ELSE VIER

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Journal of Environmental Management
journal homepage: www.elsevier.com/locate/jenvman
Research article
Does mandatory greenhouse gas emissions reporting program deter corporate greenwashing?
Nguyen H. Luua,b,d@, Chau Leb,e,*( , Hiep N. Luua. Dung Τ.Κ. Nguyend
a VNU University of Economics and Business, Vietnam National University, Hanoi, Viet Nam
b Lincoln International Business School, University of Lincoln, Lincoln, UK
c School of Banking, University of Economics HCMC, Ho Chi Minh City, Viet Nam d School of Banking and Finance, National Economics University, Hanoi, Viet Nam

ARTICLE INFO
ABSTRACT

Keywords:
Greenwashing
Greenhouse gas emissions reporting program
ESG
Environmental innovation

This study examines the impact of the mandatory greenhouse gas emissions reporting program (GHGRP) on corporate greenwashing behaviour. Utilising the GHGRP in the United States as a quasi-natural experiment, we perform a difference-in-difference analysis to a panel dataset of 2731 publicly listed US firms from 2007 to 2022. The data consist of annual observations of firm-level variables, including ESG performance and disclosure metrics, financial characteristics, and environmental innovation indicators. Our results reveal a notable reduction in greenwashing behaviour following the adoption of the GHGRP, suggesting that increased transparency and accountability discourage deceptive disclosure practices. A decomposition analysis shows that the GHGRP motivates firms to improve actual ESG performance while curbing inflated ESG claims. Larger and more profitable firms exhibit a more significant decrease in greenwashing, indicating that those under greater public scrutiny respond more strongly to regulatory oversight. Additionally, firms with higher levels of environmental innovation demonstrate a greater reduction in greenwashing post-GHGRP adoption, reflecting an alignment between sustainability commitments and corporate culture. This study offers valuable insights for firm managers, investors, and policymakers on leveraging the GHGRP framework to promote transparency in corporate reporting practices.

* Corresponding author., Lincoln International Business School, University of Lincoln. Brayford Pool, Lincoln, Lincolnshire, LN6 7TS, UK.
E-mail address: cle@lincoln.ac.uk (C. Le).
https://doi.org/!0.1016/j.jenvman.2024.123740
Received 25 July 2024; Received in revised form 9 December 2024; Accepted 11 December 2024
Available online 17 December 2024
0301-4797/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.0rg/licenses/by/4.0/).

