Search Results (23)

Annotated text datasets are increasingly reused as classifier targets, annotation candidates, and inputs to aggregate profiles, yet their labels often circulate without enough information about how they were produced. This article presents a reproducible benchmark and validation workflow for the public SASB-Aligned ESG Sentences corpus, a sentence-level sustainability disclosure dataset organized around standards-based categories such as those used in Sustainability Accounting Standards Board (SASB) analytics. Using the downloaded 6460-row version of the corpus, we construct fixed train/validation/test splits, map released child labels to parent categories, and evaluate label reuse through supervised classifiers, prompted GPT-4o classification, blind and candidate-visible Claude annotation, and Monte Carlo aggregation into ESG/Non-ESG category profiles. The reproducibility artifacts provide split metadata, label mappings, prompt templates, model predictions, LLM annotation outputs, profile sensitivity outputs, figure inputs, and scripts for reproducing the reported tables and figures. Results show that label reproduction is strongest at coarser label levels, blind annotation flags 40.3% of held-out sentences as ambiguous, candidate-visible annotation increases agreement while changing the task format, and aggregate profiles remain sensitive to label source. The benchmark supports transparent reuse of sentence-level ESG labels by reporting label source, annotation condition, prompt family, and aggregation level. Full article

Morocco has developed one of the most comprehensive sustainability governance architectures among middle-income emerging economies, yet the relationship between its formal regulatory ambition and on-the-ground implementation effectiveness remains poorly understood. This narrative literature review provides an integrated, critically analytical account of Morocco’s sustainability governance system, organised around three interlocking dimensions: (i) a progressively strengthened legislative corpus anchored by the 2011 Constitution and Framework Law 99-12; (ii) a portfolio of national sustainability strategies aligning domestic policy with Paris Agreement commitments, Nationally Determined Contributions (NDCs), and the United Nations Sustainable Development Goals (SDGs); and (iii) corporate sustainability practices driven by regulatory obligations, international supply chain pressures, and ESG disclosure norms. Drawing on 124 sources, comprising 62 peer-reviewed articles, 38 legislative texts, and 24 institutional reports, and applying institutional isomorphism theory as an integrating analytical lens, the review advances three theoretical propositions concerning the conditions under which formal governance architectures translate into effective sustainability outcomes. It further proposes a validated conceptual framework and develops a comparative positioning of Morocco against peer economies (Tunisia, Egypt, South Africa, and Turkey). Critical implementation gaps are identified in enforcement capacity, SME integration, sustainability data infrastructure, and green finance, contributing a balanced and evidence-grounded assessment of Morocco’s sustainability transition. These findings offer actionable insights for policymakers, regulators, and business leaders operating in the Moroccan and broader African context. Full article

The rapid expansion of e-commerce has reshaped global consumption systems by transforming production processes, logistics infrastructures, and consumer behaviour. While this transformation has generated significant economic opportunities, it has simultaneously intensified environmental pressures, particularly through last-mile delivery emissions, excessive packaging waste, and high return rates. At the same time, the growing diffusion of corporate sustainability reporting has raised increasing concerns about greenwashing, defined as the misrepresentation of environmental performance through selective disclosure or symbolic communication. This study aims to provide a comprehensive assessment of sustainability practices in e-commerce, focusing on the relationship between environmental performance, transparency, and economic outcomes. Particular attention is devoted to the role of blockchain technology as a potential mechanism for enhancing verifiable transparency in complex supply chains. The research adopts a multiple case study design grounded in the methodological frameworks and integrates qualitative analysis with a semi-quantitative evaluation model. Seven companies operating in different segments of the e-commerce ecosystem are analyzed through an extensive review of secondary data sources, including ESG reports, financial disclosures, NGO assessments, and industry benchmarks. The findings reveal a substantial gap between declared sustainability commitments and actual implementation, with significant heterogeneity across firms. Companies that embed sustainability into their strategic core demonstrate stronger alignment between environmental and economic performance, whereas firms relying primarily on communication-driven approaches exhibit higher implementation gaps. The study contributes to the literature by introducing an analytical framework centered on the concept of the implementation gap and by demonstrating the central role of transparency in determining sustainability effectiveness. It also highlights the potential, yet still largely unrealized, role of blockchain technology in addressing information asymmetry and reducing greenwashing in e-commerce. Full article

