As cities expand and industries grow, energy demand surges, but what fuels this transformation? Do urbanization and industrialization in developing economies accelerate a shift toward renewables, or do they deepen reliance on fossil fuels? This study examines the impacts of urbanization and industrialization on fossil fuel consumption, as well as on the consumption of renewable energy. The Autoregressive Distributed Lag (ARDL) and Canonical Cointegrating Regression (CCR) estimation procedures were used. The study found an inverted U-shape relationship between urbanization and renewable energy consumption, while an increasing monotone relationship was found for urbanization and fossil fuel consumption. The Lind and Mehlum test provided sufficient conditions for the existence of an inverted U shape between urbanization and renewable energy consumption, and the turning point was obtained for policy purposes. Another key finding from the study revealed that in Ghana, the use of fossil fuels is a key driver of industrialization, rather than renewable energy sources. Additionally, the results of the generalized forecast error variance decomposition (GFEVD) provided interesting insights. The policy implications of these findings have been thoroughly discussed.

Despite extensive investigation into the factors driving the clean energy transition, the impact of income tax on renewable energy consumption (REC) has been overlooked. This study employs a newly compiled tax dataset covering OECD economies from 1990 to 2020 to investigate the influence of income tax on REC. The study also analyzes the moderating effects of digitalization, globalization, and financial development. Applying the Cross-Section Augmented Autoregressive Distributed Lag (CS-ARDL) model, the findings reveal that income tax exerts an adverse long-run effect on REC, indicating that higher income taxes discourage the adoption of renewable energy. Digitalization and globalization have positive and significant impacts on REC, while financial development shows a negative association. Moreover, the findings reveal that digitalization and globalization mitigate the adverse influence of income tax on REC, whereas the moderating effect of financial development is insignificant. Robustness checks using the Fully Modified Ordinary Least Squares (FMOLS) model support these findings. The study recommends that fostering technological advancement and globalization are crucial to enhancing the effectiveness of fiscal policy in accelerating the low-carbon transition across OECD economies.

The primary purpose of this study is to measure the impact of economic growth, financial development (FD), and green energy defined by renewable energy (RENEW) consumption on environmental pollution in ASEAN countries, where export and population growth are used as control variables. This study considers balanced panel data from 1990–2023 and applies the panel ARDL model based on the panel unit root and other required tests. In the findings, the short-run result indicates that the coefficient of GDP growth is negative but insignificant for determining CO₂ emissions. The consumption of green energy, specifically RENEW, significantly decreases environmental pollution; a one percent increase in RENEW consumption will result in a short-term decrease of -1.172011 percent in CO₂ emissions. In the long run, exports and population growth significantly and positively impact environmental pollution. The value of error correction is 26%; that is, it shows the speed of adjustment. Through academic acceleration, environmental concern, and ensuring sustainability, this study contributes to the current body of literature by bringing value and fresh insights regarding ASEAN countries.

Energy security is essential for building resilient economies and sustaining economic growth, as it directly influences productivity, competitiveness, and overall economic performance. While existing research has explored the relationship between energy security and economic development, the asymmetric effects of energy security on economic performance remain underexamined. This paper seeks to address that gap. To test the asymmetric effects of energy security, we use various energy security proxies (domestic energy production relative to consumption, energy production per capita, renewable energy share, energy intensity, and energy consumption per capita) and employ a panel dataset of 117 countries from 2000 to 2022, and apply a panel quantile estimation method. The results reveal strong heterogeneity across income levels. Energy production relative to consumption and population, and energy consumption per capita, have stronger positive effects on GDP per capita in lower quantiles, highlighting the importance of energy availability and access in the early stages of development. By contrast, the renewable energy share has a negative effect in poorer economies but turns positive in richer economies, reflecting the high costs of renewable energy adoption for low-income countries and the benefits it generates in advanced economies. Energy intensity consistently has a negative impact, but the magnitude varies nonlinearly across quantiles, underscoring the role of energy efficiency. The results are robust to alternative sample compositions excluding microstates and to an aggregate measure of energy security constructed using principal component analysis. Overall, the findings suggest that energy policy should be tailored to countries’ development levels.

