COMMISSION STAFF WORKING DOCUMENT IMPACT ASSESSMENT Accompanying the document Proposal for a Directive of the European Parliament and of the Council amending Directive 2012/27/EU on Energy Efficiency - Hoofdinhoud

Council of the European Union

Brussels, 2 December 2016 (OR. en)

15091/16

Interinstitutional File: ADD 12

2016/0376 (COD) i

ENER 413 ENV 754 TRANS 473 ECOFIN 1149 RECH 340 IA 124 CODEC 1789

COVER NOTE

From: Secretary-General of the European Commission, signed by Mr Jordi AYET PUIGARNAU, Director

date of receipt: 1 December 2016

To: Mr Jeppe TRANHOLM-MIKKELSEN, Secretary-General of the Council of the European Union

No. Cion doc.: SWD(2016) 405 final - Part 3/3

Subject: COMMISSION STAFF WORKING DOCUMENT IMPACT ASSESSMENT Accompanying the document Proposal for a Directive of the European

Parliament and of the Council amending Directive 2012/27 i/EU on Energy Efficiency

Delegations will find attached document SWD(2016) 405 final - Part 3/3.

Encl.: SWD(2016) 405 final - Part 3/3

15091/16 ADD 12 GL/ns

EUROPEAN COMMISSION

Brussels, 30.11.2016 SWD(2016) 405 final

PART 3/3

COMMISSION STAFF WORKING DOCUMENT

IMPACT ASSESSMENT

Accompanying the document

Proposal for a Directive of the European Parliament and of the Council

amending Directive 2012/27 i/EU on Energy Efficiency

{COM(2016) 761 final i}

{SWD(2016) 406 final}

EN EN

6

6.2 Analytical approach for Articles 9-11

For Articles 9-11, no formal analytical models were used in the assessment of impacts.

The quantitative estimates of the potential for energy savings from implementation of the existing EED provisions on sub-metering of heating in multi-flat buildings were produced using an ad-hoc bottom-up/engineering spreadsheet-based model created by consultants Empirica under a specific contract. The methodology is outlined below.

As regards the estimate of each option's contribution to realising this potential, and the additional potential represented by enhanced consumption feedback, these were also based on a simple bottom-up approach set out in the main report.

There is strong evidence that introducing heat meters and heat cost allocators, to provide A) consumption-based cost allocation (i.e. "pay in relation to your actual/own consumption") and B) consumption information services (e.g. more frequent, informative billing information), leads to more careful use of energy by building occupants, and that this behaviour change results in significant energy savings. Multiple studies provide evidence of the percentage energy savings triggered, however, it is now known that the percentage resulting from the same change in user behaviour is not constant but varies with building quality. A model recently developed for

Germany 121 applies key building characteristics to convert between percentages and behaviour

effects. Extension of this energy saving conversion model for application to the EU-28 requires the following data set:

• 1) 

  Building characteristics: a) Building performance (i.e. building envelope) and user control (over settings, windows) b) Climate at the location of the building (e.g. heating degree days)

• 2) 

  Behavioral effects: a) Average reduction in internal temperature through care in temperature settings b) Average reduction in air changes per hour (ACH) through more careful ventilation (e.g. with regard to how windows are used)

Evidence of behavioural effects

Evidence of the behavioural effects is derived from savings shown in multiple studies followed

by application of the energy saving conversion model. Existing evidence 122 collected in several

studies (some of which are shown in the figure below, is that, in older buildings, the energy savings achieved by the introduction of consumption-based cost allocation amounts to around 20% of actual final consumption.

121 Bert Oschatz: Heating Cost Allocation Cost Efficiency Assessed for Buildings in Germany, Berlin 2015. 122 Cf. empirica (2016) Guidelines on good practice in cost-effective cost allocation and billing of individual consumption of heating, cooling and domestic hot water in multi-apartment and multi-purpose buildings, Available at https://ec.europa.eu/energy/sites/ener/files/documents/MBIC_Guidelines20160530D.pdf .

Figure 1: Literature review: energy savings through heat sub-metering (in %)

Source: Empirica literature review

Based on a set of studies in buildings of known performance characteristics and in known climate locations, also showing 20% savings, and assuming neither behavioural effect is dominant (50-50 split), the following behavioural effects can be shown for the introduction of consumption-based cost allocation:

o Temperature reduction by 1.1 Kelvin o Ventilation reduction by 0.25 per h (ACH)

Additional savings are achieved through changes in user behaviour by introducing consumption information service. Over many studies the median estimate for the additional savings triggered by a variety of such services amount to some 3%. Reusing the results of the energy saving conversion model for consumption-based cost allocation, the following additional behavioural effects can be shown for the introduction of consumption information services::

o Temperature: 1.1 * 3%/20% = 0.165 Kelvin o Ventilation: 0.25 * 3%/20% = 0.0375 per h

Based on figures for hot water consumption researched in the UK (DEFRA/energy saving

trust 123 ), and on an analysis of 13 studies by Sønderlund et al. 124 , the 20% saving for

consumption-based cost allocation is applied to a baseline consumption of hot tap water of 46 and 26 litres per day, per dwelling and per person respectively (total dwelling consumption = 46

+ 26*N litres / day) 125 . An additional 3% savings are achieved by introducing consumption information services. Household size is based on the most recent data available on eurostat 126 . Delivery temperature is assumed to be 60°C following health recommendations 127 .

Building stock - multi-unit buildings

The energy saving potential from EED metering and billing provisions in EU-28 depends on the building stock to benefit from the measures, that is, on the characteristics of existing buildings and their location. The building stock relevant here is the stock of multi-unit buildings not already being provided with consumption-based cost allocation (or consumption information services, respectively). The calculation of the relevant numbers in a Member State is illustrated in the figure below (with data for the UK):

Figure 2: Illustration of methodology for calculating potential energy saving (in this case for the UK)

Source: empirica calculations based on data from BPIE and estimates from JRC and EVVE

Using statistics available for all the EU-28 (see figures below), the existing residential building stock in a country is reduced to that proportion which falls under the provisions of the EED Article 9(3) and is not already provided with consumption-based cost allocation. These are the buildings able to benefit from the introduction of consumption based cost allocation.

This assessment is conservative in that commercial multi-purpose buildings are not included due to lack of data.

123 DEFRA(2008) Measurement of Domestic Hot Water Consumption in Dwellings

124 Sønderlund, A.L., Smith, J.R., Hutton, C., Kapelan, Z. (2014) Using Smart Meters for Household Water Consumption Feedback: Knowns and Unknowns, Procedia Engineering 89, 990-997.

