Search Results (102)

Environmental, Social, and Governance (ESG) disclosure plays an increasingly important role in evaluating corporate sustainability performance. However, the agricultural chemicals industry faces unique environmental and social challenges, while existing ESG assessment frameworks remain largely generic and fail to capture industry-specific disclosure requirements. To address this gap, this study develops a multi-level ESG disclosure quality evaluation framework for the agricultural chemicals industry based on the GRI 2021 Standards, China’s regulatory requirements, and sector-specific production characteristics. The framework was developed through targeted qualitative content analysis and text coding of ESG disclosures from ten listed agricultural chemical companies selected from the complete eligible population of 17 Chinese A-share agricultural chemicals enterprises that met the study’s predefined inclusion criteria and had Huazheng ESG ratings. This process resulted in a hierarchical structure comprising three dimensions, 14 first-level indicators, 61 second-level indicators, and 350 third-level observation indicators, with particular emphasis on biodiversity conservation, farmer support, and corporate governance. The Analytic Hierarchy Process (AHP) was then applied to determine the weights of the first-level and second-level indicators through expert evaluation, while a three-point scoring system (0–2) was established for the third-level indicators to construct the industry-specific ESG disclosure quality evaluation model. The framework was subsequently evaluated using an independent sample of the remaining seven listed agricultural chemical companies. A benchmarking comparison with Huazheng ESG ratings showed broad alignment in overall patterns while also revealing important company-level differences and disclosure-quality gaps not readily captured by the general ESG ratings. Furthermore, multi-level ±20% weight perturbation analyses demonstrated the framework’s stability, discriminative ability, industry suitability, and computational robustness. The proposed framework provides a practical and transparent tool for assessing ESG disclosure quality in the agricultural chemicals industry and offers a methodological foundation for developing sector-specific ESG disclosure evaluation frameworks in other high-impact industries. Full article

Environmental, Social, and Governance (ESG) metrics are increasingly used to assess corporate sustainability performance, yet their reliance on public disclosure raises questions about whether they capture actual organizational implementation. This study examines the alignment between externally reported ESG–Sustainable Development Goal (SDG) performance and internal perceptions in Kazakhstan, an emerging market characterized by uneven disclosure norms, institutional enforcement, and sectoral exposure. The analysis combines disclosure-based ESGQ scores with original survey data; matched comparisons between the two resulted in 52 responses from 25 firms. Using linear mixed models and principal component analysis, the study identifies a systematic disclosure–perception gap, in which internal perceptions exceed external disclosure-based scores in the governance and SDG dimensions, where standards are least codified and hardest to verify. Additionally, industry membership is more consistently associated with this disclosure–perception gap than ownership structure or firm size. The findings suggest that ESG measurement gaps in emerging markets are not reducible to greenwashing; instead, disclosure-based evaluation systems may under-represent practices that organizational insiders perceive as embedded, although positive response bias among respondents cannot be ruled out. The results support the idea that future research should complement disclosure-based ESG assessment with internal organizational evidence. Full article

Artificial intelligence has become increasingly embedded in digital marketing, reshaping how firms collect consumer data, personalize content, automate persuasion, and evaluate campaign performance. While these developments offer clear strategic benefits, they also raise important ethical questions concerning privacy, transparency, fairness, manipulation, and accountability within platform-mediated marketing environments. This article presents a structured thematic review of recent research on the ethics of AI-driven digital marketing. The review focuses on English-language peer-reviewed journal articles and high-quality conference proceedings published between January 2023 and September 2025. Searches were conducted across Scopus, ScienceDirect, SpringerLink, ACM Digital Library, IEEE Xplore, MDPI, PubMed, and selected academic repositories. After deduplication, screening, and full-text assessment, 91 studies were included in the final synthesis. Methodological quality was appraised using the Mixed Methods Appraisal Tool and Joanna Briggs Institute critical appraisal criteria, while the findings were examined through thematic synthesis. The review identifies five recurring ethical domains in the literature: data privacy and GDPR compliance, algorithmic transparency and explainable AI, algorithmic fairness in targeting and automated decision-making, dark patterns and deceptive interface design, and influencer or virtual influencer disclosure. The evidence suggests that privacy, consent, deceptive design, and transparency are the most extensively discussed areas, whereas the practical effectiveness of fairness interventions, explainability tools, and disclosure mechanisms remains more mixed and context-dependent. Across these themes, ethical risks appear not only as the result of individual corporate decisions but also as outcomes shaped by platform infrastructures, ranking systems, data access arrangements, and performance-oriented advertising metrics. The article contributes by organizing recent scholarship into a coherent thematic framework and by situating AI-driven digital marketing ethics within a broader platform-governance perspective. It argues that responsible implementation requires clearer distribution of accountability among businesses, platforms, regulators, and researchers. The review is limited by its focus on the 2023–2025 period, its English-language scope, and the methodological heterogeneity of the included studies, which prevents statistical meta-analysis. Full article