Dynamic characteristics of renewable energy generation and consumption using LSTM deep learning algorithm

The role and impact of batteries in the implementation of renewable energy consumption responsibilities in short-process steel mills

A comprehensive review of one year that investigates the revolutionary possibilities of integrating IoT (Internet of Things) and ML (Machine Learning) for renewable energy forecasting in Indian healthcare systems, with a focus on the rural village of Vellore, Tamil Nadu. Increased patient mortality due to unpredictable power supplies from various regions around the world. Over 300 articles indexed by SCI (Science Citation Index) from 2014 to 2025 and real case studies address power outages using IoT for real-time monitoring (solar irradiance: 1000 W/m², wind speed: 5–7 m/s) and ML models (LSTM (Long Short Term Memory), CNN-LSTM (Convolutional Neural Network-Long Short Term Memory), XG Boost (Extreme Gradient), RF (Random Forest), ANN (Artificial Neural Network) for forecasting on weekly, monthly, and yearly timescales, expanding on previous research's short and medium term emphasis. A 100-kW hybrid solar-wind system (20 kW PV, 10 kW wind) with edge-cloud optimizes demand (4000–4250 kWh/day for 200 beds) and reduces peak load (200–210 kW) by 15–20%, achieving 85–95% accuracy and 15–35% efficiency. Case studies demonstrate a 5–7-year ROI (Return on Investment), 15–40% CO₂ reduction, and 95% uptime (>99.9% with IoT). NB (Narrow band)-IoT/5G microgrids, subsidies (0.05–0.07 $/kWh), and IoT-ML to raise accuracy by 10–20% are proposed to improve healthcare resilience and global sustainability. This pioneering study lays the groundwork for hospital renewable energy infrastructure assessments. • For rural hilly microgrids, the first hybrid quantum-classical framework tailored to India (QPSO + QAOA + Q-GIS). • 30% lower levelized energy costs (₹8.00 → ₹5.60/kWh) compared to traditional PSO/GA/MILP. • Annual CO 2 emissions were reduced by 25% (from 500 to 375 tons), while the percentage of renewable energy rose to 92%. • In difficult hilly terrain, Q-GIS finds 40% more suitable sites (ANOVA p < 0.05). • Supports SDGs 7 and 13 and India's 500-GW non-fossil target with NISQ-era simulation (Qiskit + PuLP).

The integration of high-penetration renewable energy sources (RES) into global power systems necessitates advanced scheduling strategies to ensure supply-demand balance. Virtual Power Plants (VPPs) serve as critical aggregators for distributed resources; however, coordinating VPPs across multiple regions is hindered by the curse of dimensionality, partial observability, and stochastic volatility. Conventional centralized optimization lacks scalability for real-time applications, while single-agent approaches fail to effectively address complex collaborative dynamics. To overcome these limitations, this paper proposes a collaborative scheduling framework based on Multi-Agent Reinforcement Learning (MARL). We model the global system as a multi-regional environment where heterogeneous agents operate under a Centralized Training with Decentralized Execution (CTDE) architecture. A composite reward function is designed to balance economic efficiency with RES absorption, utilizing an attention-based mechanism to exploit time-zone complementarity. Simulation results demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves a global RES accommodation rate of 94.2% and maintains a minimal tie-line violation rate of 0.8%, compared to only 76.5% accommodation with rule-based heuristics. Furthermore, the approach exhibits superior robustness in extreme-volatility scenarios where standard methods degrade. This study validates the efficacy of distributed intelligence in solving large-scale energy dispatch problems, offering a scalable and privacy-preserving pathway for managing the Global Energy Interconnection.

Financing Africa’s green energy transition: do diasporas’ remittance inflows influence renewable energy consumption?

