Unmasking Greenwashing in Finance: A PROMETHEE II-Based Evaluation of ESG Disclosure and Green Accounting Alignment

Abstract

This study examines the degree of alignment between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe for the fiscal year 2024. Although ESG reporting has become a standard practice in the financial sector, there are still concerns that the quality of the disclosure may not accurately reflect substantive environmental action, which increases the risk of greenwashing. This study addresses this issue by incorporating both ESG disclosure indicators and green accounting metrics into a multi-criteria decision-making framework. This framework is supported by entropy-based weighting to assure objectivity in criterion importance, as outlined in the PROMETHEE II method. The Greenwashing Risk Index (GWI) is a groundbreaking innovation that quantifies the discrepancy between an institution’s classification based on ESG transparency and its performance in green accounting indicators, including environmental penalties, provisions, and resource usage. The results indicate that there is a substantial degree of variation in the performance of ESGs among institutions, with a significant portion of them exhibiting high disclosure scores but insufficient environmental substance. These discrepancies indicate that reputational sustainability may not be operationally sustained. The results have significant implications for regulatory supervision, sustainable finance policy, and ESG rating methodologies. The framework that has been proposed provides a replicable, evidence-based tool for identifying institutions that are at risk of greenwashing and facilitates the implementation of more accountable ESG evaluation practices in the financial sector.

1. Introduction

Greenwashing has become a systemic concern in sustainable finance, eroding trust in ESG-integrated market systems, distorting capital allocation, and undermining transparency. The objective of this study is to assess the risk of greenwashing in European financial institutions by examining the discrepancy between self-reported ESG disclosures and verifiable environmental performance. The geographic scope is expressly restricted to Europe due to the region’s sophisticated ESG regulatory architecture and data availability. The sustainability credibility of 365 publicly listed financial institutions, such as commercial banks, insurance firms, and investment service providers, is assessed using a novel Greenwashing Risk Index (GWI) that was developed through the application of a PROMETHEE II multi-criteria decision-making model and entropy-based weighting approach.

This research is situated within the broader field of sustainable finance, with a particular concentration on the reporting, implementation, and alignment of ESG policies and products by financial institutions, particularly banks and investment firms. In this context, greenwashing is not merely conceived as reputational window-dressing but as a structural misalignment between environmental disclosure narratives and actual performance indicators. This misalignment has the potential to lead to investor misinformation, poor policy guidance, and misallocated capital. It is important to note that the absence of precise regulatory standards in the domain of credit products creates a gray area in which institutions retain discretion over what constitutes as “green” or “sustainable,” further complicating the task of detecting greenwashing in finance.

As defined in the academic and policy literature, greenwashing is the deliberate exaggeration or misrepresentation of environmental initiatives, frequently achieved through selective or inflated disclosure. Recent research has underscored the fact that greenwashing impedes the redirection of capital transfers toward genuinely sustainable activities and distorts market integrity (Delmas and Burbano 2011; Erhemjamts et al. 2024; Todaro and Torelli 2024). It poses an economic and regulatory obstacle that undermines the efficacy of sustainability transitions. Although there are numerous ESG scoring and disclosure frameworks, a significant number of them rely on self-reporting, which may not accurately represent an institution’s actual environmental behavior. Divergence among ESG ratings is frequently the result of discrepancies in methodologies and disclosure emphasis, rather than actual performance, as emphasized by Berg et al. (2022).

Our research suggests a dual-metric assessment model that encompasses both green accounting indicators (e.g., penalties, environmental expenditures, and resource consumption) and ESG disclosure attributes (e.g., climate strategy, external assurance, and policy frameworks) to address these challenges. We identify instances of disclosure–performance misalignment that serve as evidence of potential greenwashing by comparing institutional rankings across these two domains. This discrepancy is the foundation of the Greenwashing Risk Index (GWI), a diagnostic instrument that assesses the credibility of environmental, social, and governance (ESG) factors in financial services.

Our study is based on the evolving European regulatory landscape, which has recently implemented comprehensive instruments to combat greenwashing. Enhancement of ESG transparency and accountability is mandated by the Sustainable Finance Disclosure Regulation (SFDR) and the Corporate Sustainability Reporting Directive (CSRD). The proposed Directive on Empowering Consumers for the Green Transition (COM/2022/143) is particularly noteworthy in that it directly addresses greenwashing by mandating that environmental claims be verifiable, explicit, and substantiated. These initiatives are indicative of the EU’s overarching objective to direct capital toward economically sustainable endeavors that are legitimately sustainable by means of credible, enforceable ESG standards.

This study provides a data-driven, replicable framework that makes a conceptual and methodological contribution to the disciplines of ESG integrity, sustainable finance, and greenwashing detection in response to these developments. Its results are pertinent to financial institutions that are striving to align their environmental narratives with operational realities, regulators that are developing supervision tools, and investors who are seeking dependable sustainability signals.

2. Literature Review

2.1. The Risk of Greenwashing in Financial Institutions and ESG Disclosure

Environmental, social, and governance (ESG) disclosure has emerged as a fundamental component of corporate communication, especially in the financial industry (Asif et al. 2023; da Silva and Vieira 2025; Lopez-de-Silanes et al. 2020; Sun et al. 2024; Sundarasen et al. 2024). Institutions increasingly depend on sustainability narratives to demonstrate accountability to regulators, investors, and customers. The reliability of these tales is disputed because of differences in reporting standards and the lack of enforceable worldwide norms. Notwithstanding the directives from organizations like the Global Reporting Initiative (GRI), the Sustainability Accounting Standards Board (SASB), and the Task Force on Climate-related Financial Disclosures (TCFD), ESG reporting continues to be predominantly voluntary and lacking standardization, resulting in considerable variability in both substance and quality (Bais et al. 2024; Krivogorsky 2024).

The financial industry, which includes banks, investment companies, and insurers, plays a crucial role in sustainable financing (Fu et al. 2024; Oliveira and Leal 2024; Rapach et al. 2024). These institutions shape the trajectory and legitimacy of the sustainable transition via capital allocation, credit assessment, and portfolio management. This position has subjected them to heightened scrutiny over the veracity of their ESG disclosures. There is increasing apprehension over the prevalence of greenwashing, defined as the purposeful deception or exaggeration of environmental performance to foster a sustainable image (Ferrara and Ciano 2023; Grijalvo and García-Wang 2023; Lagoarde-Ségot 2024; Sadiq et al. 2024).

Within the field of financial services, greenwashing may manifest in various ways: committing to net-zero objectives without viable transition strategies, issuing sustainability-linked bonds lacking quantifiable effects, or presenting selective ESG metrics that obscure environmentally detrimental activities (Biasin et al. 2024; Dicuonzo et al. 2024; Ragazou et al. 2024a). In contrast to industrial companies, whose environmental performance is readily visible, financial institutions mostly provide narrative and proxy-based measures. This creates a structural discrepancy between stated and actual performance, exacerbated by the absence of obligatory third-party verification.

Prior research has examined rating discrepancies across ESG providers, although they inadequately address purposeful exaggeration or selective reporting—key indicators of greenwashing. Our research expands on specific literature that conceptualizes greenwashing as a discrepancy between disclosure and performance—where self-reported ESG alignment does not correspond with verified environmental outcomes. This disparity is especially concerning in finance, where reputational integrity is fundamental to fiduciary trust and regulatory adherence.

