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Estimating employment from the energy transition

This indicator estimates the additional jobs (measured in full time job equivalents, FTE) per sector resulting from the energy transition. The calculation differentiates between direct employment, related to changes in one economic activity, and indirect employment induced by structural changes in the economy. The results are aggregated into the following employment categories:

  • Public transport
  • Freight transport
  • Vehicle trade & maintenance
  • Hydropower
  • Nuclear power
  • Solar photovoltaics
  • Wind power
  • Other renewables
  • Fossil industry
  • Electricity distribution and trade
  • Building & Heating
  • Industrial processes
  • Indirect employment

The energy transition pursues essentially two objectives: improving energy efficiency in all sectors (transport, building, industry, appliances) to reduce fossil energy consumption and thus CO2 emissions, and increase the share of renewable sources in the energy mix. To estimate the impact on employment, a top down methodological approach using input output tables (IOTs) with employment statistics per activity is combined with a bottom up validation of employment figures for specific sectors.

The input-output analysis assumes a constant economic structure, in the form of fixed transaction coefficients between economic activities. To adapt this structure to future scenarios, changes in economic growth and labor productivity were applied per activity as follows:

ActivitiesParameters consideredReference unit
Transportation Transport type (public/private, rail/road, vehicle type, efficiency), distance travelled, population passenger - km
Freight Rail/road, volume, distance travelled ton - km
Vehicle trade and maintenance Vehicle/type transport, distance travelled km equivalent
Energy consumption Final energy (electricity, heat), energy carriers GWh
Building and energy efficiency Heated surfaces, retrofitting rate m2 retrofitting equivalent / year
Industrial processes With/-out proactive optimization GWh saved
Energy production Production technology, energy carriers GWh
Economic growth GDP growth rate % yearly increase

Energy efficiency is accounted for in two ways in the calculation, as part of final (energy) consumption as well as in construction and manufacturing. Increased employment in consulting services and renovation is adjusted in the IOT by increasing the demand for the corresponding activities and by reducing energy consumption as a whole. The basic input-output analysis equation is represented by Eq. (1):

where X is the total output vector, I the identity matrix, A the matrix of transaction coefficient and Y the final demand in the reference year (2011) and target year (2050). EF is a vector of employment factors that can be retrieved from the current number of full time job equivalents (FTE) per activity over the value added (VA) for the given activity j as defined in Eq. (2):

where p is the annual productivity increase in percentage and n the number of years (e.g. from 2011 to 2050). FTE and VA data are released every year by the Federal Statistics Office (FSO). Productivity data p comes from the State Secretariat for Economic Affairs (SECO).

In IO analysis direct employment is considered as employment in the corresponding activity to produce a given output. To evaluate the employment contribution of each activity including intermediate uses, direct employment considers all jobs in a given activity j as well as the jobs induced by the activity amongst the tier 1 suppliers. Indirect employment considers the jobs of supporting activities to the direct one (e.g. tier 2 suppliers, services to the activity not related to the production process, etc).

With the above definition, Eq. 3 gives the direct employment from the new added value and employment factors calculated in Eq. 2.

where FTEdirect,j is the direct employment in full time equivalents for a given year, EFj the employment factor and VA the new value added.

To avoid double counting between direct and indirect jobs, the contribution of indirect employment was set to zero for all the activities immediately related to the energy transition (e.g. electricity generation, passenger transportation, waste treatment) as they are already accounted for in direct employment, as in Eq. 4.

Using the new indirect coefficient matrix Aindir, the indirect employment is then calculated with Eq. 5.

Data Sources

An extended version of the IOT for the year 2008 is used with disaggregated energy and transport sectors as well as energy accounts [1]. However, the reference year in both PROG-NOS and the Energyscope calculator is 2011, thus the IOT has first been updated to an estimat-ed 2011 IOT version and then compared to projected target years. The annual rate of economic growth was harmonized with that proposed in PROGNOS for the sake of consistency with a default value of 0.72% per year up to 2035 and 0.79% up to 2050. Energy demand and energy mix per sector (industry, services and households) comes from PROGNOS [2] and accounts for multiple energy agents. Data for transportation is disaggregated between freight and passenger travel as well as by vehicle type. Biofuels were assumed to be imported and their use is therefore accounted for as gasoline as the value added in Switzerland will be similar for these products. Productivity increase of 2.3%, 1.5% and 0.9% per year for the primary, secondary and tertiary sectors respectively was applied on employment factors from the mean over the last 18 years. The Swiss State Secretariat for Economy (SECO) predicts a stabilization of the productivity growth rate around 0.9% yearly increase, thus the mean increase was adjusted accordingly. Regarding buildings, the current annual rate of renovation stands at 0.9% per year and should be increased to achieve expected energy efficiency targets of the Swiss energy transition scenario [3]. The rate of retrofitting was calculated as the percentage of building older than 2011 and ren-ovated at the target year compared to a business as usual scenario. New constructions and ren-ovations correspond to a single construction activity in the IOT. Based on data from the Swiss Federal Office of Statistics (FSO), the mean investment ratio of renovations over total activities in the construction sector is 26% on average over the last 20 years. Approximately 61% of these renovations were motivated by energy efficiency gains [4]. The relative share of employment in renovation activities was then estimated based on national employment statistics for the con-struction sector.

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en/employment.txt · Last modified: 2019/10/22 09:17 (external edit)