Environmental sustainability disclosure has become increasingly critical as climate risks intensify and regulatory and investor demands for transparent, decision-useful information continue to grow. It plays a key role in reducing information asymmetry and supporting capital allocation, risk assessment, and regulatory oversight. However, prior studies predominantly rely on aggregated ESG indicators and linear models, which often fail to capture the structural heterogeneity and nonlinear relationships inherent in environmental data. This study develops a machine learning-based analytical framework to examine environmental disclosure using corporate data from the Taiwan Economic Journal (TEJ) from 2022 to 2024. A polarized sampling design is employed by selecting firms in the top and bottom 20% of ESG performance to identify and compare the distinctive disclosure characteristics of companies with high versus low environmental performance. Five models are evaluated using Accuracy, Precision, Recall, F1-score, and AUROC. The results show that ensemble models outperform traditional approaches, with CatBoost achieving the most robust performance. Feature importance analysis reveals a concentrated structure dominated by carbon emissions, energy efficiency, and waste management, while the importance of renewable energy variables increases over time. These findings highlight the nonlinear and multidimensional nature of environmental disclosure and demonstrate the value of machine learning in enhancing environmental sustainability analysis, investment decision-making, and regulatory effectiveness. As this study is based on a single-country dataset (Taiwan), future research may incorporate cross-country datasets to improve external validity. Full article

As sustainable finance continues to advance, an important question is how scientifically designed and well-targeted policies can curb corporate ESG greenwashing and improve the quality of firms’ ESG and sustainability disclosure. From the perspective of fiscal–financial policy coordination, we exploit the green loan interest subsidy policy (GLIS) as a quasi-natural experiment and develop an analytical framework around four policy components: commercial banks’ information screening, local governments’ green screening, the subsidy instrument’s leverage and certification effects, and firms’ internal green governance. Within this framework, we examine whether the GLIS can restrain corporate ESG greenwashing. Using Chinese listed firms from 2009 to 2022 as the sample and identifying the effect through a multi-period difference-in-differences (DID) model, we find that the GLIS significantly curbs corporate ESG greenwashing. In exploring the underlying channels, we find that the GLIS curbs corporate ESG greenwashing by strengthening commercial banks’ information screening, enhancing local governments’ green screening, easing firms’ external financing constraints, and reinforcing firms’ internal green governance. Further analysis indicates that the inhibitory effect of the GLIS on corporate ESG greenwashing is more pronounced among non-state-owned firms, firms in the growth stage, firms in heavily polluting industries, and firms located in regions with weaker resource endowments. In addition, the stronger a firm’s digital technology R&D capability and corporate governance capability, the greater the restraining effect of the GLIS on its ESG greenwashing. By systematically evaluating the governance effect of fiscal–financial policy coordination on corporate ESG greenwashing, our study provides useful insights for governments seeking to improve green finance policies and optimize the coordination of green policy instruments. Full article

The transition toward a sustainable bioeconomy and the integration of environmental, social, and governance (ESG) principles into finance have increased the demand for green skills in the financial sector. However, the literature remains fragmented, as green skills are often discussed through related constructs such as ESG competencies, sustainability knowledge, green human capital, green training, or green HRM outcomes. This study systematizes existing research and develops a finance-specific framework explaining what green skills in finance are, how they are formed, and how they support sustainable practice and bioeconomy-oriented capital allocation. A systematic literature review was conducted in accordance with PRISMA 2020 guidelines through searches in Scopus, Web of Science, and Google Scholar. After applying predefined inclusion and exclusion criteria, 47 articles were included. The findings show that green skills in finance are multidimensional and include environmental and sustainability knowledge, digital and analytical skills, behavioral and value-oriented skills, and managerial, strategic, and creative capabilities. Their formation is shaped by education and professional training, green HRM practices, and institutional and regulatory mechanisms. Overall, green skills function as human, organizational, and institutional capacities that support ESG credibility, climate-risk assessment, sustainability disclosure, responsible capital allocation, and anti-greenwashing practices in the transition toward a sustainable bioeconomy. Full article

