to a pronounced operational downturn. The pressure for innovation and transformation intensifies, significantly enhancing their pursuit of new qualitative productivity, resulting in a substantial impact from green credit policies. Table 8 Heterogeneity analysis results Variables The degree of marketization Nature of property rights Life cycle TFP TFP TFP TFP TFP TFP TFP Lower Higher SOE N-SOE Growth period Maturity Recession period DID 0.084*** (2.66) 0.043*** (2.58) 0.936*** (-0.077) 0.558*** (-0.095) 0.022 (1.07) 0.060*** (2.63) 0.225** (2.10) N 2562 11868 5110 3629 6581 5720 2024 R^2 0.891 0.875 0.14 0.051 0.911 0.924 0.903 Individual effects YES YES YES YES YES YES YES Time effect YES YES YES YES YES YES YES Industry effect YES YES YES YES YES YES YES 7. Conclusions and policy implications 7.1 Conclusions (1) The implementation of green credit policies has played an organically coordinated role in both environmental regulation and the allocation of financial resources, effectively promoting the enhancement of total factor productivity and the formation of new quality productivity in resource-based enterprises. On one hand, it guides enterprises to disclose environmental information, prompting them to avoid the opportunity cost of "false information disclosure" and to abandon short-term behaviors. Meanwhile, it provides potential signals for technological transformation, motivating enterprises to develop green technologies, processes, or products, optimize management methods, and achieve the "environmental regulation compliance cost counterbalance" effect, thereby promoting the improvement of total factor productivity. On the other hand, it internalizes the environmental negative externality costs of enterprises by facilitating investments in cleanliness with lower transaction costs, and complicating investments in pollution with higher transaction costs. This dynamically adjusts the opportunity costs of environmental damage throughout the entire production process, leading to effective allocation of financial resources and guiding the development of new quality productivity for enterprises. (2) The implementation of green credit policies alleviates the financing constraints of resource-based enterprises, thereby promoting the enhancement of their total factor productivity (TFP) and the formation of new quality productivity. Such policies can mitigate the degree of information asymmetry within enterprises, reduce occurrences of financial fraud, and lower the risk exposure for external investors and stakeholders. Additionally, these policies enable enterprises to bring favorable news to external investors, leveraging commercial credit and equity to obtain more external investment and cooperation opportunities. This facilitates the introduction of new talents and digital transformation, thereby improving TFP and aiding in the formation of new quality productivity. (3) The implementation of green credit policies compels resource-based enterprises to undergo technological transformation, thereby increasing total factor productivity and driving the formation of new forms of productivity. Such policies force enterprises to align with government directives, societal oversight, and banking preferences, thereby releasing positive signals, overcoming internal organizational inertia, strengthening motivation for transformation, and initiating green technological and management reforms. This plays a "compensatory transformation" role, enhancing resource utilization efficiency and added value, and promoting the formation of new forms of productivity. (4) The implementation of green credit policies enhances the awareness of green transformation among resource-based enterprises, thereby promoting the formation of new productive forces within these companies. Such policies employ a series of measures, including pre-loan project and enterprise behavior risk assessments, post-loan risk management and adjustments, and market transmission mechanisms. These measures increase external creditors' demands on enterprises regarding their environmental and social responsibilities, as well as their green transformation performance. This exerts pressure on enterprise owners and managers to overcome a short-term apathetic attitude of "green avoidance," driven by concerns over excessive sunk costs and the "compliance costs" of environmental regulations. Consequently, these measures enhance the awareness of environmental and social responsibilities and green transformation, while also awakening and strengthening the sense of responsibility among all members of the enterprise. This, in turn, forms an endogenous drive for the development of new productive forces. (5) The green credit policy significantly enhances the new quality productivity of resource-based enterprises, exhibiting notable heterogeneities in terms of marketization degree, ownership, and life cycle stages. Such policies have a more pronounced effect on the total factor productivity and new quality productivity of resource-based enterprises in regions with lower marketization compared to those with higher marketization. This reflects the relative advantages of economically developed regions with well-established environmental regulations and diverse financing channels. For state-owned enterprises, the promotion of total factor productivity and new quality productivity is significantly greater than for non-state-owned enterprises, highlighting the relative advantages of the policy response mechanism and the effect of being highly supervised. The promotion effects on the total factor productivity and new quality productivity of resource-based enterprises follow a descending order from the decline phase, mature phase, to the growth phase. This indicates the relative advantage of shifting from the pressure of resource depletion and environmental restoration to the drive for innovation, transformation, and development of new quality productivity. 7.2 Policy Recommendations (1) Strengthening the primary responsibility and differentiated requirements for commercial banks to implement green credit policies. Improve the professional process for identifying development projects of resource-based enterprises under green credit policies, refine the monitoring content for environmental, social, and governance (ESG) risks, strictly investigate the purposes of loans to prevent "greenwashing" behaviors; promote the innovation of green credit policies, implement resource-based enterprise credit strategies with regional and ownership differences, and avoid "one-size-fits-all" practices to continuously enhance the effectiveness of credit support; adopt a dynamic tracking credit mechanism to guide resource-based enterprises to continuously invest funds to achieve greening and digital intelligence in production operations, thereby accelerating the formation of new quality productivity. (2) Strengthening the disclosure requirements and technological innovation for Environmental, Social, and Governance (ESG) information of resource-based enterprises. Firmly establishing a corporate vision, collective understanding, and development path focused on forming new productivity; detailing the steps and goals for achieving this. Enhancing the quality of accounting information disclosure to reduce credit resource misallocation; promoting breakthroughs in the research and development of green technologies, processes, and products, as well as innovatively allocating production factors to enhance total factor productivity. Improving internal control systems, fully leveraging the benefits of green credit policies to aid in the formation of new productivity. (3) Strengthen the guidance, regulation, and performance evaluation of green credit policies by government departments. Clarify the standards for social responsibility information disclosure of resource-based enterprises, utilizing the dividends of accounting reform systems to enhance the performance of green credit policies; improve the evaluation mechanisms for technological transformation and green transition projects of resource-based enterprises, and strengthen the regular assessment of the implementation effects of green credit policies. Take measures such as material rewards or prioritizing support for corporate businesses and government investment project cooperation to motivate commercial banks to actively implement green credit policies. Guide the complementary and coordinated development of green bonds, insurance, and other instruments to build a multidimensional and three-dimensional green finance system, thereby promoting the formation of new production capacities in resource-based enterprises. 7.3 Research Deficiencies and Prospects This study is based on the quasi-natural experiment of the implementation of the China Banking Regulatory Commission's "Green Credit Guidelines" (2012). It selects the relevant data of resource-based enterprises listed on the A-shares of China from 2008 to 2022 and employs the Difference-in-Differences (DID) method to empirically examine the systemic effects of green credit policies on the formation of new quality productivity in resource-based enterprises. Although this paper conducts a substantial investigation into the systematic impact of green credit policies on the formation of new quality productivity in resource-based enterprises, some limitations still exist. Firstly, the measurement of new quality productivity in resource-based enterprises uses total factor productivity as a substitute rather than constructing a direct indicator system for measurement. Secondly, the data has not been updated to 2023. These shortcomings indicate directions for improvement in future research. Declarations Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. Funding: There is no funding. 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Fintech and enterprise digital transformation——From the perspective of enterprise life cycle[J]. Finance & Economics, ,2022, (2): 17-32. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5891402","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411036363,"identity":"210f51f1-b76d-4c73-be49-3c141ff59bf7","order_by":0,"name":"Ting LI","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"LI","suffix":""},{"id":411036364,"identity":"00976b61-0671-4f31-9d0a-0e5119f0036b","order_by":1,"name":"Wen ZHONG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACNvnDBw58qPjHzDj/YOODhIoawlr4JNgSH844c4CdeQbzYYMHZ44R1iInwWNszNt2gJ99Blua5MMWZiIcJt1jJjnjzB1p3tk9ZhWJDWwM/O3dCfi1yBwrk/hQ8cxYcs4ZsxuJO2QYJM6c3YBfC0PyNqAtzMmGDTlALWfYGAwkcglpSTCT5m1jrt9/IMesILGNmQgtEikg7x9mZpyRlsZAnBaeY6BATmNm7Dl8WCLhzDEegn6Rb28GRaUNM2N7Y+PHHxU1cvztvfi1YAAe0pSPglEwCkbBKMAKAOLrUGLG2zQOAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wen","middleName":"","lastName":"ZHONG","suffix":""},{"id":411036365,"identity":"94a30421-a9f4-42b8-ad76-07296bb19690","order_by":2,"name":"Zhiqing YAN","email":"","orcid":"","institution":"Jiangxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zhiqing","middleName":"","lastName":"YAN","suffix":""}],"badges":[],"createdAt":"2025-01-24 00:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5891402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5891402/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75632506,"identity":"9c5467b2-eff6-4586-b1a2-29e595053e2a","added_by":"auto","created_at":"2025-02-06 14:06:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17333,"visible":true,"origin":"","legend":"\u003cp\u003eMechanism of the effect of green credit policies on the new quality productivity of resource-based enterprises\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5891402/v1/954b114845d30764a9afa689.png"},{"id":75633648,"identity":"d8c629ca-4405-443a-ae76-3c31bb707230","added_by":"auto","created_at":"2025-02-06 14:14:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26534,"visible":true,"origin":"","legend":"\u003cp\u003eParallel trend test\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5891402/v1/08b11f611451b6bbca9e1f04.png"},{"id":75632509,"identity":"9f39da5e-02ec-432c-958a-b85713f5443f","added_by":"auto","created_at":"2025-02-06 14:06:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53784,"visible":true,"origin":"","legend":"\u003cp\u003ePlacebo test\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5891402/v1/f1d122ebece0c097b37ab6a2.png"},{"id":81643501,"identity":"4ce06ada-90f1-4d8a-9e5b-a288cb76e755","added_by":"auto","created_at":"2025-04-29 14:02:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1685404,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5891402/v1/6b5be8db-dd59-4829-8ae9-eb9d6b79492d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Does green credit policy promote the formation of new quality productivity in resource-based enterprises? Evidence from China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn September 2023, during an inspection tour in Heilongjiang, General Secretary Xi Jinping introduced the scientific concept and strategic task of \u0026ldquo;accelerating the formation of new quality productivity.