What Gets Measured, Gets Managed: The Role of Sustainability Assurance in Green Transformation

Abstract

Sustainability assurance (SA), an independent third-party evaluation aimed at enhancing the credibility of corporate sustainability disclosure, plays a vital role in fostering corporate green transformation. This paper systematically examines the impact of SA on corporate green transformation and the mechanisms through which it operates, using data on SA reports and green patents from A-share listed companies in China between 2010 and 2022. The findings reveal that SA significantly promotes corporate green transformation, particularly when conducted in accordance with standard ISAE 3000. Mechanism analysis indicates that SA facilitates green transformation by reducing the cost of credit and promoting risk-taking behaviors. Heterogeneity analysis further demonstrates that the positive effects of SA on green transformation are more pronounced in heavily polluting and competitive industries as well as in firms with weaker internal and external governance. This study not only underscores the beneficial impact and underlying mechanisms of SA on corporate green transformation but also contributes to the broader literature on the drivers of corporate green transformation.

1. Introduction

Escalating ecological challenges, frequent natural disasters, and increasing climate risks have made green sustainable development a defining theme of our era. Corporate green transformation has emerged as a distinctive intangible asset, enabling firms to gain a competitive advantage. This transformation not only reduces environmental penalties and enhances a company’s image and reputation, thereby attracting more consumers and investors, but also secures a sustainable competitive edge through unique eco-friendly values and environmental attributes that set it apart from competing brands [1]. However, achieving corporate green transformation demands substantial financial investment, entails a long-term profit cycle, and involves significant investment risks, alongside certain externalities. These factors often result in a lack of incentives for companies to pursue such transformation. Consequently, identifying effective strategies to encourage firms to innovate in green technologies and engage in green transformation has become a crucial concern for various stakeholders.

There is currently ongoing debate regarding whether corporate sustainability practices truly drive green transformation. Some scholars argue that environmental regulatory pressures, fueled by stakeholder demand for corporate sustainability, compel firms to undertake green transformation, thereby shifting away from traditional high-pollution, high-energy consumption production methods [2]. On the other hand, the externalities associated with sustainable activities can lead to inefficiencies in green innovation investments [3]. To further elucidate the relationship between sustainability practices and green transformation, this paper examines the impact and mechanisms of corporate sustainability practices on green transformation through the lens of sustainability assurance (SA), a distinctive third-party evaluation process. This study also aims to provide richer empirical evidence on the antecedent drivers of corporate green transformation.

Traditional audits primarily focus on evaluating economic activities and events and are part of a broader category known as assurance services. Assurance services involve engagements that provide an opinion on the credibility of written assertions, which may pertain to both economic and non-economic activities and information [4]. The primary objective of SA is to enhance the reliability of companies’ sustainability disclosure, thereby increasing stakeholders’ trust in sustainability reports [5]. In recent decades, the demand for corporate sustainability disclosure has grown significantly. To ensure credible and high-quality disclosure, regulatory bodies and standard setters worldwide have increasingly recognized the critical role of assurance. For example, the European Union has mandated compulsory SA starting in 2024. Similarly, in 2022, the U.S. Securities and Exchange Commission introduced climate-related disclosure rules, underscoring the importance of independent assurance and disclosure. Although SA is not mandatory in mainland China and Hong Kong, there is a growing positive attitude towards its adoption. As depicted in Figure 1, data indicate that the number of SA reports for A-share listed companies in China has been increasing, rising from 19 in 2010 to 102 in 2022. Previous studies have preliminarily confirmed that SA can enhance a company’s performance in the capital market [6], boost stakeholder confidence in investment decisions [7], and optimize governance structures to foster more responsible corporate behavior [8]. Investigating the mechanisms through which SA influences corporate green transformation can expand the research on the economic consequences of SA, particularly from the perspective of green innovation investments. However, the literature still lacks a focused exploration of this area.

Given these considerations, this paper systematically examines the impact of SA on corporate green transformation, utilizing data on green patents and SA reports from A-share listed companies in China. The study found that SA can indeed foster corporate green transformation, with assurance conducted under the standard ISAE 3000 proving particularly effective [9]. Mechanism tests indicated that SA reduces cost of credit and encourages risk-taking behaviors, thereby enhancing both the willingness and capacity for green transformation. Further heterogeneity analysis revealed that the effect of SA on promoting green transformation is more pronounced in heavily polluting and competitive industries as well as in firms with a lower quality of internal and external governance.

This paper makes several key contributions. First, it integrates SA and corporate green transformation within a unified analytical framework. While existing research has highlighted various economic consequences of SA, such as its impact on capital markets, investor judgments, and corporate reputation [10,11,12], few studies have explored whether and how SA affects corporate green transformation. This paper enriches and extends the body of research on the microeconomic consequences of SA by focusing specifically on green transformation. Second, this paper broadens the scope of research on corporate green transformation. While related studies have predominantly examined the effects of corporate sustainability practices on green transformation, most of the existing literature primarily focuses on the impact of sustainable investment, sustainability disclosure, or sustainability ratings on corporate green innovation. Unlike these corporate sustainability activities, this paper investigates the positive impact of SA from the perspective of third-party assurance, offering a significant contribution to the field of corporate green transformation research. Third, this paper elucidates the pathways through which SA influences corporate green transformation, focusing on two dimensions: the willingness and capacity for green transformation. It demonstrates that reducing the cost of credit and promoting risk-taking behaviors are key mechanisms through which SA affects corporate green transformation. Moreover, the study found that variations in industry characteristics and the quality of internal and external corporate governance lead to differing effects of SA on promoting green transformation. These findings deepen the understanding of the intrinsic relationship between SA and corporate green transformation.

