Introduction

China is optimizing its energy structure and reducing carbon emissions by transitioning from a coal-dominated power generation model to one reliant on renewable energy sources. Although renewable energy produces significantly fewer carbon emissions compared to fossil fuels, its advancement is marked by elevated costs and substantial technological obstacles, resulting in higher expenditures for renewable energy generation (Li et al., 2022). China has enacted several regulatory initiatives to foster the sustainable development of REPGFs. The renewable energy power price subsidy policies (REPPSPs) are designed to enhance the market competitiveness and profitability of REPGFs through financial support (Özdemir et al., 2020). While REPPSPs mitigate the generating costs of REPGFs, the rapid escalation of China’s renewable energy output has intensified the issue of insufficient funding for these initiatives. This emerging tension—between the policy’s intent to foster REPGF sustainability and the escalating fiscal pressure threatening its continuity—poses a pivotal question: How, and to what extent, do REPPSPs enhance the resilience and long-term sustainability of REPGFs in China? Therefore, the primary objective of this study is to investigate the impact of REPPSPs on the sustainability performance of China’s REPGFs, with a specific emphasis on essential aspects of firm resilience.

The renewable energy power generation industry in China faces substantial challenges, such as the absence of well-established market trading mechanisms, low resource utilization efficiency, incomplete market-oriented pricing mechanisms, and insufficient implementation of the guaranteed purchase system for renewable energy power generation. In 2016, the National Development and Reform Commission released the MMFGAREPG to promote the reform of China’s power system, implement a market-oriented pricing mechanism for renewable energy power, and reinforce the management of the guaranteed purchase of renewable energy power generation.

The MMFGAREPG stipulates that grid firms must fully purchase the on-grid electricity generated by renewable energy projects within the specified planning boundaries, based on the national benchmark electricity price and guaranteed acquisition hours, while integrating market competition mechanisms. The annual electricity production from grid-connected renewable energy projects is divided into two components: guaranteed acquisition electricity and market trading electricity. The guaranteed acquisition portion of electricity is fully purchased at the benchmark on-grid electricity price through prioritized annual generation planning and priority power purchase agreements with the grid. The market trading portion of electricity is safeguarded by REPGFs via market competition for generation contracts, which the grid implements on a priority dispatch basis.

The aforementioned policies seek to address the challenge of renewable energy power consumption and may enable REPGFs to generate stable revenue through electricity production. Furthermore, the pricing mechanism for renewable energy electricity has transitioned from full government pricing to a hybrid model, where part is set by government pricing and the rest is determined through market-oriented trading mechanisms. This approach underscores the critical role of market mechanisms in resource allocation, which may incentivize REPGFs to enhance operational efficiency and obtain more benefits via heightened electricity generation. Consequently, the MMFGAREPG may influence the sustainable development capabilities of REPGFs. The critical significance of REPGF resilience in enabling the low-carbon transition warrants focused consideration.

The Chinese energy market and regulatory framework possess distinctive characteristics. For instance, the National Development and Reform Commission spearheads the development of energy policies, which are executed efficiently but may result in a “one-size-fits-all” approach. The coexistence of benchmark coal-fired electricity prices and market-driven prices for renewable energy leads to distorted price signals. Local fiscal pressure causes delays in subsidy allocation, especially as the recent economic decline in China has imposed considerable strain on local finances, directly impacting the liquidity of REPGFs. China’s REPPSPs are the largest globally and have a complex mechanism; nonetheless, their effectiveness is constrained by the unique policy-market interaction. The research findings hold important reference value for emerging economies. Therefore, this study aims to enhance the effectiveness of the MMFGAREPG in fostering the development and growth of the renewable energy power generation industry by examining its direct impact on the resilience and long-term sustainability of REPGFs, especially within a swiftly evolving energy market.

Power generation firms disclose their overall power output and the categorization of various power generation methods in their annual reports. This study involves a manual review of annual reports from power generation firms, classifying those whose renewable energy production (from wind, solar, biomass, geothermal, ocean, and hydropower sources) exceeds 80% of their total generation, as defined by the MMFGAREPG, as REPGFs. Based on this classification, REPGFs are identified as the treatment group, whereas other power generation firms are categorized as the control group. This paper analyzes the effect of the MMFGAREPG implementation on the resilience of REPGFs, utilizing a sample of Chinese listed power generation firms and considering the 2016 introduction of the MMFGAREPG as a policy shock event. The results indicate that the implementation of the MMFGAREPG enhances the resilience of REPGFs.

We further investigate how the implementation of the MMFGAREPG influences the resilience of REPGFs through financing, efficiency, and fundamental channels, represented by financial slack, innovation efficiency, and green investors, respectively. The MMFGAREPG increases the financial slack of REPGFs, attracts more green investors to these firms, and further enhances their resilience. Furthermore, we find that the MMFGAREPG improves the innovation efficiency of REPGFs, indicating the beneficial impact of market competitive mechanisms on the innovation efficiency of REPGFs. The details can be visualized in the graphical abstract of Fig. 1.

Fig. 1
figure 1

Graphical abstract.

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We contribute to the literature on REPPSPs. Prior studies have predominantly focused on the influence of REPPSPs on carbon emissions (Kök et al., 2018), power generation costs (Zhao et al., 2014), renewable energy development (Nicolini and Tavoni, 2017), and green technology innovation (Lee et al., 2022). The literature above primarily relies on theoretical models to validate the effects of REPPSPs, lacking empirical evidence derived from real-world data. This work is, to the knowledge, the inaugural empirical investigation of the role of REPPSPs in enhancing the organizational resilience of REPGFs utilizing actual data. Specifically, we explore how multi-mechanism policies, like the MMFGAREPG, enhance the resilience of REPGFs through pathways such as financial slack, innovation efficiency, and fundamentals. The findings offer robust empirical evidence that REPPSPs positively contribute to the sustainable development of REPGFs. This paper integrates energy policy analysis with organizational resilience theory, addressing the limitations of traditional macroeconomic or technology-focused research. We elucidate the micro-level mechanisms underlying the impact of REPPSPs on the resilience of REPGFs within a mixed market context characterized by government leadership and limited competition. We provide a solid foundation for China to optimize the tapering rhythm of subsidies and design regionally differentiated policies.