1.		Introduction
With growing scientific evidence linking human actions to climate change and the rise of extreme weather events worldwide (IPCC, 2021), public awareness of climate risks has reached an all-time high, spurring global efforts to reduce greenhouse gas (GHG) emissions and climate change impacts. This heightened awareness has increased the demand for corporate accountability and transparency, leading to the adoption of GHG emissions reporting as an effective regulatory tool for monitoring firms' environmental performance (Atz et al., 2024; Chen et al., 2024; Tang and Demeritt, 2018). In response, many countries, including the United States, have introduced mandatory GHG emissions reporting programs (GHGRP) to track and help achieve GHG reduction targets (Christmann, 2000; Tornar, 2023). The GHGRP, which was established in 2009 by the US Environmental Protection Agency (EPA), requires facilities emitting over 25,000 metric tons of GHG annually to submit reports to the EPA each year (EPA Climate Change Division, 2009). This regulatory development reflects a broader, global movement toward transparency in environmental information disclosure, underscoring the critical role that firms play in tackling climate change.
However, assessing the efficiency of a real-world policy intervention is never a straightforward process, as different policy tools may generate complementary or conflicting effects (Xiufan and Decheng, 2024). Despite the increasing adoption of GHGRP, its effectiveness in promoting change in firms’ reporting practices and environmental initiatives remains contested. One strand of literature suggests that mandatory non-financial information reporting regulations increase firm's transparency, encourage tangible carbon emissions reductions (Cooper et al., 2018; Tang and Demeritt, 2018), and lower the likelihood of environmental litigation risks (Huang et al., 2023). The opposing strand of literature; however, argues that such policies raise managerial challenges, such as compliance costs and potential unethical reporting practices to obscure actual performance (Aragón-Correa et al., 2020; Ding et al., 2023; Peters and Romi, 2013). This study acknowledges both perspectives, recognising that while regulatory frameworks like GHGRP can enhance corporate accountability and transparency, their success depends heavily on the accuracy and integrity of the disclosed information (Talbot and Boiral, 2013; Warnecke, 2014).
The implementation of GHGRP and the firms’ unethical greenwashing disclosure of environmental, social, and governance (ESG) information highlight the complex relationship between regulatory requirements, corporate behaviour, and stakeholder perceptions. Greenwashing is defined as the disclosure of misleading ESG claims, where firms may promote their sustainability efforts extensively to conceal their actual poor ESG performance to enhance corporate image or avoid regulatory scrutiny (de Freitas Netto et al., 2020; Delmas and Burbano, 2011). Firms may engage in greenwashing behaviour to improve their reputation, reduce stakeholders’ pressure, or conceal subpar environmental performance. By presenting their sustainable orientation, firms seek to maintain a favourable public image and their legitimacy with stakeholders (Ruiz-Blanco et al., 2022; Yu et al., 2020). However, the negative impacts of greenwashing on firms extend beyond mere reputational damage, potentially undermining stakeholder trust, distorting market signals, and eroding investor confidence (de Freitas Netto et al., 2020; Delmas and Burbano, 2011; Ruiz-Blanco et al., 2022).
Given the negative impact of greenwashing, strong regulatory frameworks are crucial for enforcing tighter controls and compelling firms to adhere to stringent reporting standards, thus enhancing transparency and accountability (Tang and Demeritt, 2018). Such regulatory pressure discourages deceptive practices and incentivizes firms to adopt more efficient and environmentally responsible operations (Aragón-Correa et al., 2020). Stronger regulations not only mandate compliance but also create an environment where firms are encouraged to optimise their processes, reduce inefficiencies, and innovate to meet regulatory requirements cost-effectively. For instance, firms that align their operations with stringent environmental standards often find opportunities to reduce waste, enhance resource utilisation, and improve energy efficiency, all of which contribute to long-term operational efficiency and cost savings (Christmann, 2000).
While mandatory programmes like GHGRP aim to promote corporate accountability, their effectiveness relies on the integrity and accuracy of disclosed information (Talbot and Boiral, 2013; Warnecke, 2014). Combatting greenwashing, therefore, requires a multidimensional strategy that involve rigorous regulatory enforcement, robust stakeholder engagement, and corporate governance reforms. By fostering collaboration among policymakers, regulators, and companies, such strategies can help build a culture of transparency and minimise opportunities for firms to mislead stakeholders. Additionally, as firms adapt to comply with enhanced disclosure standards, the pursuit of operational improvements becomes a natural outcome, aligning business practices more closely with global environmental goals (Balluchi et al., 2020; Sun and Zhang, 2019; Zhang, 2023).
Understanding the impact of mandatory GHGRP on firms’ greenwashing behaviour is crucial for policymakers, regulators, and stakeholders seeking to enhance environmental transparency and accountability. The primary objective of this research is to investigate how implementing mandatory reporting requirements influences firms’ propensity to engage in greenwashing behaviour within their ESG disclosures. Building on previous literature highlighting the potential benefits of disclosure mandates in driving emission reductions and improving environmental performance, this study aims to identify how firms adapt their sustainability practices, both in terms of actual emissions reductions and in their public communication, in response to mandatory reporting regulations. This includes investigating whether firms enhance their ESG performance and modify their reporting strategies to improve transparency or, conversely, manipulate information to appear more sustainable than they are.
The relationship between mandatory GHGRP and firms’ greenwashing behaviour can be understood by applying Agency theory and Stakeholder theory, which help explain how mandatory reporting influences firms' responses to transparency requirements. According to Agency theory (Jensen and Meckling, 1976; Ross, 1973), firms (agents) often have more information about their environmental practices than stakeholders (principals), creating a risk of greenwashing, where firms exaggerate or misrepresent their sustainability efforts to meet stakeholder expectations without making genuine changes to operation. Mandatory reporting regulations, like the GHGRP, reduce this information asymmetry by requiring firms to disclose their emissions data transparently, thus aligning the interests of both the firm and its stakeholders. From a Stakeholder theory perspective (Freeman, 1984; Such- man, 1995), the GHGRP increases pressure from stakeholders—such as investors, creditors, customers, and regulators—who demand truthful and verifiable environmental disclosures. This heightened accountability requires firms to engage in more authentic environmental practices and reduces the likelihood of greenwashing, as firms are compelled to demonstrate their environmental commitment through accurate and verified data. Therefore, the GHGRP strengthens corporate governance by increasing transparency, improving stakeholder trust, and ultimately discouraging deceptive environmental practices.
Utilising a staggered difference-in-difference (DiD) model, this study assesses the influence of the GHGRP on firms’ tendencies to engage in greenwashing, using the data of publicly listed US companies from 2007 to 2022. To construct the sample, the study identifies firms regulated by the GHGRP and matches them with control firms that are not subject to the mandate. The US was selected as the sample country due to its pioneering role in implementing mandatory environmental regulations, which provides a meaningful and robust context for analysing the economic and managerial effects of the GHGRP. Covering approximately 85 percent of U.S emissions, the GHGRP targets large emitters across key industries, making it one of the most comprehensive national programmes, with data publicly accessible. This transparency allows researchers to verify corporate claims against reported emissions, highlighting discrepancies that may indicate greenwashing. Additionally, US markets are often at the forefront of global financial disclosure standards, as evidenced by the GHGRP and the SEC’s recent climate disclosure requirements for publicly traded companies. Therefore, findings from US-based research on GHGRP’s impact on greenwashing could have implications beyond the US, potentially influencing international policy framework.
Built upon Agency and Stakeholder theories, we expect that the GHGRP may lead to a noticeable reduction in greenwashing behaviour, as firms are likely to respond to increased transparency and accountability by reducing deceptive disclosure practices. From the principalagent perspective, mandatory reporting reduces information asymmetry, aligning corporate actions with stakeholder expectations and discouraging greenwashing. Similarly, Stakeholder theory implies that the GHGRP should enhance pressure from investors, customers, and regulators, pushing firms to truly improve their ESG performance rather than merely amplifying disclosures. These expected results reflect the GHGRP’s mandatory reporting regulation’s role in fostering transparency and genuine environmental commitment, aligning corporate behaviour with regulatory and stakeholder demands for integrity in sustainability reporting.
This study makes several contributions to the literature on environmental reporting and corporate behaviour. First, it explores how GHGRP affect firms’ ethical ESG reporting practices, enriching the existing strand of literature that examines the influence of environmental regulatory shocks on firm behaviour (Cooper et al., 2018; Huang et al., 2023; Tang and Demeritt, 2018). By focusing specifically on the ethical dimension of ESG reporting, this research sheds light on how regulatory interventions shape corporate disclosure practices, contributing to a deeper understanding of the dynamics between environmental regulations and corporate conduct.
Second, the study aims to decompose the impact of GHGRP on firms’ reporting practices and actual performance. By disentangling the effects of mandatory reporting regulations on firms’ disclosure behaviour from
their tangible environmental performance, it provides an insight into the mechanisms through which regulatory mandates influence corporate behaviour. This decomposition enables a more comprehensive assessment of the effectiveness of GHGRP in promoting environmental sustainability and responsible corporate conduct. The study aims to enhance scholarly understanding of the relationship among environmental regulations, corporate behaviour, and sustainable development.
Lastly, this study pioneers the examination of the channels through which GHGRP may induce changes in firms’ unethical behaviour. By exploring the mechanisms that mediate the relationship between regulatory mandates and corporate conduct, it seeks to uncover how GHG reporting requirements might lead to tangible shifts in firms’ ethical practices. Moreover, it investigates potential moderating factors that might influence the effectiveness of regulatory interventions in mitigating unethical behaviours, offering valuable insights into the complex dynamics of regulatory compliance.
The study is structured as follows: Section 2 presents data sample and methodology. Section 3 discusses the empirical findings and analysis. Finally, Section 4 concludes the paper with relevant theoretical and practical implications.
2.		Methodology and data sample
2.1.		Methodology
In this study, we aim to evaluate the impact of the GHGRP on a firm’s greenwashing behaviour. The greenwashing variable inherently lacks direct observability, as no firm would openly acknowledge deceptive practices in ESG reporting. Given this limitation, our approach uses a measure that aligns with prior literature (Yu et al., 2020; Zhang, 2023), which define greenwashing as the discrepancy between a firm’s extensive ESG disclosures and its actual ESG performance. This method uses quantifiable metrics of disclosure and performance from third-party, providing a systematic means of identifying greenwashing behaviour. It is believed that firms use misleading ESG disclosures as corporate strategy in an effort to give stakeholders false impressions about their performance.
In accordance with Yu et al. (2020) and Zhang (2023), we construct greenwashing score (Greenwash) for a firm by taking the difference between its ESG disclosure and ESG performance scores:
Disclosure i.t ESGDisclosure
Disclosure /

