How can multiple impacts of energy efficiency policies be quantified?

Energy efficiency first principle

According to Germany’s NECP, the EE1^st principle—as now formally anchored in Article 3 of the EED—serves as the overarching guideline across all energy sectors (BMWK 2024; European Union 2023). Germany implements EE and the EED through the German Energy Efficiency Act (EnEfG), which sets binding national primary and final energy reduction targets. However, up to now it does not include a direct transposition of Art. 3 EED, which would oblige the federal government to systematically examine demand-side measures (including their MIs) before supply-side or infrastructure investments are undertaken. The EE1^st principle is already partially reflected in some other national measures and legislation. For example, in energy infrastructure planning, the network development plan incorporates demand-side resources and efficiency measures, including electromobility, heat pumps, battery storage, and demand-side management in industry. The ongoing amendment to the Energy Industry Act (EnWG) further establishes a System Development Strategy that embeds the EE1^st principle into coordinated scenario frameworks, ensuring consistent consideration of EE across all infrastructure planning processes (BMWK 2024). The German Buildings Energy Act (GEG), decided in September 2023 with effectiveness from January 2024, promotes efficient energy use alongside RE deployment, supported by federal funding programmes for efficient new and existing buildings (BMWSB 2023). The Heat Planning Act (WPG), requires systematic heat planning, mandatory estimation of energy savings potentials, and identification of areas with high energy-saving potential.¹

While several EE policy evaluations quantify a range of MIs beyond energy and emissions reductions, these typically cover specific sectors, time horizons and impact categories, so that MIs are captured in a fragmented way rather than through a single integrated quantification that would enable a uniform approach and comparison between different measures. While some impacts are captured in addition to energy and emission reductions, such as macroeconomic effects on additional employment due to EE projects (BMWK 2023), other impacts such as health and comfort benefits, reduced air pollutants, and impacts on energy poverty are often only assessed through studies and not integrated into official monitoring processes (BPIE 2024; terSteege and Vogel 2021). In the evaluations assessed for these case studies, indicators for additional employment, and distribution by region or socio-economic status are also documented (Heinrich et al. 2025a; Heinrich et al. 2025b; Neusel et al. 2024; Rao et al. 2024).

Case studies: energy efficiency programmes

Three case studies were selected on the basis of sectoral representativeness, data availability, and policy relevance: the Federal Funding for Efficient Buildings (BEG) for the residential sector, the Environmental Bonus for the transport sector, and the Federal Funding for Energy and Resource Efficiency in Industry (EEW) for the industrial sector. All three programmes have been subject to rigorous, publicly available evaluations, providing robust input data for the MICATool and transferable methodological insights for reporting on and assessing EE1^st implementation.

Social impacts

The quantification of social impacts centres around health impacts and the improvement of living standards and include reduction of air pollution to prevent premature deaths and mitigation of lost working days, alleviation of energy poverty, avoided asthma cases and reduction of cold-weather mortality. These indicators show which programme delivers the greatest social and health benefits (Table 1). In the BEG, building renovations and heater exchanges are expected to improve indoor climate, thereby reducing excess cold weather mortality and avoided asthma cases. In addition, lower air pollution (see section “Environmental impacts”) reduces associated health effects and work absences and improved energy poverty and productivity (BPIE 2024). In the MICATool, energy poverty indicators track how the measures improve energy affordability by lowering energy costs. Overall, the BEG programme results in positive social impacts while the other two programmes, Environmental bonus and EEW, do not target the residential sector, and do not have direct impacts on energy poverty and indoor climate. However, both programmes lead to a reduction in air pollution and thus have an impact on the associated mortality, and the number of hospital admissions and lost working days.

To compare the results across the programmes, the specific impacts are calculated for some indicators. Figure 1 depicts the specific avoided health impacts linked to air pollution per unit of energy saved for each of the three EE programmes. The BEG and Environmental bonus show the greatest improvements largely due to the greater reduction in air pollutants.