125 Member state specific values on individual daily consumption were used for Denmark (18.1l), Finland (23.8l) and Sweden (49.3l)

126 Eurostat (2015) Average household size - EU-SILC survey [ilc_lvph01]

127 WHO (2007) LEGIONELLA and the prevention of legionellosis

Figure 3: Composition of residential building stock per country

Source: Odyssee (*BG; CY; CZ; IT; LV; LT; LU; PL – estimates based on entranze dataset)

Figure 4: Stock of dwellings in multi-apartment buildings with collective central heating systems

(k units)

20,000

15,000

10,000

5,000

0

without individual meters installed with individual meters already installed

Source: Empirica calculations based on JRC and EVVE estimates and ODYSSEE data

Building performance and climate

The impact of EED related sub-metering measures on different buildings in Europe vary with climate and insulation quality. These are taken into account in the energy saving conversion model. Climate is accounted for using existing statistics of degree days and production days. Differences in the quality of insulation of the elements of the building envelope - outside walls, windows and roof - are reflected in the heat transfer coefficient (U, in W/m²•K) of each element.

Recent statistics on average U values for the main building elements, coupled with transparent assumptions of the relative area of the different elements in an average building, yield the average value of the heat transfer coefficient of building stock in each Member State (see table below).

Table 1: U-values (weighted average based on stock)

Regions Countries WALL (30%) WINDOW (20%) FLOOR (25%) ROOF (25%) u-value

Southern Dry Portugal 1.31 4.07 1.97 2.48 2.32 Spain 1.76 4.61 1.74 1.15 2.17

Cyprus 1.20 2.97 0.00 1.47 1.32

Mediterranean Greece 1.34 3.77 2.29 1.96 2.22 Italy 1.47 4.98 1.68 1.76 2.30

Malta 1.61 5.80 2.44 1.87 2.72

Southern Bulgaria 1.42 2.49 0.95 1.14 1.45

Continental France 1.77 3.67 1.43 1.78 2.07 Slovenia 1.20 2.09 0.95 0.94 1.25

Belgium 1.73 4.17 0.95 1.99 2.09

Oceanic Ireland 1.38 3.99 1.12 0.73 1.67 United Kingd 1.40 4.40 1.41 1.42 2.01

Austria 1.00 2.62 1.21 0.61 1.28

Czech Rep. 0.90 2.87 1.00 0.74 1.28

Continental Germany 0.96 2.92 1.04 0.98 1.37 Hungary 1.34 2.45 0.93 0.96 1.36

Luxembourg 1.27 3.03 1.00 0.00 1.24

Netherlands 1.30 3.26 1.40 1.29 1.72

Denmark 0.75 2.50 0.57 0.34 0.95

Northern Lithuania 0.79 2.03 0.83 0.67 1.02

Continental Poland 1.11 3.05 1.23 0.62 1.41 Romania

1.57 2.44 1.29 1.23 1.59

Slovakia 1.04 3.28 1.61 1.09 1.64

Estonia 0.38 1.50 0.40 0.38 0.61

Nordic Finland 0.43 1.92 0.40 0.26 0.68 Latvia 0.95 2.54 0.78 1.05 1.25

Sweden 0.35 2.79 0.20 0.32 0.79

Source: empirica calculations based on data from iNSPiRe (2014) 128

Results – EU wide potential

The estimated impact/potential in each of the EU-28 Member States (MS) is given by applying the energy saving conversion model to the two behavioural effects (ventilation and temperature) for the relevant building stock in each MS. For each MS the thermal transfer coefficient is taken from Table 1 and weighted averages across the country's climate are used for degree days and production days.

Total outstanding annual savings in EU-28 due to full implementation of EED provisions on consumption based cost allocation is estimated at around 13.46 Mtoe in final energy consumption terms.

Table 2: Estimated savings potential from full/"perfect" implementation of current EED provisions on cost allocation and information for space heating and hot water in multi-family buildings

Measure Mtoe Space heating: Consumption based cost allocation 12.06 Space heating: Consumption information services 4.00 Hot water: Consumption based cost allocation 1.38 Hot water: Consumption information services 0.44 Total 17.88

Source: empirica estimations based on Guidelines for good practice 129

128 iNSPiRe (2014) Survey on the energy needs and architectural features of the EU building stock

The total outstanding annual savings potential in EU-28 due to implementation of EED provisions on consumption information services is estimated at around 4.4 Mtoe with the existing building stock.

Figure 5: Distribution of potential savings among EU-28 (consumption based cost allocation)

Source:empirica estimates (2016)

129 empirica (2016) Guidelines on good practice in cost-effective cost allocation and billing of individual consumption of heating, cooling and domestic hot water in multi-apartment and multi-purpose buildings, Available at https://ec.europa.eu/energy/sites/ener/files/documents/MBIC_Guidelines20160530D.pdf

Figure 6: Distribution of potential savings among EU-28 (consumption information services)

Source: empirica estimates (2016)

7 Annex – Tables and figures on Article 7 130

Table 3: Notified baselines for the calculation of the national savings requirements for period 2014-2020

Final energy Energy production Member State consumption Adjusted Transport for own use and

(ktoe) baseline (ktoe)* excluded (ktoe) non-energy use, if excluded (ktoe)

Austria 26,570 16,508 8,565 1,497

Belgium 30,171 21,940 8,231

Yes (not specified

for all regions)

Bulgaria 9,116 6,167 2,956 -

Croatia 6,151 4,113 2,037 -

Cyprus 1,863 767 1,023 73

Czech Republic 26,228 14,491 5,864 3,219

Denmark 15,086 9,833 4,973 277

Estonia 2,872 1,938 787 146

Finland 25,534 13,373 4,939 7,222

France 153,850 99,567 49,380 4,903

Germany 215,845 133,324 61,192 21,329

Greece 18,335 10,580 7,328 427

Hungary 15,859 11,681 4,172 5

Ireland 11,295 6,873 4,422 -

Italy 121,961 80,960 41,001 -

Latvia 3,970 2,702 1,109 159

Lithuania 4,768*** 3,188 1,556 -

Luxembourg 4,267 1,636 2,631 -

Malta 451 179 272 -

Netherlands 37,045 36,591 Yes (not specified) 454

Poland 64,610 47,040 17,570 -

Portugal 17,571 8,039 6,903 2,629

Romania 22,722 17,415 5,307 -

Slovakia 9,466 7,252 2,214 -

Slovenia 4,974 2,999 1,911 64

Spain 85,965 50,727 35,239 -

Sweden Not provided 27,438 - Yes (not specified)

UK 142,132 88,392 53,740 -

Total 1,078,676** 725,715 335,322** 42,404**

Source: Ricardo AEA/ CE Delft

• * 

  Adjusted means the value after subtracting ‘energy use by transport’ and ‘generation for own use’, where relevant

** Not specified by all Member States.

*** New final energy consumption for years 2010-2012 as 4768 ktoe notified without changes to the savings requirement.

130 This Annex contain the updated information per Member State (for the existing period 2014-2020) obtained trhough the structured dialogue with Member States and updates reported by Member States through the annual reports 2016.

