Search Results (897)

During the past two decades, researchers and professionals have increasingly explored the financial and macroeconomic implications of sustainable business practices, particularly through the lens of environmental, social, and governance (ESG) metrics. This review synthesizes evidence from financial economics and sectoral labor analysis to assess the impact of ESG performance and green employment on corporate financial performance (CFP) and broader economic growth. Using a discounted cash-flow framework and sectoral panel data from European economies (2008–2023), the findings reveal that robust ESG practices improve operating profits, reduce financial risk and support higher dividend distributions, while green jobs contribute significantly to Gross Value Added (GVA) and Gross Domestic Product (GDP), with each additional green job adding approximately EUR 101.920 to GVA and EUR 135.000 to GDP, in annual terms. Sectoral impacts are especially pronounced in construction, energy, and financial services, with annual contributions ranging from EUR 10.4 to EUR 11.1 million in GVA and EUR 13.7 to EUR 14.8 million in GDP. These results underscore the dual role of ESG as a financial indicator and strategic sustainability tool, advancing key United Nations Sustainable Development Goals (SDGs), including SDG 8 (Decent Work and Economic Growth), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 17 (Partnerships for the Goals). The integration of green employment metrics into national productivity frameworks and corporate ESG strategies offers practical guidance to policymakers, investors, and cross-sector partners committed to sustainable development. Full article

In response to growing pressures for sustainability in tourism, this paper examines the techno-economic evaluation of green innovations in small and medium-sized tourism enterprises (SMEs). Focusing on a single case study of a hotel in Greece, the research investigates how and why specific sustainability interventions were implemented and assesses their operational and economic impacts. The study adopts an interpretivist approach, combining process tracing with thematic analysis. The analysis is guided by innovation diffusion theory, supported by organizational learning perspectives, to explain the stepwise adoption of sustainability practices and the internal adaptation processes that enabled them. The techno-economic evaluation draws on quantitative indicators and qualitative assessments of perceived benefits and implementation challenges, offering a broader view of value beyond purely financial metrics. Data were collected through semi-structured interviews, on-site observations, and internal documentation. The findings reveal a gradual, non-linear path to innovation, shaped by adoption dynamics and organizational learning, reinforced by leadership commitment, contextual adaptation, supply chain decisions, and external incentives. Key interventions, including solar energy adoption, composting, and the formation of zero-waste partnerships, resulted in measurable reductions in energy use and landfill waste, along with improvements in guest satisfaction, operational efficiency, and local collaboration. Although it is subject to limitations typical of single-case designs, the study demonstrates how even modest sustainability efforts, when integrated into daily operations, can generate multiple types of outcomes (economic, environmental, and operational). The paper offers practical implications for tourism SMEs and policymakers and formulates propositions for future testing on sustainable innovation in the tourism sector. Full article

Engineering education in developing countries faces critical challenges that hinder progress toward achieving the United Nations Sustainable Development Goals (SDGs). In the Democratic Republic of Congo (DRC), students entering engineering programs often exhibit significant apprehension toward foundational sciences, creating barriers to developing the technical competencies required for sustainable development. This paper introduces the AI-Integrated Practical Learning in Engineering (AIPLE) Framework, an innovative pedagogical model that synergizes Problem-Based Learning (PBL), hands-on experimentation, and strategic Artificial Intelligence (AI) integration to transform engineering education for sustainability. The AIPLE framework employs a five-stage cyclical process designed to address student apprehension while fostering sustainable engineering mindsets essential for achieving SDGs 4 (Quality Education), 7 (Affordable and Clean Energy), 9 (Industry, Innovation and Infrastructure), and 11 (Sustainable Cities and Communities). This study, grounded in qualitative surveys of engineering instructors at Université Loyola du Congo (ULC), demonstrates how the framework addresses pedagogical limitations while building technical competency and sustainability consciousness. The research reveals that traditional didactic methods inadequately prepare students for complex sustainability challenges, while the AIPLE framework’s integration of AI-assisted learning, practical problem-solving, and sustainability-focused projects offers a scalable solution for engineering education transformation in resource-constrained environments. Our findings indicate strong instructor support for PBL methodologies and cautious optimism regarding AI integration, with emphasis on addressing infrastructure and ethical considerations. The AIPLE framework contributes to sustainable development by preparing engineers who are technically competent and committed to creating environmentally responsible, socially inclusive, and economically viable solutions for developing countries. Full article

