methodology and assumptions
The Institute for Sustainable Futures at the University of Technology, Sydney modelled the effects of the Reference scenario and Energy [R]evolution Scenario on jobs in the energy sector. This section provides a simplified overview of how the calculations were performed. A detailed methodology is also available. Chapters 2 and 3 contain all the data on how the scenarios were developed. The calculations were made using conservative assumptions wherever possible. The main inputs to the calculations are:
For each scenario, namely the Reference (business as usual) and Energy [R]evolution scenario:
- The amount of electrical and heating capacity that will be installed each year for each technology,
- The primary energy demand for coal, gas, and biomass fuels in the electricity and heating sectors.
- The amount of electricity generated per year from nuclear, oil, and diesel.
For each technology:
- `Employment factors’, or the number of jobs per unit of capacity, separated into manufacturing, construction, operation and maintenance, and per unit of primary energy for fuel supply.
- For the 2020 and 2030 calculations, a ‘decline factor’ for each technology which reduces the employment factors by a certain percentage per year. This reflects the fact that employment per unit falls as technology prices fall.
For each region:
- The percentage of local manufacturing and domestic fuel production in each region, in order to calculate the proportion of manufacturing and fuel production jobs which occur in the region.
- The percentage of world trade which originates in each region for coal and gas fuels, and renewable energy traded components.
- A “regional job multiplier”, which indicates how labourintensive economic activity is in that region compared to the OECD. This is used to adjust OECD employment factors where local data is not available.
The electrical capacity increase and energy use figures from each scenario are multiplied by the employment factors for each of the technologies, and then adjusted for regional labour intensity and the proportion of fuel or manufacturing which occurs locally. The calculation is summarised in the Table 6.1.
A range of data sources are used for the model inputs, including the International Energy Agency, US Energy Information Administration, US National Renewable Energy Laboratory, International Labour Organisation, industry associations for wind, geothermal, solar, nuclear and gas, census data from Australia, Canada, and India, academic literature, and the ISF’s own research.
These calculations only take into account direct employment, for example the construction team needed to build a new wind farm. They do not cover indirect employment, for example the extra services provided in a town to accommodate construction teams. The calculations do not include jobs in energy efficiency, although these are likely to be substantial, as the Energy [R]evolution leads to a 40% drop in primary energy demand overall.
Several additional aspects of energy employment have been included which were not calculated in previous Energy [R]evolution reports. Employment in nuclear decommissioning has been calculated, and a partial estimate of employment in the heat sector is included.
The large number of assumptions required to make calculations mean that employment numbers are indicative only, especially for regions where little data exists. However, within the limits of data availability, the figures presented are representative of employment levels under the two scenarios.