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Safeguarding the environment and its citizens’ health remains one of the key policy priorities of the governments of many developing and emerging countries. Using the 2017 General Household Survey (GHS) dataset, this study examines the driving factors affecting households’ recycling behaviour and payment for waste disposal in South Africa. The methods of data analysis were based on descriptive statistics and a Bivariate Probit regression model. The descriptive statistics results indicate that there are 56.29% male-headed and 43.71% female headed households, with an average age of 49 years. In addition, the study shows that 89.97% of household heads had formal education with a mean monthly income of 11,099.07 ZAR/650.504 USD. The study also revealed that 22% of the households sampled had access to social grants. The results from the Bivariate Probit regression model show that household’s income, access to social grants, formal educational attainment and the age of the household were significant (p < 0.01) driving factors affecting households’ recycling behaviour and payment for waste disposal. The study concludes that the households’ socio-economic factors affect their recycling behaviour and willingness to pay for waste management in South Africa. Actions targeted at poverty alleviation and environmental sensitization programmes are key for facilitating environmental conservation behaviours of households in South Africa in order to achieve the environmental sustainability Sustainable Development Goal (SDG) target of the United Nations.

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This study focused on analyzing the effects of human capital on income diversification among crop farmers in rural Oyo State, Nigeria. The result presented was based on primary data collected from a random sample of 120 households selected from two agricultural zones of Oyo State, Nigeria. Descriptive statistics, Poisson regression and Tobit regression were employed as analytical techniques. Both the Poisson and Tobit regression methods were respectively used to examine the determinants of income diversification. The Poisson regression result showed that educational background, value of productive assets and access to credit were statistically significant and had a positive influence on the number of income sources (NIS). In turn, the Tobit regression results revealed that years of education, years of vocational training etc. were positively significant to income diversification. The recommendations arising from this study were that government should intensify their efforts at enhancing human capital development through formal education, vocational training and extension programs for the farmers so as to make them aware of the benefits of income diversification in improving their welfare. In addition, there is need to improve the participation of poor households in formal credit with low interest rates as credit enables the households to convert their stock into physical capital within a short time to take advantage of income opportunities outside agriculture.