In response to the climate crisis, Colombia has committed to reducing greenhouse gas (GHG) emissions by 2030 through an energy transition strategy that promotes Non-Conventional Renewable Energy Sources (NCRES) and, increasingly, natural gas. Although natural gas is regarded as a transitional fuel with lower carbon intensity than other fossil fuels, existing reserves could be depleted by 2030 if no new discoveries are made. To assess this risk, a System Dynamics model was developed to project supply and demand under alternative transition pathways. The model integrates: (1) GDP, urban population growth, and adoption of clean energy, (2) the behavior of six major consumption sectors, and (3) the role of gas-fired thermal generation relative to NCRES output and hydroelectric availability, influenced by the El Niño river-flow variability. The novelty and contribution of this study lie in the integration of supply and demand within a unified System Dynamics framework, allowing for a holistic understanding of the Colombian natural gas market. The model explicitly incorporates feedback mechanisms such as urbanization, vehicle replacement, and hydropower variability, which are often overlooked in traditional analyses. Through the evaluation of twelve policy scenarios that combine hydrogen, wind, solar, and new gas reserves, the study provides a comprehensive view of potential energy transition pathways. A comparative analysis with official UPME projections highlights both consistencies and divergences in long-term forecasts. Furthermore, the quantification of demand coverage from 2026 to 2033 reveals that while current reserves can satisfy demand until 2026, the expansion of hydrogen, wind, and solar sources could extend full coverage until 2033; however, ensuring long-term sustainability ultimately depends on the discovery and development of new reserves, such as the Sirius-2 well. Full article

Corporate ESG (Environmental, Social, and Governance) reporting is increasingly envisioned as evidence of accountability in the energy transition, yet persistent gaps remain between commitments and practices. This study applied the Global Reporting Initiative (GRI) framework—specifically indicators 302 (Energy) and 305 (Emissions)—to evaluate the credibility, scope, and strategic depth of disclosures by 20 Wisconsin (WI) firms in the energy, manufacturing, food, and service sectors. Guided by accountability and legitimacy theory, a comparative content analysis was conducted, complemented by Spearman correlation to examine associations between firm size and disclosure quality. Results show that while firms consistently report basic metrics such as total energy consumption and Scope 1 emissions, disclosures on Scope 3 emissions, renewable sourcing, and energy-efficiency achievements remain partial and selectively framed. Third-party assurance is inconsistently applied, and methodological transparency—such as external audit and coding protocols—is limited, weakening credibility. A statistically significant negative correlation was observed between annual revenue and disclosure quality, indicating that greater financial capacity does not necessarily translate into greater transparency. These findings highlight methodological and governance shortcomings, including reliance on generic ESG frameworks rather than climate-focused standards such as Task Force on Climate-related Financial Disclosures (TCFD). Integrated reporting approaches are recommended to improve comparability, credibility, and alignment with Wisconsin’s Clean Energy Transition Plan. Full article

This study presents a novel integrative review of 329 review articles on sustainable buildings from 2015 to 2025, combining quantitative bibliometrics with qualitative insights to map the field’s evolution and pinpoint critical future pathways. Seven core research themes were identified in this study: (1) material and advanced construction technologies, (2) energy efficiency and renewable energy systems, (3) digitalization and smart technologies, (4) policy, standards, and certification, (5) sustainable design and optimization, (6) stakeholder and socio-economic factors, (7) other (cross-cutting) topics. Key findings reveal a surge in publications post-2020, driven by global net-zero commitments, with China, Australia, and Hong Kong leading research output. Innovations in low-carbon materials (e.g., hemp concrete, geopolymers), artificial intelligent (AI)-driven energy optimization, and digital tools (e.g., building information modeling (BIM), internet of things (IoT)) dominate recent advancements. However, challenges persist, including policy fragmentation, scalability barriers for sustainable materials, and socio-economic disparities in green building adoption. The study proposes a unique future research framework emphasizing nanotechnology-enhanced materials, interpretable AI models, harmonized global standards, and inclusive stakeholder engagement. This review provides actionable recommendations to bridge gaps between technological innovation, policy frameworks, and practical implementation in sustainable construction. Full article

This study examines the alignment between the European Union’s climate policy rhetoric and the actual fossil fuel consumption behaviours of its Member States. By combining long-term and short-term time-series data with machine learning classification techniques, the analysis captures dynamic national energy trends and decarbonisation signals. Key innovations include the use of slope acceleration metrics and the identification of label reversals to detect volatility, acceleration, or stagnation in transition trajectories. The results show that, while some countries such as France and Denmark demonstrate consistent structural progress, others show deceleration or reversal, particularly in the use of gas and liquid fuels. This indicates that the relationship between EU-level policy ambition and national implementation is asymmetric and conditionally aligned. This study concludes that ongoing empirical monitoring and targeted diagnostics are essential to prevent conflating symbolic commitments with material change, and provides practical insights for improving climate policy accountability and adaptability across the EU. Full article