Environmental disclosure and environmental, social, and governance assessments are gaining importance, using Corporate Disclosure Project (CDP) scores to evaluate corporate environmental disclosure. However, scarce evidence links CDP scores with performance and value among Japanese manufacturing firms. This study examines these associations using panel data from 2018 to 2023, combining financial data from Nikkei NEEDS Financial QUEST with CDP scores for climate change, water security, and forests. CDP ratings were converted into ordered numerical values, and regression models were estimated with firm-level controls, firm age, applicable fixed effects, and an annual macroeconomic control variable. Robustness checks included categorical score specifications, propensity score matching, and two-step system generalized method of moments estimation. The results indicate that the overall CDP score is positively associated with Tobin’s Q, whereas the evidence for return on assets and return on equity is weaker. Improvements in the overall score are also linked to increases in Tobin’s Q. By contrast, individual climate change, water security, and forest sub-scores show weak and inconsistent statistical significance. These findings should be interpreted as associations, not definitive causal effects. The study extends environmental accounting research beyond carbon-focused analyses, suggesting that aggregate CDP evaluations are more value-relevant than disaggregated sub-scores. Full article

We examine whether board activity (B_ACTIV), board size (B_SIZE), board independence (BIND), and board tenure (BOARD_TEN) are associated with the performance of South African real estate investment trusts (REITs) over the period 2013 to 2025. The REIT framework provides a rigorous setting to evaluate corporate governance theory, as statutory distribution mandates constrain payout discretion and contracted-income business models limit managerial opportunism, suggesting that governance effects concentrate within specific performance channels. We estimate dynamic panel models using a two-step system GMM framework with collapsed instruments, year fixed effects, Windmeijer-corrected standard errors, and firm-level controls for firm size (SIZE), leverage (LEV), and asset growth (GROWTH) to address endogeneity, unobserved heterogeneity, and performance persistence. We evaluate robustness through an endogenous-regressor specification, a bootstrap bias-corrected LSDVC estimator, and outlier-adjusted estimations. The sample comprises 30 JSE-listed REITs. We evaluate performance across funds from operations per share (FFO_PS), dividend yield (DIV_YIELD), return on assets (ROA), return on equity (ROE), return on invested capital (ROIC), and earnings per share (EPS). Our findings reveal that B_SIZE exhibits a statistically significant negative association with accounting profitability, where each additional director corresponds to a 1.0 percentage point reduction in ROE and a 0.32 percentage point reduction in ROA. The ROE effect remains robust across every identification strategy, including specifications treating board composition as endogenous and estimations winsorizing the dependent variables. Because firm SIZE remains statistically insignificant while LEV and GROWTH display their expected theoretical signs, the B_SIZE effect is isolated from firm scale. BIND demonstrates a directionally positive but specification-sensitive association with returns and payouts, whereas BOARD_TEN shows no robust association with any performance metric, and B_ACTIV effects attenuate once endogeneity is addressed. Overall, governance effects concentrate in operating efficiency and payout measures while remaining absent from per-share metrics, reflecting the precise channels through which boards exercise authority. Our findings caution against board expansion in this sector, highlight board scale as a transparent governance screen for investors, and demonstrate that meeting frequency and tenure benchmarks offer no reliable performance signal. Full article