Regulators have begun mitigating this danger. The EU’s Sustainable Finance Disclosure Regulation (SFDR) and Corporate Sustainability Reporting Directive (CSRD) seek to improve the openness and comparability of ESG disclosures. The proposed Green Claims Directive (COM/2022/143) intends to address deceptive sustainability marketing and may substantially influence ESG labeling and assurance methods for financial goods. Nonetheless, regulatory enforcement is still inadequate, particularly in sectors like green loans and credit portfolios, where subjective interpretation continues to prevail (Official Journal of the European Union 2024).

To address this legislative and analytical disparity, our research provides a quantitative approach that evaluates the credibility of ESG disclosures by juxtaposing them with operational green accounting measures. We use a multi-criteria decision-making framework to assess whether institutions with robust ESG narratives exhibit measurable environmental performance. This method facilitates the detection of any greenwashing practices and provides a reproducible framework for evaluating ESG integrity in European financial institutions (Chen et al. 2023; D. Zhang et al. 2023; H. Zhang and Lai 2024).

2.2. The Performance–Disclosure Gap and Green Accounting

Although ESG disclosure frameworks have become indispensable instruments for communicating sustainability objectives, an increasing body of literature contends that such narratives must be founded on verifiable environmental performance to prevent them from becoming superficial or misleading. This has resulted in a heightened focus on green accounting, a field that aims to incorporate environmental factors into corporate decision-making and financial reporting (Schaltegger et al. 2017).

Green accounting is the process of identifying, measuring, and disclosing the costs, liabilities, and investments that are linked to environmental impacts. Among these metrics are quantifiable ones, including provisions for environmental remediation, capital expenditures on green infrastructure, water and energy consumption, and refuse generation, as well as environmental fines and pollution penalties. Green accounting offers tangible, monetized indicators of an organization’s actual environmental imprint, in contrast to ESG scores, which are frequently derived from subjective assessments or policy disclosures.

Green accounting also corresponds with the growing expectation for transparency in financial risk exposure to climate change and environmental liabilities from a regulatory perspective. For instance, the EU Taxonomy Regulation and the Non-Financial Reporting Directive (NFRD) mandate the disclosure of environmental hazards in a manner that can impact investment decisions and balance sheets (Pacces 2021; Rapach et al. 2024; Yang et al. 2020). These trends indicate a transition from “soft” ESG reporting to “hard” performance-linked accountability. Furthermore, green accounting is still underutilized in academic research on ESG evaluation, particularly in comparative and cross-sectional analyses, despite its relevance. Most studies frequently depend on ESG scores or self-reported disclosure metrics, which are not always consistent with actual resource use, investments, or liabilities (Searcy and Buslovich 2014). This has resulted in a performance–disclosure divide, in which companies may achieve high ESG ratings because of the implementation of formalized policies or reporting practices, but they may not achieve measurable environmental outcomes.

The financial sector is particularly concerned about this misalignment (López-De-Silanes et al. 2025; Todaro and Torelli 2024). Financial institutions frequently function as intermediaries rather than producers, which facilitates the assertion of ESG alignment without requiring direct operational modifications. While internal environmental performance (e.g., energy use, emissions, or penalties) is either opaque or minimally reported, they may emphasize ESG-linked investment products or sustainable lending strategies. Green accounting provides empirically grounded, auditable indicators that can be compared across institutions and over time, thereby offering a solution.

By integrating green accounting data with ESG disclosure metrics in a unified analytical framework, this study addresses a critical lacuna in the literature. Although previous research has assessed either green performance or ESG disclosure in isolation, there is a scarcity of comparative frameworks that explicitly quantify the divergence between the two. This paper contributes a novel, replicable method to quantify greenwashing in financial institutions by introducing a Greenwashing Risk Index (GWI) that is based on the rank difference between disclosure-based and performance-based evaluations. Additionally, this study is in response to the demand for sector-specific, evidence-based tools that can assist stakeholders in assessing the credibility of ESG claims and identifying instances where reputational signals are not substantiated by environmental substance by applying this framework to a sample of 365 listed financial institutions in Europe (Kiohos and Sariannidis 2010; Mallidis et al. 2024; Sariannidis 2011).

3. Materials and Methods

3.1. Research Design

The dataset exclusively concentrates on financial institutions with headquarters in Europe, as the region boasts a mature sustainability regulatory framework and comprehensive ESG data coverage. This geographic concentration guarantees compliance with disclosure mandates, including the Sustainable Finance Disclosure Regulation (SFDR) and the Corporate Sustainability Reporting Directive (CSRD), which are designed to improve transparency, comparability, and accountability in ESG reporting. The credibility of sustainability disclosures has been the subject of increased scrutiny because of the increasing standardization of ESG practices in this context. The phenomenon of greenwashing, which involves the strategic exaggeration or misrepresentation of environmental actions or performance to project a misleading image of sustainability commitment, is a growing concern in literature and among regulators. The alignment between ESG disclosure and actual environmental performance in financial institutions is assessed in this study using a quantitative multi-criteria decision-making (MCDM) framework. The fundamental assumption is that the existence of significant discrepancies between these two dimensions may function as empirical indicators of greenwashing behavior.

This study is organized as a comparative assessment of financial institutions utilizing a series of environmental, governance, and transparency variables derived from the Refinitiv Eikon ESG Screener. Fourteen criteria were chosen, encompassing both green accounting indicators (such as environmental penalties, expenditures, and provisions) and ESG disclosure measures (including ESG ratings, climate initiatives, and external assurance).

The methodological approach consists of two primary analytical phases: (i) Comprehensive ESG Performance Ranking utilizing PROMETHEE II and (ii) Greenwashing Risk Detection via Subgroup Rankings. During the initial phase, the PROMETHEE II approach is utilized to calculate a comprehensive sustainability ranking for each institution. This approach facilitates pairwise comparisons grounded in weighted preferences, yielding a comprehensive and interpretable ranking of alternatives. Entropy weighting is employed to allocate objective significance to each criterion, determined by their informational variability among institutions. The Greenwashing Risk Index (GWI) is established by juxtaposing rankings obtained from two categories of criteria: (i) indicators focused on ESG disclosure and (ii) measures pertaining to green accounting and environmental performance.

A notable disparity between these rankings implies possible greenwashing since it reflects an inconsistency between institutional reports and their real environmental practices. This dual-level assessment facilitates the identification of premier institutions and the detection of discrepancies that might undermine stakeholder confidence and long-term sustainability objectives. This paper introduces an innovative and effective methodology for analyzing greenwashing risk in the financial sector by using objective weights, transparent preference modeling, and accounting-based indicators, ensuring alignment with academic rigor and policy relevance.

3.2. Data Collection and Sample

The dataset for this study was obtained from Refinitiv Eikon, a financial and ESG data platform that is frequently employed in empirical sustainability research. The analysis concentrates on financial institutions with headquarters in Europe because of the region’s advanced sustainability regulatory framework and the availability of comprehensive ESG data. The sustainability transition is significantly impacted by the financial sector, which institutionalizes ESG standards, screens investment decisions, and influences capital allocation. Nevertheless, it is also being scrutinized for potential greenwashing practices, particularly when ESG narratives rely heavily on self-reported and unaudited disclosures rather than demonstrable environmental outcomes.

The final sample consists of 365 publicly listed financial institutions that operate throughout the European Union. This ensures sectoral diversity within the sample, which includes commercial banks, insurance companies, and investment service providers. The sample encompasses both institutions classified as significant and less significant under the European Central Bank’s Single Supervisory Mechanism (SSM) to guarantee regulatory comprehensiveness. The selection was determined by the availability, completeness, and consistency of green accounting performance data and ESG disclosure attributes for the fiscal year 2024. To preserve the methodological validity of cross-institutional comparisons, entities with missing, conflicting, or internally inconsistent data were excluded from the analysis.

