Wider Public Sector Emissions Reduction Potential Research

Transcription

1 Wider Public Sector Emissions Reduction Potential Research Presented to: The Department of Energy and Climate Change Author: Gill Bryan, Robert Cohen and Paul Stepan Date: 18 July 2011 Reference no Version: Final

2 Document type: Client: Client contact: Other details: Report The Department of Energy and Climate Change File name: Wider PS Emissions Reduction Potential Final Report docx Status: Final Date: 18 July 2011 Author: Gill Bryan, Robert Cohen and Paul Stepan Signature Date:... (hard copy only)... (hard copy only) QA: Dave Worthington Signature Date:... (hard copy only)... (hard copy only) Author contact details Telephone: +44 (0) Disclaimer: This report has been prepared for the above named client for the purpose agreed in Camco's terms of engagement. Whilst every effort has been made to ensure the accuracy and suitability of the information contained in this report, the results and recommendations presented should not be used as the basis of design, management or implementation of decisions unless the client has first discussed with Camco their suitability for these purposes and Camco has confirmed their suitability in writing to the client. Camco does not warrant, in any way whatsoever, the use of information contained in this report by parties other than the above named client.

3 Contents 1 Executive Summary Introduction Baseline Methodology Emissions type Data Assumptions and extrapolations Data Discrepancies Greenhouse Gas Emissions Assumptions Overview Emission reduction potential Summary The Carbon Trust Close Out Database Results Display Energy Certificate Analysis Summary Analysis of DEC data Energy and carbon performance of public sector buildings Year on year analysis Do new buildings have better operational ratings? Appendix A: Supporting data for work stream Appendix B: Technical abatement method Appendix C: Processing of the DEC data set: Statistics and methodology for cleaning and filtering Appendix D: DECs in brief Wider Public Sector Emissions Reduction Potential 2010/11 2

4 1 Executive Summary This study assesses the level of greenhouse gas emissions within the public sector in England in 2009/10 and the potential for carbon emissions abatement by 2014/15. This is compared with recent trends in emissions from the public sector estates based on the data from the latest extract from the Display Energy Certificates (DECs) central register. Greenhouse gas emissions baseline The total greenhouse gas emissions by public sector bodies from the use of electricity and fossil fuels in 2009/10 including for administrative business transport are estimated to be Million tco 2 e. The lower bound represents a baseline generated from the most accurate primary data sources available where, for building data, figures are available for both the energy consumption by fuel type and the associated floor area. The upper bound represents an estimate of the maximum emissions from the public sector in England using more all-encompassing floor area estimates 1 and multiplying by average energy intensity values. Whilst the order of the maximum emissions is similar to the conclusions of a previous study by Camco 2, the quality of data and coverage of transport emissions have both improved considerably, with improvements in buildings data resulting from the availability of DEC data and more accurate and detailed transport data provided by Buying Solutions. There are still gaps in the transport data however for Further and Higher Education, prisons and schools which should be closed over time. The greatest contributor to the total is from Local Government with 5-8 MtCO 2 e. A breakdown of Local Government indicates that schools account for approximately MtCO 2 e, Local Authorities for MtCO 2 e and police, fire and rescue 0.6 MtCO 2 e. Health accounted for 4-6 MtCO 2 and the Ministry of Defence (MoD) a further 3.8 MtCO 2 e, Further and Higher Educational Institutes accounted for approximately 2.7MtCO 2 e, with the remaining emissions accounted for by Central Government (including prisons and excluding MoD) with MtCO 2 e. Although out of scope for this study, methane from landfill sites, commuting, and outsourced activities are all acknowledged to have a significant contribution to public sector emissions albeit with reduced public sector control and influence. It is recommended that further work is undertaken to quantify these emissions and identify associated abatement opportunities. Table 1.1 Source of emissions 2009/10 emissions baseline breakdown and comparison Buildings emissions Baseline Maximum (tco 2 e) (tco 2 e) Transport emissions Baseline Maximum (tco 2 e) (tco 2 e) Total emissions Baseline Maximum (tco 2 e) (tco 2 e) Central Government (exc. prisons) 580,716 2,265, , , ,868 2,765,399 Central government (MoD specific) 1,450,226 1,450,226 2,350,046 2,350,046 3,800,272 3,800,272 Central government (prison specific) 377, , , ,781 Health 3,737,659 5,837, , ,593 3,866,141 6,035,824 Local Authorities (exc schools and police and fire) 1,941,588 4,089,480 99, ,853 2,041,234 4,317,333 Local Authorities (schools) 2,439,586 2,965, ,439,586 2,965,006 Local Authorities (police, fire and rescue) 335, , , , , ,948 Further Education Institutions 719, , , ,774 Higher Education Institutions 1,983,962 1,983, ,983,962 1,983,962 Total 13,566,629 20,102,994 3,129,877 3,512,304 16,696,505 23,615,298 1 The lower bound primary data sources generally exclude buildings with floor area < 500m 2 (SOGE) or < 1,000 m 2 (DECs) 2 Cross Cutting Review of Low Carbon Potential in the Public Sector (Camco, November 2009) Wider Public Sector Emissions Reduction Potential 2010/11 3

5 The associated annual cost of energy is Billion for buildings and Billion for transport fuel. The wide variation in reported floor areas and lack of transport data for some sub-sectors suggests the need for re-baselining on an annual basis and the measurement of carbon reduction on a normalised as well as absolute basis. Carbon abatement potential Central Government is planning to establish a stretching level of greenhouse gas emissions reductions from 09/10 by 14/15 to support the Greening Government Commitments. This report explores the potential for the expansion of any level of ambition to the wider public sector, defined as Schools, Police Fire and Rescue, NHS, Further Education and Higher Education. Based on the emissions baseline work outlined above, this represents a total reduction in building emissions of 2.3 MtCO 2 across those sub-sectors. Transport emissions have been excluded from the target as there is insufficient evidence in the abatement modelling work to support the adoption of cost-effective emissions reductions from transport-related opportunities. The most cost effective way to achieve this target is to implement measures where they have the lowest cost per tonne of carbon abated rather than to aim to achieve a 25% reduction in each sub-sector. Our analysis suggests that this strategy would result in a commercial cost saving of 577m across the life of the measures including a capital cost saving of ~ 1 bn compared with a 25% reduction in each sub-sector. Table 1.2 Summary of implications of low carbon target scope Entire Public 25% in each Difference Sector subsector (%) Initial Cost 1482 Million 2541 Million 72% Lifetime Discounted Financial Savings 3651 Million 4133 Million 13% Annual Savings (Based on Year 0) 498 Million 538 Million 8% Lifetime Traded CO MTCO MTCO 2-1% Lifetime NonTraded CO MTCO MTCO 2 42% MAC % Our analysis suggests that the most cost effective opportunities to achieve the target exist within the Further and Higher Education sectors with an average cost of carbon abatement of - 170/tCO 2 compared with - 115/tCO 2 for schools, police and fire and rescue, and - 33/tCO 2 for health. An alternative approach to achieving the target would be to prioritise measures by payback instead of by Marginal Abatement Cost (MAC), defined as the Net Present Value of a measure divided by its lifetime CO 2 savings. This would reduce the capital cost requirement of the policy and the associated financial savings but would be more reflective of a typical approach to energy management in industry where the general expectation is that a 10-20% reduction in CO 2 emissions can be achieved from low cost behavioural measures and fine tuning of controls systems. Wider Public Sector Emissions Reduction Potential 2010/11 4

6 Display Energy Certificate trend analysis Based on the latest DECs for 28,859 public sector buildings (or sites), the third element of this project calculated DEC coverage as a total floor area of 118 km 2 and an emissions footprint of 11 million tonnes CO 2 /year. Overall, 57% of these buildings have ratings < 100 i.e. better than the benchmark and 83% are in the A to E grades. 10% have an F grade and there are about 2,100 buildings with an official G grade (7.4%). The full breakdown of grades is shown in Figure 1.1 which includes a disaggregation of the ratings over 150 (conventionally all labelled as a G-grade) into grade bandwidths of 25 (see section for details). We also found a year-on-year improvement in CO 2 intensity, on a building by building basis, of 0.4% per year. This value does mask some ups and downs with central government and local authorities leading the way with reductions of 1.3% and 1.5% respectively. The data set we analysed contained all DECs lodged up to February Typically DECs are lodged a couple of months after the end of the year of energy measurement, so the most recent DECs in the data set will cover the year up to November Furthermore, because DECs can be produced for any 365 day period, the set of current DECs that form the basis of our analysis will have measurement years which fall within the period from December 2008 through to November This means that changes in energy use from the previous financial year during the April 2010 to March 2011 financial year (used for the 10:10 achievement analysis) will be only partially taken into account in our analysis. Further differences with the 10:10 methodology arise from our DEC analysis being a like-for-like calculation (on a building by building basis) whereas the 10:10 assessment has measured absolute emissions, which will take into account changes in the central government estate. We used a like-for-like approach in order to identify changes in the energy efficiency of the buildings concerned. 12,000 10,000 10,103 8,000 7,251 6,000 5,144 4,000 2,853 2, ,103 1, A B C D E F G G1 G2 G3 G4 G5 H Figure 1.1 DEC grades for all public sector buildings with valid current DECs DECs cover 90% of the baseline emissions footprint and 60% of the maximum footprint. They act as a low cost quality assured tool for verification of CO 2 emissions and motivate improvement, a) by making performance visible to managers and occupiers, b) by rewarding carbon reduction activities and behaviour with lower ratings and c) by applying reputational pressure by virtue of a DEC s public display. Wider Public Sector Emissions Reduction Potential 2010/11 5

7 2 Introduction The reduction of greenhouse gas emissions in the UK is one of seven strategic objectives of the Department of Energy and Climate Change (DECC). Leading by example, Central Government set a target for a 10% reduction in emissions across its central estate in 2010/11. This report explores the potential for a stretching level of emissions reductions over the medium term expanded to include the wider public sector. Where references are made to the wider public sector, this should be taken as Schools, Police Fire and Rescue, NHS, Further Education and Higher Education. Where references are made to the total public sector this should be taken as all of the sub sectors previously listed as well as Local Authorities, Central Government Core Departments, the Ministry of Defence, Executive Agencies, Non-Departmental Public Bodies and other organisations. This report covers the following three areas of research: Workstream 1: Re-assessment of public sector emissions: An assessment of public sector estates emissions, covering scopes 1, 2 as defined by the WBCSD/WRI GHG Reporting Protocol 3 and scope 3 business travel emissions using data updated to 09/10 or as close to that year as possible. Our aim was to select the most robust datasets with the best available energy and floor area data whilst also obtaining an estimate of the upper limit of public sector emissions using estimates of maximum floor area for each sector and multiplying by the baseline energy intensity figures. Workstream 2: Detailed assessment of abatement potential: Analysis of cost-effective and non-cost-effective carbon abatement in the public sector applied evenly to each sub-sector or as a top level target achieved using the most cost effective measures regardless of sub-sector distribution. The modelling is bottom-up using data from the Carbon Trust s close-out database, listing measures recommended to public sector bodies through its Carbon Management Plans. Workstream 3: Display Energy Certificate analysis: Analysis of the latest extract from the DEC registry to investigate overall public sector and sub-sectoral trends in building energy and carbon emissions performance. The work builds on earlier research by Camco for the Department of Energy and Climate 4 Change (DECC), Her Majesty s Treasury (HMT) and the Carbon Trust with the aim of updating the analysis were data has improved. 3 World Business Council for Sustainable Development (WBCSD) and the World Resource Institute (WRI) Greenhouse Reporting Protocol: A Corporate Accounting and Reporting Standard and ISO Cross Cutting Review of Low Carbon Potential in the Public Sector (Camco, November 2009) Wider Public Sector Emissions Reduction Potential 2010/11 6

8 3 Baseline 3.1 Methodology The quantification of emissions for the Wider Public Sector in England follows the reporting principles of the World Business Council for Sustainable Development (WBCSD) and the World Resource Institute (WRI) Greenhouse Gas Reporting Protocol: A Corporate Accounting and Reporting Standard, and ISO We have endeavoured to use the most robust data available with coincident energy and floor space information to avoid the need for pro rata adjustments using energy intensity data not related to floor space information. For comparison we have also generated an estimate of total emissions from the public sector in England using the maximum floor area data for each subsector and applying energy intensity values to this. The GHG Protocol provides a three scope reporting framework. Scope 1 covers direct GHG emissions from organisation-owned vehicles and facilities. Scope 2 includes net indirect emissions from energy imports and exports, particularly imported and exported electricity (and other energy carriers such as steam). Scope 3 includes other indirect GHG emissions, such as employee business travel, product transport by third parties, outsourcing of core activities and off-site waste disposal/management activities. In accordance with recognised reporting principles above, emissions arising from agreed activity areas have been quantified, and then allocated emissions by government sector. Emissions contributions by scope have also been demonstrated. The GHG Protocol recommends that scopes 1 and 2 are reported as a minimum. This assessment has included scopes 1 and 2 and some scope 3 emissions. The emission sources included within the scope of this project include energy consumption from electricity and fossil fuels, owned vehicles and employee business travel. Employee commuting was excluded from the study as a scope 3 emission, as was waste given the difficulties in obtaining data and quantifying emissions belonging to Public Sector. Refrigerant gases were excluded as a scope 1 emission source, due to difficulties in obtaining recharge quantities from premises. Figure 3.1 gives a graphical representation of the emissions scope of the project that was undertaken. Wider Public Sector Emissions Reduction Potential 2010/11 7