\u0026rdquo; This innovative approach to productivity is characterized by significant technological breakthroughs, novel factor configurations, and comprehensive industrial transformations. Resource-based enterprises, particularly those involved in mineral extraction and primary processing, are notorious for their substantial negative externalities, which include surface subsidence, water system destruction, land desertification, and atmospheric pollution. Their reliance on traditional, extensive operational methods hampers the enhancement of resource extraction efficiency and total factor productivity. Presently, China hosts approximately 67,000 resource-based enterprises, affecting over 3.75\u0026nbsp;million hectares of land through excavation, subsidence, or occupation. These enterprises annually contribute around 795\u0026nbsp;million tons of waste rock and discharge 6.004\u0026nbsp;billion cubic meters of mine water, inflicting damage to 22\u0026nbsp;billion cubic meters of groundwater resources [1]. It is evident that transforming the developmental strategies of these enterprises and hastening the emergence of new productive forces is crucial and urgent for achieving high-quality development.\u003c/p\u003e \u003cp\u003eThe development of new productive capacities in resource-based enterprises requires a balanced approach that avoids over-reliance on market forces or purely governmental directives. Green credit policy serves as a middle ground, integrating financial mechanisms with environmental strategies. This hybrid approach mitigates the rigidity associated with administrative control and enhances the market\u0026rsquo;s regulatory influence. Since the release of the \u0026ldquo;Green Credit Guidelines\u0026rdquo; by the China Banking Regulatory Commission in 2012 and the subsequent \u0026ldquo;Green Finance Evaluation Scheme for Banking Financial Institutions\u0026rdquo; by the People\u0026rsquo;s Bank of China in 2021, there has been continuous refinement in green credit management and evaluation standards. These policies are designed to bolster green industries by directing essential financial resources toward their development. Simultaneously, they aim to curb financial support for enterprises characterized by high pollution, energy consumption, and emissions \u0026ndash; commonly referred to as the \u0026ldquo;Three Highs\u0026rdquo; \u0026ndash; through raised loan thresholds. Consequently, these policies reallocate financial resources to promote environmentally sustainable practices. The critical questions remain: How do these policies impact resource-based enterprises, and are they effective in encouraging the formation of new, environmentally-friendly productive capacities within these sectors?\u003c/p\u003e"},{"header":"2. Literature review","content":"\u003cp\u003eReviewing the literature related to this research topic reveals that both domestic and international scholars primarily focus on three dimensions: Firstly, the effectiveness of the implementation of green credit policies. Some scholars, examining from the perspective of banks, have found that green credit policies explicitly guide banks to strictly control credit for projects categorized as \"three highs\" (high pollution, high energy consumption, high emissions), while providing low-interest rate credit support for green projects. This inevitably leads to a structural adjustment in the flow of credit resources, thereby promoting the development of green, low-carbon, and circular industries \u003csup\u003e[\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. This policy not only reduces the risk for banks but also regulates the impact of bank liquidity on profitability \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Hence, it is evident that commercial banks recognize the necessity of implementing green credit policies. Additionally, some scholars have investigated from the perspective of enterprises and found that green credit policies can promote adjustments in corporate decision-making and enhancement of technological protection levels, thereby improving corporate performance \u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Specifically, through the aspects of financing constraints and investment efficiency, these policies enhance the economic, social, and governance performance evaluations of enterprises \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, ultimately fostering technological innovation within firms. Further research by Berikhanovna et al \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. found that green credit policies significantly promote technological innovation in large enterprises but do not have a significant effect on technological innovation in small and medium-sized enterprises. This indicates a considerable disparity in existing research conclusions and a lack of in-depth studies on the heterogeneity of implementation effects across different industries. Secondly, attention is given to the influencing factors of productivity development in resource-based enterprises. Some scholars, based on the rational allocation of production factors in economic growth theory, believe that capital substitution for labor, i.e., capital deepening, has a significant impact on the productivity development of enterprises \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Other scholars have pointed out that imperfect industrial policies, inadequate legal systems, and weak social awareness can hinder the green transformation of resource-based enterprises \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, revealing barriers to the development of enterprise productivity. On the other hand, some scholars, using a panel threshold model for empirical testing, have found that digital transformation can free resource-based enterprises from time and space constraints, allowing them to leverage the comparative advantages of various production factors and achieve enhanced production efficiency \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. More specific empirical findings indicate that digitization factors in strategic planning, marketing, and operations management significantly enhance the productivity development of resource-based enterprises \u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. Meanwhile, some scholars have argued that resource tax reform and higher executive compensation can significantly promote green innovation in resource-based enterprises \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Further research by other scholars has found that environmental tax reform drives productivity development in resource-based enterprises by encouraging technological innovation, restricting opportunistic behavior, and facilitating factor mobility \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Clearly, there is a lack of systematic research on these influencing factors, and the impact of green credit policies has not been considered. Thirdly, the focus is on the impact of green credit policies on corporate productivity development. Based on classical economic theory, some scholars have suggested that green credit policies reinforce the inherent risk aversion of commercial banks, increase the difficulty of obtaining loans for high-pollution enterprises, create a \"penalty effect,\" causing enterprises to fail to secure sufficient funds and thus reduce green innovation. This may also induce \"greenwashing\" behaviors, ultimately suppressing productivity development \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In contrast, other scholars, examining the \"Porter Hypothesis,\" believe that green financial policies promote green development by increasing the financing constraints and debt financing costs for high-pollution enterprises, thereby stimulating enterprise technological innovation and social responsibility \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, some other studies recognize the positive incentives green credit policies have on the productivity development of heavily polluting enterprises \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Clearly, existing studies have not reached a consensus, and since high-pollution enterprises represent an extreme type, further research is needed to explore and deepen the understanding of the impact on the majority of enterprises, which possess certain pollution levels but also serve as pillars of socio-economic development.\u003c/p\u003e \u003cp\u003eIn conclusion, previous research, whether focused on different subjects or utilizing varying theories, methods, and data, has not fully elucidated the theoretical \"black box\" of the relationship between green credit policies and the new productivity of resource-based enterprises. Delving into the specific pathways through which green credit policies influence the formation of new productivity in resource-based enterprises is also a crucial prerequisite for comprehensively evaluating the micro-effects of green finance. Resource-based enterprises possess certain environmental negative externalities and hold strategic importance in national resource development. The formation of their new productivity is an organizational behavior characterized by industry-specific and Chinese-specific traits. In exploring the mechanisms by which green credit policies affect the formation of new productivity in these enterprises, existing research lacks systematicity and specificity. Particularly, there is a notable absence of systematic examination of the pathways through which effects on their financing constraints, technological transformation, and green transition awareness contribute to the formation of new productivity.