Search Results (1,306)

This study analyzes the impact of firm digital transformation on environmental, social, and corporate governance (ESG) performance, incorporating financing constraints as a moderating factor. Based on dynamic capability theory and resource orchestration theory, the analysis is conducted using panel data from China’s A-share listed firms between 2009 and 2025. Results show that digital transformation is positively associated with ESG performance, and that this association weakens as financing constraints intensify and may reverse when constraints are severe. The association is strongest among mature firms and among firms followed by more analysts. These findings identify financing constraints as a boundary condition for the conversion of digital transformation into ESG performance and suggest that easing financing constraints may help digital investment translate into sustainability outcomes. Full article

Geopolitical conflict, tariff shocks, and rapidly evolving environmental, social, and governance (ESG) regulation have made global supply chains markedly less predictable. This study examined how manufacturing supply chain decision-makers perceive six regulatory-volatility risks, including labor, environmental, customs, ownership, military-logistics, and distribution, and whether those perceptions predict recorded engagement outcomes. Adopting an information systems perspective, we retrospectively analyzed 1988 fully anonymized decision-maker records from the digital lessons-learned repository of a multinational supply chain logistics firm, applying descriptive statistics, exploratory factor analysis with parallel analysis, and binary logistic regression with classification diagnostics. The perceived risks did not converge on a unified regulatory-volatility construct: sampling adequacy was weak (KMO = 0.470), and parallel analysis retained three single-indicator factors (distribution, environmental, and ownership risk) that were close to orthogonal apart from one moderate environment-distribution association. Critically, the sustainability-motivated risks were rated lowest, with a median labor severity of 0 and an environmental severity of 1 on 0–5 scales, during a period of record forced-labor enforcement; this perception gap invites sustainability leakage, whereby surprise enforcement provokes supplier exit and sourcing flight rather than remediation within scrutinized regions. The logistic regression separated success from failure perfectly in-sample (accuracy = AUC = 1.000), driven jointly by the near-collinear distribution and supply items—a complete-separation pattern warning of label leakage when digitized organizational records are mined for artificial intelligence-based decision support. The two items are near-duplicate measures (r = 0.915), so no individual predictor effect is identified. The findings favor multidimensional rather than composite digital risk dashboards, provenance-aware data governance for supply chain analytics pipelines, and digitally enabled ESG compliance under volatile regulation, contributing an empirically grounded information systems lens to sustainable supply chain management. Full article

China’s dual-carbon goals require firms to pursue low-carbon transformation, while digital infrastructure offers new opportunities for corporate emission reduction. Using data from Chinese A-share listed firms from 2010 to 2024, this study treats the establishment of National Big Data Comprehensive Pilot Zones (NBDCPZ) as a quasi-natural experiment and applies a multi-period difference-in-differences model. The results indicate a statistically significant negative relationship between policy and estimated corporate carbon emissions. This result remains robust to parallel-trend tests, propensity-score-matching difference-in-differences (PSM-DID) estimation, and lagging control variables by one period. Mechanism tests suggest that the policy is negatively associated with estimated corporate carbon emissions through pathways consistent with green innovation, digital transformation, and easing financing constraints. Heterogeneity analysis indicates stronger effects among firms in highly competitive industries, non-heavy-polluting sectors, and southern China. Moreover, the pilot policy enhances corporate Environmental, Social, and Governance (ESG) performance. Overall, this study provides evidence that big-data-related policies can facilitate corporate decarbonization and offers policy implications for carbon reduction through data sharing, openness, and governance. Full article