2. Literature Review and Theoretical Analysis

2.1. Literature Review

The relevant literature related to this study can be broadly divided into two main strands: one focusing on SA and the other on the drivers of corporate green transformation. The first strand has been expanding rapidly, with numerous studies examining various aspects of SA [13,14,15]. The existing research has explored assurance models and processes [16], the quality and reliability of assurance [17], the selection of assurance standards and provider [18], the determinants of SA [19,20], and the economic consequences of SA, such as its impact on capital markets, investor judgments, and corporate reputation [10,11,12].

The second strand of literature investigates the impact of various corporate sustainability practices on green transformation. However, there are differing views on the effectiveness of these practices in driving green transformation. For instance, Chouaibi et al. (2022) argued that improved sustainability ratings enhance green innovation and output, suggesting a phenomenon of strategic innovation [21]. Similarly, Tan and Zhu (2022), using data from A-share listed companies in China, demonstrated that sustainability ratings can improve the quantity and quality of green innovation [22]. Conversely, Cohen et al. (2020) found a negative relationship between sustainability ratings and green innovation in U.S. energy companies [23].

It is important to recognize that corporate sustainability practices encompass various aspects, such as sustainable investment, sustainability disclosure, sustainability ratings, and SA [24]. In sustainable investment, asset owners or managers are the primary participants, with companies merely being the recipients of investments. Sustainability disclosure is carried out by the companies themselves, while sustainability ratings involve external third-party agencies that provide comprehensive evaluations of companies’ sustainability from environmental, social, and governance (ESG) perspectives, serving as a tool to support sustainable investment. However, sustainability ratings may suffer from inconsistencies [25]. On the other hand, SA focuses on the evaluation of key content in sustainability disclosures by independent third-party agencies following assurance standards. As such, it can partially validate or enhance the quality of sustainability disclosures and sustainability ratings, providing decision-useful information for sustainable investment [26]. Despite this, there is a lack of literature examining the impact of corporate sustainability practices on green transformation from the perspective of SA. This paper aims to fill this research gap by providing further evidence on whether corporate sustainability practices and corporate green transformation are complementary or conflicting.

2.2. Theoretical Analysis and Hypothesis Development

2.2.1. SA and Corporate Green Transformation

This section first reviews the primary factors influencing corporate green transformation and then analyzes the intrinsic mechanisms through which SA affects this transformation. A company must possess both the capability and willingness to achieve genuine green transformation. Green innovation is the core and key element of corporate green transformation. However, investments in corporate green R&D activities are substantial, long-term, and fraught with risk. The social benefits often outweigh the private benefits, creating significant positive externalities. This discrepancy results in inadequate corporate R&D investment capabilities or willingness, with actual R&D investment levels falling far below the optimal level [27,28]. The factors hindering corporate green transformation mainly include the following two aspects.

On the one hand, capital shortages constrain a company’s practical ability to pursue green transformation. Compared to conventional investments, green innovation investments are riskier, have longer payback periods, and involve higher sunk costs, requiring greater stability in capital input. Additionally, information asymmetry between companies and capital providers regarding green innovation projects increases a company’s cost of credit. Therefore, addressing capital shortages is a critical challenge for promoting corporate green transformation.

On the other hand, a low level of risk-taking behavior within a company leads to insufficient intrinsic motivation for green transformation. This reluctance to take risks stems from the agency problem within companies. Corporate owners often employ equity incentives or other short-term profit-oriented mechanisms to align the interests of owners and managers. To protect their reputations and ensure performance for private gain, managers may favor stable cash flows and low-risk conventional investments, showing less willingness to invest in green projects that favor long-term growth. Moreover, due to a lack of effective external oversight of corporate operations, external parties also tend to push companies towards short-term projects with stable cash flows, further reducing the company’s willingness to take risks [29].

As an independent third-party evaluation aimed at enhancing the credibility of corporate sustainability disclosure, SA has the theoretical potential to alter cost of credit and risk-taking behaviors, thereby increasing both the capability and willingness for corporate green transformation. Given the characteristics of SA and the features of corporate green transformation, the following analysis specifically examines how SA influences corporate green transformation by reducing cost of credit and promoting risk-taking behaviors. The theoretical framework of this paper is illustrated in Figure 2.

Firstly, SA can help companies reduce the cost of credit, thereby alleviating the capital shortages that hinder corporate green transformation. Due to investor preferences and information asymmetry, companies with lower corporate social responsibility often attract a smaller investor base. By auditing and verifying the authenticity and completeness of corporate sustainability reports, SA enhances the quality and credibility of sustainability disclosure, providing investors with more reliable non-financial information. This increased transparency can reduce the perceived risk among financial institutions and creditors, ultimately leading to lower credit costs [30]. Furthermore, according to signaling theory, SA effectively communicates positive signals to stakeholders regarding a company’s commitment to environmental protection, social responsibility, and sustainability practices. This, in turn, enhances the company’s image, reputation, and customer recognition [10,31,32]. Improved trust between companies and capital providers further alleviates the financial constraints associated with green transformation. Empirical evidence from existing studies also supports the idea that SA can reduce the cost of credit [33,34]. Therefore, by reducing the cost of credit, SA strengthens a company’s ability to pursue green transformation.