The present Brief Report examines the role of data governance and regulatory complexity in shaping the integration of Environmental, Social, and Governance (ESG) factors in asset management and corporate reporting within the European Union. As sustainable finance expands, ensuring the transparency, comparability, and reliability of ESG information remains a key challenge. The study adopts a qualitative and analytical approach, drawing on academic literature, institutional reports, and EU regulatory frameworks, including the Corporate Sustainability Reporting Directive (CSRD), the Sustainable Finance Disclosure Regulation (SFDR), the EU Taxonomy, and the European Sustainability Reporting Standards (ESRS). Through a documentary and comparative analysis, it assesses the consistency and interoperability of European and international sustainability frameworks. The findings highlight persistent challenges such as data fragmentation, lack of standardization, and regulatory complexity, while emphasizing the role of ESRS and robust data governance in enhancing data quality and transparency. The present Brief Report therefore provides potentially useful insights for stakeholders such as policymakers and regulators, managers and institutional investors, corporate issuers and academicians. Strengthening governance structures and regulatory alignment is hence essential not only to foster investor trust and improve access to sustainable finance, but also to support evidence-based policy reform and long-term creation across European capital markets. Full article

This conceptual and analytical study examines how digitalization may reduce the cost and complexity of ESG and circular economy reporting for women-led SMEs within the evolving EU sustainability reporting framework. Particular attention is given to selected contextual examples from the Danube Region. Using a conceptual accounting approach grounded in EU regulatory documents, the academic literature, and prior bibliometric research, it identifies four key challenge domains: measurement, valuation, disclosure, and professional judgment. The analysis is complemented by an exploratory public data illustration based on publicly available documents and illustrative cases of women-led SMEs from the Danube Region. The empirical illustration is intended solely to contextualize and demonstrate the practical visibility of the proposed accounting domains rather than to validate the conceptual framework statistically. It develops an accounting-oriented problem matrix linking these challenges to digital enablers such as data platforms, automation tools, and traceability technologies. The findings suggest that digital accounting capabilities may support more efficient, reliable, comparable, and scalable ESG reporting. A conceptual framework is proposed, connecting regulatory drivers, digital accounting capabilities, and reporting outcomes, including enhanced assurance readiness and potentially improved access to finance. The study also outlines practical recommendations, including minimum viable ESG datasets and a staged digital adoption approach, alongside policy implications related to harmonized data requests and targeted capacity-building for SMEs. The study contributes to the literature by integrating ESG reporting, circular economy, digitalization, and gender-related constraints affecting women-led SMEs within an explicitly accounting-centered analytical framework. Full article

Expanding ESG disclosure mandates under the Corporate Sustainability Reporting Directive (CSRD) and the International Sustainability Standards Board (ISSB) have driven rising demand for artificial intelligence (AI) and data analytics capable of supporting sustainability reporting and verification at scale. Nevertheless, the scholarly literature remains dispersed across discrete disciplinary fields—natural language processing, machine learning, auditing, and regulatory compliance—with limited integrative synthesis. To address this gap, the present study conducts a PRISMA 2020-compliant systematic review of 45 peer-reviewed articles indexed in Scopus and published between 2020 and 2025. The methodology combines bibliometric mapping through VOSviewer with qualitative thematic content analysis. Findings document a rapidly expanding field exhibiting a compound annual growth rate of 91.9%. Four principal thematic dimensions emerge: (i) NLP and text mining for ESG disclosure analysis; (ii) machine learning for ESG scoring and corporate performance; (iii) AI-enabled ESG assurance, auditing, and governance; and (iv) regulatory frameworks and the digital transformation of sustainability reporting. The evidence indicates that AI is progressively reshaping ESG disclosure from a largely narrative and self-reported practice into a data-driven, independently verifiable transparency system. These developments carry substantive implications for regulators, corporate practitioners, assurance providers, and investors seeking to strengthen the reliability and comparability of sustainability disclosures. Full article