Sustainability policies introduced under the European Green Deal have strengthened climate-related disclosure requirements for agri-food companies. In particular, the Corporate Sustainability Reporting Directive requires in-scope companies to transparently disclose information on their environmental performance. However, the academic literature on decarbonisation in the olive oil sector remains fragmented. This systematic literature review synthesises findings by considering cultivation, milling and retail, and waste management as interconnected stages of the olive oil supply chain and by developing a matrix linking decarbonisation strategies to the relevant European Sustainability Reporting Standards (ESRS) environmental, social and governance (ESG) topics. Following the PRISMA protocol, 42 peer-reviewed studies from Scopus and Web of Science were included in the final synthesis, covering cultivation (RQ1), milling and retail (RQ2), and waste management (RQ3). The cultivation stage represents an important part of the emission profile of the chain while also offering potential for carbon sequestration through sustainable management practices, such as reduced tillage, cover crops, organic amendments and biochar application. In the downstream stages, the mill and its retail interface rely on a different set of measures, including two-phase extraction, rooftop photovoltaic systems, thermal recovery from pits, and lighter bottles transported in bulk. Waste management also offers opportunities to recover value from pomace, mill wastewater and pruning waste through biogas, biochar, compost or phenolic extracts. The potential for a net-negative carbon balance is context-dependent and varies with system boundaries, the balancing period, functional units, and the methods used to account for carbon sequestration. The matrix offers a clear classification of decarbonisation strategies and ESRS topics, opening valuable avenues for upcoming studies to extend its practical utility. Full article

Balancing production resilience with environmental, social and governance (ESG) performance is difficult when disruptions, policy constraints and stakeholder expectations interact over time. This paper proposes a six-node dynamic network data envelopment analysis architecture whose admissible weight intervals are adjusted by an LSTM learner using IoT-derived shock states. The application covers 96 aggregate monthly periods from 2018 to 2025 across three countries and four technology-based manufacturing groups. Figure-grounded diagnostics indicate mean resilience scores of 0.7016 for the basic IoT-DEA benchmark and 0.7134 for the complete model (paired difference = 0.0118; 12-month moving-block bootstrap 95% CI = 0.0015–0.0245; p = 0.001), while mean uncertainty falls from 0.0536 to 0.0205, a 61.8% reduction. Threshold sensitivity shows that governance-delay and skill boundaries vary by industry and carbon-constraint severity rather than constituting universal standards. In the calibrated policy simulation, the combined carbon-tax and green-subsidy path reaches approximately 0.90 by month 96, compared with 0.79 under no policy; this contrast is interpreted as a scenario result, not a firm-level causal treatment effect. NSGA-III and SHAP then connect the measured constraints to Pareto-efficient portfolios and sector-specific managerial priorities. The framework’s main supported contribution is more stable, temporally explicit ESG-resilience diagnosis with transparent limits on causal and cross-sectional inference. Full article

As a widely recognized measure of corporate sustainability, ESG performance exerts a significant influence on corporate exports and risk-coping capabilities. Using product–destination-level panel data of Chinese listed firms from 2009 to 2016, compiled from the China Customs Database and the CSMAR Database, this paper employs the High-Dimensional Fixed Effects (HDFE) model to empirically examine the impact of ESG performance on corporate export resilience and its underlying mechanisms. The findings reveal that improved ESG performance significantly strengthens firm export resilience, a conclusion that remains robust after a series of robustness checks and addressing endogeneity concerns. ESG performance directly enhances export resilience through its environmental, social, and governance dimensions, with the social dimension exhibiting the strongest effect. Mechanism analysis indicates that ESG performance enhances export resilience by promoting diversification in both export products and export markets. Heterogeneity analysis reveals that the effect varies significantly across countries, products, and firms. Specifically, the positive effect is more pronounced for exports to developed countries, Belt and Road Initiative (BRI) participating countries, and coastal countries. Moreover, ESG performance contributes more strongly to the export resilience of final goods, high-technology products, and products with comparative advantages. At the firm level, the enhancing effect is more evident for state-owned enterprises, capital-intensive firms, and large-scale enterprises. Further analysis reveals that ESG performance and export resilience exhibit a positive joint effect in enhancing overseas market profitability and overseas revenue sustainability. These findings offer actionable implications for policymakers and exporters aiming to embed ESG principles into their export operations, which may further facilitate the sustainable and high-quality transformation of foreign trade. Full article

