This study introduces an AI-driven integrated framework for predicting and optimizing the performance of turbo air classifiers, addressing the limited application of advanced intelligence techniques in fine-particle processing. A turbo air classifier was examined using three operational inputs, rotor speed (561–1739 rpm), primary air flow (98.87–351.13 m3/h), and secondary air flow (6–74 m3/h), to predict two key performance indicators: cut size (CS) and classification accuracy index (CAI). Multilayer perceptron neural networks (MLPNNs) were optimized using modified particle swarm optimization (MPSO), marine predators algorithm (MPA), and gray wolf optimizer (GWO). MPSO-MLPNN yielded the best CS predictions (R > 0.999), while GWO-MLPNN achieved the most accurate CAI predictions (R > 0.99). Pareto-based multi-objective bat algorithm (MOBA) was then applied to minimize CAI while constraining CS within 15–18 μm and 18–21 μm. The Pareto results revealed a clear trade-off: CAI decreased from ∼2.30 to ∼1.65 as CS increased slightly in the fine separation regime and stabilized at ∼1.58–1.60 for coarser separation. Optimal conditions showed that fine separation requires high rotor speed with moderate–high airflow, whereas coarser, energy-efficient operation is achievable with lower rotor speeds and high airflow.
Amid intensifying climate change and escalating environmental degradation, achieving environmental sustainability has become a pressing global challenge, particularly for highly industrialized and energy-intensive economies in the OECD region. Despite growing scholarly attention, prior empirical studies have not sufficiently examined how financial stability, environmental policy stringency, and renewable energy collectively influence environmental sustainability. Addressing this gap, the present study investigates the heterogeneous effects of economic growth, renewable energy consumption, non-renewable energy consumption, hydro energy consumption, foreign direct investment (FDI), environmental policy stringency, and financial stability on environmental sustainability. Using balanced panel data for OECD countries from 2000 to 2020, the study employs the innovative Method of Moments Quantile Regression (MMQR) estimator to capture variation across the conditional distribution of environmental sustainability. The empirical results reveal that renewable energy consumption, hydro energy consumption, stringent environmental policies, and financial stability enhance environmental sustainability across heterogeneous quantiles. Conversely, economic growth, non-renewable energy consumption, and FDI undermine environmental sustainability across heterogeneous quantiles. The study recommends that OECD economies should expand the share of renewable and hydro energy through clean energy investments and subsidies, strengthen the enforcement of environmental regulations, incentivize green FDI through environmental screening and tax benefits, and integrate climate-related risks into financial supervision. Furthermore, implementing carbon pricing mechanisms and allocating the resulting revenues toward renewable energy R D and green innovation are crucial for achieving long-term sustainable development.
This study examines the relationship between sustainability expenditures and Environmental, Social, and Governance performance, emphasizing the moderating role of Financial Reporting Quality, proxied by value relevance and income smoothing. Using a panel of 1770 firm-year observations from 177 industrial, energy, and basic materials firms in the United Kingdom over the period 2014-2023, the study employs Fixed Effects regressions as the main estimation strategy, complemented by Heckman correction for sample selection bias and GMM to address potential endogeneity. The results indicate that Environmental Expenditure, Environmental Expenditure Investments, and Research and Development expenditures have a positive and significant impact on ESG performance. This effect is significantly moderated by FRQ; value relevance amplifies the impact of sustainability expenditures, while income smoothing supports long-term strategic consistency. These findings highlight the importance of transparent, informative reporting to enhance the effectiveness of sustainability investments. The study contributes to the ESG literature by showing that sustainability expenditures function as strategic investments rather than mere costs. From a practical perspective, the results suggest that managers should align sustainability strategies with high-quality financial reporting practices, while policymakers should promote clearer disclosure standards to improve ESG outcomes.
The present study investigates the relationship between corporate environmental disclosure and dividend policy. The study also examines the moderating role of environmental controversies in the relationship between corporate environmental disclosure and dividend policy. A sample of 2579 listed firms from European countries was selected, and fixed-effect, GMM, and quantile regression methodologies were employed. The result suggests that corporate environmental disclosure reflects the importance of environmental practices in shaping payout policies. The result also suggests that firms become more effective at enhancing dividend outcomes when they actively manage controversies. The findings are robust with GMM and quantile regression. This study is the first to combine various measures of corporate environmental disclosure, including CSR strategy scores, Environmental Pillar Scores, ESG Scores, Green Building Scores, and Emissions Scores. It also incorporates financial metrics such as cash dividends paid, net common stock buybacks, dividend payout ratio, dividend yield, and common stock dividends. The aim is to thoroughly examine the relationship between these factors and dividend policy in European countries.
This study explores the transformative potential of digital twin technology within the solar photovoltaic (PV) ecosystem, emphasizing its applications, performance metrics, challenges, and future directions. By synthesizing insights from cutting-edge research, the paper provides a holistic framework that integrates digital twin features—such as architecture, modeling, software, and IoT-based integration—with the core functionalities of solar PV systems. These features enable real-time simulation, predictive analytics, and system interoperability, thereby optimizing energy generation, streamlining maintenance processes, and extending lifecycle management. The review highlights critical performance metrics, including output efficiency, cost-effectiveness, fault detection, and sustainability, offering a data-driven perspective on enhancing system reliability and reducing environmental impact. The paper also addresses significant challenges confronting the adoption of digital twins in the solar PV sector. Technological hurdles, such as data synchronization and system scalability, are explored alongside financial barriers that impede widespread deployment. Regulatory complexities, including compliance with energy standards and cybersecurity protocols, are analyzed to offer actionable recommendations for policymakers. By identifying gaps in current research—such as the lack of scalable, real-time frameworks and cost-effective deployment strategies—the study lays the groundwork for future advancements. Furthermore, the review outlines promising trends and opportunities, including the integration of artificial intelligence, blockchain, and circular economy principles in PV system management. Emerging applications in predictive fault diagnosis and enhanced grid interaction underscore the potential of digital twins to revolutionize renewable energy. The paper concludes by presenting a roadmap for researchers, practitioners, and policymakers to collaboratively advance the digital twin-powered solar PV ecosystem. By bridging interdisciplinary domains, this study aims to accelerate the transition toward sustainable, resilient, and highly efficient solar energy systems, aligning with global energy and climate goals. This comprehensive review not only elucidates the current landscape but also serves as a catalyst for innovation, equipping stakeholders with a strategic vision to harness the full potential of digital twins in driving the next generation of solar PV technologies.