\u003c/p\u003e \u003cp\u003eConsidering that the \"Guidelines\" is China's first green credit policy document, this study takes the effective date of the \"Guidelines\" as the benchmark to design a quasi-natural experiment. By adopting the difference-in-differences method, the study aims to elucidate the mechanism through which the green credit policy influences the formation of new productivity in resource-based enterprises. The study also examines whether this effect is incentivizing or inhibiting and provides systematic insights to promote the comprehensive implementation and effective improvement of green credit policies. This is expected to accelerate the formation of new productivity and high-quality development of enterprises. Research on this issue effectively fills the gap in the theoretical study of green finance in the resource industry, making it of significant importance.\u003c/p\u003e \u003cp\u003eCompared to previous literature, the marginal contributions and main innovations of this study are as follows: First, it discovers that green credit policies can effectively synergize the functionalities of environmental regulation and financial resource allocation, significantly promoting the total factor productivity (TFP) of resource-based enterprises and the formation of new productive capacities. Secondly, it reveals the partial mediating effects of financing constraints, technological transformation, and awareness of green transition in the functioning of green credit policies. Specifically, the implementation of these policies helps alleviate financing constraints, catalyze technological innovation, and enhance awareness of the green transition, thereby supporting the rise in TFP and the formation of new productive capacities in resource-based enterprises. Thirdly, it clarifies the heterogeneous impacts of green credit policies on the promotion of new productive capacities across different marketization levels, property rights, and lifecycle stages of resource-based enterprises. The effects are more pronounced in regions with lower levels of marketization, stronger for state-owned enterprises (SOEs) compared to non-SOEs, and decrease in order from declining, mature, to growing enterprises. Fourthly, it provides empirical evidence supporting the need for strengthened collaboration among commercial banks, resource-based enterprises, and governmental departments in the implementation of green credit policies to promote the formation of new productive capacities in resource-based enterprises, along with important policy implications for improvements.\u003c/p\u003e"},{"header":"3. Theoretical analysis and research assumptions","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The role of green credit policy on the development of new productivity of resource-based enterprises\u003c/h2\u003e \u003cp\u003eThe essence of green credit policy is to guide credit allocation under environmental constraints, involving two major functions: environmental regulation and financial resource allocation \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. From the perspective of the environmental regulation function of green credit policy, based on signaling theory, financial institutions extend credit based on resource-based enterprises' environmental compliance and social responsibility levels. Consequently, enterprises face higher transaction costs due to expanded environmental information disclosure and social responsibility activities, which compel them to avoid the opportunity cost of \"pseudo-disclosure,\" correct short-term behaviors, adapt to the regulatory requirements of financial institutions, and develop new qualitative productivity. Meanwhile, based on the \"Porter Hypothesis,\" green credit policy can imply innovation for enterprises, incentivizing them to engage in technological and managerial transformations, actively contributing to the development of green technologies, processes, or products, and optimizing management methodologies. This not only offsets the \"compliance costs\" of environmental regulations but also generates a \"transformation compensation\" effect, promoting the formation of new qualitative productivity in enterprises. From the perspective of the financial resource allocation function of green credit policy, the policy internalizes the environmental negative externality costs of resource-based enterprises. By facilitating and reducing transaction costs for clean investments and complicating and increasing transaction costs for polluting investments, it dynamically adjusts the opportunity costs of environmental destruction by enterprises. This approach implements environmental governance starting from the production process and throughout its entirety, forming an effective path for financial resource allocation to guide the formation of new qualitative productivity in enterprises \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Consequently, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH1a: The implementation of the green credit policy will promote the formation of new qualitative productivity of resource-based enterprises.\u003c/p\u003e \u003cp\u003eHowever, the role of green credit policies may also result in counterproductive effects. Based on neoclassical economic theory, the environmental regulation function of green credit policies, under unchanged conditions of capital, labor, and technology in resource-based enterprises, leads to an increase in environmental governance costs, occupying production resources and crowding out funding for green technology development and green deep processing of products \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. This worsens the balance sheet and restricts the capacity to develop new productive forces. The financial resource allocation function of green credit policies, when facing the substantial investment, long cycles, and high risks of green technological transformation in resource-based enterprises, raises the threshold for bank credit and increases the risk of funding gaps for enterprises. This challenge may be unbearable for enterprises with single financing channels, inhibiting actions to develop new productive forces. Thus, it proposes the following hypothesis:\u003c/p\u003e \u003cp\u003eH1b: The implementation of green credit policy will inhibit the formation of new qualitative productivity of resource-based enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Green credit policy and financing constraints for resource-based enterprises\u003c/h2\u003e \u003cp\u003eFinancing constraints are the main measure of the extent to which resource-based enterprises face limitations in external financing. With the implementation of green credit policies, the role of banks has become more prominent. Resource-based enterprises may not receive credit approval if their projects do not meet policy standards. Even if they receive credit, they may face temporary or permanent suspension of funding due to significant risks identified during process risk assessment and monitoring. This indicates that banks, inherently profit-seeking and risk-averse, have significantly raised the regulatory requirements for environmental and social risks, as well as the credit thresholds for enterprises. Consequently, the difficulty, cost, and social pressure for these enterprises to obtain bank loans have increased considerably \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Moreover, resource-based enterprises exhibit strong environmental negative externalities, making end-of-pipe treatment challenging, costly, and resource-intensive \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. They also face the risk of environmental litigation and the withdrawal or termination of credit extensions by external creditors. Additionally, the development cycle for green technologies and new productive capacities is long, requires substantial investment, and carries high risks, further exacerbating financing constraints. Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH2a: The green credit policy has increased the financing constraints of resource-based enterprises and inhibited the formation of new quality productivity of enterprises.\u003c/p\u003e \u003cp\u003eSimultaneously, due to the enhancement of environmental information disclosure by resource-based enterprises under the green credit policy, the degree of information asymmetry has been significantly reduced. Based on the pecking order theory under an imperfect market, the degree of corporate financing constraints is positively correlated with the degree of information asymmetry. As corporate information becomes more transparent, financial fraud is curbed, reducing the information collection costs and investment risks for external investors. Furthermore, because most resource-based enterprises are state-owned and have implicit government guarantees, developing resources becomes \"profitable,\" signaling a \"greenwashing\" image to society. This sends positive messages to external investors, allowing the enterprises to alleviate financing constraints through means with low restrictions and low financing costs, such as financing via current liabilities and trade credit. For example, they could settle payments through delayed payment or accounts payable, acquiring certain funds for developing new types of productivity such as introducing new talents and advancing digital and intelligent transformations. In other words, the green credit policy reduces the difficulty for resource-based enterprises to obtain funds through informal financing channels. This leads to the hypothesis:\u003c/p\u003e \u003cp\u003eH2b: The green credit policy has alleviated the financing constraints of resource-based enterprises and promoted the formation of new quality productivity of enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Green Credit Policy and Technological Transformation of Resource-based Enterprises\u003c/h2\u003e \u003cp\u003eTechnological innovation is the main avenue for enhancing the total factor productivity of resource-based enterprises. Policies such as the \"Guidelines\" explicitly instruct financial institutions to increase credit support and optimize processes for green and low-carbon development, thereby incentivizing the development of green products and services. This makes resource-based enterprises acutely aware that to alleviate financing constraints, enhance competitive advantages, and achieve high-quality development, they must align with government directives, social supervision, and bank preferences, sending positive signals by implementing technological changes and developing new productive capacities. Hence, the motivation for technological innovation within these enterprises continuously strengthens \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Existing research indicates that green credit policies lead to more active development of green technologies among heavily polluting enterprises \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Additionally, the fundamental purpose of policies like the \"Guidelines,\" which serve as financial instruments of environmental policy, is not to force the closure or halting of resource-based enterprises but to compel them to overcome internal organizational inertia, implement technological changes, and realize the \"compensation for change\" effect \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. This aligns with the development motivations and long-term goals of enterprise owners. Therefore, under the constraints of policies like the \"Guidelines,\" banks should provide credit support for potential technological innovation projects of resource-based enterprises, particularly in the realm of green technology, processes, or products, following scientific risk assessments. This will in turn promote the improvement of resource utilization efficiency, the added value of enterprises, and the formation of new productive capacity \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Thus, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH3a: The green credit policy will force the technological change of resource-based enterprises and promote the formation of new quality productivity of enterprises.