This paper explores how green digital technologies (GDTs)—ERP systems, cloud software, IoT, artificial intelligence, and big data analytics—can be used to improve the quality of ESG disclosures of industrial listed companies in the Amman Stock Exchange (ASE). Based on the institutional isomorphism theory, we examine how the relationship between coercive, mimetic, and normative institutional pressures and adopting green technology interacts to effect sustainability reporting practices. Using panel data pertaining to 30 ASE-listed industrial companies during the 2020–2024 period (N = 146 firm-year observations), we applied pooled OLS and random effects frameworks characterized by a strong clustering of standard errors. The findings show that the Green Digital Technology Index is positively and significantly associated with ESG disclosure scores (Pooled OLS: β = 5.448, t = 2.367, p = 0.019; Random Effects: β = 5.941, t = 2.507, p = 0.024), with adopting firms having an average score that is 1.73 points higher. Its largest effect is on the environmental dimension (β = 3.460, p = 0.074). Institutional pressures do not moderate the GDT–disclosure relationship; however, mediation analysis indicated that institutional pressure significantly predicts GDT adoption (β = 0.098, p < 0.001), suggesting that institutional forces are linked to disclosure quality through their association with technology adoption rather than through direct effects, indicating that institutional forces exert their influence through technology adoption. Disclosure quality is negatively associated with CEO duality (β = −4.863, p < 0.001). These results are consistent with the interpretation that green digital technologies serve as a transmission channel through which institutional pressures are associated with enhanced sustainability disclosure in emerging markets. Full article

Environmental, Social, and Governance (ESG) metrics have become a cornerstone to sustainable finance, yet their measurement and predictability remain constrained by data heterogeneity, methodological divergence, and disclosure bias. This study develops a comprehensive ESG prediction framework grounded in the Design Science Research paradigm, integrating advanced machine learning techniques with rigorous data preprocessing, feature selection, and temporal validation. Using firm-level data from Refinitiv and Bloomberg, the analysis distinguishes between ESG composite performance and disclosure-based robustness, addressing a critical gap in the literature. Ensemble learning models, including Random Forest and XGBoost, are evaluated alongside deep learning architectures using multiple sampling strategies and rolling-window validation. The results demonstrate that ESG performance is moderately forecastable, with ensemble methods consistently outperforming neural networks in structured datasets. In contrast, disclosure robustness exhibits lower predictability, reflecting its dependence on discretionary strategic reporting and institutional factors. The findings highlight the importance of data quality, model selection, and validation design in ESG analytics, while emphasizing the limitations of deep learning in tabular financial contexts. The integration of explainable artificial intelligence further enhances interpretability by identifying key predictors of ESG outcomes. Overall, the study contributes to the literature by providing a robust, interpretable, and methodologically rigorous framework for ESG prediction, with implications for investors, regulators, and corporate decision-making. Full article

