Search Results (255)

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

Environmental, social, and governance (ESG) evaluation has become increasingly critical for company sustainability assessments, especially for enterprises in the construction industry with a high environmental burden. However, existing methods face limitations in subjective evaluation, inconsistent ratings across agencies, and a lack of industry-specificity. To address these limitations, this study proposes a large language model (LLM)-based intelligent ESG evaluation model specifically designed for the construction enterprises in China. The model integrates three modules: (1) an ESG report information extraction module utilizing natural language processing and Chinese pre-trained language models to identify and classify ESG-relevant statements; (2) an ESG rating prediction module employing XGBoost regression with SHAP analysis to predict company ratings and quantify individual statement contributions; and (3) an ESG intelligent evaluation module combining knowledge graph construction with fine-tuned Qwen2.5 language models using Chain-of-Thought (CoT). Empirical validation demonstrates that the model achieves 93.33% accuracy in the ESG rating classification and an R² score of 0.5312. SHAP analysis reveals that environmental factors contribute most significantly to rating predictions (38.7%), followed by governance (32.0%) and social dimensions (29.3%). The fine-tuned LLM integrated with knowledge graph shows improved evaluation consistency, achieving 65% accuracy compared to 53.33% for standalone LLM approaches, constituting a relative improvement of 21.88%. This study contributes to the ESG evaluation methodology by providing an objective, industry-specific, and interpretable framework that enhances rating consistency and provides actionable insights for enterprise sustainability improvement. This research provides guidance for automated and intelligent ESG evaluations for construction enterprises while addressing critical gaps in current ESG practices. Full article

The increasing pressure for environmental, social, and governance (ESG) accountability in publicly funded institutions has raised concerns about the authenticity and efficiency of ESG implementation. This study investigates the relationship between public ESG funding, disclosure quality, and organizational efficiency across Greek public and financial entities. Using a mixed-methods approach—data envelopment analysis (DEA), qualitative ESG content scoring, and bibliometric mapping—we reveal that symbolic compliance remains prevalent, often decoupled from actual sustainability outcomes. Our DEA findings show that technical efficiency is strongly associated with reporting clarity, the use of verifiable metrics, and governance integration, rather than the mere volume of funding. The qualitative analysis further confirms that many disclosures reflect reputational signaling rather than impact-oriented transparency. Bibliometric results highlight a systemic underrepresentation of the public sector in ESG scholarship, particularly in Southern Europe, underscoring the need for regionally grounded empirical studies. This study provides practical implications for improving ESG accountability in publicly funded institutions and contributes a novel approach that integrates efficiency, content, and bibliometric analysis in the ESG context. Full article

Sustainability reporting has evolved into a multidimensional field encompassing Environmental, Social, and Governance (ESG) disclosure, integrated reporting (IR), and circular economy (CE) practices. This study aims to map the intellectual and thematic landscape of sustainability reporting research over the past decade, with a focus on sectoral differentiation. Drawing on bibliometric analysis of 1611 scientific articles indexed in Scopus, this research applies co-word analysis, thematic mapping, and bibliographic coupling to identify prevailing trends, conceptual clusters, and knowledge gaps. The results reveal a clear progression from fragmented debates toward a more integrated discourse combining ESG, IR, and CE frameworks. In the real economy, sustainability reporting demonstrates a mature operational focus, supported by standardized frameworks and extensive empirical evidence. In contrast, the banking sector exhibits emerging engagement with sustainability disclosure, while the public sector remains at an earlier stage of conceptual and practical development. Despite the increasing convergence of research streams, gaps persist in linking reporting practices to tangible sustainability outcomes, integrating digital innovations, and addressing social dimensions of circularity. This study concludes that further interdisciplinary and sector-specific research is essential to advance credible, comparable, and decision-useful reporting practices capable of supporting the transition toward sustainable and circular business models. Full article

