Does Corporate ESG Performance Enhance Sustained Green Innovation? Empirical Evidence from China
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Abstract
Examining how corporate environmental, social, and governance (ESG) performance impacts the sustainability of green innovation holds significant theoretical and practical value for achieving China’s “dual carbon” goals and advancing corporate sustainability. This study contributes by providing novel empirical insights into the mechanisms and heterogeneous effects of ESG on sustained green innovation, addressing gaps in understanding ESG’s role in emerging markets like China. Using data from Shanghai and Shenzhen A-share listed firms (2009–2023), this study investigates the relationship between corporate ESG performance and sustained green innovation, revealing three key findings. First, corporate ESG performance significantly promotes sustained green innovation – a conclusion robust to rigorous sensitivity tests. Second, mechanism analysis confirms that strong ESG performance elevates innovation capacity by alleviating financing constraints and reducing agency costs, while stricter environmental regulations and higher levels of digital transformation further amplify ESG’s positive impact. Third, heterogeneity tests demonstrate that ESG’s effect is more pronounced in non-state-owned enterprises, non-high-pollution industries, growth-stage firms, and enterprises with higher ESG rating divergence. These insights offer critical theoretical and practical implications for ESG and innovation literature, as well as practical implications for ESG implementation and green policy design in China, guiding firms and policymakers toward more effective sustainability strategies.
1 Introduction
Corporate ESG (environmental, social, and governance) performance has emerged as a critical driver of sustainable business practices and green innovation. As firms face increasing pressure to align with environmental sustainability goals, understanding how ESG initiatives translate into sustained innovation outcomes becomes paramount. This study examines the relationship between corporate ESG performance and sustained green innovation using comprehensive data from China’s A-share listed companies (2009–2023). Our analysis reveals a significant sustainability challenge: among innovative firms in China, only 36.11% practice sustained green innovation, while 63.89% engage in non-sustained green innovation[1]. This nearly 2:1 ratio underscores the critical need to understand what drives innovation sustainability beyond initial implementation.
Current academic research has established substantial consensus regarding the influence of corporate ESG performance on green innovation activities. On the one hand, ESG performance enhances market linkages and reduces information asymmetry. Strong ESG performance strengthens a firm’s connections with the market and mitigates information asymmetry with various stakeholders (Usman et al., 2020). It signals to capital markets and financial institutions the firm’s commitment to social responsibility and sustainable development (Zheng et al., 2023). From a sustainability perspective, the optimization of the external environment exhibits characteristics of path dependence. Firms with high ESG ratings are more likely to receive policy benefits such as green credit (Cheng et al., 2014) and R&D subsidies (Flammer, 2015), which helps establish a stable path for acquiring green innovation resources. This path dependence further incentivizes firms to maintain and improve their ESG performance to consolidate their resource advantages. Moreover, institutional investors’ preference for firms with strong ESG performance (Dyck et al., 2019) not only alleviates current financing constraints (FC) but also facilitates the establishment of long-term investment relationships. These relationships provide firms with cross-cycle financial and technological support for green innovation, thereby mitigating the adverse effects of short-term funding fluctuations on innovation continuity.
On the other hand, ESG performance improves governance structures. ESG implementation optimizes governance structures, reducing agency costs (AC) and curbing managerial short-termism (Amore & Bennedsen, 2016). This governance optimization contributes to sustainability by fostering the development of dynamic capabilities. The implementation of ESG principles encourages firms to establish long-term strategic frameworks incorporating environmental objectives, prompting management to integrate green innovation into the core system of competitive advantage development, rather than treating it as a short-term political or performance project (Schiederig et al., 2012). For instance, ESG-driven initiatives such as the establishment of board-level sustainability committees and internal environmental risk control mechanisms provide institutional safeguards for green innovation, reducing the risk of innovation disruption caused by leadership changes or strategic shifts. Moreover, the internal innovation culture shaped by ESG principles not only motivates employees to engage in innovative activities but also facilitates the accumulation of green technological knowledge and experience through organizational learning mechanisms. This enhances the firm’s dynamic adaptability to changes in environmental policies and the evolution of market demand for green products. As a result, green innovation can continuously respond to external environmental shifts, transitioning from isolated efforts to systemic and iterative innovation, thereby ensuring its long-term sustainability (Teece et al., 1997).
However, the impact of ESG performance on the sustainability of corporate green innovation remains relatively under-explored. Existing research has paid relatively limited attention to the topic, and the theoretical boundaries of sustained green innovation (SGI) have yet to be clearly established. SGI builds upon the foundations of green innovation and sustainable innovation (Triguero & Córcoles, 2013). It refers to innovation efforts through which firms reduce the environmental externalities of their production activities over time (Rennings, 2000), while continuously improving performance across ecological, economic, and social dimensions (Carruthers & Vanclay, 2012). Theoretically, SGI differs from other related concepts. Compared to eco-innovation, which emphasizes environmental performance across the full life cycle and focuses more on classifying the different types of innovations (Kemp & Pearson, 2007), SGI highlights the sustained outcomes of green innovation, such as improvements in financial performance, competitiveness, and market value. In contrast to incremental green innovation, which refers to small-scale enhancements and alterations to existing products and processes and is relatively conservative, SGI stresses systematic and strategic continuous improvement. This approach may encompass both radical and incremental innovations simultaneously (Jing & Liu, 2024).
Building on this theoretical foundation, this study addresses three key research questions: First, does corporate ESG performance significantly enhance sustained green innovation? Second, what are the underlying mechanisms through which ESG influences innovation sustainability? Third, how do firm characteristics and external environments moderate this relationship?
In this article, we conduct a multifaceted analysis to investigate these effects. Specifically, we utilize econometric models, including fixed-effects regressions, to test the direct impact of ESG performance on sustained green innovation, followed by robustness checks and endogeneity treatments. We further explore mediating mechanisms, such as alleviating FC and reducing AC, and moderating factors like environmental regulations (ERs) and digital transformation (DT). Heterogeneity analyses are performed across enterprise types, including ownership (state-owned vs non-state-owned), industry pollution levels, firm life cycle stages, and ESG rating divergence. Our key findings indicate that strong ESG performance significantly enhances sustained green innovation, with pronounced effects in non-state-owned enterprises, non-high-pollution industries, growth-stage firms, and those with higher ESG rating divergence. These results are robust and provide actionable insights into how ESG can drive long-term sustainability.
Compared to existing research, this study contributes in the following aspects:
First, it expands corporate innovation theory and enriches research on sustained green innovation. Substantive differences exist between green innovation and sustained green innovation. The latter emphasizes long-term, cumulative, and dynamic green innovation activities within a sustainable development framework, aligning more closely with green development strategies. Achieving sustained green innovation requires both external support and internal management coordination. Corporate ESG performance can simultaneously influence both dimensions. Investigating ESG’s impact on sustained green innovation helps firms achieve green transformation and sustainable development, offering significant practical guidance for Chinese enterprises.
Second, this study enriches micro-level evidence on the relationship between corporate ESG performance and sustained green innovation from a value-creation perspective. It positions corporate ESG practices within a long-term sustainable development framework, providing critical insights for assessing whether corporate operations align with green development strategies.
Third, through heterogeneity analysis, we test whether this impact varies across different types of enterprises. Identifying which firms benefit most from ESG’s effect on sustained green innovation offers new perspectives for policymakers and managers across diverse enterprises, fostering healthier development in green finance markets and corporate green innovation practices.
The article is structured as follows. Section 2 summarizes the existing literature and proposes theoretical hypotheses. Section 3 details the study design, including sample selection, variable definition, and model construction. Section 4 first empirically tests whether corporate ESG performance affects sustained green innovation, followed by robustness checks, endogeneity tests, mechanism analysis, heterogeneity analysis, and policy implications. Section 5 concludes.
