The research team, led by José Alejandro Fernández Fernández with Renata Kubus and Inés Martín de Santos, drew its data from the Refinitiv Eikon database and constructed a balanced panel of 666 unique firms, yielding 2,612 estimation observations after excluding a small number of records with missing ESG outcomes. Rather than relying on conventional linear econometrics, which the authors argue can obscure threshold-dependent and nonlinear relationships, they deployed random forests built from 500 regression trees, each constrained to a maximum depth of eight, alongside regression-tree benchmarks, permutation importance, SHAP values, and partial dependence plots.

The methodological care is notable. All preprocessing, including winsorization of extreme values at the first and ninety-ninth percentiles, median imputation of missing predictors, and a logarithmic transformation of total assets, was fitted strictly within each training fold and then applied to validation data, preventing information leakage. The train-test split was grouped by firm, so no company’s observations appeared in both partitions. A separate temporal validation trained the models on 2019 to 2021 and tested them on 2022, and out-of-bag estimates provided an additional ensemble-based diagnostic.

Predictive performance varied meaningfully across industries. Financials proved the most predictable sector, achieving a grouped cross-validated R-squared of 0.415 and a temporal test score of 0.417, with out-of-bag performance reaching 0.571. Utilities followed with a temporal R-squared of 0.363, while Industrials and Basic Materials landed in the mid-range. Energy showed the least stable results, with a negative single grouped holdout but positive five-fold and temporal scores, a dispersion the authors attribute to the sector’s smaller sample and warrant cautious interpretation.

When the researchers jointly permuted six analytical blocks of predictors on the 2022 holdout, the size signal proved overwhelming. Disrupting the size block degraded test performance by a mean R-squared decline of 0.650 in Financials, 0.566 in Utilities, 0.488 in Energy, and 0.487 in Industrials, with Basic Materials at 0.348. Leverage ranked second in four of the five sectors, while profitability took that position in Financials. This block-level evidence matters because correlated accounting ratios can redistribute individual importance scores; by permuting whole blocks at once, the team confirmed that the dominance of firm size is not an artifact of importance being split among overlapping variables.

Beyond the shared size effect, the sectors diverged sharply. In Energy and Utilities, the predictive structure was comparatively concentrated, centered on firm scale and capital expenditure intensity. Intriguingly, capital expenditure showed predominantly negative nonlinear associations with predicted ESG scores beyond moderate thresholds in both sectors, suggesting that spending more on plant and equipment does not automatically translate into stronger sustainability ratings. The authors caution that this pattern may reflect differences in the orientation and efficiency of capital allocation rather than investment volume itself, though the observational design cannot establish the mechanism.

Industrials and Basic Materials displayed the most heterogeneous and nonlinear configurations. In Industrials, the assets-to-equity ratio emerged as the second most relevant predictor, with free operating cash flow showing negative associations beyond levels near 0.14 and debt service contributing threshold-dependent effects across firm-size regions. Basic Materials exhibited a U-shaped relationship between EBITDA-to-equity and predicted ESG scores among smaller firms, with the direction changing around values close to 0.28. Financials, by contrast, presented the most concentrated and stable structure of all, with size dominating and profitability indicators such as EBITDA-to-equity and EBITDA-to-total-assets providing secondary contributions, while debt variables played a comparatively minor role.

The study also documents a broader convergence story in the raw ESG data. Average scores rose across all five sectors between 2019 and 2022, with Energy posting the strongest growth at 39 percent, followed by Basic Materials at 27 percent and Industrials at 24 percent, while Utilities and Financials improved more modestly at 18 and 17 percent. Meanwhile, the coefficient of variation declined across sectors, indicating that ESG practices are converging, even as Financials and Energy retain higher internal dispersion, pointing to uneven adoption within those industries.

The policy implications are interpretive rather than causal, as the authors emphasize, but they are consequential. The dominance of firm size suggests that ESG integration may be structurally easier for large corporations with sophisticated governance and reporting infrastructure, implying that smaller and medium-sized firms face proportionally higher compliance costs and could benefit from more proportional disclosure requirements. The sectoral heterogeneity in how leverage, profitability, and investment relate to ESG scores further argues against one-size-fits-all regulation, particularly for capital-intensive industries where financing structure matters most. And the finding that higher investment intensity sometimes accompanies weaker ESG outcomes underscores the need to distinguish investment quality from quantity when designing sustainability incentives.