Greenwashit =

/ESLrPerformance it ESCperformance) (1)
\Performance /
We standardise these scores by subtracting the mean and dividing by the standard deviation (σ). The standardised Greenwash score generally falls within a range of approximately —3 to +3, as standardized scores typically center around a mean of zero with a standard deviation of one. Scores near zero suggest alignment between a firm’s ESG disclosures and actual performance, while higher positive values (e.g., closer to +3) indicate potential greenwashing behaviour, where disclosures exceed actual practices. Conversely, lower negative values (e.g., closer to -3) suggest conservative disclosure practices relative to actual performance. In a nutshell, the difference between a firm’s normalised ESG disclosure and ESG performance score is known as the firm's peer-relative greenwashing behaviour. A firm is considered to have missing data for the relative greenwashing score if it lacks indicators for either or both types of scores. While this indirect measure does have limitations—such as the reliance on third-party ESG assessments and the potential for subjective interpretations—its strength lies in its ability to capture peer-relative discrepancies objectively. This enables an empirical, standardized analysis of greenwashing behaviour across firms, allowing for comparisons within the sample and providing a practical proxy for assessing greenwashing risk in firms’ ESG strategies.
To evaluate the causal impact of GHGRP on corporate greenwashing practices, we employ a quasi-natural experiment design combined with difference-in-difference (DiD) estimations. DiD stands out as one of the most widely used research methodologies in economics and social sciences to evaluate the impacts of policy intervention. It is particularly useful in evaluating the impact of a treatment (or intervention) by comparing the changes in the outcomes overtime between a treatment group and a control group (Li et al., 2024). Similar to other empirical studies, potential endogeneity issues could lead to bias in estimating the causal impacts of GHGRP. Arguably, the greenwashing behaviour may be correlated to unobserved corporate characteristics, raising endogeneity concerns that make parameter estimates skewed and inconsistent (Nguyen et al., 2020). In addition, environmental regulation like GHGRP is often implemented systematically, making it difficult to disentangle the actual economic effect of the policy change (i.e., the adoption of GHGRP) from other time-varying factors. To address these empirical challenges, we use staggered DiD estimators with panel data using two-way fixed effects (TWFE) to control for unobserved time-invariant heterogeneity while evaluating the treatment effect. Stagged DiD approach is particularly suitable for capturing the phased implementation of the GHGRP, as it allows us to measure the impact of the policy over different timelines across firms, rather than treating it as a one-time event affecting all firms simultaneously. This staggered timing reflects the real-world adoption process, making the analysis more realistic and relevant. The TWFE model further controls for unobserved, time-invariant characteristics and broader time trends, which strengthens the causal inference and reduces bias from confounding factors (Li et al., 2024).
To alleviate concerns that omitted shocks coinciding with the implementation of the GHGRP might also influence firms’ manipulative reporting behaviour, we perform several falsification tests on adopted years and adopted firms to evaluate the robustness of our findings. A key assumption of the DiD approach is that, in the absence of an external shock, the treated and control groups should exhibit similar trajectories regarding changes in their outcome variables. Consistent with this assumption, we posit that, prior to the implementation of GHGRP, the greenwashing behavior of controlled and treated firms followed a similar trend. This similarity in pre-GHGRP trends allows us to estimate how firms’ greenwashing behaviour might have evolved in the absence of the policy. To validate the parallel trend assumption central to the DiD design and to address potential anticipation effects, we examine the dynamics of firms’ greenwashing behaviour in the period leading up to the GHGRP implementation.
The empirical model is specified as follows:
Greenwashit = a + p GHGRP t + Controls t + op + rt+ Eit (2) where i denotes firm and t denotes year; @i and rt represent firm and year-fixed effects, respectively; Eit is the standard error clustered at the state level.
The dependent variable, Greenwash, measures the extent to which a firm engages in ESG greenwashing behaviour. Our main independent variable of interest is GHGRP, a binary variable that takes the value of one if the firm operates a facility under GHGRP and zero otherwise. GHGRP identifies firms that have adopted the mandate during the postimplementation period (i.e., GHGRPLt = Treated x Postt). A positive (negative) coefficient of βι would indicate that firms increase (decrease) their greenwashing level following the GHGRP implementation.
We also incorporate into the model a set of control variables (Controls) capturing firm-specific characteristics that could affect firms' decision to engage in ESG misleading disclosure practices. In particular, we include firm size (Size), determined by the natural logarithm of total assets; financial leverage (Leverage), calculated by dividing total liabilities by total assets; profitability (Profit), measured by dividing the firm's profits before interest and tax (EBIT) by total assets; firm sale revenue (Sale), measured as the ratio of total revenue to total assets.; firm level of environmental innovation (Envijnnovation), measured as the score ranging from 0 to 100 indicates firm's level of innovative environmental technologies, processes, or eco-designed products.
2.2.		Data sample and descriptive statistics
We collect firm-level data from the Thomson Reuters Refinitiv (Refinitiv) database for our empirical studies. We start with all US publicly listed firms in the Refinitiv database from 2007 to 2022, excluding firms that operate in the financial industry due to significantly different disclosure mandates and accounting standards compared to other industries. We also eliminate firms with missing ESG data and other firm-specific variables from the sample. Refinitiv provides both ESG disclosure and ESG performance scores. The ESG disclosure score, which runs from 0 to 100, indicates how much ESG information a firm discloses to the public through its reports. Conversely, the ESG performance score, also ranging from 0 to 100, evaluates a firm’s performance with respect to the environmental, social, and governance factors.
To construct the treatment and control groups for the DiD analysis, we gather data on facilities subject to the GHGRP requirements through the EPA Facility Level Information on Greenhouse Gases Tool (FLIGHT). We identify firms that own and operate facilities falling under the GHGRP. We then manually match the firms’ tickers listed in the FLIGHT with the parent firms of each facility (EPA, 2023). This results in 11,182 facilities from 8321 firms that prepared emissions reports between 2010 and 2019. Using the firm's ticker, we match the facility ownership information with the ESG and financial firm-level data from Refinitiv. This enables us to divide our whole sample into two groups: a treatment group consisting of firms that are under the GHGRP-regulated facilities, and a control group consisting of firms not subject to regulations. The US EPA launched the GHGRP on October 30, 2009, mandating that, beginning in 2010, firms emitting more than 25,000 metric tons of carbon annually report their emissions each year. In this study, we choose the sample period of 2007-2022 to provide a balanced pre- and post-regulation analysis window, capturing firm behaviour three years before and three years after the GHGRP implementation. This timing allows us to examine changes in firm behaviour surrounding the policy’s introduction. Therefore, the final sample comprises 2731 listed US firms, including 134 treated firms and 2597 control firms between 2007 and 2022.
The definition, measurement, and source of all variables included in our empirical analysis are summarised in Table 1. Table 2 presents the descriptive statistics for each of the study’s main variables. As can be seen from Table 2, the mean value of Greenwash is 1.747, meaning that, on average, firms typically report 1.747 points higher than their actual ESG performance. This positive mean Greenwashing score indicates that,
Table 1
Variable description.
Variable	Description	Sources	
Greenwash	The extent to which a firm engages in ESG greenwashing, measured by the difference between ESG performance and ESG disclosure	Authors’ calculation using data retrieved from Thomson Reuters Refinitiv	
GHGRP	Dummy variable, taking the value of 1 if the firm has facility under GHG reporting program, and zero otherwise.	Author’s own complication from FLIGHT	
Firm Size	The natural logarithm of total assets	Authors’ calculation using data retrieved from	
Leverage Profit Sale	Total liabilities to total assets Net income to total assets Revenues to total assets	Thomson Reuters Refinitiv	
Envijnnovation	Score indicates firm’s level of innovative environmental technologies, processes, or ecodesigned products	Thomson Reuters Refinitiv	
across the sample, firms tend to overstate their ESG disclosures relative to their actual ESG practices, suggesting a general tendency toward greenwashing behaviour. The GHGRP dummy variable has a mean of 0.125, showing that 12.5% of sample observations are firms with facilities under the mandatory GHG reporting program. Our sample includes medium and large firms, with Size (the natural logarithm of total assets) ranging from about 17 to 27.3, resulting in a mean value of 23. Regarding profitability, firms in our sample report generate 4.8% of profits over total assets on average. They also report that firms' capital structure differs substantially, as Leverage ranges from 4.6% to 351%. Our sample firms also have average sales revenue of 77.9% of total assets and average level of environmental innovation of 36.173 points.
Table 3 exhibits pairwise correlations between our main variables. It could be observed that correlations among the independent variables are quite low, with the highest correlation coefficient of 0.299 between firm size and greenwashing score. Moreover, all the VIF values are well below 3, indicating that multicollinearity is not an issue in our regressions (Menard, 2002).
3.		Empirical results and analysis
3.1.		The effect of the mandatory GHGRP on greenwashing behaviour: baseline results
The baseline results on the impact of GHGRP on a firm’s greenwashing behaviour, based on the empirical model (1), are summarised in Table 4. Specifically, column (1) presents the outcomes of univariate regression, which excludes all control variables and fixed effects. Column (2) incorporates all control variables (Size, Leverage, Profit, and Sale) along with firm fixed effects (FEs) to reduce the possibility that unobservable firm-specific features will eventually affect our findings. Column (3) includes industry FEs, while column (4) incorporates year and industry FEs. Column 5 presents results for our main specification, with all controls and firm-industry FEs to take into account industryspecific time shocks, allowing for more granular control of the unobserved factors that might influence the relationship between GHGRP and Greenwash.
In all model specifications, the estimated coefficients of GHGRP are negative and statistically significant, suggesting a decrease in Greenwash following the adoption of the mandatory GHGRP. In other words, firms reduce their greenwashing behaviour when they experience an exogenous regulation that promotes transparency and accountability in firms’ environmental reporting practices. The effect is also economically significant. As an illustration, in the fully specified model (Column 5) that includes all control variables and firm-industry fixed effects, adopting the GHGRP is associated with an approximately 30.74% (i.e., 0.537/ 1.747*100) reduction in greenwashing behaviour of treated firms compared to control firms.
Our results support the proposition that adopting the mandatory GHGRP may discourage firms from manipulating their ESG reports. By requiring firms to disclose detailed information about their GHG emissions, sources, and reduction efforts, the GHGRP may reduce information asymmetry between firms and stakeholders. Moreover, firms are held accountable for the accuracy and completeness of their disclosure, reducing the incentive to engage in greenwashing practices to mislead stakeholders. Non-compliance with reporting requirements can result in legal consequences, reputational damage, and loss of market share. This finding is in line with the Agency theory (Ross, 1973; Jensen and Meckling, 1976) and Stakeholder theory (Freeman, 1984), and prior literature (Kurpierz and Smith, 2020; Ruiz-Blanco et al., 2022) which shows that the reporting regulations could address the information asymmetry between firms and stakeholders to align the interests of managers with those of stakeholders. The result suggests that mandatory reporting regulations lower agency costs of supervising non-financial information disclosure by improving transparency and promoting sustainability awareness.

