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While there is a consensus on the multifaceted advantages of wind farms, only a handful of developing countries harness it to the fullest potential. Among the various factors that contributed to the low development of wind farms in those countries, the dearth of methods for identifying a suitable site is the leading one. Studies conducted elsewhere on wind farm industry identifies criteria such as slope, wind speed, land use/land cover, geology, distance from electric transmission grids, roads, and settlements. However, the relative magnitude of each factor to the optimum location of the wind farm is inadequately examined. This study focuses on the combined use of geographic information systems (GIS) and spatial multi-criteria decision analysis (MCDA) for selecting the most appropriate sites for wind-farm development. Using a pairwise comparison method of the analytical hierarchical process, we computed the relative importance and weight of parameters affecting wind farm sites in a weighted overlay technique. Based on the analysis of pertinent data collected from Bahir Dar City and its surroundings, northwestern Ethiopia, we categorized the study area into five suitability classes ranging from extremely suitable to not suitable. Our result, revealed a spatial pattern of the potential sites suitable for wind farm investments that are economical, environmentally sound, and aesthetically amenable. We recommend replicating the method developed in this study in other areas of developing countries where transitioning to renewable energy sources through windfarm development is sought.

While there is a consensus on the multifaceted advantages of wind farms, only a handful of developing countries harness it to the fullest potential. Among the various factors that contributed to the low development of wind farms in those countries, the dearth of methods for identifying a suitable site is the leading one. Studies conducted elsewhere on wind farm industry identifies criteria such as slope, wind speed, land use/land cover, geology, distance from electric transmission grids, roads, and settlements. However, the relative magnitude of each factor to the optimum location of the wind farm is inadequately examined. This study focuses on the combined use of geographic information systems (GIS) and spatial multi-criteria decision analysis (MCDA) for selecting the most appropriate sites for wind-farm development. Using a pairwise comparison method of the analytical hierarchical process, we computed the relative importance and weight of parameters affecting wind farm sites in a weighted overlay technique. Based on the analysis of pertinent data collected from Bahir Dar City and its surroundings, northwestern Ethiopia, we categorized the study area into five suitability classes ranging from extremely suitable to not suitable. Our result, revealed a spatial pattern of the potential sites suitable for wind farm investments that are economical, environmentally sound, and aesthetically amenable. We recommend replicating the method developed in this study in other areas of developing countries where transitioning to renewable energy sources through windfarm development is sought.

Abstract The ecological footprint is a measure of the resources necessary to produce the goods that an individual or population consumes. It is also used as a measure of sustainability, though evidence suggests that it falls short. The assumptions behind footprint calculations have been extensively criticized; I present here further evidence that it fails to satisfy simple economic principles because the basic assumptions are contradicted by both theory and historical data. Specifically, I argue that the footprint arbitrarily assumes both zero greenhouse gas emissions, which may not be ex ante optimal, and national boundaries, which makes extrapolating from the average ecological footprint problematic. The footprint also cannot take into account intensive production, and so comparisons to biocapacity are erroneous. Using only the assumptions of the footprint then, one could argue that the Earth can sustain greatly increased production, though there are important limitations that the footprint cannot address, such as land degradation. Finally, the lack of correlation between land degradation and the ecological footprint obscures the effects of a larger sustainability problem. Better measures of sustainability would address these issues directly.

Abstract The ecological footprint is a measure of the resources necessary to produce the goods that an individual or population consumes. It is also used as a measure of sustainability, though evidence suggests that it falls short. The assumptions behind footprint calculations have been extensively criticized; I present here further evidence that it fails to satisfy simple economic principles because the basic assumptions are contradicted by both theory and historical data. Specifically, I argue that the footprint arbitrarily assumes both zero greenhouse gas emissions, which may not be ex ante optimal, and national boundaries, which makes extrapolating from the average ecological footprint problematic. The footprint also cannot take into account intensive production, and so comparisons to biocapacity are erroneous. Using only the assumptions of the footprint then, one could argue that the Earth can sustain greatly increased production, though there are important limitations that the footprint cannot address, such as land degradation. Finally, the lack of correlation between land degradation and the ecological footprint obscures the effects of a larger sustainability problem. Better measures of sustainability would address these issues directly.

The purpose of this paper is to research the relation among environmental quality and renewable energy in the RECAI country group. The study used per capita CO2 emission, energy intensity, and Aggregate National Savings as a measure of environmental quality. Other variables used in the study are renewable energy consumption, fossil fuel consumption, GDP per capita, and foreign direct investments. In the study, three different models to see different environmental quality indicators by panel quantile method for 19202090-. According to the results obtained, unlike other models, renewable energy consumption in model 1 positively affects energy intensity in all quantiles. In other words, renewable energy consumption negatively affects environmental quality. In model 1 and model 3, the coefficients of fossil fuel consumption were positive and negative, respectively. Unlike model 2, the coefficient estimates of fossil fuel consumption in model 3 were predominantly negative. Fossil fuel consumption shows a positive effect on environmental quality, which is similar to model 1. Economic growth negatively affects environmental quality in all models. There is a one-way causal relationship from renewable energy consumption to energy intensity and energy intensity to growth.

