Predicting Structural Changes of the Energy Sector in an Input-Output Framework

Abstract

This study examines how changes in a country's energy mix affect its energy sector's input coefficients within an input-output framework. Our empirical analysis is based on a fractional multinomial logit model and panel data on the energy mix compositions and input structures of national energy sectors for 26 European countries. We illustrate the prediction of future input coefficients for the case of Austria's renewable expansion act with its planned increase of renewable energy production by 27 TWh until 2030 as a case study. We find that inputs from the agricultural sector and professional & administrative services to Austria's energy sector would increase, while inputs from the mining sector and the value added share would decrease. The marginal effects of our model show that increasing the share of renewable energy sources in the energy mix generally comes with a strong decrease in the share of value added. This is especially true for solar power, which also mainly drives the increased demand for professional & administrative services. Our novel and time-efficient approach can be readily applied to 26 European countries. The presented method allows to assess renewable energy policy plans to anticipate the effects of structural changes in national energy sectors.

Keywords: Input coefficientsFractional multinomial logitRenewable EnergyFIGARONational accounts