\u003c/p\u003e \u003cp\u003eHowever, the implementation of green credit policies may also have unintended consequences. Resource-based enterprises, which exhibit environmental negative externalities and some characteristics of \"three-high\" enterprises (high energy consumption, high pollution, and high emissions), face higher financing thresholds and costs due to the issuance of policies such as the \"Guidelines.\" Consequently, these enterprises can only allocate their limited working capital to urgent production and operational needs, resulting in insufficient investment in technological transformation and the development of new qualitative productivity. Furthermore, the technological transformation and formation of new qualitative productivity in resource-based enterprises require significant investments over an extended period, characterized by longer return cycles and higher risks. The issuance of policies such as the \"Guidelines\" also leads risk-averse financial institutions to reduce their enthusiasm for investing in such long-term, high-risk projects that lack immediate economic benefits. Existing studies have suggested that the introduction of green credit policies has restricted corporate credit financing, particularly long-term borrowing, thereby negatively inhibiting the output of new technologies for enterprises \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Other research indicates that the green credit policy does not significantly enhance the quality of technological development \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Thus, we propose the following hypothesis:\u003c/p\u003e \u003cp\u003eH3b: The green credit policy will hinder the technological change of resource-based enterprises and inhibit the formation of new quality productivity of enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Green credit policy and green transformation awareness of resource-based enterprises\u003c/h2\u003e \u003cp\u003eGreen transition awareness refers to the concept wherein enterprises take sustainable development as their responsibility and shift towards low-carbon, zero-pollution, digital intelligent, and high-efficiency operational modes. After the introduction of the green credit policy, financial institutions can access more information about companies' environmental damage, governance, and green transition, thereby conducting more precise risk assessments for loan projects and corporate behaviors to reduce the default risk and environmental harm risk associated with corporate credit. Additionally, policies such as the \"Guidelines\" emphasize post-loan management by financial institutions, requiring banks to monitor the behavior of loan-receiving companies, and based on risk management, to reassess or adjust loan disbursements, thereby eliminating motivations and behaviors related to \"greenwashing\". Furthermore, market transmission mechanisms will be used to create new requirements from external creditors regarding companies' environmental and social responsibilities, as well as green transition performance. In response, resource-based enterprises will place greater emphasis on green, low-carbon, environmentally friendly, and new quality productivity development behaviors to gain the trust of banks and external creditors \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In other words, the punitive mechanisms of the green credit policy will effectively compel corporate managers to overcome the short-term \"green evasion\" mentality, induced by concerns over excessive sunk costs and compliance costs related to environmental regulations, thus enhancing their awareness of environmental and social responsibilities and green transition. This also strengthens the overall awareness of environmental, social responsibility, and green transition within enterprises, which can serve as an endogenous driving force for the formation of new quality productivity. Accordingly, we propose the following hypothesis:\u003c/p\u003e \u003cp\u003eH4: The green credit policy can improve the awareness of green transformation of resource-based enterprises and promote the formation of new quality productivity of enterprises.\u003c/p\u003e \u003cp\u003eBased on the aforementioned four types of hypotheses and their intrinsic logical relationships, a conceptual model can be established to elucidate the mechanism by which green credit policies impact the new productivity of resource-based enterprises, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Research Design","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Model settings\u003c/h2\u003e \u003cp\u003eTo systematically analyze and test the impact of the implementation of green credit policies on the new quality productivity of resource-based enterprises, based on the aforementioned theoretical analysis and research hypotheses, and considering that the Difference-in-Differences (DID) method effectively avoids endogeneity issues when analyzing the effects of macro policies on micro-individuals, and can also combine with fixed effects to mitigate omitted variable bias, inspired by previous studies \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, the following DID model is constructed:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{TFP}_{itd}={\\alpha\\:}_{0}\\)\u003c/span\u003e \u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}DID+{\\alpha\\:}_{2}{Controls}_{itd}+{\\mu\\:}_{i}+{}_{t}+{\\rho\\:}_{d}+{}_{itd}\\)\u003c/span\u003e\u003c/span\u003e ⑴\u003c/p\u003e \u003cp\u003eIn formula (1), TFP represents the dependent variable\u0026mdash;Total Factor Productivity (TFP) of resource-based enterprises. The subscripts \u003cem\u003ei\u003c/em\u003e, \u003cem\u003et\u003c/em\u003e and d represent the enterprise, year, and industry, respectively. DID denotes the explanatory variable, i.e., the Difference-in-Differences term Treat\u0026times;Post, where Treat indicates whether it is a resource-based enterprise, and Post indicates whether the resource-based enterprise is affected by the green credit policy. Controls represent the control variables.\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\mu\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}_{t}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\rho\\:}_{d}\\)\u003c/span\u003e\u003c/span\u003e represent enterprise, time, and industry fixed effects, respectively; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{}_{itd}\\)\u003c/span\u003e\u003c/span\u003e denotes the random error term.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable Design\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1 Explanatory variables\u003c/h2\u003e \u003cp\u003eTo measure the performance of new productivity formation for resource-based enterprises, considering the characteristics of \"three highs\" and previous experiences, this study uses total factor productivity (TFP) as the explained variable, which is the core indicator of new productivity development. TFP not only reflects the average unit output of various production input factors under general conditions, indicating the overall efficiency of input and output, but is also related to technological progress and the combination of production factors. It embodies factors such as the technological level, management proficiency, institutional quality, and calculation errors, thus clearly observing the efficiency changes in the formation of new productivity in resource-based enterprises. Inspired by the research approach of Olley and Pakes \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, the following production function model is established:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{lnP}_{it}={\\beta\\:}_{0}+{\\beta\\:}_{1}{lnK}_{it}+{\\beta\\:}_{2}{lnL}_{it}+{\\beta\\:}_{3}{lnZ}_{it}+\\sum\\:Year+\\sum\\:industry+{}_{it}\\)\u003c/span\u003e \u003c/span\u003e ⑵\u003c/p\u003e \u003cp\u003eIn Eq.\u0026nbsp;(2), P represents the output of the enterprise, measured by operating revenue and deflated using the producer price index. K represents the capital stock of the enterprise, measured by the net amount of fixed assets. L represents the end-of-period number of employees. Z represents the proxy variable, measured by the operating revenue minus the value added, where the latter includes the sum of depreciation, wages, net production taxes, and operating profit. According to the definition of Total Factor Productivity (TFP), the residuals from the estimation of the above production function are the TFP values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2 Explanatory variables\u003c/h2\u003e \u003cp\u003eThe explanatory variable involved in the model is the green credit difference-in-difference term (Treat\u0026times;Post). Considering that the \u0026ldquo;Guideline\u0026rdquo; came into effect in February 2012, the experimental period is chosen as 2008 to 2022. For resource-based enterprises, the sample takes Treat as 1, otherwise Treat is 0; for sample years from 2012 onwards, Post is taken as 1, otherwise Post is 0.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e4.2.3 Mediation variables\u003c/h2\u003e \u003cp\u003eTo examine whether the green credit policy affects the new-quality productivity of resource-based enterprises through financing constraints, technological innovation, and green transition awareness, this study adopts the methodologies of existing research \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. It utilizes the enterprise financing constraint index (SA, absolute value), enterprise research and development intensity (RD, logarithm of the sum of patent applications and granted patents plus one), and enterprise green transition focus (GF, comprehensive score of five indicators: establishment of environmental management systems, provision of environmental training, implementation of environmental actions, and acquisition of ISO9001 and ISO14001 certifications) as mediating variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.4 Control variables\u003c/h2\u003e \u003cp\u003eTo mitigate the potential influence of other factors on the dependent variable, a set of control variables is designed: (1) Firm size (Size): the natural logarithm of total assets at the end of the year. (2) Firm age (Age): the natural logarithm of the number of years since the firm's registration. (3) Leverage ratio (Lev): the ratio of total liabilities to total assets at the end of the year. (4) Return on assets (ROA): the ratio of net profit to the average balance of total assets. (5) Total asset growth rate (Growth): the ratio of the increase in total assets during the year to the total assets at the beginning of the year. (6) TOP1: the shareholding ratio of the largest shareholder. (7) Separation of ownership and control (Dual): the difference between the actual controller's controlling rights and ownership rights in the listed company. (8) Firm growth (TQ): the ratio of the sum of the market value of circulating shares, the value of preferred shares, and net liabilities to the book value of total assets. (9) Board size (Board): the natural logarithm of the total number of directors.