The main objective of this study is to analyse the relevance of financial performance as an accurate predictor of ESG scores for French companies from 2010 to 2022. To this end, Machine Learning techniques such as linear regression, polynomial regression, Random Forest, and Support Vector Regression (SVR) were employed to provide more accurate and reliable assessments, thus informing the ESG rating attribution process. The results obtained highlight the excellent performance of the Random Forest method in predicting ESG scores from company financial variables. In addition, the approach identified specific financial variables (operating income, market capitalisation, enterprise value, etc.) that act as powerful predictors of companies’ ESG scores. This modelling approach offers a robust tool for predicting companies’ ESG scores from financial data, which can be valuable for investors and decision-makers wishing to assess and understand the impact of financial variables on corporate sustainability. Also, this allows sustainability investors to diversify their portfolios by including companies that are not currently rated by ESG rating agencies, that do not produce sustainability reports, as well as newly listed companies. It also gives companies the opportunity to identify areas where improvements are needed to enhance their ESG performance. Finally, it facilitates access to ESG ratings for interested external stakeholders. Our study focuses on using advances in artificial intelligence, exploring machine learning techniques to develop a reliable predictive model of ESG scores, which is proving to be an original and promising area of research. Full article

This study examines the influence of ethical leadership on corporate sustainability and financial performance, highlighting the moderating effect of firms’ commitment to the United Nations Sustainable Development Goals (SDGs). Utilizing panel data from 420 automotive companies spanning 2015 to 2024, the analysis applies the System Generalized Method of Moments (GMM) to control for endogeneity and unobserved heterogeneity. All data were gathered from the Refinitiv Eikon Platform (LSEG) and annual reports. Panel GMM regression is used to estimate the relationship to deal with the endogeneity problem. The results reveal that ethical leadership significantly improves corporate sustainability performance—measured by ESG scores from Refinitiv Eikon and Bloomberg—as well as financial indicators like Return on Assets (ROA) and Tobin’s Q. Additionally, firms that demonstrate breadth (the range of SDG-related themes addressed), concentration (the distribution of non-financial disclosures across SDGs), and depth (the overall volume of SDG-related information) in their SDG disclosures gain greater advantages from ethical leadership, resulting in enhanced ESG performance and higher market valuation. This study offers valuable insights for corporate leaders, policymakers, and investors on how integrating ethical leadership with SDG alignment can drive sustainable and financial growth. Full article

Packaging waste is a major environmental issue, making the transition to sustainable solutions imperative. This article proposes the concept of Shared Producer Responsibility (SPR) as a key approach to advancing sustainable packaging in the fast-moving consumer goods (FMCG) sector. The study explores how the United Nations Sustainable Development Goals (SDGs), environmental, social, and governance (ESG) reporting, and stakeholder engagement converge to support this transition. The research identifies current trends, challenges, and gaps in sustainable packaging practices through a systematic literature review (SLR) and analysis of sustainability and ESG reports from leading FMCG and packaging companies. The findings highlight the need for standardised reporting frameworks and improved stakeholder cooperation to enhance transparency and accountability in sustainability efforts. This study proposes a conceptual framework for accelerating sustainable packaging adoption through combining strategies like consumer education, regulatory incentives, and clear product labelling. The proposal to implement the concept of Shared Producer Responsibility emphasises the shared accountability of FMCG companies and packaging manufacturers in managing the full environmental lifecycle of packaging materials. This approach is crucial for achieving SDG 12 (responsible consumption and production) and SDG 13 (climate action) and driving more effective and sustainable packaging practices across the FMCG industry. Full article

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. Full article

This study provides a PRISMA-guided bibliometric analysis of scholarly research at the intersection of Environmental, Social, and Governance (ESG) efforts, green accounting, and sustainable urbanization. This study employs 130 peer-reviewed articles obtained from the Scopus database (2014–2025) and applies Biblioshiny (version 4.1) and VOSviewer (version 1.6.20) to analyze publishing trends, topic clusters, conceptual frameworks, and citation patterns. The results demonstrate a growing convergence of ESG frameworks and environmental accounting practices in urban governance discussions, driven by elevated demands for transparency, performance evaluation, and sustainable change. Green accounting is an essential instrument for executing ESG principles at the municipal level, enhancing the credibility of sustainability reporting and enabling data-driven urban decision-making. Thematic mapping and hierarchical clustering illustrate a dynamic and varied research ecosystem, defined by distinct clusters focused on ESG transparency, urban resilience, governance innovation, and green technology integration. This study contributes to the literature by clarifying the structural and conceptual evolution of this emerging field and by suggesting a research agenda to promote integrated governance models that align financial, environmental, and social goals within urban systems. Full article