The 14 ESG-related indicators utilized in this study were chosen based on their conceptual relevance to the assessment of greenwashing, data quality and consistency, and regulatory alignment with prominent frameworks such as the European Sustainability Reporting Standards (ESRS E1), the Green Loan Principles, and the Sustainability-Linked Loan Principles. These indicators represent two analytically distinct but complementary dimensions of sustainability performance. By capturing tangible environmental and financial engagements, such as environmental expenditures, regulatory penalties, water withdrawal, and refuse generation, green accounting metrics reflect the institution’s operational responsibility. In this context, penalties are financial sanctions that are imposed by national or supranational authorities, such as environmental protection agencies or financial regulators, for violations of environmental legislation, emissions thresholds, or other sustainability-related obligations. The credible proxies for verified environmental underperformance are these externally imposed penalties, which are systematically reported by institutions and collated by ESG data providers such as Refinitiv.

Conversely, institutional transparency, strategic commitment, and governance practices are reflected in ESG disclosure attributes. These encompass the formalization of climate policy frameworks, the presence of external assurance mechanisms, and the extent of greenhouse gas disclosures. To facilitate comparative assessment and standardize the dataset, all indicators were preprocessed. Quantitative data were normalized to a standard [0, 1] range by converting Boolean fields to binary values and applying min–max scaling. To guarantee that higher values indicated inferior environmental performance, cost-type indicators—including penalties, emissions, and waste—were reverse normalized. Using the PROMETHEE II outranking method, this preprocessing facilitated the integration of the selected indicators into a unified decision-making model.

Greenwashing is defined in this study as the quantifiable discrepancy between accounting-based environmental performance and disclosure-based ESG indicators. This study identifies instances in which sustainability narratives substantially exceed actual environmental behavior by constructing distinct institutional evaluations based on each dimension. The resulting Greenwashing Risk Index (GWI) offers a scalable and replicable instrument for evaluating the financial sector’s exposure to greenwashing risk and ESG credibility in Europe.

This study also integrates recent policy developments in addition to referencing established regulations, including the Corporate Sustainability Reporting Directive (CSRD) and the Sustainable Finance Disclosure Regulation (SFDR). The regulatory urgency surrounding accurate ESG communication is underscored by the inclusion of the 2024 Omnibus Directive (Directive (EU) 2024/825), which explicitly addresses green-washing by requiring sustainability claims be substantiated and verifiable. The policy relevance of developing comprehensive empirical methods capable of identifying discrepancies between environmental disclosure and environmental performance is affirmed by the evolving regulatory context.

3.3. Criteria Selection and Classification

To evaluate the congruence between ESG disclosures and actual environmental performance, this study implements a collection of 14 evaluation criteria that were chosen from the Refinitiv Eikon ESG Screener for the fiscal year 2024. The selection of these criteria was determined by their relevance to sustainability performance, empirical availability across the dataset of 365 European financial institutions, and conceptual alignment with either green accounting or ESG disclosure and governance domains.

Fines, expenditures, and resource utilization are tangible environmental outcomes that are reflected in green accounting criteria. Conversely, ESG disclosure and governance criteria are directed toward strategic alignment with recognized frameworks, such as the Paris Agreement or CDP (Carbon Disclosure Project), as well as transparency and assurance.

Each criterion is categorized as either a cost or a benefit indicator. Benefit criteria are those that indicate superior ESG performance when their values are higher. In contrast, cost criteria indicate that higher values are linked to less favorable environmental outcomes, such as increased fines or water withdrawal. Standardization and type-specific normalization were implemented to ensure that all criteria were prepared for inclusion in the PROMETHEE II model. This classification enables the criteria to be categorized into two thematic subgroups: (i) disclosure-oriented indicators and (ii) green accounting-based indicators, which serve as the foundation of the model’s structure. This dual classification is additionally employed in the following section, where is computed the Greenwashing Risk Index (GWI), which quantifies the ranked discrepancy between operational environmental outcomes and reported ESG practices.

The evaluation criteria are summarized in the table below (

Table 1. Description of the criteria.

Code of Criterion	Criterion	Description	Type	Category
C1	Environmental Pillar Score (last 4 FY)	Refinitiv’s assessment on environmental policy and performance	Benefit	ESG Disclosure
C2	GHG Emissions Scope 1 and 2 Paris Agreement Aligned	Assesses the organization’s comprehensive dedication to minimizing its carbon impact in absolute terms	Benefit	ESG Disclosure
C3	GHG Emissions Intensity Scope 1 and 2 Paris Agreement Aligned	Evaluates the company’s efficacy in dissociating economic growth from carbon emissions	Benefit	ESG Disclosure
C4	Energy Consumption External Assurance	Specifies if energy data are externally verified	Benefit	ESG Disclosure
C5	Waste Total (last 4 FY)	The institution’s total waste across four fiscal years	Cost	Green Accounting
C6	Water Withdrawal Total	The total volume of water that was withdrawn over a four-year period	Cost	Green Accounting
C7	Self-Reported Environmental Fines (USD)	Fines assessed for environmental violations (in US dollars)	Cost	Green Accounting
C8	Environmental Expenditures (USD)	Capital expenditures allocated to environmental initiatives	Benefit	Green Accounting
C9	Environmental Provisions (USD)	Estimated financial obligations associated with environmental obligations	Cost	Green Accounting
C10	ESG Combined Score	Environmental, social, and governance dimensions are combined to create a composite ESG score	Benefit	ESG Disclosure
C11	ESG Score	ESG grade that is solely determined by performance metrics	Benefit	ESG Disclosure
C12	ESG Controversies Score	Exposure to ESG-related controversies is reflected in the score	Benefit	ESG Disclosure
C13	CDP Climate Strategy (Presence)	The existence of a formal climate strategy that is aligned with the CDP (1 = Yes; 0 = No)	Benefit	ESG Disclosure
C14	Board Meeting Attendance (%)	The average attendance at board meetings, as a proxy for government engagement	Benefit	Governance

).

3.4. PROMETHEE II Framework: Data Normalization, Entropy Weighting, and Ranking Application

This study applies a multi-criteria decision-making (MCDM) approach using the PROMETHEE II method, supported by entropy-based weighting and standardized data preparation, to evaluate the ESG performance of financial institutions. This integrated framework enables a robust ranking system that accommodates the complexity and diversity of sustainability indicators, both in form (e.g., quantitative, binary) and orientation (benefit or cost).

3.4.1. Methodological Rationale

PROMETHEE II (Preference Ranking Organization Method for Enrichment Evaluation II) is a widely used outranking method in the disciplines of environmental management, financial sustainability analysis, operations research, and decision sciences. It is particularly effective for evaluating complex, multi-dimensional decision problems due to its capacity to provide a comprehensive ranking of alternatives through pairwise comparisons across multiple weighted criteria. PRO-METHEE II is particularly suitable for sustainability and ESG assessments because of its non-compensatory nature. This means that a company’s subpar performance in one criterion (e.g., carbon emissions) cannot be entirely compensated for by its superior performance in another (e.g., policy disclosure). This characteristic is consistent with the normative logic of ESG evaluation, which emphasizes the need to scrutinize trade-offs across environmental, social, and governance domains rather than just assuming them. PROMETHEE II facilitates a structured and transparent assessment of these trade-offs without imposing compensatory assumptions.