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Sample selection and data sources\u003c/h2\u003e \u003cp\u003eThe study selected A-share listed companies in China from 2008 to 2022 as the initial sample. Following the methodology of Ma Jie et al. [19], and based on the Chinese \"Industry Classification for National Economic Activities\" standard, we categorized companies in 15 industries, such as petroleum, coal, and non-ferrous metals, as resource-based enterprises. The remaining listed companies were assigned to the control group. We meticulously excluded three types of companies: listed financial enterprises, ST, *ST, and PT companies, as well as companies with severely incomplete financial and related research data. Ultimately, we obtained 958 companies with a total of 14,369 observations. Additionally, to mitigate the impact of extreme values on regression results, we winsorized the continuous variables at 1% and 99% quantiles. The financial data used in this study were sourced from the CSMAR database and the Wind database, while patent-related data were obtained from the National Intellectual Property Administration and the WIPO database. Indicators concerning corporate green transition focus were extracted from environmental information disclosed in the annual reports of listed companies.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistical results of the various variables involved in the study are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, firstly, there is a significant difference between the maximum and minimum values of TFP, indicating a substantial disparity in the development levels of new productivity among resource-based enterprises. Therefore, the effect of green credit policy on this matter requires further empirical investigation. Secondly, except for the large standard deviation of the shareholding ratio of the largest shareholder, the fluctuations of other control variables are within normal range, providing suitable conditions for further research.\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 statistical results of variables\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\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\u003eSt.d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRD\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSOE\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e89.986\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003earea\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Correlation analysis\u003c/h2\u003e \u003cp\u003eThe results of the correlation tests between the dependent variable and control variables, as well as among the control variables themselves, are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As evident from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the correlation coefficients between variables are all significantly less than 0.7. The variance inflation factor (VIF) test results indicate that all VIF values are well below 10. According to the standards of statistics and econometrics, it can be concluded that there is no multicollinearity among the variables, and all variables can be effectively distinguished.\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\u003eTable of correlation coefficients of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTQ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\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 \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.035\u003c/p\u003e \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\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Preliminary Regression Results\u003c/h2\u003e \u003cp\u003eTo examine the impact of implementing green credit policies on the formation of new quality productivity in resource-based enterprises, total factor productivity (TFP) is used as the dependent variable to initiate the regression test of model (1), and the results are listed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Column I include control variables and fixes individual, industry, and time effects; Column II includes control variables and fixes individual and industry effects without fixing time effects; Column III includes control variables and fixes time and industry effects without fixing individual effects; Column IV includes control variables and fixes individual and time effects without fixing industry effects. The DID coefficients are significantly positive at the 1% and 10% levels, indicating that, with the implementation of the green credit policy, the total factor productivity of resource-based enterprises has significantly increased, effectively promoting the formation of new quality productivity in these enterprises, thereby confirming hypothesis H1a. The fundamental reason for this is that the green credit policy has effectively coordinated the dual functions of environmental regulation and financial resource allocation: on one hand, it directs resource-based enterprises to disclose environmental information, thereby encouraging them to avoid the opportunity costs of \"false information disclosure,\" correcting short-term behaviors, and simultaneously providing potential signals for technological transformation. This incentivizes enterprises to engage in research and development of green technologies, processes, or products, and optimization of management methods, thereby not only offsetting the \"compliance costs\" of environmental regulations but also producing a \"transformation compensation\" effect that promotes the formation of new quality productivity. On the other hand, it internalizes the environmental externality costs of resource-based enterprises by facilitating clean investments with low transaction costs and complicating polluting investments with high transaction costs, dynamically adjusting the opportunity costs of environmental destruction throughout the production process, forming an effective path of financial resource allocation that guides the formation of new quality productivity in enterprises.\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\u003eBenchmark 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\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\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\u003eⅠ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003cp\u003e(2.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024*\u003c/p\u003e \u003cp\u003e(1.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003cp\u003e(2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.061***\u003c/p\u003e \u003cp\u003e(2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\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\u003eFixed year\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\u003eIndividual fixation\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\u003eNO\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\u003eIndustry fixed\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\u003eNO\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The figures in parentheses indicate the robust standard deviations clustered at the firm level; *, **, and *** denote significance levels of 10%, 5%, and 1%, respectively. The same notations apply hereinafter.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Robustness test\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Parallel trend test\u003c/h2\u003e \u003cp\u003eA prerequisite for applying the difference-in-differences (DID) method is to satisfy the parallel trends assumption, namely, that resource-based enterprises and non-resource-based enterprises exhibit similar trends in total factor productivity (TFP) prior to the policy implementation. To verify this hypothesis, the event study method was employed to examine the dynamic impact of the green credit policy on firms' TFP. The sample window period was restricted to three years before and after the policy issuance, with 2012 as the base period. A time dummy variable (Post) was constructed and interacted with the firm dummy variable (Treat), and the interaction term was included in Model (1) regression as the explanatory variable. The results are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the interaction term coefficient is not significant before the implementation of the green credit policy, indicating no significant difference in TFP between resource-based and non-resource-based enterprises prior to the policy implementation. However, the interaction term coefficients are significant in the post-implementation period, suggesting that the green credit policy has a distinct impact on the TFP of resource-based and non-resource-based enterprises, thereby satisfying the parallel trends assumption.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e5.4.2 Placebo test\u003c/h2\u003e \u003cp\u003eTo assess the potential influence of omitted variables and other disturbances on the regression results, Bootstrap technology was employed to randomly select samples, determine policy timing, and construct interaction terms to be included in model (1) regression. A distribution chart of the virtual regression coefficients for total factor productivity was obtained through 500 repeated experiments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As indicated by Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the virtual regression coefficients from the repeated experiments are evidently concentrated around the zero point and approximately follow a normal distribution, which is significantly smaller than the benchmark regression's coefficient of 0.064. Clearly, this confirms that the likelihood of the virtual policy shock exerting a significantly positive or negative effect on the regression coefficient of total factor productivity for resource-based enterprises is small. Therefore, it can be inferred that the effect of the green credit policy is unlikely to be driven by other unobservable factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e5.4.3 PSM-DID test\u003c/h2\u003e \u003cp\u003eTo reduce the impact of endogeneity on the regression results, the propensity score matching method is employed to conduct the examination (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the standard deviation of the matched data decreases significantly, indicating that the data becomes more concentrated. The propensity score matching method reduces the selection bias of variables.\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\u003eComparison differences before and after variable matching\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSample matching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandardization bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDeviation Reduction (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProcessing groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-31.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-14.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-3.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e98.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-7.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter performing the PSM-DID test and subsequent regression analysis, the results are displayed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the regression outcome of the core explanatory variable DID on the total factor productivity of resource-based enterprises remains positive. Additionally, the coefficients of other control variables are consistent with expectations. This confirms that, after accounting for selection bias, the baseline regression results remain robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePSM-DID regression results\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Err.