Table 4: Notified sum of expected cumulative energy savings (and share by EEOS) by 2020, perMember

State 131

Member State Notified target (ktoe) Notified sum of expected Percentage to be savings (ktoe) delivered by EEOS (%)

Austria 5,200 9,145 42%

Belgium 6,911 7,268

Bulgaria 1,942 1,943 100%

Croatia 1,296 1,295 41%

Cyprus 242 243

Czech Republic 4,841 5,186

Denmark 3,841* 7,355* 100%

Estonia 610 611 5%

Finland 4,213 7,531

France 31,384 31,131 87%

Germany 41,989 45,302

Greece 3,333 3,333 Not provided

Hungary 3,680 3,689

Ireland 2,164 2,243 48%

Italy 25,502 25,800 62%

Latvia 851 851 65%

Lithuania 1,004 699

Luxembourg 515 515 100%

Malta 56 67 14%

Netherlands 11,512 11,270

Poland 14,818 14,818 *** 100%

Portugal 2,532 2,532

Romania 5,817 5,863

Slovakia 2,284 2,288

Slovenia 945 945 33%

Spain 15,979 14,361** 44%

Sweden 9,114 11,505

UK 27,859 34,041 24%

Total 230,434 251,830 35%

Source: Ricardo AEA/ CE Delft

• * 

  Denmark’s notified the energy savings target is 4,130 ktoe, this however includes savings in energy transformation, distribution and transmission sectors. Savings in these sectors accounted for 6% of the total reported savings in 2012, in 2013 for 5% and in 2014 for 7%. A reduction of 7% has been applied for the purposes of this report and the energy savings target and expected savings have been reduced accordingly.

** Excludes 1,619 ktoe of savings notified by Spain in related taxation measures, as these arise in 2013, so cannot count towards the 2014 - 2020 saving period.

*** The expected amount of savings is the same as the target, as only annual savings for 2016 and 2020 were notified by Poland.

131 The total amount of expected energy savings contain also the savings achieved under exemptions (c) and (d) of Article 7(2) for the relevant Member using these exemptions.

Table 5: Overview of policy measures per Member State (period 2014-2020) 132

n l y n , io or ar s

di ng m le i

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na es em ion i

gy ur

ic y at tax es lu

nc ol

unt or ic ab ch at eas c lear

y obl e cy N O

2

 (i v s ion f pol

r C chem s) or ent and n appl

U law pt y m y not o es

ic nc hem en iv

es

ant d

 E educ se ener

el ling s -u nd lic nd um ber ur

fic sc fici Fund y o ing s ent gr io ns eem ds at y an po

egor eas

 ef erg agr or S under

 lab ng a her c at num m

gy y Ef inanc inc erg ) En F egul tandar M

gy ni ing e cons cal S

at

ner rai ot

or al

E ner En (a (b) fis ) R (c (d) and m E T

ny and/ Tot (e) (f)

reduc i) A

Austria 1 1 4 1 1 1 9

Belgium 1 12 4 3 1 21

Bulgaria 1 1

Croatia 1 10 11

Cyprus 3 2 5

Czech Republic 23 23

Denmark 1 1

Estonia 1 1 1 3

Finland 1 1 2 1 3 8

France 1 1 1 3

Germany 133 1 1 20 3 1 13 67 106

Greece 1 15 1 1 1 19

Hungary 29 1 4 19

Ireland 1 2 4 3 10

Italy 1 2 3

Latvia 1 4 1 1 7

Lithuania 1 4 1 2 8

Luxembourg 1 1

Malta 1* 14 19 34

Netherlands 31 31

Poland 1 1

Portugal 1 1 1 2 5

Romania 20 1 2 6 28

Slovakia 134 7 59 66

Slovenia 1 1 2

Spain 1 1 10 2 1 15

Sweden 1 1

UK 3** 1 4 6 3 3 20 Total [number of

measures] 18 4 8 180 41 13 1 33 179 477

Total [number of MS] 16 4 8 20 12 6 1 8 13 28

132 These measures were notified by Member States and are subject to possible changes. Notified EEOSs do

not necessary mean that they are all operational , -four Member States are still to put in place the scheme.

133 Germany notified 65 policy measures that are implemented by the German States (Länder).

134 Slovakia provided savings per group of policy measures, targeted to a specific sector; not savings per individual policy measure.

Source: Ricardo AEA/ CE Delft

• * 

  Malta notified 4 measures labelled as EEOS (which are individually included in the total of 35 measures for Malta). In practice these are four separate measures that form part of a single EEO scheme, and thus represents just one policy measure. This is recorded as a single EEOS, but as 4 measures in the total column.

** The UK notified three EEOS. Two of the schemes ran from 2010-2012 and are now expired, so only one scheme is planned to be operational for the 2014 to 2020 commitment period.

Figure 7: Breakdown of expected energy savings by type of policy measure (ktoe)

Source: Ricardo AEA/ CE Delft

Figure 8: Notified measures per sector for the period 2014-2020

Source: Ricardo AEA/ CE Delft

Figure 9: Energy savings per target sector in the period 2014-2020 (ktoe)

Source: Ricardo AEA/ CE Delft

Figure 10: Division of energy savings in buildings sector (long lifetimes over type of measure)

Source: Ricardo AEA/ CE Delft

Table 6: Application of exemptions under paragraph, per Member State for period 2014-2020

Sum of exemptions

Member % used

Calculated effect per exemption (ktoe)

State exemptions (ktoe) used

slow start ETS supply early actions Industry side 7(2)(c ) 7(2)(d)

7(2)(a) 7(2)(b)

Austria 25% 1,733 - - - 1,733

Belgium 25% Yes (not Yes (not Yes (not Yes (not specified) specified) specified) - specified)

Bulgaria 25% 648 540 - - 108

Croatia 25% 431 359 72 - -

Cyprus 25% 81 41 40 - -

Czech

Republic 25% 1,604 1,268 - - 336 Denmark 7%* 289 - - 289 -

Estonia 25% 204 170 25 - 9

Finland 25% 1,404 - - - 1,404

France 25% 27,750 - 14,500 - 13,250

Germany 25% 13,996 - - - 13,996

Greece 25% 1,111 554 557 - -

Hungary 25% 1,226 1,022 204 - -

Ireland 25% 721 601 120 - -

Italy 25% 8,501 7,083 - - 1,418

Latvia 25% 283 236 47 - -

Lithuania 25% 335 279 - 28 28

Luxembour

g 25% 172 143 29 - -

Malta 25% 19 16 - - 3

Netherlands 25% 3,794 3,187 607 - -

Poland 25% 4,939 - 3,439 - 1,500

Portugal 25% 844 703 141 - -

Romania 21% 1,531 1,531 - - -

Slovakia 25% 761 635 - - 126

Slovenia 25% 314 262 - 52 -

Spain 25% 5,326 4,438 888 - -

Sweden 21% 2,408 2,408 - - -

UK 25% 9,286 7,739 1,548 - -

Total 89,711 33,215 22,217 369 33,911

Source: Ricardo AEA/ CE Delft

• * 

  The energy savings under exemption paragraph 2(c) are calculated in Denmark on the basis of the achieved savings. Savings in these sectors accounted for 6% of the total reported savings in 2012, in 2013 for 5% and in 2014 for 7%. A 7% reduction has been assumed for purposes of this report.