The entropy weighting procedure is implemented to guarantee objectivity in the attribution of relative importance to criteria. Entropy weighting is a data-driven methodology that determines the weights of each indicator across the dataset by considering its intrinsic variability. Criteria with more dispersion are considered to be more informative and, as a result, have a greater impact on the classification. This prevents biases that are associated with expert-driven or arbitrarily assigned weights and guarantees that the study is consistent with its empirical nature.

3.4.2. Data Normalization and Preparation

All criteria were normalized using min–max scaling to standardize their values on a [0, 1] range prior to the application of PROMETHEE II. This transformation was essential for the purpose of achieving a comparable magnitude between a variety of indicators, including monetary environmental expenditures and binary governance signals. The formula for normalization is as follows:

$$x^{\prime }={\displaystyle \frac{x-min\left(x\right)}{\mathit{max}\left(x\right)-min\left(x\right)}}$$

Normalization was implemented directly for benefit criteria that indicate superior performance (e.g., environmental assurance, ESG scores). To ensure that higher normalized values reflect stronger sustainability performance, an inverse transformation was implemented for cost criteria, including environmental penalties, water withdrawal, and waste generation. Binary indicators, such as the presence of a CDP strategy, were encoded as 1 (positive) and 0 (negative) without any additional modifications.

3.4.3. Entropy Weighting Procedure

Following normalization, entropy weighting was utilized to determine the relative importance of each of the 14 ESG criteria. Entropy measures the degree of disorder or uncertainty inherent in each criterion over all options. A criterion with low entropy has significant discriminating power and is therefore allocated a higher weight. The process includes:

• Determine the ratio of each value inside a criterion in relation to the total sum of that criterion across all institutions.
• Compute the entropy value $E_j$ for each criterion.
• Determine the degree of diversification $d_j$ = 1 − $E_j$.
• Determine the conclusive weight for each condition utilizing: $w_j={\displaystyle \frac{d_j}{\sum _{j=1}^n{d}_j}}$

This weighting method guarantees that the model emphasizes criteria according to their informative value, eliminating the necessity for subjective input.

3.4.4. Application of PROMETHEE II

Once normalized values and entropy-based weights were prepared, the PROMETHEE II method was applied to calculate the overall ESG performance ranking. For each pair of institutions ($a_i,a_j)$a preference index is computed based on the weighted difference in performance across all criteria. The preference of one institution over another on a given criterion is determined by a linear preference function, commonly used for continuous data.

The aggregated preference index $\pi (a_i,a_j)$summarizes how much $a_i$is preferred to $a_j$, across all weighted criteria. Based on these pairwise comparisons, the following flows are computed:

Positive outranking flow ${\Phi }^{+}\left(a_i\right)$: how much institution i dominates others.

Negative outranking flow ${\Phi }^{-}\left(a_i\right)$: how much institution iii is dominated by others.

Net outranking flow $\Phi \left(a_i\right)={\Phi }^{+}\left(a_i\right)$ − ${\Phi }^{-}\left(a_i\right)$: overall performance score.

Institutions are ranked in descending order based on their net flow values $\Phi$ with higher scores indicating stronger overall ESG performance. This ranking forms the empirical foundation for assessing both sustainability leadership and inconsistencies in ESG reporting behavior, as further explored in Section 3.5.

3.5. Greenwashing Risk Index (GWI)

The PROMETHEE II method offers a comprehensive evaluation of financial institutions based on their ESG performance; however, it does not, in isolation, distinguish between the substance of environmental action and the transparency of disclosure. The Greenwashing Risk Index (GWI) is a novel metric that the present study introduces to identify discrepancies between the environmental responsibility that institutions report and the actions they take to resolve this limitation. Greenwashing behavior may be suspected when there is a substantial discrepancy between ESG disclosure and actual environmental performance.

GWI is determined by comparing two distinct PROMETHEE II rankings for each institution. The initial ranking is determined by a subset of criteria that reflect the quality of ESG disclosure and governance, such as ESG scores, external assurance of environmental data, participation in recognized frameworks like the CDP, and governance indicators like board meeting attendance. The second ranking is determined by a unique set of criteria that are associated with green accounting practices, such as environmental expenditures, environmental penalties, provisions, and natural resource usage.

The GWI is defined as the discrepancy between the disclosure-based ranking and the green accounting-based ranking of each financial institution. The index is mathematically represented as follows:

$${GWI}_i={Rank}_{Disclosure,i}-{Rank}_{GreenAccounting,i}$$

3.6. Sensitivity Analysis and Visualization Strategy

A sensitivity analysis is conducted in conjunction with targeted data visualizations to guarantee the interpretability and robustness of the ESG performance evaluation and the Greenwashing Risk Index (GWI). These tools are designed to both verify the consistency of the PROMETHEE II results and improve the transparency of model-based inferences for a wider range of stakeholders.

In the event of variations in methodological assumptions, the stability of institutional rankings is evaluated through the sensitivity analysis. This entails the modification of the entropy-based weight distribution to evaluate the extent to which the overall ranking is influenced by changes in criterion importance and to simulate alternative informational scenarios. In addition, the model’s dependence on individual indicators is assessed by examining the impact of removing specific criteria or adjusting normalization parameters. This analysis allows for the identification of rankings that are highly sensitive to specific input conditions, thereby advising caution in their interpretation.

A complementary strategy is implemented to facilitate the interpretation of PROMETHEE II outputs and GWI scores through the use of visualization. Bar charts are employed to illustrate positive, negative, and net preference flows, thereby facilitating a straightforward comparison between institutions. The normalized performance scores across all 14 criteria are displayed using heatmaps, which emphasize the institutional strengths and deficits. In addition, rank comparison graphs are developed to illustrate the magnitude and direction of GWI values by contrasting disclosure-based and green accounting–based rankings. These visual tools not only improve the accessibility of results but also offer intuitive evidence of potential misalignments between environmental action and ESG communication. By utilizing a combination of sensitivity testing and graphical representation, the study enhances the credibility of its findings and provides stakeholders with actionable insights into the financial sector’s environmental integrity, ESG consistency, and greenwashing risk.

4. Results

This chapter presents the outcomes of the ESG performance evaluation and greenwashing risk analysis for 365 European financial institutions for the fiscal year 2024. The results are derived from the application of the PROMETHEE II method using entropy-based weights and the construction of a Greenwashing Risk Index (GWI). The analysis unfolds in three stages. First, the entropy weighting outcomes are discussed to establish the objective importance of each ESG criterion. Second, the PROMETHEE II ranking is presented, providing a comprehensive view of the institutions’ sustainability performance. Third, the Greenwashing Risk Index is calculated and interpreted, revealing potential inconsistencies between ESG disclosure and environmental action.

4.1. Entropy-Based Criterion Weighting

Entropy weighting was employed to ascertain the relative significance of each ESG criterion, which quantifies the variability (or information richness) of each indicator across the entire sample. The degree of uniformity of each criterion is represented by its entropy value $Ej$; a lesser entropy value suggests a greater degree of differentiation among institutions. The final entropy weights $wj$ used in the PROMETHEE II evaluation were derived by calculating and normalizing the degree of diversification $dj=1-Ej$ from these entropy values.

The findings indicate that the highest weights are assigned to criteria such as the Environmental Pillar Score (C1) and the ESG Combined Score (C10), which indicate a significant amount of informational variation among institutions (

Table 2. Entropy values, diversification scores, and final weights here.

