This paper investigates the relationship between economic development and the formation of thermal hotspots, defined as areas of geographically concentrated thermal pollution associated with fossil-fuel use. A novel measure of potential thermal energy is developed from the higher heating values of fossil fuels. A spatial error-correction framework examines the relationship between economic development and thermal pollution across 95 countries, accounting for spatial interdependence. The findings reveal significant spatial dependence in thermal pollution across neighboring countries in both the short and long runs. Thermal pollution and economic development, measured by GDP per capita, exhibit an inverted-U relationship, consistent with the Environmental Kuznets Curve. Innovation, measured by patents per capita, is positively associated with thermal pollution in the long run, potentially reflecting productivity gains, lower production costs, and rebound effects rather than improvements in energy efficiency. Trade openness is positively associated with thermal pollution in the short run, potentially reflecting increased fossil-fuel consumption in transportation and logistics. The long-term consequences of persistent thermal hotspots remain uncertain. The Precautionary Principle suggests a proactive approach to mitigation. Policy recommendations include regional coordination to improve fossil-fuel efficiency, policies that align economic growth with energy efficiency, and incentives for technologies with smaller thermal footprints.
This study examines the impact of aggregate and disaggregated renewable energy consumption while considering economic growth, remittances, urbanisation and foreign direct investment (FDI) on environmental degradation, proxied by the ecological footprint, across major renewable energy-consuming countries over the period 1984-2020. Employing the Dynamic Common Correlated Effects (DCCE) estimator, the study captures long-run impacts while accounting for cross-sectional dependence and heterogeneity. The findings reveal that aggregate renewable energy consumption significantly reduces environmental degradation. However, the disaggregated analysis uncovers heterogeneous effects across energy types: solar energy consumption reduces the ecological footprint, whereas hydropower and wind energy increase environmental pressure, likely due to lifecycle and infrastructure-related impacts. Furthermore, economic growth, remittances and FDI are found to exacerbate environmental degradation, while urbanisation contributes to environmental improvement. These results highlight the importance of moving beyond aggregate energy measures and adopting a component-specific approach to renewable energy policy. The study provides policy-relevant insights for advanced renewable energy-consuming countries, emphasising the need to prioritise solar energy expansion while mitigating the environmental impacts of other renewable sources.
Despite the decrease in global energy intensity, OPEC countries still have relatively low productivity and high energy intensity. Therefore, this study examines the impact of the influencing variables, such as technology, financial development, and trade openness, on energy intensity in OPEC countries between 2008 and 2020. For this purpose, the spatial econometric method has been used. The Spatial Durbin model was employed to ascertain the direct effect of technology, trade openness, financial development, and economic growth on OPEC members, as well as the spillover effect on each other. Our analysis revealed that technological innovation has the most significant direct effect on energy intensity, while the spillover effect between OPEC countries is not significant. The direct effect of economic growth is negative, but the indirect effect of economic growth is positive and significant. In contrast, financial development exhibits a positive direct effect on energy intensity, suggesting that policies promoting financial development may need to be revisited to align with energy efficiency goals. Finally, trade openness has no effect on energy intensity among OPEC countries. The study suggests that OPEC member countries should focus on policies that foster technological advancement while simultaneously addressing financial efficiency and trade patterns.
This study explores the relationship between trade, industrial energy efficiency, energy losses, and natural gas consumption on China's ecological sustainability from 1990 to 2021, with a focus on the Inverted Load Capacity Factor (ILCF). Using advanced econometric methods, including Fourier Engle-Granger cointegration and various regression techniques (FMOLS, CCR, DOLS) and ARDL, the analysis identifies a stable long-term relationship between ILCF and key economic indicators. The findings support the existence of an inverted U-shaped relationship between trade and ILCF, thus proving the validity of the Inverted Load Capacity Curve (ILCC) hypothesis. This hypothesis suggests that trade initially has a negative impact on ecological sustainability, but increases in trade volume and improvements in trade practices can positively affect ecological sustainability once a certain threshold is exceeded. These findings emphasize that the environmental impacts of trade may change over time. Industrial energy efficiency emerges as a crucial factor in reducing ecological impact, highlighting the need for sustained policy efforts in this area. Additionally, addressing energy losses and accelerating the transition from natural gas to renewable energy are identified as important for achieving ecological goals. These insights provide valuable guidance for China's policy strategies, emphasizing the balance between economic growth and ecological sustainability.
Despite growing policy efforts toward sustainable development, the mechanisms through which artificial intelligence, institutional quality, and macroeconomic policy variables jointly shape green growth remain insufficiently understood in advanced economies. This study investigates the key determinants of green growth across the G7 economies from 2000 to 2023, employing a panel ARDL framework combined with wavelet-based time-frequency analysis. The model incorporates AI research activity, renewable energy use, natural resource rents, government effectiveness, environmental taxation, and education expenditure. The findings reveal that AI research and renewable energy adoption serve as major engines of sustainable growth, whereas natural resource rents and education spending exert adverse long-run effects, suggesting inefficiencies and misalignment between fiscal and environmental objectives. Distinct national patterns emerge: innovation-oriented economies such as Germany, Japan, and the United States benefit from the synergy between digitalization and green investment, while more resource-dependent economies face institutional barriers that slow their green transition. The study further illustrates how the integration of AI and institutional effectiveness can reshape policy frameworks. The results underscore the importance of data-informed policymaking, education reform toward environmental competencies, and smarter environmental taxation, providing actionable insights for governments aiming to align technological innovation with ecological resilience in advanced economies.