Contemporary food systems are increasingly shaped by financialization, corporate concentration, and unequal distributions of power that influence food production, food environments, dietary exposures, and population health. However, the relationships among food-system financialization, food sovereignty, and oral health remain insufficiently integrated within food-security, sustainability, and One Health research. This structured narrative review critically synthesized interdisciplinary evidence from Scopus, Web of Science, PubMed/MEDLINE, Google Scholar, citation searching, and authoritative institutional sources to examine these relationships and develop an integrative conceptual framework. The synthesis indicates that financialization may influence health through market concentration, commodity dependence, corporate control of food environments, and the expansion of ultra-processed foods, whereas food sovereignty may modify these pathways by strengthening agency, equitable resource distribution, local governance, and ecological resilience. Dietary exposures and related biological mechanisms provide plausible pathways through which these structural processes may contribute to oral-health outcomes and inequalities. The proposed framework integrates food-system structures, governance, food environments, dietary exposures, biological pathways, oral health, and One Health implications within a common analytical model. Oral health is therefore proposed as a potential biological interface through which food-system transformations and inequalities may become measurable. Empirical research is required to test these pathways and evaluate the framework’s applicability across populations and food-system contexts. Full article

This study examines how Corporate Social Responsibility (CSR) can contribute to the development of a responsible governance orientation in hotel companies located in inland tourism destinations through internal organizational capabilities. Unlike approaches focused primarily on the external dimension of CSR, this research directs attention to the internal mechanisms that enable responsible commitments to be transformed into ethical, verifiable, and sustained management practices. Drawing on stakeholder theory and the resource-based view, the proposed model analyzes the effects of ethical organizational design, internal CSR implementation, and accountability and evaluation mechanisms on responsible hotel governance orientation. The empirical analysis is based on a sample of 112 hotel managers in Extremadura, Spain, and uses partial least squares structural equation modeling (PLS-SEM), complemented by a multigroup analysis based on hotel category. The results show that ethical organizational design is the model’s primary antecedent, positively influencing internal CSR implementation, accountability and evaluation mechanisms, and responsible hotel governance orientation. Internal CSR implementation and accountability and evaluation mechanisms also act as explanatory pathways linking ethical structures to responsible governance. The multigroup analysis reveals distinct patterns across hotel categories, particularly in the relationships associated with ethical organizational design. At the practical level, this study offers guidance for strengthening responsible management in hotel companies through ethical structures, internal training, and continuous monitoring and improvement systems. Full article

Cross-border remanufacturing trade is developing steadily. Carbon tariffs and carbon cap-and-trade regulation have emerged as two important instruments for carbon governance, yet their independent and combined effects on supply chain operations remain insufficiently discussed, which delivers supplementary analytical space for cross-border remanufacturing supply chain research. This paper examines a cross-border supply chain consisting of an exporting manufacturer and an importing retailer that distributes both new and remanufactured products. Four research scenarios are established: no regulation, carbon tariffs only, cap-and-trade only, and dual mixed regulation. Adopting a two-stage Stackelberg game, we analyze equilibrium pricing, production, and carbon abatement decisions, and further evaluate environmental performance and overall social welfare. Firms with different initial carbon emission levels respond differently to regulatory stringency. The results show that carbon tariffs reduce total emissions but erode corporate profits and social welfare, while cap-and-trade regulation can mitigate such adverse effects. When carbon tariffs are stringent and carbon quotas are sufficient, dual regulation improves all participants’ profitability alongside better environmental quality and higher consumer surplus only within this paper’s simplified analytical context. This study offers tentative operational references for remanufacturing firms and theoretical and analytical implications for governments to design compatible cross-border carbon regulatory systems. Full article

In the context of increasing global economic uncertainty, the financial risks of listed companies exhibit significant dynamic evolution characteristics. Traditional methods based on static indicators are no longer sufficient to meet the requirements of forward-looking early-warning. Considering the intertemporal transmission and path dependence characteristics of enterprise financial risks, from the perspective of deep temporal modeling, this study constructs a Gated Recurrent Unit model (Residual GRU, RGRU) that integrates a residual connection mechanism to enhance the feature transmission ability in the deep network and alleviate the problem of gradient propagation difficulties. Taking Chinese A-share listed companies from the first quarter of 2010 to the fourth quarter of 2024 as the research sample, and using the Special Treatment (ST) or Delisting Risk Warning (*ST) status as the distress determination criterion, an enterprise financial distress early-warning system is constructed based on multi-dimensional financial and corporate governance indicators. On this basis, ablation experiments are set up from two dimensions—the type of recurrent units (GRU and LSTM) and the connection mechanism (residual connection and dense connection)—and performance is evaluated through systematic parameter optimization and multi-layer network structure experiments. The empirical results show that the RGRU model outperforms the comparison model in classification indicators such as accuracy, F1 score, recall, and probability prediction indicators such as Brillouin score and logarithmic loss, and demonstrates better stability and generalizability. The research results indicate that the introduction of residual connections can effectively improve the prediction performance and practical value of recurrent neural networks in the enterprise financial distress early-warning task. Full article