Secondly, SA can effectively promote risk-taking behaviors, thereby enhancing a company’s willingness for green transformation. From an internal perspective, SA, as an independent third-party evaluation, serves as a key tool for mitigating agency problems between managers and other stakeholders [30]. Given the high risks associated with green innovation projects and the emphasis on short-term profits in performance evaluations, managers often lack the willingness to invest in green innovation. By alleviating agency conflicts within the company, SA can better align the incentives of managers and other stakeholders (such as shareholders), thereby increasing managers’ willingness to take risks. From an external perspective, SA is vital for enhancing the credibility of sustainability disclosures [26]. It can improve a company’s reputation and help establish a responsible corporate image [10,31,32]. This fosters long-term, stable business relationships and boosts external parties’ trust in the company as well as their tolerance for risk. As a result, external parties are more likely to attribute failures in corporate green innovation investments to unpredictable external factors rather than to opportunistic behaviors such as internal expropriation. This ultimately creates a more supportive and trusting environment for corporate green transformation, enhancing the company’s willingness to take risks and motivating green initiatives. Thus, by promoting risk-taking behaviors, SA strengthens a company’s confidence and intrinsic motivation for green transformation. In summary, this paper proposes the following research hypotheses:

H1. 

Holding other conditions constant, SA can promote corporate green transformation.

H1a. 

SA promotes corporate green transformation by reducing the cost of credit.

H1b. 

SA promotes corporate green transformation by promoting risk-taking behaviors.

2.2.2. The Differential Impact of Assurance Standard Types on Corporate Green Transformation

Companies have the discretion to decide whether to engage a third-party assurance provider to verify their sustainability reports. The assurance standards employed by different providers can vary significantly, affecting both the foundation and direction of the assurance work. In mainland China, two primary assurance standards are widely used: the International Standard on Assurance Engagements 3000 (ISAE 3000) and the AccountAbility 1000 (AA1000) [35]. These standards differ in their application conditions and levels of assurance.

ISAE 3000 covers a broad range of assurance engagements, applying to all engagements other than audits or reviews of historical financial statements. Additionally, ISAE 3000 requires practitioners to adhere to the Code of Ethics for Professional Accountants issued by the International Ethics Standards Board for Accountants. Accounting firms must apply and comply with ISQC 1, ensuring that assurance reports are prepared by firms adhering to this standard [36]. In practice, third-party assurance providers using ISAE 3000 are typically accounting firms. In contrast, the AA1000 is a comprehensive and publicly available series of assurance standards developed by the Institute of Social and Ethical Accountability for the social responsibility disclosure and related performance by organizations. This standard is based on four principles: inclusivity, materiality, responsiveness, and impact. In practice, other third-party professional assurance providers, in addition to accounting firms, often use this standard.

When comparing ISAE 3000 with AA1000, ISAE 3000 has distinct accounting and auditing characteristics, providing a relatively stringent assurance framework. Accounting firms adhering to ISAE 3000 demonstrate higher levels of professionalism and independence [33]. Assurance practitioners must follow standardized procedures and quality control steps, which reduces variability in assurance quality across different subjects. Additionally, because practitioners following ISAE 3000 must adhere to strict professional and ethical codes, the market and investors tend to have greater trust in SA provided under this standard [34,37,38]. As a result, SA under ISAE 3000 is generally considered to be of higher quality [39].

High-quality assurance services can more effectively convey positive signals about a company’s social responsibility and sustainability performance [40,41], thereby more effectively mitigating information asymmetry and reducing agency costs, which helps companies attract more capital and increases their willingness to take on additional risk. In summary, SA based on ISAE 3000 is more likely to enhance both the credit cost reduction effect and the risk-taking increase effect of SA, thereby more effectively promoting corporate green transformation.

H2. 

Compared to the standard AA1000, SA based on the standard ISAE 3000 has a more significant effect on promoting corporate green transformation.

3. Research Design

3.1. Model Specification and Variable Description

This study designed the following model to identify the impact of SA on corporate green transformation:

GT_it = α₀ + α₁SA_it + α_j∑Controls_it + λ_i + η_t + ε_it

(1)

3.1.1. Explanatory Variables: SA (SA1, SA2)

This study constructed two core explanatory variables, SA1 and SA2, to represent “SA quality” and “whether the sustainability report is assured”, respectively. SA1 is a comprehensive index composed of 19 indicators, adapted from the SA quality assessment frameworks proposed by O’Dwyer and Owen (2011) and Perego and Kolk (2012) [32,42]. The score of this index ranges from 0 to 27, based on the assurance report of a company’s sustainability report. The scoring is performed item-by-item using content analysis and then summed up as a measure. Apart from SA1, SA2 is defined as 1 if a professional institution provides assurance for a company’s sustainability report in the current year and 0 otherwise.

3.1.2. Dependent Variable: Corporate Green Transformation (GT)

GT is the dependent variable in this study. Referring to Chang (2011), this study used corporate green innovation to measure green transformation (GT) and selected the natural logarithm of the number of green invention patent applications plus one as a proxy for corporate green transformation [43]. The reasons are twofold: First, green patents, due to their quantifiability and spillover effects both within and outside the industry, most intuitively reflect the output of corporate green transformation. Second, green patent application data are more stable, reliable, and timely compared to grant data. Additionally, in the robustness check section, this paper also uses the natural logarithm of the sum of the number of green invention patent applications and green utility model patent applications plus one (GT2) as an alternative measure of corporate green transformation.