The purpose of this paper is to research the relation among environmental quality and renewable energy in the RECAI country group. The study used per capita CO2 emission, energy intensity, and Aggregate National Savings as a measure of environmental quality. Other variables used in the study are renewable energy consumption, fossil fuel consumption, GDP per capita, and foreign direct investments. In the study, three different models to see different environmental quality indicators by panel quantile method for 19202090-. According to the results obtained, unlike other models, renewable energy consumption in model 1 positively affects energy intensity in all quantiles. In other words, renewable energy consumption negatively affects environmental quality. In model 1 and model 3, the coefficients of fossil fuel consumption were positive and negative, respectively. Unlike model 2, the coefficient estimates of fossil fuel consumption in model 3 were predominantly negative. Fossil fuel consumption shows a positive effect on environmental quality, which is similar to model 1. Economic growth negatively affects environmental quality in all models. There is a one-way causal relationship from renewable energy consumption to energy intensity and energy intensity to growth.

In developing economies, there is a lack of certainty in energy and environmental economic policy. In contrast, research into the interplay between population, economic growth (GDP), renewable energy, nuclear energy, fossil fuels, and greenhouse gas (GHG) emissions has received comparatively little attention. Thus, the main motive of this exploration is to analyze the influence of population, economic growth, and various energy sources consumption on the selected South Asian economy's GHG emissions using the STIRPAT model. The period 1972–2021 is used to estimate results by considering cross-sectional dependence (CSD) and slope homogeneity (SH) tests, as well as second-generation unit root and cointegration tests. Due to the presence of SH, CSD, and mixed-order unit root problems, the cross-sectional autoregressive distributive lag (CS-ARDL) model has been used to estimate the value of selected variables. The impact of GDP and population is positive but insignificant for South Asian countries in the short run, but GDP is significant in the long run. The research also revealed that burning fossil fuels significantly contributes to atmospheric gas emissions level. In addition, renewable and nuclear energy play useful and substantial roles in reducing pollution in South Asian countries. Therefore, it is recommended that the economies of the South Asian countries maintain a uniform approach to economic policy to fully benefit from the advantages of increased green and safe energy production.

In developing economies, there is a lack of certainty in energy and environmental economic policy. In contrast, research into the interplay between population, economic growth (GDP), renewable energy, nuclear energy, fossil fuels, and greenhouse gas (GHG) emissions has received comparatively little attention. Thus, the main motive of this exploration is to analyze the influence of population, economic growth, and various energy sources consumption on the selected South Asian economy's GHG emissions using the STIRPAT model. The period 1972–2021 is used to estimate results by considering cross-sectional dependence (CSD) and slope homogeneity (SH) tests, as well as second-generation unit root and cointegration tests. Due to the presence of SH, CSD, and mixed-order unit root problems, the cross-sectional autoregressive distributive lag (CS-ARDL) model has been used to estimate the value of selected variables. The impact of GDP and population is positive but insignificant for South Asian countries in the short run, but GDP is significant in the long run. The research also revealed that burning fossil fuels significantly contributes to atmospheric gas emissions level. In addition, renewable and nuclear energy play useful and substantial roles in reducing pollution in South Asian countries. Therefore, it is recommended that the economies of the South Asian countries maintain a uniform approach to economic policy to fully benefit from the advantages of increased green and safe energy production.

Abstract To empirically verify whether financial risk affects global carbon emissions, this study investigates the financial risk-emission nexus by employing a global balanced panel dataset of 62 countries over the period 2003–2018. Furthermore, we explore the mediation effect of technological innovation on the financial risk-emission nexus. Fully considering potential regional heterogeneity and asymmetry, this study further analyzes the heterogeneous and asymmetric relationships among the variables, such as the difference between regional comprehensive economic partnership countries and other countries. The empirical results indicate that: (1) a mediation effect between financial risk and global carbon emissions exists; in other words, increased financial risk not only reduces global carbon emissions directly, but can also have an indirect impact in mitigating carbon emissions by promoting technological innovation; (2) the impacts of financial risk and technological innovation on global carbon emissions show significant regional heterogeneity; and (3) financial risk and technological innovation show asymmetry across different quantiles. To be specific, technological innovation and financial risk have a significant inhibitory effect on global carbon emissions only in the 10th quantile, while promoting carbon emissions in other quantiles.