Technology is increasingly central to sustainability, yet frameworks built around the environmental–social–economic (E–S–Ec) triad and ESG disclosure regimes do not fully capture the governance problems created by interconnected digital and cyber–physical infrastructures. In this conceptual paper, Tech4.0 is used in a deliberately narrow working sense, focusing on AI-mediated decision systems, data/platform/cloud infrastructures, software dependency chains, and cyber–physical control environments in which opacity, infrastructural dependence, interdependence, and cascading failures create distinctive problems of governability and resilience. Against this background, the paper examines whether making the technological dimension explicit adds analytical value within sustainability architecture. It examines the case for treating Technological Sustainability (T) as a distinct analytical dimension/pillar insofar as it foregrounds system properties of the Technosphere that tend to be diluted when distributed across environmental, social, and economic categories. The paper then discusses the hierarchy T → Corporate Technological Responsibility (CTR) → Corporate Digital Responsibility (CDR) as a possible corporate-level operational pathway and outlines an exploratory measurement agenda structured around exposures, capabilities, and outcomes. Rather than offering empirical proof or a validated reporting architecture, the article provides a conceptual research program for later empirical inquiry into technological accountability under Tech4.0 conditions. Full article

The Taskforce on Nature-related Financial Disclosures (TNFD) refers to both the international organizing body and the disclosure framework it developed. Throughout this article, the term TNFD is used to encompass both the organization and the framework to ensure precision while maintaining conciseness. TNFD has emerged as a key mechanism for integrating nature-related risks and opportunities into corporate decision-making, extending the scope of existing environmental, social, and governance (ESG) and climate-related disclosures. As TNFD adoption remains at an early diffusion stage, empirical evidence on its global uptake and sectoral characteristics is still limited, particularly in nature-dependent industries such as forestry and environmental services. This study provides an exploratory mapping of global TNFD adoption patterns using the complete list of TNFD adopting organizations disclosed on the official TNFD platform as of June 2025. A total of 584 organizations across 54 countries were analyzed, with a focused examination of forestry- and environment-related entities. Rather than testing causal relationships, this research adopts a descriptive and structural analytical approach to identify geographic, institutional, and sectoral patterns of adoption. The results reveal a strong concentration of TNFD adoption in developed economies and corporate entities, while forestry-specific adopters remain limited in number. Notably, TNFD adoption does not appear to correlate with forest resource endowment, suggesting that governance capacity and financial disclosure readiness are more influential than ecological conditions. Based on these findings, the study discusses strategic implications for forestry and environmental organizations and proposes a conceptual framework for advancing nature-related financial disclosure in the sector. This research contributes an early-stage empirical foundation for understanding TNFD diffusion and offers practical insights for policymakers, corporations, and researchers seeking to operationalize nature-related disclosure frameworks. Full article

The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes. Full article

Traditional Environmental, Social, and Governance (ESG) assessments rely heavily on corporate disclosures and third-party ratings, which are often delayed, inconsistent, and prone to bias. These limitations leave stakeholders without timely visibility into rapidly evolving ESG events. These assessment frameworks also fail to capture the dynamic nature of ESG issues reflected in public news media. This research addresses these limitations by proposing and implementing an automated framework utilising Artificial Intelligence (AI), specifically Natural Language Processing (NLP) and Knowledge Graphs (KG), to analyse ESG news data for companies listed on major stock indices. The methodology involves several stages: collecting a registry of target companies; retrieving relevant news articles; applying Named Entity Recognition (NER), sentiment analysis, and ESG domain classification; and constructing a linked property knowledge graph to structure the extracted information semantically. The framework culminates in an interactive dashboard for visualising and querying the resulting graph database. The resulting knowledge graph supports comparative inferential analytics across indices and sectors, uncovering divergent ESG sentiment profiles and thematic priorities that traditional reports overlook. The analysis also reveals comparative insights into sentiment trends and ESG focus areas across different exchanges and sectors, offering perspectives often missing from traditional methods. Findings indicate differing ESG sentiment profiles and thematic focuses between the UK (FTSE) and Australian (ASX) indices within the analysed dataset. This study confirms AI/KG’s potential for a modular, dynamic, and semantically rich ESG intelligence approach, transforming unstructured news into interconnected insights. Limitations and areas for future work, including model refinement and integration of financial data, are also discussed. This proposed framework augments traditional ESG evaluations with automated, scalable, and context-rich analysis. Full article