Figure 1

In 2022, Germany exhibited the third-highest attributable mortality in Europe from long-term exposure to PM_2.5 and NO₂, and the highest for O₃ with corresponding average years of life lost (YLL) per 100,000 inhabitants of 535 for PM_2.5, 155 for NO₂, and 231 for O₃ (EEA 2024). Over the duration of the programmes, the estimated reduction in years of life lost (YLL) per 100,000 inhabitants amounts to less than 1 for each of the programmes, but as these programmes address only a limited share of emission sources within individual sectors, the estimated reductions in years of life lost and air pollution related mortality are within the expected range. Direct validation remains difficult because comparable values are not reported in the literature or evaluation reports. The transport sector remains a major source of air pollution in Germany. The Environmental bonus BEV switches oil-based fuels to electricity resulting in significant reductions in CO₂ and NO_x, but lead to further SO_x pollutants emitted due to the additional electricity generation required, which would potentially decrease in the future as the electricity supply sector is continually decarbonised (see also Section “Environmental impacts” (Table 4) and the supplementary data for a comparison for this measure with a renewable electricity supply (see Table 12)).

Economic impacts

The economic impacts comprise indicators related to the implementation of the EE programmes and capture the impact of EE measures on the energy system and the overall Gross Domestic Product (GDP) over the duration of the programmes, as shown in Table 2.

The EE programmes reduce energy intensity and trigger investments, which in turn produces an added value to the GDP and macroeconomic effects such as increasing employment (Rajbhandari and Zhang 2018). The BEG programme exhibited the greatest reduction in energy and import dependency, while increasing GDP and additional employment. On average, the construction industry contributed about 4.9% to the total GDP in Germany between 2021 and 2023 (equating to a total of 523 billion EUR), of which the BEG contributes 13% (Destatis 2026). The EEW contributed considerably towards reducing the energy intensity while adding to the GDP, which is corroborated in high income countries such as Germany (Rajbhandari and Zhang 2018).

Figure 2 compares the GDP impacts per euro invested generated using the MICATool across the EE programmes. These results suggest that the BEG—primarily through construction activity—and the EEW—through industrial activity—exert the strongest effects on GDP growth. The specific impact on additional full-time employment years was also calculated with the MICATool and, as shown in Figure 3, exhibits less deviation across the programmes.

Figure 2

Figure 3

The evaluation reports on the EE programmes provided estimates of the annual macroeconomic impacts, e.g. on GDP and additional employment, which can be directly compared with the MICATool outputs, as shown in Table 3. For the BEG, MICATool estimates fall within the same order of magnitude as those reported in the evaluation report. In 2020, national assessments reported an estimated 393,000 full-time equivalent (FTE) years of additional employment and a GDP contribution of €79.1 billion, whereby these estimates cover both residential and non-residential renovations (BMWK 2023). This consistency between the MICATool results and the official estimates supports the validity of the tool’s sector-specific impact assessments. The evaluation report for the environmental bonus did not report values on the additional employment as specific trends attributed to the programme could not be clearly identified although the markets share of BEV in the German market while the additional number of employees in this sector have grown in general, and a direct comparison for the national GDP contributions was not possible as these were given as contributions to the regional gross value added (GVA) (Rao et al. 2024). For the EEW, the report provides estimates for the contribution to the GDP and additional employment for the improvement action 3a for process-specific savings, which are within range with the outputs from the MICATool. National estimates estimate that EE investments in the industrial sector for manufacturing led to an additional 11,600 employees in 2020 (BMWK 2023), which is of comparative value with the outputs from the MICATool (in 2020 14,070 additional full-time employment years were added in EEW 3a).

Environmental impacts

The quantification of the environmental impacts includes an overview of the impacts related to savings in primary energy, savings contribution to RE targets, and reduction in air pollution and GHG emissions over the lifetime of each programme as shown in Table 4 with the greatest reduction for the EEW, followed by BEG, while the Environmental bonus has a net positive decrease in the use of oil, but an increase in energy in the supply sector due to the fuel switch to electricity consumption (as seen by the negative values shown, which represent increased consumption of these specific fuels).