2 Literature Review and Theoretical Hypotheses
2.1 The Impact of ESG Performance on the Sustained Green Innovation
Corporate ESG performance conveys a firm’s commitment and actions regarding environmental protection, social responsibility, and sound governance. It represents a vital, multi-faceted signal. According to Spence’s (1974) signaling theory, firms send signals to communicate internal quality or values. A strong ESG rating effectively provides specialized information about a firm’s environmental responsibility. This reduces information asymmetry with stakeholders and boosts investor confidence, enhancing social trust (Tang & Yang, 2023), and helping investors better understand the firm’s green innovation efforts and short-term performance fluctuations. Consequently, investors become more tolerant of potential early-stage failures in green innovation, supporting management’s commitment to sustained green innovation investments. Meanwhile, firms with superior ESG performance possess robust governance structures and strategic planning capabilities. These ensure efficient resource allocation and the smooth implementation of sustained green innovation activities (Aljebrini et al., 2025).
Additionally, drawing on Resource Dependence Theory, firms with excellent ESG performance accumulate strategic resources, including green technologies, management expertise, and reputational capital (Barney, 1991). These resources are scarce and difficult for competitors to imitate, providing foundational support for sustained green innovation. Policymakers are more inclined to provide R&D subsidies to ESG-leading firms, such as those under China’s “14th Five-Year Plan” for green technology initiatives. The resulting policy shocks are transmitted through market expectations and investor confidence, ultimately reshaping capital allocation patterns (Gong et al., 2024). Enhancing ESG performance thus becomes a strategic means for firms to hedge against policy uncertainty (Dai & Jiang, 2025). At the same time, although ESG funds may exhibit greenwashing behavior (Sun et al., 2024), investors’ preference for such funds still alleviates FC for ESG-oriented firms (Gillan et al., 2021). This expands firms’ access to financial resources and makes sustained investment in green innovation more feasible. Furthermore, based on Stakeholder Theory, linking managerial performance to relative firm performance means ESG ratings directly impact managerial evaluations. This compels managers to dedicate more resources and effort to sustained green innovation activities, driving continuous improvement in the firm’s green innovation capabilities (Duque-Grisales & Aguilera-Caracuel, 2021). Thus, we propose Hypothesis 1:
Hypothesis 1: Corporate ESG performance has a significant positive relationship with sustained green innovation.
2.2 ESG Performance, FC, and Sustained Green Innovation
Corporate green innovation requires high investment and long cycles (Hall & Lerner, 2010), while sustained green R&D increases business risk. Without strategic innovation support or policy intervention, firms struggle to pursue green innovation independently and heavily rely on external funding (Aghion et al., 2013). Especially in the context of escalating climate risks, climate-related uncertainties, such as rising sea levels and extreme heat, further increase firms’ financing costs (Fu et al., 2025). According to the Pecking Order Theory (Myers & Majluf, 1984), information asymmetry drives FC. ESG performance mitigates this by building dynamic reputational assets through continuous disclosure. It signals verifiable commitments to sustained innovation, reducing market friction and information asymmetry. Investors thus form positive expectations and accept higher premiums, lowering interest rates and collateral requirements for green credit, directly easing FC (Dyck et al., 2019). Moreover, unlike one-time subsidies, high-ESG firms secure ongoing policy support (e.g., government grants, green finance), replenishing innovation funds long-term. Reduced FC enable continuous green technology iteration, complementary R&D, and supply chain collaboration. This significantly boosts green patents (Zhang & Jin, 2021), accelerates green technology upgrades (Chen et al., 2020), improves commercialization success (Li & Wang, 2024), and through competition and imitative learning among peer firms (Seng & Zhang, 2025). Although ESG compliance may raise short-term costs, its long-term funding benefits far outweigh initial expenditures (Flammer, 2015). Thus, we propose Hypothesis 2(a):
Hypothesis 2(a): ESG Performance enhances sustained green innovation by alleviating FC.
2.3 ESG Performance, AC, and Sustained Green Innovation
Agency cost theory (Meckling & Jensen, 1976) highlights that goal conflicts between stakeholders cause resource misallocation. Managers may prioritize short-term profits over long-term green innovation by: slashing green R&D budgets to embellish financial reports, diverting innovation funds to speculative activities, or avoiding high-uncertainty frontier technologies. Such actions disrupt sustained innovation and weaken corporate sustainability (Acharya & Xu, 2017). ESG governance embeds long-term green goals into managerial incentives via environmental committees and ESG-linked compensation. This aligns managerial behavior with innovation objectives, curbing short-termism. ESG-oriented institutional investors further reduce fund misappropriation through oversight (Dyck et al., 2019), mitigating principal–agent conflicts. Strategically, elevated ESG coherence lowers coordination costs and enables sustained innovation (Eccles et al., 2014). Thus, we propose Hypothesis 2(b):
Hypothesis 2(b): ESG Performance enhances sustained green innovation by reducing AC.
2.4 Moderating Role of ER
ER critically amplifies the ESG-innovation link. Policies like carbon trading, emission permits, and environmental taxes internalize governance costs, forcing firms to reevaluate environmental risks versus innovation returns. Behind this lies an “induced pressure” mechanism, where firms, facing increasingly stringent environmental compliance pressures, are compelled to seek new development paths to reduce costs and enhance competitiveness (Jaffe & Palmer, 1997). High-ESG firms excel here: Porter’s “Innovation Offset Effect” (Porter & Linde, 1995) suggests their mature environmental capabilities turn compliance into competitive gains. China’s revised Environmental Protection Law, green patent filings grew 2.3 times faster in high-ESG firms than in low-ESG peers, proving the regulation’s amplifying effect (Tan & Zhu, 2022). A more nuanced analysis of how ER amplifies the relationship between ESG performance and SGI beyond mere compliance pressure can be understood from the following perspectives.
On one hand, ER facilitates more efficient market-based resource allocation by highlighting differences in corporate ESG performance. As ERs become increasingly stringent, market sensitivity to firms’ environmental responsibility rises. This prompts financial institutions to treat ESG ratings as a core criterion for capital allocation, directing policy incentives such as green credit and targeted subsidies toward firms with high ESG scores (Delis et al., 2024). Concurrently, consumers show a growing preference for products and services offered by environmentally responsible companies, allowing high-ESG firms to capture greater market share and enjoy brand premiums (Delmas & Burbano, 2011). This alignment between market incentives and ER creates a synergistic effect, motivating high-ESG firms to continuously invest in green innovation to maintain their competitive edge.
On the other hand, ER strengthens the linkage between ESG and SGI by shaping corporate strategic decision-making. Under stringent regulatory conditions, investors tend to adjust their risk preferences, reallocating capital toward firms with strong ESG performance (Bolton & Kacperczyk, 2021). To mitigate risk and attract investment, firms are compelled to emphasize long-term strategic planning and embed ESG principles into their corporate strategies. This shift encourages firms to prioritize sustainable development goals and allocate more resources to green innovation projects (Bansal & Roth, 2000).