3.1.3. Control Variables

This paper controls for potential factors that simultaneously affect both SA and corporate green transformation, including company financial and governance characteristics such as firm size (Size), leverage (Lev), return on assets (Roa), cash flow (Cfo), revenue growth rate (Growth), board size (Board), proportion of independent directors (IDB), management shareholding ratio (Mhold), institutional investor shareholding ratio (Insti), firm age (Age), CEO duality (Dual), largest shareholder’s shareholding ratio (Top1), and fixed asset ratio (Fixed). The specific definitions of each variable are provided in

Table 1. Definitions of Variables.

Variable Type	Variable Name	Variable Definition
Dependent Variable	GT	Corporate green transformation, measured as the natural logarithm of the number of green invention patent applications plus one
Explanatory Variables	SA1	Quality of SA, scored using content analysis based on the frameworks by O’Dwyer and Owen (2007) and Perego and Kolk (2012) [42,44]
	SA2	Whether the sustainability report is assured, defined as 1 if the company’s sustainability report for the year has been assured by a professional institution and 0 otherwise
Control Variables	Size	Firm size, measured as the natural logarithm of total assets
	Lev	Leverage, measured as the ratio of total liabilities to total assets
	Roa	Return on assets, measured as the ratio of net profit to total assets
	Cfo	Cash flow, measured as the ratio of net cash flow from operating activities to total assets
	Growth	Revenue growth rate, measured as the ratio of the increase in operating revenue to the total operating revenue of the previous year
	Board	Board size, measured as the natural logarithm of the number of board members
	IDB	Proportion of independent directors, measured as the ratio of the number of independent directors to the total number of board members
	Mhold	Management shareholding ratio, measured as the ratio of the number of shares held by management to the total number of shares
	Insti	Institutional investor shareholding ratio, measured as the ratio of the number of shares held by institutional investors to the total number of shares
	Age	Firm age, measured as the natural logarithm of the number of years since listing plus one
	Dual	CEO duality; equals 1 if the chairman and CEO are the same person and 0 otherwise
	Top1	Largest shareholder’s shareholding ratio, measured as the ratio of the number of shares held by the largest shareholder to the total share capital
	Fixed	Fixed asset ratio, measured as the ratio of fixed assets to total assets
	Year	Year dummy variable
	Firm	Firm dummy variable

. Additionally, the model includes individual fixed effects (λ_i), year fixed effects (η_t), and a random error term (ε_it), with standard errors clustered at the firm level. This study focuses on the coefficient α₁ of SA_it, which represents the impact of SA on corporate green transformation after excluding other interfering factors.

3.2. Sample and Data

To examine the impact of SA on corporate green transformation, this study focused on non-financial A-share listed companies that have published sustainability reports. The sustainability reports and their assurance reports were sourced from the Cninfo website and the official websites of the listed companies and were manually verified and organized. Corporate green patent data were obtained from the CNRDS database, while financial and governance data were primarily sourced from the CSMAR database. Considering that sustainability report assurance by Chinese listed companies has shown an upward trend since 2010 [45], this paper focuses on the period from 2010 to 2022. After excluding samples with missing variables, the final sample consists of 9754 firm-year observations. To mitigate the influence of extreme values on the estimation results, key continuous variables were winsorized at the 1% and 99% levels.

3.3. Descriptive Statistics

Table 2. Descriptive Statistics.

	Sample Size	Mean	SD	Med	Min	Max
GT	9754	0.819	1.197	0	0	6.805
SA1	9754	0.471	3.053	0	0	25
SA2	9754	0.026	0.159	0	0	1
Size	9754	0.213	0.410	0	23.063	1
Lev	9754	23.174	1.400	19.872	0.487	26.179
Roa	9754	0.474	0.197	0.050	0.040	0.888
Cfo	9754	0.047	0.060	−0.221	0.054	0.224
Growth	9754	0.056	0.067	−0.160	0.109	0.242
Board	9754	0.160	0.341	−0.544	2.197	2.275
IDB	9754	2.172	0.202	1.609	0.364	2.639
Mhold	9754	0.538	0.230	0.003	0	0.909
Insti	9754	2.407	0.772	0	0.568	3.332
Age	9754	0.376	0.055	0.333	2.639	0.571
Dual	9754	0.071	0.148	0	0	0.690
Top1	9754	0.362	0.160	0.079	0.345	0.757
Fixed	9754	0.225	0.175	0.002	0.184	0.736

presents the descriptive statistics of the variables. The mean value of corporate green transformation (GT) is 0.819, with a standard deviation of 1.197 and a median of 0. For SA (SA1 and SA2), the mean values are 0.471 and 0.026, respectively, indicating that approximately 2.6% of the companies in the sample (excluding those in the financial industry and those with missing values) conducted SA. This finding aligns with the results of Shen et al. (2017) and Shen et al. (2023) [45,46]. Additionally, the other control variables are generally consistent with those reported in the existing literature, with no significant differences observed.

4. Empirical Results and Analysis

4.1. Baseline Regression Results

To examine the impact of SA on corporate green transformation, this study performed a regression analysis using model (1). The results are summarized in

Table 3. SA and Corporate Green Transformation: Baseline Regression Results.