This study conducts a comparative assessment of the environmental, social, and governance (ESG) integration strategies of three leading companies in Hong Kong—H&M Group, China Gas Company Limited (Towngas), and MTR Corporation Limited (MTR)—each operating in distinct sectors with unique sustainability challenges and opportunities. The analysis adopts the Challenge–Evaluation–Planning–Action–Review (CEPAR^®) framework developed by the International Chamber of Sustainable Development to examine how these companies identify and evaluate ESG-related risks, formulate action plans, implement sustainability initiatives, and refine their strategies. The findings reveal H&M’s strong emphasis on sustainable fashion, with a target of using 100% sustainable materials by 2030 and reducing greenhouse gas emissions by 56%. Towngas faces the complex challenge of transitioning from fossil fuels to cleaner energy and is investing in zero-carbon technologies to meet regulatory standards and stakeholder expectations. MTR focuses on sustainable urban development and efficient mass transit, prioritizing community engagement and reducing environmental impact. This study underscores the importance of sector-specific ESG approaches tailored to a company’s operational context. It also demonstrates how ESG integration is enhanced by proactive planning, transparent reporting, and alignment with long-term corporate values. By showcasing both successful practices and areas requiring further attention, this research contributes to the broader discourse on sustainable business practices in Hong Kong. Moreover, it provides actionable policy implications for government agencies and regulatory bodies. The insights gained can inform strategic decision-making across sectors and support the development of a more sustainable, resilient, and inclusive economy aligned with Hong Kong’s long-term climate and governance goals. Full article

This study examines the interplay of financial performance, corporate governance, and sustainability reporting quality (SRQ), addressing the need to enhance corporate transparency and accountability in an emerging market context. Guided by agency and stakeholder theories, this study investigated the effects of board size, independence, diversity, and experience on SRQ, along with the moderating role of governance in the financial performance–SRQ relationship. Using an explanatory research design and quantitative approach, data from 88 listed firms in premier markets from 2015 to 2024 were analyzed using ordered logistic regression. All data were gathered from the Refinitiv Eikon Platform (LSEG), annual reports. Panel GMM regression is used to estimate the relationship to deal with the endogeneity problem. The findings reveal that board size and experience positively influence SRQ, highlighting the role of diverse expertise and seasoned directors in fostering sustainability. Financial performance alone was not significantly associated with SRQ. However, robust governance structures enhanced the translation of financial resources into improved reporting quality. Firm size emerged as a key determinant, with larger firms exhibiting higher SRQ, while financial firms lagged compared to non-financial sectors. This study concludes that corporate governance is pivotal in shaping SRQ, particularly in resource-constrained environments. This study contributes to the literature by bridging governance, financial performance, and sustainability, offering practical insights for policymakers and corporate leaders to improve SRQ through regulatory frameworks and governance reforms. Full article

The sustainable supply chain management (SSCM) literature does not directly address Scope 3 emissions despite their role as primary drivers of greenhouse gas emissions. This study aims to provide an overview of the main themes through which the SSCM literature has considered Scope 3 emissions and identify further avenues for research. A systematic literature review (SLR) was conducted. Scopus and Web of Science were the databases considered. Sixty-one papers were included in the analysis. Most papers focus on assessing and estimating Scope 3 emissions, followed by papers that discuss the reporting of Scope 3 emissions. These papers shed light on how firms may not report Scope 3 emissions if the information is negative to improve investors’ perception of the firm. The last group of papers discusses practices and strategies to manage Scope 3 emissions. The main challenge identified in establishing strategies to manage Scope 3 emissions is engagement with stakeholders, as, generally, only one or two tiers of the value chain cooperate. This study is the first to organize the literature on Scope 3 emissions under the lens of SSCM. If supply chains are to become more sustainable, focal enterprise coordination must be effective and leverage practices such as Scope 3 emissions metrics and measurement, data sharing, and green product development for all stakeholders. Full article