Table 7: Impact on energy consumption due to the measures implemented under the EEOS 135

Reduction of final Final energy savings energy consumption Time period per year (ktoe) per year Sector

UK 2008-2012 237 0.5% household sector

Denmark 2015 291 4.2% all sectors

France 2011-2013 377 0.4% all sectors

Italy 2015 500 0.4% all sectors

Austria 2015 136 0.9% household and industry sectors

Vermont, U.S. 2012-2014 10 1.7% all sectors except transport

California,

U.S. 2010-2012 384 1%

all sectors except transport

Source: Regulatory Assistance Project

Figure 11: Illustrative long-term impact of EEOSs on energy bills 136

Source: Regulatory Assistance Project

Figure 12: Breakdown of the average household energy bill in the UK (2014)

135 The reduction of final energy consumption per year is expressed in both absolute values and as a percentage of anticipated consumption under a BAU scenario).

136 The data presented are based on: 3 year operational period and termination thereafter; assuming no EEOS in place before; only applies to household sector; average yearly savings of 1%; average cost as share of total energy bill of 3%; split of lifetimes of measures: 25% 5 years, 25% 10 years, 25% 15 years and 25% 20 years; and average annual household energy bill of 1,500 Euro.

Source: DECC (2014a)

Figure 13: Breakdown of the average household energy bill in Italy (2014)

Source: Regulatory Assistance Project

Table 8: Reported energy savings achieved in 2014 under Article 7, ktoe 137

Compared to Compared to Compared to Estimated estimated total

Expected Cumulative expected savings on savings on cumulative Member State Savings savings in savings savings in the basis the basis of savings

achieved in 2014 (if requiremen 2014 (if of annual annual requirement

2014 notified 138 ) t by 2020 notitified) rate 2014 139 rate 140 by 2020

Austria 714 400 5,200 186 384% 14%

Belgium 180 141 247 6,911 247 73% 4%

Bulgaria 15 69 1,942 22% 69 22% 0%

Croatia 2.5 29 1,296 9% 46 7% 0%

Cyprus 2.2 7 242 34% 9 22% 1%

Czech Republic 65 173 4,841 173 38% 1%

Denmark 204 238 142 3,841 86% 137 149% 5%

Estonia 41 48 610 87% 22 186% 7%

Finland 561 4,213 150 374% 13%

France 1,585 738 31,384 215% 1121 141% 5%

Germany 2,548 2,844 41,989 90% 1500 170% 6%

Greece 74 100 3,333 74% 119 62% 2%

Hungary 75 75 3,680 100% 131 57% 2%

Ireland 71 73 2,164 97% 77 92% 3%

Italy 1,232 850 25,502 145% 911 135% 5%

Latvia 5 6 851 78% 30 17% 1%

Lithuania 38 1,004 36 106% 4%

Luxembourg 8.6 25 515 35% 18 50% 2%

Malta 1.5 1 56 238% 2 50% 3%

Netherlands 666 373 11,512 179% 411 162% 6%

Poland 403 14,818 529 76% 3%

137 All savings reported by Member States have been converted into ktoe to ensure consistency of data

presented. 138 Expected savings in 2014 were not notified for all policy measures therefore is it not reflected in column 4. 139 This column provides an indication of savings estimated for 2014 on the basis of the annual rate of the notified total cumulative savings requirement (target) by 2020 per each Member State on the assumption that Member States would achieve new savings each year (in reality Member States have freedom how they phase the achievement of their savings over the whole obligation period, which most of the Member States have notified to the Commission). It serves purely as a theoretical reference to allow monitoring progress of the savings per country and across EU-28.

140 This column provides an indication of savings estimated for 2014 on the basis of the annual rate of the

notified total cumulative savings requirement (target) by 2020 per each Member State on the assumption that Member States would achieve new savings each year (in reality Member States have freedom how they phase the achievement of their savings over the whole obligation period, which most of the Member States have notified to the Commission). It serves purely as a theoretical reference to allow monitoring progress of the savings per country and across EU-28. 141 Belgium has notified 301.85 ktoe in energy savings in total (summed up for each region). Since these savings contain also 122.03 ktoe stemming from early actions, this of have been deducted. 142 Denmark has notified the energy savings target and expected savings inclusive of savings in energy transformation, distribution and transmission sectors (exemption (c) under paragraph 2). Savings in these sectors accounted for 6% of the total reported savings in 2012, in 2013 for 5% and in 2014 for 7%. The expected savings have therefore been reduced by 7%.

Portugal 46 53 2,532 88% 90 51% 2% Romania 364 346 5,817 105% 208 175% 6%

Slovakia 72 71 2,284 101% 82 88% 3% Slovenia 18 23 945 76% 34 53% 2%

Spain 565 493 15,979 571 99% 4% Sweden 252 997 9,114 25% 326 77% 3%

UK 2,382 143 2,347 27,859 101% 995 239% 9%

Total 12,191 10,626 230,434 95% 8,230 113% 4%

Source: Ricardo AEA/ CE Delft

Figure 14: Multiple benefits of Energy Efficiency Obligation Schemes 144

Participant benefits

•Bill savings

•Health

•Comfort

•Disposable income

•Asset values

•Other resource savings

•Operations & Maintenance

•Employee productivity

Utility system benefits Societal benefits

•Avoided transmission capacity EEOSs •Greenhouse gas emission

costs reduction •Avoided generation operation costs •Energy security

•Avoided CO2 costs •Reduced energy prices •Avoided other env regulations •Employment

costs •Macroeconomic impacts •Avoided line losses •Industrial productivity •Minimising reserve requirements •Poverty alleviation

•Reduced credit and collection costs •Local air pollution •Reduced financial risk •Fiscal benefits

•Improved customer retention •Reduced cost for RES targets

143 UK notified total for all policy measures 27.7 TWh (28 TWh as rounded). 144 Rosenow and Bayer (2016) based on IEA (2014) report on multiple benefits of energy efficiency

8 Annex – Energy efficiency investments

The exact size of the energy efficiency market is difficult to estimate. Investments in energy efficiency are challenging to track because they are carried out by a multitude of agents, private households and companies, often without external financing. They also frequently constitute only a portion of broader investments and are not accounted for separately. There are broadly two possible methodologies to estimate energy efficiency investment flows 145 :

• Bottom-up approaches involve counting the individual exchanges of goods and services that increase energy efficiency. This method can provide a robust estimate of the size of the market, as long as the appropriate data are available and aggregation systems are in place. A bottom-up approach tracks the many individual activities that take place within homes and businesses. Bottom-up calculation requires relatively detailed data over time to compute stock adoption, the energy performance of each different stock type and behaviour changes down to the individual or business level. Typically, these data are not currently available, at least at an economy-wide or other broad level.

• In the absence of available granular data, a top-down method can evaluate trends in energy consumption and economic growth to estimate the scale of investment required to improve efficiency. In light of data challenges, this can be a more practical approach. Top-down methods sacrifice accuracy but still provide insight on the size of the market and changes over time.

The market size also varies significantly depending on the definition of energy efficiency investment. For example, it is possible to make the distinction between autonomous investments and motivated investments. Autonomous investments happen by themselves (e.g. replacement of equipment, normal refurbishment of buildings, etc.). In that case, energy efficiency is not the primary motivation for investing, and market actors might undertake such investment without knowing that it will deliver energy savings. On the contrary, motivated investments are typically induced by policies, where investments are explicitly designed to achieve energy efficiency objectives.