Background: Monocytes can commit to different phenotypes associated with specific features required in inflammation and homeostasis. Classical and alternative activation are two extremes of monocyte polarization and are both influenced by glucocorticoids (GCs). Methods: Human monocytes were sorted from the blood of healthy individuals and activated with LPS or IL-4 and IL-13, either in the absence or presence of dexamethasone (Dex). Metabolic adjustments were analyzed using Seahorse stress tests, SCENITH, and RT-qPCR. Results: LPS enhanced glycolysis and also, to a lesser extent, oxidative phosphorylation (OXPHOS), whereas addition of Dex induced a metabolic switch in favor of the latter. In contrast, activation of monocytes with IL-4 and IL-13 exclusively stimulated OXPHOS, which was suppressed by concomitant Dex treatment. The glycolytic function of monocytes matched alterations in gene expression of glucose transporters and metabolic enzymes, which were upregulated by LPS and inhibited by Dex via interference with the mTORC1 pathway but remained unaltered in response to IL-4 and IL-13. Although the dependency of classically and alternatively activated monocytes on OXPHOS and glucose usage markedly differed, modulation by GCs was limited to the latter polarization state. Conclusions: Our findings unravel a highly selective regulation of human monocyte energy metabolism by different activating stimuli as well as by GCs. Full article

In the last decades, the introduction of intermittent renewable energy sources has transformed the operation of power systems. In this framework, ancillary service markets (ASMs) play an important role, due to their contribution in supporting system operators to balance demand and supply and managing real-time contingencies. Usually, ASMs require that energy is committed before actual participation, hence scheduling systems of plants and microgrids are required to compute the dispatching program and bidding strategy before needs of the market are revealed. Since possible ASM requirements are given as input to scheduling systems, the chance of accessing accurate estimates may be helpful to define reliable dispatching programs and effective bidding strategies. Within this context, this paper proposes a methodology to estimate the revenue range of energy exchange proposals in the ASM. To this end, the possible revenues are discretized into ranges and a classification pattern recognition algorithm is implemented. Modeling is performed using extreme gradient boosting. Input data to be fed to the algorithm are selected because of relationships with the production unit making the proposal, with the location and temporal indication, with the grid power dispatch and with the market regulations. Different tests are set up using historical data referred to the Italian ASM. Results show that the model can appropriately estimate rejection and the revenue range of awarded bids and offers, respectively, in more than 82% and 70% of cases. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

The burgeoning Internet of Medical Things (IoMT) offers unprecedented opportunities for real-time patient monitoring and predictive diagnostics, yet the current systems struggle with scalability, data confidentiality against quantum threats, and real-time privacy-preserving intelligence. This paper introduces Med-Q Ledger, a novel, multi-layered framework designed to overcome these critical limitations in the Medical IoT domain. Med-Q Ledger integrates a permissioned Hyperledger Fabric for transactional integrity with a scalable Holochain Distributed Hash Table for high-volume telemetry, achieving horizontal scalability and sub-second commit times. To fortify long-term data security, the framework incorporates post-quantum cryptography (PQC), specifically CRYSTALS-Di lithium signatures and Kyber Key Encapsulation Mechanisms. Real-time, privacy-preserving intelligence is delivered through an edge-based federated learning (FL) model, utilizing lightweight autoencoders for anomaly detection on encrypted gradients. We validate Med-Q Ledger’s efficacy through a critical application: the prediction of intestinal complications like necrotizing enterocolitis (NEC) in preterm infants, a condition frequently necessitating emergency colostomy. By processing physiological data from maternal wearable sensors and infant intestinal images, our integrated Random Forest model demonstrates superior performance in predicting colostomy necessity. Experimental evaluations reveal a throughput of approximately 3400 transactions per second (TPS) with ~180 ms end-to-end latency, a >95% anomaly detection rate with <2% false positives, and an 11% computational overhead for PQC on resource-constrained devices. Furthermore, our results show a 0.90 F1-score for colostomy prediction, a 25% reduction in emergency surgeries, and 31% lower energy consumption compared to MQTT baselines. Med-Q Ledger sets a new benchmark for secure, high-performance, and privacy-preserving IoMT analytics, offering a robust blueprint for next-generation healthcare deployments. Full article