This study is also closely related to REPGFs. Limited research has been conducted on REPGFs, with current studies predominantly addressing excess profits (Jaraitė and Kažukauskas, 2013), incentive policies (Zhao et al., 2016), resource allocation (Weigelt and Shittu, 2016), and portfolio strategies (Zhang et al., 2022). However, these studies do not establish definitive criteria for classifying REPGFs. This study involves a manual analysis of annual data from power generation firms, identifying those classified as REPGFs if their renewable energy generation—encompassing wind, solar, biomass, geothermal, ocean, and hydropower, as defined by the MMFGAREPG—constitutes above 80% of their overall power output. This approach effectively mitigates sample bias in REPGF classification arising from self-declaration and inaccurate database categorization. Furthermore, the resilience of REPGFs has not been sufficiently addressed in previous literature. The resilience of firms denotes their ability to endure external risk shocks (Dormady et al., 2019; Iftikhar et al., 2021), and robust resilience signifies enhanced potential for sustainable development. We examine the sustainable development of REPGFs via the perspective of organizational resilience and confirm the beneficial impact of market competition mechanisms on the resilience of REPGFs. This study contributes to the literature on REPGFs by providing theoretical support for promoting their sustainable development through market competition mechanisms.

Hypotheses development

In physics, resilience denotes a material’s capacity to absorb energy during plastic deformation and fracture. Higher resilience correlates with less susceptibility to brittle fracture. In the context of firms, resilience represents the ability to withstand risk shocks. Resilience refers to a firm’s ability to maintain its core functions and achieve sustainable development by absorbing shocks, adapting to changes, and proactively transforming. Firms with higher resilience can better mitigate external risks and minimize their adverse effects (Croci et al., 2024). For REPGFs, resilience is distinctly exhibited in the following dimensions. In terms of absorptive capacity: short-term resistance to shocks such as electricity price fluctuations, policy changes (including subsidy reduction), or natural disasters (such as extreme weather causing power generation). Regarding adaptive capacity: mid-term modifications of business models (for instance, transitioning from centralized to distributed power generation) or technological routes (such as enhancing energy storage efficiency). In terms of transformative capacity: the long-term reconstruction of the value chain to cope with fundamental changes in the energy system.

The renewable energy industry is characterized by high capital intensity, long payback durations, and sensitivity to policy changes. The importance of financial buffers greatly surpasses that of industries with stable cash flows, such as manufacturing. The government’s assurance to acquire green power at a fixed feed-in tariff beyond the market price secures the fundamental revenue for REPGFs. The fixed feed-in tariff purchase income assists REPGFs in mitigating risks, including delayed renewable energy subsidy payments and electricity price fluctuations, while maintaining their normal operations. The MMFGAREPG secures the revenue from power generation for REPGFs, reducing the risk of cash flow disruption caused by wind and solar curtailment or electricity price fluctuations. Stable cash flow might facilitate the replacement of short-term high-interest loans with long-term debt (such as converting short-term financing into green bonds), hence diminishing leverage and interest burdens. REPGFs need not excessively reserve liquidity funds to address market concerns, allowing them to focus capital on effective capacity expansion or technical research and development, thus systematically improving their risk resilience. The MMFGAREPG effectively ensures the consumption of renewable energy power, reduces the additional costs faced by REPGFs in electricity sales, and thus improves their financial flexibility.

In the face of external uncertainty and risks, financial slack serves as a crucial financial cushion for firms, which in turn reduces their reliance on external financing, decreases financing costs, and enhances their financial stability (John et al., 2017). Firms with substantial financial slack can more flexibly adjust their business strategies in response to market fluctuations, thereby adapting promptly and sustaining a competitive advantage under complex, dynamic market environments (Lungeanu et al., 2016). Higher financial slack functions as precautionary savings, buffering firms against future risks and uncertainties. This process enables more effective responses to risk shocks and alleviates adverse impacts (Vanacker et al., 2017), thus alleviating resilience. The analysis presented above leads to the following hypothesis:

Hypothesis 1: The implementation of the MMFGAREPG enhances the resilience of REPGFs.

Hypothesis 2: The implementation of the MMFGAREPG enhances the resilience of REPGFs by increasing their financial slack.

The MMFGAREPG categorizes power generation from renewable energy projects into two segments: the guaranteed acquisition electricity portion and the market transaction electricity portion. REPGFs can augment their sales revenue by selling surplus electricity through competitive markets, beyond the guaranteed purchase quota. According to the economic man hypothesis, which posits that economic agents seek to maximize profits (Stoelhorst, 2023), REPGFs may leverage profits from guaranteed electricity purchases to invest in technological innovation, thereby enhancing power generation capacity. This augmented capacity allows REPGFs to concurrently earn elevated revenue and profits through guaranteed purchases and market trading avenues.

The MMFGAREPG mitigates uncertainty in renewable energy power consumption, alleviates income instability for REPGFs due to wind and solar power curtailment or electricity price fluctuations, reduces excess funds retained by REPGFs for risk management, and releases additional cash flow for technological research and development. Stable income expectations enhance the credit ratings of REPGFs, thereby drawing financial backing, including green bonds and venture capital, for high-cost innovation projects. A stable cash flow improves the balance sheets of REPGFs, facilitating their attainment of international certifications and their inclusion in the green power procurement lists of multinational firms. Efficient renewable energy technologies decrease equipment failure rates and enhance the stability of renewable energy grid integration, reducing production suspension losses due to natural disasters and lessening the risk of power curtailment owing to grid fluctuations. The reduction in losses and enhanced capacity to withstand risk shocks resulting from these technological innovations can motivate REPGFs to continuously invest in technological research and development, thus fostering a virtuous cycle.