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSize\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLev\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eROA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTop\u003c/em\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDual\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTQ\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGrowth\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBoard\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.356\u003c/p\u003e \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 \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFixed year\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual fixation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry fixed\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e5.4.4 Other robustness tests\u003c/h2\u003e \u003cp\u003eTo further validate the robustness of the aforementioned regression model and results, multiple strategies were employed. First, the method of measuring the total factor productivity (TFP) of enterprises was changed. Specifically, the LP method was used to measure the TFP of resource-based enterprises and was re-incorporated into Model (2) for regression; the results are shown in Column I of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. These results are essentially consistent with the previous baseline regression results, confirming the robustness of the model. Second, the regression method was changed. The ordinary least squares (OLS) method was replaced with the generalized least squares (GLS) method for regression, and the results are shown in Column II of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The DID regression coefficient is positive at the 1% significance level and is also similar to the baseline regression results, indicating that the research results are robust. Third, the sample range was adjusted. Given that the sample window period (2008\u0026ndash;2022) spans a significant amount of time and is susceptible to major events, such as the global financial crisis in 2008 and the sudden outbreak of COVID-19 at the end of 2019, which could impact the production and operation as well as the implementation of green development strategies of resource-based enterprises, the window period was changed to three years before and after the issuance of the \"Guideline.\" The samples were re-selected for regression, with the results shown in Column III of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The DID regression coefficient is 0.039 and passes the 5% significance test, verifying that the research conclusions are relatively robust. Fourth, the impact of the \"Environmental Protection Law\" was excluded. As the new \"Environmental Protection Law\" issued in 2015 stipulates measures for enterprises to reduce emissions and pollution, which may affect the TFP of resource-based enterprises, a dummy variable for 2015 and subsequent years was added for regression. The results are shown in Column IV of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. After adding the dummy variable, the green credit DID coefficient and significance remain consistent with the baseline regression coefficient, ruling out the effect of the \"Environmental Protection Law\" policy on TFP, reflecting the robustness of the research results. Fifth, provincial-year fixed effects were added. Considering that the TFP of resource-based enterprises may be influenced by other environmental protection policies, and most environmental policies in China are implemented by administrative divisions, provincial-year fixed effects were further included in the regression model to control for the potential influence of environmental policies on the regression results. The test results shown in Column V of Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, clearly indicate that the results remain robust.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOther robustness test results\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\u003eTFP-LP\u003c/p\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTFP(GLS)\u003c/p\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReplace the sample\u003c/p\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExclusion of the Environmental Protection Act\u003c/p\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFixed provinces \u0026ndash; year\u003c/p\u003e \u003cp\u003eⅤ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.025***\u003c/p\u003e \u003cp\u003e(1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003cp\u003e(2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039**\u003c/p\u003e \u003cp\u003e(2.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003cp\u003e(4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.064***\u003c/p\u003e \u003cp\u003e(4.26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\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\u003eFixed effect\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\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Further test the analysis","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Mechanism deepening test\u003c/h2\u003e \u003cp\u003eTo test the aforementioned hypothesis and clarify whether the implementation of green credit policy impacts the formation of new quality productivity in resource-based enterprises through financing constraints, technological innovation, and green transition awareness, a stepwise method for mediation effect testing is adopted. First, the following regression model is established:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{itd}={\\beta\\:}_{0}\\)\u003c/span\u003e \u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}DID+{\\beta\\:}_{2}{Controls}_{it}+{\\mu\\:}_{i}+{}_{t}+{\\rho\\:}_{d}+{}_{itd}\\)\u003c/span\u003e\u003c/span\u003e ⑶\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{TFP}_{itd}={\\theta\\:}_{0}\\)\u003c/span\u003e \u003c/span\u003e+\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{1}DID+{\\theta\\:}_{2}{M}_{it}+{\\theta\\:}_{3}{Controls}_{it}+{\\mu\\:}_{i}+{}_{t}+{\\rho\\:}_{d}+{}_{itd}\\)\u003c/span\u003e\u003c/span\u003e ⑷\u003c/p\u003e \u003cp\u003eIn Equations (3) and (4), M represents the mediating variable, while the definitions of other variables remain consistent with Model (1).\u003c/p\u003e \u003cp\u003eThen proceed with the actual test step by step: Perform regression on Model (1); if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is not significant, the testing stops. If \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is significant, proceed with tests for Models (3) and (4). If both \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e are significant, mediation effects exist. Furthermore, if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is also significant, it indicates partial mediation effects; if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e is not significant, it indicates full mediation effects. If either \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e or \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e, or both, are not significant, a Bootstrap test is necessary. In Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Columns Ⅰ, Ⅲ, and Ⅴ present the regression results for Model (3), while Columns Ⅱ, Ⅳ, and Ⅵ present the regression results for Model (4).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanism analysis results\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFinancing constraints\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTechnological change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGreen Transition Awareness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSA\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eRD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eⅠ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eⅡ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eⅢ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eⅣ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eⅤ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eⅥ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDID\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003cp\u003e(6.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.043***\u003c/p\u003e \u003cp\u003e(2.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.110**\u003c/p\u003e \u003cp\u003e(4.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045***\u003c/p\u003e \u003cp\u003e(2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.070**\u003c/p\u003e \u003cp\u003e(3.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.032**\u003c/p\u003e \u003cp\u003e(1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003cp\u003e(0.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003cp\u003e(1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003cp\u003e(1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl variables\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual effects\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime effect\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry effect\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e6.1.1 The test of financing constraints\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the coefficient for the interaction term (DID) in Column I is 0.024, which is significantly positive at the 1% level, indicating that the green credit policy has reduced the financing constraints for resource-based enterprises. The interaction term (DID) coefficient in Column II is 0.043 and is also significant at the 1% level. However, the coefficient for the mediating variable (M) is 0.040 and not significant. A subsequent Bootstrap test showed that the product of the coefficients \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e is significantly different from zero at the 99% confidence interval. Additionally, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{1}\\)\u003c/span\u003e\u003c/span\u003e、\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{1}\\:\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\theta\\:}_{2}\\)\u003c/span\u003e\u003c/span\u003e are of the same sign, confirming that the financing constraint variable plays a partial mediating role in the effect of green credit policy on the total factor productivity of resource-based enterprises. Hypothesis H2b is thereby verified. The fundamental reason is that the implementation of the green credit policy has strengthened the environmental information disclosure by resource-based enterprises, alleviated the degree of information asymmetry, reduced instances of financial fraud, and decreased the risk exposure for external investors and stakeholders. This is particularly beneficial for resource-based enterprises, which largely possess state-owned attributes and implicit government guarantees, find resource development profitable, and signal their \"greenwashing\" to society. All these factors bring favorable news to external investors, making it easier for resource-based enterprises to obtain financing through commercial credit and equity. This allows them to maintain normal production and operations while securing more external investments and cooperation opportunities, facilitating the introduction of new talents, digital transformation, and ultimately enhancing total factor productivity and the formation of