About ODYSSEE-MURE: an indicator approach to the employment effects of energy efficiency
What are the main characteristics of the ODYSSEE-MURE project?
The ODYSSEE-MURE project  offers a comprehensive monitoring of energy efficiency (EE) trends and policy evaluation in all EU Member States, Norway, Serbia and Switzerland. It relies on two complementary internet databases and analytical tools which are regularly updated by a network of national teams in EU Member States, Norway, Switzerland and Serbia:
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- ODYSSEE: detailed EE and CO2 indicators with data on energy consumption, the activities driving energy demand and the related CO2 emissions.
- MURE: database on all EE measures implemented at EU or national level (including searchable classifiers, a description and impact evaluation).
Analytical support tools have been developed in order to make the analysis interactive and attractive to decision makers and other actors involved in EE.
One of these tools, the Facility on the Multiple Benefits of Energy Efficiency (MB-EE) , aims to quantify the impacts of EE policies on twenty different indicators, covering environmental, social and economic aspects (Figure 1).
How are employment effects addressed as part of the Facility on the Multiple Benefits of Energy Efficiency (MB-EE)?
One of the MB-EE indicators focuses on the employment effects of EE measures in the residential sector, using input-output (IO) analysis. EE measures in residential buildings generally require up-front investment, which is recouped through reduced energy costs in subsequent years. Additional investment in EE triggers shortterm economic demand impulses, leading to higher production in the relevant industries. To estimate the resulting effects on employment from EE (e.g. from energy saved in heating residential buildings) we considered investments made for the insulation of the building envelope and for the renewal of heating systems. These investments were used as an input for the change in demand in the IO analysis, which results in a change in gross value added (GVA) in various sectors affected directly and indirectly (through intersectoral flows). Based on these changes in GVA, we estimate the additional jobs in fulltime equivalents (FTE) using national statistics on the average FTE per GVA in the branches considered.
Which energy efficiency measures and other influencing factors are considered in the employment estimation?
The EE measures we consider in our analysis, which aim to improve the thermal insulation of the building envelope, include insulating material, plastering and heat-absorbing glazing. The measures are thus matched to the sector ‘Constructions and construction works’ (F), whereas investments in EE technology for heating, ventilation and air conditioning are matched to the sector ‘Machinery and equipment’ (C28) . Changes in demand by households for fuels and electricity due to energy efficient refurbishment are represented in the sector ‘Coke and refined petroleum products’ (C19)  as well as in the sector ‘Electricity, gas, steam and air conditioning’ (D35) . For the latter, we also address the energy saved in households in monetary terms to calculate the changes in demand in these sectors.
To estimate the resulting effects on employment from energy efficiency, we considered investments made for the insulation of the building envelope and for the renewal of heating systems’
What types of investment are taken into account as part of the analysis?
The investments associated with energy savings regarding heating were provided by the Invert/EE-Lab Model , run by TU Wien, which provides projections for annual net investments in building envelopes and in Heating, Ventilation, Air Conditioning technologies (HVAC) in residential buildings for European countries up to 2030. Only investments improving the EE of buildings are considered. Regular construction and refurbishment costs are excluded from the analysis.
What is the underlying approach in terms of calculating energy savings when calculating employment effects?
The MB-EE Facility of ODYSSEE-MURE determines employment effects based on top-down (TD) savings, i.e. calculated using the energy statistics of the ODYSSEE database, or based on bottom-up (BU) savings, i.e. based on policy evaluations from the MURE database. The first also captures savings (and hence employment effects) which cannot be related directly to a policy measure but which may be due to market transformation.
For the nine European countries considered, employment estimated with the top-down savings approach amounts to around 1,400,000 FTE.
Figure 4 compares employment effects for Germany from top-down and bottom-up savings, showing that employment effects are generated beyond policy programmes. Top-down savings show 569,000 FTEs in total, mostly associated with the branches ‘machinery and equipment’ (234,000 FTE) and ‘construction and construction works’ (224,000 FTE). Considering only bottom-up savings related to EE policies targeting heating consumption in households (mainly the KfW programmes regarding residential buildings) , about 535,000 FTE were generated.
What are the potential weaknesses and advantages of the MB-EE Employment effects approach?
The employment effects calculated are gross effects, excluding factors such as displacement effects and indirect second order effects through additional tax revenues, export/imports of EE related goods, etc. However, the indicator approach developed may be gauged with more detailed modelling studies and can easily be extended from year to year, making it attractive for policymakers to include MB-EE in their reporting.
According to NACE rev. 2 classification for economic activities in the European Union
The ‘Kreditanstalt für Wiederaufbau’ (KfW) is a promotional bank offering financing of purchase, renovation and energy-efficien modification of existing or new properties.