Most of the studies presented below have tried to estimate the additional investment costs for improving energy efficiency. This means the capital expenditure necessary to go beyond business-as-usual investment for autonomous investments, and the whole up-front costs for the motivated investments. For instance, in the case of energy efficient equipment, the additional investment cost represents the difference of purchasing costs between an energy efficient appliance and a "regular" one. The main challenge is therefore to define what is meant by "regular" (i.e. to define a baseline), which is by definition moving over time because of

continued technological improvements 146 .

145 https://www.iea.org/publications/freepublications/publication/EEMR2014.pdf.

146 A caveat of this methodology is that it does not show larger market dynamics that also contribute to energy efficiency improvements. For instance, for some appliances, one can buy a more energy efficient equipment without any additional costs. In that case, no monetary contribution is taking into account in the estimated energy efficiency investment flows.

At the global level, several top-down and bottom-up studies estimate energy efficiency

investments in the range of EUR 100 – 300 billion per annum 147 . This is summarised in the table

below.

Table 9: Studies estimate energy efficiency investments

Source Estimate Comments

World Energy

$130 billion per The estimate refers to energy efficiency investments by end-users in

Investment Outlook

(IEA, 2014) 148 year

2013 to increase the efficiency of devices above the 2012 stock efficiency level (bottom-up estimate).

Energy Efficiency

Market Report $310 – 360

In their 2014 Energy Efficiency Market Report, IEA presents six different top-down methods to estimate the size of the energy

(IEA, 2014) 149 billion per year efficiency market.

Sizing energy efficiency $365 billion per The estimate refers to 2012 and includes investment in the purchase

investment (HSBC,

2014) 150 year

of energy efficient equipment in the transport, buildings and industry sectors.

The HSBC study (referred above) also provides a detailed break-down by sector. The following graph illustrates the segments leading to their estimated total market size of $365 billion.

Figure 15: Global market size for energy efficiency products (HSBC study)

Source: HSBC

At the EU level, a number of bottom-up and top-down studies broadly outline current or expected energy efficiency investments in different market sectors, as shown in the table below.

147 The average EUR/USD exchange rate in 2000-2015 (1.21) is used to convert the estimates provided in

USD to EUR

148 https://www.iea.org/publications/freepublications/publication/WEIO2014.pdf

149 https://www.iea.org/publications/freepublications/publication/EEMR2014.pdf

150 https://www.research.hsbc.com/R/20/K2kb6gL5ynU7

Table 10: Sectorial bottom-up and top-down studies estimating energy efficiency investments

Source Sector Estimate Comments

BEAM² model All buildings This figure refers to the estimated current costs of building (new and envelope related measures (such as insulation and windows)

refurbished) €120 and the costs of energy efficient technical building systems. It billion per includes both new and refurbished buildings. This capital year (in expenditure should be compared with the overall EU market 2016) for building renovation which represents annually around EUR 500 billion and the market for new construction of around EUR 400 billion.

Supporting study Residential €80 In this study, the EE-related market for buildings renovations for the fitness check buildings billion per is defined as the value of the works and related goods and on the construction (new and year (in services utilized to upgrade the energy efficiency of dwellings.

industry 151 refurbished) 2010- Around €73 billion is for renovations, and €7 billion would be

2014) the additional energy efficiency cost for new buildings. Ecodesign Impact Ecodesign This is an estimate of the extra acquisition costs for more

Accounting Products €62

report 152 billion per

energy efficient products in 2020. These acquisition costs

year (in represent around 12% of the yearly capital expenditures and

2020) they are expected to trigger €173 billion of gross savings on running costs (91% energy).

These studies show that the European market for energy efficiency is already sizeable and that it represents investments well above €100 billion per year.

One important question related to investment is to identify, for different policy scenarios, the sectors where additional energy efficiency investments will be the most needed in the future. One way to answer that question is to use the PRIMES model by looking at the investment gap between the EUCO27 policy scenario and the more ambitious ones for the period 2021-2030. By taking this approach, it is possible to disregard the investment related to the 2030 GHG and RES targets that are included in PRIMES investment figures, and solely focus on energy efficiency investments. The table below shows the results of this approach.

151 Supporting Study for the Fitness Check on the Construction Industry – Draft Final Report.

152 https://ec.europa.eu/energy/sites/ener/files/documents/Ecodesign%20Impacts%20Accounting%20%20-

%20final%2020151217.pdf .

Table 11: Energy efficiency investment gap

EUCO27

Average annual

Investment Expenditures values 2021- EUCO30 EUCO+33 EUCO+35 EUCO+40

2030 (billion €'13)

Total energy related investment

Expenditures 1,036 8% 19% 28% 51%

Industry 17 6% 36% 69% 192%

Residential 168 28% 71% 101% 171%

Tertiary 40 72% 200% 295% 547%

Transport 153 731 1% 0% 0% 1%

Grid 39 -8% -12% -21% -33%

Generation and boilers 42 0% -4% -11% -14%

Source: PRIMES

According to the PRIMES projections, the energy efficiency investment expenditure increases in all scenarios compared to EUCO27 - more significantly in more ambitious scenarios and mostly in the residential and tertiary sectors. For instance, in the EUCO30 scenario, the model estimates the need to increase by 28% the energy related investment expenditures in the residential sector, and by 72% in the tertiary sector, compared to the investments foreseen in the EUCO27 scenario.

When estimating future energy efficiency investments, the level of cost intensity 154 of future

energy efficiency measures is as important as the level of achievable energy savings. However, predicting the cost intensity of future energy saving measures is difficult as it depends on many factors. For instance, it depends on the nature of the remaining energy saving potential, on future technological progress or on future price reductions of energy efficiency solutions due to e.g. increased sales volumes, more efficient installation procedures, or improved productivity. The table below illustrates the disparity in cost intensity factor based on past experiences and modelling assumptions.

153 Investment in transport equipment for mobility purposes (e.g. rolling stock but not infrastructure) and energy efficiency; excluding investments in recharging infrastructure.

154 The capital expenditure required to achieve 1 Mtoe of energy saving per year (e.g. billion EUR/Mtoe).

Table 12: Cost for energy efficiency improvement measures 155

Energy

Source Methodology Sector efficiency cost intensity [bn

EUR/Mtoe]

CONCERTO Cost intensity based on the monitoring of 58 pilot cities Buildings: energy

database in 23 Member States renovation 11,6

Projects supported Cost intensity based on the monitoring of 21 energy Buildings: energy

under ELENA efficiency projects renovation and street 15,7 lighting

Study Fraunhoferbottom- up and top down approach estimating the Buildings: additional

ECOFYS ISI 2011 required upfront-investments for the period 2011-2020 upfront investments 5,3

Buildings: renovation BEAM² building cost modelling and new buildings 20,1

(2016-2030) This report investigates a number of scenarios for

Study on renovating improving the energy performance of Germany's Germany's building building stock. The focus is on the economic viability of Buildings: renovation stock - BPIE different levels of renovation from the perspective of the (2015-2030)

23,6

investor or building owner. The reported figure is the one from the Business as usual scenario.