9 Figure 3.1 Scoping boundary for Work Stream Emissions type Six gases are identified in the Kyoto Protocol as having a climate forcing effect, or contributing to global warming. These are Carbon Dioxide (CO 2 ), Methane (CH 4 ), Nitrous Oxide (N 2 O), Hydrofluorocarbons (HFCs), Perfluorocarbons (PFCs) and Sulphur Hexafluoride (SF 6 ). Work Stream 1 calculates CO 2, CH 4 and N 2 O. The Global Warming Potential (GWP) factors, expressed in Carbon Dioxide Equivalents (CO 2 e) are given by IPCC (1996) as: CO 2 1 GWP CH 4 21 GWP N 2 O 310 GWP HFCs, PFCs and SF6 have been excluded from the scope of this work stream, as they are related mainly to industrial processes and would be a very minor contributor to the public sector Buildings Buildings The data used for this study builds on previous work for DECC by Camco in 2009 with the aim of improving both emissions coverage and data quality. Central Government operations data was sourced from the Sustainable Operations on the Government Estates (SOGE) database. The SOGE data highlighted the energy consumption figures for all Central Government (CG) operations (including the MoD). The Central Government operations account for all Executive Agencies, Non-Departmental Public Bodies (NDPBs) and other organisations. As there is uncertainty around the MoJ figures inclusion or exclusion within the SOGE data. For robustness, Display Energy Certificates returns data for CG was also assessed and the results include the respective figures from both datasets for MoJ as a separate line item for comparison. Wider Public Sector Emissions Reduction Potential 2010/11 8

10 For the Health Sector, the Estates Return Information Collection (ERIC) data provided details on energy consumption figures for all primary care trusts and mental health and learning centres. GP surgeries and dentists are not included in the ERIC data. Energy consumption figures for Local Authorities were previously calculated based on the National Indicators 185 (NI 185) returns. However LA data for 2009/10 will not be available until Q3 of It was agreed with the project team to use data from the Display Energy Certificates (DEC) analysis from work stream 3. As data for schools and police, fire and rescue services was previously taken from the NI 185 returns, the 2011 DEC analysis data from work stream 3 was also used to calculate energy consumption figures for these sources. Energy consumption data for Further and Higher Educational institutions is covered by the HESA and emandate datasets which were provided by the Higher Education Funding Council for England and Department for Business Innovation & Skills respectively. These datasets contain energy consumption and floor areas for all buildings. At the time of the data analysis stage of this project the holders of this information were in the initial stages of the 2011 data collection process. Due to this, in consultation with the project team we used data provided from the previous study as the most robust figures for energy consumption for the financial year 2009/10 data period Owned vehicles Camco were provided with data on Government fuel cards for 2009/10 for Central Government, Health sector, Local Authorities and police, fire and rescue. The data, provided by Buying Solutions, represents 80% of all fuel purchased through fuels card by Public Sector. Further details of coverage, fuel and sector splits were also provided by Buying Solutions. The data was extrapolated to give 100% coverage for the year. We also applied a scaling factor of 83.6% to account for England-only operations. This figure was calculated based on Census population figures in Ministry of Defence (MoD) owned vehicle fuel consumption data was provided from the Sustainable Development Annual Report 2009/10. To account for England-only operations, a 83.6% scaling factor was applied. Fuel consumed by overseas fuel consumption was excluded as this was outside the scope boundary for the project Business Travel Data associated with business travel was taken from the Government Carbon Offsetting Fund (GCOF) which collects data from all Central Government and Ministry of Defence operations. In order to scale these back to England-only operations, a 83.6% scaling factor was applied to remove any non-england operations from the data. All Central Government and MoD flights have been accounted for within the Government Carbon Offsetting Fund (GCOF) and emissions were calculated based on distances travelled, with the appropriate Defra emissions factors applied. 3.3 Data Assumptions and extrapolations Central Government figures have been rolled in to one reporting area and include Core Departments, Executive Agencies, Non Departmental Public Bodies (NDPB's) and other agencies as a single reporting line. MoD figures are presented as a separated line item as these were reported in this manner originally. Wider Public Sector Emissions Reduction Potential 2010/11 9

11 Where scaling factors were used to obtain England only figures, devolved regions were excluded using scaling factors derived from Census population data from 2001, with a split of England 83.6%, Scotland 8.6%, Wales 4.9% and NI 2.9%. SOGE data: the Welsh, Scottish and Northern Irish data was removed, by applying a 16.4% scaling factor (based on the populations from each country taken from the 2001 census). Floor area data from the SOGE data set is specified as Net Internal Area (NIA), and was scaled up using a factor of 1.25 to account for the total Gross Internal Area (GIA) of the government estates. This is consistent with the methodology used for Display Energy Certificates 5. Where results have been calculated using DEC data floor area for Central Government, Welsh Estates have been excluded. NHS data includes hospital estates and PCT clinics. Data excludes GP surgeries and dentists. 3.4 Data Discrepancies As part of work stream 1, to assess confidence in data, we triangulated various data sets in order to establish variances. The following is a list of all data sets analysed to ensure the accuracy of the baseline emissions: SOGE 2007/08 and 2009/10 - floor areas for Central Government and MoD buildings greater than 500m 2, energy consumption; e-pims - floor area for Central Government buildings greater than 500m 2 ; Carb Stock Model all non-domestic buildings; DECODE data derived from the Carb Stock Model; and DEC analysis All non-domestic buildings with a floor area greater than 1,000m 2. The analysis from work stream 3 highlighted some data discrepancies in the SOGE data set, and floor areas were analysed (as any variance in floor areas would also reflect a variance in energy consumption) from the above data sets in order to establish any variances. In order to test the robustness of the data sources, data was triangulated, and two scenarios were presented to the team on a baseline and maximum emissions basis. The rationale for the baseline scenario was that figures were based on received primary data. However floor areas provided within the SOGE dataset, in comparison with floor areas from the DEC analysis (WS 3) highlighted variances in data coverage. To establish confidence in floor area data, figures were examined from various datasets as shown in Table 3.1 and baseline and maximum areas were established. From this exercise, the project team agreed to the use of DEC data for Local Government and for prisons. Higher and Further Educational Institutions were the same in both scenarios as 2007/08 data had a high confidence level associated with it. 5 The Government s Methodology for the production of Operational Ratings, Display Energy Certificates and Advisory Reports (DCLG, October 2008) Wider Public Sector Emissions Reduction Potential 2010/11 10

12 Table 3.1 Summary of floor areas from various datasets Source of emissions OPTION A OPTION C Total floor area (m 2 ) Total floor area (m 2 ) Central Government (exc. prisons) 10,976,115 17,368,051 Central government (MoD specific) 89,870,000 89,870,000 Central government (prison specific) 3,650,699 4,515,139 Health 28,379,463 43,865,700 Local Authorities (exc schools and police and fire) 17,795,804 40,691,968 Local Authorities (schools) 44,538,706 58,851,175 Local Authorities (police, fire and rescue) 2,734,059 2,734,059 Further Education Institutions 8,713,282 8,713,282 Higher Education Institutions 21,731,720 21,731,720 Total 228,389, ,341,094 Legend DECs Carb stock model DECC PVP study SOGE ERIC It is important to note that the data sets have different thresholds for floor area coverage: SOGE data excludes buildings less than 500m 2 DEC data covers buildings over 1,000 m 2 The Carb Stock model is based on floor areas from the Valuation Office Agency used for valuation and business rates calculations and should therefore represents full coverage of the stock. All data used in floor area comparisons has had the scaling factor described in 3.4 applied where necessary to remove devolved regions, and have been extrapolated to represent the Gross Internal Area (GIA) of the buildings. Within the datasets, a wide variance in floor areas was observed in most sub-sectors falling within the scope of this project. The Ministry of Defence shows the most exaggerated variance in floor areas between both the SOGE datasets (2007/08 and 2009/10) and the DEC returns. It is understood that in addition to building coverage differences described above, the SOGE dataset includes all storage/warehouse/hangar areas, whereas DEC returns account for office space only. It was agreed with the project team that the preferred approach would be to use data from preferred primary sources. This is also the case with Central Government buildings. ERIC Return data does not account for GP surgeries and clinics. This has been excluded from the scope of Work Stream 1. This exclusion also accounts for the difference in floor areas between the Carb Stock model and the ERIC data as the Carbon Stock Model accounts for all building stock as it is greater by 13 million m 2. As data could not be provided for Local Government (Local Authorities, schools and police, fire and rescue), due to NI 185 data not being available until the end of Q2 2011, we defaulted to the DEC data return energy intensity figures, which were applied to DEC data floor areas. There were no other reliable datasets that could be used to compare floor areas. Further and Higher Educational Institutions datasets from HESA and emandate were not available until the end of April When comparisons on floor areas are made against the DEC returns, there is a variance of approximately 17 million m 2. Because of this variation, it was agreed with the project team to use the actual data from the 2007/08 project, provided by the emandate and HESA which included floor area and energy consumption. Wider Public Sector Emissions Reduction Potential 2010/11 11

13 3.5 Greenhouse Gas Emissions A summary of the emissions baseline is included in the following figures and tables. A detailed list of the data sources and assumptions used within the calculation of the baseline can be found in section Baseline Summary Totals Table 3.2 Summary of baseline emissions by sector Source of emissions Total floor area (m 2 ) Total CO 2 e emissions by buildings (tonnes/yr) Total CO 2 e emissions by owned vehicles (tonnes/yr) Total CO 2 e emissions by Business travel (tonnes/yr) Total CO 2 e emissions (tonnes/yr) Central Government (exc. prisons) 10,976, , ,826 66, ,868 Central government (MoD specific) 89,870,000 1,450,226 2,341,622 8,423 3,800,272 Central government (prison specific) 3,650, , ,721 Health 28,379,463 3,737, ,482-3,866,141 Local Authorities (exc schools and police and fire) 17,795,804 1,941,588 99,647-2,041,234 Local Authorities (schools) 44,538,706 2,439, ,439,586 Local Authorities (police, fire and rescue) 2,734, , , ,948 Further Education Institutions 8,713, , ,774 Higher Education Institutions 21,731,720 1,983, ,983,962 Subtotals (exc, MoD) 138,519,848 12,116, ,505 66,325 12,896,233 Total 228,389,848 13,566,629 3,055,128 74,749 16,696, Baseline Emissions Analysis The total carbon emissions from the English public sector in this study are estimated to be 16.7MtCO 2 e. The greatest contributor to this total is from Local Government with 5MtCO 2 e. A breakdown of Local Authorities indicates that schools account for approximately 2.5 MtCO 2 e, Local Authorities account for 2MtCO 2 e and police, fire and rescue 0.5MtCO 2 e. Health accounted for 3.8MtCO 2 and the Ministry of Defence a further 3.84MtCO 2 e, Further and Higher Educational Institutes accounted for approximately 2.7MtCO 2 e, with the remaining emissions accounted for by Central Government (including prisons) with 1.24MtCO 2 e. A graphical representation of these emissions is given in Figure 3.2. Wider Public Sector Emissions Reduction Potential 2010/11 12

14 Baseline GHG Emissions: Public Sector - England 3.4% 4.3% 11.9% 5.4% 22.8% Central Government (exc. prisons) Central government (MoD specific) Central government (prison specific) Health 14.6% 12.2% 23.2% 2.3% Local Authorities (exc schools and police and fire) Local Authorities (schools) Local Authorities (police, fire and rescue) Further Education Institutions Higher Education Institutions Figure 3.2 Summary of baseline emissions by sector Buildings accounted for approximately 80% of the total baseline GHG emissions. A breakdown by sector of emissions from buildings is shown below in Figure 3.3. Baseline GHG Emissions: Public Sector Buildings - England 2.5% 5.3% 14.6% 4.3% 10.7% 2.8% Central Government (exc. prisons) Central government (MoD specific) Central government (prison specific) Health 18.0% 27.6% Local Authorities (exc schools and police and fire) Local Authorities (schools) 14.3% Local Authorities (police, fire and rescue) Further Education Institutions Higher Education Institutions Figure 3.3 Summary of baseline buildings emissions by sector Wider Public Sector Emissions Reduction Potential 2010/11 13

15 Baseline GHG Emissions from all sources: Public Sector - England 0 2,000,000 4,000,000 Tonnes of CO 2 e per year Buildings Owned-vehicles Business travel Figure 3.4 Summary of baseline emissions by sector: Buildings, owned vehicles & business travel Figure 3.4 highlights the split of emissions between buildings, owned vehicles and business travel. Figure 3.5 shows the fuel split of emissions from Buildings energy (stationary). Figure 3.6 shows total emissions split by scope. Baseline GHG Emissions - Breakdown of emissions by fuel source Electricity Natural gas LPG Oil Coal Steam Undisclosed fuels 0 2,000,000 4,000,000 6,000,000 8,000,000 Tonnes of CO 2 e per year Figure 3.5 Summary of baseline emissions by fuel source Wider Public Sector Emissions Reduction Potential 2010/11 14

16 Baseline GHG emissions - Breakdown of emissions by scope 0.4% 46.8% 52.7% Scope 1 GHG emissions Scope 2 GHG emissions Scope 3 GHG emissions Figure 3.6 Summary of baseline emissions by scope The breakdown of emissions by scopes shows 52.74% as scope 1 emissions (direct), scope 2 (indirect) emissions as 46.8%, with a remaining 0.4 scope 3 (indirect) emissions Baseline Energy Spend TOTAL ENERGY SPEND Sector Electricity spend ( ) Natural gas spend ( ) LPG spend ( ) Oil spend ( ) Coal spend ( ) Steam spend ( ) Undisclosed fossil fuel spend ( ) Total energy spend ( ) Central Government 191,814,254 60,547, ,873, ,235,098 Central government (MoD specific) 199,062,656 83,464, ,143, ,669,916 Central government (prison specific) 28,188,786 16,684, ,873,427 Health 562,589, ,589,280 Local Authorities (exc schools and police and fire) 180,489,490 72,780, ,304, , ,597, ,343,743 Local Authorities (schools) 180,413, ,837, ,197, , , ,895,126 Local Authorities (police, fire and rescue) 34,259,282 10,515, ,611, ,914 47,559,697 Further Education Institutions 52,383,347 16,371, ,436 8,894,889 63, ,334, ,811,143 Higher Education Institutions 204,924,667 62,842, ,715, , ,878,720 Totals 1,634,125, ,044, ,436 83,722, , ,427,084 2,539,856,151 The Efficiency and Reform Group (ERG) provided the ERG PSPES Draft v0.2 document with highlighted energy spend data for Central Government and the Ministry of Defence. Energy spend for the Health Department was taken directly from the ERIC 2009/10 data. All remaining energy spend data was calculated based on the cost per unit of energy consumed. These figures were as follows: Electricity per kwh Natural gas per kwh LPG per kwh Oil per kwh Coal per kwh Undisclosed fuels per kwh Wider Public Sector Emissions Reduction Potential 2010/11 15

