Search Results (169)

This study examines how institutional investors adopt and utilize Environmental, Social, and Governance (ESG) information by integrating the Unified Theory of Acceptance and Use of Technology (UTAUT). Using the Analytic Hierarchy Process (AHP) with expert-based pairwise comparisons from 20 senior investment professionals at major South Korean financial institutions, we identify and weight key determinants influencing ESG information use among South Korean institutional investors. The results show that performance expectancy emerged as the most influential determinant (33.7%), followed by facilitating conditions (24.6%), social influence (22.8%), and effort expectancy (18.9%). At the sub-criterion level, usefulness for investment decision-making (11.2%), institutional encouragement (10.2%), and utilization of ESG information as a fiduciary duty (9.4%) recorded the highest global weights, whereas psychological comfort in utilizing ESG information (2.0%) and practical guidelines and training programs (3.7%) exhibited the lowest. These findings suggest that ESG adoption has evolved beyond early legitimacy-seeking behavior toward substantive and performance-driven integration, consistent with UTAUT predictions that performance expectancy and facilitating conditions gain salience in mature adoption phases, while effort expectancy and social influence diminish. This weight distribution indicates that ESG has been internalized as core analytical infrastructure informing investment decision-making and risk management, rather than functioning as a peripheral compliance tool. By empirically mapping ESG adoption determinants into a hierarchical structure, this study contributes to the literature on ESG diffusion, institutional investor behavior, and adoption theory, offering practical implications for regulators and financial institutions seeking to deepen substantive ESG integration. Full article

This study presents a comparative analysis of five gender equality certification schemes alongside the ISO 53800 standard with the aim of distilling shared conceptual foundations and design principles that can inform progress toward Sustainable Development Goal (SDG) 5 on gender equality. The comparative analysis reveals marked heterogeneity in scope, design architecture, indicators, and transparency. Methodologically, the study draws on the relevant literature, documentary evidence, and semi-structured consultations with five experts in gender equality, diversity management, auditing, and ESG reporting. Building on the most effective and robust features across gender equality schemes, the study proposes a universal framework for gender equality certification. Under this framework, an ideal universal certification model should apply the same core requirements to both public and private organizations, while including simplified procedures tailored to small- and medium-sized enterprises (SMEs). Moreover, the model should rely on a limited set of key performance indicators (KPIs), focusing on the most material dimensions and prioritizing quantitative measures. It should also strengthen employee feedback mechanisms and enhance accountability in corporate governance. The framework should also pay attention to intersectional dimensions, extend responsibility across the value chain, and address the gender-related implications of artificial intelligence (AI). Importantly, an ideal universal gender equality certification should ensure a high level of transparency through the public disclosure of certified organizations, assessment criteria, KPIs, and levels or scores achieved. Furthermore, it should be supported by a free digital self-assessment tool and robust auditing arrangements, underpinned by a sufficiently large pool of accredited certification bodies and gender-balanced audit teams. Finally, it should undergo periodic review and align with Environmental, Social, and Governance (ESG) principles and other related SDGs. Full article

Amid the new wave of technological revolution and industrial transformation, new quality productive forces (NQPFs) have become the key to a firm’s sustainable development. To help enterprises accelerate the improvement of their NQPFs, grounded in signaling theory, this paper takes data of China’s A-share listed companies from 2015 to 2024 as the research sample and uses a two-way fixed effects model to empirically examine whether and how superior ESG performance, serving as a high-quality signal, fosters NQPFs. The results show the following. There is a significant positive relationship between corporate ESG performance and NQPFs. This finding remains robust across a series of checks, including replacing the key explanatory variable, removing outlier years and cities, and addressing endogenous problems through instrumental-variable estimation. Heterogeneity tests reveal that the effect is more pronounced among non-state-owned firms and those located in Northeast China, whereas it is statistically insignificant for firms in Western China. Mechanism analysis indicates that ESG performance boosts NQPFs indirectly by raising analyst attention and investor confidence. Overall, this paper not only enriches research perspectives on the relationship between corporate ESG performance and NQPFs, but also offers theoretical support and practical reference for the formulation of corporate ESG strategies and the precise policy-making of governments. Full article

