Economies are making environmental regulations to achieve sustainable development and mitigate environmental pollution. However, these regulations cannot provide effective results unless implemented properly. The role of the government is fundamental in this regard. In this context, this research probes the impacts of democracy, environmental regulations, renewable energy, globalization, and economic growth on ecological footprints in N-11 countries from 1990 to 2018. For statistical analysis, this work applies the cross-sectional autoregressive distributed lags (CS-ARDL) methods. This method efficiently provides robust findings for panel time series data because they counter the cross-sectional dependence and slope heterogeneity while providing the results. Moreover, augmented mean group (AMG) and fully modified ordinary least squares (FMOLS) are used to check the robustness of the findings. The results show that environmental regulation significantly mitigates ecological footprint, while economic growth escalates footprints in N-11 countries. In addition, democratic quality, renewable energy consumption, and globalization are contributing factors to environmental quality. Hence, this research presents important policy implications for the N-11 countries in that they need to enhance democratic accountability. This will assist them to launch an effective environmental policy. Effective environmental policy will assist in increasing renewable energy, which will ultimately enhance the environmental quality.

Every year, the problem of environmental degradation becomes more severe globally. It is widely believed that technological innovation and economic complexity are understood as structural transformations toward a more sophisticated and knowledge-based means of production as a viable way to fight against climate change. However, the studies integrating these two elements into the same environmental policy framework are still scant. With this in view, this study investigates the dynamic linkage between economic complexity, technological innovations, economic growth, and nonrenewable energy on CO₂ emissions in the N-11 nations. This study uses data from 1980 to 2020. It applies the recent method of cross-sectional autoregressive distributed lags (CS-ARDL). The cointegration method shows a strong association among the variables. The findings of the CS-ARDL show that technological innovations are negatively related to environmental degradation, while nonrenewable energy deteriorates the environment by escalating CO₂ emissions. This study fails to validate the EKC in the N-11 nations. In addition, economic complexity is helping these economies to achieve environmental sustainability by lowering environmental pollution. Based on the findings, this work recommends that the N-11 countries restructure their industrial sectors with low-carbon energy sources. For this purpose, these countries should increase their research and development budgets. This will help in launching environmentally friendly energy sources in their economic development model.

The energy sector holds critical importance for South Africa, particularly as a developing country grappling with persistent economic challenges and energy insecurity. These pressures have stimulated growing scientific and policy interest in renewable energy as a pathway to sustainable development. This study examines public perceptions and awareness of renewable energy technologies and estimates willingness to pay (WTP) for their increased integration into South Africa’s energy mix. By linking these objectives, the study provides insights into the social and economic factors that shape a just energy transition and informs targeted policies, investments, and engagement strategies to accelerate the adoption of renewable energy. A descriptive research design was employed, incorporating a systematic random sampling approach to ensure reliability and representativeness. Data were collected through structured questionnaire surveys conducted in both urban and rural households across Limpopo Province, South Africa. Findings reveal a generally positive public attitude toward the expansion of renewable energy, although knowledge levels remain moderate and are most pronounced with respect to solar energy systems. The mean household WTP for increased renewable energy penetration was estimated at ZAR 163.4 per annum. Binary logistic regression analysis identified eight statistically significant predictors of WTP: Education, Occupation, Income, Recognised Advantages (A1), Financial Incentive Schemes for RES (A3), Expansion Strategies for Renewable Energy (A4), Price Parity with Fossil Fuels (A7), and Interest-Free Financing Options (A8). These results highlight the importance of affordability, policy support, and tangible benefits in driving public acceptance. Overall, the findings highlight the potential for targeted policy and educational interventions to foster household participation and advance South Africa’s just energy transition.

Determining the solar radiation for the use of energy generation involves a number of procedures, estimations and calculations using the climatological weather data measurements. The case study was conducted in the Vhembe District area through the nine installed Weather Stations (WS). The analysis determines the minimum and maximum solar radiation equations associated with the local climate patterns in accommodating the theoretical bases and its time period changes. The paper contributes to the main project objectives on renewable (i.e. solar, wind, biomass/biogas and hydro) energy assessment for their potentials and generating at small/ micro scale in the district. These parameters are very important in estimating the solar energy radiation to the area using its extraterrestrial solar radiation per day/ weekly/ monthly and annual periods. The metrology estimations through graphical representations were performed. These provided calculations in which territorial solar energy was determined through climatic conditions and analysis found to be usable.

This study addresses the global challenge of assessing renewable wind resources by evaluating wind energy potential, spatial availability, and localised applications—such as household electricity, irrigation, and small-scale community projects—within South Africa's Vhembe District, Limpopo Province. The research integrates meteorological data from local weather stations with Geographic Information Systems (GIS) and Remote Sensing (RS) techniques. This approach maps spatial wind distribution while accounting for environmental constraints, weather conditions, and regional land suitability. The empirical results reveal highly variable wind energy prospects across the Vhembe District and broader Limpopo locations. Furthermore, the study demonstrates the efficacy of the Conceptual Framework for Identification of the Rural Energy Deployment Application Tool in optimising site selection. Utilising integrated GIS and RS methodologies provides a robust framework for identifying viable inland wind resources, confirming that Vhembe possesses strategic pockets capable of supporting decentralised rural energy projects. These findings provide community leaders and regional decision-makers with a data-driven tool to successfully plan, implement, and accelerate sustainable wind energy deployment in remote rural areas.