Criterion	C1	C2	C3	C4	C5	C6	C7	C8	C9	C10	C11	C12	C13	C14
Entropy_Ej	0.0000	0.8115	0.8773	0.7998	0.9995	0.9991	0.9995	0.1935	0.9995	0.9840	0.9827	0.9932	0.0000	0.9770
Diversification_dj	1.0000	0.1885	0.1227	0.2002	0.0005	0.0009	0.0005	0.8065	0.0005	0.0160	0.0173	0.0068	1.0000	0.0230
Weight_wj	0.2956	0.0557	0.0363	0.0592	0.0001	0.0003	0.0001	0.2384	0.0001	0.0047	0.0051	0.0020	0.2956	0.0068

). Conversely, criteria such as Waste Total (C5) and Water Withdrawal (C6) were assigned to extremely low weights, which implies that there is a lack of differentiation in these variables within the sample.

These weights were then applied to the normalized decision matrix to objectively evaluate the ESG performance of each institution.

4.2. PROMETHEE II-Based ESG Performance Ranking

A comprehensive preference-based evaluation of 365 publicly listed financial institutions across Europe was achieved through the implementation of the PROMETHEE II method, which included entropy-weighted and normalized ESG indicators. The net flow score (Φ) of each institution is indicative of its overall sustainability performance in comparison to the rest of the sample. Higher values indicate more balanced and superior ESG practices. The distribution of PROMETHEE II scores that results indicates a significant degree of heterogeneity in the implementation and strategies of ESG. Institutions that incorporate environmental performance into both internal operations and financial decision-making are characterized by the top-performing decile. These institutions exhibit advanced green accounting practices, as evidenced by substantial environmental investments and reduced ecological penalties. Additionally, they maintain comprehensive, externally verified ESG disclosures that are consistent with frameworks such as the TCFD, CSRD, and the Paris Agreement.

In contrast, the bottom segment of the distribution represents institutions that exhibit significant deficiencies in either their green accounting metrics or the credibility of their ESG disclosures. It is intriguing that the middle stratum, which spans the 40th and 60th percentiles, is composed of institutions that demonstrate moderate performance in both environmental outcomes and disclosure. These institutions typically adhere to the minimum regulatory requirements without demonstrating strategic innovation or strong leadership in sustainability. The PROMETHEE II analysis reveals a critical policy implication: while certain financial institutions are genuinely aligning ESG narratives with operational commitments, others are vulnerable to greenwashing concerns due to observable gaps between stated ambitions and actual impact (Pohl and Geldermann 2024; Zobeidi et al. 2024).

The core segment of the ranking (about the 40th to 60th percentile) consists of universities demonstrating average performance. These enterprises often demonstrate strong transparency or environmental initiatives, but seldom both. A business may demonstrate proficiency in disclosure practices—such as ESG transparency, policy execution, and board engagement—while displaying subpar performance in financial metrics pertaining to actual environmental investments or operational sustainability. Institutions at the lowest end of the spectrum, characterized by negative net flows, consistently exhibit underperformance in several dimensions. These underperformers often lack transparency on climate initiatives, have minimal or no external verification of environmental data, and exhibit either insufficient ESG ratings or a general absence of openness. Furthermore, they demonstrate little to absent investment in environmental efforts or policies and frequently display inadequate performance in resource efficiency indicators, such as water use and waste generation.

The range of net flow numbers reveals two unique ESG strategy characteristics. The initial profile includes institutions demonstrating a balanced ESG approach, displaying simultaneous expertise in both green accounting and transparency. These entities are likely really committed to sustainability and may serve as industry benchmarks. The second profile includes organizations that emphasize symbolic disclosure over substantive action; these may attain modest rankings in overall ESG metrics due to proficient policy communication, despite making negligible contributions to environmental investments or operational efficiency. Section 4.3 of the Greenwashing Risk Index further analyzes this discrepancy.

The PROMETHEE II rating functions as a practical tool for decision-makers. Regulators may employ net flow ratings to evaluate which banks excel or underperform in ESG performance. Investors may employ the rankings to inform portfolio screening and engagement strategies, while institutions may assess their performance relative to peers and industry leaders. The PROMETHEE II results offer a comprehensive and detailed analysis of ESG practices in the European banking sector. The rankings highlight both excellent firms with credible environmental commitments and underperformers that may need more scrutiny or intervention.

4.3. Greenwashing Risk Index (GWI) Analysis

Although the PROMETHEE II method integrates both environmental action and disclosure indicators to provide a comprehensive ranking of ESG performance, it does not independently disclose inconsistencies between these two domains. To mitigate this issue, the Greenwashing Risk Index (GWI) was developed as a metric for the rank divergence between the green accounting performance of institutions and their disclosure-based ESG performance (

Table A2. Final Greenwashing Risk Index scores with PROMETHEE II, disclosure, and green accounting ranks.