	(1)	(2)	(3)	(4)	(5)	(6)
	GT	GT	GT	GT	GT	GT
SA1	0.017 ***		0.016 ***		0.015 **
	(2.666)		(2.683)		(2.449)
SA2		0.246 **		0.231 ***		0.202 *
		(2.107)		(2.729)		(1.767)
Size	0.438 ***	0.440 ***	0.272 ***	0.272 ***	0.436 ***	0.438 ***
	(16.855)	(16.872)	(6.472)	(6.475)	(16.934)	(16.948)
Lev	−0.099	−0.104	−0.313 **	−0.312 **	−0.068	−0.074
	(−0.778)	(−0.816)	(−2.358)	(−2.352)	(−0.555)	(−0.603)
Roa	−0.180	−0.174	−0.505 ***	−0.499 **	−0.118	−0.108
	(−0.666)	(−0.643)	(−2.581)	(−2.550)	(−0.448)	(−0.411)
Cfo	0.036	0.029	−0.230	−0.231	−0.023	−0.029
	(0.173)	(0.137)	(−1.545)	(−1.550)	(−0.108)	(−0.138)
Growth	−0.083 ***	−0.082 ***	−0.022	−0.022	−0.075 ***	−0.074 **
	(−2.743)	(−2.703)	(−0.996)	(−0.977)	(−2.580)	(−2.540)
Board	0.049	0.046	−0.048	−0.048	−0.004	−0.008
	(0.416)	(0.392)	(−0.414)	(−0.416)	(−0.031)	(−0.064)
IDB	0.387	0.397	0.081	0.089	0.393	0.402
	(0.980)	(1.005)	(0.230)	(0.254)	(0.943)	(0.965)
Mhold	−0.166	−0.166	0.626 ***	0.629 ***	−0.218	−0.216
	(−0.989)	(−0.989)	(3.524)	(3.540)	(−1.225)	(−1.211)
Insti	−0.238 *	−0.237 *	0.187	0.188	−0.256 *	−0.253 *
	(−1.804)	(−1.794)	(1.252)	(1.256)	(−1.894)	(−1.871)
Age	−0.013	−0.013	0.009	0.010	−0.015	−0.015
	(−0.460)	(−0.462)	(0.183)	(0.203)	(−0.530)	(−0.523)
Dual	0.018	0.017	−0.025	−0.025	0.022	0.021
	(0.391)	(0.378)	(−0.750)	(−0.747)	(0.502)	(0.473)
Top1	0.121	0.117	−0.305	−0.307	0.226	0.220
	(0.755)	(0.731)	(−1.294)	(−1.303)	(1.359)	(1.315)
Fixed	−0.373 **	−0.374 **	−0.140	−0.142	−0.237	−0.238
	(−2.532)	(−2.538)	(−0.920)	(−0.928)	(−1.547)	(−1.552)
Constant	−9.321 ***	−9.359 ***	−5.245 ***	−5.259 ***	−9.214 ***	−9.258 ***
	(−16.044)	(−16.078)	(−5.307)	(−5.320)	(−15.701)	(−15.747)
Firm	Yes	Yes	Yes	Yes	Yes	Yes
Year	Yes	Yes	No	No	No	Yes
Year-City	No	No	Yes	Yes	No	No
Year-Ind	No	No	No	No	Yes	Yes
N	9754	9754	9754	9754	9754	9754
Adj_R2	0.436	0.435	0.448	0.448	0.486	0.485

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

. Columns (1) and (2) include control variables and account for both individual and time fixed effects. The coefficients for the SA variables (SA1 and SA2) are 0.017 and 0.246, respectively, both significant at the 1% level. Columns (3) and (4) control for individual fixed effects and year–city combined fixed effects, with coefficients for SA1 and SA2 being 0.016 and 0.231, respectively, also significant at the 1% level. Columns (5) and (6) account for individual fixed effects and year–industry combined fixed effects, showing coefficients for SA1 and SA2 of 0.015 and 0.202, respectively, significant at the 5% and 10% levels. These results suggest that SA promotes corporate green transformation, thus supporting hypothesis H1.

4.2. Robustness Tests

Several factors may affect the identification of causal relationships and the estimation results of the model in this study. The literature on voluntary assurance suggests that SA is often endogenously determined [6]. Additionally, both the decision to undertake SA and the decision to pursue corporate green transformation may be influenced by unobservable variables. To address these concerns, this study conducted robustness checks using the following methods.

4.2.1. Addressing Endogeneity: Instrumental Variable (IV) Test

The study used the average SA of other firms within the same industry (excluding the firm being studied) and the average SA of other firms within the same city (excluding the firm being studied) as instrumental variables for the IV test. Specifically, the study constructed instrumental variables based on the average SA quality and the decision to undertake SA at the industry level (excluding the firm being studied) (IV1, IV2) and at the city level (excluding the firm being studied) (IV3, IV4).

Table 4. Instrumental Variable Test.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	SA1	GT	SA2	GT	SA1	GT	SA2	GT
IV1	0.446 ***
	(0.129)
IV2			0.581 ***
			(0.142)
IV3					0.182 **
					(2.519)
IV4							0.143 **
							(2.384)
SA1		0.013 *				0.011 **
		(1.863)				(2.463)
SA2				0.132 *				0.202 *
				(1.881)				(1.934)
Controls	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Firm/Year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
N	9672	9672	9672	9672	8935	8935	8935	8935
Adj_R2	0.101	0.471	0.119	0.469	0.228	0.443	0.216	0.491

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

reports the results of the two-stage least squares (2SLS) regression using these instrumental variables. Columns (1), (3), (5), and (7) present the first-stage results of the IV test, where the coefficients for IV1, IV2, IV3, and IV4 are 0.446, 0.581, 0.182, and 0.143, respectively, all significantly positive. Columns (2), (4), (6), and (8) show the second-stage results of the IV test, indicating that the coefficients for the SA variables (SA1 and SA2) remain significantly positive. The Kleibergen–Paap rk LM statistics are 8.334, 9.749, 8.510, and 8.807, respectively, rejecting the null hypothesis of under-identification of the instrumental variables at the 1% level. The Cragg–Donald Wald F statistics are 83.214, 97.413, 31.368, and 22.750, all exceeding the critical values for weak identification tests at the 10% significance level and rejecting the null hypothesis of weak instruments. These results confirm the validity of the instrumental variables. Overall, the findings from the IV test support the conclusions drawn from the baseline regression.