In addition, considering the possibility of counterintuitive outcomes, this study also discusses the challenges state-owned enterprises (SOEs) may face under intensified ER. From one perspective, SOEs often carry heavy administrative responsibilities, such as ensuring regional energy supply stability and maintaining employment in strategic industries. As a result, when facing stringent environmental standards, SOEs must carefully balance compliance with these standards against fulfilling administrative mandates. For example, in the energy sector, SOEs may prioritize short-term projects that quickly increase clean energy capacity to meet high ER targets. This can crowd out long-term green technology innovation initiatives that require sustained R&D investment and have extended payback periods, thereby constraining the adaptability of technological progress to real market demand for green solutions (Zhang et al., 2014). From another perspective, SOEs typically operate with complex and bureaucratic decision-making processes, often characterized by multi-tiered principal–agent relationships that delay information transmission (Qian, 1996). In a high-ER context where policy changes occur frequently, SOEs may struggle to promptly capture and effectively respond to policy-driven opportunities for green innovation. For instance, in response to newly implemented, stringent carbon accounting and trading regulations, non-state firms may leverage their organizational agility to swiftly redirect R&D efforts toward low-carbon technologies. In contrast, SOEs, hindered by lengthy internal approval procedures, may experience significant delays from identifying policy opportunities to formulating innovation strategies and initiating R&D activities. Such delays can lead to missed windows for innovation and ultimately hinder the enhancement of long-term green innovation capacity. Thus, we propose Hypothesis 3(a):
Hypothesis 3(a): ER positively moderates the relationship between ESG performance and sustained green innovation.
2.5 Moderating Role of DT
The moderating role of DT in the relationship between corporate ESG performance and SGI can be understood within the framework of dynamic capabilities (Teece et al., 1997). The deep integration of digital technologies – such as big data, blockchain, and artificial intelligence – significantly enhances firms’ dynamic capabilities in risk mitigation, stakeholder engagement, and innovation cycle acceleration. These improvements, in turn, increase transparency in ESG governance, optimize resource allocation efficiency, and enhance organizational responsiveness, thereby amplifying the marginal effect of ESG performance on green innovation outcomes (Li et al., 2024).
From the perspective of risk mitigation, DT enables firms to establish more sophisticated systems for risk forecasting and response. For instance, through big data analytics of vast environmental datasets, firms can proactively identify potential environmental risks, such as climate-related disruptions to supply chain stability or compliance risks arising from new ERs (Bharadwaj et al., 2013). Firms with strong ESG performance can integrate environmental risk management into daily operations using digital tools, thereby reducing the probability and severity of adverse events and ensuring that green innovation activities are not interrupted by major shocks. Additionally, blockchain technology guarantees the immutability of environmental data and enables real-time tracking of green technology funding flows (Guo et al., 2022). This reduces information asymmetry (Cheng et al., 2014), enhances the credibility of ESG commitments in capital markets, and mitigates investment risks, thereby providing a stable financial foundation for sustained green innovation.
In terms of stakeholder engagement, digital technologies offer firms efficient platforms for interaction with stakeholders, enhancing their capacity for dynamic responsiveness (Nambisan et al., 2017). Digital governance platforms based on big data can track R&D funding flows in real time, effectively reducing the risk of fund misallocation (Chang & Wang, 2024). These platforms can also embed ESG performance metrics into executive compensation algorithms, aligning short-term interests with long-term green objectives (Amore & Bennedsen, 2016). This sends a strong signal to both internal employees and external investors regarding the firm’s commitment to ESG values, thereby increasing stakeholder motivation to participate in green innovation initiatives. Moreover, through digital channels such as social media and online forums, firms can quickly gather insights into consumer preferences for green products and community concerns about environmental impacts. These insights enable firms to refine their green innovation strategies to better match market and societal demands, fostering a collaborative model of green innovation between stakeholders and firms.
From the perspective of accelerating innovation cycles, digital technologies such as artificial intelligence and the industrial internet significantly enhance firms’ capacity to optimize resource allocation and streamline innovation processes. AI can accurately identify technological gaps in green innovation; for example, Tesla has leveraged AI simulation to reduce battery development cycles by 40%, thereby accelerating the transformation of ESG-aligned resources into tangible innovation outcomes. Industrial internet platforms break down organizational silos, enabling rapid integration of green technology resources across the supply chain. Using digital twin technologies, firms can simulate innovation pathways, thus shortening the time from ESG strategic planning to commercialization (Nambisan et al., 2017). Furthermore, machine learning can identify intersectional innovation opportunities arising from both regulatory and market forces – such as clean technology substitution opportunities in response to carbon tariffs – allowing firms to swiftly mobilize R&D resources. In contrast, firms with low levels of digitalization may struggle with fragmented ESG data and delayed decision-making processes (Eccles et al., 2014), hindering their ability to seize innovation opportunities and respond effectively to policy and market changes. Thus, we propose Hypothesis 3(b):
Hypothesis 3(b): DT positively moderates the relationship between ESG performance and sustained green innovation.
3 Study Design
3.1 Variable Selection
Variable selection involves three main categories: (1) The dependent variable: Firm-level Sustained Green Innovation (SGI); (2) The independent variable: Corporate ESG Performance; (3) Control variables: Factors potentially influencing SGI, mitigating confounding effects beyond ESG.
3.1.1 The Explained Variable
Following Triguero and Córcoles (2013), SGI is measured as the product of the sequential growth rate of green patent applications and the current level of green patent output. This captures both the scale and dynamic growth trend of a firm’s green innovation. Specifically:
where OIN represents the number of green patent applications in year t, t−1, and t−2. Economically, SGI reflects a firm’s long-term knowledge accumulation and technological progress in areas like green product development, renewable energy substitution, process improvement, and energy efficiency enhancement, emphasizing temporal continuity.
3.1.2 The Explanatory Variable
Academic research primarily employs third-party ESG ratings. Huazheng ESG Ratings, a leading domestic authority, is widely recognized and utilized. This system incorporates China-specific indicators while removing unsuitable or inaccessible ones (Wang et al., 2024a). Consequently, Huazheng ESG scores serve as the measure for corporate ESG performance.
3.1.3 The Mediating Variables
FC: Common measures include the KZ index (Kaplan & Zingales, 1997; Lamont et al., 2001), WW index (Whited & Wu, 2006), SA index (Hadlock & Pierce, 2010), and FC index (Xu et al., 2020). The FC index, based on objective financial data, avoids subjective assessment bias and enhances cross-firm comparability. Given the Chinese context, the FC index is the primary measure, with the KZ index used for robustness checks.
AC: Focusing on conflicts between managers and shareholders, AC are proxied by the operating expense ratio (sum of management and sales expense ratios), following Zhen et al. (2015).
3.1.4 The Moderating Variables
ER: Measurement approaches vary, including: policy enforcement intensity (e.g., frequency of environmental inspections, fines levied – Dasgupta et al., 2001; Dean et al., 2009); outcome-based metrics (e.g., wastewater compliance rate – Zhang & Fu, 2008, though limited by regional homogeneity); and firm-level pollution control costs (e.g., equipment investment, operating expenses – Zhang & Fu, 2008). This study employs the ratio of a listed firm’s environmental protection expenditure (e.g., pollution discharge fees) to its annual sales revenue.
DT: Initial measurement involved a two-dimensional matrix of digital readiness and intensity (Chanias & Hess, 2016). Drawing on Wu et al. (2021), this study uses Python-based text analysis of annual reports. Keywords related to DT are extracted across two dimensions: underlying technologies and practical applications. The total keyword frequency is aggregated, and the natural logarithm of this total (plus one) measures the degree of DT.
3.1.5 The Control Variables
The specific control variables are defined in Table 1. In addition, this study controls for year-fixed effects, firm-fixed effects, and industry-fixed effects.