17 3.5.4 Baseline Owned-Vehicle Fuel Spend Spend on owned-vehicles has been calculated by applying average fuel prices for the financial year 2009/10. These prices are as follows: Petrol per litre Diesel per litre LPG per litre Aviation fuel (Kerosene) per litre Gas oil (red diesel) per litre These prices were applied to the total fuel consumption figures to calculate an average spend as shown in table 3.4 below. Transport costs could not be calculated for business travel due to inherent inaccuracies in calculating spend from distances travel. For example ticket prices for domestic flights can vary massively depending on when the ticket is purchased. Table 3.4 Summary of baseline owned-vehicle fuel spend Department Total fuel spend Central Government 113,910,705 Central government (MoD specific) 889,085,583 Central government (prison specific) 0 Health 58,582,648 Local Authorities (exc schools and police and fir 45,441,864 Local Authorities (schools) 0 Local Authorities (police, fire and rescue) 107,401,521 Further Education Institutions 0 Higher Education Institutions 0 Total owned-vehicle fuel spend 1,214,422, Maximum Summary Totals Table 3.3 Summary of Maximum emissions by sector Source of emissions Total floor area (m 2 ) Total CO 2 e emissions by buildings (tonnes/yr) Total CO 2 e emissions by owned vehicles (tonnes/yr) Total CO 2 e emissions by Business travel (tonnes/yr) Total CO 2 e emissions (tonnes/yr) Central Government (exc. prisons) 17,368,051 2,265, , ,950 2,765,399 Central government (MoD specific) 89,870,000 1,450,226 2,341,622 8,423 3,800,272 Central government (prison specific) 4,515, , ,781 Health 43,865,700 5,837, ,593-6,035,824 Local Authorities (exc schools and police and fire) 40,691,968 4,089, ,853-4,317,333 Local Authorities (schools) 58,851,175 2,965, ,965,006 Local Authorities (police, fire and rescue) 2,734, , , ,948 Further Education Institutions 8,713, , ,774 Higher Education Institutions 21,731,720 1,983, ,983,962 Subtotals (exc, MoD) 198,471,094 18,652,768 1,057, ,950 19,815,027 Total 288,341,094 20,102,994 3,398, ,373 23,615,298 Wider Public Sector Emissions Reduction Potential 2010/11 16

18 3.5.6 Maximum Emissions Analysis The maximum carbon emissions from the English public sector in this study are estimated to be 23.6MtCO 2 e. The greatest contributor to this total is from Local Government with 7.8MtCO 2 e. A breakdown of Local Authorities indicates that they account for 4.3MtCO 2 e. Schools account for approximately 2.9MtCO 2 e, and police, fire and rescue 0.5tCO 2 e. Health accounted for 6MtCO 2 e, whilst the Ministry of Defence generated for 3.8MtCO 2 e. Central Government (including prisons) generated 3MtCO 2 e, with the remaining emissions accounted for by Further and Higher Educational Institutes accounted for approximately 2.7MtCO 2 e. A graphical representation of these emissions is given in Figure 3.7. Maximum GHG Emissions: Public Sector - England 2.4% 3.0% 8.4% 11.7% Central Government (exc. prisons) Central government (MoD specific) Central government (prison specific) Health 12.6% 16.1% Local Authorities (exc schools and police and fire) Local Authorities (schools) 18.3% 25.6% 1.9% Local Authorities (police, fire and rescue) Further Education Institutions Higher Education Institutions Figure 3.7 Summary of maximum emissions by sector Buildings accounted for approximately 80% of the total maximum GHG emissions. A breakdown by sector of emissions from buildings is shown below in Figure 3.8. Wider Public Sector Emissions Reduction Potential 2010/11 17

19 Maximum GHG Emissions: Public Sector Buildings - England 1.7% 3.6% 14.7% 9.9% 11.3% 7.2% 2.3% Central Government (exc. prisons) Central government (MoD specific) Central government (prison specific) Health 20.3% 29.0% Local Authorities (exc schools and police and fire) Local Authorities (schools) Local Authorities (police, fire and rescue) Further Education Institutions Higher Education Institutions Figure 3.8 Summary of baseline buildings emissions by sector Maximum GHG Emissions from all sources: Public Sector - England 0 2,000,000 4,000,000 Tonnes of CO 2 e per year Buildings Owned-vehicles Business travel Figure 3.9 Summary of maximum emissions by sector: Buildings, owned vehicles & business travel Wider Public Sector Emissions Reduction Potential 2010/11 18

20 Electricity Natural gas LPG Oil Coal Steam Undisclosed fuels Maximum GHG Emissions - Breakdown of emissions by fuel source 0 2,000,000 4,000,000 6,000,000 8,000,000 Tonnes of CO 2 e per year Figure 3.10 Summary of maximum emissions by fuel source Maximum GHG emissions - Breakdown of emissions by scope 0.4% 46.8% 52.7% Scope 1 GHG emissions Scope 2 GHG emissions Scope 3 GHG emissions Figure 3.11 Summary of maximum emissions by scope Maximum Energy Spend TOTAL ENERGY SPEND Sector Electricity spend ( ) Natural gas spend ( ) LPG spend ( ) Oil spend ( ) Coal spend ( ) Steam spend ( ) Undisclosed fossil fuel spend ( ) Total energy spend ( ) Central Government 552,580, ,815, ,134, ,530,688 Central government (MoD specific) 199,062,656 83,464, ,143, ,669,916 Central government (prison specific) 97,869,854 20,176, , ,288,327 Health 875,732, ,732,902 Local Authorities (exc schools and police and fire) 380,157, ,294, ,491, , ,577, ,881,810 Local Authorities (schools) 219,269, ,355, ,085, , ,104, ,468,332 Local Authorities (police, fire and rescue) 34,259,282 10,515, ,611, ,914 47,559,697 Further Education Institutions 52,383,347 16,371, ,436 8,894,889 63, ,334, ,811,143 Higher Education Institutions 204,924,667 62,842, ,715, , ,878,720 Totals 2,616,239, ,836, , ,797,611 1,079, ,105,110 3,720,821,535 Wider Public Sector Emissions Reduction Potential 2010/11 19

21 In order to calculate the energy spend in proportion to the ERG PSPES Draft v0.2 document, energy consumption figures were calculated on a per unit basis from the total energy spent and the total energy consumed. For maximum energy spend, the energy cost per unit was taken from the baseline figures and applied to maximum energy consumption for Central Government, Ministry of Defence and the Health Department. All other sectors energy spend were calculated from the maximum energy consumption data and the average unit cost as used in the baseline energy spend Maximum Owned-Vehicle Fuel Spend Table 3.7 indicates the estimated spend on fuel from owned-vehicles. Section highlights the assumptions made in order for fuel spend to be calculated. Table 3.7 Summary of maximum owned-vehicle fuel spend Department Total fuel spend Central Government 180,246,556 Central government (MoD specific) 889,085,583 Central government (prison specific) 0 Health 90,550,299 Local Authorities (exc schools and police and fire) 103,907,577 Local Authorities (schools) 0 Local Authorities (police, fire and rescue) 107,401,521 Further Education Institutions 0 Higher Education Institutions 0 Total owned-vehicle fuel spend 1,371,191, Assumptions Overview All emissions factors used to calculate the public sector are taken from 2010 Guidelines to Defra/DECC s GHG Conversion Factors for Company Reporting, with the except of emissions from steam, which was calculating by deriving an emissions factor from the 2010 Ecoinvent database and the GWP s from IPCC A table of assumptions made within the baseline calculations is included in Appendix A. Wider Public Sector Emissions Reduction Potential 2010/11 20

22 4 Emission reduction potential This section summarises the analysis of cost-effective and non-cost-effective abatement in the wider public sector, defined as schools, police fire and rescue, NHS, further education and higher education. The assessment is based on data from the Carbon Trust Close Out Database, a data set of all abatement opportunities identified through Carbon Trust programmes combined with an abatement model based on IAG commercial energy price and assumptions 6. This section of the report provides an overview of the workstream and the base data, a justification for the analysis, a detailed description of method and key assumptions and finally the analysis findings are presented. 4.1 Summary The purpose of workstream 2 is to calculate the abatement potential for the wider public sector, particularly with respect to near term reduction targets (such as 25% by 2014/2015). To achieve the objective, carbon reduction potential must be calculated by subsector and then apportioned across traded and non-traded carbon savings. This enables the calculation of not only total abatement potential but also the most cost effective route towards achieving it by sub sector. The key findings are set out below. Cost effective potential (measures with a negative MAC value) for the entire public sector would cost 1,660 Million with an annual return (based on year 0 of 2014) of 729 Million with annual carbon savings 3.4 Million tco 2. Table 4.1 Summary of low carbon potential assessment THEORETICAL Total Initial Cost Total Identified savings pa 5,512 M 839 M Total Identified tco 2 pa 4.0 MTCO 2 Total lifetime CO 2 Savings 27.5 MTCO 2 Total Lifetime financial Savings 6,061M MAC Lifetime cost / lifetime CO 2 savings - 20/ tco 2 COST EFFECTIVE Total Initial Cost Total Identified savings pa 1,660 M 729 M Total Identified tco 2 pa 3.4 MtCO 2 Total lifetime CO 2 Savings 21.0 MtCO 2 Total Lifetime financial Savings 4,924 M MAC Lifetime cost / lifetime CO 2 savings - 155/tCO 2 Based on the above data it is possible to calculate the cost required to achieve medium-term reduction targets. 6 Interdepartmental Analysts Group Wider Public Sector Emissions Reduction Potential 2010/11 21

23 Two options are available for target setting. Either targets are set for the entire wider public sector enabling the most cost effective reduction to be implemented regardless of sector; or targets are set with a requirement for each subsector to achieve the same reduction. We have concluded that it would be more cost effective to set a top level target rather than at subsector level. The commercial net present value of setting a top level abatement target is 577 Million over 25 years. 4.2 The Carbon Trust Close Out Database The Carbon Trust Close Database collates information on all opportunities identified by the Carbon Trust through its programmes. With over 52,000 opportunities for public sector organisations across the United Kingdom, it is a unique resource for data on measure cost and reduction potential for a given organisation. A bottom up assessment of carbon abatement potential has been generated using this data. A list of the data fields captured within the database is included in Appendix B. Further detail on key elements of the database is provided below Actual savings against identified savings The Carbon Trust undertakes annual data reconciliation exercises with programme alumni s. This enables the Carbon Trust to both track implementation arising from programmes and also correct the energy saving potential and cost figures for a given measure as more detailed and accurate information becomes available. When a measure is implemented, the figures are listed as actual although the energy savings are typically still estimates rather than metered values Double counting of savings An organisation may have received support from the Carbon Trust across a range of products. To avoid double counting of emission reduction potential for a particular measure, the most up to date appraisal of potential is used to supersede previous estimates Measure categorisation In order to generate the bottom-up assessment of low carbon potential, we have used a categorisation of measures which is adapted from a categorisation system used by the Carbon Trust for recording measures identified via their Carbon Management programmes. This is included for reference in Appendix B. The main technology classifications were used for the bottom up abatement calculations as in some cases, the number of data points at the subtechnology level were too small to be statistically significant Method The analysis steps taken in order to build the bottom-up assessment of low carbon potential are described in detail in Appendix B. In summary, the data on measures identified through the Carbon Trust Public Sector Carbon Management (PSCM) programmes was used to build up a model of total potential across the public sector by pro rata multiplication of the potential of each category of measure using the number of organisations per sub-sector and total floor area as scaling factors. A judgement was made as to the applicability of some individual technologies e.g. swimming pool related measures, across the wider public sector when multiplying up the potential. Wider Public Sector Emissions Reduction Potential 2010/11 22

24 Figure 4.1 Method Schematic The schematic above documents the process undertaken: 1. Measure screening Measures were screened against geographic scope and validity. 2. Simple MACC A simple 7 MAC (marginal abatement cost) value was produced for each measure to aid outlier screening. Measures with erroneous MAC values were removed. 3. Reviewed list of measures The final number of measures was 5, Base metric data the final list of measures was used to produce basic outputs, such as capital cost and project lifetime, for the financial analysis 5. Financial analysis the financial analysis plotted MWh savings over time and applied the appropriate in-year costs and carbon factors. A discount rate of 3.5% is used. 6. Final dataset this combines outputs from steps 4 and 5 7. Outputs measure information is increased pro rata to calculate sector abatement potential after which a number of factors are applied, such as optimism bias, hidden cost and penetration rate Identification of appropriate product types for base data The products covered by the Carbon Trust database range from year-long deep engagements with an entire organisation (through a carbon management programme for instance) to shallow engagements via one day site surveys. We compared outputs from all product types and found a high degree of variance in output Simple MAC values. To ensure the appropriate scale and range of measures was included, our study focused only on opportunities captured through the large scale carbon management programmes. These will typically be identified after a year-long engagement with an organisation and outputs will focus on organisation wide measures with an aim of targeting aggressive long-term reductions Profile allocation There are four product types that we used as our base data. These were Central Government Carbon Management, Local Authority Carbon Management, NHS Carbon Managements and Higher Education Carbon Management. These profiles types need to be attributed to the nine public sector entities that we are assessing. As a result, profiles were allocated and then opportunities pro-rated based on number of organisations and average square meterage. The profile allocations were as follows: 7 A simple MAC is based on undiscounted savings Wider Public Sector Emissions Reduction Potential 2010/11 23

