Project
EcoEff- Macroeconomic Inefficiency and Emission Reduction Potentials: Accounting for Heterogeneous Industry Structures
EcoEff investigates the potential for reducing greenhouse gas emissions by a more efficient use of existing production possibilities. The analysis relies on nonparametric methods for efficiency analysis which are suited to incorporate undesired outputs such as greenhouse gas emissions or pollutants. Therefore, the macroeconomic environmental efficiency of the EU countries is analyzed and compared in order to quantify and statistically validate potential emission reductions in various industrial sectors. The project also attempts to estimate the costs (in terms of foregone output enhancement) which are associated with the efficiency improvements leading to emission reductions. In a final workpackage, the long-run trends of the potential emission reductions for a broad country sample are projected into the future to assess their contribution to reach the climate stabilization targets.
Project results
The results show that the magnitude of emission reduction potentials varies widely both between sectors and between countries. There is a concentration in the sectors agriculture, manufacturing, energy generation and transport. CH4 and N2O play a role mainly in agriculture, while CO2 dominates in the other sectors. The costs of avoiding CO2 emissions also show that comparatively low-cost emission reductions would be possible in the energy generation and agriculture sectors, whereas eliminating inefficiencies in the manufacturing sector in particular would be associated with high costs. The detailed results, which are published in scientific publications, can serve as an orientation for the formulation of country and sectoral targets that can support a (cost-)efficient achievement of climate targets.
Illustration of project results

The figure shows the aggregate emission reduction potentials (computed using medians of the inputs and outputs over the period 2012-2016 in the efficiency analysis) for the sectors A to H (on the left) and 16 European countries (on the right). The y-axis is specified in megatons of CO2 equivalents. Shown are the aggregates (over countries or industries) of the actual emissions of greenhouse gases as open circles conntected by a dashed line. The potential emission reductions computed from the efficiency analysis are depicted in the form of bootstrap-bias corrected estimates as solid dots connected by a solid line. The vertical lines show the size of 95% confidence intervals. The aggregate potential emission reductions are found to be concentrated in certain sectors (manufacturing C, energy and water DE and transport H) and certain countries (Germany, United Kingdom and Poland). To which extent and over which time frame the reduction potentials can be realized must remain an open question.
Main Findings
- Savings potentials through efficiency improvements are substantial
- Efficiency improvements can make a significant contribution to achieving the climate targets, but are not sufficient alone
- Heterogeneous industrial structures should be taken into account when formulating reduction targets, as the size of savings potentials varies greatly between economic sectors
- Current emissions prices do not correctly reflect the actual abatement costs of emissions
Flagship-Paper
Fait, L., Wetzel, H. (2021):
The value of greenhouse gas emission reductions in the EU: A non-parametric and sector-wise approach. MAGKS Discussion Paper No. 21-2022.
Fait, L., Krüger, J.J., Tarach, M., Wetzel, H. (2022):
Trend Projections of Greenhouse Gas Emission Reduction Potentials: A Bootstrap-Based Nonparametric Efficiency Analysis. SSRN Working Paper 4107819.
Krüger, J.J., Tarach, M. (2022):
Greenhouse Gas Emission Reduction Potentials in Europe by Sector: A Bootstrap-Based Nonparametric Efficiency Analysis. Environmental and Resource Economics 81: 867-898.
Krüger, J.J., Tarach, M. (2020):
Greenhouse Gas Emission Reduction Potentials in Europe: A Nonparametric Efficiency Analysis Approach Using Sectoral Data. SSRN Working Paper 3716203.