Table 4
Baseline results.
(1)	(2)	(3)	(4)	(5)	
VARIABLES	No Controls	Firm FEs	Industry FEs	YearIndustry FEs	FirmIndustry FEs	
GHGRP	-0.15***	-0.54**	-0.11**	-0.12***	-0.54**	
	(0.05)	(0.23)	(0.04)	(0.04)	(0.23)	
Size		-0.60***	-0.27***	-0.29***	-0.60***	
		(0.05)	(0.01)	(0.01)	(0.05)	
Leverage		-0.58***	-0.14**	-0.11	-0.58***	
		(0.13)	(0.07)	(0.07)	(0.13)	
Profit		-0.14	-0.14	-0.16	-0.14	
		(0.19)	(0.18)	(0.20)	(0.19)	
Sale		-0.12	-0.12***	-0.14***	-0.12	
		(0.09)	(0.03)	(0.03)	(0.09)	
Constant	1,81***	16.01***	8.11***	8.52***	16.01***	
	(0.02)	(1.29)	(0.24)	(0.26)	(1.29)	
Observations	6054	5723	6000	5995	5723	
R-squared	0.002	0.63	0.13	0.15	0.63	
Year FEs	NO	NO	NO	YES	NO	
Firm FEs	NO	YES	NO	NO	YES	
Industry FEs	NO	NO	YES	YES	YES	
Note: This table reports the results of the baseline regression model to estimate the impact of mandatory GHGRP on corporate greenwash behaviour. Column (1) shows the baseline model results using no fixed effects. Column (2) shows the baseline model results using only firm-fixed effects. Column (3) reports the results of the baseline regression model using industry fixed effects. Column (4) reports the results of the baseline regression model using industry-year fixed effects. The dependent variable is greenwashing behaviour (Greenwash). The main independent variable, GHGRP, is a dummy variable, taking the value of 1 if the firm is under the GHG program, and zero otherwise. The definitions for all other variables are outlined in Table 1. Robust standard errors are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

	Ν	Mean	Std. Dev.	Min	25th percentile	Median	75th percentile	Max	
Greenwash
GHGRP
Size
Leverage
Profit
Sale
Envijnnovation	5723	1.75	1.23	-4.45	1.24	1.89	2.53	5.03
5723	0.13	0.33	0	0	0	0	1
5723	23.01	1.43	17.03	22.02	22.98	24	27.30
5723	0.63	0.22	0.05	0.50	0.62	0.74	3.52
5723	0.05	0.09	-1.48	0.02	0.05	0.09	0.66
5723	0.78	0.62	-0.02	0.35	0.63	1.00	5.41
5723	36.18	31.53	0	0	34.30	61.46	99.38	

Table 2
Descriptive statistics.