new quality productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e6.1.2 Technological change test\u003c/h2\u003e \u003cp\u003eThe interaction term (DID) coefficient in Column III of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e is 0.11, which is significantly positive at the 5% level, corroborating that the green credit policy compels technological innovation in resource-based enterprises. The interaction term (DID) coefficient in Column IV is 0.045 at the 1% significance level, and the coefficient of the mediating variable (M) is 0.009 at the 10% significance level, indicating that the technological innovation variable exerts partial mediation effects. The green credit policy enhances the total factor productivity by elevating the level of technological innovation in resource-based enterprises, promoting the formation of new-quality productivity. Thus, hypothesis H3a is confirmed. The fundamental reason lies in the fact that the implementation of the green credit policy has made resource-based enterprises acutely aware that to alleviate financing constraints, enhance competitive advantages, and achieve sustainable and high-quality development, they must align with government guidance, social oversight, and banking preferences, thereby emitting a favorable signal. This fundamentally forces enterprises to overcome internal organizational inertia, strengthen their motivation and actions towards technological innovation, and utilize the \"innovation compensation\" effect, thereby enhancing the efficiency and value-added of resources and promoting the formation of new-quality productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e6.1.3 Green Transformation Awareness Test\u003c/h2\u003e \u003cp\u003eThe coefficient of the interactive term (DID) in Column V of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e is 0.07, significantly positive at the 5% level, indicating that the implementation of green credit policies can promote internal reflection within resource-based enterprises and enhance the recognition of the importance of green, low-carbon, and environmental protection. The coefficient of the interactive term (DID) in Column VI is 0.032 at the 5% significance level, and the coefficient of the mediating variable (M) is 0.012 at the 10% significance level. This confirms that in the impact of green credit policies on the total factor productivity of resource-based enterprises, the variable of green transition awareness exerts a partial mediating effect, which is a positive shock effect. Thus, hypothesis H4 is verified. The fundamental reason lies in the implementation of green credit policies through measures such as risk assessment of pre-loan projects and corporate behaviors, post-loan risk management and adjustment, and market transmission mechanisms. These measures increase external creditors' requirements for enterprise environmental, social responsibility, and green transition performance, thereby compelling enterprise managers to overcome the short-term \"green evasion\" mentality due to concerns over high sunk costs and \"compliance costs\" of environmental regulation. This enhances the awareness of environmental and social responsibility and green transition, as well as strengthens the awareness of environmental and social responsibility and green transition among all employees, forming an endogenous driving force for the new quality productivity of resource-based enterprise development.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Heterogeneity analysis\u003c/h2\u003e \u003cp\u003eBased on model (1), a heterogeneity analysis was conducted. First, the heterogeneity analysis considered the degree of marketization in different regions. According to the marketization index, the locations of resource-based enterprises were categorized into high, medium, and low marketization regions. The specific marketization index was derived from the \"China Provincial Marketization Index Report,\" and the econometric analysis results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. From Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, it can be seen that the impact of green credit policies on the total factor productivity of resource-based enterprises varies significantly with the degree of marketization. Specifically, green credit policies have a significant and strong impact on enterprises in regions with low marketization, no significant impact on enterprises in regions with medium marketization, and a significant but weak impact on enterprises in regions with high marketization. The main reason for this is that economically developed regions with higher marketization have more comprehensive environmental regulations and diverse funding channels, thereby reducing the impact of green credit policies on resource-based enterprises. In contrast, resource-based enterprises in regions with lower marketization are more constrained by environmental, transportation, and funding limitations, making them more susceptible to the influence of green credit policies.\u003c/p\u003e \u003cp\u003eThe second aspect is the heterogeneity analysis considering the nature of property rights. Resource-based enterprises are categorized into state-owned and non-state-owned enterprises based on their ownership nature. The results of the quantitative analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. As can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the implementation of green credit policies has a more significant impact on state-owned enterprises, indicating that state-owned enterprises respond more promptly and enforce green credit policies more rigorously compared to non-state-owned enterprises. The primary reason is that state-owned enterprises possess a higher national mission, social responsibility, and a complete mechanism for responding to national policies. They respond faster to policy changes and specific demands. Furthermore, they receive more attention and supervision from the government, the public, and stakeholders, which compels them to urgently convey positive signals to the outside world and actively promote the formation of new productive capacities.\u003c/p\u003e \u003cp\u003eThirdly, consider the heterogeneity analysis of the enterprise lifecycle stages. The lifecycle of resource-based enterprises often aligns with the exploitable cycle of their mineral resources. At different lifecycle stages, profitability, operational status, and innovation awareness exhibit distinct characteristics, thereby varying the impact of green credit policies. Inspired by the research of Tang Song et al. [45], enterprises are classified into growth, maturity, and decline stages for grouping regression analysis. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the impact of green credit policies on the total factor productivity of resource-based enterprises in the decline, maturity, and growth stages decreases sequentially. The primary reason is that resource-based enterprises in the growth stage have weaker profitability and greater incentives for production expansion and revenue increase. Their pursuit of economic performance significantly surpasses that of environmental performance, resulting in a lesser impact from green credit policies. Resource-based enterprises in the maturity stage, with stronger profitability, stable production and cash flows, and a higher inertia for innovation and transformation, tend to place greater importance on social reputation and environmental policies, thus experiencing a moderate impact from green credit policies. In the decline stage, resource-based enterprises face dual constraints from the depletion of resources and environmental rehabilitation, leading to a pronounced operational downturn. The pressure for innovation and transformation intensifies, significantly enhancing their pursuit of new qualitative productivity, resulting in a substantial impact from green credit policies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeterogeneity analysis results\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\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eThe degree of marketization\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eNature of property rights\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eLife cycle\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eTFP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.936***\u003c/p\u003e \u003cp\u003e(-0.077)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.558***\u003c/p\u003e \u003cp\u003e(-0.095)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003cp\u003e(1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.060***\u003c/p\u003e \u003cp\u003e(2.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.225**\u003c/p\u003e \u003cp\u003e(2.10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR^2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.903\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndividual effects\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\u003eTime effect\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 effect\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusions and policy implications","content":"\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Conclusions\u003c/h2\u003e \u003cp\u003e(1) The implementation of green credit policies has played an organically coordinated role in both environmental regulation and the allocation of financial resources, effectively promoting the enhancement of total factor productivity and the formation of new quality productivity in resource-based enterprises. On one hand, it guides enterprises to disclose environmental information, prompting them to avoid the opportunity cost of \"false information disclosure\" and to abandon short-term behaviors. Meanwhile, it provides potential signals for technological transformation, motivating enterprises to develop green technologies, processes, or products, optimize management methods, and achieve the \"environmental regulation compliance cost counterbalance\" effect, thereby promoting the improvement of total factor productivity. On the other hand, it internalizes the environmental negative externality costs of enterprises by facilitating investments in cleanliness with lower transaction costs, and complicating investments in pollution with higher transaction costs. This dynamically adjusts the opportunity costs of environmental damage throughout the entire production process, leading to effective allocation of financial resources and guiding the development of new quality productivity for enterprises.\u003c/p\u003e \u003cp\u003e(2) The implementation of green credit policies alleviates the financing constraints of resource-based enterprises, thereby promoting the enhancement of their total factor productivity (TFP) and the formation of new quality productivity. Such policies can mitigate the degree of information asymmetry within enterprises, reduce occurrences of financial fraud, and lower the risk exposure for external investors and stakeholders. Additionally, these policies enable enterprises to bring favorable news to external investors, leveraging commercial credit and equity to obtain more external investment and cooperation opportunities. This facilitates the introduction of new talents and digital transformation, thereby improving TFP and aiding in the formation of new quality productivity.