25 Table 4.2 Profile Allocation Subsector Core Departments Executive Agencies Non-Departmental Public Bodies Other organisations NHS Bodies Ministry of Defence Local Authorities (in school and police and fire) Further Education Institutions Higher Education Institutions Product type CM GOV CM GOV CM GOV CM GOV NHSCM CM GOV LACM HECM HECM Optimism Bias Optimism bias is the tendency towards optimistic assessments of cost, output and life expectancy at the project identification stage. It is calculated by comparing actual tonnes CO 2 savings against identified tonnes of CO 2 savings in the Carbon Trust Close Out Database. The average reduction in potential from the identification stage to implementation is set out below, with the final value adopted being 33%. A figure of 0% means that no adjustment was required and 100% means that nothing was installed. Table 4.3 Optmism Bias Analysis by Measure Measure Category Actual Annual Carbon Savings Identified Annual Carbon Savings Optimism Bias Air conditioning and cooling 2,959 3,572 17% Building construction, installation and commissioning 1,630 1,630 0% Building fabric 8,615 14,486 41% Building instrumentation and control 23,649 37,569 37% Building services distribution systems 8,751 13,407 35% Carbon and Energy Management 112, ,768 33% Combined heat and power 10,701 11,678 8% Compressed air % Equipment 13,058 17,556 26% Lighting 23,426 40,704 42% Motors and drives 2,000 2,648 24% Process heating and cooling 2,035 4,199 52% Refrigeration % Renewable energy sources 12,321 25,987 53% Space heating 31,565 42,862 26% Swimming pools 1,374 1,374 0% Ventilation 3,991 4,814 17% Estate Rationalisation 3,330 4,381 24% Grand Total 262, ,343 33% Wider Public Sector Emissions Reduction Potential 2010/11 24

26 4.2.8 Hidden cost To reflect hidden costs an uplift factor to capital cost was applied. Based on the previous AEA 8 and Enviros 9 research a figure of between 10 30% has been recommended. For this analysis we used 20%. Following the uplift of costs the MAC figures were recalculated. The weakness of this approach is that where measures have no capital cost (awareness campaigns for instance) associated against them their performance would be improved relative to the others. An alternative, as Enviros have previously done, is to apply hidden costs based on estimates of resource requirements Penetration rate Residual potential after the consideration of already implemented measures was accounted for by taking the average annual penetration level of efficiency measures across non domestic buildings in 2007 from analysis undertaken by the Committee on Climate Change (3.6%) 10, as per the Camco 2009 study. This was applied to the top level totals on a per measure basis. For renewable opportunities the penetration rates were assumed to be 0%. Data is available from the Carbon Trust database to provide an alternative source of information for project implementation. It is possible using the Carbon Trust data to compare counts of measures with actual savings recorded against the total of all measures to calculated assumed implementation rates. This data is set out below but was not used for the study as it appears to indicate overly optimistic rates of implementation: Table 4.4 Implementation Rate Analysis By Measure Measure Category Actual Annual Carbon Savings Identified Annual Carbon Savings Implementation rate Air conditioning and cooling % Building construction, installation and commissioning % Building fabric % Building instrumentation and control % Building services distribution systems % Carbon and Energy Management % Combined heat and power % Compressed air % Equipment % Lighting % Motors and drives % Process heating and cooling % Refrigeration % Renewable energy sources % Space heating % 8 AEA, 2008, Review and update of UK abatement cost curves for the industrial, domestic and non-domestic sectors available at 9 Enviros, 2006, Review and development of Carbon Dioxide abatement curves for available technologies as part of energy efficiency innovation review available at 10 Based on analysis of CCC data for the building the low carbon economy report. The 2006 public sector footprint was compared to the projected 2020 value to produce an implied straight-line rate of annual implementation of energy efficiency measures. Wider Public Sector Emissions Reduction Potential 2010/11 25

27 Table 4.4 Implementation Rate Analysis By Measure Measure Category Actual Annual Carbon Savings Identified Annual Carbon Savings Implementation rate Swimming pools % Ventilation % Estate Rationalisation % Grand Total % Use of IAG Energy Prices and Carbon Factors In this analysis IAG carbon and energy prices 11 were used to calculate lifetime savings. In contrast, in previous work the Carbon Trust outputs for in-year financial and carbon savings were used. In the Carbon Trust outputs both are typical annual savings and based on present carbon factors and fuel prices, whilst the IAG guidance values plot steadily increasing energy prices and also a decarbonising grid. The implication of using IAG guidance values will therefore improve the MAC performance of the same measures. Consider for instance the MAC formula: MAC = Net Present Cost/Lifetime Carbon Savings The IAG guidance values will result both in a decreasing NPC (due to increasing energy prices over time) and decreasing lifetime carbon savings (due to grid decarbonisation). It is also worth noting that there is a substantial difference in carbon factors between the Carbon Trust and the IAG recommendations even in the early years: Table 4.5 IAG carbon factors compared to Carbon Trust carbon factors for 2009 Fuel Type IAG Carbon Factor Carbon Trust Carbon Factor Difference (%) Coal % Diesel % Gas Oil % Grid Electricity % LPG % Natural Gas % Table 4.6 IAG fuel prices compared to CT fuel prices for 2014 Fuel Type IAG ( /MWh) Carbon Trust ( /MWh) Difference (%) Coal % Diesel % Gas Oil % Grid Electricity % Natural Gas % 11 Interdepartmental Analysts Group Wider Public Sector Emissions Reduction Potential 2010/11 26

28 Summary of Assumptions The table below is a reference table of key assumptions made: Table 4.7 Key assumptions Assumption Value Source Fuel Price NA IAG Guidance Carbon Factor NA IAG Guidance CT Product Scope Carbon Management Programmes only CT close out data 3.6% for EE Implementation Rate 0% Renewables CCC Hidden costs 20% Enviros, 2006 Optimism Bias 33% Analysis of CT Close Out Database Discount factor 3.5% IAG Guidance Floor area NA Work Stream 1 Baseline data NA Work Stream Factors not accounted for The following is a description of the main factors not accounted for in the abatement model and commentary on the impact on the results: Estimated vs. Actual the values provided for implemented projects in the Carbon Trust Close-out database are best estimates of energy saving but do not take account of unexpected lack of performance i.e. it is assumed that all installed measures work to the expected standard and are used correctly across the board. This is accounted for to some extent by the use of measure-specific persistence factors which adjust the lifetime of a measure based on experience of its real persistence in the field. Measures which are hard to remove or adjust, such as building insulation, typically have persistence values which are close to their full economic lifetime. Measures which are easy to remove or adjust, such as lighting controls, have values which are much shorter than their economic life. Heat replacement the model does not take account of heat replacement effect where the replacement of high energy consuming appliances with low energy using equivalents results in the need for additional heating to make-up for the difference in appliance heat output. This is not expected to impact on the capital cost estimates but would marginally increase the operational costs associated with lighting and appliance-related measures which would result in a slight increase in their respective MAC values. Ongoing maintenance costs of measures The general assumption in the model is that the operational and maintenance costs associated with a given measure are either negligible or the same as a business as usual scenario. This will be slightly conservative for plant replacements and result in a slight over-estimate of the MAC values for measures with no business as usual e.g. renewable energy and CHP. Sequential implementation of measures The raw data used for the model consists of opportunities which have been identified to meet a given carbon reduction target for a particular public sector body. In arriving at a list of opportunities that will meet the target, the public sector body and their advisors will generally have given some consideration to the issues of inter-dependent and incompatible measures although they are unlikely to have undertaken the detailed analysis which would eradicate this potential source of error completely. Wider Public Sector Emissions Reduction Potential 2010/11 27

29 Rebound effects The model does not incorporate either direct or indirect rebounds effects. Direct rebound effects such as the issue of comfort take where the internal space temperature is increased as energy costs are reduced through efficiency measures is well documented in the domestic market but is thought to be less significant in non-domestic buildings where professional energy managers are tasked with achieving year-on-year reductions in measured energy consumption. Indirect effects have been excluded on the basis that the overall response to climate change would reduce GDP and increase energy costs preventing greater spend on energy-intensive goods and services. This is consistent with the approach taken by the Committee on Climate Change. In addition, the complexity of budget allocation and management within public sector bodies means that this effect is likely to be less pronounced than for domestic consumers. 4.3 Results The outputs of the analysis are set out below. Marginal Abatement Cost Curves (MACCs) are produced at both public sector and subsector levels and the implications for target setting discussed. Next, the value at stake is considered and the investment costs against financial return and then finally key outputs are subject to a sensitivity analysis Public Sector MACC The MAC curve is based on the aggregated performance of a given technology group across the entire public sector stock. The results are broadly the same as we would expect from a topdown assessment with the exception of renewable energy measures which we would not have expected to be cost effective, albeit only marginally, given that the model does not include the effect of support mechanisms such as Feed-in Tariffs or Renewable Obligation Certificates. Figure 4.2 Public Sector MACC A breakdown of the contribution from each renewable energy technology to the total is shown in Figure 4.3. This shows that around half of the potential reductions are from biomass boilers. As the model does not assume a year-on-year increase in the price of biomass compared with the IAG assumptions for gas price increases, this is likely to over-estimate the associated cost Wider Public Sector Emissions Reduction Potential 2010/11 28

30 savings leading to a lower than expected average cost of carbon abatement for this technology category. In addition, despite screening of the dataset, some optimistic assessments of renewable savings may still reside in the dataset. The average result for Combined Heat and Power (CHP) is consistent with the results of other studies such as the recent report 12 for DECC by Pöyry which suggest a cost of around /tCO 2 depending on the scale of CHP engine and associated district heating network. There was a wide range of values in the data set however with a number of cost effective installations where CHP can be incorporated into an existing district heating network or building heating system, and there is sufficient baseload heat demand e.g. for Local Authority swimming pools or hospitals. Figure 4.3 Breakdown of saving by renewable energy technology Within the broad categories used, there are a number of extremely cost effective sub-categories such as building controls optimisation, requiring little or no cost to correct for avoidable energy wastage. These opportunities appear in multiple main technology categories and if consolidated together would appear to the far left of the MACC curves. The replacement of life expired plant with more efficient models is also a very cost effective opportunity which is hidden within several different categories and will be improving the average result. Figure 4.4 retains the order of categories from the public sector MACC curve and shows the associated total carbon abatement potential. As discussed above, the use of averages hides the fact that a large number of CHP opportunities will be cost effective which could still be an important technology in achieving a given target due to the potential size of abatement available (5 MtCO 2 ). Based on our experience and other similar studies, it is expected that around a 20% reduction should be achievable as a result of the optimisation of existing building systems. These opportunities are included within the Building Construction, Installation and Commissioning and 12 The Potential and Costs of District Heating Networks (Pöyry, April 2009) Wider Public Sector Emissions Reduction Potential 2010/11 29

31 Building Instrumentation and Controls categories but are generally distributed throughout the data set. Figure 4.5 provides a side-by-side comparison of the key metrics associated with each category of measure. It can be seen from this that the measures within the renewable energy, CHP and lighting categories are more capital intensive on average than the other categories. Cost effective Non cost effective Figure 4.4 Lifetime traded and non traded carbon savings by main technology Figure 4.5 Key Financial Metrics Wider Public Sector Emissions Reduction Potential 2010/11 30

32 4.3.2 Sub Sector MACCs Sub sector performance is considered below. Key variance in measure effectiveness occurs in CHP and renewable energy sources. This variation reflects the context sensitive nature that impact on their performance. CHP for instance will perform well where large consistent heat loads are present (such as with Swimming pools) but more poorly in other environments without a consistent demand for heat. Figure 4.6 Central Government MACC Wider Public Sector Emissions Reduction Potential 2010/11 31

33 Figure 4.7 NHS MACC Wider Public Sector Emissions Reduction Potential 2010/11 32

34 Figure 4.8 LA MACC Wider Public Sector Emissions Reduction Potential 2010/11 33

35 Figure 4.9 FE and HE MACC Wider Public Sector Emissions Reduction Potential 2010/11 34

36 4.3.3 Wider Public Sector target setting Based on the previously presented data it is possible to calculate the cost required to achieve levels of emissions reduction in the public sector. The example below considers the costs of delivering a 25% reduction in emissions (a high level of ambition in keeping with the UK s national requirement to deliver 80% emissions reductions by 2050) by 2014/15 on a baseline of 2009/10 for the wider public sector, defined as schools, police fire and rescue, NHS, further education and higher education. In this example, two options are considered for apportioning emissions reductions: Either setting a reduction level for the entire wider public sector enabling the most cost effective reduction to be implemented regardless of sector; or setting a requirement for each subsector to achieve the same reduction. Furthermore, to assess the investment required it is also necessary to consider the expected Business As Usual (BAU) reductions in the estate. The following data was provided by DECC economists as representative of the expected reductions in consumption over this period: Table 4.9 BAU projections for footprint reductions by 2014/15 on 09/10 Sub sector Entire Public Sector Core Departments % Executive Agencies % Non-Departmental Public Bodies % Other organisations % NHS Bodies 0.90% Ministry of Defence -5.40% Local Authorities (in school and police and fire) % Further Education Institutions -2.40% Higher Education Institutions -2.40% The costs associated with each approach are set out below: Table 4.10 Summary of implications of carbon reduction scope Wider Public Sector at lower cost per tco 2 25% in each subsector Difference (%) Initial Cost 1482 Million 2541 Million 72% Lifetime Discounted Financial Savings 3651 Million 4133 Million 13% Annual Savings (Based on Year 0) 498 Million 538 Million 8% Lifetime Traded CO MTCO MTCO 2-1% Lifetime NonTraded CO MTCO MTCO 2 42% MAC % Wider Public Sector Emissions Reduction Potential 2010/11 35

37 It implies that it would be far more cost effective to set a top level target rather than at subsector level. The net present value of setting a top level abatement target is 577 Million over 25 years. The contribution by sector is set out below: Figure 4.10 Contribution by subsector Table 4.1 below provides further detail on the cost and value of the two target approaches by subsector. Table 4.1 Comparison of target approach by subsector HE and FE Wider Public Sector at lowest cost per tonne 25% in each subsector Required Reduction (ktonnes CO 2 ) Initial Cost 614 m 473 m Lifetime Discounted Financial Savings 1,507 m 1,296 m Annual Savings (Based on Year 0) 197 m 149 m Lifetime Traded CO MTCO MTCO 2 Lifetime NonTraded CO MTCO MTCO 2 MAC NHS Bodies Required Reduction (ktonnes CO 2 ) Initial Cost 476 m 1,600 m Lifetime Discounted Financial Savings 1,278 m 1,906 m Wider Public Sector Emissions Reduction Potential 2010/11 36