Perhaps the most sobering takeaway for investors is that market valuation signals, captured through the historic earnings-to-price ratio, showed weak and often negative alignment with ESG performance in several sectors, hinting that sustainability information may not be fully priced into American equities. The authors argue that strengthening ESG transparency, comparability, and interpretability could improve market efficiency and channel capital more effectively. What the study ultimately delivers is a nuanced map: ESG performance carries a common scale-related backbone, but the financial characteristics that matter beyond scale depend fundamentally on the economic structure of each industry, a conclusion that resists the temptation of a single homogeneous financial profile for sustainability success.

Subject of Research: Machine learning analysis of the relationship between financial health indicators and ESG scores in US firms

Article Title: “Machine learning analysis of ESG score and financial health in the USA”

Article References: “Machine learning analysis of ESG score and financial health in the USA”. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: ESG, machine learning, random forests, SHAP values, S&P 1500, firm size, financial health, sustainable finance, corporate governance, sectoral analysis, explainable AI, Refinitiv Eikon

The framework, developed by researchers at the Technical University of Košice in the Slovak Republic, integrates six distinct concerns that the literature has typically handled separately: heterogeneous industrial data, a discrete-event simulation-based digital twin, MQTT and OPC UA communication protocols, external predictive analytics, operational ESG-oriented evaluation, and planner decision support. Rather than treating each as a standalone project, the authors arrange them into a six-layer, event-driven architecture in which information flows between layers as discrete events, triggering analysis, prediction, and ultimately human-approved decisions on the factory floor.

At the heart of the proposal is the idea that events, not periodic reports, should drive production planning and control. In an event-driven design, every meaningful occurrence on the shop floor, such as a machine fault, a material shortage, or a completed batch, becomes a message that propagates through the architecture. Lightweight publish-subscribe protocols like MQTT can carry high-frequency telemetry from sensors to cloud services, while OPC UA provides the standardized, semantically rich interface that industrial equipment uses to expose its state. Prior performance studies of these protocols, cited in the paper, show they are well suited to exchanging data between industrial plants and cloud servers, which makes them natural candidates for the communication backbone of a digital twin.

The digital twin itself is built on discrete-event simulation, a modeling technique that represents a production system as a chronological sequence of events and state changes. This choice matters because discrete-event simulation can answer the questions production planners actually ask: what happens to throughput if a machine goes down for two hours, how a rush order reshapes the schedule, or where buffers will overflow. By coupling the simulation model to live industrial data streams, the twin becomes a predictive instrument rather than a static mirror, allowing planners to test candidate schedules and interventions in silico before committing resources on the floor.

The second major contribution is what the authors call a four-layer transformation logic, and it is here that the framework makes its most distinctive claim. Digital technologies, the authors argue, do not directly improve environmental, social, or governance outcomes. Instead, they influence ESG-relevant results only through changes in production planning and control processes and in managerial evaluation. A sensor network by itself reduces nothing; it is the rescheduled batch, the avoided machine failure, or the reweighted performance indicator, approved by a human planner, that translates digital capability into measurable sustainability impact. This mediating logic is intended to correct what the authors see as a common weakness in the literature, where digitalization and sustainability are linked loosely without specifying the causal pathway.

To make the framework concrete, the paper illustrates it with a hypothetical manufacturing scenario, a complete event trace showing how messages move through the six layers, and formal definitions of measurable indicators. The indicator definitions are designed to be operational, meaning they specify exactly what is measured, from what data, and how the resulting values feed into ESG-oriented evaluation. This level of specification is what separates a genuine decision-support architecture from a conceptual diagram: planners can, in principle, implement the indicators directly and audit how each recommendation was derived from underlying events.

Human oversight is built into the architecture rather than bolted on. The framework explicitly positions planner decision support as the final layer, so that predictive outputs and ESG evaluations arrive as recommendations that a human decision-maker reviews and approves before any change to production planning takes effect. This design reflects the broader Industry 5.0 movement, which, as the European policy literature cited in the paper emphasizes, seeks a sustainable, human-centric, and resilient industry rather than full autonomy. The authors position their work in this tradition, drawing on recent scholarship that frames Industry 5.0 as a corrective to the technology-first ethos of Industry 4.0.

The intellectual lineage of the framework is broad. It builds on established digital twin reference models and six-layer architectural patterns from the manufacturing literature, on systematic reviews of machine learning applications in production lines and predictive quality, and on a growing body of work connecting intelligent manufacturing to ESG performance, including empirical studies of Chinese manufacturing firms and recent analyses asking whether smarter production is also greener. By synthesizing these strands, the authors aim to provide what they describe as an explicit integration of technical event flows with human-approved, ESG-oriented production planning and control decisions, something they argue no single existing framework delivers.