More than one type of policy is necessary to advance the sustainability transition and stimulate innovation. However, limited empirical evidence exists regarding the synergistic effects of environmental and innovation policies on corporate green innovation. Using data on Chinese listed companies in heavy-pollution industries from 2007 to 2022 as samples, this study employs a multi-phase difference-in-differences (DID) approach to examine the synergistic effect of China’s Innovative City Pilot Policy (ICPP) and Low-Carbon City Pilot Policy (LCPP) on corporate green innovation. The results show that the ICPP-LCPP synergy promotes green innovation among enterprises in heavily polluting industries. Further analyses reveal that this effect is concentrated among leading enterprises, with large state-owned leading enterprises (LSLEs) exhibiting a particularly pronounced response. Meanwhile, environmental, social, and governance (ESG) performance positively moderates the relationship between the ICPP–LCPP synergy and green innovation among leading enterprises. Green innovation by leading enterprises has a positive effect on follower enterprises’ green patent grants, particularly green invention patent grants. This study provides important insights into how policy synergy can foster corporate green innovation and facilitate a collaborative sustainability transition involving both leading and follower enterprises. Full article

Given the limited environmental, social, and governance disclosure (ESGD) among Palestinian firms, this study’s purpose is to explore potential connections between diversity of board gender and nationality, as well as representation of non-executive directors, environmental, social, and governance factors and aggregate ESG disclosure. The study focuses on industrial companies listed on the Palestine Exchange (PEX), employing random-effects panel-data regression with firm-clustered robust standard errors on a balanced panel of 11 industrial companies, all listed on PEX from 2018 to 2024. The results show that ESGD is positively associated with female directors, foreign directors, and non-executive directors and provide support for agency theory, resource dependence theory, and stakeholder theory. This study furthers the literature via empirical evidence (from a conflict-affected emerging market) on the impact of board gender diversity, nationality diversity, and non-executive directors on ESGD. The study recommends that policymakers and stakeholders promote board independence and diversity of gender and nationality, with a view to enhancing ESG disclosure in Palestine. Full article

Manufacturing firms face tightening environmental requirements while seeking to maintain supply continuity under disruption. This study examines whether environmental regulatory pressure (ER) is associated with supply chain resilience (SCR) and whether informational, relational, and structural governance adjustments accompany this relationship. Using 22,638 firm-year observations from Chinese A-share listed manufacturers during 2013–2023, we construct a continuous exposure-based measure from province-year environmental-governance intensity and predetermined two-digit industry pollution exposure. Because ER varies continuously rather than reflecting staggered policy adoption, the analysis is not a staggered difference-in-differences design. Firm and year fixed-effects estimates show a positive ER–SCR association. The association remains positive across alternative exposure, timing, matching, clustering, first-difference, province-by-year fixed-effects, and province-specific-trend specifications. Pathway analyses are consistent with greater digital integration, lower supplier payment pressure, and lower supplier dependence, with the strongest formal evidence observed for supplier payment governance. The positive association is stronger among firms with higher lagged environmental, social, and governance (ESG) scores and among non-state-owned firms, with suggestive evidence for light-asset firms. Overall, the results indicate that environmental regulatory pressure is systematically associated with resilience-relevant supply chain governance adjustment. Full article

We examine whether digital industry cluster policy is associated with corporate environmental, social, and governance (ESG) performance. We study digital industry clusters within China’s Innovative Industrial Cluster program, a spatial industrial policy combining cluster designation, fiscal support, digital infrastructure, and innovation platforms. Using Chinese A-share listed firms from 2009 to 2023, we exploit staggered cluster designation across cities and estimate difference-in-differences models with firm and year fixed effects. Cluster designation is associated with higher ESG scores, mainly through the governance dimension and, to a lesser extent, the environmental dimension. Additional analyses show that treated firms receive more government innovation subsidies, face lower financing constraints, and increase R&D intensity, consistent with resource provision channels. The results are robust to industry-by-year and province-by-year fixed effects and to wild cluster bootstrap inference. Effects are stronger for firms with greater market attention and in high-technology industries, and larger in regions with weaker digital infrastructure. The findings provide firm-level evidence on how spatially targeted digital industrial policy may support corporate sustainability practices. Full article