4.2.2. Propensity Score Matching (PSM) Test

To address potential biases arising from unobservable variables, especially given that most sample firms did not conduct SA, this study employed the propensity score-matching (PSM) method as a robustness check. Specifically, a 1:3 nearest neighbor matching with a caliper of 0.05 was utilized. The matching covariates are those used as control variables in the main analysis. The results are presented in columns (1) and (2) of

Table 5. Propensity Score-Matching Test.

	(1)	(2)
	GT	GT
SA1	0.014 ***
	(3.131)
SA2		0.197 ***
		(4.123)
Controls	Yes	Yes
Firm/Year	Yes	Yes
N	1052	1052
Adj_R2	0.564	0.570

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

. The coefficients for the SA variables (SA1 and SA2) are 0.014 and 0.197, respectively, both significant at the 1% level. These findings align with the main test results, thereby reinforcing the conclusions drawn from the baseline regression.

4.2.3. Replacing the Dependent Variable

In the previous analysis, the primary measure of corporate green transformation was the natural logarithm of the number of green invention patent applications plus one (GT). To further validate the robustness of the results, this section employs an alternative measure: the natural logarithm of the sum of green invention patent applications and green utility model patent applications plus one (GT2). The results, reported in columns (1) and (2) of

Table 6. Replacing the Dependent Variable and Lagged Effect of SA.

	(1)	(2)	(3)	(4)
	GT2	GT2	F.GT	F.GT
SA1	0.013 *		0.018 ***
	(1.880)		(3.442)
SA2		0.157 *		0.203 ***
		(1.825)		(4.039)
Controls	Yes	Yes	Yes	Yes
Firm/Year	Yes	Yes	Yes	Yes
N	9754	9754	7555	7555
Adj_R2	0.483	0.483	0.467	0.474

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

, show that the coefficients for the SA variables (SA1 and SA2) are 0.013 and 0.157, respectively, both significant at the 10% level. These results are largely consistent with those of the main analysis, providing additional support for the conclusions of the baseline regression.

4.2.4. Lagged Effect of ESGRA

The previous analysis examined the immediate impact of SA on corporate green transformation within the same year. To further investigate the lagged effects, this study conducted a lagged effect test using the future value of corporate green transformation (F.GT) after a company has undertaken SA. Columns (3) and (4) of Table 6 report the results of this test. When using the future period’s corporate green transformation (F.GT) as the dependent variable, the coefficients for the SA variables (SA1 and SA2) are 0.018 and 0.203, respectively, both significant at the 1% level. These results are consistent with the main findings, reinforcing the conclusions of the baseline regression.

4.3. Further Tests Based on Types of Assurance Standards

This section conducts further tests to explore the differences in the effects of SA based on different assurance standards. The sample consists of listed companies that have undertaken SA. The study distinguished between assurance based on ISAE 3000 and AA1000. It constructed variables for the quality of assurance based on ISAE 3000 (SA1_ISAE), the quality of assurance based on AA1000 (SA1_AA), whether assurance is based on ISAE 3000 (SA2_ISAE), and whether assurance is based on AA1000 (SA2_AA). These variables were included simultaneously in the model to assess the differences in their effects.

Table 7. SA and Corporate Green Transformation: Based on ISAE 3000 or AA1000.

	(1)	(2)
	GT	GT
SA1_ISAE	0.019 **
	(2.443)
SA1_AA	0.009
	(1.361)
SA2_ISAE		0.271 *
		(1.840)
SA2_AA		0.168
		(1.353)
Controls	YES	YES
Firm/Year	YES	YES
N	263	263
Adj_R2	0.436	0.435

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

presents the test results. Column (1) shows that the coefficient for the quality of assurance based on ISAE 3000 (SA1_ISAE) is 0.019 and is significant at the 5% level, whereas the coefficient for the quality of assurance based on AA1000 (SA1_AA) is 0.009 and not significant. Column (2) indicates that the coefficient for whether assurance is based on ISAE 3000 (SA2_ISAE) is 0.271 and significant at the 10% level, while the coefficient for whether assurance is based on AA1000 (SA2_AA) is 0.168 and not significant. These findings suggest that SA based on ISAE 3000 has a significantly more positive effect on promoting corporate green transformation compared to AA1000, thereby supporting hypothesis H2.

5. Further Analysis

5.1. Mechanism Test

The previous sections analyzed and tested the impact of SA on corporate green transformation. This section follows the theoretical framework proposed to investigate how SA influences corporate green transformation through two mechanisms: reducing the cost of credit and promoting risk-taking behaviors.

5.1.1. Reducing Cost of Credit

According to the theoretical framework, SA enhances the credibility of corporate sustainability disclosure, thereby alleviating information asymmetry between investors, financial institutions, and companies. This improvement in credibility enhances the company’s image and reputation, which can lead to lower credit costs and better access to financial resources needed for green transformation [33,34]. To test this mechanism, the study measured the cost of credit (DebtCost) as the ratio of interest expenses to the average total of short-term and long-term debt. A direct regression analysis was performed using model (1) to assess the impact of SA on cost of credit.