Corporate governance is increasingly recognised as a key mechanism supporting sustainability transparency, accountability, and long-term value creation. While prior research has examined governance–performance relationships and sustainability outcomes using proprietary ESG ratings, evidence on how governance structures enable sustainability disclosure remains limited, particularly in Central and Eastern Europe (CEE). This gap reflects heterogeneous institutional environments and uneven ESG data availability in emerging European markets. To address this limitation, this study develops and applies a Corporate Governance–Sustainability Index for Central and Eastern Europe (CGSI–CEE). The index integrates core governance mechanisms (such as board effectiveness, leadership structure and ownership discipline) with sustainability transparency indicators, namely ESG report publication and CO₂ emissions disclosure. The CGSI–CEE is constructed using publicly available firm-level data from CEE blue-chip companies over the 2018–2024 period and follows a transparent, theory-driven weighting scheme. The results reveal substantial heterogeneity in governance-enabled sustainability capacity across firms, sectors, and countries. Bivariate results indicate a negative association with short-term accounting profitability and a positive association with market valuation; however, these relationships weaken once firm-level characteristics are controlled for, reinforcing the interpretation of CGSI–CEE as a structural governance-capacity measure rather than a direct performance determinant. Full article

The correct valuation of collateral supporting real estate loans has always been a key issue for the stability of the credit system. Substandard lending practices and the absence of uniform valuation approaches have historically contributed to the accumulation of non-performing loans. In recent years, several regulatory measures operating at both the European and national level have introduced principles, rules and procedures aimed at standardizing the valuation of properties pledged as collateral for credit exposures. These interventions seek to promote greater transparency, consistency, and prudence in property appraisals, thereby enhancing the soundness and resilience of the financial system. In January 2025, the updated Regulation (EU) 575/2013 came into force, incorporating the Basel III reform (also referred to as Basel 3+ or Basel IV). Among the innovations introduced, the concept of property value (PV) is particularly relevant, a prudential value that excludes expectations of price growth and considers the sustainability of the value over time in relation to the duration of the loan. PV is defined as a derived value with respect to market value (MV), determined by considering the main current and forward-looking risk factors that may arise during the life of the loan, including environmental, social and governance (ESG) risks, the intrinsic characteristics of the property and expectations regarding the economic cycle. This paper proposes a quantitative model for the determination of PV, applied to a practical case involving a residential property located in a medium-sized city in Italy’s Veneto region. The model adopts a deterministic and a probabilistic approach, the latter implemented through Monte Carlo simulation, which is indeed a generalization of the deterministic one. The model links the assessment of PV to the possible evolution of the property’s key parameters and the real estate cycle over the duration of the loan. It was tested under the assumption of a twenty-year mortgage originated in 2025 for the purchase of a residential property in Italy, considering two alternative locations: a suburban area and a city-centre area. The analysis conducted showed a substantially higher MV haircut for the suburban property compared with the central location. This difference reflects the fact that PV is less sensitive to real estate cycle fluctuations in more premium, central locations. Furthermore, the use of Monte Carlo simulation in the probabilistic approach enabled the calibration of the haircut according to a predefined confidence level, confirming the pattern observed in the deterministic framework. The combined evidence strengthens the empirical robustness of the model and highlights the importance of locational and cyclical dynamics in collateral valuation. Full article

Generative AI has surfaced as a key driving force for corporate sustainable development and strategic transformation, offering new perspectives for effectively enhancing corporate ESG performance practices. Utilizing panel data sourced from Chinese A-share listed firms spanning the years 2012 to 2024, this research establishes and substantiates a model elucidating the mechanism by which generative AI impacts corporate ESG performance. The findings reveal the subsequent points: First, generative AI can effectively drive improvements in corporate ESG performance. Second, the caliber of information disclosure acts, in part, as an intermediary factor influencing the correlation between generative AI and corporate ESG performance enhancement. Third, sustainable innovation partially mediates the relationship between generative AI and corporate ESG performance enhancement. Fourth, environmental regulations weaken the beneficial influence exerted by generative AI on a company’s ESG achievements. Fifth, compared to non-manufacturing firms, companies situated in the central and western parts of China, and non-technology-intensive firms, the application of generative AI exerts a more pronounced enhancing impact on ESG achievements in manufacturing firms, firms in eastern regions, and technology-intensive firms. The research findings provide new insights for improving corporate ESG performance and provide strategic guidance for businesses aiming to attain long-term sustainable growth through reliance on generative AI. Full article

The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, and multi-criteria optimization of KPIs, rather than as a direct virtual replica of a specific physical production system. This paper proposes a hybrid simulation–prediction model based on a digital twin framework for optimization of KPIs in sustainable digital printing, with particular emphasis on overall equipment effectiveness (OEE). Due to the limited availability of structured industrial data, the model is developed using a synthetically generated dataset constructed in accordance with industry-reported operating ranges and technically realistic digital printing process variables. Random Forest and XGBoost algorithms are applied to model nonlinear relationships between process parameters and KPIs, including material waste, energy consumption, machine downtime, and OEE. Based on these predictive models, a constrained multi-objective optimization procedure is performed to identify Pareto-efficient configurations that reduce material waste and energy consumption while maintaining acceptable downtime and OEE levels. The results characterize structural trade-offs among environmental and operational KPIs within a formally defined decision space. Full article