This study examines how environmental, social and governance (ESG) pressures are translated into verifiable governance practices in European banking and asks why this translation remains uneven in the Western Balkans. Drawing on institutional theory, Europeanisation and the evolving architecture of the Corporate Sustainability Reporting Directive (CSRD), European Sustainability Reporting Standards (ESRS), prudential ESG risk governance and sustainability assurance, the article distinguishes between ESG pressure and ESG measure. The empirical analysis uses an original, manually coded dataset of 55 banks across eleven European countries. A governance-oriented maturity framework captures the progression from CSR-dominant disclosure to formal reporting standards, board-level integration and external assurance. Descriptive statistics and group comparison tests reveal a pronounced institutional divide: EU-core and Croatian banks occupy the highest maturity category, while banks in selected Western Balkan systems remain concentrated around partial integration and lack local assurance. A robustness comparison using ESG_core, which excludes reporting standards and assurance, confirms that the regional divide persists beyond those mechanically related components. A double-coded subsample of 17 banks further demonstrates substantial-to-perfect inter-coder reliability across the principal coded dimensions. The article then develops a complementary policy architecture for a permissioned, blockchain-enabled ESG data platform based on standardized application programming interfaces, off-chain data storage, on-chain hashes and shared attestations. The proposed design links triple-entry accounting principles with regulatory supervision and independent assurance while explicitly addressing data protection, interoperability and the oracle problem. The article contributes by integrating comparative evidence on ESG governance maturity with a technologically realistic pathway for reducing data and assurance gaps in transition economies. Full article

Environmental, Social, and Governance (ESG) ratings increasingly shape capital allocation, corporate strategy, and regulatory oversight, yet their credibility is constrained by methodological opacity, rating divergence, and greenwashing risk. Prior reviews treat machine learning (ML) in ESG as a prediction problem. We identify an emerging research trajectory in which ML is increasingly used not only to consume ESG signals but also to verify their construction and credibility. Drawing on signaling theory, we conduct a PRISMA-guided systematic review of 127 peer-reviewed studies from Scopus and Web of Science to examine how machine learning (ML), deep learning (DL), Natural Language Processing (NLP), and Explainable AI (XAI) are transforming ESG rating analysis. We develop a tripartite framework classifying studies by the functional role of the ESG score: predicted (n = 29), used (n = 57), or assessed (n = 41). Our central contribution is the first synthesis of the methodological-assessment stream, organized into four clusters: XAI reverse-engineering of proprietary scoring functions, divergence reconciliation, greenwashing detection, and unsupervised industry-materiality clustering. The evidence assembled in this stream indicates that ESG ratings weight low-cost aspirational disclosure heavily relative to costly performance evidence, suggesting that greater reliance on aspirational disclosure relative to performance evidence may increase greenwashing risk, consistent with signaling-theory concerns. A study-level validation appraisal further shows that the most extreme fit statistics often arise in target-proximal reconstruction or non-temporal validation settings, cautioning against interpreting high R² as evidence of transferable out-of-time forecasting. Full article

Lending to small and medium-sized enterprises (SMEs) on the basis of their environmental, social, and governance (ESG) performance is difficult for commercial banks. The data needed for a proper assessment are rarely at hand, and existing tools cannot capture experts’ fuzziness, complex hesitation, and preference characteristics at the same time. This paper introduces the probabilistic picture hesitant fuzzy set (PPHFS) into SME ESG evaluation and develops a multi-attribute group decision-making (MAGDM) method that rests on the correlation coefficient of PPHFSs. The method has three components. It first defines the information energy of PPHFSs, builds a correlation measure on that foundation, and then derives several correlation coefficients together with their weighted forms. A separate procedure converts the multi-dimensional voting information that evaluators provide through questionnaires and balloting into probabilistic picture hesitant fuzzy information, which keeps the approach close to practice. These components are assembled into a complete MAGDM framework for banks and illustrated on a numerical case of five SME loan applicants. Sensitivity analysis and comparative experiments corroborate the feasibility and effectiveness of the method. By aligning bank credit decisions with ESG screening, the framework supports the extension of green credit to SMEs and contributes to sustainable finance and the Sustainable Development Goals (SDGs). Full article