Corporate sustainability reporting has become a central component of corporate governance under the European Sustainability Reporting Standards (ESRS); yet organizations continue to face challenges in integrating sustainability data into decision-making processes. This study addresses this gap by developing a system dynamics (SD) model for analyzing corporate sustainability performance based on ESG indicators. The proposed framework integrates environmental, social, and governance variables into a unified dynamic structure, capturing feedback loops, time delays, and interdependencies among sustainability dimensions. The model is empirically tested using a ten-year dataset from a fuel retail company, enabling both historical validation and scenario-based analysis. The results demonstrate that the model successfully reproduces observed trends in resource efficiency, safety performance, and social indicators while highlighting trade-offs associated with business growth, particularly in energy consumption and CO₂ emissions. Sensitivity analysis demonstrates that the sustainability index remains stable under uncertainty in normalized ESG indicator values. Scenario simulations indicate that several sustainability targets for 2030 are achievable under current development trajectories; however, maintaining progress requires continuous investment and organizational commitment. The study contributes to sustainability research by integrating ESRS-based indicators into a dynamic modeling framework and provides a practical decision-support tool for corporate sustainability management, enabling more informed planning, performance evaluation, and strategic alignment with long-term sustainability goals. Full article

The transition to a circular economy (CE) in the automotive sector is increasingly critical amid rising resource pressures and climate imperatives. In India, this shift is influenced by regulatory initiatives, corporate sustainability goals, and life-cycle-wide environmental challenges. However, current studies remain fragmented, often neglecting the linkages between policy drivers, material issues, and firm-level responses. This study aims to evaluate how CE strategies are operationalized across the Indian automotive value chain using a Drivers–Materiality–Response (DMR) analytical framework. A multiple-case qualitative analysis was conducted involving six major automotive firms and associated ecosystem actors, with data sourced from corporate reports, national policies, and third-party assessments from 2018 to 2024. Semi-structured interviews with 11 industry experts were incorporated to strengthen triangulation, validate firm-level circular economy claims, and support the reliability of the DMR-based interpretation. Findings reveal strong alignment with national CE policies among leading firms, particularly Tata Motors and Mahindra, with comprehensive integration of electrification, battery reuse, zero-waste goals, and digital mobility solutions. However, challenges remain in end-of-life vehicle (ELV) formalization and circularity in downstream systems. The DMR model effectively bridges gaps in existing frameworks by offering a life-cycle-based lens that links Environmental, Social and Governance (ESG), Life Cycle Assessment (LCA), and policy–firm dynamics. The study contributes a scalable diagnostic tool for assessing CE maturity in emerging economies. While limited by reliance on secondary data, the triangulated approach enhances reliability and provides actionable insights for policymakers and industry leaders. Full article

Environmental, Social, and Governance (ESG) performance has become a central criterion for evaluating corporate sustainability. Yet the empirical relationship between ESG and financial performance remains contested, especially in emerging markets where institutions are evolving. This study examines how ESG performance relates to corporate financial performance among Chinese A-share listed companies and tests whether corporate innovation functions as a transmission mechanism. Using a balanced panel (2015–2023), we combine System Generalized Method of Moments (System GMM) with a non-parametric Bootstrap mediation procedure. We decompose ESG into environmental, social, and governance dimensions and distinguish between innovation input (R&D investment) and innovation output (patent generation). The results indicate a positive directional association between overall ESG performance and return on assets (ROA), but the direct financial effect is primarily driven by the governance dimension. Environmental and social performance do not show robust direct effects. However, ESG significantly promotes corporate innovation, especially patent output. Bootstrap mediation results confirm that patents represent a robust and universal channel through which ESG contributes to financial performance, while the R&D pathway is more conditional. The findings also indicate ownership heterogeneity between state-owned and private enterprises. By distinguishing between innovation input and output, this study explains how ESG practices may be translated into economic value in an emerging market context distinct from advanced economies. Full article