Table 8. Mechanism Test: Alleviating Financing Constraints and Promoting Risk-taking Behaviors.

	(1)	(2)	(3)	(4)
	DebtCost	DebtCost	RiskTake	RiskTake
SA1	−0.000 ***		0.022 *
	(−6.771)		(1.932)
SA2		−0.001 ***		0.049 *
		(−5.821)		(1.821)
Controls	Yes	Yes	Yes	Yes
Firm/Year	Yes	Yes	Yes	Yes
N	9754	9754	9754	9754
Adj_R2	0.282	0.291	0.312	0.319

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

presents the results. In columns (1) and (2), the coefficients of the SA variables (SA1 and SA2) are −0.0004 and −0.001, respectively, both significant at the 1% level. These findings indicate that SA significantly reduces cost of credit. This supports the hypothesis H1a that SA can enhance the capability for corporate green transformation.

5.1.2. Promoting Risk-Taking Behaviors

According to the theoretical framework, SA not only reduces agency cost but also helps companies establish long-term, stable business relationships [30,32]. These factors can increase managers’ willingness to enhance external stakeholders’ trust and tolerance for corporate risks, thereby promoting corporate green transformation. The existing literature typically measures risk-taking behaviors through the volatility of accounting profits or stock returns. This approach reflects higher risk-taking behavior through greater financial volatility. However, this method may not fully capture the research question of this study. Since SA helps companies gain stakeholders’ trust, build long-term stable business relationships, and reduce business risk [32], an increase in risk-taking behavior due to SA might not necessarily lead to higher earnings or stock price volatility. To address this, the study used the percentage of R&D expenditures relative to total assets (RiskTake) as a measure of a company’s investment in risky projects. Based on model (1), this section regresses SA on risk-taking behaviors (RiskTake) to evaluate its impact. Table 8 presents the results. In columns (3) and (4), the coefficients for the SA variables (SA1 and SA2) are 0.022 and 0.049, respectively, both significant at the 10% level. These findings suggest that SA positively promotes risk-taking behaviors. This supports hypothesis H1b, indicating that SA can enhance the willingness for corporate green transformation.

5.2. Heterogeneity Analysis

The previous analysis confirmed the impact of SA on corporate green transformation, its underlying mechanisms, and the differential effects of various assurance standards. However, industry-specific attributes and firm characteristics also influence corporate green transformation. This section conducts a heterogeneity analysis from the perspectives of industry attributes and corporate governance to further refine the understanding of how SA impacts corporate green transformation.

5.2.1. SA, Industry Attributes, and Corporate Green Transformation

(1)

Industry Classification: Heavily Polluting vs. Non-Heavily Polluting Industries

In recent years, China has implemented various green credit and finance policies to direct capital toward environmentally friendly and resource-efficient enterprises. The financing costs for heavily polluting companies have significantly increased. Consequently, firms in heavily polluting industries may face more severe financing constraints compared to those in less polluting sectors. As a result, companies in heavily polluting industries are expected to benefit more from SA in terms of reducing credit costs and enhancing their green transformation efforts. Additionally, heavily polluting companies often face heightened scrutiny from stakeholders concerned with sustainability, amplifying the impact of SA on their green transformation. To test this hypothesis, industries were classified based on the “Guidelines for Environmental Information Disclosure by Listed Companies” issued by the Ministry of Environmental Protection of China in 2010. This classification identifies 16 industries, including thermal power, steel, and mining, as heavily polluting. The sample was divided into firms in heavily polluting industries (HPI = 1) and those in non-heavily polluting industries (HPI = 0). Columns (1) and (2) of

Table 9. Heterogeneity Analysis: SA, Industry Attributes, and Corporate Green Transformation.

	(1)	(2)	(3)	(4)
	GT	GT	GT	GT
SA1	0.013 ***		0.013 ***
	(2.891)		(2.771)
SA2		0.189 ***		0.161 ***
		(2.921)		(2.621)
SA1 × HPI	0.007 **
	(2.27)
SA2 × HPI		0.092 *
		(1.737)
SA1 × HHHI			−0.005 ***
			(−5.821)
SA2 × HHHI				−0.062 ***
				(−5.417)
Controls	Yes	Yes	Yes	Yes
Firm/Year	Yes	Yes	Yes	Yes
N	9754	9754	9754	9754
Adj_R2	0.455	0.461	0.450	0.450

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

present the regression results for the interaction terms between SA and industry classification (SA1 × HPI, SA2 × HPI). The coefficients of these interaction terms are significantly positive, indicating that SA has a more substantial effect on promoting green transformation in heavily polluting industries.

(2)

Whether a Company Belongs to a Competitive Industry

Monopolistic companies often wield significant bargaining power, allowing them to secure excess profits through price control, which can result in less incentive for green innovation [27]. Conversely, firms in competitive industries, which face lower profit margins and higher pollution costs due to market competition, are generally more motivated to adopt SA. This motivation stems from the need to gain stakeholder support and leverage green innovation to enhance their competitive edge. To test this hypothesis, the study used the Herfindahl–Hirschman Index (HHI) as a measure of market competition. A higher HHI indicates a lower level of market competition. Companies with an HHI above the industry median are categorized as facing low market competition (HHHI = 1), while those below the median are categorized as facing high market competition (HHHI = 0). Columns (3) and (4) of Table 9 present the regression results for the interaction terms between SA and the Herfindahl–Hirschman Index (SA1 × HHHI, SA2 × HHHI). The coefficients for these interaction terms are significantly negative, demonstrating that in industries with more intense market competition (i.e., a lower HHI), SA has a more pronounced positive impact on corporate green transformation.