In absolute terms, the scale of the overall reductions in primary energy and emissions in the EEW are doubled compared to the BEG over the lifetime of the EE programmes (Table 4). Comparing the specific impact of each programme, the BEG and EEW perform similarly for both CO₂ emissions (Figure 4) and air pollutants (Figure 5). The Environmental bonus has the greatest specific impact on net CO₂ emissions saved owing to the reduction in reliance of oil (Figure 4) and reduction in NO_x (Figure 5), but due to the increased electricity consumption leads to additional air pollutants (SO_x) related to additional electricity generation from other fuel sources including fossils (see also supplementary data for a comparison for this measure with a renewable electricity supply (see Table 14)). Whereas the EEW saves more energy consumption from coal, gas and district heating, the BEG saves more oil, which has a higher emission factor.

Figure 4

Figure 5

The specific impact on the primary energy savings per unit of investment is shown in Figure 6, which shows that overall the EEW performs the best with 0.45 units of energy saved per unit of investment, followed by the Environmental bonus with 0.04 and BEG with 0.03.

Figure 6

The evaluation reports provided estimates of the annual impact on primary energy savings and the reduction in GHG emissions (adjusted to reflect the consolidations of the programme to fit into the tool) which can be compared with the values produced by the MICATool, as shown in Table 5. While the values for BEG and the Environmental bonus are within range with each other, the values for EEW vary greatly due to the variation in the types of technologies and processes implemented and the simplification of these reflected as generalised processes in the tool. The observed variation in the estimated GHG emission reductions for the BEG arises from differences in the consolidation of inputs within the MICATool due to insufficient information regarding the affected fuels in the BEG and assumptions are therefore based on default values in the tool.

Monetisation

Monetising indicators translates non-financial, qualitative, and environmental/social impacts into monetary terms. By establishing a single, comparable unit for a broad spectrum of often intangible effects, this approach supports more informed decision-making, enhances cost-benefit analyses, and enables externalities to be incorporated into business or policy assessments. However, not all indicators discussed above are monetised in the tool, e.g. because they are of a purely theoretical nature and do not generate a direct, measurable benefit, or because assigning a monetary value is either not feasible or not appropriate. This may be the case, for example, when no robust valuation approach is available or when monetisation would distort results through double counting. For example, the environmental indicator “reduction in air pollution” captures the avoided emissions of PM_2.5, NO_x and SO₂, but if these effects were also expressed in monetary terms, they would be double accounted as they are already reflected in the first two social indicators on air quality. Table 6 shows the results of the monetisation of the EE programmes over the analysed duration of the programmes given as undiscounted values. Across all programmes, monetised benefits are dominated by GDP effects, although their relative importance declines from the BEG to the Environmental Bonus and the EEW. By contrast, reductions in end user energy costs constitute the second largest benefit and become more prominent from the BEG to the Environmental Bonus and the EEW. It is important to note that some benefits, such as GDP, are once-off, while other benefits, such as energy savings, are accrued annually over the lifetime of the related savings. Therefore, analysing the MIs over their lifetime may alter the relative importance/size of the different indicators compared to the assessment of a dedicated timeframe for each measure (see also CBA analysis). Overall, compared to the investments made, the EEW generated the greatest multiple benefits owed largely to the energy cost and related emission reductions accrued.

Figure 7 compares the total investment and monetised benefits across the three programmes. The benefits over the lifetime of the measures are assessed in the Cost-Benefit Analysis (CBA) in the following section.

Figure 7

Cost-Benefit Analysis

The cost-benefit analysis (CBA) provides information about the annuity, net present value (NPV), levelised costs of energy savings (LCOE) and levelised costs of carbon dioxide (LCOCO2), cost-benefit ratio and benefit-cost ratio over the lifetime of the implemented EE measures or programmes.² The weighted annuity is the annuatised value of a project’s costs and benefits expressed as yearly payments over its lifetime. A positive annuity indicates that annual benefits exceed costs, signalling financial viability, while a negative value indicates the opposite. Similarly, a positive NPV indicates the projected earnings exceed costs, whereas a negative NPV reflects net costs over the project lifetime. As illustrated in Figure 7, the inclusion of macroeconomic effects, such as the impacts on GDP, has the strongest influence on the financial performance of the EE programmes. Beyond improving EE, these programmes were intended to also stimulate economic activity, which is reflected in GDP-related indicators.