Summary of the main variables
| Category | Variable selection | Symbols | Explanation |
|---|---|---|---|
| Explained variable | Sustained green innovation level | SGI | The year – on – year growth rate of the sum of green patent applications between years t−1 and t, relative to the sum between years t−2 and t−1, multiplied by the sum of green patent applications between years t−1 and t |
| Explanatory variable | Corporate ESG performance | ESG | Huazheng ESG score |
| Mediating variables | FC | FC KZ | FC KZ index |
| Agency costs | AC | Management expense ratio + Sales expense ratio | |
| Moderating variables | Environmental regulation | ER | Pollution – control – expenses/Annual sales revenue |
| Digital transformation | DT | Digital transformation index | |
| Control variables | Firm size | Size | Ln (total assets) |
| Debt-to-asset ratio | Lev | Total liabilities/Total assets | |
| Return to total assets | Roa | Net profit/Total assets | |
| Growth capability | Growth | (Current period operating revenue – Previous period operating revenue)/Previous period operating revenue | |
| Capital intensity | Density | Ln (Total fixed assets/Number of employees) | |
| Firm value | Tobin | Market value/Replacement cost | |
| Dual role of CEO | Dual | 1 if the chairman also serves as the general manager, otherwise 0 | |
| Equity concentration | Top10_HHI | Top 10 shareholders’ equity ratio | |
| Board size | Board | Ln (number of board members) | |
| R&D intensity | RD_ratio | R & D expenditure/Sales revenue |
3.2 Model Construction
To examine the impact of corporate ESG performance on sustained green innovation performance, this study constructs the following empirical model (1):
In equation (1), i denotes the firm, and t denotes the year. SGI is the dependent variable, representing the level of continuous green innovation of the firm. ESG is the core independent variable, with β 1 being the key parameter that reflects the impact of corporate ESG performance on the level of continuous green innovation. A significantly positive β 1 indicates that ESG performance can effectively enhance continuous green innovation, whereas a negative correlation would be suggested otherwise. Controls refer to the series of control variables mentioned earlier. To mitigate potential endogeneity issues, this study controls for time-fixed effects, firm-fixed effects, and industry-fixed effects. ε it represents the random error term.
3.3 Sample Selection and Data Source
Considering data continuity for ESG performance evaluation and the macro-institutional context of ecological civilization construction since the 18th CPC National Congress, this study utilizes data from China’s Shanghai and Shenzhen A-share listed companies spanning 2009 to 2023. To ensure result reliability, the following data preprocessing steps were applied: (1) Excluding financial and insurance firms; (2) Removing samples with missing variables; AND (3) Eliminating ST, *ST, and PT companies. Continuous variables were winsorized at the 1% and 99% levels to mitigate outlier effects. Data on sustained green innovation (SGI) originate from the CNRDS database. Firm-level financial data and industry characteristics are sourced from CSMAR and WIND databases, while provincial-level data comes from respective provincial statistical yearbooks. The descriptive statistics of the data are presented in Table 2.
The descriptive statistics of variables
| Variables | Mean | Std.dev | Min. | Max. | Obs. |
|---|---|---|---|---|---|
| SGI | 21.21 | 160.7 | 0 | 14,281 | 38,687 |
| ESG | 4.143 | 0.937 | 1 | 8 | 42,681 |
| FC | 39,698 | 0.489 | 0.280 | 0.000 | 0.997 |
| KZ | 39,697 | 1.088 | 2.526 | −11.460 | 12.051 |
| AC | 45,660 | 0.647 | 68.062 | 0.001 | 13959.844 |
| ER | 6,377 | 0.002 | 0.032 | −0.014 | 2.497 |
| DT | 41,123 | 1.364 | 1.417 | 0.000 | 6.380 |
| size | 22.14 | 1.305 | 19.65 | 26.22 | 45,691 |
| lev | 0.422 | 0.212 | 0.0510 | 0.949 | 45,690 |
| Roa | 0.0350 | 0.0680 | −0.296 | 0.201 | 45,690 |
| Growth | 0.358 | 1.012 | −0.722 | 7.184 | 45,482 |
| Density | 12.56 | 1.180 | 9.069 | 15.70 | 45,675 |
| Tobin | 2.037 | 1.341 | 0.843 | 8.911 | 45,690 |
| dual | 0.297 | 0.457 | 0 | 1 | 44,545 |
| Top10_HHI | 0.433 | 0.200 | 0.131 | 0.918 | 45,666 |
| Board | 2.085 | 0.188 | 1.609 | 2.485 | 27,614 |
| RD_ratio | 5.131 | 5.210 | 0.0300 | 30.66 | 30,760 |
Notes: The descriptive statistics show the mean (Mean), standard deviation (Std. dev), minimum (Min.), maximum (Max.), and observation(Obs.) of the variables.
4 Empirical Analysis
4.1 Benchmark Regression
Columns (1) and (2) in Table 3 show the regression results without and with control variables. As shown in Table 3, the coefficient of ESG is 2.988 and significant at the 5% level (t = 2.45). This indicates that a one-unit increase in ESG performance is associated with an average increase of 2.988 units in corporate green innovation output. Thus, Hypothesis 1 proposed in this article is confirmed.
Benchmark regression results
| Variables | (1) | (1) |
|---|---|---|
| ESG | 4.272*** | 2.988** |
| (4.03) | (2.45) | |
| Size | 19.114*** | |
| (7.85) | ||
| Lev | 2.892 | |
| (0.32) | ||
| Roa | 0.614 | |
| (0.04) | ||
| Growth | −0.287 | |
| (−0.21) | ||
| Density | 0.658 | |
| (0.36) | ||
| Tobin | 2.144** | |
| (2.20) | ||
| Dual | −0.913 | |
| (−0.34) | ||
| Top10_HHI | −0.580 | |
| (−0.06) | ||
| Board | 13.987* | |
| (1.68) | ||
| RDSpendSumRatio | 1.244*** | |
| (3.40) | ||
| Constant | 4.200 | −464.074*** |
| (0.96) | (−8.16) | |
| Year FE | YES | YES |
| Firm FE | YES | YES |
| Industry FE | YES | YES |
| R-squared | 0.329 | 0.288 |
| Observations | 36,782 | 15,819 |
Notes: *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
4.2 Endogeneity Test
4.2.1 Explanatory and Control Variables Lagged by One Period
Considering the potential influence of corporate ESG performance in t−1 period on the current level of continuous green innovation, this study employs lagged explanatory and control variables to address endogeneity issues. The introduction of lagged terms mitigates the reverse causality problem to some extent (Li et al., 2022). In column (1) of Table 4, the lagged ESG performance is only significant at the 10% level. Conducting the Hausman test reveals that Prob > F = 0.6623, indicating that the endogeneity of lagged ESG performance with respect to current continuous green innovation is not significant. This suggests that short-term ESG performance may not directly drive continuous green innovation.
The results of the endogeneity test
| VARIABLES | (1) | (2) | (3) |
|---|---|---|---|
| Step | Step 1 | Step 2 | |
| L_ESG | 2.069* | ||
| (1.87) | |||
| 7.089*** | |||
| (1.719) | |||
| ESG_mean | 0.517*** | ||
| (64.78) | |||
| Controls | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES |
| R-squared | 0.440 | 0.269 | 0.002 |
| Observations | 12,437 | 16,094 | 16,094 |
| F-stat | 416.71*** | ||
| Wald χ² | 45.41*** |
Hausman test: χ² = 0.19, p = 0.662. Notes: *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
4.2.2 Instrumental Variable (IV) Method
Despite the benchmark regression indicating that ESG performance significantly promotes green innovation, there may be a bidirectional causal relationship between the two: on the one hand, high ESG firms drive innovation through resource integration and governance optimization; on the other hand, green innovation outcomes (such as low – carbon technology patents) can enhance corporate environmental performance and social reputation, thereby feeding back into ESG ratings (Flammer, 2015). This reverse causality can bias the OLS estimates, failing to accurately identify the net effect of ESG on innovation. To mitigate endogeneity bias, this study employs the instrumental variable method (IV). Following the strategy of Amore and Bennedsen (2016), the average industry-level ESG performance (ESG_mean) is selected as an instrumental variable. The rationale is as follows: first, firms within the same industry face similar policy pressures and technological standards, resulting in a co-movement trend in ESG performance (relevance); second, the industry-level ESG mean affects innovation by influencing the strategic choices of individual firms (e.g., benchmarking against industry best practices), but the overall industry trend does not directly determine a single firm’s innovation capability (exclusivity).