The Polish energy sector is undergoing a profound transformation driven by decarbonization targets and the implementation of the European Union’s sustainability governance framework, including the Corporate Sustainability Reporting Directive, the European Sustainability Reporting Standards and the EU Taxonomy Regulation. These policy instruments aim to align corporate behavior, capital allocation, and risk management with long-term sustainability and climate objectives, particularly in energy systems characterized by high carbon intensity. This study examines how ESG reporting requirements are perceived by professionals involved in ESG reporting in Poland’s energy sector and how they are expected to influence economic performance and investment decisions. The analysis is based on survey data from 43 entities. Although the sample size is limited, it covers the key energy-sector entities in Poland, providing a comprehensive sector-level perspective. Non-parametric statistical tests, binary and ordinal logit models, principal component analysis, Kendall’s tau correlations, and cluster analysis are used to assess perceived economic benefits, compliance capacity, and cost-related challenges associated with ESG reporting. The results indicate that ESG reporting is perceived as an economically relevant instrument improving transparency and supporting the integration of environmental performance into investment and strategic decision-making. At the same time, respondents identify significant economic barriers, including high administrative costs, regulatory complexity, and legal uncertainty, particularly affecting carbon-intensive entities. Full article

Under the constraints of carbon peaking and carbon neutrality targets, corporate emission reduction is shifting from fragmented governance toward integrated governance that aligns pollution control with carbon reduction and long-term sustainable development. New energy transition policies have become a key instrument for restructuring urban energy, environmental, and economic systems, yet it remains unclear how these macro-level policies reshape firms’ marginal abatement cost–benefit structures and under what governance conditions they generate the synergy within corporate pollution reduction, rather than merely shifting burdens. It is valuable to identify whether, how, and under which governance conditions new energy demonstration city policies enhance the synergy between corporate pollution reduction and carbon mitigation. Guided by system synergy theory and a marginal abatement cost perspective, we use panel data on listed firms to construct a synergy index that jointly reflects multiple pollutant emissions and abatement costs, capturing both environmental effectiveness and economic efficiency. A DID model based on the staggered rollout of new energy demonstration cities is then employed to estimate the policy’s impact on the synergy between corporate pollution reduction and carbon mitigation and its contextual conditions. The results show the following: (1) Inclusion in a new energy demonstration city significantly increases the synergy within corporate pollution reduction. (2) Mechanism analysis indicates that higher municipal attention to green and environmental development and higher corporate ESG (environmental, social, and governance) performance strengthen the positive policy influence. (3) Heterogeneous effects are mainly concentrated in non-energy intensive industries, state-owned enterprises, and small firms, which indicates structural divergence in policy incentives across different types of firms. Overall, this study enriches the studies about the synergy between pollution reduction and carbon mitigation to the firm level, embeds a marginal abatement cost perspective into synergy measurement, and provides an evaluative framework that is consistent with how firms balance environmental and financial objectives. The findings contribute to the sustainability literature by informing the design and assessment of energy transition policies and by offering evidence to refine new energy demonstration city programs so that limited governance resources are directed toward more cost-effective joint gains. Full article

This study examines the impact of interlocking directorate networks on the ESG performance of listed companies in China, offering insights into the role of informal institutions in shaping sustainable business practices. Employing social network analysis and multiple regression methods, the results demonstrate that firms embedded in stronger interlocking directorate networks exhibit significantly superior ESG performance. Mechanism analysis identifies absorptive capacity as a key mediator, suggesting that network position improves ESG outcomes by enhancing a firm’s ability to assimilate and apply external knowledge. Furthermore, heterogeneity tests indicate that this positive effect is notably stronger among non-state-owned enterprises and firms where directors lack prior green experience. These findings have valuable implications for enterprises seeking to cultivate ESG competitive advantages in the era of network relationships. Full article

Guided by the Sustainable Development Goals, the ESG concept has increasingly become a key reference factor in corporate investment decisions. Given existing research on ESG practices in corporate risk management and the research gap regarding China’s media industry, this study examines the role of ESG practices in corporate risk-taking among China’s listed new media companies from 2009 to 2024. It proposes the following key hypotheses: there exists an inverted U-shaped relationship between ESG performance and corporate risk-taking in new media firms; Confucian culture moderates this nonlinear relationship. The findings confirm these hypotheses, demonstrating that ESG ratings have a nonlinear, rising-then-falling effect on corporate risk-taking, which is attenuated by Confucian culture. Furthermore, this inverted U-shaped relationship is more pronounced among firms in highly marketized regions, low-growth firms, and firms with low organizational inertia. This study extends the understanding of the economic consequences and mechanisms of corporate ESG performance, providing empirical evidence to help new media enterprises balance ESG responsibilities with strategic risk-taking in pursuit of sustainable development. Full article