The authors are candid about the limits of the current study. The framework is derived through a structured conceptual synthesis, and the paper does not report implemented or empirically validated performance results. No datasets were generated or analyzed, and the hypothetical scenario exists to illustrate the architecture, not to prove it. The authors list as future work a set of concrete validation steps: discrete-event simulation experiments, construction of an MQTT and OPC UA testbed, expert assessment, and industrial validation in real manufacturing settings. Until those studies are complete, the framework should be read as a rigorous design proposal and a research agenda rather than a demonstrated solution.

Even so, the timing of the proposal is significant. Manufacturers worldwide face simultaneous pressure to digitize operations and to report credible environmental, social, and governance performance, and regulators and investors increasingly demand that sustainability claims rest on verifiable operational data. A framework that traces a straight line from a sensor event, through a predictive simulation, to a planner-approved scheduling decision and a defined ESG indicator offers exactly the kind of auditable causal chain that both engineers and sustainability officers need. If the planned testbed and industrial validation bear out the design, event-driven digital twins could become the connective tissue that finally unifies the smart factory’s many brains, turning streams of industrial telemetry into decisions that are simultaneously faster, more predictive, and demonstrably more sustainable.

Subject of Research: An event-driven digital twin framework for predictive production planning and ESG-oriented decision support in smart manufacturing

Article Title: A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing

Article References: Karakai, M., & Bobko, D. (2026). A Conceptual Framework for Event-Driven Digital Twin-Based Predictive Production Planning and ESG-Oriented Decision Support in Smart Manufacturing. Mobile Networks and Applications. https://doi.org/10.1007/s11036-026-02535-3

Image Credits: AI Generated

DOI: 10.1007/s11036-026-02535-3

Keywords: digital twin, smart manufacturing, Industry 5.0, discrete-event simulation, MQTT, OPC UA, predictive production planning, ESG, decision support, industrial IoT, sustainability, conceptual framework

The research, conducted by Detcharat Sumrit and Rodsak Kongsakul, focuses on Thailand’s SME logistics sector, a setting the authors argue is emblematic of challenges facing emerging economies worldwide. Thai policymakers have embraced a Bio-Circular-Green economy model and carbon neutrality commitments, but the small firms that dominate the sector still wrestle with outdated technology, manual reporting systems, fragmented data governance, and subcontractors with limited sustainability capabilities. The study treats these firms not as laggards but as organizations caught between internal resource limits and powerful external pressures, and it builds its entire analytical framework around that tension.

Theoretically, the work weaves together three complementary lenses. The resource-based view explains how internal assets, from financial capital to digital infrastructure, shape what the authors call endogenous barriers. Institutional theory captures the exogenous pressures that compel action: coercive forces such as the European Union’s Corporate Sustainability Reporting Directive, mimetic imitation of sustainability leaders like DHL and Maersk, and normative expectations from customers demanding low-carbon logistics. The technology-organization-environment framework then provides the structure for organizing potential responses. No prior study, the authors contend, has integrated all three perspectives while simultaneously linking barrier identification to strategy prioritization under uncertainty.

The methodological machinery is where the study becomes technically distinctive. The researchers deployed a hybrid multi-criteria decision-making pipeline combining the Full Consistency Method, the Decision-Making Trial and Evaluation Laboratory, and Quality Function Deployment, all operating within an interval-valued spherical fuzzy environment. Spherical fuzzy sets extend classical fuzzy logic by adding an explicit hesitancy parameter alongside membership and non-membership degrees, subject to the constraint that the squared components sum to at most one. This gives experts a larger, more flexible decision space than intuitionistic or Pythagorean fuzzy sets, and the interval-valued variant captures the variability inherent when twelve experts must reconcile subjective, linguistically expressed judgments.

An expert panel of twelve, drawn from government, industry, and academia through a purposive sampling process with a 60 percent response rate, validated seventeen candidate obstacles using Lawshe’s content validity ratio. Eleven survived, and the fuzzy FUCOM analysis then weighted them with striking clarity. The single most influential barrier, at 0.123, was fragmented collaboration among supply chain actors, followed almost immediately by capital constraints on sustainability investment at 0.122. Regulatory misalignment and inadequate governmental incentives rounded out the top four. The message is unambiguous: ESG failure in small logistics firms is less about unwillingness and more about an ecosystem in which no single actor can move alone.