155 Sources: Concerto ( http://smartcities-infosystem.eu/concerto/concerto-archive ); Study on renovating

Germany's building stock, BPIE (http://bpie.eu/wp-content/uploads/2016/02/BPIE_Renovating-Germany-s Building-Stock-_EN_09.pdf ), Study Fraunhofer-ECOFYS ( http://www.isi.fraunhofer.de/isiwAssets/docs/x/de/publikationen/Building-policies_Brochure_Final_November-2012.pdf ); BEAM² (EPBD Impact Assessment SWD).

9 Annex – Review of the default coefficient – Primary Energy

Factor for electricity generation referred to in Annex IV of

Directive 2012/27 i/EU

CONTEXT

In the context of energy efficiency implementation, a so-called Primary Energy Factor (PEF) has been used to determine the primary energy consumption to generate one kWh of electricity. Directive 2012/27 i/EU on energy efficiency (EED) establishes in Annex IV a default coefficient

of 2.5 for savings in kWh electricity 156 , to transform electricity savings into primary energy

savings. This coefficient is a single value for the EU. Member States may apply a different coefficient provided they can justify it.

Article 22 of the EED empowers the European Commission to review the default coefficient.

For the PEF review a study was tendered from August 2015 to April 2016 157 and three meetings 158 took place at the European Commission premises:

• 1. 

  On 11 December 2014 and on 17 June 2016, two consultative joint meetings of Member States' representatives for the EED with the consultation forum under art. 18 of the Ecodesign of energy-related products Directive 2009/125/EC i, including stakeholders

  (minutes are available online 159 ). The reason for the joint meetings is that the PEF value

  from the EED is used by several implementing regulations under the Ecodesign and Energy Labelling Directives, for comparing the efficiency of products using electricity and products using other fuels such as gas or liquid fuels. The PEF review in the EED would have

  implications in existing or forthcoming Ecodesign and Energy Labelling Regulations 160, 161 .

• 2. 

  On 21 January 2016, a technical meeting with Member States' representatives for the EED

  and stakeholders: this meeting was a relevant input to the tendered study 162 .

Most Member States and stakeholders argued that the current 2.5 value is outdated and should be revised.

156 Which means an average, European-wide conversion efficiency of 40% (excluding grid losses).

157 Contract No. Reference: ENER/C3/2013-484/02/FV2014-558/SI2.710133 "Review of the default primary energy factor (PEF) reflecting the estimated average EU generation efficiency referred to in Annex IV of Directive 2012/27 i/EU and possible extension of the approach to other energy carrier" – Contractor: Trinomics. Technical leadership: Fraunhofer ISI.

158 Together with EU Member States, EEA countries and over 50 European associations were involved.

159 11 December 2014 meeting minutes: http://ec.europa.eu/transparency/regexpert/

index.cfm?do=groupDetail.groupDetailDoc&id=18412&no=2 17 June 2016 meeting minutes: http://ec.europa.eu/transparency/regexpert/index.cfm?do=groupDetail.groupDetailDoc&id=24733&no=2 160 However, even if the value is revised in the EED, no instantaneous change of its value within the Ecodesign or the Energy Labelling Regulations should take place. Any review would take place in the context of the relevant regulation.

161 The discussion about the PEF value is also relevant in the context of the establishment of a common EU voluntary certification scheme for non-residential building under the Directive 2010/31 i/EU on the energy performance of buildings where a PEF for electricity has to be determined to calculate, in a default setting, the energy performance of buildings.

162 The scope of this meeting was to provide an analysis of the whole range of calculation options from a scientific perspective. Main points of discussion were on marginal or average approach, which method to adopt for renewables – and non-combustible renewables – and the weighting of the options.

The tendered study was requested to look in particular at how to measure the efficiency of electricity generation, including the following aspects: average vs. marginal electricity generation; current, future or desired efficiency of the electricity generation; time of use of energy. The study also looked at if the use of PEF should be extended to other energy carriers.

APPROACH

The basic concept to calculate the PEF for electricity is to relate the raw primary energy demand of electricity generation with the electricity produced.

The calculation process of the PEF for electricity is made of two consequential steps that can be structured according to the following formula:

𝑷𝑷𝑷𝑷𝑷𝑷 𝑷𝑷𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬 = 𝑷𝑷𝑷𝑷𝑷𝑷 𝒐𝒐𝒐𝒐 𝑷𝑷𝑭𝑭𝑬𝑬𝑬𝑬 𝑪𝑪𝒐𝒐𝑪𝑪𝑪𝑪𝑬𝑬𝑬𝑬𝑪𝑪𝑬𝑬𝒐𝒐𝑪𝑪 𝑬𝑬𝒐𝒐𝒐𝒐𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑬𝑪𝑪𝑬𝑬𝑬𝑬

The first step is to determine the "PEF of Fuel", i.e. how much energy was needed to get one unit of ready-to-use fuel (before being converted into electricity). This is done for each fuel. In

this document, all energy sources are named as “fuel” 163 . In this step, issues like system

boundaries counts, e.g. transmission and distribution losses or the energy used to extract, clean and transport coal.

The second step is to determine the conversion efficiency of the electricity generation process,

for each ready-to-use fuel. 164 Hence, a PEF for electricity for each fuel is calculated (e.g. a PEF

for electricity from coal; a PEF for electricity from wind; etc). The total PEF for electricity is the weighted sum of the single PEFs according to the relative amount of every fuel in the total primary energy.

The tendered study selected four calculation methods for examination that looked into different options for the two steps:

• Calculation method 1 is designed to be in line with the Eurostat calculation for primary energy and electricity production.

• Calculation method 2 is designed to reflect the total consumption of non-renewable sources only.

• Calculation method 3 is a variation of method 1 in order to analyse the impact of changing the allocation method for CHP from the “IEA method” to the “Finish

method” 165 .

• Calculation method 4 modifies calculation method 3 by adding the life cycle perspective to the conventional fuels.

163 This also includes wind, solar or hydro which are normally not called “fuel” in the classical sense E.g.

Eurostat refers to them as energy products. Elsewhere (e.g. some UN standards) they are also called energy sources or carriers.

164 Regarding non-conventional fuels, such as wind, solar PV, hydro, geothermal or nuclear, there is a range of methodological choices to be made to define the primary energy content.

165 The IEA method attributes the primary energy to the outputs power and heat in relation to their relative output shares. The Finish method takes into account the average efficiency in single heat and power plants as a reference. The Finish method attributes a higher share of primary energy consumption to electricity. The Finish method is the method in Annex II of the EED for determining the efficiency of the cogeneration process..

All calculated PEF values after the year 2015 are below 2.5.

Calculations are based on the PRIMES 2016 Reference Scenario – the most recent available version. PRIMES contains projections of the development of the European electricity mix by taking into account the impact that will generate from current policies (e.g. from EU energy policies to 2030 a higher share of renewable sources of energy). The historical years in PRIMES are calibrated based on official statistics from Eurostat, i.e. reaching consistency with real data as for the previous years. The focus is on the time framework 2005-2020.