REPGFs exhibiting strong innovation efficiency can timely launch products and services with differentiated competitive advantages in response to market demands so as to maintain their market position and competitiveness. This capability helps them better cope with various uncertainties, such as policy adjustments and economic fluctuations (Cooper et al., 2022). REPGFs with high innovation efficiency can optimally leverage diverse resources and allocate them to high-potential projects, resulting in higher resource utilization efficiency. The optimization of resource allocation improves operational efficiency for organizations and offers supplementary resource buffers to alleviate external risk shocks, thus diminishing their adverse effects and augmenting resilience (Kong et al., 2023). The preceding analysis gives rise to the following hypothesis:

Hypothesis 3: The implementation of the MMFGAREPG enhances the resilience of REPGFs by improving their innovation efficiency.

The MMFGAREPG can increase the future income of REPGFs, diminish profit volatility, and elevate the price-earnings ratio and price-to-book ratio, therefore aiding in the attraction of green investors. Investors who prioritize low-carbon initiatives, environmental protection, and sustainable development are termed green investors. Green investors emphasize the green attributes of their investment targets over returns, making them less sensitive to the returns of green assets (Wang, 2024). Green investors emphasize the environmental qualities of their investment aims rather than financial returns, rendering them less responsive to the profitability of green assets (Wang, 2024). Green investors are less inclined to liquidate green assets, preferring to retain them long-term due to their perceived financial value. The low sensitivity of green investors to asset returns allows them to hold assets for a longer period rather than engaging in frequent short-term trading. Green investors increase their holdings in REPGFs, optimizing the equity structure of REPGFs, reducing the issuance rate of green bonds, and bolstering stock price stability, thereby enhancing resilience against capital market volatility. The long-term holdings of green investors send a stability signal to the market, promoting an upgrade in the credit ratings of REPGFs and subsequently reducing their financing costs.

The financial infusion from green investors supports REPGFs in breaking through high-risk technologies and avoids the interruption of innovation due to short-term profit pressure. The MMFGAREPG is usually accompanied by supporting measures (such as tax credits and priority land use rights), and when combined with the resource integration capabilities of green investors, it reduces policy fluctuations and market access risks. Green investors should advocate for REPGFs to employ derivative tools to mitigate risks and align long-term liabilities with asset cycles, so preventing liquidity crises resulting from short-term borrowing for long-term investments. Green investors would also enhance ESG governance, shape brand value, and build trust with stakeholders (Flammer, 2013; Chi et al., 2023), thereby enhancing the reputation of REPGFs and obtaining product premiums and stakeholder support (Martin and Moser, 2016; Tang and Zhang, 2020; Pastor et al., 2021). The preceding analysis gives rise to the following hypothesis:

Hypothesis 4: The implementation of the MMFGAREPG enhances the resilience of REPGFs by attracting green investors.

Research design

Sample construction

The MMFGAREPG applies to renewable energy sources, including wind, solar, biomass, geothermal, ocean, and hydropower generation. First, information on all listed power generation firms in the electricity industry is collected from the Sina Finance website, involving a total of 76 firms. Subsequently, firms are classed based on the condition that the total power generation from the specified six renewable energy sources, as outlined in the MMFGAREPG, constitutes over 80% of their overall power generation. Firms disclose details regarding their main business operations, as well as the share and revenue proportions of different business segments, in their annual reports. To precisely identify firms affected by the MMFGAREPG, we manually review each firm’s annual report and calculate the ratio of renewable energy power generation across the six designated categories. Ultimately, 32 firms are identified as REPGFs through this manual review process. This classification method ensures that REPGFs exhibit the essential characteristics of renewable energy power generation. Furthermore, it highlights notable disparities in the impact of the MMFGAREPG on REPGFs and non-REPGFs.

The sales revenue and stock returns of REPGFs, along with other firm-level data included in this paper, are all sourced from the CSMAR and Wind databases. The digital economy index is derived from the China City Statistical Yearbook and the Peking University Digital Inclusive Finance Index, while the green finance development index is obtained from the China City Statistical Yearbook and the Environmental Status Bulletin. Ultimately, after excluding missing data, 1071 firm-year observations are acquired, with the sample period spanning from 2007 to 2023.

Model and variables

We utilize the following model to validate the influence of the MMFGAREPG’s implementation on the resilience of REPGFs:

$${{RESILIENCE}}_{i,t}=\alpha +\beta {{TREAT}}_{i}\times {{POST}}_{t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(1)

where \({\rm{RESILIENCE}}_{i,t}\) indicates the resilience of firm \(i\) in year \(t\). Firms with robust resilience typically demonstrate sustained high-speed growth alongside lower-risk attributes. Consequently, firm resilience can be characterized by two dimensions: high-performance growth and low financial volatility. Drawing on the methodology of Ortiz‐de‐Mandojana and Bansal (2016), we employ the year-on-year growth rate of sales revenue as a proxy for high-performance growth and stock return volatility as an indicator of low financial volatility. We calculate the weights of these two indicators utilizing the entropy method to obtain a comprehensive score that reflects firm resilience. Initially, we standardize the year-on-year growth rate of sales revenue and the volatility of stock returns. Subsequently, we compute the proportions and information entropy values of these indicators to ascertain their respective contributions and weights. Finally, we aggregate these weighted contributions into a composite score that acts as an indicator of firm resilience. The growth in a firm’s revenue signifies its market penetration capability and sensitivity to policy changes, thereby directly indicating business resilience. Meanwhile, stock price volatility functions as a measure of investors’ risk perception, encompassing factors like technological uncertainty or policy fluctuations, making it particularly relevant for firms with high policy dependence.

The entropy method, a multi-index evaluation approach grounded in information theory, quantifies a firm’s risk resistance by calculating the information entropy of diverse indicators. The entropy value automatically adjusts the weight in response to the market environment, exemplifying dynamic adaptability. The entropy method is widely used in the analysis of complex systems within the energy economy sector, owing to its distinctive benefits in managing nonlinear and high-dimensional data. We control for the firm fixed effects to address firm-level factors that remain constant across time and to prevent omitted variable bias. We also control for the year fixed effects to absorb macroeconomic shocks (such as the 2008 financial crisis and COVID-19) and isolate the impact of policy changes.