The rapid transformation of urban centers into smart environments introduces complex challenges at the intersection of technological advancement, environmental stewardship, and social justice. This study evaluates Lisbon’s smart city transition by establishing an integrated framework that links digital equity with Environmental, Social, and Governance principles. Employing a convergent qualitative research design, this paper triangulates a comprehensive regulatory policy analysis with primary empirical data gathered from twenty-five semi-structured interviews with municipal officials, academic experts, and residents of marginalized communities. The findings expose critical systemic disparities in digital infrastructure deployment, device affordability, and platform literacy across socio-economic strata, demonstrating how localized digital divides directly impede the execution of urban ESG objectives. While green financing mechanisms offer robust pathways for sustainable energy and transit infrastructure, their equity outcomes remain constrained without mandatory, transparent information disclosure systems that mitigate agency costs. Cultivating urban resilience requires shifting from tokenistic e-governance to genuine citizen empowerment. This study offers a novel theoretical contribution by operationalizing corporate ESG metrics within public urban governance frameworks, providing an empirical roadmap for municipal policymakers globally to balance digital innovation with structural inclusion and environmental accountability in smart city agendas. Full article

In bank-dominated financial systems, the accumulation of non-performing loans is a recognised source of systemic vulnerability, as correlated corporate defaults can erode bank capital, impair liquidity, and propagate stress across interconnected portfolios. Firm-level default detection thus constitutes a microprudential foundation of macroprudential stability: the reliable early identification of risky borrowers reduces both individual credit losses and the aggregate exposures that drive system-level fragility. Yet the use of structured data-mining pipelines for this task remains underexplored in Central and Eastern Europe. This study applies the CRISP-DM methodology to predict corporate loan default using data on 302 Slovak corporate borrowers, combining financial ratios from publicly available financial statements with selected company and loan-related information from internal bank records. Seven individual classifiers were developed and compared: decision trees (CART, CHAID, C5.0), logistic regression, discriminant analysis, and neural networks (MLP, RBF), together with a stacked ensemble based on their outputs. Model performance was evaluated using sensitivity, overall classification accuracy, and area under the ROC curve (AUC), with sensitivity treated as the primary criterion because of the asymmetric costs of misclassification in credit risk assessment. The results confirm that historical firm-level information provides a reliable basis for default prediction, with tree-based models consistently outperforming statistical and neural network approaches. The stacked ensemble achieved the strongest overall performance, whereas C5.0 and CHAID showed that interpretable classifiers can also deliver competitive predictive accuracy. A champion–challenger deployment architecture is proposed, in which the ensemble serves as the performance-oriented champion and interpretable models act as challengers; this arrangement contributes to the operational resilience of the credit-risk assessment process and aligns with macroprudential expectations of model governance, auditability, and explainability. The study offers a replicable methodological framework for integrating data-driven decision support into credit evaluation in comparable banking settings. Full article

With intensifying measures related to investor and policy requirements, corporate governance and sectoral environmental performance became a focal point for sustainability disclosure, especially in energy-intensive industries with high environmental externalities. This study evaluates whether corporate environmental governance practices in key sectors correspond to their pollution intensity and economic output, analysing a panel dataset across EU member states, for the 2000–2021 period. The empirical methodology includes ordinary least squares (OLS), fixed- and random-effects models, and dynamic system generalised method of moments (GMM) panel estimation to account for sectoral heterogeneity. Results prove that sectoral value added is an influential factor of greenhouse gas emissions, with carbon dioxide exhibiting the highest elasticity to economic activity, followed by methane emissions, and nitrous oxide displaying cross-country variations due to structural and regulatory differences. While services and manufacturing sectors partially decouple via cleaner technologies, overall growth positively correlates with emissions, and renewable energy offers limited mitigation due to scale and integration challenges. Conclusions emphasise robust governance frameworks in high-value energy sectors to meet EU climate-neutrality goals, as stronger environmental accountability attracts capital and supports sustainable development, underscoring the needs for targeted decarbonisation, regulatory coordination, and accelerated technological innovation within persistent industry disparities. Full article