The analysis looked into 51 options in total (Table 1) and the results were weighted according to policy objectives (Table 2). Each calculation method was the result of a decision tree (Table 3).

Table 13: Options for PEF calculation

Category Option

Strategic and political considerations

PEF purpose Desired

Calculated

Applicability Abolish the use of a PEF

No differentiation

Different for different policies

Different for different electric appliances

Different for different policies and electric appliances

Different for delivered and produced electricity

Adjustment and review Constant over time

process Regular review/adjustment

Database and calculation Based on statistics and studies

method Advanced calculations based on statistics and studies

Power sector model calculations

Representation of the electricity sector

Geographical resolution Bigger EU With Power Exchange (PEX) correction

(EU+Norway) No PEX Correction

EU With PEX correction

No PEX Correction

Member States With PEX correction

No PEX Correction

Market regions With PEX correction

No PEX Correction

Subnational With PEX correction

regions No PEX Correction

Development over time Constant

Dynamic

Time resolution Average over several years

Annual average

Seasonal

Hourly time of use

Market position Average electricity production

Marginal electricity production

General PEF methodology

PEF indicator Total primary energy

Non-renewable energy only System boundaries Entire supply chain

Energy conversion and transmission/distribution

Accounting method for Technical conversion efficiencies nuclear electricity (and heat)

generation Direct equivalent method

Physical energy content method

Accounting method for Zero equivalent method power (and heat) generation

using non-combustible RES Substitution method

Direct equivalent method

Physical energy content method

Technical conversion efficiencies

Accounting method Zero equivalent method electricity (and heat)

generation using biomass Technical conversion efficiencies

Accounting method for IEA method

cogeneration (CHP) Efficiency method

Finish method

Methodological consistency Same method in all Member States

Different methods in different Member States

Different methods in different Member States with correction mechanism

Table 14: Policy evaluation criteria with weightings

Methodological Suitability Acceptance

70 % 30 %

Preci Data Availability Target: Target: Target: Target: Compl Trans

sion internal 2020 2020 Long-term exity parenc

20 % market climate securit decarbonisati y

(includin y of on (including Effort Credib Data Uncert Flexi g Energy supply Electrificatio required ility quality ainty bility Union) n)

Figure 69: Decision tree

RESULTS

The following conclusions apply to all the four calculation methods:

• It appears appropriate for the approach of single PEF value for electricity in the EU to be kept (for use in the contexts where it is currently used) and the same PEF value for electricity to be used in all EU legislation where it is appropriate. This is to avoid distortions, take account of the interconnected European electricity system and be consistent with the EU Internal market vision. Where the same requirements or labels are applied to products using different fuels, a PEF is needed in order to obtain comparable information. In addition, since the Regulations published under the Ecodesign and the Energy Labelling Directives are directly applicable in all EEA countries (Norway, Liechtenstein and Iceland) and the free movements of goods needs to be maintained, a single European PEF value needs to be used.

• The analysis covers EU28 and Norway, because of the relevance of Directive 2012/27 i/EU for the EEA countries, of which Norway is the most relevant trading partner. This choice is a trade-off between precision and data availability and complexity. Since the PRIMES dataset does not contain Norway, the contractor

developed an extra dataset for Norway based on ENTSO-E 166 data, which the Norwegian

representatives verified at the Technical meeting.

166 ENTSO-E is the European network of transmission system operators for electricity. It provides freely accessible data on the electricity system in Europe. https://www.entsoe.eu/disclaimer/Pages/default.aspx

• It seems appropriate for the PEF value to be a calculated value and to be revised regularly, in order to reflect reality (and forthcoming reality) at best. The projected development of the electricity sector changes regularly and especially technologies such as nuclear, renewables and CHP are subject to political influence, which may change their future development over time.

• The time of use of energy is based for all methods on annual average values. Seasonal values – the most relevant alternative option – are excluded because they would require complex calculations: most statistical and projected data exists on a yearly basis and hence seasonal values would need to be deduced from a power sector model, with detriment to transparency and impartiality of the results.

• Regarding the accounting methods for primary energy, as for nuclear electricity (and heat) generation, the Physical energy content method is used. As for electricity (and heat) generation using biomass, the Technical conversion efficiency method is used. This is in line with the Eurostat approach.

• An average market position is favoured for all calculation methods over a marginal position. The dimension "Market position" concerns the question, which power generator is taken as the basis for the calculation. While the average generation mix is easy to estimate, determining the marginal generation unit requires more complex assumptions. The rationale behind using the marginal generation unit is that relatively small changes in consumption lead to changes only in the generation of electricity in the last units used to cover demand. If an efficiency measure reduces power consumption in hours of high demand, renewable energies and base load power plants will continue to produce and only the peak load plants (mostly gas and oil turbines) will adjust their power generation accordingly. The primary energy consumption of the marginal generator often differs substantially from the average generation: the party in favour of a marginal position claims this would better show the primary energy consumption of new appliances. Yet, normally the effect of one single new appliance in the system is marginally low. Complex and time-consuming power system model calculations would have to be carried out to determine the marginal supplier for a specific point in time.

• For fossil fuels and directly combustible renewable fuels, the conversion efficiency is given by the heat value generated during combustion of the fuels (output) divided by the raw primary energy demand (input). For non-combustible renewables a conversion efficiency of 100% is assumed. For geothermal power stations a conversion efficiency of 10% is assumed, while for nuclear power stations a conversion efficiency of 33% applies. These values are commonly applied and in line with Eurostat.

The four calculation methods differ for three aspects:

• 1) 

  the system boundaries,

• 2) 

  the treatment of renewable energy sources (RES), and

• 3) 

  the allocation method used for CHP.

These three aspects are represented in the last five columns of the decision tree in Table 3.

The category “System boundaries” defines if only the primary energy that is used within the conversion and distribution process is considered or if also additional energy consumption, related to the (entire or partial) life cycle of the conversion, transmission and distribution infrastructure. Calculation methods 2 and 4 take into account the life cycle perspective.

As for RES, the issue is if to consider the primary energy at the origin of RES as total primary energy or non-renewable primary energy. In the latter case, the guiding question being "How much non-renewable primary energy was used to get 1 unit of fuel to be converted into electricity?" and the answer being "Zero", the Zero equivalent method is applied. The PEF of fuel for all RES would therefore be 0. It would instead be of value 1 with the Total primary energy method ("How much total primary energy was used to get 1 unit of fuel to be converted into electricity?"). The Zero equivalent method is applied in Calculation method 2, while methods 1, 3 and 4 apply the Total primary energy method.

As regards CHP, there is the need to identify how much of the fuel input that goes into a CHP plant is used to produce heat and electricity, i.e. what is the quota of primary energy that is used to produce respectively heat and electricity. Various methods exist. The study shed light on two methods: the IEA method and the Finish method (also known as Alternative production method). The IEA method attributes the primary energy to the power and heat outputs in relation to their relative output shares. The Finish method takes into account the average efficiency of single heat plants and single power plants as a reference. As a result, the IEA attributes a higher share of primary energy to heat than the Finish method, i.e. the efficiency of electricity production in CHP with the IEA method results higher than with the Finish method. Thus, heat production in CHP appears less efficient with the IEA method than in reality is: the Finish method allows for results that are more realistic. The IEA method is used by Eurostat as a default method when Member States do not provide own calculations.