Note: This table reports the summary statistics of all main variables used in our empirical analysis. The definitions for all variables are outlined in Table 1. Number of observation (N) for each variable is 5723.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	VIF	
(1)		Greenwash
(2)		GHGRP
(3)		Size
(4)		Leverage
(5)		Profit
C6) Sale
(7) Envijnnovation	1.00
-0.03*	1.00	1.05
-0.29***	0.17***	1.00	1.16
-0.08***	0.08***	0.19***	1.00	1.08
-0.07***	-0.04***	0.09***	-0.07***	1.00	1.08
-0.02	-0.15***	-0.16***	0.10***	0.21***	1.00	1.13
0.26***	0.06***	0.20***	-0.02	0.09***	0.06***	1.00	1.06	

Table 3
Correlation matrix.

Note: This table presents the pairwise correlation coefficients between the main variables used in the paper. The last column reports the results of the VIF test.

The results of other control variables also offer valuable insights. The coefficients of Size and Leverage consistently show a negative and strong significance across all columns. These findings suggest that larger firms and those more reliant on debt are less likely to engage in greenwashing, as they are subject to greater public scrutiny and regulatory pressure, which can deter deceptive practices in ESG reporting (Marquis et al., 2016). In contrast, smaller firms may not receive the same level of attention, while facing intense competitive pressure to differentiate themselves in the market. Furthermore, they typically have fewer resources to invest in genuine sustainability initiatives, which may incentivise them to resort to greenwashing as a cost-effective means of appearing environmentally responsible without making significant investments. This aligns with (Zhang, 2022a,b), who shows that companies’ greenwashing decisions are often motivated by financial constraints. Additionally, consistent with Agency and Stakeholder theories, smaller firms and those relying on equity financing may prioritise short-term gains over long-term sustainability to meet the expectations of specific stakeholder groups, such as investors and customers. They may also feel heightened pressure to enhance their image, making them more prone to greenwashing in an effort to attract investors or improve their perceived performance.
To explore the mechanism of GHGRP effect, we extend our analysis by decomposing the greenwashing measurement. Our study defines greenwashing as the discrepancy between a firm's ESG disclosure and its actual ESG performance (Yu et al., 2020; Zhang, 2022a,b, 2023). It represents the gap between what a firm claims or communicates about its ESG practices and the reality of its actions and outcomes. To verify our baseline findings about the potential impact of non-financial information mandatory disclosure regulation on a firm’s unethical ESG disclosure, we re-estimate the baseline regression by dividing Greenwash variable into two components: ESG Performance and ESG Disclosure. We posit a significant enhancement of ESG Performance and a significant reduction of ESG Disclosure after adopting GHGRP in treated firms compared to control firms in our sample.
Table 5 presents the findings of our decomposition analysis. The estimated coefficients confirm that the GHGRP, which creates a regulatory framework that incentivizes firms to adopt more sustainable practices, prompts firms to improve their overall ESG performance. On the other hand, before mandatory GHGRP were implemented, firms may have engaged in voluntary ESG disclosure to enhance their public image and reputation, thus engaging in greenwashing behaviour to cover up their poor performance. However, with the GHGRP’s introduction, firms need to adhere to strict regulations and standards for reporting their GHG emissions, which may lead to the reduction of their overall overstating ESG disclosure. Thus, the baseline results of overall reduction of greenwashing behaviour post stringent disclosure mandates could be explained by the reduction of excessive or exaggerated ESG disclosure

Table 5
Decomposition analysis.
(1)	(2)	
VARIABLES	ESG Performance	ESG Disclosure	
GHGRP	2.07***	-6.78**	
	(0.56)	(3.45)	
Size	5.49***	-1.28	
	(0.13)	(0.78)	
Leverage	1.01	1.10	
	(0.85)	(1.61)	
Profit	6.92***	0.64	
	(2.24)	(2.19)	
Sale	2.11***	-0.44	
	(0.34)	(1.23)	
Constant	69.40’**	124.33***	
	(2.88)	(18.49)	
Firm-Industry Fes	YES	YES	
Observations	5995	5723	
R-squared	0.30	0.60	
Note: This table reports the results of the greenwashing decomposition analysis. Column (1) shows the results of the baseline model using ESG Performance as dependent variable. Column (2) shows the results of the baseline model using ESG Disclosure as dependent variable. The main independent variable, GHGRP, is a dummy variable, taking the value of 1 if the firm is under the GHG program, and zero otherwise. The definitions for all other variables are outlined in Table 1. Robust standard errors are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

and enhancement of ESG performance.
3.2.		The heterogenous impact of GHGRP
3.2.1.		The heterogeneous impact of public scrutiny
Vie have documented thus far that, on average, firms reduce their greenwashing behaviour following the implementation of mandatory GHGRP. In this section, we explore whether the impact of GHGRP is homogeneous across firms. Firms subject to higher levels of public scrutiny - due to their size, market prominence, or financial profitability - are more likely to face intensified regulatory oversight (Kim and Yoo, 2022; Lourenco et al., 2012). Furthermore, larger and more profitable firms typically have established market reputations to uphold. For these firms, non-compliance with mandatory GHG reporting requirements can not only lead to legal repercussions but also erode reputation and stakeholder trust, ultimately impacting sales, market share, and investor confidence. Building on this reasoning, we hypothesise that the effects of mandatory reporting regulation are likely to be more pronounced for larger and more profitable firms.
We partitioned our full sample into sub-groups based on firm profitability and size to empirically test our hypothesis. To create a balanced division that accurately reflects central tendencies, we categorize firms with profitability (Profit) above the median as having high profitability, and those with Profit below the median as having low profitability. Using the median, rather than the mean, helps avoid skewing the results due to extreme values or outliers in profitability (Aguinis et al., 2013; Rous- seeuw and Hubert, 2011; Tian et al., 2021). For each corresponding sub-sample, we re-estimate the baseline model. In Table 6, Columns 2 and 4 reveal that GHG report regulations significantly mitigate greenwashing behaviour among more profitable and larger firms. Notably, the estimated coefficient of GHGRP lacks statistical significance in Column 1 but exhibits a statistically significant, albeit lower, coefficient in Column 3. These results lend support to our hypothesis that the impact of the GHGRP is more pronounced for high-profile firms. Such firms face heightened public scrutiny and are more vulnerable to reputation risk if they fail to comply with sustainability practices. While mandatory reporting may impose additional compliance costs, it also enhances firm transparency, potentially bolstering investor confidence, especially among firms under greater public scrutiny. Consequently, the net effect on financial statements may not necessarily be negative. Firms proactively comply with GHGRP may actually benefit by attracting greater stakeholder trust and mitigating risks related to non-compliance, such as reputational damage and regulatory penalties. This heterogenous impact underscores that public scrutiny may intensify the regulatory effect, particularly for firms with greater visibility and market stakes.
3.2.2.		The role of environmental innovation on the impact of GHGRP