\u003c/p\u003e \u003cp\u003e(3) The implementation of green credit policies compels resource-based enterprises to undergo technological transformation, thereby increasing total factor productivity and driving the formation of new forms of productivity. Such policies force enterprises to align with government directives, societal oversight, and banking preferences, thereby releasing positive signals, overcoming internal organizational inertia, strengthening motivation for transformation, and initiating green technological and management reforms. This plays a \"compensatory transformation\" role, enhancing resource utilization efficiency and added value, and promoting the formation of new forms of productivity.\u003c/p\u003e \u003cp\u003e(4) The implementation of green credit policies enhances the awareness of green transformation among resource-based enterprises, thereby promoting the formation of new productive forces within these companies. Such policies employ a series of measures, including pre-loan project and enterprise behavior risk assessments, post-loan risk management and adjustments, and market transmission mechanisms. These measures increase external creditors' demands on enterprises regarding their environmental and social responsibilities, as well as their green transformation performance. This exerts pressure on enterprise owners and managers to overcome a short-term apathetic attitude of \"green avoidance,\" driven by concerns over excessive sunk costs and the \"compliance costs\" of environmental regulations. Consequently, these measures enhance the awareness of environmental and social responsibilities and green transformation, while also awakening and strengthening the sense of responsibility among all members of the enterprise. This, in turn, forms an endogenous drive for the development of new productive forces.\u003c/p\u003e \u003cp\u003e(5) The green credit policy significantly enhances the new quality productivity of resource-based enterprises, exhibiting notable heterogeneities in terms of marketization degree, ownership, and life cycle stages. Such policies have a more pronounced effect on the total factor productivity and new quality productivity of resource-based enterprises in regions with lower marketization compared to those with higher marketization. This reflects the relative advantages of economically developed regions with well-established environmental regulations and diverse financing channels. For state-owned enterprises, the promotion of total factor productivity and new quality productivity is significantly greater than for non-state-owned enterprises, highlighting the relative advantages of the policy response mechanism and the effect of being highly supervised. The promotion effects on the total factor productivity and new quality productivity of resource-based enterprises follow a descending order from the decline phase, mature phase, to the growth phase. This indicates the relative advantage of shifting from the pressure of resource depletion and environmental restoration to the drive for innovation, transformation, and development of new quality productivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Policy Recommendations\u003c/h2\u003e \u003cp\u003e(1) Strengthening the primary responsibility and differentiated requirements for commercial banks to implement green credit policies. Improve the professional process for identifying development projects of resource-based enterprises under green credit policies, refine the monitoring content for environmental, social, and governance (ESG) risks, strictly investigate the purposes of loans to prevent \"greenwashing\" behaviors; promote the innovation of green credit policies, implement resource-based enterprise credit strategies with regional and ownership differences, and avoid \"one-size-fits-all\" practices to continuously enhance the effectiveness of credit support; adopt a dynamic tracking credit mechanism to guide resource-based enterprises to continuously invest funds to achieve greening and digital intelligence in production operations, thereby accelerating the formation of new quality productivity.\u003c/p\u003e \u003cp\u003e(2) Strengthening the disclosure requirements and technological innovation for Environmental, Social, and Governance (ESG) information of resource-based enterprises. Firmly establishing a corporate vision, collective understanding, and development path focused on forming new productivity; detailing the steps and goals for achieving this. Enhancing the quality of accounting information disclosure to reduce credit resource misallocation; promoting breakthroughs in the research and development of green technologies, processes, and products, as well as innovatively allocating production factors to enhance total factor productivity. Improving internal control systems, fully leveraging the benefits of green credit policies to aid in the formation of new productivity.\u003c/p\u003e \u003cp\u003e(3) Strengthen the guidance, regulation, and performance evaluation of green credit policies by government departments. Clarify the standards for social responsibility information disclosure of resource-based enterprises, utilizing the dividends of accounting reform systems to enhance the performance of green credit policies; improve the evaluation mechanisms for technological transformation and green transition projects of resource-based enterprises, and strengthen the regular assessment of the implementation effects of green credit policies. Take measures such as material rewards or prioritizing support for corporate businesses and government investment project cooperation to motivate commercial banks to actively implement green credit policies. Guide the complementary and coordinated development of green bonds, insurance, and other instruments to build a multidimensional and three-dimensional green finance system, thereby promoting the formation of new production capacities in resource-based enterprises.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Research Deficiencies and Prospects\u003c/h2\u003e \u003cp\u003eThis study is based on the quasi-natural experiment of the implementation of the China Banking Regulatory Commission's \"Green Credit Guidelines\" (2012). It selects the relevant data of resource-based enterprises listed on the A-shares of China from 2008 to 2022 and employs the Difference-in-Differences (DID) method to empirically examine the systemic effects of green credit policies on the formation of new quality productivity in resource-based enterprises. Although this paper conducts a substantial investigation into the systematic impact of green credit policies on the formation of new quality productivity in resource-based enterprises, some limitations still exist. Firstly, the measurement of new quality productivity in resource-based enterprises uses total factor productivity as a substitute rather than constructing a direct indicator system for measurement. Secondly, the data has not been updated to 2023. These shortcomings indicate directions for improvement in future research.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data presented in this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThere is no funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, W.Z.; methodology, W.Z.; software, Z.Y.; validation, W.Z.; formal analysis, W.Z.; investigation, T.L.; resources, T.L.; data curation, T.L.; writing\u0026mdash;original draft preparation, T.L.; writing\u0026mdash;review and editing, T.L.; visualization, T.L.; supervision, T.L.; project administration, Z.Y.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang J X, Ju Y, Zhang Q, et al. Low ecological environment damage technology and method in coal mines[J]. Journal of Mining and Strata Control Engineering, 2019,1(1):013515.\u003c/li\u003e\n\u003cli\u003eChen Y K, Guan J, Tian D D. Research on the micro impact effect of green credit policy: Punishment or incentive? \u0026mdash;\u0026mdash;A re-test of the Porter effect of green credit policy[J]. Journal of Financial Development Research,2022, (9):50-61.\u003c/li\u003e\n\u003cli\u003eXing C, Zhang Y M, Tripe D. Green credit policy and corporate access to bank loans in China: The role of environmental disclosure and green innovation[J].International Review of Financial Analysis,2021,77: 101838.\u003c/li\u003e\n\u003cli\u003eTian C, Li X Q, Xiao L M, et al. 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Finance \u0026amp; Economics, ,2022, (2): 17-32.\u003c/li\u003e\n\u003c/ol\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":"new quality productive forces, green credit policy, financing constraints, technological change, green transformation awareness, DID, fixed effects model, resource-based enterprise","lastPublishedDoi":"10.21203/rs.3.rs-5891402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5891402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe green credit policy is designed to foster sustainable and high-quality development within enterprises. However, it currently lacks focus on the development of new productive forces in resource-based enterprises, particularly concerning the systemic effects of financing constraints, technological changes, and awareness of green transformation. These areas warrant further investigation. This study leverages a quasi-natural experiment based on the original Green Credit Guidelines by the China Banking Regulatory Commission, initiated in 2012. Using data from resource-based enterprises listed in China\u0026rsquo;s A-share market from 2008 to 2022, a difference-in-difference approach assesses the impact of green credit policies on the emergence of new quality production capacities within these firms. The research indicates that green credit policies can effectively integrate environmental regulation with financial resource allocation, substantially enhancing total factor productivity and fostering new quality productivity within resource-based enterprises. Mechanism analysis reveals that these policies mitigate financing constraints, stimulate technological advancements, and strengthen green transformation awareness, thereby boosting total productivity and quality of production. Heterogeneity analysis points out that the influence of green credit policies is more pronounced in less marketed regions compared to highly marketed ones, and it is more significant in state-owned enterprises than in non-state-owned enterprises. Additionally, throughout various enterprise lifecycles\u0026mdash;decline, maturity, and growth\u0026mdash;the need to bolster primary responsibilities and differentiate requirements for policy implementation becomes evident.\u003c/p\u003e","manuscriptTitle":"Does green credit policy promote the formation of new quality productivity in resource-based enterprises? Evidence from China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-06 14:06:43","doi":"10.21203/rs.3.rs-5891402/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":"e5b76841-8f8e-44a1-bae8-488dc0d8e288","owner":[],"postedDate":"February 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43824246,"name":"Business and commerce/Economics"},{"id":43824247,"name":"Business and commerce/Finance"},{"id":43824248,"name":"Social science/Development studies"},{"id":43824249,"name":"Social science/Economics"},{"id":43824250,"name":"Social science/Finance"}],"tags":[],"updatedAt":"2025-04-29T13:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-06 14:06:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5891402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5891402","identity":"rs-5891402","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}