Extreme weather events resulting from the effects of climate change coupled with a global call for countries to reduce their greenhouse gas (GHG) emissions imply that the transition to a “green” economy is non-negotiable. The societal and policy discussions have moved on to trying to understand the transition possibilities and the economic and social implications of these in each country’s context. This research project will focus on the case of South Africa.
Context
South Africa is one of the largest GHG emitting countries due to its heavy reliance on coal for most of its energy needs. The South African government is cognisant of the fact that shifting away from carbon-intensive forms of technology to more sustainable ways of production means that some jobs will be destroyed, and new ones will be created. A concern therefore for policy makers is ensuring that the transition is just and that it will not exacerbate existing inequalities.
South Africa comes into this employment transition discussion facing a triple challenge: persistent high unemployment, inequality, and poverty. This situation has worsened since the 2007-2008 financial crisis and was further exacerbated by the recent COVID-19 pandemic. This complicates the discussion of an optimal social transition to a “green” economy.
This project is part of a wider research program on the just transition in South Africa, conducted with several South African research centres and in close collaboration with the South African authorities.
See also: Research on inequalities
Goal
The project will carry out a study of the South African labour market with the aim of identifying the proportion and distribution of workers engaged in “green” jobs and “brown” jobs – in other words, jobs that are ecologically sustainable and jobs that are not. It will also examine the possibilities of transitioning labour from brown jobs into low emitting sectors.
Method
We will measure green intensity as the share of total tasks in an occupation that are green. We will also identify the share of workers in green jobs using employment information from surveys such as the Quarterly Labour Force Surveys (QLFSs) and the Census. Using industry level information on pollution, we will go further to identify occupations more likely to be in highly polluting sectors than in any other sectors. This will be described as brown jobs. Next, we will utilise occupational tasks, skills, and knowledge information from the O*NET dataset to identify important skills for brown and green jobs. This will enable us to estimate the probability of transitioning workers to green jobs. Finally, to map the location of green jobs, we will use employment information from the Census, the Community Survey, and the Spatial Tax data.
Contact:
- Anda David, research officer, AFD