VARIABLES	Panel A: Profitability	Panel Β: Firm size	
	(1)	(2)	(3)	(4)	(5)	(6)	
	Lower profitability	Higher profitability	Impact of Profit on Greenwash	Smaller firms	Larger firms	Impact of Firm Size on Greenwash	
GHGRP	0.02	-0.26***		-0.39**	-0.93**		
	(0.06)	(0.07)		(0.19)	(0.41)		
Size	-0.27***	-0.28***	-0.28***	-0.56***	-0.68***	-0.61***	
	(0.02)	(0.02)	(0.01)	(0.08)	(0.08)	(0.06)	
Leverage	-0.26*	-0.12	-0.04	-0.48***	-0.84***	-0.58***	
	(0.15)	(0.08)	(0.07)	(0.14)	(0.23)	(0.13)	
Profit	0.48	0.10	-0.48**	-0.11	-0.20	-0.13	
	(0.35)	(0.43)	(0.19)	(0.22)	(0.32)	(0.19)	
Sale	-0.13***	-0.13***	-0.13***	-0.26**	-0.17	-0.13	
	(0.05)	(0.04)	(0.03)	(0.11)	(0.15)	(0.09)	
Age Constant	8.28***	8.40***	8.33***	14.78***	18.58***	16.17***	
	(0.42)	(0.39)	(0.24)	(1.80)	(2.06)	(1.29)	
Firm-Industry FEs	YES	YES	YES	YES	YES	YES	
Observations	2860	2849	5999	2820	2827	5723	
R-squared	0.14	0.16	0.11	0.72	0.59	0.63	

Table 6
The heterogeneous impact of public scrutiny.

Note: Robust standard errors are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.

In this section, we aim to examine whether firms’ orientation toward sustainable and environmental responsibility affects the impact of GHGRP on greenwashing behaviour. Firms that invest more in environmentally related research and development (R&D) tend to prioritise sustainability and environmental responsibility to align with their culture and principles (Ma et al., 2022; Xue et al., 2022; Zameer et al., 2022). This emphasis on environmental innovation signifies a dedication to improving environmental performance through technological advancements, suggesting that firms with higher levels of environmental investment likely view greenwashing as contradictory to their mission and values. Despite the potential reputation risks and stakeholder distrust that greenwashing brings (de Freitas Netto et al., 2020; Ruiz-Blanco et al., 2022), some firms may still resort to these practices under pressure to appear environmentally responsible in a cost-effective manner (Yu et al., 2020). By misleadingly promoting sustainability efforts or selectively disclosing information, such firms aim to capture the benefits of perceived "sustainable" credibility without fully committing to substantial operational changes. However, firms with sincere environmental commitments are less inclined to greenwash, as these deceptive practices undermine the trust of stakeholders and counteract their genuine efforts to mitigate environmental impacts. This rationale supports our hypothesis that firms with higher levels of environmental innovation are likely to exhibit less greenwashing compared to firms with lower levels of such investments in response to GHG report regulations.
We divide our entire dataset into sub-groups according to firms’ levels of environmental innovation and re-estimate the baseline regression model for each sub-sample. We use environmental innovation score at the firm level as a proxy for their commitment to environmental responsibility and split the samples into two groups based on the median of this score (Envijnnovation). Firms in the top quartile constitute the subsample with a greater focus on sustainability, whereas firms in the bottom quartile make up the subsample with a lesser focus on sustainability. The results in Columns 1 and 2 of Table 7 illustrate that firms with higher investments in environmental innovation exhibited a noteworthy reduction in ESG deceptive disclosure compared to their peers. Moreover, the estimated coefficient of Envijnnovation is statistically and negatively significant in Column 3, indicating that environmental innovation plays a significant role in curbing greenwashing practices within firms. The results support our hypothesis that environmental orientation has a significant impact on the effectiveness of GHGRP in mitigating firms' ESG reporting behaviour.
3.3.		Robustness tests
3.3.1.		Falsification tests
Vie employ the falsification tests to determine the robustness of our findings by incorporating artificial treatment time periods and treatment groups for the policy exogenous shock (Degl'Innocenti et al., 2022; Xiao et al., 2023). The falsification test is intended to verify the observed changes in greenwashing practices between GHGRP and non-GHGRP-required firms are attributed to the implementation of the GHGRP, rather than being influenced by a temporal trend or other unknown confounding regulations. Therefore, we designate the policy shock year as one year before the actual GHGRP adoption year in each treated firm. Next, for each treated firm, we choose a random year as the shock year and replace the original GHGRP dummy variable with RandomYear. Finally, for each actual shock year, we choose an equivalent
Table 7
The impact of Environmental Innovation.
	(1)	(2)	(3)	
VARIABLES	Lower Environmental Innovation	Higher Environmental Innovation	Impact of Environmental Innovation	
GHGRP	0.05	-0.25**		
	(0.115)	(0.115)		
Envijnnovation			-0.01*	
			(0.001)	
Size	-0.28***	-0.22***	-0.54***	
	(0.04)	(0.03)	(0.06)	
Leverage	-0.46***	0.25	-0.51***	
	(0.16)	(0.26)	(0.13)	
Profit	0.26	-0.20	-0.10	
	(0.33)	(0.64)	(0.19)	
Sale	-0.086	-0.156**	-0.138	
	(0.07)	(0.08)	(0.09)	
Constant	8.70***	6.47***	14.90***	
	(0.78)	(0.73)	(1.29)	
Observations	2875	2842	5723	
R-squared	0.16	0.14	0.64	
Note: Robust standard errors are in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively.
number of random firms as the artificial treatment groups and replace the dummy variable with RandomFirm. Vie then replicate our baseline analyses using these artificial shocks and present the results for each test in Table 8 - Columns 1, 2, and 3, respectively. The falsification tests' outcomes indicate that the GHGRP coefficients are statistically insignificant in all columns. These results support the validity of the parallel trend assumption of the DiD research design by suggesting similar greenwashing behaviour patterns between treated and control groups before adopting GHGRP.
3.3.2.		Dynamic greenwashing model
In the DiD methodology, the identification of causal impacts depends on the validity of the parallel trend assumption (Nguyen et al., 2020; Xiao et al., 2023). In our context, this requires that the greenwashing behaviour of treatment firms (that are subject to the GHGRP regulation) and those in control firms (that are not subject to the GHGRP regulation) exhibit similar time trends before the introduction of the GHGRP. To assess the validity of this parallel trend assumption, we examine the timing of the mandatory GHGRP by incorporating in equation (1) three additional binary variables denoting the years relative to the adoption of the GHGRP: Beforel, Before2, and Before3, which equal to one if it is one, two, and three-year(s) prior the year a given firm was required to report GHG. This approach allows us to observe whether any pre-existing differences in greenwashing behaviour existed between treated and control firms in the years leading up to GHGRP implementation. We then re-run our regression model and present the results in Table 9.
Table 9 shows that the estimated coefficients of the three dummy variables Beforel, Before2, and Before3 are all statistically insignificant. Meanwhile, the coefficient estimate for the GHGRP is still statistically significant at the 5% level. This supports the parallel trend assumption and indicates that our results are valid.
3.3.3.		Sensitivity analysis and robustness tests
In this section, we perform sensitivity analysis by including several robustness tests to confirm the reliability and stability of our baseline result. The findings of these tests are reported in Table 10. In Column (1), we control for state and firm-fixed effects to account for unobserved
Table 8
Falsification tests.
	(1)	(2)	(3)	
VARIABLES	GHGLag	RandomYear	RandomFirm	
GHGRP	-0.06	-0.07	-0.13	
	(0.23)	(0.09)	(0.11)	
Size	-0.15***	-0.15***	-0.60***	
	(0.06)	(0.05)	(0.06)	
Leverage	0.18	0.18	-0.57***	
	(0.13)	(0.11)	(0.13)	
Profit	0.18	0.18	-0.12	
	(0.17)	(0.14)	(0.19)	
Sale	-0.14	-0.14	-0.13	
	(0.09)	(0.10)	(0.90)	
Constant	5.12***	5.13***	16.11***	
	(1.325)	(1.3)	(1.29)	
Firm-Industry FEs	YES	YES	YES	
Observations	5722	5722	5723	
R-squared	0.67	0.67	0.63	
Note: This table reports the results of the falsification tests. Column (1) shows the results of the first falsification test, where the years of implementation were lagged by 1 year. Column (2) shows the results of the year falsification test, where the years of implementation were randomly assigned. Column (3) reports the result of the falsification test where the treated firms were randomly selected. The dependent variable is greenwashing behaviour (Greenwash). The main independent variable, GHGRP, is a dummy variable, taking the value of 1 if the firm is under the GHG program, and zero otherwise. The definitions of all the other control variables are provided in Table 1. Robust standard errors are in parentheses. ***, is and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Table 9
Dynamic timing effects.
(1)	(2)	(3)	(4)	
VARIABLES	Beforel	Before2	Before3	Dynamic timing	
Beforel	-0.31			-0.420	
	(0.41)			(0.433)	
Before2		-0.07		0.093	
		(0.27)		(0.305)	
Before3			-0.003	0.080	
			(0.231)	(0.248)	
GHGRP	-0.60**	-0.56**	-0.539**	-0.544**	
	(0.24)	(0.28)	(0.275)	(0.275)	
Constant	16.03***	16.03***	16.02***	15.99***	
	(1.29)	(1.29)	(1.30)	(1.30)	
Controls	YES	YES	YES	YES	
Firm-Industry Fes	YES	YES	YES	YES	
Observations	5723	5723	5723	5723	
R-squared	0.63	0.63	0.63	0.63	