38 Wider Public Sector at lowest cost per tonne 25% in each subsector Annual Savings (Based on Year 0) 163 m 241 m Lifetime Traded CO MTCO MTCO 2 Lifetime NonTraded CO MTCO MTCO 2 MAC Schools and police, fire and rescue Required Reduction (ktonnes CO 2 ) Initial Cost 392 m 469 m Lifetime Discounted Financial Savings 865 m 931 m Annual Savings (Based on Year 0) 137 m 148 m Lifetime Traded CO MTCO MTCO 2 Lifetime NonTraded CO MTCO MTCO 2 MAC Wider Public Sector Emissions Reduction Potential 2010/11 37

39 4.3.4 Sensitivity A number of variables were subjected to sensitivity analysis: Discount factor Energy prices Optimism bias Hidden cost Penetration rates The factors which have the greatest impacts are optimism bias, energy prices and the penetration rate used. Use of a less conservative optimism bias could increase delivered savings substantially (up to 60% more annual carbon savings). In contrast, if the low energy price scenario is modelled, cost effective annual financial savings reduce by approximately 40%. Finally, if CT measure penetration rates are adopted, annual cost effective abatement potential reduces by 18%. Table 4.12 Sensitivity Analysis Outputs Base Case 10%DF 0% DF High Energy Price Low Energy Price Low Energy Price - 10% DF 0% Optimism Bias 0% Hidden Cost 30% Hidden Cost CT Pen. Rate COST EFFECTIVE Total Initial Cost 2536 M 2605 M 1199 M 2748 M 1050 M 822 M 2748 M 2290 M 1534 M 1184 M Total Identified savings pa 692 M 717 M 591 M 872 M 393 M 369 M 1108 M 743 M 616 M 569 M 3.3 Total Identified tco 2 pa 3.2 MtCO 2 MTCO MtCO MtCO MtCO MtCO MtCO MtCO MtCO MtCO Total lifetime CO 2 Savings MtCO 2 MtCO MtCO MtCO MtCO MtCO MtCO 2 MtCO 2 MtCO MtCO 2 Total Lifetime financial Savings 4822 M 5959 M 2688 M 5931 M 2397 M 1671 M 7486 M 5016 M 3694 M 3348 M MAC Lifetime cost / lifetime CO 2 savings Wider Public Sector Emissions Reduction Potential 2010/11 38

40 180% 160% 140% 120% 100% 80% 60% 40% 20% 0% 10%DF 0% DF High Energy Price Scenario Low Energy Price Scenario Low Energy Price - 10% DF 0% Optimism Bias 0% Hidden Cost 30% Hidden Cost CT Pen. Rate Total Initial Cost Total Identified savings pa Total Identified tco2 pa Total lifetime CO2 Savings Total Lifetime financial Savings MAC Lifetime cost / lifetime CO2 savings Figure 4.11 Sensitivity analysis outputs Table 4.13 Sensitivity Analysis Outputs (Variance around base case) Base Case 10%D F 0% DF High Energy Price Low Energy Price Low Energy Price - 10% DF 0% Optimism Bias 0% Hidden Cost 30% Hidden Cost CT Pen. Rate COST EFFECTIVE Total Initial Cost 100% 103% 47% 108% 41% 32% 108% 90% 60% 47% Total Identified savings pa 100% 104% 85% 126% 57% 53% 160% 107% 89% 82% Total Identified tco 2 pa 100% 103% 85% 107% 79% 72% 160% 107% 89% 82% Total lifetime CO 2 Savings 100% 101% 72% 105% 68% 59% 156% 105% 76% 71% Total Lifetime financial Savings 100% 124% 56% 123% 50% 35% 155% 104% 77% 69% MAC 100% 110% 69% 101% 66% 48% 101% 87% 94% 102% Wider Public Sector Emissions Reduction Potential 2010/11 39

41 5 Display Energy Certificate Analysis 5.1 Summary Workstream 3 has used the DEC data records available from the Landmark register to calculate the numbers of public sector buildings in England with a current DEC, their floor area and CO 2 emissions. After cleaning and filtering of the data, we found there are 36,622 public sector buildings or sites in England with valid energy performance data. Some DECs have been lodged for both buildings and the sites they are on. After excluding such overlaps, the estate covered by DECs reduces to 28,859 buildings (or sites) with a valid current DEC, which has a floor area of 118 km 2, and an emissions footprint of 11 million tonnes CO 2 /year. We have then reviewed the energy and carbon performance of the buildings within each subsector. Overall, 57% have ratings < 100 i.e. better than the benchmark and 83% are in the A to E grades. 10% have an F grade and there are about 2,100 buildings rated over 150 i.e. with an official G grade (7.4%). Nearly all ratings (98%) are less than 200, but 655 buildings (2%) are rated over 200 of which 140 are over 300. Lastly, we set out to identify the year-on-year changes in performance on a building by building basis, using the evidence from those buildings with one or more annual DEC renewals. Overall there has been a small improvement, a reduction in CO 2 intensity (kg CO 2 /m 2 ) of 0.4% per year. This value does mask some ups and downs with central government and local authorities leading the way with reductions of 1.3% and 1.5% respectively. However, these are small improvements given the challenge of the UK s carbon reduction plans. DEC data provides quality-assured information at the individual building level, which is where energy demand reductions are actionable by individuals, whether energy and facilities managers, building managers or occupants. DECs are also an essential verification tool for all carbon reduction policies aimed at non-domestic buildings (NDBs). It is too early to judge the motivational impact of DECs themselves in their ability to engender carbon reduction activities and behaviour, especially as 40% of the sample have failed to obtain a single DEC renewal. This does not detract from their ability to provide a robust means for tracking CO 2 emissions from NDBs on a year-on-year and like-for-like basis. The introduction of organisational league tables based on DECs can be expected to intensify the pressure on building managers and occupiers to do more to achieve continuous improvement in their energy efficiency. The superb example set by DECC themselves (and by other high profile government buildings, such as Portcullis House in Westminster) demonstrates how it can be done. 5.2 Analysis of DEC data Introduction This report provides an analysis of the data in the 76,500 Display Energy Certificate (DEC) records lodged on the Landmark central register from the time DECs were introduced in mid up to February Each lodgement comprises an XML file containing all data relevant to the DEC, including administrative and performance calculation details (see box in section 5.2.3). This report provides a comprehensive analysis of this new and significantly increased DEC data set compared with the previous analysis which was on a data set of 45,000 records (Camco 2010). It covers the energy and carbon performance of nearly 42,000 buildings (there were some 32,000 buildings covered by the 2010 data set), nearly all in the public sector. Wider Public Sector Emissions Reduction Potential 2010/11 40

42 The data set has been filtered to exclude buildings in Wales, buildings in the private sector and invalid records (defaults 13, corrections 14 and outliers 15 ). The resultant data set has some 68,000 records which cover 36,622 public sector buildings or sites in England with valid energy performance data 16. These records include 7,763 individual buildings on sites for which there is also a valid site DEC. After excluding such buildings, there are 28,859 buildings (or sites) with a valid DEC, which has a total floor area of 118 km 2, and an emissions footprint of 11 million tonnes CO 2 /year. The average floor area of all the buildings with a DEC is almost 4,000 m 2 and their average CO 2 intensity is 95 kg/m 2 /year. This means that on average every 10m 2 of floor space is associated with nearly one tonne of CO 2 emissions per year. Appendix C contains full details of how we processed the DEC data set, the methodology for cleaning and filtering and some detailed statistics on the different categories of records. The Energy Performance of Buildings regulations require DECs to be renewed annually (see Appendix D). We have found that over 14,000 buildings (39% of all buildings with a valid DEC) have only ever had one DEC, whilst another 14,000 have had only one renewal. Only some 9,000 have had the two renewals that might have been expected by now (see Table 5.1). The vast number of buildings with a valid DEC but no renewal is compelling evidence of the lack of enforcement of the DEC Regulations by trading standards officers (TSOs). Table 5.1 Summary of number of buildings in England with one or more DEC Objectives of the DEC analysis The DEC analysis has had three key objectives: 1. Determine the baseline building stock covered by DECs for each sub-sector of the Public Sector, and its area, energy use and CO 2 emissions. 2. Review the energy and carbon performance of the buildings within each sub-sector 3. Identify the year-on-year changes in performance on a building by building basis, using the evidence from those buildings with one or more annual DEC renewals. The report allocates each building to one of 8 agreed sub-sectors of the Public Sector: 1. Central government (ex MoD and prisons) 2. Central government (MoD specific) 13 In the initial implementation of DECs, buildings for which compliant meter data were not available were given default ratings of 200 in order that a DEC could be produced and the occupier could comply with the regulations. Unfortunately, the Central Register record did not distinguish default ratings from genuine ratings which happened to be 200. From 7th March 2010, DEC software was upgraded to allocate such defaults a rating of 9999, watermark the DEC display poster as an Unmetered Rating and place a default flag in the DEC s record on the Central Register. 14 Records for the same building (same UPRN and same floor area) where the assessment period overlapped by more than 6 months (regulations stipulate a maximum of 3 months overlap) were assumed to be corrections. Where such overlaps occurred, a record with a later issue date was assumed to supercede one with an earlier issue date. 15 Records for buildings with floor area < 50 m2 or > 100,000 m2 or with a rating > 1,000 are assumed to arise from unreliable data and are classified as not credible outliers. It is recognised that using such criteria for automatic filtering may exclude some valid records. 16 In this report, valid does not necessarily mean that a DEC has not expired its 12 month validity period. Wider Public Sector Emissions Reduction Potential 2010/11 41

43 3. Central government (prison specific) 4. Health 5. Local Authorities (ex schools and emergency services) 6. Schools 7. Emergency services (police, fire and rescue) 8. Further & Higher Education Institutions Our analysis has analysed the data set in two distinct ways: 1. To meet objectives 1 and 2, we have identified all current valid DECs i.e. the latest DEC for every building which has a valid DEC. This data set comprises 28,859 buildings (or sites). 2. To meet objective 3, we have identified all those buildings which have had at least one DEC renewal, and calculated the change in rating and CO 2 emissions for those buildings. This data set comprises 22,496 buildings (or sites) The data in each DEC record DECC provided Camco with the records for all the DECs lodged in the England and Wales DEC register operated by Landmark, from their inception in June 2008 up to February Records include more data items than the previous extract (Camco 2010). These included: 1. RRN: the unique DEC Report Reference Number 2. UPRN: the 12 digit Unique Property Reference Number, comprising an 8 digit base building identifier plus a 4 digit supplementary identifier for buildings on a site or separate parts (or hereditaments) in the same building. 3. Benchmark Categories and Building Types their floor area and hours of use. This is more than one for a mixed-use building or site e.g. an office with a restaurant. 4. DEC status: identifies defaults and Asset Rating only DECs, but only after the software upgrade on 7th March DEC reason: identifies if the lodged record relates to a site DEC or to a building DEC, but only after the software upgrade on 7th March 2010, 6. Occupier organisation name and address: this comprises Occupier, four address fields plus the post town and the post code. 7. Rating: The DEC rating i.e. actual CO 2/m2 times 100 divided by benchmark CO 2/m2. 8. Electricity CO 2: Tonnes of CO 2 calculated from 365 day kwh of electricity 9. Heating CO 2: Tonnes of CO 2 calculated from 365 day kwh of all types of non-electricity. 10. Issue date: the date the DEC was created by software 11. Assessment end date: the end date of the 365 day assessment period for the DEC. 12. Energy use by each fuel type (kwh/year): includes any renewable sources 13. Separable energy uses 14. Annual Energy Use Fuel Thermal: delivered 365 day kwh/m2 summed for all non-electric energy sources excluding on-site RES 15. Annual Energy Use Electrical: 365 day kwh/m2 electricity use excluding on-site RES 16. Renewable Thermal: % total fuel thermal kwh supplied by RES 17. Renewable Electrical: % total electrical kwh supplied by RES 18. Asset rating 19. Main heating fuel 20. Type of servicing (air conditioning, mechanical or natural ventilation, etc) Wider Public Sector Emissions Reduction Potential 2010/11 42

44 5.2.4 Breakdown by sub-sector of buildings in England with DECs The total floor area of the buildings in England with a DEC (valid or default) is km 2. Some 9.5 km 2 (6%) currently is represented by a default, leaving a floor area of km 2 having a valid DEC. Table 5.2 shows this breakdown by sub-sector. Table 5.2 Floor area by sub-sector of buildings with valid and default DECs in England There are 36,622 buildings with a valid current DEC (total floor area of km 2 ). The numbers and areas of buildings (and sites) with a current DEC is shown in Table 5.3 for each sub-sector. Figure 5.1 shows the average area of the buildings in each sub-sector and the overall average which is almost exactly 4,000 m 2 per building. Table 5.3 Floor area and numbers of buildings by sub-sector with valid DECs in England Wider Public Sector Emissions Reduction Potential 2010/11 43

45 Figure 5.1 Average floor area of buildings with valid DECs in each sub-sector Mean floor area of buildings in sub-sector (m2) 30,000 24,667 25,000 20,000 15,000 10,000 7,325 7,387 2,834 2,858 3,529 3,990 4,657 5,009 5,110 5,000 0 Schools Emergency (police, fire and rescue) Local Authorities (ex schools and emergency) Mean of data set Central government (MoD specific) Further & Higher Education Institutions Central government (ex MoD & prisons) Private sector Health Central government (prison specific) Initially the DEC regulations allowed a single site DEC to be used to cover all buildings on a site. A site DEC covers a multi-building site with meters at the site level. These sites may be, for example, a whole hospital complex or complete university campus or a multi-building school. In November 2009, this transitional arrangement was ended. From this date, all buildings over 1,000 m 2 on a site have been required to have their own DEC, whilst a site DEC is now allowed only as an additional voluntary option. Many lodgements in the data set are for site DECs obtained by schools, universities and hospitals prior to November The cessation of site DECs was problematic in many respects, especially because a majority of the affected buildings are not sub-metered and, in the case of schools, the benchmarks and most energy management activities are done on a whole school basis. Additionally it means that the system captures only that portion of the site s footprint which is represented by buildings over 1,000 m 2. It is believed that one consequence of this change has been an increase in the number of DECs that are not being renewed. The change was needed for large university and hospital sites where sub-metering is essential for good energy management, but the rationale for schools is less compelling. Because DECs have been lodged for some buildings and also the sites they are on, we needed to exclude such overlaps to calculate the size and footprint of the estate covered by DECs. The structure of the UPRN enabled us to identify these overlaps and, for the purpose of calculating the footprint covered by DECs, we excluded the buildings (and kept the sites). 7,763 buildings (of the 36,622 in England with a current DEC) with an area of 28.2 km 2 were excluded to get 28,859 buildings (or sites) with a current DEC and a true floor area footprint of km 2 (see Table 5.4), km 2 excluding the private sector. Wider Public Sector Emissions Reduction Potential 2010/11 44