This study investigates the relationship between energy consumption, labour productivity, economic growth, and poverty in selected West African countries, with emphasis on distributional heterogeneity. The study covered the period of 1990-2024. The motivation for this study stems from persistent poverty crisis in the region despite rising energy use and economic growth, suggesting that while mean-based estimators provide average insights, they may overlook differences driven by initial poverty conditions. Quantile regression addresses this by capturing distribution-specific effects, revealing whether energy interventions are more effective in low-, middle-, or high-poverty contexts. The study employs generalized method of moment (GMM), Panel Nonlinear Autoregressive Distributed Lag (NARDL), and Panel Quantile Regression techniques to capture long-run, asymmetric, and distributional effects. Empirical result showed that across the various estimation methods, renewable energy consumption (RENC) consistently exhibits a negative relationship with poverty, confirming its poverty-reducing role. GMM shows a modest negative effect, while NARDL highlights stronger asymmetric impacts, especially from negative shocks. Quantile regression reveals robust and significant effects across all distributions, with stronger impacts at higher poverty levels. Non-renewable energy exhibits mixed and often insignificant effects, while in some cases worsening poverty outcomes due to inefficiencies and volatility. Economic growth (GDP) shows weak and sometimes non-inclusive effects, indicating that growth does not automatically translate into poverty reduction. Labour productivity demonstrates context-specific impacts, becoming more relevant in certain quantiles and short-run dynamics. The findings further confirm significant asymmetry and heterogeneity in the energy–poverty nexus, supporting the superiority of quantile regression in capturing distributional effects. The study concludes that inclusive energy transitions, productivity enhancement, and targeted pro-poor growth strategies are essential for sustainable poverty reduction in West Africa.

Rapid expansion in urbanisation, along with the rising demand for energy consumption, has deepened environmental apprehensions among developing economies and intensified their concerns about long-run environmental sustainability. This article examines how urban expansion and rising energy consumption impact environmental sustainability, and whether environmental taxes moderate this relationship, by using a panel of 110 developing countries over the period of 2010 to 2024. To capture both static and dynamic relationships among the variables, we have applied complementary econometric methodologies that allow for cross-country heterogeneity and persistence in emissions. The estimated outcomes show that urban expansion and energy consumption are significantly increasing gas emissions, and this outcome is consistent with the idea that environmental costs of urban-led growth and energy-intensive development. But as we have added environmental taxes as a moderating policy instrument, the positive impact of energy consumption and urbanisation on emissions becomes negative in most specifications. The significant impact of both interaction terms, i.e., environmental taxes and urbanisation, and environmental taxes and energy consumption, across different estimation strategies, suggests that environmental taxation weakens emissions and encourages structural change with rising energy use. Renewable energy consumption and foreign direct investment have significant influences on emissions, emphasising the role of energy structure and investment composition in shaping environmental outcomes, whereas the income effect varies across models. The outcomes of dynamic models also confirm emissions persistence, but over time, environmental taxes reduce the degree of emissions persistence. The estimated outcomes imply that environmental taxes can support a decoupling of urbanisation and energy-driven growth from environmental degradation. Thus, developing countries should balance urban development, energy demand, and environmental sustainability through credible market-based regulations.

• This study examines the impact of carbon emissions (CO₂) and renewable energy consumption (REC) on firms’ financial performance (ROA and ROE) and market value (Tobin’s Q and EPS). • The study analyzes 3,416 firm-year observations from the NSE CNX500 index (India) over 10 years (2011–2020), using data from Eikon Thomson Reuters and the World Bank. • Both CO₂ and REC show a consistent and significant influence on Tobin’s Q across all tests, indicating a strong link between environmental factors and firm valuation. • The study includes robustness checks by firm size and leverage, revealing that CO₂ and REC significantly affect ROE in low-leverage firms, though not in the full sample. • The findings provide critical insights for investors, fund managers, and policymakers aiming to integrate sustainability into financial decision-making. This study examines the impact of carbon emissions (CO₂) and renewable energy consumption (REC) on financial performance (ROA, ROE) and market value (Tobin’s Q, EPS) using 3,416 firm-year observations from NSE CNX500 firms (2011–2020), sourced from Eikon Thomson Reuters and the World Bank. Results show that CO₂ and REC consistently and significantly influence Tobin’s Q across all models, including by firm size and leverage. REC also significantly affects EPS in the full sample. These findings highlight the strong and persistent relevance of environmental factors for market valuation, offering important insights for investors, fund managers, and policymakers.