5.2.2. SA, Corporate Governance, and Corporate Green Transformation

(1)

External Governance

From the perspective of external governance, if a company lacks strong external oversight, SA—as a third-party external governance mechanism—can have a more substantial marginal effect in mitigating information asymmetry and reducing agency conflicts. Consequently, the expected benefits in terms of reduced of credit and encouraged risk-taking behaviors are likely to be more pronounced, thus significantly boosting corporate green transformation. To test this hypothesis, the study used the proportion of institutional investor shareholding as a measure of external governance. Institutional investors, representing external shareholders, can exert a positive governance influence [47]. Companies with institutional investor shareholding proportions above the industry median were classified as high institutional shareholding companies (Hnsti = 1), while those below the median were classified as low institutional shareholding companies (Hnsti = 0). Columns (1) and (2) of

Table 10. Heterogeneity Analysis: SA, Corporate Governance, and Corporate Green Transformation.

	(1)	(2)	(3)	(4)
	GT	GT	GT	GT
SA1	−0.011 ***		0.014 ***
	(−2.772)		(2.921)
SA2		−0.169 ***		0.148 ***
		(−3.011)		(2.921)
SA1 × Hnsti	0.001 *
	(1.802)
SA2 × Hnsti		0.017 *
		(1.901)
SA1 × HRPT			0.006 ***
			(5.802)
SA2 × HRPT				0.082 ***
				(4.901)
Controls	Yes	Yes	Yes	Yes
Firm/Year	Yes	Yes	Yes	Yes
N	9754	9754	9754	9754
Adj_R2	0.446	0.456	0.449	0.452

Note: The values in parentheses in the table are t-statistics adjusted for firm-level clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

present the regression results for the interaction terms between SA and the proportion of institutional investor shareholding (SA1 × Hnsti, SA2 × Hnsti). The coefficients of these interaction terms are significantly negative, indicating that the impact of SA on corporate green transformation is more pronounced in firms with a lower quality of external governance.

(2)

Internal Governance

From the perspective of internal governance, severe agency problems within a company can render SA—a third-party external governance mechanism—more effective in substituting for inadequate internal governance. In such cases, the influence of SA is expected to be more substantial, leading to a stronger promotion of corporate green transformation. To test this hypothesis, the study used the ratio of related-party transactions to total assets (RPT) in a given year to measure the severity of a company’s internal agency problems. High levels of related-party transactions among major shareholders often reflect significant internal agency issues [48]. Companies with an RPT ratio above the industry average were classified as high related-party transaction companies (HRPT = 1), while those with an RPT ratio below the average were classified as low related-party transaction companies (HRPT = 0). Columns (3) and (4) of Table 10 present the regression results for the interaction terms between SA and related-party transactions (SA1 × HRPT, SA2 × HRPT). The coefficients of these interaction terms are significantly positive, indicating that SA has a more pronounced impact on corporate green transformation in companies with higher quality of internal governance.

6. Conclusions and Implications

Sustainability assurance (SA) can enhance the credibility of corporate sustainability disclosures; however, few studies have explored its positive role in promoting corporate green transformation. This paper primarily examines the impact of SA on corporate green transformation and its underlying mechanisms. From a theoretical standpoint, SA promotes corporate green transformation by reducing the cost of credit and encouraging risk-taking behaviors. Empirical research based on green patents data and SA reports data from A-share listed companies in China from 2010 to 2022 provided evidence supporting this theoretical analysis. The conclusions of this study are as follows. First, SA can significantly promote corporate green transformation, with assurance based on the standard ISAE 3000 being more effective. Second, the mechanism analysis found that SA facilitates corporate green transformation through channels such as reducing the cost of credit and promoting risk-taking behaviors. Third, heterogeneity analysis revealed that the positive impact of SA on corporate green transformation is stronger in heavily polluting and competitive industries as well as in companies with lower quality of internal and external governance. This paper contributes to the literature by integrating SA and corporate green transformation within a unified framework. It also highlights the positive effects of third-party assurance on green transformation, offering a fresh perspective compared to the focus on other corporate sustainability activities. Additionally, it identifies key mechanisms through which SA influences green transformation, such as reducing the cost of credit and promoting risk-taking behaviors, while accounting for variations in industry characteristics and corporate governance. These findings deepen our understanding of the relationship between SA and corporate green transformation.

The findings of this study offer new insights for promoting corporate green transformation and optimizing SA practices, which have important policy implications. First, at the policy level, it is crucial to emphasize the role of SA and continually improve its quality. Specifically, standard-setting bodies for information disclosure and relevant regulatory authorities should expedite the establishment and refinement of SA standards and regulations, clarify and refine the implementation details of SA services, and ensure that assurance providers adhere to high industry standards. This will help maintain the credibility and reliability of SA and provide more accurate and high-quality decision-support information for investors, rating agencies, and other stakeholders. Second, at the corporate level, as sustainability increasingly becomes a core business philosophy, companies that actively fulfill their social responsibilities and voluntarily engage in SA not only help promote green transformation but also significantly enhance their brand image and market reputation, thereby facilitating long-term stable development. Therefore, companies should deeply integrate the concept of sustainability into all aspects of their operations and management, placing a strong emphasis on and investing in SA. This is not only a key measure for achieving green transformation and upgrading but also an important strategy for pursuing sustainable competitive advantage.