Note:. This table reports the result of the dynamic effect on greenwashing behaviour before and after implementing the GHGRP. Column (1-3) shows the baseline model results replacing the DiD term Pilot with four dummies representing four years before the GHGRP implementation year (i.e. Beforel, Before2, and Before3). Column (4) reports the results of the baseline regression model replacing the DiD term Pilot with all of the dummies representing three years prior and current year of the GHGRP implementation year. The dependent variable is greenwashing behaviour (Greenwash). The main independent variable, GHGRP, is a dummy variable, taking the value of 1 if the firm is under the GHG program, and zero otherwise. The definitions of all the other control variables are provided in Table 1. Robust standard errors are in parentheses. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively. state heterogeneity and common time shocks that may confound the relationship between the GHGRP and the firm’s unethical ESG disclosure. In Column (2), we keep Industry-Year FEs but cluster standard errors at the firm level to account for potential within-firm autocorrelations, which can address potential dependence among observations and improve the accuracy of the standard errors. Our main findings are still intact.
In column (3), we incorporate state-level macroeconomic variables, such as GDPGrowth (GDP growth rate) and PopGrowth (population growth), to account for external macroeconomic factors that could influence the effectiveness of GHGRP on a firm behaviour. We also incorporate corporate governance variables including board size (BrdSz) and duality (Duality) in Column (4) to account for the governance structure of firms that could influence their behaviour. By testing the stability and reproducibility of these baseline results, these additional variables reduce the omitted variable bias and offer a more thorough understanding of the factors influencing a firm’s unethical reporting practices. The result is consistent with our baseline model.
Our study period spans from 2007 to 2022, encompassing when the COVID-19 pandemic emerged and spread globally. Previous research has expressed concern regarding firms’ possible operational disruptions during the COVID-19 pandemic (Baker et al., 2020; Engelhardt et al., 2021). Therefore, including this period in the analysis could introduce estimation bias. To address this concern, we exclude 2020 and 2021 from our analysis and re-estimate the baseline regression model in column (5).
Finally, in column (6), all continuous variables are winsorized at the 1% and 99% levels to reduce the potential impact of outliers. The estimated results remain unchanged.
4.		Conclusion and implications
Economic theory and anecdotal evidence have long argued that government intervention through regulatory frameworks can address market failure and promote sustainability, though evaluating their true impact can be challenging. Our analysis examines the GHGRP’s influence on greenwashing behaviour among publicly listed US firms, utilising DiD analysis with robustness checks, placebo tests, and dynamic timing analyses to validate our findings. The results indicate that the implementation of GHGRP effectively mitigates unethical ESG reporting practices. This reduction in greenwashing behaviour tendency likely arises from the diminished information asymmetry and decreased incentives for firms to engage in deceptive reporting, ultimately fostering greater transparency and aligning managerial actions with stakeholder interests.