The increasing vulnerability of African nations to climate change underscores the need to identify the institutional, structural, and technological drivers of CO 2 emissions to advance progress toward SDG 13. However, the role of institutional, technological innovation, and trade in influencing CO 2 emissions remains insufficiently examined in an integrated and multi-channel framework within the African context. Using panel data of 15 African countries from 2000 to 2022, this study utilizes the Bias-Corrected Method of Moments and system GMM to examine the effects of the rule of law, green technology innovation, renewable energy consumption, international trade, and GDP on CO 2 emissions. The results reveal that the rule of law reduces CO 2 emissions, highlighting the role of strong governance and regulatory enforcement in improving environmental performance. Renewable energy consumption is found to reduce CO 2 emissions, reaffirming the environmental benefits of a clean-energy transition in Africa. In contrast, green technology innovation increases CO 2 emissions, suggesting a rebound effect and the predominance of carbon-intensive innovation pathways. GDP similarly contributes to rising CO 2 emissions, consistent with scale-driven environmental pressure. Trade is positively and insignificantly associated with CO 2 emissions. The results underscore the need for Africa to strengthen institutional quality, scale up clean energy systems, and reorient trade toward sustainable sectors to achieve SDG 13. • Examined the effect of rule of law, green technology innovation, andtrade on CO 2 emissions. • Rule of law significantly reduces CO 2 emissions across the African countries. • Green innovation raises CO 2 emissions, showing early-stage rebound effects. • Renewable energy use lowers emissions, supporting Africa's clean transition. • GDP increases CO 2 emissions, reflecting scale-driven pressures.

Funding the supply of critical materials for the transition to renewable energy (RE) is crucial to addressing climate change. For the world’s leading economies in copper and lithium mining, this paper investigates the association among foreign direct investment (FDI), governance, carbon emissions (CO2) and renewable energy consumption (REC). Using 2002–2023 panel data, a unit root test was applied to determine the stationarity of the variables and cointegration tests revealed cointegration in first differences. The variables were cointegrated at the 1% significance level, as indicated by the Kao Residual Cointegration Test. Next, the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) panel regression methods were employed. The FMOLS model findings indicated a long-term negative relationship between FDI and RE. Specifically, a 1% increase in FDI (as a percentage of GDP) reduces REC by 0.24% in the major copper- and lithium-producing economies. Governance, measured by control of corruption, has a positive effect on clean energy consumption, and CO2 emissions are significantly negatively associated with REC. Using the DOLS model, we confirmed the robustness of these long-term panel relationships. Policymakers should strengthen the quality of governance, including combating corruption and encouraging FDI in RE. This strategy should also support sustainable mining practices and responsible consumption, aligning with Sustainable Development Goals (SDGs) 7 and 12, respectively.

Synergizing renewable energy, women empowerment, and policy for emissions reduction

The rapid development of the economy impacts environmental degradation, where climate change is the major issue. Despite all efforts of international organisations, governments, businesses, and ordinary citizens, the problem cannot be successfully managed. The main objective of the study was to test the relationship between CO₂ and economic growth and two other indicators. In addition, the share of renewable energy and energy efficiency level indicators was included to testing environmental investments in the energy sector. The study used a sample of 23 EU member states over the period 2009-2023. Data were obtained from Eurostat and normalised using the mean-standardisation approach. Regression analysis was used to examine the relationships among the selected variables. The regression analysis confirmed that GDP and renewable energy consumption had positive effects on CO₂ emissions, whereas energy intensity had no statistically significant effect. To more deeply reveal regional structural development tendencies, the chosen sample was categorized into three groups using K-means clustering. Despite efforts to delink economic growth from CO₂ emissions, the findings indicate that this objective has not yet been achieved. The study emphasised the need for stronger political commitment and more effective strategies to achieve meaningful progress in the emission-growth delinking process. The analysis covered a specific period and the ceteris paribus assumption may not hold fully due to the presence of other factors. Further examination of additional explanatory factors may also provide new insights and policy implications.