\({\rm{TREAT}}\) takes a value of 1 for REPGFs defined as in Section “Sample construction” and a value of 0 for other power generation firms. \({\rm{POST}}\) takes 1 for the years 2016 and beyond; otherwise, it is 0. The coefficient of \({\rm{TREAT\times POST}}\) reflects the impact of the implementation of the MMFGAREPG on the resilience of REPGFs. The following variables that may affect the resilience of firms are controlled: Firm size (SIZE), large firms may experience economies of scale, allowing them to spread fixed costs and lower unit costs, thus gaining an edge in price competition or during a downturn in demand. At the same time, large firms may possess greater resources to deal with crises. Firm age (AGE), older firms may have accumulated rich experience and resources, established stable customer relationships, and developed resilient supply systems, which enable them to endure risks. Leverage ratio (LEV), a high leverage ratio may increase financial risks, especially during economic recessions. The interest burden may increase, leading to constrained cash flow and the potential risk of bankruptcy. Return on assets (ROA), firms with higher ROA have superior operational efficiency and profitability, which may bolster their ability to withstand risks, as substantial profits might furnish more internal financial buffers. Number of board members (BOARD), the size of the board of directors may affect decision-making efficiency. A greater quantity may yield more diverse perspectives, which is advantageous for a thorough assessment of risks. The shareholding ratio of the largest shareholder (TOP1), coupled with a highly concentrated equity structure, may enhance the largest shareholder’s incentive to supervise management and ensure the firm’s stable operation. However, an excessively high shareholding ratio may result in the predominant shareholder’s arbitrary behavior, neglecting the interests of minority shareholders, and potentially facilitating interest transfer, heightening corporate governance risks and undermining resilience. The book-to-market ratio (BM), a high book-to-market ratio may reflect the market’s pessimistic expectations for the firm’s prospects. Such firms may be undervalued during a crisis; nevertheless, if their fundamentals are solid, they may exhibit superior performance throughout the recovery phase. Fixed assets ratio (FIXED), a high proportion of fixed assets may imply higher sunk costs, hindering rapid adjustments to the asset structure during industrial downturns, and thereby diminishing resilience.

Descriptive statistics

In Table 1, the resilience scores at the 10th and 90th percentiles for the sample firms are 0.0225 and 0.8573, respectively, with a standard deviation of 0.3863. This indicates a low average resilience level and considerable variation among different REPGFs. The average asset return rate of the sample firms is 0.0160, suggesting a relatively low profitability for REPGFs. The average financial slack level is 0.0798, signifying a low proportion of cash and cash equivalents for REPGFs. The average proportion of green investors’ holdings in the sampled firms is 0.0912, suggesting that the appeal of REPGFs to green investors is still quite low. Significant potential exists for augmenting the resilience of REPGFs by attracting a greater number of green investors. The Table A1 of the appendix details the variable definitions.

Table 1 Descriptive statistics.
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Results

Baseline regression results

Table 2 illustrates the impact of the MMFGAREPG’s implementation on the resilience of REPGFs. In Column (2), the coefficients of TREAT × POST indicate that the implementation of the MMFGAREPG enhances the resilience of REPGFs. Countries worldwide widely adopt REPPSPs to support the development of REPGFs. The coefficient of TREAT × POST in column (4) signifies that the implementation of MMFGAREPG leads to an average increase of 0.0385 in the resilience of REPGFs, equivalent to 10% of the sample mean.

Table 2 The MMFGAREPG and the resilience of REPGFs.
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Zhao et al. (2014) and Nicolini and Tavoni (2017) have pointed out that REPPSPs substantially enhance the installed capacity of renewable energy and facilitate technological dissemination. We confirm the beneficial impact of this policy from the perspective of micro-firm resilience while elucidating its underlying mechanism. Current research underscores the direct motivation provided by subsidies for capacity augmentation, whereas we demonstrate that REPPSPs indirectly bolster industry stability by enhancing the risk-buffering capabilities of REPGFs, therefore elucidating the divergence in policy transmission logic. The results indicate that the model combining guaranteed acquisition of electricity and market trading of electricity, as highlighted by MMFGAREPG, improves the ability of REPGFs to cope with risk shocks. In light of this, amid an increasing subsidy gap, regulators may consider a dual-track financing model comprising “subsidy funds + green certificate trading.” REPGFs with green certificates are more apt to attract environmentally conscious investments, while green certificates contribute to cultivating a sustainable brand image and bolstering competitiveness.

The coefficients of SIZE indicate that larger-scale REPGFs demonstrate greater resilience, are more prone to scale effects, exhibit superior business performance, and have the ability to cope with risk shocks, culminating in stronger resilience. The coefficients of AGE indicate that older REPGFs may have more mature business models and enhanced market competitiveness, thus possessing stronger resilience. The coefficients of LEV confirm the lower resilience of REPGFs with higher debt levels. A higher level of debt decreases firms’ capacity to handle risk shocks, escalates their operating costs, and undermines their resilience. The coefficients of BOARD may validate the supervisory role of a diversified board of directors in bolstering firm resilience. Meanwhile, the coefficients of BM reflect that firms with a higher book-to-market ratio demonstrate greater resilience.

Robustness tests

Parallel trend test

The reliability of the baseline regression outcomes depends on the alignment of resilience trends between the treatment and control groups before the implementation of the MMFGAREPG. The following model is constructed to validate the above trend:

$${{RESILIENCE}}_{i,t}=\alpha +\mathop{\sum }\limits_{-4}^{4}{\beta }_{k}{{TREAT}}_{i}\times {{POST}}_{t}^{k}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(2)

where \({\sum }_{-4}^{-1}{\rm{POST}}_{t}^{k}\) and \({\sum }_{1}^{4}{\rm{POST}}_{t}^{k}\) represent the values of 1 for REPGFs before and after \(k\) years of the implementation of the MMFGAREPG, respectively; otherwise, they are 0. The first year of the sample is designated as the base period. In Fig. 2, the coefficients of \({\sum }_{-4}^{-1}{\rm{POST}}_{t}^{k}\) are insignificant, while those of \({\sum }_{1}^{4}{\rm{POST}}_{t}^{k}\) are significant and positive, indicating that the resilience of REPGFs compared to other firms remains largely unchanged before the implementation of the MMFGAREPG, thus verifying the parallel trend.