That diagnosis directly shaped the remedy. Eleven mitigation strategies, organized within the technology-organization-environment structure, were mapped against the validated barriers in a House of Quality, with DEMATEL analysis revealing the causal web of interdependencies among them. When the dust settled, one strategy towered above the rest: collaborating with green suppliers and clients, earning a normalized priority of 0.148. Partnering with industry groups and embedding ESG into core corporate strategy followed closely. Notably, carbon footprint accounting software ranked last, suggesting that digital tools alone, however fashionable, cannot substitute for the relational and strategic work of building collaborative capacity.

The robustness testing is unusually thorough for this genre. The researchers varied every barrier weight across a range of minus thirty to plus thirty percent, generating sixty-six alternative scenarios, and the strategy ranking held steady in every one. A comparative assessment against crisp, triangular fuzzy, intuitionistic fuzzy, and spherical fuzzy versions of QFD showed the proposed approach correlating very strongly with spherical fuzzy QFD at a Spearman coefficient of 0.850, while traditional crisp methods managed only a weak 0.373. In other words, the sophistication of the fuzzy machinery is not decorative; it materially changes which conclusions survive scrutiny.

For managers, the practical implications are refreshingly concrete. Rather than attempting every ESG practice at once, the authors recommend phased investment in measures with rapid payback, such as energy-efficient equipment, route optimization, and digital documentation, financed through government grants, green financing, equipment leasing, and cost-sharing with supply chain partners. Joining industry associations and adopting affordable cloud platforms for shared sustainability data can attack the collaboration deficit at low cost. Designating staff to track evolving regulations and folding ESG indicators into existing performance reviews, rather than building separate reporting bureaucracies, addresses regulatory uncertainty without new overhead.

The study also sketches a phased policy roadmap. In the short term, governments should harmonize ESG standards, simplify reporting, and offer incentive-based compliance schemes, an approach the authors note resembles Singapore’s combination of structured reporting guidance with targeted financial support. Medium-term efforts should build standardized certification and capacity-building programs, drawing on Germany’s sector-wide logistics coordination. Long term, the goal is an adaptive regulatory ecosystem that embeds sustainability into national logistics strategy, exemplified by the Netherlands’ low-emission logistics zones. For a sector that keeps global commerce moving, the study’s central finding may prove its most viral idea: the greenest thing a small logistics firm can do is stop trying to go green alone.

Subject of Research: ESG mitigation strategies for sustainable transition in small and medium-sized third-party logistics providers

Article Title: Mitigation strategies for addressing environmental, social, and governance challenges in third-party logistics: A sustainable transition perspective

Article References: Sumrit, D., & Kongsakul, R. (2026). Mitigation strategies for addressing environmental, social, and governance challenges in third-party logistics: A sustainable transition perspective. Cleaner Engineering and Technology, 34, Article 101317. https://doi.org/10.1016/j.clet.2026.101317

Image Credits: AI Generated

DOI: 10.1016/j.clet.2026.101317

Keywords: ESG, third-party logistics, sustainability transition, SME logistics, fuzzy MCDM, FUCOM, DEMATEL, Quality Function Deployment, institutional theory, resource-based view, TOE framework, green supply chain collaboration

The study, spanning April 2015 to December 2024, addresses a methodological gap that has plagued earlier attempts to link sustainability scores with returns. Many prior studies simply correlated ESG ratings with stock performance, a approach vulnerable to confounding by well-known return drivers such as firm size, valuation and profitability. Karkaria and colleagues instead embedded ESG directly into the workhorse frameworks of modern empirical finance: the Fama–French three-factor and five-factor models, which explain stock returns through market exposure, size, value, profitability and investment factors. By augmenting these models with a dedicated ESG factor, the authors could test whether sustainability information carries explanatory power beyond everything mainstream asset pricing already accounts for.

The construction of the ESG factor itself followed the characteristic-based portfolio approach that has become the gold standard since Fama and French popularized it in the early 1990s. Stocks within each BRICS market were sorted into portfolios based on their ESG characteristics, and the return spread between high-ESG and low-ESG portfolios became the factor’s empirical return series. This design matters because it converts a subjective rating into a tradable return stream—precisely the kind of object that asset pricing theory is built to evaluate. If that spread earns a persistent premium that standard factors cannot explain, financial economists have good reason to treat ESG as a distinct dimension of risk or mispricing rather than noise.

The statistical tests the authors deployed are the field’s harshest judges. The Gibbons, Ross and Shanken F-statistic, a classical test of whether a multifactor model’s pricing errors are jointly zero, evaluated whether augmented models outperformed the standard ones. Spanning tests asked an even more pointed question: can the existing Fama–French factors fully reproduce, or ‘span,’ the returns to the ESG factor? If ESG returns were spanned, they would contain no information beyond size, value, profitability, investment and the market itself. The spanning tests rejected that proposition, confirming that ESG returns are not fully absorbed by the conventional factor zoo. Factor-loading estimates and Sharpe ratio comparisons across model specifications pointed in the same direction, consistent with ESG carrying a priced risk premium in these markets.