46 Table 5.4 Floor area by sub-sector of buildings with valid DECs excluding site duplications The split of floor area by sub-sector is shown in Figure 5.2. Schools are still comfortably the largest sub-sector (38% by floor area), although this is markedly down on their 50% by number (see Appendix C) because their average floor area is the lowest (see Figure 5.1). Health is 20% of the total and local authorities 15% by floor area. Central government is 13% by floor area (cf 7% by number). Figure 5.2 Floor area of buildings with current DEC split by sub-sector (Total km 2 ) Central government (MoD specific) Emergency (police, fire and rescue) Central government (prison specific) Central government (ex MoD & prisons) Further & Higher Education Institutions Local Authorities (ex schools and emergency) Health Schools 25.3 The prevalence of default DECs is shown in Figure 5.3. Nearly 10 km 2 of buildings have a default DEC, 7.5% of the total floor area. The proportion for central government is the highest and is the same order for Further & Higher Educations, excluding the results for MOD which is a very small sample size. Wider Public Sector Emissions Reduction Potential 2010/11 45

47 Figure 5.3 Area of buildings/sites in England with a default DEC (total 9.5 km 2 ) 35% Buildings in England currently with default DECs (% by area) 30% 25% 20% 15% 10% 5% 0% 11.8% Central government (ex MoD & prisons) 28.9% Central government (MoD specific) 2.2% Central government (prison specific) 4.7% Health 6.9% Local Authorities (ex schools and emergency) 7.5% Schools 6.7% Emergency (police, fire and rescue) 10.3% Further & Higher Education Institutions 5.2% Private sector 7.5% TOTAL Renewals of DECs Figure 5.4 shows for each sector the proportion of buildings which only have a first DEC i.e. no annual renewal has been lodged. Overall, almost 40% of the buildings in the data set have not obtained a renewal. But there are some marked differences: over 50% of health buildings do not have a renewal and nearly 40% of schools, whilst 75% of central government buildings have done their renewals. The renewal rate in England is significantly better than Wales, where 50% of buildings have not done a renewal DEC (see Appendix C). Figure 5.4 Proportion of buildings in England with a DEC but no renewal (total 14,126) 100% 90% 80% 86% 83% 70% 60% 50% 62% 54% 40% 35% 38% 37% 39% 30% 20% 25% 21% 10% 0% Central government (ex MoD & prisons) Central government (MoD specific) Central government (prison specific) Health Local Authorities (ex schools and emergency) Schools Emergency (police, fire and rescue) Further & Higher Education Institutions Private sector Total Wider Public Sector Emissions Reduction Potential 2010/11 46

48 5.2.6 CO 2 footprint of DEC records for England The analysis now moves on to show the annual energy use by fuel and sector (see Table 5.5), and the CO 2 emissions footprint 17 by fuel and sector (see Table 5.6), for the 28,859 buildings (or sites) with a valid current DEC. Totals are also shown for electricity and non-electricity (essentially directly supplied fossil fuel or heat). Table 5.5 Annual energy use by fuel and by sub-sector of buildings with valid DECs The energy use breakdown shows that over 90% of the fossil fuel (non-electricity) used by public sector buildings comes in the form of natural gas, with oil supplying 5% and coal 2%. We note that the figures for oil and coal are artificially lowered by the fact that about 50% more default DECs have been issued to buildings which use oil and coal as their main heating fuel. The energy used by these buildings is therefore, of course, not included in this analysis. Most of the fossil fuel is used by the health and schools sectors (35% and 27% of the total respectively) Table 5.6 Annual emissions by fuel and by sub-sector of buildings with valid DECs The CO 2 emissions breakdown in Table 5.6. shows that nearly 60% of emissions are from electricity use whilst only 2% are not from electricity, gas or oil. The proportion of emissions for each sub-sector is also shown in Figure 5.5. Health is the largest with 31% of total CO 2 emissions (cf 20% by area and 12% by number of buildings/sites). Schools come next with 23% of the total (cf 38% by area and 50% by number). Local authorities are responsible for 18% of emissions, central government 15% and higher education 11%. It is notable that prisons account for a higher proportion than all the emergency services. It is useful to note the purchase cost of the energy producing these carbon emissions. The average tonne of CO 2 emitted as a result of energy use in public sector buildings comprises 572 kg from the use of electricity and 428 kg from the use of non-electricity. At typical current prices 17 Using the DEC CO2 factors to convert kwh to CO 2. Wider Public Sector Emissions Reduction Potential 2010/11 47

49 of 10p/kWh for electricity and 3p/kWh for non-electricity, this means the energy cost of the CO 2 is about 170/tonne. The tentative carbon cost proposed by the CRC of 12/tonne will add 7% to this energy cost. It can also be calculated that the energy costs amount to some 17 per year per m 2 of floor space. Assuming some 20m 2 per FTE, would mean the energy costs can also be expressed as 340/year per FTE, which is in the order of 1% of typical labour costs. Figure 5.5 Public sector buildings carbon footprint split by sub-sector (total 11m tonnes) 0.3% 3.1% 3.5% 31.1% 10.8% 10.9% Central government (MoD specific) Emergency (police, fire and rescue) Central government (prison specific) Central government (ex MoD & prisons) Further & Higher Education Institutions Local Authorities (ex schools and emergency) Schools Health 17.9% 22.6% Total emissions by sector are shown in Figure 5.6, split by whether their source is electricity or non-electricity. Figure 5.6 Total CO 2 emissions from each sub-sector split by emissions source 4,000,000 3,500,000 Total emissions (tonnes CO2/year) 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 Non-electricity Electricity 500,000 0 Central government (MoD specific) Emergency (police, fire and rescue) Central government (prison specific) Central government (ex MoD & prisons) Further & Higher Education Institutions Local Authorities (ex schools and emergency) Schools Health Wider Public Sector Emissions Reduction Potential 2010/11 48

50 Electricity is the dominant source in most sectors, and even in schools, prisons and the health sector, it accounts for around 50% of the total. In Central Government buildings, electricity accounts for almost three quarters of emissions (see Figure 5.7). Figure 5.7 Contribution of electricity to each sub-sector s emissions 100% 90% 80% 70% 60% 50% Non-electricity Electricity 40% 30% 20% 10% 0% Central government (ex MoD & prisons) Central government (MoD specific) Central government (prison specific) Health Local Authorities (ex schools and emergency) Schools Emergency (police, fire and rescue) Further & Higher Education Institutions The energy and carbon intensity per m 2 of floor area of each sub-sector is markedly different 18. Energy intensities for electricity and non-electricity use by sub-sector are shown in Table 5.7. The highest electricity use is by central government buildings, whilst the highest non-electricity use is in the health sector, followed by prisons, reflecting their residential character. Table 5.7 Electricity and non-electricity use by sub-sector CO 2 emission intensities for electricity and non-electricity use and the total by sub-sector are shown in Table 5.8 and Figure 5.8. The health sector has the highest emissions intensity at 143 kg/m 2 /year, followed by the emergency services at 125 kg/m 2 /year. The emissions intensity of 18 To calculate intensities, first the 152 buildings with Asset Rating only DECs were removed as these have no operational energy data but contribute 0.7 km2 to the area footprint. Wider Public Sector Emissions Reduction Potential 2010/11 49

51 schools is noticeably lower than all the other sub-sectors at 56 kg/m 2 /year. The average CO 2 intensity is 95 kg/m 2 /year. This means that on average every 10m 2 of floor space is associated with nearly one tonne of CO 2 emissions per year. Table 5.8 CO 2 emissions for electricity and non-electricity use by sub-sector Figure 5.8 Average building carbon intensity for each sector Emissions Intensity (kg CO2/m2/year) Non-electricity Electricity 0 Schools Central government (MoD specific) Further & Higher Education Institutions Central government (prison specific) Local Authorities (ex schools and emergency) Central government (ex MoD & prisons) Emergency (police, fire and rescue) Health 5.3 Energy and carbon performance of public sector buildings Summary The grades of all the current valid DECs for buildings/sites in England are shown in Figure 5.9. Considering initially the performance of all the DECs in the dataset, 57% have ratings < 100 i.e. better than the benchmark and 83% are in the A to E grades. 10% have an F grade and there are about 2,100 buildings rated over 150 i.e. with an official G grade (7.4%). We have disaggregated the ratings over 150, conventionally labelled as a G-grade, into grade bandwidths of 25, as used for all the other grades, as follows: Wider Public Sector Emissions Reduction Potential 2010/11 50

52 G G G G G G H > 300 Nearly all the current ratings (98%) are less than 200, but 655 buildings (2%) are rated over 200 of which 140 are over 300. Figure 5.9 DEC grades for all public sector buildings with valid current DECs 12,000 10,000 10,103 8,000 7,251 6,000 5,144 4,000 2,853 2, ,103 1, A B C D E F G G1 G2 G3 G4 G5 H The split of floor area of all public sector buildings by DEC grade is shown in Figure Wider Public Sector Emissions Reduction Potential 2010/11 51

53 Figure 5.10 Floor area of all public sector buildings split by DEC grade 1% 9% 4% 11% 26% 17% 32% A B C D E F G Performance by sector Central government (ex MoD & prisons) Central government has 2,122 buildings with a valid current DEC (see Figure 5.11). 48% of these have ratings < 100 i.e. better than the benchmark 68% are in the A to E grades. 13% have an F grade 420 buildings are rated over 150 i.e. with an official G grade (20%). Nearly 200 buildings (9%) have ratings over buildings (2%) are rated over 300. Figure 5.11 DEC grades for all central government buildings (ex MoD & prisons) A B C D E F G G1 G2 G3 G4 G5 H Wider Public Sector Emissions Reduction Potential 2010/11 52

54 Central government (MoD specific) Only 78 MoD buildings have been given a DEC (see Figure 5.12). 66% of these have ratings < 100 i.e. better than the benchmark 78% are in the A to E grades. 7% have an F grade 11 buildings are rated over 150 i.e. with an official G grade (16%). 8 buildings (11%) have ratings over buildings (3%) are rated over 300. Figure 5.12 DEC grades for all central government MoD buildings A B C D E F G G1 G2 G3 G4 G5 H Central government (prison specific) A substantial proportion of prisons (148) have been given a DEC (see Figure 5.13). 66% of these have ratings < 100 i.e. better than the benchmark 82% are in the A to E grades. 9% have an F grade 13 prisons are rated over 150 i.e. with an official G grade (9%). Only 2 prisons (1%) have ratings over 200 No prisons are rated over 300. Wider Public Sector Emissions Reduction Potential 2010/11 53

55 Figure 5.13 DEC grades for all prisons A B C D E F G G1 G2 G3 G4 G5 H Health The health sector has 3,177 buildings with a valid current DEC (see Figure 5.14). 58% of these have ratings < 100 i.e. better than the benchmark 83% are in the A to E grades. 10% have an F grade 220 buildings are rated over 150 i.e. with an official G grade (7%). Nearly 70 buildings (2%) have ratings over buildings (0.4%) are rated over 300. Figure 5.14 DEC grades for all health sector buildings 1,200 1,101 1, A B C D E F G G1 G2 G3 G4 G5 H Local Authorities (ex schools and emergency) Local authorities have nearly 5,000 buildings with a valid current DEC (see Figure 5.15). Wider Public Sector Emissions Reduction Potential 2010/11 54

56 62% of these have ratings < 100 i.e. better than the benchmark 81% are in the A to E grades. 9% have an F grade 500 buildings are rated over 150 i.e. with an official G grade (10%). Nearly 170 buildings (3%) have ratings over buildings (0.8%) are rated over 300. Figure 5.15 DEC grades for all local authority buildings (ex schools and emergency) 1,400 1,303 1,200 1,179 1, A B C D E F G G1 G2 G3 G4 G5 H Schools There are over 15,000 schools or school buildings with a valid current DEC (see Figure 5.16). 55% of these have ratings < 100 i.e. better than the benchmark 85% are in the A to E grades. 11% have an F grade 700 buildings are rated over 150 i.e. with an official G grade (5%). Just 130 buildings (0.8%) have ratings over buildings (0.2%) are rated over 300. Figure 5.16 DEC grades for all schools 7,000 6,000 6,024 5,000 4,554 4,000 3,000 2,000 2,134 1,593 1, A B C D E F G G1 G2 G3 G4 G5 H Wider Public Sector Emissions Reduction Potential 2010/11 55

57 We have generated separate grade distributions for primary and secondary schools, the median rating of secondary schools is marginally higher than primary schools (100 cf 97). Figure 5.17 shows that secondary schools have more E, F and G grade buildings than primary schools, whilst primary schools have more B, C and D grade buildings than secondary schools. Figure 5.17 DEC grades for primary and secondary schools 45% 40% 35% Median ratings Nursery: 99 Primary: 97 Secondary: % % of all grades 25% 20% Nursery Primary Secondary 15% 10% 5% 0% A B C D E F G We also used the data set for schools to explore the impact of extended hours of use on the ratings achieved. Schools have variable hours of use, depending on the presence of breakfast clubs, after-school clubs, the use of the school as a community centre, etc. The schools DEC benchmark makes allowances for this variability and Figure 5.18 shows virtually no visible trend in the rating as the hours of use increase. Figure 5.18 DEC ratings for schools with extended hours of use Operational Rating y = x R 2 = ,000 1,500 2,000 2,500 3,000 3,500 4,000 Extended use (hours/year) Emergency (police, fire and rescue) There are nearly 1,000 emergency services (police, fire and rescue) buildings with a valid current DEC (see Figure 5.19). Wider Public Sector Emissions Reduction Potential 2010/11 56