Amid rapid industrialization and the growing integration of financial technologies, emerging economies face increasing pressure from rising ecological footprints (ECOF). This study examines the environmental impacts of natural resource rents (NRES) and digital financial technology (DFIN), emphasizing the moderating role of governance (INST), using data from the top 10 emerging economies between 1995 and 2023. The Method of Moments Quantile Regression (MMQR) approach is employed to capture heterogeneous effects across different levels of environmental stress. The results reveal that both NRES and DFIN exacerbate ECOF, particularly in economies facing higher ecological pressures. However, strong governance significantly reduces these adverse effects, especially at higher ECOF quantiles, highlighting its pivotal role in aligning resource management and digital innovation with environmental sustainability goals. Interaction terms further confirm that effective institutional quality can buffer the ecological risks associated with resource exploitation and FinTech expansion. Additionally, Dumitrescu–Hurlin panel causality tests reveal a unidirectional causality from NRES and economic growth (EGRO) to ECOF, while bidirectional relationships are observed between DFIN, INST, education, urbanization, renewable energy, and ECOF. These findings underscore the complex interlinkages between economic growth, technological advancement, and institutional frameworks. In the context of post-COP28 climate commitments and Sustainable Development Goals, this study provides timely policy recommendations to promote sustainable growth through robust governance, responsible resource utilization, and balanced FinTech integration. Full article

Carbon neutrality has emerged as a critical issue in the 21st century, particularly in the Middle East and North Africa (MENA) region. These nations have demonstrated significant commitment to investing in renewable energy and implementing initiatives aimed at achieving carbon neutrality. The global spotlight on environmental concerns, encompassing the responsibilities of all economic stakeholders, has prompted the convening of COP 27, a pivotal meeting dedicated to reducing carbon emissions on a global scale. However, research on carbon neutrality in the MENA region remains relatively limited, particularly in terms of in-depth analysis of green energy, green technology, oil revenues, and the efficacy of government interventions. This study seeks to address this gap in existing research by investigating the factors influencing the attainment of carbon neutrality in the MENA region from 2000 to 2022. Specifically, the research focuses on the roles of green energy, green technology, oil revenues, and government effectiveness in this context. Utilizing the Method of Moments’ Quantile Regression, this study aims to analyze the impact of location and scale on the conditional distribution of carbon emissions. The findings reveal that investments in green energy, adoption of green technology, increases in service-added value, and oil revenues are associated with decreased carbon emissions, while greater trade openness correlates with emission reductions. However, all governance metrics examined exhibit a positive correlation with carbon emissions. These results underscore the importance of prioritizing investments in green energy and enhancing the effectiveness of governmental initiatives to steer economic growth towards achieving carbon neutrality. Moving forward, policymakers in the MENA region are encouraged to place greater emphasis on sustainable energy solutions and to implement strategies that enhance the efficacy of government interventions to accelerate progress towards carbon neutrality. Full article

Artificial intelligence is transforming industrial marketing by reshaping processes, decision-making, and inter-firm relationships. However, research remains fragmented, with limited evidence on how adoption drivers create new capabilities and sustainability outcomes. This study develops and empirically validates an integrated framework that combines technology, organization, environment, user acceptance, resource-based perspectives, dynamic capabilities, and explainability. A convergent mixed-methods design was applied, combining survey data from industrial firms with thematic analysis of practitioner insights. The findings show that technological readiness, organizational commitment, environmental pressures, and user perceptions jointly determine adoption breadth and depth, which in turn foster marketing capabilities linked to measurable improvements. These include shorter quotation cycles, reduced energy consumption, improved forecasting accuracy, and the introduction of carbon-based pricing mechanisms. Qualitative evidence further indicates that explainability and human–machine collaboration are decisive for trust and practical use, while sustainability-oriented investments act as catalysts for long-term transformation. The study provides the first empirical integration of adoption drivers, capability building, and sustainability outcomes in industrial marketing. By demonstrating that artificial intelligence advances competitiveness and sustainability simultaneously, it positions marketing as a strategic lever in the transition toward digitally enabled and environmentally responsible industrial economies. We also provide a simplified mapping of theoretical lenses, detail B2B-specific scale adaptations, and discuss environmental trade-offs of AI use. Given the convenience/snowball design, estimates should be read as upper-bound effects for mixed-maturity populations; robustness checks (stratification and simple reweighting) confirm sign and significance. Full article