Alternative	Net_Flow	PROMETHEE_Rank	Disclosure_Rank	Green_Accounting_Rank	GWI
1	0.0613	56	52	121	−69
2	−0.0258	254	175	348	−173
3	0.0720	19	17	5	12
4	0.0688	44	41	354	−313
5	0.0281	85	79	318	−239
6	0.0141	98	88	6	82
7	−0.0279	266	184	332	−148
8	−0.0401	318	308	121	187
9	0.0292	80	74	121	−47
10	0.0702	28	25	315	−290
11	−0.0236	251	339	121	218
12	−0.0563	349	276	121	155
13	−0.0298	288	201	121	80
14	−0.0562	348	273	121	152
15	−0.0539	334	240	121	119
16	0.0705	24	21	297	−276
17	0.0683	49	46	342	−296
18	0.0682	50	47	350	−303
19	−0.0189	199	250	121	129
20	−0.0279	268	186	325	−139
21	0.0727	6	2	339	−337
22	−0.0124	183	362	121	241
23	0.0094	136	128	308	−180
24	0.0304	72	66	356	−290
25	0.0311	67	61	284	−223
26	0.0703	25	22	278	−256
27	0.0699	33	30	341	−311
28	0.1663	3	155	3	152
29	0.0017	164	160	121	39
30	0.0698	35	32	352	−320
31	0.0152	92	81	346	−265
32	−0.0184	197	243	121	122
33	0.0696	37	34	121	−87
34	−0.0220	233	318	231	87
35	−0.0220	234	319	121	198
36	−0.0380	310	264	121	143
37	0.0693	39	36	121	−85
38	0.0686	46	43	349	−306
39	−0.0550	339	251	121	130
40	−0.0131	185	209	121	88
41	−0.0535	331	236	233	3
42	−0.0210	214	289	121	168
43	−0.0552	341	256	121	135
44	0.2663	1	82	1	81
45	−0.0138	188	213	121	92
46	−0.0215	223	304	121	183
47	−0.0223	241	327	121	206
48	−0.0211	217	293	121	172
49	0.0669	52	49	121	−72
50	−0.0288	278	195	121	74
51	−0.0219	228	312	121	191
52	−0.0383	311	266	121	145
53	−0.0169	193	229	121	108
54	−0.0127	184	207	121	86
55	−0.0107	180	161	347	−186
56	0.0012	166	173	121	52
57	−0.0220	232	317	121	196
58	0.0135	102	93	263	−170
59	0.0687	45	42	243	−201
60	0.0309	70	64	320	−256
61	−0.0523	323	226	253	−27
62	−0.0356	303	232	121	111
63	0.0689	42	39	343	−304
64	−0.0272	261	180	293	−113
65	−0.0209	213	287	121	166
66	−0.0221	239	324	121	203
67	−0.0230	248	335	121	214
68	−0.0391	315	220	121	99
69	−0.0219	230	314	121	193
70	−0.0283	272	190	336	−146
71	0.0016	165	172	121	51
72	−0.0222	240	326	121	205
73	−0.0220	231	316	121	195
74	−0.0220	235	320	121	199
75	−0.0361	304	238	121	117
76	0.0689	41	38	345	−307
77	−0.0149	189	214	121	93
78	0.0139	99	89	121	−32
79	0.0047	141	134	237	−103
80	−0.0215	222	303	121	182
81	0.0685	47	44	262	−218
82	−0.0192	201	254	254	0
83	0.0297	74	68	273	−205
84	0.0104	132	124	121	3
85	0.0129	108	99	290	−191
86	0.0118	117	109	121	−12
87	0.0030	154	136	121	15
88	0.0611	57	55	311	−256
89	−0.0002	168	166	275	−109
90	−0.0310	298	206	244	−38
91	0.0283	83	77	358	−281
92	0.0310	69	63	276	−213
93	0.0436	59	54	280	−226
94	−0.0389	314	278	121	157
95	0.0157	91	169	340	−171
96	−0.0096	179	153	121	32
97	−0.0219	229	313	121	192
98	0.0292	79	73	329	−256
99	0.0109	123	115	285	−170
100	−0.0054	176	338	121	217
101	0.0086	137	130	271	−141
102	0.0310	68	62	351	−289
103	−0.0309	297	364	121	243
104	0.0599	58	57	291	−234
105	0.0720	18	15	294	−279
106	0.0040	146	142	121	21
107	0.0433	60	56	353	−297
108	0.0722	14	11	327	−316
109	−0.0281	270	188	121	67
110	−0.0307	296	363	121	242
111	−0.0214	220	301	121	180
112	−0.0293	283	196	248	−52
113	−0.0315	301	210	121	89
114	−0.0209	212	286	121	165
115	−0.0206	210	279	121	158
116	0.0213	89	129	289	−160
117	0.0105	129	121	246	−125
118	0.0127	111	102	121	−19
119	0.0725	11	7	279	−272
120	0.0725	9	6	292	−286
121	−0.0214	219	300	121	179
122	−0.0555	344	260	235	25
123	0.0031	153	149	121	28
124	−0.0211	218	296	121	175
125	0.0105	130	122	121	1
126	0.0143	97	87	232	−145
127	0.0311	66	60	319	−259
128	0.0698	36	33	295	−262
129	0.0035	149	145	363	−218
130	−0.0247	252	341	250	91
131	0.0036	148	144	121	23
132	−0.0226	247	334	121	213
133	0.0031	152	148	121	27
134	−0.0296	287	358	121	237
135	−0.0217	226	309	121	188
136	−0.0037	174	315	121	194
137	0.0693	38	35	121	−86
138	−0.0231	249	336	121	215
139	0.0106	128	120	357	−237
140	0.0685	48	45	306	−261
141	0.0727	5	3	11	−8
142	0.0725	10	8	9	−1
143	0.0690	40	37	121	−84
144	−0.0280	269	187	121	66
145	−0.0289	279	354	121	233
146	−0.0303	294	360	121	239
147	0.0358	63	165	121	44
148	−0.0583	359	328	121	207
149	−0.0552	340	255	121	134
150	0.0105	131	123	121	2
151	0.0131	106	97	121	−24
152	0.0623	55	51	300	−249
153	−0.0118	182	162	121	41
154	0.0724	13	10	281	−271
155	−0.0262	256	176	121	55
156	−0.0210	216	292	121	171
157	0.0226	87	135	361	−226
158	0.0149	93	83	121	−38
159	−0.0220	236	321	121	200
160	−0.0361	305	218	322	−104
161	0.0128	109	100	359	−259
162	−0.0032	172	170	335	−165
163	0.0005	167	163	121	42
164	0.0192	90	132	121	11
165	−0.0521	322	224	121	103
166	−0.0158	191	222	255	−33
167	0.0133	104	95	236	−141
168	−0.0134	186	211	121	90
169	−0.0526	324	227	249	−22
170	0.0712	22	19	270	−251
171	0.0700	31	28	287	−259
172	−0.0561	346	271	121	150
173	0.0109	124	116	247	−131
174	−0.0267	259	178	121	57
175	0.0713	21	18	268	−250
176	−0.0310	299	365	121	244
177	0.0074	138	131	310	−179
178	0.0043	144	139	313	−174
179	−0.0541	335	241	121	120
180	−0.0083	178	346	121	225
181	−0.0233	250	337	121	216
182	−0.0299	289	359	121	238
183	0.0681	51	48	256	−208
184	0.0281	84	78	304	−226
185	0.0138	100	91	288	−197
186	0.0033	150	146	331	−185
187	−0.0514	320	221	338	−117
188	−0.0058	177	340	121	219
189	0.0289	81	75	238	−163
190	0.0145	95	85	277	−192
191	0.0127	110	101	317	−216
192	−0.0273	262	181	316	−135
193	−0.0573	357	298	121	177
194	0.0121	116	108	121	−13
195	−0.0196	203	263	121	142
196	0.0126	114	105	239	−134
197	0.0700	29	26	344	−318
198	−0.0521	321	223	299	−76
199	−0.0220	237	322	121	201
200	0.0144	96	86	337	−251
201	0.0421	61	58	121	−63
202	0.0708	23	20	305	−285
203	0.0221	88	127	296	−169
204	0.0134	103	94	121	−27
205	0.0297	75	69	241	−172
206	−0.0191	200	253	121	132
207	0.0057	139	164	121	43
208	−0.0554	343	259	121	138
209	−0.0380	309	262	121	141
210	−0.0626	362	347	121	226
211	−0.0401	317	307	121	186
212	−0.0284	274	192	121	71
213	−0.0279	267	185	328	−143
214	0.0336	65	80	364	−284
215	0.0116	120	112	121	−9
216	0.0018	163	159	334	−175
217	−0.0547	336	247	121	126
218	0.0296	76	70	121	−51
219	0.0101	133	125	121	4
220	0.0024	160	157	324	−167
221	0.0125	115	106	274	−168
222	0.0703	26	23	121	−98
223	−0.0291	282	357	121	236
224	−0.0155	190	171	121	50
225	−0.0283	273	191	121	70
226	−0.0176	195	217	121	96
227	−0.0562	347	272	121	151
228	−0.0302	291	203	121	82
229	0.0101	134	126	121	5
230	−0.0285	276	351	121	230
231	0.0021	162	137	121	16
232	0.0729	4	1	229	−228
233	−0.0204	209	277	121	156
234	0.0300	73	67	251	−184
235	0.0132	105	96	242	−146
236	−0.0034	173	299	365	−66
237	−0.0553	342	257	264	−7
238	−0.0111	181	200	121	79
239	−0.0568	351	282	121	161
240	−0.0210	215	290	121	169
241	−0.0201	206	269	121	148
242	−0.0466	319	352	360	−8
243	−0.0579	358	311	121	190
244	−0.0385	312	219	121	98
245	−0.0281	271	189	121	68
246	−0.0184	198	244	121	123
247	0.0727	7	4	333	−329
248	−0.0216	224	305	121	184
249	0.0117	119	111	121	−10
250	0.0640	53	53	259	−206
251	−0.0221	238	323	121	202
252	−0.0225	246	333	121	212
253	−0.0528	325	228	121	107
254	0.0688	43	40	303	−263
255	0.0027	157	152	121	31
256	−0.0273	264	350	121	229
257	−0.0265	258	177	330	−153
258	−0.0225	245	332	121	211
259	0.0724	12	9	265	−256
260	−0.0533	328	233	121	112
261	−0.0136	187	212	121	91
262	−0.0261	255	344	234	110
263	0.0051	140	133	240	−107
264	−0.0278	265	183	121	62
265	0.0039	147	143	267	−124
266	0.2507	2	65	2	63
267	0.0702	27	24	321	−297
268	0.0307	71	107	258	−151
269	−0.0535	330	235	121	114
270	0.0032	151	147	230	−83
271	0.0107	127	119	121	−2
272	−0.0264	257	345	269	76
273	−0.0531	327	231	312	−81
274	0.0117	118	110	121	−11
275	−0.0363	306	245	12	233
276	−0.0373	308	252	121	131
277	−0.0569	354	285	121	164
278	−0.0200	204	267	121	146
279	−0.0558	345	265	121	144
280	0.0717	20	16	121	−105
281	−0.0028	171	291	121	170
282	0.0030	155	150	257	−107
283	−0.0225	244	331	121	210
284	−0.0628	364	349	121	228
285	0.0114	121	113	121	−8
286	−0.0273	263	182	326	−144
287	−0.0161	192	225	121	104
288	−0.0571	355	295	121	174
289	0.0722	15	12	10	2
290	0.0726	8	5	286	−281
291	−0.0302	292	204	323	−119
292	−0.0530	326	230	261	−31
293	−0.0534	329	234	121	113
294	0.0042	145	140	252	−112
295	−0.0608	360	342	121	221
296	−0.0039	175	325	121	204
297	−0.0195	202	261	121	140
298	0.0024	159	156	121	35
299	−0.0312	300	208	121	87
300	0.0721	17	14	121	−107
301	0.0021	161	158	355	−197
302	−0.0181	196	242	121	121
303	−0.0216	225	306	121	185
304	−0.0290	280	355	121	234
305	0.0262	86	90	121	−31
306	−0.0200	205	268	121	147
307	−0.0303	293	205	121	84
308	−0.0371	307	246	121	125
309	−0.0169	194	216	121	95
310	−0.0201	207	270	121	149
311	0.0101	135	141	4	137
312	0.0108	126	118	298	−180
313	−0.0305	295	361	121	240
314	0.0043	143	168	121	47
315	−0.0290	281	356	121	235
316	−0.0539	333	239	121	118
317	−0.0285	275	193	121	72
318	0.0148	94	84	121	−37
319	−0.0256	253	174	302	−128
320	−0.0286	277	194	7	187
321	−0.0271	260	179	283	−104
322	0.0699	32	29	121	−92
323	0.0362	62	59	121	−62
324	0.0126	113	104	121	−17
325	−0.0293	284	197	121	76
326	0.0029	156	151	121	30
327	0.0027	158	154	8	146
328	−0.0225	243	330	121	209
329	−0.0011	169	258	121	137
330	−0.0548	338	249	121	128
331	0.0357	64	167	272	−105
332	0.0138	101	92	121	−29
333	−0.0627	363	348	121	227
334	−0.0536	332	237	121	116
335	−0.0648	365	353	301	52
336	0.0111	122	114	121	−7
337	−0.0395	316	294	121	173
338	0.0288	82	76	309	−233
339	0.0296	77	71	121	−50
340	−0.0568	352	283	121	162
341	−0.0568	353	284	121	163
342	−0.0294	285	198	121	77
343	−0.0217	226	309	121	188
344	−0.0300	290	202	121	81
345	−0.0295	286	199	121	78
346	0.0045	142	138	260	−122
347	−0.0203	208	275	121	154
348	−0.0351	302	215	13	202
349	0.0294	78	72	121	−49
350	0.0721	16	13	282	−269
351	−0.0207	211	281	121	160
352	−0.0548	337	248	121	127
353	−0.0616	361	343	314	29
354	−0.0214	221	302	121	181
355	−0.0566	350	280	245	35
356	0.0127	112	103	121	−18
357	−0.0386	313	274	121	153
358	0.0130	107	98	121	−23
359	0.0699	34	31	266	−235
360	0.0629	54	50	307	−257
361	0.0108	125	117	121	−4
362	0.0700	30	27	121	−94
363	−0.0224	242	329	121	208
364	−0.0571	356	297	121	176
365	−0.0027	170	288	362	−74