Fig. 2
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Parallel trend test.

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Alternative measures of firm resilience

This section evaluates firm resilience across three dimensions: profitability, growth ability, and debt-paying ability. Profitability encompasses return on equity, return on total assets, and net profit from sales; growth ability comprises the year-on-year growth rates of operational revenue and net profit; and debt-paying ability includes the ratio of net cash flow to liabilities. Profitability, growth ability, and debt-paying ability reflect a firm’s ability to manage risk shocks from different aspects. The entropy method is adopted to calculate the weights of the above indicators, resulting in a comprehensive indicator that serves as a proxy for firm resilience (RESILENCE1). The resilience indicators in the baseline regression are replaced with new proxy indicators for firm resilience, and Eq. (1) is re-estimated. The coefficients of TREAT × POST in Table 3 demonstrate that the resilience of REPGFs markedly improves after the implementation of the MMFGAREPG. The above results confirm that the baseline regression conclusion remains valid after replacing the proxy indicator of firm resilience.

Table 3 Alternative measures of firm resilience.
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Changing the screening criteria for REPGFs

In this section, firms whose total power generation from the six specified renewable energy sources, consistent with the MMFGAREPG, exceeds 60% of their total power generation are classified as REPGFs. This classification seeks to further examine whether the resilience of REPGFs and other power generation firms has undergone significant changes subsequent to the implementation of the MMFGAREPG. The coefficients of TREAT × POST in Table 4 align with the findings in Section “Baseline regression results”, indicating that firms primarily generating power from renewable energy are considerably and positively affected by the MMFGAREPG.

Table 4 Changing the screening criteria for REPGFs.
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Propensity score matching

To address potential issues associated with sample selection bias, the propensity score matching (PSM) method is employed. Specifically, propensity score values are calculated using a logit model with control variables as covariates. Subsequently, the caliper nearest neighbor matching technique is utilized to identify firms in the control group, thereby constructing a new matched sample. This new sample is then used to re-estimate Eq. (1). As shown in Table 5, the treatment and control groups exhibit comparable characteristics after matching. Furthermore, the coefficients of TREAT × POST presented in Table 6 provide additional evidence supporting the robustness of the findings, confirming that the implementation of the MMFGAREPG enhances the resilience of REPGFs.

Table 5 Balance tests.
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Table 6 PSM-DID.
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Placebo test

To mitigate the effects of unobservable factors or omitted variables on the baseline regression, treatment and control groups are randomly generated from the total sample, and 500 regressions are conducted to derive the kernel density estimation distribution depicted in Fig. 3. The coefficient estimates in Fig. 3 are centered around zero and follow a normal distribution, indicating that unobservable factors or omitted variables are unlikely to affect the results.

Fig. 3
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Coefficient distribution.

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Channel tests

The financing channel

Due to a lack of market competitiveness, renewable energy electricity experiences sluggish sales, which in turn affects REPGFs’ cash flow and normal production and operation activities. The poor operating conditions of REPGFs exacerbate their financing difficulties, creating a vicious cycle. The MMFGAREPG mandates the complete acquisition of on-grid electricity from renewable energy power generation projects. This policy effectively addresses the issue of slow sales of renewable energy electricity; it enhances the operating conditions of REPGFs, increases their financial slack, and ultimately enhances their resilience. In this study, the following models are estimated to validate the financing channel through which the MMFGAREPG affects the resilience of REPGFs:

$${{FS}}_{i,t}=\alpha +\beta {{TREAT}}_{i}\times {{POST}}_{t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(3)
$${{RESILIENCE}}_{i,t}=\alpha +\lambda {{FS}}_{i,t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(4)
$${{RESILIENCE}}_{i,t}=\alpha +\mu {{TREAT}}_{i}\times {{POST}}_{t}+{\mu }_{1}{{TREAT}}_{i}\times {{POST}}_{t}\times {{FS}}_{i,t}+{\mu }_{2}{{FS}}_{i,t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(5)

where \(F{S}_{i,t}\) indicates the financial slack of firm \(i\) in year \(t\). Referring to the research of Bhandari and Javakhadze (2017), the proportion of cash and cash equivalents relative to total assets serves as a proxy variable for firm financial slack. The coefficients of TREAT × POST in Table 7 indicate that the implementation of the MMFGAREPG leads to an average increase of 0.0150 in the financial slack of REPGFs, equivalent to 18.7% of the sample mean. The coefficients of FS indicate a positive correlation between the financial slack of REPGFs and their resilience. The coefficients of TREAT × POST × FS indicate the impact of the MMFGAREPG on the resilience of REPGFs is more pronounced compared to those with a lower degree of financial slack. According to the above results, the MMFGAREPG makes REPGFs more resilient by increasing their financial slack. This finding proves the proposed hypothesis about the financing channel.

Table 7 The financing channel.
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Klemun and Trancik (2020) emphasize that feed-in tariffs (FIT) subsidies facilitate bank credit and equity investment by stabilizing revenue expectations, mitigating project risks, and enhancing corporate credit ratings, thereby effectively alleviating the financing constraints faced by firms. These insights align with the conclusion of this paper, which posits that the MMFGAREPG enhances the financial stability of REPGFs. Collectively, these findings suggest that REPPSPs can serve as a critical institutional mechanism to promote the sustainable development of REPGFs by addressing their financing challenges. This study provides a robust theoretical foundation for policymakers to utilize policy instruments in alleviating the financing constraints of REPGFs, thereby fostering the sustainable development of the renewable energy generation sector.