Environmental, social, and governance (ESG) quality cannot be directly observed because substantial disagreement across rating providers contaminates the observed ESG scores with measurement error. This paper addresses this problem by modelling the true ESG state as a latent, vector-valued Itô diffusion defined on a filtered probability space whose information set is progressively enlarged by FinTech signals. We establish the well-posedness of the latent ESG process, prove the existence of an equivalent martingale measure under an explicit exponential-moment condition, and solve an ESG-constrained portfolio problem under a wealth-scaled sustainability constraint through a Hamilton–Jacobi–Bellman verification theorem. Computationally, raw sustainability information is extracted from SEC Form 10-K filings using a FinBERT transformer architecture and incorporated into a linear Gaussian state-space model, where the latent ESG state is recovered via Kalman filtering. Theoretical results are then linked to asset pricing, portfolio allocation, and systemic risk networks through a common filtered ESG factor. Using an unbalanced panel of 1086 U.S. listed firms over 2011–2023 and ESG information from MSCI, Refinitiv, and Sustainalytics, we document substantial provider disagreement and show that the observed ESG ratings contain significant transitory measurement noise. The filtered ESG state exhibits higher reliability, lower noise, and greater persistence than individual provider scores. In asset pricing tests, the latent ESG state predicts future excess returns, whereas a composite provider-based ESG measure does not; a one-standard-deviation increase in the latent ESG state is associated with approximately 0.35 percentage points higher monthly excess returns (about 4.3% annualised). When both measures are included simultaneously, only the filtered ESG state retains explanatory power. Out-of-sample portfolio tests show that a latent ESG strategy achieves a Sharpe ratio of 0.72, significantly exceeding both an unconstrained benchmark (0.59) and a composite ESG screen strategy (0.55). At the network level, ESG-adjusted weighting attenuates systemic fragility by reducing the spectral abscissa from 0.34 to 0.21, with the mitigating effect remaining significant under permutation-based placebo tests. Overall, the evidence supports the central hypothesis that ESG measurement error attenuates the observed pricing effects and that FinTech-enabled filtering recovers economically meaningful sustainability information relevant for asset pricing, portfolio construction, and systemic risk assessment. Full article

Tropical perennial systems in Southeast Asia, including oil palm, rubber, and cocoa, are established on highly weathered soils under monsoonal climates that differ fundamentally from temperate systems for which most soil health frameworks were developed. Growing certification, environmental, social, and governance (ESG) reporting, and regenerative agriculture requirements demand demonstrable soil health outcomes, yet the diagnostic infrastructure needed to generate and verify such outcomes remains limited. Existing approaches rely on episodic, indicator-based assessment that captures static conditions rather than functional state, applying benchmarks from dissimilar agroecological contexts with limited basis for resolving system readiness, biological constraint, or functional trajectory. This creates a persistent diagnostic gap between measurement and decision-making, disproportionately affecting smallholders required to meet compliance frameworks calibrated for fundamentally different systems. This critical review synthesizes literature on conventional soil testing, indicator-based frameworks, monitoring programs, and biological diagnostics to identify the structural origins of this gap. Soil health surveillance is identified as a review-derived diagnostic logic, i.e., longitudinal, function-oriented, and crop-calibrated, integrative across chemical, physical, and biological dimensions, and tiered from field-level screening to longitudinal datasets supporting monitoring, reporting, and verification (MRV) reporting. Empirical calibration, context-specific threshold development, and longitudinal validation at the individual system scale are identified as research priorities for operationalizing governance-ready soil health assessment in tropical perennial agriculture. Full article