As shown in columns (2) and (3) of Table 4, the instrument ESG_mean exhibits a strong correlation with the endogenous variable L_ESG (F-statistic = 416.71), satisfying the relevance condition for IV validity. After addressing endogeneity, the second-stage regression reveals that the positive impact of lagged ESG on the dependent variable significantly strengthens, with a coefficient of 7.089 that is highly significant at the 1% level. The IV estimation results confirm that, after accounting for the bidirectional causality, the positive effect of ESG on green innovation remains robust, highlighting the necessity of policy interventions to strengthen ESG development.
4.3 Robustness Test
4.3.1 Substitution of Dependent Variables
Considering the disparity in the proportions of green invention patents and green utility model patents, we observe a gradually increasing trend in the former and a declining trend in the latter. In most years, green utility model patents account for a larger share; however, in certain years, such as 2013 and after 2021, green invention patents represent a higher proportion (results are reported in Appendix A). These two distinct types of green patents may exert different economic implications for the sustainability of green innovation. Therefore, in this section, we substitute the indicators of the explained variable to facilitate a more robust empirical examination.
This study replaces the number of green utility model patent applications, the citation-weighted number of green patent applications, and the number of granted green patents as the dependent variables. Using the same calculation method, the sustainability levels of these four variables are obtained, denoted as SUGI, SCWGI, and SAGI. Regression analysis is then conducted, and the empirical results are presented. As shown in Panel A of Table 5, columns (1), (2), and (3), the regression coefficients are all significantly positive, indicating that after substituting the dependent variables, corporate ESG performance continues to significantly improve the level of SGI.
The results of the robustness test
| Panel A: Substitution of dependent variables | |||
|---|---|---|---|
| VARIABLES | (1) | (2) | (3) |
| ESG | 2.319** | 0.178** | 4.090** |
| (2.52) | (1.98) | (2.55) | |
| Constant | −342.326*** | 3.521 | −439.061*** |
| (−7.21) | (1.36) | (−5.17) | |
| Controls | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES |
| R-squared | 0.414 | 0.684 | 0.306 |
| Observations | 7,449 | 5,041 | 7,863 |
| Panel B: Substitution of independent variables & adjusting the sample range | ||||
|---|---|---|---|---|
| Variables | (1) | (2) | (3) | (4) |
| ESG | 2.622* | 0.178** | 4.090** | 3.580* |
| (1.92) | (1.98) | (2.55) | (1.75) | |
| Post_2020 | −2.453 | |||
| (−0.33) | ||||
| ESG*Post_2020 | 0.420 | |||
| (0.23) | ||||
| ESG_std | 3.095** | |||
| (2.57) | ||||
| Bloomberg_ESG | 0.851** | |||
| (2.48) | ||||
| Constant | −446.594*** | −447.618*** | −455.404*** | −446.637*** |
| (−9.20) | (−7.80) | (−4.60) | (−4.46) | |
| Controls | YES | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES | YES |
| R-squared | 0.287 | 0.288 | 0.440 | 0.258 |
| Observations | 15,819 | 15,819 | 12,437 | 9,263 |
Notes: *, **, and ***indicate statistical significance at the 10, 5, and 1% levels, respectively.
4.3.2 Substitution of Independent Variables
Considering that Huazheng recalibrated its ratings after 2020, which may undermine comparability across periods, and taking into account the issue of reflecting real-world implementation quality, this paper conducts robustness checks on the independent variables using several methods. First, a dummy variable for post-2020 (Post_2020) is added, and a new regression model is constructed based on the interaction term between this dummy variable and the Huazheng ESG rating. The empirical results are shown in column (1) of Panel B in Table 5. Both the Post2020 variable and its interaction term have insignificant regression coefficients, indicating that the Huazheng ESG rating system itself is comparable across periods, thereby making the baseline regression results more robust. Second, ESG scores are standardized by year, and the standardized ESG scores are used as the independent variable in the regression. The results are shown in column (2) of Panel B in Table 5. The regression coefficient is significantly positive, confirming the robustness of the results. Finally, Bloomberg ESG scores are used as the independent variable, and the regression is rerun. The empirical results are shown in column (3) of Panel B in Table 5, where the regression coefficient is significantly positive, further validating the robustness.
4.3.3 Adjusting the Sample Range
The outbreak of COVID-19 in 2019 had a significant impact not only on the economy but also on market-based corporate entities. Therefore, based on the original data sample, this study excludes data from 2020 to 2022 to avoid potential interference from the pandemic on corporate green innovation. As shown in column (4) of Panel B in Table 5, the conclusion remains robust.
4.4 Mechanism Test
4.4.1 Mediating Effect Test
To further explore the pathways through which corporate ESG performance affects the level of continuous green innovation, we construct the following model using the mediating variable M to test the mediating effects:
In the formula, the mediator variable M it refers to the degree of FC or AC, using ESG it as the explanatory variable, controlling for variables and fixed effects consistent with the benchmark model.
4.4.1.1 Mediating Effect of FC
This study uses the FC and KZ indices to measure the degree of corporate FC, where lower FC and KZ values indicate more relaxed FC. Columns (1) and (2) of Table 6 present the estimation results of the impact of corporate ESG performance on FC. The negative and significant coefficients suggest that good ESG performance alleviates corporate FC. Possible explanations include the following: ESG performance conveys the company’s commitment to sustainable development and its ability to manage environmental risks to the capital market through standardized disclosure frameworks (such as GRI and TCFD), reducing investors’ concerns about “greenwashing” or hidden risks (Cheng et al., 2014). High-quality ESG information, as an important non-financial component of corporate reports, can effectively mitigate information asymmetry between the firm and external investors, enhance financing credibility, and make it easier for the firm to attract investor favor, thereby obtaining financial resources at a lower cost (Goss & Roberts, 2011). The funds released from the alleviation of FC can be used to jointly build laboratories and sponsor basic research in universities, accelerating the efficiency of transforming green technologies from the laboratory to commercialization. Therefore, corporate ESG performance can enhance continuous green innovation by alleviating FC, thus verifying Hypothesis 2(a).
The results of mediating effect tests
| Variables | (1) | (2) | (3) |
|---|---|---|---|
| ESG | −0.008*** | −0.103*** | −0.005** |
| (−4.80) | (−5.49) | (−2.39) | |
| Constant | 1.940*** | 1.003 | 0.861*** |
| (28.50) | (1.33) | (8.88) | |
| Controls | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES |
| R-squared | 0.802 | 0.730 | 0.531 |
| Observations | 14,140 | 14,140 | 15,634 |
Notes: *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
4.4.1.2 Mediating Effect of AC
The sum of the sales expense ratio and the management expense ratio is used as a measure of AC. Column (3) of Table 6 shows the estimation results of the impact of corporate ESG performance on AC. The negative and significant coefficient indicates that ESG performance reduces AC. Possible explanations are as follows: The ESG governance system can systematically mitigate the goal conflicts between owners and managers. Through regular training, companies embed the concept of sustainable development into the cognitive framework of managers, prompting them to actively balance short-term gains and long-term environmental responsibilities in strategic decision-making (Kim & Lyon, 2011). Meanwhile, linking executive compensation to green innovation outcomes (such as the number of green patents granted and the achievement rate of carbon reduction targets) guides managers to focus on the long-term value of technology R & D (Flammer et al., 2019). Therefore, corporate ESG performance can enhance continuous green innovation by reducing AC, thus verifying Hypothesis 2(b).