The efficiency channel

The MMFGAREPG mandates a full guarantee for the acquisition of renewable energy-generated power and permits firms to secure contracts for excess electricity via market competition. Furthermore, these contracts are granted priority dispatch by power grid firms during their execution. Such regulations may encourage REPGFs to enhance their power generation efficiency, thereby increasing power output and achieving higher profitability. Based on this premise, we examine the innovation efficiency channel through which the implementation of the MMFGAREPG affects the resilience of REPGFs using the models outlined below:

$${{INNOE}}_{i,t}=\alpha +\beta {{TREAT}}_{i}\times {{POST}}_{t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(6)
$${{RESILIENCE}}_{i,t}=\alpha +\lambda {{INNOE}}_{i,t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(7)
$$\begin{array}{l}{{RESILIENCE}}_{i,t}=\alpha +\mu {{TREAT}}_{i}\times {{POST}}_{t}+{\mu }_{1}{{TREAT}}_{i}\times {{POST}}_{t}\\\qquad\qquad\qquad\qquad\quad \times\,{{INNOE}}_{i,t}+{\mu }_{2}{{INNOE}}_{i,t}\\\qquad\qquad\qquad\qquad\quad+\,\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}\end{array}$$
(8)

where \({\rm{INNOE}}_{i,t}\) represents the innovation efficiency of firm \(i\) in year \(t\). Innovation efficiency is quantified by the ratio of the number of patents to the natural logarithm of R&D expenditure, with 1 added to the expenditure value. The coefficients of TREAT × POST in Table 8 indicate that the implementation of the MMFGAREPG leads to an average increase of 0.0373 in the innovation efficiency of REPGFs, equivalent to 21.8% of the sample mean. The coefficients of INNOE indicate a positive correlation between innovation efficiency and the resilience of REPGFs. The coefficient of TREAT × POST × INNOE suggests that the impact of the MMFGAREPG on the resilience of REPGFs is more pronounced than that of firms with lower innovation efficiency. The results reveal that the implementation of the MMFGAREPG enhances the innovation efficiency of REPGFs, which in turn increases their resilience, thereby validating the efficiency channel.

Table 8 The efficiency channel.
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Borenstein (2012) asserts that market competition compels firms to enhance technological efficiency. The above research places greater emphasis on the “survival-driven effect,” suggesting that excessive competition may compress the space for R&D investment and have a crowding-out effect on innovation. The results indicate that the combination of financial subsidies and market competition can promote the intrinsic innovation motivation of REPGFs and thereby enhance their resilience. Unlike the internal mechanism of the “survival-driven effect” proposed by Borenstein (2012), the findings emphasize the intrinsic innovation motivation of REPGFs on the basis of maintaining survival. Regulators can stimulate the intrinsic innovation motivation of REPGFs by optimizing the model that combines guaranteed acquisition of electricity and market trading of electricity, and actively shape the market structure to avoid innovation crowding-out.

The fundamental channel

The MMFGAREPG releases positive signals about REPGFs to the market, attracting further green investors. Green investors mitigate information asymmetry between REPGFs and stakeholders, secure essential resources for business development, and reduce the stock price volatility of REPGFs, thereby enhancing their capacity to manage risk shocks and improve resilience. Therefore, we infer that the MMFGAREPG can improve the fundamentals of REPGFs and enhance their resilience by attracting more green investors. We develop the following models to examine the fundamental channel by which the implementation of the MMFGAREPG affects the resilience of REPGFs:

$${{GI}}_{i,t}=\alpha +\beta {{TREAT}}_{i}\times {{POST}}_{t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(9)
$${{RESILIENCE}}_{i,t}=\alpha +\lambda {{GI}}_{i,t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t}$$
(10)
$${\begin{array}{ll}{{RESILIENCE}}_{i,t}=\alpha +\mu {{TREAT}}_{i}\times {{POST}}_{t}+{\mu }_{1}{{TREAT}}_{i}\times {{POST}}_{t}\times {{GI}}_{i,t}+{\mu }_{2}{{GI}}_{i,t}+\gamma {{Controls}}_{i,t}+{\eta }_{i}+{\theta }_{t}+{\varepsilon }_{i,t} \end{array}}$$
(11)

where \({\rm{GI}}_{i,t}\) is the natural logarithm of 1 added to the number of green funds holding firm \(i\) in the year \(t\). We qualify a fund as a green fund by analyzing its subject information for the presence of sustainability-related keywords. The coefficients of TREAT × POST in Table 9 indicate that implementation of the MMFGAREPG leads to an average increase of 0.0242 in the green investors of REPGFs, equivalent to 26.5% of the sample mean. The coefficients of GI indicate a positive correlation between green investors and the resilience of REPGFs. Furthermore, the coefficient of TREAT × POST × GI signifies that the impact of the MMFGAREPG on the resilience of REPGFs is more significant compared to those with fewer green investors. The results presented above demonstrate that the implementation of the MMFGAREPG attracts more green investors to REPGFs, enhancing their resilience and validating the fundamental channel.

Table 9 The fundamental channel.
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Tang and Zhang (2020) assert that green investors contribute to the improvement of the stock liquidity of REPGFs. The results offer novel evidence illustrating the long-term positive impact of green investors on REPGFs, thereby expanding upon the research conducted by Tang and Zhang (2020). We also offer micro-level evidence about the role of green capital in promoting the sustainable development of the renewable energy industry. Regulators may effectively guide green capital into the renewable energy sector through a comprehensive policy framework and enhance its role as a value investor. REPGFs can draw green capital and obtain long-term stable financial support by enhancing transparency and ESG performance, among other strategies.

Heterogeneity tests

Digital economy

In regions with a well-developed digital economy, REPGFs may leverage big data, the Internet of Things, and other technologies to optimize power generation forecasting, equipment maintenance, energy storage, etc., thereby enhancing efficiency and diminishing volatility. In conjunction with the stable income generated by guaranteed purchase policies, REPGFs can more effectively strategize long-term investments and enhance their financial and technical resilience. Advanced smart grids in these regions can monitor and dispatch electricity in real time, while the guaranteed purchase policy ensures the absorption of renewable energy power. Smart grid technology can more efficiently integrate this power, minimizing electricity waste. This approach improves the operational efficiency and market access capabilities of REPGFs, enhancing their resilience. These regions may possess more sophisticated data platforms that deliver real-time market information, aiding REPGFs in accurately predicting market demand and price fluctuations. The guaranteed purchase reduces income uncertainty, while data tools assist REPGFs in enhancing resilience in domains such as cost control and risk management.