, Appendix A). Thus, GWI functions as a quantitative proxy for evaluating the substantive support of sustainability disclosures by environmental action.

The results indicate a substantial variation in GWI values throughout the sample, with large negative scores suggesting underreporting of environmental accomplishments and large positive values suggesting potential greenwashing. Institutions with GWI values that are nearly zero are regarded as aligned, indicating that their operational behavior is generally consistent with their sustainability communication.

A subset of institutions demonstrates positive GWI scores that exceed five ranking positions, indicating that their public-facing ESG performance—as measured by ESG scores, external assurance, and climate-related governance—exceeds their actual green accounting commitments (Ragazou et al. 2024b, 2024c; Ragazou and Sklavos 2020). These institutions frequently disclose ambitious ESG policies, engage in high-profile sustainability frameworks, and maintain robust governance visibility. Nevertheless, their environmental investments, liabilities, or resource efficiency performance (e.g., low environmental expenditures, high penalties, or limited provisions) do not align with the ambition of their disclosures. In such instances, the positive GWI functions as a warning sign for symbolic ESG behavior and reputational signaling, which may be indicative of greenwashing tendencies (Lagasio 2024; D. Li et al. 2024; J. Li 2024; Pizzetti et al. 2021; Todaro and Torelli 2024).

In contrast, organizations that exhibit substantial negative GWI values, those whose green accounting rank is substantially higher than their disclosure ranks seem to be under communicating their sustainability initiatives. These organizations prioritize environmental sustainability and sustain robust operational metrics; however, they lack formal ESG reporting structures and transparency. Despite their substantial contributions to sustainability outcomes, this group may be penalized in traditional ESG assessments. These “quiet performers” offer potential opportunities for constructive engagement and support in the enhancement of ESG disclosure practices for investors and regulators.

The final computed results for all institutions are presented in Table A2, which includes the PROMETHEE II net flows and rankings, disclosure-based ranks, green accounting–based ranks, and the resulting Greenwashing Risk Index (GWI). This table provides a practical tool for identifying greenwashing-prone firms within the financial sector and provides a transparent view of institutional alignment or misalignment between reported and actual ESG performance. A bell-shaped curve centered near zero is evident in a visual distribution of GWI scores with a perceptible skewness toward positive values. This implies that greenwashing may be more prevalent than green underreporting in the current sample. The results also indicate that larger institutions with greater media visibility are more likely to produce high GWI scores. This may be attributed to the reputational incentives to appear sustainable, even when the underlying environmental practices are feeble or non-transparent.

Collectively, the GWI facilitates a nuanced, second-layer analysis of ESG performance that surpasses net flows or total scores. It encapsulates the discrepancy between ESG rhetoric and environmental action, enabling institutions to be classified along a credibility spectrum, ranging from high-integrity performers to probable greenwashers. This has direct implications for regulatory monitoring, sustainability risk assessment, and ESG-integrated investment.

5. Discussion

This study investigates the correlation between environmental performance and ESG disclosures to offer a comprehensive and multifaceted evaluation of ESG integrity in the European financial sector. The research unmasks significant discrepancies that challenge the reliability of narrative-based sustainability communication, highlighting the evolving nature of ESG credibility using a hybrid MCDM framework—entropy-weighted PROMETHEE II—and the novel Greenwashing Risk Index (GWI).