4.4.2 Moderating Effect Test
4.4.2.1 Moderating Effect of ER
In column (2) of Panel A in Table 7, the coefficient of the interaction term between ESG and the ER indicator is 9.833, which is significantly positive. This indicates that as ER becomes stricter, the positive effect of corporate ESG performance on continuous green innovation becomes stronger. In regions with stricter ERs, companies tend to invest more in ESG, and their stakeholders place greater emphasis on ESG ratings. This can enhance the governance effect of external stakeholders on green innovation to some extent, ultimately promoting the improvement of green innovation capabilities. Thus, Hypothesis 3(a) is confirmed.
The results of Moderating effect tests
| Panel A: Moderating effect | ||||
|---|---|---|---|---|
| Variables | (1) | (2) | (3) | (4) |
| ESG | 2.988** | −0.582 | 2.988** | 2.516** |
| (2.45) | (−0.48) | (2.45) | (1.98) | |
| MR (ER/DT) | −221.850 | 1.613 | ||
| (−0.79) | (1.29) | |||
| ESG*MR | 9.833** | 1.918** | ||
| (2.58) | (2.03) | |||
| Constant | −464.074*** | −262.144*** | −464.074*** | −484.268*** |
| (−8.16) | (−3.79) | (−8.16) | (−8.06) | |
| Controls | YES | YES | YES | |
| Year/Firm/Ind FE | YES | YES | YES | |
| R-squared | 0.288 | 0.435 | 0.288 | 0.289 |
| Observations | 15,819 | 1,814 | 15,819 | 15,759 |
| Panel B: Further analysis | ||||
|---|---|---|---|---|
| (1) | (2) | (3) | (4) | |
| Margin | −6.01 | 10.538 | 0.467 | 2.485 |
| z | −2.37 | 2.42 | 0.27 | 1.99 |
| P>|z| | 0.018 | 0.015 | 0.790 | 0.047 |
| [95% Conf. Interval] | −10.974 | 2.013 | −2.959 | 0.037 |
| −1.043 | 19.063 | 3.892 | 4.933 | |
| J-N | 0.036 | 1.404 | ||
Notes: *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
To further investigate the level of ER at which the amplification effect is triggered, this study examines the marginal effects of ER across different quantiles. Additionally, the Johnson–Neyman technique is employed to identify the threshold of ER. The empirical results, presented in columns (1) and (2) of Panel B in Table 7, show that at the 1st percentile, ER exerts a negative moderating effect on the relationship between ESG performance and SGI. However, once the level of ER exceeds the threshold value of 0.036, the moderating effect becomes positive and continues to strengthen.
4.4.2.2 Moderating Effect of DT
In column (4) of Table 7, the coefficient of the interaction term between ESG and the DT indicator is significantly positive. This suggests that DT acts as a “technology enabler” in the relationship between corporate ESG performance and continuous green innovation. In firms with a higher degree of DT, ESG resources are more efficiently converted into innovation outcomes. Therefore, Hypothesis 3(b) is verified.
To further explore the facilitating effect at different levels of DT, this study empirically analyzes the marginal effects of DT at various quantile levels. The Johnson–Neyman method is used to identify the threshold at which the marginal effect becomes significant. The empirical results, presented in columns (3) and (4) of Panel B in Table 7, show that at the 1st percentile, the moderating effect of DT on the relationship between ESG performance and SGI is positive but not significant. However, once ER exceeds the threshold value of 1.404, the moderating effect remains significantly positive.
4.5 Heterogeneity Analysis
4.5.1 Ownership Heterogeneity
The sample is divided into state-owned enterprises (SOEs) and non-SOEs based on ownership. Columns (1) and (2) in Table 8 (Panel A) report the results. The ESG coefficient is insignificant for SOEs but significantly positive (1% level) for non-SOEs. This indicates that ESG performance more effectively promotes sustained green innovation in non-SOEs. The divergence stems from distinct ESG-driven pathways: SOEs leverage policy advantages (e.g., subsidies, credit) to ease FC (Wang et al., 2024b), yet administrative priorities may hinder market adaptability. Non-SOEs rely on market governance, where ESG’s agency cost reduction is critical (Amore & Bennedsen, 2016).
The results of the heterogeneity analysis
| Panel A | ||||
|---|---|---|---|---|
| Variables | (1) | (2) | (3) | (4) |
| ESG | 13.786 | 2.826** | 0.735 | 4.178** |
| (1.39) | (2.36) | (1.37) | (2.39) | |
| Constant | −843.111 | −519.649*** | −147.074*** | −582.471*** |
| (−1.50) | (−8.87) | (−5.07) | (−6.91) | |
| Controls | YES | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES | YES |
| R-squared | 0.300 | 0.307 | 0.307 | 0.294 |
| Observations | 984 | 14,683 | 4,531 | 11,219 |
| Panel B | |||
|---|---|---|---|
| Variables | (1) | (2) | (3) |
| ESG | 3.093** | 1.444 | 5.364 |
| (2.17) | (0.62) | (1.15) | |
| Constant | −548.041*** | −487.955*** | −127.476 |
| (−7.70) | (−4.05) | (−0.50) | |
| Controls | YES | YES | YES |
| Year/Firm/Ind FE | YES | YES | YES |
| R-squared | 0.392 | 0.668 | 0.518 |
| Observations | 10,802 | 1,231 | 2,465 |
| Panel C | ||
|---|---|---|
| Variables | (1) | (2) |
| ESG | 8.381** | 1.879 |
| (2.19) | (0.87) | |
| Constant | 54.714 | −57.538 |
| (0.69) | (−1.38) | |
| Controls | YES | YES |
| Year/Firm/Ind FE | YES | YES |
| R-squared | 0.361 | 0.279 |
| Observations | 5,748 | 6,245 |
Notes: *, **, and *** indicate statistical significance at the 10, 5, and 1% levels, respectively.
Policy formulation should adopt a differentiated approach. For state-owned enterprises (SOEs), it is necessary to mandate the disclosure of ESG reports that comply with the Task Force on Climate-related Financial Disclosures (TCFD) standards. A certain proportion of board members should possess professional backgrounds in environmental or social domains. Additionally, incentives for the market-oriented transformation of green technologies should be strengthened. Environmental performance indicators – such as carbon emissions per unit of revenue, the conversion rate of green patents, and the transaction volume of environmental technologies – should be incorporated into the performance evaluation systems of SOE executives, with evaluation outcomes directly linked to compensation and promotion decisions. For non-state-owned enterprises, ESG-related tax incentives should be introduced. These may include tax deductions on interest income from green bonds and investment tax credits for environmental protection equipment, thereby reducing the compliance costs associated with ESG implementation.
4.5.2 Pollution Intensity Heterogeneity
Following Guo et al. (2019), firms are classified as high-pollution or non-high-pollution industries based on China’s 2008 Listed Company Environmental Verification Catalog and the 2012 CSRC Industry Classification Guidelines. Columns (3) and (4) in Table 8 (Panel A) show that ESG significantly boosts innovation in non-high-pollution firms but not in high-pollution firms. High-pollution industries face stringent ERs, where ESG unlocks innovation via policy compliance and resource allocation (Li et al., 2017). Non-high-pollution industries depend more on market-driven governance optimization (Amore & Bennedsen, 2016). Consequently, policy design should be tailored to the specific characteristics of different industries. For heavily polluting sectors, industry-specific carbon quota benchmarks should be established. Initial allocation quotas should be determined based on sectoral emission intensity. For ultra-high-emission industries such as chemicals and steel, a benchmark of “15% reduction from historical intensity” can be adopted, while for medium- to high-emission industries such as power generation and cement, the benchmark can be based on the “industry average intensity.” This ensures that quota allocation aligns with each sector’s emission reduction potential.
