Human population growth and the environment: the Africa perspective

This study investigates the relationship between population dimensions (population growth, population density, and population size and population age structure) and carbon dioxide (CO2) emissions across African countries at different levels of per capita income. The approach is conceptual and empirical. The conceptual part critically compares the Malthusian and Boserupian traditions. The empirical part uses econometric models to analyse 51 of the 54 sovereign countries in Africa, according to World Bank income groupings.

The study is motivated by the observation that an epidemic of population explosion is sweeping across the continent in relation to the fixed environment. In previous works, both the theoretical and empirical frameworks were formulated to address population-environment impacts of developed economies without accounting for institutional changes, institutions and the economic development of the developing countries. Our main claim is that only an analysis of the relationships over time between population dimensions, the manufacturing and services sectors, consumption, the practice of corruption, net trade and CO2 emissions can help understand the key driving forces of environmental impacts.

The study used time series data 1960-2010 available for CO2 emissions on the basis of income groupings. We estimated fixed effects, random effects, Generalized Least Square/Feasible Generalized Least Square and Panel-Corrected Standard Errors. Our findings suggest the average effect of population growth on CO2 emissions when the population growth change across time and between countries in low income countries in Africa (LICA) increases by 1 per cent, CO2 emissions increase by about 0.38 per cent, holding all other predictors constant.

The average effect of population growth over CO2 emissions, when the population growth change across time and between countries in lower middle income countries in Africa (LMICA) increases by 1 per cent, CO2 emissions increase by about 1.08 per cent, holding all other predictors constant. A 1 per cent increase in the corruption perception index, when the corruption perception index change across time and between countries, decreases CO2 emissions by about 3.98 per cent, when all other predictors are constant. The average effect of net trade over CO2 emission, when the net trade change across time and between countries increases by 1 per cent, increases CO2 emissions by about 2.47 per cent for LMICA, when all other predictors are constant.

Additionally, the findings suggest the average effect of population growth on CO2 emissions when the population growth change across time and between countries in upper income countries in Africa (UICA) increases by 1 per cent, increases CO2 emissions by about 0.30 per cent, holding all other predictors constant. A 1 per cent increase in the corruption index, when the corruption index changes across time and between countries, decreases CO2 emissions by about 3.79 per cent, when all other predictors are constant. The average effect of net trade over CO2 emissions, when the net trade changes across time and between countries increases by 1%, increases CO2 emissions by about 1.11 per cent for UICA, when all other predictors are constant.

The study provides some new empirical findings, while simultaneously supporting and reinforcing previous findings in the empirical analysis of the population-CO2 emissions nexus. The implications of the study are: the population pressures of African countries are likely to contribute significantly to CO2 emission loads in the long term; the impacts of affluence on CO2 emissions are likely to be highly significant in the long term; the economic structure (manufacturing and service sectors) presents empirical evidence consistent with the expectation that the percentage of GDP in the manufacturing sector supports an abating effect on CO2 emissions in the shift to a service economy; the practice of corruption and net trade appear as important variables having been typically neglected in the analysis of the driver-triggers of CO2 emissions in Africa. These findings have important implications for the literature and development policies. Population density, population growth, population size, population age structure, net trade, service and manufacturing sectors, and corruption, in that order, are the most important variables to consider in determining the key drivers of the environmental impacts. Therefore, it can be concluded that policies aimed at reducing environmental impacts in Africa will have to take population pressures into account.