Empirical evidence on households' fuel choices for lighting is lacking in literature in developing countries. We use the World Bank's socioeconomic survey data to study households' lighting choices in Ethiopia. Using a panel multinomial logit model, we investigate the determinants of households' lighting choices and the transition to cleaner fuel sources. In addition, we use a recent cross-sectional survey to complement the results of the panel data. We find that the energy ladder characterizes the households' fuel choices for lighting. As income increases, the probability of households choosing cleaner fuel sources also increases. We also found that education, fuel prices, and location significantly affect fuel choices for lighting. In addition, there is a significant percentage of fuel transition probabilities across different households. Households' education levels and higher incomes facilitate a transition to modern fuel sources. Investments in education increase households' productivity and incomes which will enable their faster and more general transition to cleaner fuels. 

Empirical assessment of relationships between land use and land cover and drinking water chemical treatment cost is lacking in developing countries. This study is conducted to assess the impact of forest and non-forest cover on water purification chemical costs in Ethiopia. A panel fixed effects regression model was applied and analysed at the watershed, upstream parts of the watershed, and different buffer distances ranging from 2.5 to 30 kilometers. Findings indicated that forest cover both at watershed and upstream level has a significant effect on water treatment chemical cost. Result showed that watershed forest cover contributes significantly to reduction of treatment chemical costs as compared to non-forest cover, but the contribution to the reduction of treatment cost declines as the buffer distance increases. Thus, the findings highlighted that protecting forest enhances water quality and reduces the chemical costs incurred to treat potable water.

The purpose of this study is to provide empirical evidence on the impact of a climate-smart agricultural practice (row planting) on the welfare of rural households. Data collected from 260 households in Gubalafto woreda of Amhara region in Ethiopia were analyzed using Propensity Score Matching (PSM) and a Semi-parametric Local Instrumental Variable (LIV) version of the generalized Roy model. The results from the PSM model revealed that adoption of row planting technology has a positive and significant impact on per capita consumption and on crop income per hectare. Covariates are well balanced and the impact estimate is insensitive to unobserved selection bias indicating that the estimates are a pure effect of the row planting technology. Marginal benefit of row planting is increasing with higher propensity of the farmer to adopt the climate smart planting technology. Therefore, scaling up the technology will significantly contribute to farmers’ resilience against the adverse effects of climate change through enhancing household's income and food security. 

The purpose of this study is to provide empirical evidence on the impact of a climate-smart agricultural practice (row planting) on the welfare of rural households. Data collected from 260 households in the North Wollo Zone of Ethiopia were analyzed using Propensity Score Matching (PSM) and a semi-parametric Local Instrumental Variable (LIV) version of the generalized Roy model. The results from the PSM revealed that adoption of row planting technology has a positive and significant impact on per capita consumption and on crop income per hectare. Covariates are well balanced and the effect of unobserved selection bias on the impact estimate is insignificant, indicating that the estimates are largely the effect of row planting. Similarly, the semi-parametric LIV model suggests that average treatment effect is positive and significant for crop income. Marginal benefit of row planting is increasing with higher propensity of the farmer to adopt this practice. Therefore, scaling up the technology will significantly contribute to farmers’ resilience against the adverse effects of climate change through enhancing household’s income and food security.