To isolate the contribution of different MI components, the CBA is first assessed considering energy cost reductions only. Under this perspective, financial performance varies substantially across programmes, reflecting differences in their nature and cost structures. When only energy cost reductions are considered, investment costs outweigh cash inflows, resulting in negative financial viability across all programmes. Correspondingly, the LCOE and LCOCO₂ values are comparatively high, driven by the high upfront investment costs relative to the energy and CO₂ savings achieved.

When monetised multiple impacts are included in the assessment (while still excluding the impact of GDP) the improvement in the financial viability of the programmes become visible. The LCOE represents the average cost per Megawatt-hour of energy produced or saved over a project’s lifetime and serves as a benchmark for comparing technologies. Within the MICATool framework, this indicator additionally incorporates monetised multiple impacts, which can outweigh costs and result in negative LCOE values. These values are averaged over the programme duration and weighted by each year’s new annual savings. Under this broader assessment, the financial viability of the BEG and EEW 3a programmes becomes positive, while the Environmental bonus shows a substantial improvement. The same trends are observed for both LCOE and LCOCO₂.

The performance of the Environmental bonus programme is strongly influenced by assumptions regarding the electricity supply sector, which in the default MICATool settings is not fully decarbonised in order to reflect current conditions. Assuming a fully decarbonised electricity sector leads to a marked improvement in performance in which financial viability increases significantly, while LCOE and LCOCO₂ decline substantially, decreasing by 54% and 75%, respectively thereby representing lower costs to save energy and reduce emissions (see Table 11 in the supplementary data).

When the economic benefits associated with GDP effects are included, all EE programmes become financially viable, as was illustrated in Figure 7. Under this comprehensive assessment, both LCOE and LCOCO₂ decrease sharply across all programmes. This result highlights how the perceived performance of EE programmes shifts when a broader set of MIs is considered, particularly given that stimulating economic activity was an explicit objective of these programmes alongside EE improvements. A comparison of the differences between considering the reduction in energy costs and including all MIs without impact of GDP is shown in Figure 8 and additional results are shown in Table 17–Table 19 in the supplementary data.

Figure 8

Comparative assessment of RE vs. EE

Energy planning involves deciding between investing in new RE capacity to meet demand and prioritising EE measures that lower final energy demand and thus reduce the need for additional generation capacity. The MICATool enables the quantitative assessment of RE impacts and their systematic comparison with EE impacts, thereby supporting an empirical evaluation of the EE1^st principle. This comparison was undertaken by comparing the impact of EE through residential building envelope renovations (improvement action 1) under the BEG programme with investments in renewable electricity supply through rooftop photovoltaic (PV) systems, in which fundamental differences in their impacts on the system are highlighted. While PV investments primarily aim to meet existing electricity demand with low carbon-supply (the assumption for the calculations were that the PV produces the amount of electricity saved through the EE measures for building envelope renovations), BEG renovations directly reduce final energy consumption through efficiency improvements.³ A comparison of a sample of monetisation results generated over the lifetime of the programme (2021–2023) for both examples is provided in Table 7.

Comparing the different investment approaches (EE vs. RE) can yield different benefits. While investing in RE with rooftop PV contributes to emissions reductions, reducing health effects linked to reduced air pollution and associated lost working days, achieving RE targets and stimulates the economy, renovation activities as in the BEG generate higher macroeconomic benefits, reflected in a stronger positive impact on GDP, due to their labour-intensive nature and domestic value chains. Renovations also achieve a broader variety of MIs, such as avoided cold winter mortality and have (in this case) a higher impact on reducing emissions. Assessing the benefits of each approach in parallel enables a fair comparison across measures and provides a basis for selecting the solution, or combination of solutions, that delivers the most favourable overall balance with respect to the pursued policy objectives thereby illustrating the best practice in operationalising the EE1^st principle in the residential sector.