In regions with a well-developed digital economy, there is typically greater technological innovation and knowledge sharing. REPGFs may more readily access advanced technologies and management practices. The stable environment created by guaranteed purchases can accelerate technical advancements and improve risk management capabilities. The digital economy, characterized by the fluidity of data and the permeability of digital technologies, promotes disruptive innovations in REPGFs in technology research and development, business models, and organizational management. Digital infrastructure enhances the resilience of REPGFs to market volatility and supply chain disruptions. In highly digitalized regions, REPGFs can rapidly assimilate data flows and modify production strategies, thereby developing “dynamic capabilities” that allow them to sustain resilience against energy price volatility or policy alterations while simultaneously augmenting their competitive edge in the market. Therefore, for REPGFs located in regions with a well-developed digital economy, the implementation of MMFGAREPG may yield a more pronounced impact on their resilience.

The entropy method is applied to four primary indicators—digital infrastructure construction, digital industry, industrial digitization, and digital innovation capability—alongside twenty secondary indicators to formulate a comprehensive index that acts as a proxy for the digital economy (DE). We classify the samples into high- and low-level digital economy groups based on whether their levels exceed the median value for each year. As shown in Table 10, the coefficient of TREAT × POST in the high digital economy group exceeds that of the low-level digital economy group, suggesting that the implementation of the MMFGAREPG exerts a more significant positive effect on the resilience of REPGFs in regions with high-level digital economy levels.

Table 10 The heterogeneity of digital economy.
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Brynjolfsson and McElheran (2016) argue that big data and AI technologies improve firms’ capabilities in demand forecasting, equipment operation and maintenance, and risk management, thereby enhancing operational resilience. The findings provide novel micro-level evidence regarding the supportive function of regional digital infrastructure in bolstering the REPGFs’ risk-resistance capabilities and further reveal how the maturity of the regional digital economy, as a key environmental factor, significantly amplifies the positive effects of policy intervention (MMFGAREPG). We also extend the impact of the digital economy on physical firms to encompass the renewable energy generation sector. Regulators should incorporate digitalization into green financial support, increase investment in regional digital infrastructure, and foster the coordinated advancement of policies and digital infrastructure.

Green development

In regions with higher levels of green development, governments typically prioritize environmental sustainability, providing robust legislative support and imposing stringent fines for corporate pollution emissions. Renewable energy power generation can effectively diminish carbon emissions by substituting traditional thermal power generation and is projected to garner substantial support from local governments, including policy subsidies and loans. Regions with a high level of green development may possess more sophisticated carbon trading markets, allowing firms to earn additional income by selling carbon quotas. This, together with the consistent profits from assured purchases, bolsters their financial resiliency. Moreover, the clustering of green technologies may promote technology sharing and innovation among firms, reducing R&D costs and improving technological resilience.

We examine three dimensions: the degree of economic growth, resource and environmental carrying capacity, and government policy support. These dimensions encompass nine secondary indicators, including the green growth efficiency index, resource and ecological protection index, green investment index, and environmental governance index, as well as 53 tertiary indicators. The entropy method is utilized on the specified indicators to derive a comprehensive index (GD) as a proxy for the green development level. We classify the samples into high- and low-level green development groups based on their green development levels relative to the annual median value. In Table 11, the coefficient of TREAT × POST in the high-level green development group is larger than that in the low-level green development group.

Table 11 The heterogeneity of green development.
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The above results imply that implementing MMFGAREPG significantly enhances the resilience of REPGFs in areas characterized by higher levels of green development. The findings greatly align with the macro impact proposed by Aghion et al. (2016) that green policies mitigate risks in the new energy industry. However, the breakthrough of this study lies in revealing the specific transmission path through micro-firm data, thereby filling the empirical gap in the implementation effect of policies. Promoting green development facilitates the nonlinear positive impact of the MMFGAREPG on the resilience of REPGFs.

ESG performance

The resource-based view theory posits that ESG performance constitutes the “intangible strategic resources” amassed by firms. Firms with better ESG performance are more likely to gain support from key stakeholders and serve as an “accelerator” for policy implementation. REPGFs with better ESG performance are more inclined to obtain low-cost green financing, including green bonds and ESG-themed funds. The stable revenue stream from fully guaranteed purchases can act as a credit endorsement, further reducing the financing risk premium. The certainty of policy guarantees, combined with ESG reputation, creates a “double credit enhancement” effect, markedly improving the financial resilience of REPGFs. REPGFs exhibiting better ESG performance are more likely to receive priority support from the government, community recognition, and long-term procurement commitments from customers. REPGFs with better ESG performance usually possess more comprehensive internal control systems, can quickly adapt to policy changes (such as flexibly adjusting power generation plans), and efficiently allocate resources, thus demonstrating stronger resilience. The implementation of MMFGAREPG could potentially enhance the resilience of REPGFs with better ESG performance.

We use the Huazheng ESG score to measure a firm’s ESG performance. Samples are divided into better and worse ESG performance groups based on whether their ESG scores exceed or fall below the median value for each year. Table 12 demonstrates that the coefficient of TREAT × POST in the better ESG performance group exceeds that of the worse ESG performance group, indicating that the implementation of the MMFGAREPG exerts a more pronounced influence on the resilience of REPGFs with better ESG performance.

Table 12 The heterogeneity of ESG performance.
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Superior environmental performance, exemplified by the application of low-carbon technologies, can reduce policy risks, including carbon taxes and environmental fines, while also improving energy efficiency (Fatemi et al., 2018). For instance, REPGFs attract green investors through ESG disclosures and improve financing conditions. Prior studies have inadequately addressed the impact of ESG factors within the renewable energy industry. For the first time, we present micro-level evidence demonstrating that, in the renewable energy power generation sector, adherence to ESG obligations serves not merely as a compliance necessity but also as a strategic measure for enhancing resilience. This discovery transcends the cognitive limitation established by Fatemi et al. (2018) concerning the universal value of environmental performance. The active fulfillment of ESG responsibilities and improvement of ESG performance by REPGFs help them obtain more resources and support from stakeholders essential for development, thereby augmenting their ability to cope with risk shocks.