58 60% of these have ratings < 100 i.e. better than the benchmark 81% are in the A to E grades. 9% have an F grade Nearly 100 buildings are rated over 150 i.e. with an official G grade (10%). Nearly 30 buildings (3%) have ratings over buildings (0.6%) are rated over 300. Figure 5.19 DEC grades for all emergency services buildings A B C D E F G G1 G2 G3 G4 G5 H Further & Higher Education Institutions There are nearly 2,100 HE/FE buildings with a valid current DEC (see Figure 5.20). 71% of these have ratings < 100 i.e. better than the benchmark 86% are in the A to E grades. 6% have an F grade 170 buildings are rated over 150 i.e. with an official G grade (8%). 64 buildings (3%) have ratings over buildings (1%) are rated over 300. Figure 5.20 DEC grades for all HE/FE buildings A B C D E F G G1 G2 G3 G4 G5 H Wider Public Sector Emissions Reduction Potential 2010/11 57

59 5.4 Year on year analysis We have identified year-on-year changes in performance on a building by building basis, using the evidence from those buildings with one or more annual DEC renewals. The results are shown in Table 5.9 using the evidence from 31,120 data records. The table shows the sums of the year-on-year change in CO 2 emissions for every DEC renewal that has been lodged. Table 5.9 Calculation of year-on-year improvement in renewal DECs Overall there has been a small improvement, a reduction in CO 2 intensity (kg CO 2 /m 2 ) of 0.4% per year. This value does mask some ups and downs, as shown in Figure Central government and local authorities are leading the way with reductions of 1.3% and 1.5% respectively. Figure 5.21 Are public sector buildings improving year-on-year? 5% Annual change in CO2 emissions intensity (kg CO2/m2/year) 4% 3% 2% 1% 0% -1% Emergency (police, fire and rescue) Schools Local Authorities (ex schools and emergency) Health Central government (prison specific) Central government (MoD specific) Central government (ex MoD & prisons) Further & Higher Education Institutions -2% Wider Public Sector Emissions Reduction Potential 2010/11 58

60 Year-on-year analysis example Table 5.10 shows the operational ratings and CO 2 footprint recorded by the six DECs that have been lodged to date for 3-8 Whitehall Place (DECC s headquarters). Apart from the first one, all of these DECs are renewals. The 5th and 6th columns of the table show respectively the absolute change in CO 2 emissions (tonnes per year) on each renewal and the change in CO 2 intensity (kg/m 2 /year). In the final column, this change is shown as a percentage of the average CO 2 intensity for central government buildings (given in the 7th column). The overall result for these five renewals is obtained by summing the five changes in CO 2 emissions intensity (-435 tonnes per year), converting this into an average overall change in CO 2 intensity (-8 kg/m 2 /year) and expressing this as a percentage of the mean CO 2 intensity value for central government (ex MoD and prisons), 115 kg/m 2 /year. This gives a result for these five renewals of a reduction in CO 2 intensity (kg CO 2/m 2 ) of 6.9% per year. Table 5.10 Example calculation for year-on-year improvement based on DECC s DECs Change in CO 2 (tonnes/ year) Change in CO 2 intensity (kg/m 2 / year) Central government CO 2 intensity (kg/m 2 ) Change in kg CO 2/ m 2 / year (%) Total CO 2 Issue date Rating (tonnes/ year) Area (m2) 30/09/ ,336 10, /09/ ,397 10, % 19/11/ ,342 10, % 17/05/ ,090 10, % 12/11/ ,029 10, % 12/04/ , % Renewal totals 54, % DECC s DEC renewals shown in Figure 5.22 reveal a continuously downward trend in rating and a steady improvement in the A to G grade. Whilst impressive, it is not at all typical: most buildings see annual changes in their ratings of only a small number of points, which means only those with ratings near a grade boundary are likely to experience a change in grade. Figure 5.22 Changes in DEC ratings for 3-8 Whitehall Place (DECC s headquarters) DEC rating of 3-8 Whitehall Place (DECC) G G F 121 E 114 E 100 D /09/ /09/ /11/ /05/ /11/ /04/2011 Wider Public Sector Emissions Reduction Potential 2010/11 59

61 The year-on-year analysis also examined the association of rating improvement with grade 19. The results (see Figure 5.23) suggest that the better a building s grade, the greater the propensity to see a year-on-year improvement. In other words, statistically, good buildings get better, whilst bad buildings get worse. In some ways, this is counter-intuitive, given that the opportunities for improvement are likely to be greater for buildings with worse grades. And of course, the statistics show a trend which covers a lot of moves in both directions. Nevertheless, the inference is clearly there of the potential for virtuous and vicious circles. Figure 5.23 Are changes in CO 2 intensities associated with the building s performance grade Change in CO2 intensity (kg CO2/m2/year) A B C D E F G All grades Do new buildings have better operational ratings? We considered the issue of how much a building s intrinsic energy efficiency i.e. the energy efficiency of the fabric and plant making up a building as represented by the building s asset rating, is likely to contribute to a better operational rating. The empirical evidence for this is limited because few buildings are required to get both an EPC (which is based on the asset rating) and a DEC (which is based on the operational rating). The new DEC data set has 236 public buildings with both DEC and EPC ratings. It might be assumed that these buildings have asset ratings because they are new, and therefore are built to more exacting Building Regulations than the bulk of the existing stock. But it could be the case that a building has an asset rating because it has been recently bought or let. The evidence suggests a weak link between the operational and intrinsic energy performance of a building, as shown in Figure 5.24, but this finding is provisional due to the small sample size. 19 The records were grouped by their current grade to determine the total effect for all records with the same grade. For the majority of records, the previous year s grade will have been the same, given that most buildings only shift their rating by a few points each year. Wider Public Sector Emissions Reduction Potential 2010/11 60

62 Figure 5.24 How much is the operational rating of a building affected by its asset rating? Operational rating y = x R 2 = Asset Rating Wider Public Sector Emissions Reduction Potential 2010/11 61

63 Appendix A: Supporting data for work stream 1 A1.1 Data sources Source Data year Provided by Departments included Activity area covered Sustainable Operations on the Government 2009/10 Efficiency and Reform Group Central Government (exc Prisons) Electricity & Fossil fuel consumption Estate (SOGE) (ERG) MOD Estate Return Information Collection (ERIC) 2009/10 NHS bodies (exc GP's & Dentists) Electricity & Fossil fuel consumption Total spend for all energy emandate Energy Data 2007/08 Higher Education Funding Council for England Further Education Electricity & Fossil fuel consumption Higher Education Statistics Agency (HESA) Data 2007/08 Department for Business Innovation & Skills Higher Education Electricity & Fossil fuel consumption Display Energy Certificates returns data 2010 Robert Cohen, Camco (Ws3) Central Government-Prisons Local Authorities Police, Fire & Rescue Services State Schools Electricity & Fossil fuel intensity per m 2 Carb stock model 2007 Harry Bruhns, University College London Police, Fire & Rescue State schools Floor area Government Carbon Offsetting Fund (GCOF) 2009/10 Buying Solutions Central Government MoD Business Travel Air, rail, taxi, bus mileage Fuel Card data 2009/10 Buying Solutions Central Govt Police Local Authorities Health Sustainable Development Annual Report 2009/10 DECC Quarterly Energy Prices, Sept 2010 Electricity & Fossil fuel prices 2009/10 Sustainable Development & Continuity Division, MoD MoD Sep-10 BERR Central Government-Prisons Local Authorities Police, Fire & Rescue Services State Schools Census Population data 2001 All sectors Scaling Factors - extrapolation of England specific data ERG PSPES Draft v /10 Efficiency and Reform Group (ERG) Central government MoD Owned and lease vehicles (exc bulk fuels) Fuel Card purchase consumption Owned vehicles Transport Energy spend Non-Domestic Energy prices, average user, as at Oct 10 (inc CCL) Electricity & Fossil fuel consumption Owned and Business travel data Energy spend Ecoinvent v Proprietary database Health Steam emissions factor Guidelines to Defra / DECC's GHG Conversion Factors for Company Reporting 2010 Defra All Emissions Factors Electricity & Fossil fuel expenditure (buildings) Wider Public Sector Emissions Reduction Potential 2010/11 62

64 A1.2 Assumptions Table A1 highlights all assumptions that have been made within the baseline calculations. Table A1 Calculations assumptions made Calculations section Assumption made Electricity consumption for surgeries and clinics is not included within included within the ERIC Returns for the NHS Bodies. Electricity Prison data is not included in SOGE data, and has been calculated using DEC returns data Camco has not calculated the emissions associated with CHP plants or on-site renewable operated by Central Government as it is assumed that all electricity and heat generated from these facilities is consumed by Central Government facilities. Natural gas Oil consumption Coal consumption Natural gas consumption for surgeries and clinics is not included within the ERIC Returns for the NHS Bodies. Prison data is not included in SOGE data, and has been calculated using DEC returns data Oil consumption for surgeries and clinics is not included within the ERIC Returns for the NHS Bodies. Prison data is not included in SOGE data, and has been calculated using DEC returns data Coal consumption for surgeries and clinics is not included within the ERIC Returns for the NHS Bodies. Prison data is not included in SOGE data, and has been calculated using DEC returns data Steam consumption Steam consumption for surgeries and clinics is not included within the ERIC Returns for the NHS Bodies. Undisclosed/other fossil fuels Camco has derived an emissions factor based on fossil fuel supply in the UK being 87.5% from natural gas, 1% from coal consumption, 2.5% from LPG and the remaining 9% from oil, from Carbon Trust split used in the 2008/09 study. MOD fuel consumption in Afghanistan and Iraq has been excluded from the scope of this project. Owned petrol (excluding grey fleet) Camco has extrapolated the MOD fuel data to represent emissions based on English operations based on a scaling factor of 83.6% based on UK populations in Scaling factors (used to remove Scottish, Welsh and Northern Irish operations from the baseline) have been calculated based on the 2001 Census population figures. Owned diesel (excluding grey fleet) MOD fuel consumption in Afghanistan and Iraq has been excluded from the scope of this project. Camco has extrapolated the MOD fuel data to represent emissions based on English operations based on a scaling factor of 83.6% based on UK populations in Wider Public Sector Emissions Reduction Potential 2010/11 63

65 Calculations section Owned LPG (excluding grey fleet) Owned aviation vehicles (excluding grey fleet) Assumption made MOD fuel consumption in Afghanistan and Iraq has been excluded from the scope of this project. Camco has extrapolated the MOD fuel data to represent emissions based on English operations based on a scaling factor of 83.6% based on UK populations in MOD fuel consumption in Afghanistan and Iraq has been excluded from the scope of this project. Camco has extrapolated the MOD fuel data to represent emissions based on English operations based on a scaling factor of 83.6% based on UK populations in Owned Gas Oil vehicles (excluding grey fleet) MOD fuel consumption in Afghanistan and Iraq has been excluded from the scope of this project. Camco has extrapolated the MOD fuel data to represent emissions based on English operations based on a scaling factor of 83.6% based on UK populations in Wider Public Sector Emissions Reduction Potential 2010/11 64

66 Appendix B: Technical abatement method B. 1 Close out Database Data fields The key fields captured by the close out database are as follows: Region The location of the organisations Organisation Name The organisation name Product Type The programme under which the opportunity was identified Sector Whether the organisation is private or public sector Sub sector The relevant subsector in which the organisation resides Recommendation/ Opportunity a description of the opportunity Fuel Type The fuel type for which the MWh reductions relate. Where savings reduce multiple fuel types a multi-fuel option is available Technology Group A top level technology grouping Main Technology A mid level technology category Sub Technology A low level technology category Identified Initial Cost Identified annual savings in Identified annual savings in MWh Identified annual savings in carbon TCO 2 Identified lifetime savings in carbon TCO 2 The previous five metrics above against actual performance (see section for definition of actual ). C. 2 Method The analysis steps taken in order to build the bottom up assessment of low carbon potential is as follows: PSCM Geographic Screening The PSCM set was screened for all opportunities identified for organisations based outside of the geographic scope of this study. PSCM Opportunities Screening It was observed that a significant number of opportunities had erroneous metrics associated with them. All measures which did not have an identified per annum CO 2 saving associated with them were removed. All measures which had blank initial costs were removed All measures with lifetime carbon savings of 0 or less were removed All measures with negative initial costs were removed All measures identified under any product type s other than HECM, NHS CM, CM GOV and LACM are removed. Wider Public Sector Emissions Reduction Potential 2010/11 65

67 To remove outlier ratios between actual and identified financial savings and simple MACs were calculated: All measures where actual capital cost was 10 times greater than identified measure cost were removed Removal of all measure with 0 MWh savings All MAC values outside /tCO 2 were removed. Data correction A number of measures seemed plausible but had erroneous fuel types or measure categories: All Aviation Turbine Fuel and Aviation Spirit savings were reallocated to Gas oil as these all referred to boiler opportunities All measures with fuel types of CHP, Wood pellets and process were removed. Alternative energy sources measures were reallocated to alternative energy The Carbon and Energy Management was sorted by the following key words and manually reallocated to correct measure types; heat controls, boilers, zoning, exchangers, recovery, volt + opt, draught, insulation, CHP, Light, Chiller, renewable, Cool, controls covers, time, motor and VSD. Allocation of traded and non-traded savings As per the IAG guidance, it is necessary to discriminate between those emissions which are captured under the EU Emission Trading System (Traded) and those which are not (Nontraded). They are governed by different short to medium term targets and should essentially be treated as separate commodities. In our method, in order to allocate savings to traded or non-traded the fuel type was used. Where savings were attributed Grid Electricity they would be classed as traded with the remaining fuel types deemed to be non-traded. Importantly some measures had multi fuel or other fuel types allocated. For these it was assumed that the saving would be a combination of natural gas and grid electricity savings and savings were allocated in according proportions using the following formula to calculate the electricity proportion: I tco2 =(E cf *(x*i MWh ))+(G cf *((1-x)*I MWh )) Where I tco2 is identified tonnes of CO 2, E cf is the electricity carbon factors, G cf is the gas carbon factor and I MWh is the identified consumption saving. Implementation factor To account for optimism bias as well as other factors which may reduce the actual carbon savings delivered from a measure an adjustment factor was applied to all identified savings. For a representative proportion of measures, actual CO 2 saving values were available. These were collected through follow-on survey work by the Carbon Trust. The percentage of actual savings relative to the identified savings for all these opportunities was the adjustment factor adopted. All identified savings and lifetime savings were therefore reduced by this factor. Pivot data The data sheet was then pivoted by product type and efficiency measure. Product type relates to the carbon management programme under which the opportunity was identified. There are four product types; Local Authority Carbon Management, Central Government Carbon Management, National Health Service Carbon Management and Higher Education Carbon Management. Wider Public Sector Emissions Reduction Potential 2010/11 66