For enterprises with high ESG ratings, the initial quota allocation should be increased. These firms should also be allowed to carry forward a certain proportion of surplus quotas across compliance years and be permitted to use a portion of that year’s national Certified Emission Reduction (CCER) credits to offset their compliance obligations.
For non-heavily polluting sectors, industry-specific standards for green digitalization should be developed. For example, in the information technology industry, ESG certification should include indicators such as “carbon footprint tracking of data centers.” In the modern service sector, “digital collaboration in green supply chains” should be incorporated into ESG assessment frameworks, while wholesale and retail enterprises may be eligible for subsidies targeting green logistics initiatives.
4.5.3 Lifecycle Heterogeneity
Firms exhibit divergent strategic goals, innovation incentives, and risks across lifecycle stages. Moreover, ESG performance influences a firm’s capacity for sustained green innovation through mechanisms such as alleviating financial constraints and optimizing capital allocation. Among these pathways, the differentiated responsiveness of financing channels constitutes a key mechanism. Using Dickinson’s (2011) cash flow classification, firms are grouped into growth, maturity, and decline stages. Table 8 (Panel B) reveals that ESG significantly enhances green innovation in growth-stage firms but not in mature or declining firms. Growth-stage firms prioritize market expansion and core competency building, driving strong R&D urgency. At this stage, ESG-related financing instruments, such as Venture ESG Bonds, which are specifically designed for start-ups or growth-stage firms with high potential and strong ESG practices, and R&D Loan Guarantees provided by governments or financial institutions, effectively mitigate financial constraints. In comparison, mature firms tend to have more stable cash flows and more complex operations, but their willingness to make continuous investments in innovation may be diluted by intense market competition (e.g., price wars). While ESG ratings may still influence their financing costs or reputational standing, the marginal effect of ESG in driving incremental or breakthrough green technology innovation is relatively limited. For firms in this stage, financing tools such as Sustainability-Linked Bonds (SLBs), which tie borrowing costs to the achievement of specific ESG or green innovation targets (e.g., R&D intensity or patent application volume), may serve as more effective incentives. In contrast, firms in the decline stage typically prioritize survival and strategic restructuring. With constrained resources and weak innovation momentum, the role of ESG ratings in promoting green technological innovation becomes less pronounced.
4.5.4 ESG Rating Disagreement Heterogeneity
ESG ratings convey a company’s green and sustainable development philosophy to the external market, playing an important role in the connection between the firm and the market. Discrepancies in ESG ratings may lead to variations in the impact of ESG performance on a company’s ability to sustain green innovation. This study uses ESG ratings or scores from five rating agencies (Hexun, Huazheng, Bloomberg, SynTao Green Finance, and CNRDS) and, drawing on the measurement method for rating discrepancies proposed by Avramov et al. (2022), constructs an ESG rating discrepancy index. The data is divided into two groups – high and low – based on the median and analyzed through group regressions. For the samples with missing ESG ratings in the current period, this article did not interpolate but excluded them to ensure the accuracy of the data.
Columns (1) and (2) of Panel C in Table 8 present the regression results for the high and low ESG rating disagreement groups, respectively. Based on these results, it can be concluded that companies with higher ESG rating discrepancies exhibit a significant positive effect of ESG performance on green innovation capacity, while companies with lower discrepancies show no significant effect. This may be due to the fact that ESG rating discrepancies can undermine a company’s green image and reputation, causing stakeholders to perceive a misalignment between the firm’s business philosophy and its environmental commitment. When facing discrepancies in ESG ratings, companies may experience negative impacts such as increased capital market financing costs, customer attrition, or heightened compliance pressures. To mitigate these risks, firms typically adjust their business decisions, such as increasing investments in green technology innovation and improving environmental performance, thereby reducing operational risks and enhancing market competitiveness. As a result, the economic consequences of ESG rating discrepancies may compel companies to engage in green technological innovation activities, facilitating their green transformation.
4.6 Policy Implications
The above empirical analysis addresses two core questions: whether corporate ESG performance enhances sustained green innovation and the mechanisms underlying this relationship. Based on the findings, the following policy recommendations are offered for enterprises, investors, and regulators.
Current ESG practices reveal significant divergence. Leading firms achieve dual environmental and revenue growth through green manufacturing and smart factories, while others struggle with inconsistent or selective disclosure, particularly in meeting stringent standards like the EU’s Corporate Sustainability Due Diligence Directive. Firms must integrate ESG into strategic cores, especially non-SOEs and growth-stage enterprises. Accelerating DT – such as adopting AI-driven carbon footprint tracking for supply chain optimization – can reduce innovation costs. Proactive alignment with international ESG standards mitigates carbon tariff risks. Multinationals should adopt regionalized ESG strategies: enhancing supply chain transparency in Western markets to address regulatory pressures, while fostering ESG localization via technology partnerships in developing economies, balancing compliance with cost efficiency.
Global ESG investments exceed $30 trillion, yet inconsistent ratings across agencies exacerbate greenwashing risks, prompting some funds to exit ESG due to short-term pressures. Evidence confirms ESG alleviates FC to boost sustained innovation. Investors should thus prioritize firms’ green innovation capabilities, developing dynamic evaluation models and focusing support on innovation projects in environmentally sensitive sectors. Internationally, institutional investors should innovate ESG financial instruments: issuing “transition bonds” in Western markets to decarbonize high-carbon industries and deploying green industrial funds in Asia–Pacific emerging markets to reduce risks. Blockchain technology can further enhance ESG asset credibility and resilience against policy volatility.
Divergent global ESG policies persist: the EU enforces supply chain compliance via mandatory disclosure and carbon tariffs; US policy volatility triggers ESG fund outflows; China’s hybrid “1+N” framework (universal standards + sector-specific rules) differs from the EU’s CSRD and the US SEC climate disclosure rules in its governance structure, emphasizing state guidance alongside market mechanisms, and adopts a phased approach to mandatory disclosure, contrasting with the immediate stringent requirements in the EU. Moreover, China’s green financial tools, such as green credit guidelines and regional carbon markets, are tailored to domestic industrial transition needs. To strengthen outcomes, regulators in high-compliance regions should enhance carbon data oversight paired with fiscal incentives, while providing targeted financing for growth-stage firms. Globally, China could champion a cross-regional ESG collaboration mechanism: co-developing differentiated industry standards with Belt and Road partners, promoting mutual recognition through technology exports, and establishing a Sino-European green patent mutual recognition mechanism to enhance technical interoperability and reduce market entry barriers. Additionally, advocating “common but differentiated responsibilities” within G20 could counter trade barriers like the EU’s full-lifecycle carbon accounting, thereby energizing corporate green innovation.
5 Conclusions
Although extensive research exists on ESG’s impact on green innovation, the relationship between corporate ESG performance and sustained green innovation remains unexplored. Fully considering innovation persistence, this study measures sustained green innovation level using the product of sequential growth in green patent applications and current patent output. Empirical analysis of China’s A-share listed firms (2009–2023) reveals the following conclusions:
First, corporate ESG performance significantly enhances sustained green innovation. This result holds across benchmark regressions, endogeneity tests, and robustness checks. Specifically, benchmark results confirm a positive ESG coefficient. For endogeneity, lagged explanatory variables failed the Hausman test, indicating weak delayed effects, while instrumenting ESG with industry-year averages resolved reverse causality. Robustness checks further validated conclusions through variable substitution and sample adjustments.
Second, ESG boosts sustained green innovation by alleviating FC and reducing AC. Strong ESG signals sustainable competence to markets, lowering information asymmetry and easing access to green finance (e.g., green bonds), thus securing stable innovation funding. Concurrently, ESG governance curbs managerial short-termism, directing resources toward long-term green R&D. Critically, this relationship is moderated by contextual factors: stricter ERs amplify ESG’s impact by combining policy pressure with market demand, whereas advanced DT magnifies effects through precise resource allocation and innovation opportunity identification.
