This study examines the impact of clean energy technologies on environmental sustainability in 29 sub-Saharan African (SSA) countries while controlling for income, industrialization and trade from 2002 to 2018. We used the generalized quantile regression,which controls variable endogeneity using lagged instruments. In addition, Bayesian panel regression was used for robustness checks. We used the load capacity factor (LCF) as a broad measure of environmental sustainability that captures both nature's supply and man's demand for the environment. The findings show that clean energy technologies (clean fuels and renewable energy), have positive and statistically significant effects on environmental sustainability for nearly all quantiles in SSA. The findings are still the same after verifying the robustness analysis, showing that the coefficients for clean fuels and renewable energy technologies in quantile regression are within the Bayesian probability credible intervals and all have positive impacts on ensuring environmental sustainability in SSA. Furthermore, the results show that economic growth (income) has asymmetric (both negative and positive) effects on environmental sustainability across different quantile, confirming the Load Capacity Curve (LCC) hypothesis in SSA while accounting for clean energy technologies in the model. The findings further indicate that industrialization and trade have heterogeneous impacts on environmental sustainability. Overall, our findings imply that clean energy technologies improve environmental sustainability in SSA. Our main recommendation to policymakers is that sub-Saharan Africa needs to reduce the cost of energy services (i.e., renewable energy and clean fuels for cooking) in order to achieve greater environmental sustainability.
This study analyzed the moderation effect of demographic variables on the trust in mobile phone banking services among smallholder farmers in the Dodoma Region. The study employed a quantitative research design with cross-sectional field surveys and structured questionnaires were employed as the research methods. The study employed a sample size of 355 smallholder farmers who were drawn by simple random sampling from grapes farmers. SPSS was used as an analytical tool for quantitative data analysis. Multiple linear regressions and Fisher’s Z-transformation were involved to test the moderating effect of demographic variables. Results show that demographic factors namely sex, age, experience, level of income, level of education, and marital status were significant moderating variables. However, the level of education did not show any moderating effect. Our results suggest that by integrating the accessibility and ease of use from the Technology Acceptance Model (TAM), age, sex, and experience from the Unified Theory of Acceptance and Use of Technology (UTAUT), and security and privacy from the Protection Motivation Theory (PMT), the research provides insights into the factors influencing consumers’ trust in mobile phone banking services. Besides, the results of moderating effect improve our understanding of the demographic differences, which influence the degree of mobile banking adoption. Besides, the results of moderating effect improve our understanding of the demographic differences, which influence the degree of mobile banking adoption. This study will help researchers and service providers to come up with improved mobile phone trust frameworks with a greater understanding of the influence of demographic variables. No similar study had been done in sub-Saharan African countries. Therefore, the study provides new knowledge and insight into the influence of demographic variables on the trust in mobile phone banking services.
This study examines the impact of clean energy technologies on environmental sustainability in 29 sub-Saharan African (SSA) countries while controlling for income, industrialization and trade from 2002 to 2018. We used the generalized quantile regression,which controls variable endogeneity using lagged instruments. In addition, Bayesian panel regression was used for robustness checks. We used the load capacity factor (LCF) as a broad measure of environmental sustainability that captures both nature's supply and man's demand for the environment. The ndings show that clean energy technologies (clean fuels and renewable energy), have positive and statistically signi cant effects on environmental sustainability for nearly all quantiles in SSA. The ndings are still the same after verifying the robustness analysis, showing that the coe cients for clean fuels and renewable energy technologies in quantile regression are within the Bayesian probability credible intervals and all have positive impacts on ensuring environmental sustainability in SSA. Furthermore, the results show that economic growth (income) has asymmetric (both negative and positive) effects on environmental sustainability across different quantile, con rming the Load Capacity Curve (LCC) hypothesis in SSA while accounting for clean energy technologies in the model. The ndings further indicate that industrialization and trade have heterogeneous impacts on environmental sustainability. Overall, our ndings imply that clean energy technologies improve environmental sustainability in SSA. Our main recommendation to policymakers is that sub-Saharan Africa needs to reduce the cost of energy services (i.e., renewable energy and clean fuels for cooking) in order to achieve greater environmental sustainability.2.1 Empirical literature review
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