68 The efficiency measures were based on Carbon Trust categories outlined earlier. The following metrics from these measures were collated by product type: Table B5.1 Key output fields Initial Cost C Average of Lifetime Count of Fuel Type Coal (MWh) Diesel (MWh) Domestic Coal (MWh) Fuel Oil (MWh) Gas Oil (MWh) Grid Electricity (MWh) LPG (MWh) Multi-Fuel Traded (MWh) Multi-Fuel Non Traded (MWh) Natural Gas (MWh) Other Traded (MWh) Other - Non Traded (MWh) Petrol (MWh) LIFETIME TRADED MWh LIFETIME NON TRADED MWh ANNUAL LIFETIME UD LIFETIME D TCO 2 TRADED ANNUAL TCO 2 TRADED LIFETIME TCO 2 NONTRADED ANNUAL TCO 2 NONTRADED LIFETIME TCO 2 BOTH ANNUAL TCO 2 BOTH LIFETIME Profile Gaps For some product types the inventory of measures was not complete. Where gaps were found these were filled using the average metrics for such a measure based on all other profile types. Once the gaps were filled all product types had a comprehensive suite of measures reflecting the types of opportunities available to those organisations. Profile allocation There are four product types but nine public sector entities that we are assessing. As a result, profiles were allocated and then opportunities pro-rated based on number of organisations, energy intensity and average square meterage. Bottom up scale up The resultant profiles were multiplied up by number of organisations to assess the bottom up reduction potential for that type of organisation across England. These were then aggregated to produce a total figure for each measure an overall total. Wider Public Sector Emissions Reduction Potential 2010/11 67

69 Accounting for existing measures Existing measures were accounted for by taking the average penetration level of efficiency measures across non domestic buildings in 2007 from the CCC analysis (3.6%). This was applied to the top level totals on a per measure basis. For renewable and transport opportunities the penetration rates were assumed to be 0%. Incorporating hidden costs To reflect hidden costs an uplift factor to capital cost was applied. Based on the previous AEA and Enviros research a figure of between 10 30% has been recommended. For this analysis we used 20%. Following the uplift of costs the MAC figures were recalculated. Calculating Theoretical Potential The resultant outputs provide the theoretical potential across all carbon reduction measure for the public sector. Calculating Cost Effective Potential By sorting measures in ascending order according to their MAC and then creating a cut off point at 0/tCO 2 the cost effective portfolio of measures is identified. D. 3 Technology Categories The technology types are listed below: Table B.2 Low Carbon Measure Categories Technology Group Main Technology Sub technology Combined heat and power Gas, diesel, gasoil or biodiesel engine CHP Biomass boiler Building integrated wind power (0-50kW) Alternative Energy Heat pump PV Small hydropower Renewable energy sources Solar thermal Building technologies Air conditioning and cooling Estate rationalisation Building construction, installation and commissioning Building fabric Chillers - other Condensers Controls Cooling tower - open - galvanised metal or other Free cooling Heat recovery Other Estate rationalisation Commissioning Air tightness Glazing - metal or plastic frames Insulation - cavity wall Wider Public Sector Emissions Reduction Potential 2010/11 68

70 Table B.2 Low Carbon Measure Categories Technology Group Main Technology Sub technology Insulation - loft Other Shading - short life Structure BMS hardware - bureau remotely managed BMS hardware - not remotely managed Local Control - optimiser Local Control - timeclock Building instrumentation and control Other Electrical supply, switchgear and distribution Other Pipe insulation - Internal Building services distribution systems Equipment Steam traps Voltage control device Computers, printers and office equipment Controls (including ballasts) Lighting Controls (not including ballasts) Luminaires (internal) and fittings Luminaires (internal) retrofit Lighting Other Boilers Condensing boilers Controls - other Direct fired systems Heat recovery Low temperature systems Other Space heating Valve flange and ancillary insulation Controls Distribution system ducting Fans - AHU Fans - destratification Ventilation Other Metering and monitoring Management measures Other Policy and strategy Carbon and Energy Management Programme - Policy, M and T, training Wider Public Sector Emissions Reduction Potential 2010/11 69

71 Table B.2 Low Carbon Measure Categories Technology Group Main Technology Sub technology Training and awareness - Embedded Training and awareness - one-off Compressed air Air compressors High efficiency motors Other Motors and drives Variable Speed Drives Absorption systems Boilers - other Controls sensors, actuators, relays and other interface equipment Process technologies Direct fired systems Heat exchangers Heat recovery Other Process heating and cooling Refrigeration Steam pipework Controls Controls Swimming pools Energy Efficiency Measures Covers - Liquid Covers - Manual Wider Public Sector Emissions Reduction Potential 2010/11 70

72 Appendix C: Processing of the DEC data set: Statistics and methodology for cleaning and filtering C.1 DEC records for England and Wales The results of the first analysis of the DEC data records is shown in Table C1. Overall there were a total of 76,543 records. Using the UPRN and assessment date, each record can be categorised as a building which has only ever had one DEC or a building which has had at least one DEC renewal 20, and in the latter case whether the record is for the first DEC, an interim renewal or the last renewal that exists. Each record has also been allocated to a sub-sector. This was done firstly on the basis of the building type with the largest floor area (some buildings are defined as a mix of building types) using a pre-defined allocation for each of the 239 permitted building types. [NB a multitude of rogue Building Type descriptions had to be dealt with]. About 10% of records were for types where the allocation is not certain and these were initially allocated to a temporary sector for more detailed scrutiny. The records in the uncertain sector were then allocated on the basis of the occupier s name. If this was not definitive, the address was used as it sometimes contains the occupiers name or affiliation. About 400 records could not be allocated using these methods and were allocated after manual examination. Table C1 First analysis of the DEC data records Table C1 shows that about 17,000 buildings have only ever had one DEC and 25,000 have had at least one renewal. In principle, the number of records for buildings which have a first DEC and a subsequent renewal should equal the number of records for the last DEC for those buildings which have had a renewal. However, due to the vagaries of the automated allocation process, a few buildings (same UPRN) initially ended up with a different sector for their first DEC and their renewal(s), since these can have different occupier names and even different entries in the address fields. Of the 25,000 buildings with at least one renewal, Table C2 shows that 15,000 buildings have had one renewal and 10,000 have had two renewals. Overall some 42,000 buildings have been given at least one DEC. The fact that 17,000 buildings with a DEC (40% of the total) have not yet been given a renewal is compelling evidence of poor enforcement of the Regulations by trading standards officers (TSOs). Efforts are being stepped up to improve on this: Landmark is developing a system whereby they are issuing to every TSO a list of all buildings in their local area which have failed to renew their first DEC in a timely manner. 20 The EPB regulations stipulate that buildings requiring a DEC must renew the DEC every 12 months. Wider Public Sector Emissions Reduction Potential 2010/11 71

73 Table C2 Summary of DEC renewal rates C.2 Defaults In the initial implementation of DECs, buildings for which compliant energy data were not available were given default ratings of 200 (i.e. twice the benchmark for that building) in order that a DEC could be produced and the occupier could comply with the regulations to display a DEC. From 7th March 2010, defaulting buildings were given a rating of 9999, rather than 200, as, predictably, it proved impossible to distinguish a DEC with a genuine 200 rating from a default 200. From 14 April 2011, default DECs are no longer allowed at all, the theory being that public buildings have now had sufficient time to sort out any energy metering issues. Table C3 shows that over 3,100 buildings currently have a default DEC. The average incidence of defaults is 8% across the whole data set. Some 1,800 have only ever had one DEC (i.e. the default) and 1,300 have been serial defaulters. The prevalence of defaults is highest for Central Government which currently has 18% of its buildings showing a default DEC. However, this could be a perverse consequence of a conflict of KPIs: having a default helps to increase the proportion of the portfolio covered by a DEC. Table C3 Summary statistics for DEC defaults C.3 Implausible records Records for buildings with floor area < 50 m 2 or > 100,000 m 2 or with a rating > 1,000 are assumed to arise from poor data and are classified as outliers. It is recognised that using such criteria for automatic filtering may exclude the odd valid record. The number of buildings with an implausible current DEC is 94, with over half of these being in the health sector. Excluding defaults and outliers means that there are 38,417 buildings with a valid current DEC in the data set (see Table C4). Wider Public Sector Emissions Reduction Potential 2010/11 72

74 Table C4 Summary statistics for DEC data set C.4 Corrections We found there were 670 cases where records for the same building (same UPRN and same floor area) had an assessment period which overlapped another record by more than 6 months (regulations stipulate a maximum of 3 months overlap). These duplications were assumed to be corrections. A record with a later issue date was assumed to supercede one with an earlier issue date. Table C5 shows their distribution by sector. Table C5 Numbers of records in data set appearing to be duplicates (corrections) Table C6 shows the data set summary statistics with the numbers of current valid DECs (37,746) excluding the 3,145 defaults, 94 outliers and 670 corrections. The bottom row shows how many buildings (same UPRN) still have a different sub-sector for their first DEC and their latest renewal. Table C6 Summary statistics for DEC data set C.5 Final analysis Next, the 670 records categorised as corrections were deleted (leaving 75,873 records) and the analysis of first and renewal DECs was repeated. Then those buildings with the same UPRN but different baseline sector for first DEC and renewal(s) were examined manually and the subsector for all records for that UPRN harmonised to the most applicable sub-sector. It was assumed that differences in sub-sector were false i.e. were not a genuine change of use, but rather caused by the imprecise nature of the automated sector allocation process described above. The revised data set statistics are shown in Table C7. It can be seen that now, for all Wider Public Sector Emissions Reduction Potential 2010/11 73

75 sectors, the number of records for a first DEC where there is a renewal equals the number of records for the last renewal. All buildings have a coherent set of records (only DEC, first DEC, renewals). There are 41,656 buildings with a current DEC, including 3,145 defaults and 94 outliers, leaving 38,417 buildings with a valid current DEC. Table C7 Summary statistics for data set of DECs excluding corrections Lastly, the 110 records with implausible area (< 50 m 2 or > 100,000 m 2 ) and 16 records with implausible rating (> 1,000 and < 9999) were deleted (94 of these were for records representing a current DEC). With these records deleted, analysis of first and renewal DECs was repeated to give Table C8 which has 75,747 records, 41,570 buildings with a current DEC, including 3,138 defaults, leaving 38,432 buildings with a valid current DEC. Table C8 Summary statistics for data set of DECs excluding corrections and outliers Figure C1 shows the split between sub-sectors of the buildings with current valid DECs. Over 50% are schools. Next comes local Authorities with 15%, health with 12% and higher education with 13%. Only 7% of the buildings are for central government. Wider Public Sector Emissions Reduction Potential 2010/11 74

76 Figure C1 Number of buildings/sites with current valid DEC in data set (total 38,432) ,073 2,342 19,461 4,445 4,835 Central government (MoD specific) Private sector Central government (prison specific) Emergency (police, fire and rescue) Central government (ex MoD & prisons) Health Further & Higher Education Institutions Local Authorities (ex schools and emergency) Schools 5,902 Figure C2 shows the number of buildings currently showing a default DEC. It would appear that many buildings, especially schools, have been given defaults due to assessors having difficulty calculating the annual use of bulk fuels (oil and coal). Figure C2 Number of buildings/sites with default DEC in data set (total 3,138) 1,200 1,188 1, Central government (ex MoD & prisons) 32 Central government (MoD specific) 4 Central government (prison specific) Health Local Authorities (ex schools and emergency) Schools 88 Emergency (police, fire and rescue) Further & Higher Education Institutions 9 Private sector C.6 DEC records for Wales We separated the 38,432 buildings with current valid DECs between England and Wales, using post codes. This identified there are 36,622 buildings with a valid DEC in England and 1,810 in Wales. The split by sub-sector for the buildings in Wales is shown in Table C9. Wider Public Sector Emissions Reduction Potential 2010/11 75

77 Table C9 Summary statistics for buildings with valid DECs in Wales The total floor area of the buildings in Wales is 7.1 km 2 of which 0.4 km 2 (5%) currently have a default leaving 1,810 buildings with a valid current DEC and a total floor area of 6.7 km 2 (average 3,721 m 2 per building). Table C10 shows this breakdown by sub-sector. Table C10 Floor area by sub-sector of buildings with valid and default DECs in Wales Overall the buildings in Wales have 4.4% of the total floor area of the buildings in the data set (including those with defaults). Their share by sub-sector is shown in Figure C3. Figure C3 Percentage of buildings/sites in data set that are in Wales 6% 5.7% Buildings in data set in Wales by area 5% 4% 3% 2% 4.5% 2.4% 4.5% 4.2% 4.6% 3.9% 4.4% 1% 0% Central government (ex MoD & prisons) 0.2% Central government (MoD specific) Central government (prison specific) Health Local Authorities (ex schools and emergency) Schools Emergency (police, fire and rescue) Further & Higher Education Institutions 0.1% Private sector TOTALS Wider Public Sector Emissions Reduction Potential 2010/11 76