	(1)	(2)	(3)	(4)	(5)	(6)	
VARIABLES	State and Firm FEs	Alternative	Macro control	Adding Corporate Governance	Exclude COVID	Winsorize data	
		clustering	variables	variables	period		
GHGRP	-0.54**	-0.12**	-0.49**	-0.50**	-0.15**	-0.49**	
	(0.24)	(0.06)	(0.229)	(0.23)	(0.06)	(0.20)	
GDPgrowth			-2.15***				
			(0.32)				
Popgrowth			1.20				
			(1.900)				
BrdSz				0.01			
				(0.09)			
Duality				0.23***			
				(0.05)			
Constant	16.01***	8.52***	14.72***	16.10***	7.80***	15.91***	
	(1.17)	(0.40)	(1.28)	(1.29)	(0.41)	(1.28)	
Controls	YES	YES	YES	YES	YES	YES	
Firm-Industry Fes	YES	YES	YES	YES	YES	YES	
Observations	5723	5995	5723	5717	3006	5723	
R-squared	0.63	0.146	0.639	0.636	0.13	0.646	

Table 10
Robustness tests.

Note: This table reports the results of the models for evaluating the impact of GHGRP on firm greenwashing behaviour. Column (1) shows the results of the baseline model using state and firm-fixed effects. Column (2) shows the baseline model results with Industry-Year FEs but cluster standard errors at the firm level. Column (3) includes state-level macroeconomic variables. Column (4) incorporates corporate governance variables. Column (5) reports the results of the baseline regression model excluding the COVID pandemic period from 2020 to 2021. Column (6) winsorized all variables at the 1% and 99% levels. The dependent variable is greenwashing behaviour (Greenwash). The main independent variable, GHGRP, is a dummy variable, taking the value of 1 if the firm is under the GHG program, and zero otherwise. The definitions of all the other control variables are provided in Table 1. Robust standard errors are in parentheses. ***, " " and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

Our findings provide empirical support to Agency theory, which suggests that in the absence of oversight, managers may prioritise their own interests over those of shareholders, leading to greenwashing. The GHGRP serves as an external control mechanism that reduces information asymmetry and aligns managerial actions with the interests of shareholders and other stakeholders, enhancing accountability. Additionally, our results support Stakeholder theory, which emphasises that firms must consider the interests of various stakeholders—such as investors, customers, and regulators—to maintain legitimacy and longterm success. By adopting the GHGRP, firms not only comply with regulatory expectations but also enhance their reputation and legitimacy, fostering genuine sustainability efforts and building trust with stakeholders. Our study is also in line with previous research suggesting that reporting regulations can help reduce information asymmetry between firms and stakeholders, thereby aligning the interests of managers with those of stakeholders (Kurpierz and Smith, 2020; Ruiz-Blanco et al., 2022).
Additionally, the study explores the heterogeneity of the GHGRP’s impact across firms, focusing on the impact of public scrutiny and environmental orientation. By analysing the differential effects of the GHGRP across various types of firms, the study highlights the importance of heterogeneity in regulatory impact. This finding reinforces the notion that "one-size-fits-all” regulatory approaches may yield varying outcomes depending on firm characteristics (Delmas and Toffel, 2008). The result indicates that that larger and more profitable firms exhibit a more pronounced reduction in greenwashing behaviour in response to the reporting mandate, indicating that heightened public scrutiny enhances regulatory effectiveness. Firms subject to stricter reporting regulations are more likely to reduce greenwashing behaviour when regulatory oversight increases reputational risks form non-compliance or perceived greenwashing. Regulatory interventions, when combined with public oversight, can amplify these risks, thereby incentivising greater transparency (Braam and Peeters, 2018). Moreover, the findings reveal that firms emphasizing environmental innovation are found to be more significantly affected by the GHGRP, underscoring the importance of corporate culture orientation in shaping firms’ response to regulatory interventions. This lends support to the resource-based theory, which argues that internal resources and capabilities - such as commitment to environmental innovation - are key determinants of a firm’s ability to adapt to external pressures (Hart, 1995; Russo and Fouts, 1997).
From the managerial perspective, this research addresses the urgent need for heightened attention to ESG disclosure practices across industries. Drawing on our study results, we offer several recommendations. First, our findings demonstrate that the GHGRP effectively curbs greenwashing behaviour, particularly in sectors with higher environmental footprints, such as manufacturing, energy, and transportation, where emissions and sustainability concerns are more critical. These insights emphasize the significance of implementing transparency- focused regulations tailored to the unique needs of different sectors. Policymakers should consider adopting industry-specific reporting requirements that account for variations in environmental impact and operational practices. Secondly, managers in high-impact sectors should prioritise the accurate and comprehensive disclosure of GHG emissions and integrate sustainability considerations into their corporate strategies to align with regulatory expectations and stakeholder interests. For sectors with lower environmental exposure, firms should proactively adopt similar practices to stay ahead of evolving regulatory landscapes. Thirdly, firms across all sectors should emphasize environmental R&D efforts by investing in innovative solutions and green technologies that reduce their environmental footprints and support their compliance with sustainability regulation. For instance, energy-intensive industries might focus on renewable energy integration, while service-oriented sectors could adopt sustainable procurement practices. Lastly, investors and stakeholders should actively scrutinize ESG reporting, particularly in industries prone to greenwashing, and demand greater transparency to discourage deceptive practices. Regulatory policies like GHGRP can be most effective when combined with mechanisms that increase public accountability, support environmental innovation, and consider firm-specific factors like resources that shape compliance behaviour.
Despite its valuable insights, this study has certain limitations. One limitation is the measurement of greenwashing behaviour based on publicly available ESG disclosures and ESG performance, which may be subject to biases in reporting. These scores may not fully capture the complexity of greenwashing, as firms may engage in subtle forms of misleading reporting that are difficult to quantify with standardized metrics. Future studies could improve the measurement of greenwashing by integrating qualitative content analysis, industry-specific benchmarks, and a longitudinal approach to better capture the complexities of firms’ actual sustainability disclosure practices. Additionally, while the study provides evidence of the GHGRP’s impact on reducing greenwashing behavior, it does not capture the long-term effects and the interaction of the program with other regulations, which could be an area for further investigation. Future research could explore the interaction between multiple regulatory frameworks and their cumulative effect on greenwashing behaviour. Moreover, examining the role of corporate governance and leadership in influencing ESG practices could offer additional insights into how firms respond to transparency- focused regulations.
CRediT authorship contribution statement
Nguyen H. Luu: Writing - original draft, Methodology, Formal analysis, Data curation, Conceptualization. Chau Le: Writing - review & editing, Validation, Supervision, Formal analysis, Conceptualization. Hiep N. Luu: Writing - review & editing, Supervision, Resources, Data curation, Conceptualization. Dung T.K. Nguyen: Writing - review & editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability
The data that support the findings of this study are available from the corresponding author upon request.
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