The 365 specified financial institutions analyzed were found to exhibit a diverse range of ESG performance in the PROMETHEE II analysis. A combination of robust ESG disclosures and robust green accounting practices was frequently displayed by high-ranking institutions, often accompanied by their involvement in internationally recognized climate frameworks and third-party data assurance. Conversely, institutions with negative net flows demonstrated underperformance in both dimensions, suggesting that there are systemic deficiencies in both disclosure transparency and environmental action. It is intriguing that institutions in the mid-tier of the classification exhibited a significant degree of variability. While some institutions excelled in ESG transparency but underperformed operationally, others performed well in terms of environmental sustainability but lacked comprehensive disclosures. These patterns required a more comprehensive diagnostic, which was provided by the GWI.

The GWI results emphasize a fundamental imbalance in the integrity of ESGs. A substantial number of institutions achieved positive GWI scores, indicating that they performed better in green accounting than in ESG disclosure. Although not all such instances suggest deliberate obfuscation, this misalignment raises concerns about the strength of ESG communication and encourages examination of potential underreporting. In contrast, institutions with negative GWI values, which are indicative of strong performance but inferior disclosure, are at risk of being penalized in investor evaluations that heavily rely on surface-level ESG ratings. The conflation of disclosure quality with actual sustainability performance is a broader structural vulnerability in ESG frameworks, as evidenced by this duality.

This study thereby serves to substantiate a critical insight: ESG performance cannot be accurately predicted solely based on disclosures. The risk of greenwashing may be exacerbated if symbolic conformance is incentivized over substantive environmental engagement in the absence of performance-linked reporting mandates.

5.1. Theoretical, Practical, and Regulatory Implications

This study represents a triple contribution to academic literature. To begin, it operationalizes greenwashing as a quantifiable construction by employing the GWI, a data-driven, replicable instrument that surpasses qualitative and anecdotal typologies. This allows for the quantification of the ESG performance–disclosure disparity across a broad institutional landscape by researchers and practitioners (da Silva and Vieira 2025; Lopez-de-Silanes et al. 2020; Schiemann and Tietmeyer 2022). The second aspect is that it advocates for a disaggregated approach that differentiates between environmental action and narrative strategies, thereby challenging the hegemony of composite ESG scores. In this way, it is consistent with the growing appeal in the literature to reorient ESG evaluation around accountability rather than communication (Asif et al. 2023; Sundarasen et al. 2024). Third, it reaffirms the importance of green accounting metrics—in particular, environmental penalties, expenditures, and provisions—as empirically founded indicators of environmental performance that are frequently disregarded in ESG rating systems.

For institutional stakeholders, the results indicate a paradigm shift. ESG credibility is no longer a reputational asset that is solely derived from disclosures; rather, it is a material risk factor that should be verified. Considering the 2024 Omnibus Directive (Directive (EU) 2024/825), firms with high GWI scores—i.e., significant misalignment between disclosures and actions—are subject to increased reputational, regulatory, and investor scrutiny (Official Journal of the European Union 2024). The necessity of alignment between reported intentions and realized environmental outcomes is emphasized by these frameworks, which also prioritize verifiability and impact.

Investors are provided with a more sophisticated evaluative prism by the proposed framework, which allows them to identify “silent performers” and minimize their exposure to entities that demonstrate narrative compliance but lack operational proficiency. ESG rating providers may implement the GWI as a modification mechanism to mitigate the risk of greenwashing, while regulators may employ it as a screening instrument to identify institutions that require additional audit or policy engagement (Chau et al. 2025; Šević et al. 2024).

5.2. Limitations

This study has certain limitations, despite its methodological rigor and empirical contribution. Initially, the generalizability of the findings to other regions with varying regulatory maturity levels or disclosure cultures may be compromised by the geographical scope, which is restricted to financial institutions based in the EU. Secondly, the methodological consistency of the reliance on Refinitiv Eikon data is inherently influenced by a degree of self-reporting bias. Additionally, certain critical indicators, including Scope 3 emissions and biodiversity metrics, were excluded from the dataset due to their lack of uniform availability.

Third, entropy weighting guarantees objectivity by designating weights based on data variability; however, it may not completely convey the strategic or material relevance of specific indicators across institutional contexts. Increased relevance and balance may be achieved by incorporating stakeholder-informed weighting schemes. In conclusion, PROMETHEE II implies linear preference functions and independence across criteria, which may oversimplify the intricate trade-offs that are present in ESG decision-making. In order to more accurately represent uncertainty and interdependence, future research could investigate fuzzy, stochastic, or hybrid MCDM models.

5.3. Future Research Avenues

The current framework could be improved in numerous significant ways through future research. Longitudinal studies that examine the evolution of GWI scores over time could provide insight into the regulatory impact and trends in disclosure–performance alignment. Differential greenwashing patterns in high-impact industries such as energy, manufacturing, and technology may also be revealed by sector-specific studies. In terms of methodology, the implementation of sophisticated fuzzy models (e.g., fuzzy-VIKOR, hybrid AHP-TOPSIS, and q-ROFS) could enhance the sensitivity of ESG assessments in the presence of uncertainty. Additionally, the use of machine learning techniques, particularly explainable AI models, could be implemented to anticipate greenwashing risk by incorporating financial, governance, and ESG predictors. Importantly, future research should also investigate the repercussions of greenwashing in terms of regulatory penalties, stakeholder trust erosion, or market reactions. The GWI would be validated as a predictive instrument for reputational or systemic risk and would facilitate more actionable ESG supervision because of these analyses.

6. Conclusions

This research examined the correlation between the actual environmental performance and the ESG disclosures of 365 listed financial institutions in Europe during the fiscal year 2024. The research introduces the Greenwashing Risk Index (GWI) as a novel empirical tool to measure disclosure–performance asymmetries by integrating green accounting indicators and ESG transparency metrics into a PROMETHEE II-based multi-criteria decision-making framework supported by entropy weighting.

The results indicate that there are substantial discrepancies in the ESG profiles of financial institutions. While some institutions exhibit high transparency and limited operational engagement, others demonstrate coherent and demonstrable sustainability strategies. This misalignment, which is evident in the elevated GWI scores, implies that reputational sustainability is frequently not supported by environmental action, thereby emphasizing the limitations of relying solely on self-reported ESG data.

This study theoretically contributes to the expanding literature on greenwashing by providing a rank-based, replicable method for its detection. It also serves to emphasize the necessity of disentangling symbolic ESG communication from actual sustainability performance, promoting green accounting metrics as essential indicators of environmental credibility. In practice, the GWI provides a diagnostic instrument for investors, regulators, and institutions that are interested in identifying ESG inconsistencies and enhancing accountability. This study is consistent with recent regulatory developments, such as the CSRD, SFDR, and Directive (EU) 2024/825, from a policy perspective (Official Journal of the European Union 2024). These developments underscore the verifiability and impact of environmental performance, as well as the accuracy of ESG reporting. This regulatory imperative is addressed by the framework proposed in this study, which establishes a strong, data-driven foundation for the improvement of ESG ratings and supervision (Kartal et al. 2024; Peng et al. 2023; Schiemann and Tietmeyer 2022).

Despite its methodological robustness, the study’s geographic scope, dependence on partially self-reported data, and reliance on entropy-based weighting introduce limitations that require consideration. In the future, research should broaden the scope to include regions and sectors, incorporate alternative weighting approaches, and investigate longitudinal trends and predictive outcomes of greenwashing exposure.

In summary, this research illustrates that disclosures alone are insufficient to evaluate the credibility of ESG practices. Tools such as the GWI will be essential in guaranteeing that institutional claims are not only visible but also verifiable, actionable, and consistent with genuine environmental impact as sustainability becomes a fundamental component of financial legitimacy.