The impact of Environmental, Social, and Governance (ESG) scores on financial performance remains a subject of debate, as the literature reports mixed evidence regarding their effect on stock returns. This research aims to examine the relationship between ESG ratings and the change in ESG scores, or ESG Momentum, concerning both returns and risk of a large sample of stocks traded on U.S. exchanges. The study examined a sample of 3856 stocks traded on U.S. exchanges, considering 20 years of quarterly data from December 2002 to December 2022. We applied multi-factor models and tested them through pooled ordinary, fixed effects, and random effects panel regression methods. Our results show negative relationships between ESG scores and stock returns and between ESG Momentum and volatility. Contrarily, we find positive associations between ESG Momentum and returns and between ESG scores and volatility. Although high ESG scores are generally associated with lower long-term stock returns, an increase in a company’s ESG rating tends to translate into immediate positive returns and reduced risk. Accordingly, investors may benefit from strategies that focus on companies actively improving their ESG performance, while firms themselves stand to gain by signaling continuous advancement in ESG-related areas. Full article

This study investigates voluntary compliance with the Task Force on Climate-Related Financial Disclosures (TCFD) framework in 64 financial, Environmental, Social, and Governance (ESG) reports from six Spanish IBEX-35 energy firms (2020–2023) and explores the implications for intangible assets and corporate reputation, employing empirical quantitative text mining and Natural Language Processing (NLP) in Python. A validated scale-based taxonomy within the TCFD framework applies query-driven rules to extract relevant text. This enables an evaluation of aspects of the reports, facilitating the development of a compliance index measuring each company’s adherence to TCFD recommendations. All companies showed year-on-year improvements (2023 was the most comprehensive), yet none fully adhered due to information gaps. Disparities in the disclosures of Scope 1,2 and 3, persisted, suggesting reputational risks. A replicable methodological model generating a compliance index that assesses the ‘being’ (‘true performance’) versus ‘seeming’ (‘external perception’) dichotomy within sustainability reports and acts as a potential reputational barometer for stakeholders. By providing unprecedented evidence of TCFD reporting in the Spanish energy sector, this study closes a significant academic gap. Future research may analyze ESG reports using AI agents, study the impact of ESG on energy-intensive companies from AI data centers, supporting services like Copilot, ChatGPT, Claude, Gemini, and extend this methodology to other industrial sectors. Full article

This study investigated how corporate maturity—measured through firm age and lifecycle stage—moderates the value relevance of Environmental, Social, and Governance (ESG) disclosures in a frontier market context, using Bangladesh as a case study. Drawing on panel data from 2011–2012 to 2023–2024 for 86 publicly listed non-financial firms, the study employed a modified Ohlson valuation framework, panel regression analysis, and multiple robustness techniques (2SLS, PSM). ESG disclosure was measured using a researcher-developed index aligned with international reporting standards (GRI, SASB, TCFD, UN SDGs). ESG disclosures are positively associated with firm value, but this relationship is significantly moderated by corporate maturity. Younger firms exhibit a stronger valuation effect from ESG transparency, driven by higher signaling and legitimacy needs. In contrast, mature firms experience a diminished marginal benefit, reflecting routine compliance rather than strategic differentiation. These findings challenge the uniform application of ESG assessment models and suggest the need for lifecycle-adjusted disclosure ratings, particularly in nascent regulatory environments like Bangladesh. Investors and regulators should tailor ESG evaluation criteria by firm age and industry sustainability exposure. Younger firms, often overlooked, may carry outsized ESG signaling value in emerging markets. Enhancing ESG transparency among younger firms can foster greater stakeholder trust, support inclusive growth, and strengthen social accountability in emerging economies. This study contributes to the ESG literature by introducing corporate maturity as a key moderating variable in value relevance analysis. It provides new empirical insights from a developing economy and proposes lifecycle-based adaptations to global ESG rating methodologies. Full article