Purpose — This study investigates the impact of Information and Communication Technology (ICT) trade flow components, specifically ICT service exports, ICT goods exports, and ICT goods imports, alongside economic complexity and renewable energy share on carbon emissions.Methodology — A panel of BRICS countries from 2000 to 2022 is estimated using a second-generation cross-sectional autoregressive distributed lag (CS-ARDL) model that accounts for cross-sectional dependence and slope heterogeneity across countries. Findings — Gross domestic product per capita and economic complexity are positively associated with carbon emissions. ICT trade flows have heterogeneous effects on emissions. ICT services, exports, and renewable energy consumption significantly reduce carbon emissions. However, ICT goods exports and imports have an insignificant effect on carbon emissions.Implications — The results suggest that the BRICS countries must emphasise policy measures that promote the export of ICT services, accelerate renewable energy adoption, and promote industrial transformation policies towards sustainable production practices.Originality — This study focuses on supply-side ICT trade channels and disaggregates them into ICT goods exports, imports, and service exports. Furthermore, this study applies second-generation estimation techniques that are robust to cross-sectional dependence and slope heterogeneity

This study examines the association between environmental sustainability factors and Domestic Credit to Private Sector (CPS) across selected EU countries, specifically focusing on Germany, France, Italy, Spain, Netherlands, Sweden, Finland, and Denmark. Using advanced econometric techniques, including MMQR, the study investigates how renewable energy consumption (REC), research and development expenditure (RDE), income inequality (GIN), and under-5 mortality (MUR) influence CPS. The results show that CPS is significantly impacted by REC and RDE, suggesting that financial access is closely tied to investments in renewable energy and technological innovation. Furthermore, CPS is negatively associated with income inequality, highlighting the potential of financial systems to reduce disparities and promote more inclusive economic growth. Although CPS is positively associated with forest area (FAR), the relationship with under-5 mortality is limited, implying that healthcare infrastructure and social policies play a more direct role in improving child health outcomes. These findings emphasize the importance of integrating green finance policies, enhancing R&D investments, and addressing income inequality to foster long-term sustainable development and financial inclusion in EU countries.

This study examines the determinants of environmental pressure in selected Asian economies, with a focus on the roles of Information and Communication Technology (ICT) trade openness, energy intensity, renewable energy consumption, access to clean cooking fuels, and sustainable development. Using a balanced panel dataset and estimation techniques robust to cross-sectional dependence and heterogeneity, namely Panel Corrected Standard Errors and Feasible Generalized Least Squares, CO₂ emissions per capita are employed as a proxy for environmental pressure. The empirical results indicate a differentiated pattern of environmental dynamics. The ICT trade is found to be negatively associated with emissions, suggesting that technological diffusion improves production efficiency and reduces environmental pressure. In contrast, energy intensity exhibits a strong positive relationship with emissions, confirming that inefficient energy use remains a central driver of environmental degradation. Renewable energy consumption shows a positive association with emissions, reflecting the dominance of scale effects and the limited substitution away from fossil fuels in the early stages of energy transition. Access to clean cooking fuels contributes to lower emissions, highlighting the importance of household-level energy transition. Sustainable development is also associated with reduced environmental pressure, although its effect appears gradual and context-dependent. The findings suggest that environmental sustainability in Asian economies depends on the interaction between technological openness, energy structure, and structural transformation. Policies should prioritize improving energy efficiency, ensuring effective substitution toward clean energy, strengthening absorptive capacity for technological adoption, and promoting inclusive access to modern energy services.

ABSTRACT This study aims to provide a thorough bibliometric analysis of the existing literature on financial development, foreign direct investment (FDI), CO 2 emissions, and governance in renewable energy consumption towards achieving Sustainable Development Goal (SDG) 7. This study analyses 2,050 publications indexed in Scopus and Web of Science from 1997 to 2025. It aims to outline the evolution of the global research agenda on finance, investment, institutional quality, and CO 2 emissions, considering renewable energy consumption. Results show that the academic interest in the finance‐energy‐environment nexus is on the rise, especially after the Paris Agreement and the implementation of the 2030 Sustainable Development Agenda. A shift in focus from emissions and economic growth to the increasing importance of green finance, governance, and cross‐border investment to facilitate a clean energy transition is observed. This study highlights that, over the past years, FDI and sustainable development have become a relevant topic of research, emphasising the importance of securing finance and building institutional capacities to support a cleaner energy transition. Notably, the study uncovers significant variations in research across regions. A reliable financial and institutional base is crucial for an inclusive and sustainable energy transition, and this trend provides useful directions for policymaking based on findings from the past three decades. JFL Classifications : Q01; Q42; F21; G20; C83