Regional heterogeneity

There are significant differences among various regions in China in terms of economic development, policy measures, and resource endowments. In the western regions, the amalgamation of subsidies and resource advantages leads to high power generation efficiency and low unit costs, facilitating the attainment of economies of scale for REPGFs and exhibiting enhanced resilience. In the eastern regions, subsidies are relied upon to compensate for resource deficiencies; yet, proximity to consumer markets necessitates a balance between reliance on subsidies and the benefits of grid connection. The eastern regions have strong local fiscal support capabilities, and the coordination of supporting policies, such as tax exemptions and land preferences, with central subsidies bolsters the risk-resistance of REPGFs. In the western regions, subsidies may emerge as the primary income source, but local support is insufficient, and delayed subsidies could precipitate liquidity crises for REPGFs, posing challenges to their resilience after the subsidy phase-out. The timeliness and transparency of subsidy distribution in the eastern regions affect the stability of REPGFs’ capital chains. The legislative framework in the eastern regions is supportive, and the synergistic interaction among subsidies, carbon trading mechanisms, and green certificate markets enhances the long-term competitiveness of REPGFs. Based on this, we hypothesize that the impact of the MMFGAREPG’s implementation on the resilience of REPGFs may vary across different regional REPGFs.

We categorize the samples into eastern and western regions according to the locations of the REPGFs and perform regressions on Eq. (1) separately. As shown in Table 13, the absolute value of the coefficient of\(TREAT\times POST\) in the eastern region sample is larger, indicating that the implementation of the MMFGAREPG has a more substantial impact on enhancing the resilience of REPGFs in the eastern region.

Table 13 The heterogeneity of region.
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Zhao et al. (2016) indicate that the eastern power grid possesses a superior capacity for power accommodation, facilitating a more efficient implementation of the guaranteed purchase policy. This mitigates the risk of cash flow disruption caused by the abandonment of wind and solar energy. Furthermore, local governments in the eastern region have reinforced the policy’s effectiveness by providing supplementary subsidies and tax incentives, thereby directly enhancing the risk-resistance capabilities of REPGFs. The findings echo those of Zhao et al. (2016) regarding the effect of the guaranteed purchase policy on augmenting the consumption capacity of the eastern power grid. The implementation of the MMFGAREPG should consider regional disparities, enhance focus on the western regions, and mandate local governments in those areas to furnish appropriate policy and infrastructural support.

Conclusion

Renewable energy power generation corresponds with the concept of green development and has emerged as the future trajectory of the power industry. Countries worldwide have implemented REPPSPs to support the development of renewable electricity and lower carbon emissions by switching from conventional energy sources to renewable energy production. Although REPPSPs can alleviate the funding deficit of REPGFs, their comparatively high generation costs diminish market competitiveness, hence presenting a significant obstacle to their sustained development. Therefore, elucidating the influence of REPPSPs on the resilience of REPGFs is critical for optimizing the policy’s positive role in fostering their sustainability.

We demonstrate that the implementation of the MMFGAREPG markedly enhances the resilience of REPGFs. Specifically, this policy not only increases financial slack for REPGFs but also improves their innovation efficiency and draws more green investors to these firms, thereby enhancing their resilience. Moreover, the heterogeneity test results indicate that the implementation of the MMFGAREPG exerts a more pronounced positive effect on the resilience of REPGFs with better ESG performance, as well as those situated in eastern regions and areas characterized by a well-developed digital economy and green development.

The conclusion provides policy insights for guiding regulatory authorities to promote the better development of REPGFs. Regulatory authorities should continue to improve the MMFGAREPG, combine price guarantee acquisition with market competition mechanisms, and employ these mechanisms to stimulate innovation among REPGFs. Regulators can promote the synergy between digitalized power grids and the full guarantee purchase policy. For instance, they can optimize renewable energy consumption through smart grids and power big data platforms, thus minimizing the curtailment of wind and solar electricity. They can also support the application of blockchain technology in the traceability and trading of green electricity. A connection should be established between the full guarantee purchase policy and local green finance policies (such as green credit interest subsidies and carbon emission rights trading), and the infrastructure for cross-regional power allocation should be improved to ensure the transmission capacity of renewable energy. ESG indicators should be incorporated into the priority assessment system of the full guarantee purchase policy, offering preferential power price subsidies and quota allocations to firms with elevated ESG ratings, thereby fostering healthy competition within the sector.

For REPGFs located in regions with relatively lower levels of digital economy and green development, they can actively fulfill their ESG responsibilities to secure greater support from stakeholders. These firms can pursue low-cost digital transformation strategies, collaborate with firms or technology providers in regions characterized by advanced digital economies to obtain technical support, participate in cross-regional green power transactions, and sell electricity to regions with higher demand. Such measures can enhance revenue stability. Additionally, these REPGFs can proactively apply for green subsidies and tax incentives from local governments while advocating for policy support that facilitates cross-regional cooperation and technology transfer. For example, they can negotiate with local governments to integrate renewable energy projects into local infrastructure construction initiatives, thereby securing additional resources.

Investors can prioritize REPGFs in regions with advanced digital economies, as these firms are more resilient under MMFGAREPG and have lower policy dependency. They should comprehensively assess the technological iteration capabilities and market-oriented transformation potential of these firms. Investors may consider the ESG performance of REPGFs as a fundamental criterion for investment decisions, as REPGFs exhibiting better ESG performance are more likely to obtain policy backing and long-term financing advantages.

The study exclusively focuses on REPPSPs, disregarding other renewable energy power generation policies, which may impose certain constraints. Green power certificates enable firms to shape a green and environmentally friendly brand image, improve competitiveness, attract more consumers, and earn additional profits through trade. Therefore, exploring the combined impact of REPPSPs and other policies (e.g., green power certificates) or analyzing the long-term implications of these policies on the sustainability of REPGFs are valuable directions for future research.