Perhaps the most striking findings emerged from the cross-country analysis, which revealed heterogeneous rather than uniform patterns of ESG pricing across the BRICS bloc. Within each market, portfolios of low-ESG firms displayed significantly negative loadings on the ESG factor, while high-ESG portfolios showed significantly positive loadings—a clean, internally consistent signature that ESG characteristics divide firms along a priced dimension. The effect was most pronounced in Brazil and China, suggesting that in these economies sustainability disclosures convey information that investors meaningfully price. In Brazil, decades of environmental regulation and deforestation-related scrutiny have made ecological performance a salient business risk, while China’s state-driven push toward green finance and carbon intensity targets has similarly sharpened investor attention to ESG profiles.

The study also uncovered a subtle substitution effect with implications for how sustainable investing frameworks are built in emerging markets. In India and China, the ESG factor effectively substituted for the investment factor of the five-factor model—the component that captures differences in firms’ asset growth and investment aggressiveness. In practical terms, ESG information in those two markets appears to encode some of the same economic content that investment patterns otherwise capture, perhaps because conservatively managed, low-growth firms are also those with stronger governance and sustainability commitments. Across all five markets, however, ESG augmented the five-factor model, adding explanatory power even where full substitution did not occur.

Why should these results matter beyond the seminar room? Trillions of dollars in institutional capital now flow through ESG-screened mandates, and the academic controversy over whether ESG investing sacrifices, enhances, or leaves unchanged returns remains unresolved, particularly for emerging markets where disclosure standards and enforcement vary widely. The BRICS economies represent a critical test bed: they combine rapid industrialization, evolving regulatory regimes, and increasingly sophisticated capital markets. If ESG is a priced factor there, then asset managers constructing portfolios for these regions are implicitly taking or hedging ESG risk whether they intend to or not, and mean-variance optimization that ignores the factor may be quietly mis-specified.

The market-dependent nature of the findings is itself a contribution. Much of the ESG-finance literature, dominated by US and European data, implicitly assumes that results generalize across geographies. This study’s evidence that ESG pricing relevance in BRICS asset markets is contingent rather than universal cautions against transplanting conclusions from developed markets. It also gives sustainable-investment practitioners a map of where ESG integration is most likely to improve portfolio efficiency—and where it may add cost without commensurate information value. The authors frame this as insight into where and how ESG integration meaningfully improves sustainable investing frameworks across these heterogeneous economies.

Methodologically, the paper’s triangulated evidence—GRS tests for model completeness, spanning regressions for factor redundancy, factor-loading significance, and out-of-sample-style Sharpe ratio comparisons—represents a template that future studies of other emerging regions can adopt. The decade-long window captures a period of dramatic change in ESG disclosure: the rise of mandatory sustainability reporting in parts of Asia, the growth of global ESG data providers, and the post-2015 surge in climate-related investor pressure following the Paris Agreement. That the ESG factor retained incremental pricing power through this evolving landscape strengthens the case that its effects are structural rather than transient.

Limitations and open questions remain, as the authors acknowledge through their careful framing. ESG ratings from different providers correlate imperfectly, and disclosure-based scores may reflect what firms report rather than what they do—a concern amplified in markets with weaker disclosure enforcement. The authors’ published version, released as open access under a Creative Commons license and citable through its permanent DOI, invites replication across other emerging-market blocs and with alternative ESG data sources. Still, the central message stands: in the BRICS world, sustainability information is not financial decoration. It loads onto returns in statistically significant, economically interpretable ways, and any serious account of asset pricing in these rapidly growing economies now has to reckon with ESG as a factor in its own right.

Subject of Research: Whether ESG disclosures function as a priced risk factor in asset pricing models across BRICS equity markets

Article Title: Nexus between ESG disclosures and asset pricing efficiency in BRICS markets

Article References: Dusmanta, K., V R, K., & Mohanty, S. P. (2026). Nexus between ESG disclosures and asset pricing efficiency in BRICS markets. Discover Sustainability. https://doi.org/10.1007/s43621-026-04712-6

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04712-6

Keywords: ESG, asset pricing, BRICS markets, Fama–French models, sustainable investing, emerging markets, risk premium, portfolio returns, factor models, sustainability, capital markets, financial economics