For the calculation in the Finish method, it is necessary to get data on average conversion efficiencies. The most recent data available from Eurostat are used: 40% for reference power plants, 90% for reference heat plants and 70% overall efficiency for CHP plants.

Calculation method 1 applies the IEA method, while methods 2, 3 and 4 apply the Finish method.

The calculations below show the difference between the IEA method and the Finish method:

STARTING DATA

(FROM PRIMES 2016) Operator Indicator 2015 Unit

CHP El. Generation 397 TWh CHP OUTPUT + CHP Heat Generation 941 TWh

• = 

  Total CHP Output 1337 TWh

CHP INPUT Primary energy 1911 TWh

RESULTS

With IEA method With Finish method

Primary Energy share Primary Energy share

of electricity 567 TWh of electricity 931 TWh

PEF for electricity PEF for electricity

from CHP 1.43 from CHP 2.34

PEF for heat from CHP 1.43 PEF for heat from CHP 1.03

The results show that according to the IEA method 1.43 TWh of primary energy are needed to produce 1TWh of electricity from a CHP plant (and the same amount is needed to produce 1TWh of heat), while with the Finish method the result is 2.34 TWh to get 1 TWh of electricity and 1.03 to get 1TWh of heat. The Finish method is closer to reality, because heat production is much more efficient than electricity production (in single plants, as well as in CHP), as

confirmed by latest studies and documents by the European Commission 167 .

CHP stakeholders and Member States investing in CHP are in favour of getting heat production valorised as much as possible: the Finish method allows for this more than the IEA method.

CONCLUSIONS AND PROPOSAL

The PEF of 2.5 is not adequate and should be revised: all calculation methods show a decrease of the PEF due to the projected growth of electricity generation from RES.

Table 15: Results PEF for electricity from the tendered study 168

Calculation method 2005 2010 2015 2020

Method 1 2,35 2,25 1,98 1,88

Method 2 2,33 2,12 1,73 1,54

Method 3 2,48 2,38 2,09 1,99

Method 4 2,60 2,48 2,17 2,06

The analysis shows that no calculation method can claim absoluteness. On balance, it appears appropriate to proceed with Calculation method n.3 and an appropriate value for the default coefficient in the EED for electricity production is 2.0. The reasons for choosing method n.3 are the following:

• With the exception of CHP, it is in line with the primary energy calculation made by Eurostat, the official EU statistics body fed with national statistics;

• Calculation method n.3 applies the Finish method for CHP, which gives a more realistic result of the primary energy share used for electricity production in CHP plants than the IEA method, applied by Eurostat. This choice is also justified by the fact that Eurostat is working with DG Energy on CHP reporting forms to be integrated in the annual Eurostat questionnaire to Member States probably in the next 2-3 years, in the context of the requirements under Art. 24(6) of the EED. The new reporting forms will allow moving from aggregation on plant level to the aggregation on the unit level and will enable to

make calculations in line with the Finish method 169 ;

• The Finish method is the methodology in the EED – Annex II to determine the efficiency of the CHP process;

• As for RES, calculation method n.3 applies the Total primary energy method for the primary energy at the origin of RES. The reasons to prefer this method are the following:

167 See Eurostat energy balances. See Review of the Reference Values for High-Efficiency Cogeneration – RICARDO-AEA. Report for EC DG Energy ENER/C3/2013-424/SI2.682977 ED59519. See Best Available Techniques (BAT) Reference Document for Large Combustion Plants Industrial Emissions Directive 2010/75 i/EU (Integrated Pollution Prevention and Control) JOINT RESEARCH CENTRE Institute for Prospective Technological Studies Sustainable Production and Consumption Unit European IPPC Bureau Final Draft (June 2016), http://eippcb.jrc.ec.europa.eu/reference/BREF/LCP_FinalDraft_06_2016.pdf . Other calculation methods exist, some of which aim to valorise the heat production in CHP (e.g. the 200% heat efficiency in Denmark). 168 Compared to the tendered study, these calculations are updated with the last available PRIMES Reference Scenario from 2016.

169 Eurostat will continue using the IEA method only in case no better data exist for the preparation of energy balance (annual questionnaires) at national level.

o The PEF value from the EED is used by several implementing regulations under the

Ecodesign and Energy Labelling Directives, to compare the performance of products such as electric heaters and gas heaters. The share of renewable energy in electricity

generation is heading for 35%. By using a PEF of 0 for RES, that would mean that 35% of the electricity used would be ignored when comparing the performance of

electricity and gas appliances. The choice for PEF of 0 for RES could undermine the credibility of a consumer-serving label;

o A PEF as 1 for RES recognises that it makes sense to place value on, and save where

possible, all types of energy including renewable energy;

o The role of RES for sustainable and climate policies is already recognised by the

assumption of full conversion efficiency into electricity (100%) – i.e. by the use of a factor of 1 rather than the higher values used for other technologies.

• As for system boundaries, calculation method n.3 applies no life cycle approach. The reasons are the following:

o Neither the tendered study nor literature and Member States' experiences show clear

and consistent data on the consumption of primary energy in the upstream chain of fuels from being raw to becoming fuels ready to be converted into electricity. There

are also doubts on how far to go in the upstream chain;

o The application of the PEF for electricity in the Ecodesign and Energy Labelling

Directives to compare the performance of products leads to the question, whether or not a similar method has to be applied to other energy carriers as well, such as coal or gas. Currently, their final energy consumption is calculated to be equivalent to its primary energy consumption. By choosing method n.3 there is consistency with the approach adopted so far in the Ecodesign and Energy Labelling Directives.

The value of 2.0 is the projected result for the year 2020. The choice of the year 2020 seems reasonable to take into account the effect of on-going energy policies in the forthcoming years and at the same time to keep limited the uncertainty from modelling. This approach is in line with the intention to have a regular review of the PEF value, notably every five years.

An alternative option would be to make an extrapolation (linear or exponential) of the η factor

developed by Eurostat 170 . The η factor is the efficiency of electricity generation: PEF would be

• = 

  1/ η. As of 2020, the extrapolated PEF would result in 2.1 (see Tables 5 and 6).

Before comparing the result from method n.3 and the Eurostat extrapolation, two passages are needed. First, the extrapolated value has the IEA method for CHP and it is necessary to adapt the value with the Finish method. According to calculations from the study, a factor of 0.1 needs to be added (2.1+0.1=2.2). Second, the extrapolation of historical data from Eurostat does not show the evolution of on-going energy policies (notably growing quota of RES, which mean a lower PEF) – while PRIMES do. 1/η will be higher than the result of any method from the study.

170 http://ec.europa.eu/eurostat/documents/38154/43500/ETA_time_series.xlsx/8d4ae449-8795-44d8-b903- ddd6ff36ba42

Figure 70: Extrapolation of η factor by Eurostat (as of 2020: η =48%, PEF=2,08)

In conclusion, the result from method n.3 is counter proven and based on robust assumptions.

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