Environmental tax is a key market-based instrument for promoting sustainability and reshaping corporate strategy. Using the panel data of Chinese listed firms from 2010 to 2023, this study employs text mining to measure digital transformation and examines the impact of environmental tax on corporate digitalization. The results show that environmental tax significantly promotes digital transformation. The mechanism analyses reveal that green technology innovation and ESG performance serve as important transmission channels. Furthermore, the effect is positively moderated by regional marketization, environmental information disclosure, and low-carbon city policies. The heterogeneity analyses indicate stronger effects in economically developed regions and firms with greater resource endowments. The additional analysis demonstrates that environmental tax enhances both total factor productivity and green governance performance through accelerating digital transformation, achieving a synergistic green–digital transition. This study provides empirical evidence on how market-based environmental policies can foster corporate digital transformation as a pathway toward sustainable development. Full article

This study investigates the financial and strategic implications of ESG implementation in the hotel sector, focusing on cost structures, stakeholder engagement, and risk-related outcomes. Empirical evidence remains limited in tourism-intensive economies, particularly regarding operational practices. Using a mixed-methods approach, we combine survey data from hotel managers in Crete, Greece, with a case study of a leading hotel enterprise. The findings reveal that environmental initiatives require substantial investment but can lead to operational efficiencies and regulatory alignment. Social and governance practices, while less capital intensive, play a key role in internal stakeholder trust. The study concludes that the integration of ESG into measurable key performance indicators, when strategically aligned with corporate objectives, contributes to long-term financial viability. These insights reinforce ESG’s growing role in enhancing resilience and governance effectiveness within the hospitality sector. Full article

Against the backdrop of a booming digital economy, manufacturing faces a conflict between its high energy consumption and sustainable development goals, positioning digital transformation as a key solution. Enterprise digital transformation is often categorized into management, production, and service digitalization. During transformation, it is challenging to advance all domains simultaneously, and interactions exist between them, so these effects should be considered. We use a sample of Chinese A-share listed manufacturing enterprises using data from 2012 to 2023 to investigate how digital transformations and their internal interactions affect enterprise sustainability performance (ESG performance), analyzing the differences between these effects in different types of enterprises. The findings demonstrate that digitalization across the management, production, and service domains enhances ESG performance. Significant interaction effects exist between management and production digitalization, between production and service digitalization, and across all three domains, while the interaction between management and service digitalization is insignificant. Heterogeneity analysis reveals that in private enterprises, the interaction between management and service digitalization positively affects ESG performance, whereas its effect is insignificant in state-owned enterprises. In large enterprises, interactions involving service digitalization are insignificant; in contrast, in SMEs, although service digitalization alone negatively impacts ESG, its interaction with management digitalization strengthens the latter’s positive effect. Mediation tests indicate that production efficiency partially mediates the effect of production digitalization on ESG, and innovation output mediates the effects of both management and service digitalization. Full article

Managerial decision-making is a core component of business management and plays a particularly critical role in Sustainable Business Models (SBMs), where it supports long-term competitiveness, adaptability, and positive environmental and social impact. SBMs are inherently complex, dynamic, and data-intensive, requiring advanced analytical capabilities to continuously monitor and optimize sustainability performance across Environmental, Social, and Governance (ESG) dimensions. Artificial Intelligence (AI) introduces new technological opportunities that fundamentally transform managerial decision-making by enabling advanced modeling, simulation, and the analysis of incomplete and heterogeneous data. The purpose of this research is to systematically analyze and synthesize existing AI-supported decision-making approaches used in sustainable business models, with a focus on how these methods transform traditional managerial decision-making frameworks through the integration of Environmental, Social, and Governance (ESG) criteria, and to assess the key benefits, limitations, and implementation conditions of AI-supported decision systems for achieving long-term organizational sustainability. Using a systematic literature review and comparative synthesis of recent theoretical and empirical studies, the research maps key AI-based decision-making approaches applied in sustainable business models and compares their managerial relevance across ESG dimensions. The results provide a structured overview of how different AI techniques contribute to sustainability monitoring, resource optimization, and risk assessment, while also outlining critical organizational, governance, and ethical constraints affecting their practical deployment. Full article