Third, heterogeneity patterns reveal that the efficiency of ESG depends on firm characteristics: non-SOEs show stronger responses due to agile governance; non-high-pollution industries exhibit greater sensitivity with lower policy disruption; and growth-stage firms benefit most as ESG ratings critically influence external financing for innovation scaling. The economic consequences brought about by enterprises with higher ESG rating divergences usually force them to carry out green technological innovation activities, and the ESG performance of these enterprises has a more significant impact on the level of sustained green innovation.
However, there were some limitations to this study. First, this study is subject to geographical limitations that may affect the external validity of our findings. Our analysis is based exclusively on data from Chinese listed companies, which, while providing a rich sample size and comprehensive empirical evidence, may limit the generalizability of our conclusions. As an emerging economy, China possesses distinctive institutional environments, regulatory frameworks, and market structures that differ significantly from those in developed countries. Specifically, China’s environmental regulatory enforcement mechanisms, state-owned enterprise-dominated ownership structures, and relatively unique ESG evaluation and disclosure systems may result in differentiated relationship mechanisms between ESG performance and green innovation across different countries and regions. Consequently, the applicability and universality of our findings in other national or regional contexts require further validation through cross-national comparative studies.
Second, this study also faces certain limitations in variable measurement that may affect the precise estimation of the ESG–green innovation relationship. On one hand, we primarily rely on third-party rating agencies’ ESG scores to measure corporate ESG performance, which may suffer from subjective evaluation standards and imprecise weighting allocations across different dimensions, making it difficult to fully capture the differentiated impact mechanisms of various ESG components on green innovation. On the other hand, our study predominantly employs patent data to measure corporate green innovation levels; however, there exists a temporal lag and efficiency variance between patent applications and final commercialized innovation outcomes, and some green innovations may exist in the form of trade secrets or other forms that cannot be adequately reflected in patent data. These measurement limitations may introduce certain biases in estimating the strength of the relationship between ESG performance and green innovation.
Acknowledgments
The authors are grateful for the reviewer’s valuable comments that improved the manuscript.
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Funding information: This work was supported by the National Natural Science Foundation of china (72172029, 71971046, 72403033), and Humanities and Social Science Fund of Ministry of Education of China (24YJAZH192).
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and consented to its submission to the journal, reviewed all the results, and approved the final version of the manuscript. The authors’ contributions are as follows: G.W., J.L., J.W., X.W., Y.S., and Z.W. contributed equally to the conception, design, execution, and interpretation of the study, as well as to the preparation and critical revision of the manuscript. All authors contribute equally to the article.
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Conflict of interest: Authors state no conflict of interest.
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Data availability statement: The data will be made available on reasonable request. Requests should be directed to the corresponding author, Yongdong Shi.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
Appendix A
Descriptive statistics and trends on the proportion of green invention patents and green utility model patents
Trends in the proportion of green invention patents and green utility model patents.
Descriptive statistics on the proportion of green invention patents and green utility model patents
| Year | GIP_ratio | GUP_ratio |
|---|---|---|
| 2009 | 0.453 | 0.547 |
| 2010 | 0.471 | 0.529 |
| 2011 | 0.472 | 0.528 |
| 2012 | 0.474 | 0.526 |
| 2013 | 0.502 | 0.498 |
| 2014 | 0.486 | 0.514 |
| 2015 | 0.473 | 0.527 |
| 2016 | 0.474 | 0.526 |
| 2017 | 0.477 | 0.523 |
| 2018 | 0.495 | 0.505 |
| 2019 | 0.476 | 0.524 |
| 2020 | 0.470 | 0.530 |
| 2021 | 0.502 | 0.498 |
| 2022 | 0.500 | 0.500 |
| 2023 | 0.631 | 0.369 |
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- Research on the Coupled Coordination of the Digital Economy and Environmental Quality
- Optimal Consumption and Portfolio Choices with Housing Dynamics
- Regional Space–time Differences and Dynamic Evolution Law of Real Estate Financial Risk in China
- Financial Inclusion, Financial Depth, and Macroeconomic Fluctuations
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- Estimating the Size of Fiscal Multipliers in the WAEMU Area
- Impact of Green Credit on the Performance of Commercial Banks: Evidence from 42 Chinese Listed Banks
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- Cultural Catalysts of FinTech: Baring Long-Term Orientation and Indulgent Cultures in OECD Countries
- Loan Loss Provisions and Bank Value in the United States: A Moderation Analysis of Economic Policy Uncertainty
- Collaboration Dynamics in Legislative Co-Sponsorship Networks: Evidence from Korea
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- Multidimensional Poverty in Rural China: Human Capital vs Social Capital
- Property Registration and Economic Growth: Evidence from Colonial Korea
- More Philanthropy, More Consistency? Examining the Impact of Corporate Charitable Donations on ESG Rating Uncertainty
- Can Urban “Gold Signboards” Yield Carbon Reduction Dividends? A Quasi-Natural Experiment Based on the “National Civilized City” Selection
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- Unveiling Ecological Unequal Exchange: The Role of Biophysical Flows as an Indicator of Ecological Exploitation in the North-South Relations
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- Informal Finance and Enterprise Digital Transformation
- Wealth Effect of Asset Securitization in Real Estate and Infrastructure Sectors: Evidence from China
- Consumer Perception of Carbon Labels on Cross-Border E-Commerce Products and its Influencing Factors: An Empirical Study in Hangzhou
- How Agricultural Product Trade Affects Agricultural Carbon Emissions: Empirical Evidence Based on China Provincial Panel Data
- The Role of Export Credit Agencies in Trade Around the Global Financial Crisis: Evidence from G20 Countries
- How Foreign Direct Investments Affect Gender Inequality: Evidence From Lower-Middle-Income Countries
- Big Five Personality Traits, Poverty, and Environmental Shocks in Shaping Farmers’ Risk and Time Preferences: Experimental Evidence from Vietnam
- Academic Patents Assigned to University-Technology-Based Companies in China: Commercialisation Selection Strategies and Their Influencing Factors
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- A Bibliometric Analysis of Digital Financial Inclusion: Current Trends and Future Directions
- Targeted Poverty Alleviation and Enterprise Innovation: The Mediating Effect of Talent and Financing Constraints
- Does Corporate ESG Performance Enhance Sustained Green Innovation? Empirical Evidence from China
- Can Agriculture-Related Enterprises’ Green Technological Innovation Ride the “Digital Inclusive Finance” Wave?
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- The Role of Generative Artificial Intelligence in Shaping Business Innovation: Insights from End Users’ Perspectives and Practices
- The Mediating Role of Climate Change Mitigation Behaviors in the Effect of Environmental Values on Green Purchasing Behavior within the Framework of Sustainable Development
- The Mediating Role of Psychological Safety in the Relationship Between Paradoxical Leadership and Organizational Citizenship Behavior
- Special Issue: The Path to Sustainable And Acceptable Transportation
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- Special Issue: Shapes of Performance Evaluation - 2nd Edition
- Redefining Workplace Integration: Socio-Economic Synergies in Adaptive Career Ecosystems and Stress Resilience – Institutional Innovation for Empowering Newcomers Through Social Capital and Human-Centric Automation
- Knowledge Management in the Era of Platform Economies: Bibliometric Insights and Prospects Across Technological Paradigms
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- The Impact and Mechanism of the Creation of China’s Ecological Civilization Building Demonstration Zones on Labor Employment
- From Social Media Influence to Economic Performance: The Capital Conversion Mechanism of Rural Internet Celebrities in China
