3.0 University Energy Management Questionnaire

Transcription

1 3.0 University Energy Management Questionnaire 3.1 INTRODUCTION To supplement the individual case studies, a postal survey was undertaken of energy management in UK universities. The survey was based very closely on the Pre Interview Questionnaire (PIQ) and was estimated to require around 15 minutes to complete, with the majority of questions being simple tick boxes. Questions were organised under the following headings: Annual energy costs Energy policy and management Environmental policy and management Energy information systems Energy management self assessment profile Energy efficiency investment Energy saving opportunities Information sources on energy efficiency opportunities Use of contract energy management Implementation of specific energy efficient technologies to energy efficiency improvement A copy of the survey questionnaire is contained in Annex 1. The survey was sent to the individual responsible for energy management at 94 UK universities (the federal colleges of the University of London were treated as a single institution). Contact names were originally obtained from a database of Higher Education Energy Share Fair members, held by the Buildings Research Establishment. Additional contacts for universities that were not members of the Share Fair network were obtained from the institutions web sites and through phone calls. Some of the respondents were full time energy managers, but more usually their job description suggested wider responsibilities: e.g. Estates manager; Estates maintenance supervisor; mechanical engineer; procurement manager; building services officer; and so on. The six institutions used as case studies were excluded from the survey. A total of 32 replies were obtained, representing 33% of the survey population. Three of the replies did not report their annual electricity and fuel consumption. The remaining 29 were classified by their total annual expenditure on energy, as follows: Small: < 1 million/year 11 respondents Medium: 1-2 million/year 9 respondents Large: > 2 million/year 9 respondents The smallest reported annual energy expenditure was 228 thousand while the largest was 4.8 million. Institutions with a total energy expenditure of > 1 million/year are recommended by the UK Energy Efficiency Office to appoint a full time energy manager. Of 37

2 the respondents, 15 out of the 18 institutions with annual bills exceeding 1 million had an energy manager, while less than half of the small institutions did. The above categories are therefore a convenient basis for comparing the performance of different sized institutions. The performance of the universities on energy management could in principle be assessed by means of energy use per square meter floor area. However, experience suggests that reporting floor area accurately may be difficult for some institutions and may discourage completion of the questionnaire. Even if they had the data, efficiency figures would be expected to vary widely according to the age of building and the type of building use (notably residential vs. academic). Meaningful data would therefore require more detailed surveys and the use of disaggregated benchmarks, such as discussed in section The performance measures here are therefore based on self assessment of variables such as technology adoption. This is indirect and impressionistic, but nevertheless useful. The sample is self selecting and it seems reasonable to assume that a response to the questionnaire indicates an interest in, and willingness to devote time to, energy related issues. It is therefore likely that the sample will be biased towards those universities with a more proactive approach to energy efficiency. This, together with the small sample size, suggests that the results should be treated with caution and may not be representative of the total university population. Nevertheless the results provide a useful picture of energy management practices in one third of UK universities in 1999 and a useful benchmark against which to compare the subsequent case study results. The results of the survey are summarised in the following sections. In most cases, results are presented as the percentage of valid replies to each question, where a non-valid reply means that no answer was given. The number of valid replies varied between questions. For most questions, results are broken down into the small, medium and large size categories, but the small sample size must be borne in mind when interpreting results expressed as percentages. 3.2 ENERGY POLICY AND MANAGEMENT A total of 81% of respondents had an energy policy of some form, although this fell to 64% in the small category. Integration of policy objectives into new build & refurbishment specifications was high at 93% overall and 100% for large institutions. Integration into maintenance policy was poorer at 71%, while integration into purchasing policy was poorer still with only 54% of institutions considering energy issues. Clearly, integration here is a subjective concept and the effectiveness of such integration is considered further in the case studies. The DETR Making a Corporate Commitment (MACC) campaign aims to increase awareness of energy efficiency among top management. Signatories pledge to develop and publish a formal energy policy and set specific targets for energy use. MACC may therefore be indicative of a stronger commitment to energy efficiency than in-house policies. Only 46% of respondents were signatories to MACC, compared to 81% with some form of energy policy. In the small size category, only 22% were signatories. 38

3 Table 3.1 Energy Policy (% of valid responses) Question Small Medium Large Total Does the university have an energy policy? Are energy policy objectives integrated into new build & refurbishment specifications?. Are energy policy objectives integrated into maintenance policy? Are energy policy objectives integrated into purchasing policy? Is the university a signatory to the MAAC campaign? Energy management may be the full-time or part-time responsibility of one or more individuals. An average of 72% of respondents had an energy manager, but less than half the small institutions did. On average, 1.43 staff had responsibility for energy management, with the respective figures for the individual size categories being 0.53 (small), 1.63 (medium), and 2.28 (large). Establishment of an energy committee was a key recommendation of the HEFCE energy management study (HEFCE, 1996). Only 39% of respondents had such a committee, with the number varying from 18% in small institutions to 63% in large. Annual energy reports were produced by 58% of respondents, while a high proportion (83%) had conducted energy audits in major buildings. A notable feature of both the survey and the case studies is the extent to which responsibility for energy management is centralised in the Estates department. Energy budgets are rarely devolved to individual departments, with important consequences for incentive structures. Here we note that only 22% of respondents delegated responsibility for energy to building managers. Table 3.2 Energy management (% valid responses) Question Small Medium Large Total Is there an energy manager? Does the university have an energy committee? Is responsibility for energy delegated to building managers? Have energy audits been conducted in major buildings? Is an annual energy report produced? ENVIRONMENTAL POLICY & MANAGEMENT Energy management is well established as a an essential element of cost control. In contrast, environmental management is a more recent response to social concerns and is less clearly 39

4 linked to the bottom line. Take up is more limited and universities are lagging behind manufacturing firms in the adoption of accredited management systems such as ISO No respondents were registered to an accredited system, although 60% of institutions stated that they intended to develop such a system in the future. An average of 59% of respondents had adopted an environmental policy, but only 16% (5 institutions) had an environmental manager. In contrast, environmental committees were established at 40% of institutions, a comparable proportion to that for energy committees. Environmental concerns are diverse and while energy use forms an important component of a universities environmental impact it is frequently not the highest profile environmental issue, nor the one where initiatives are easiest to undertake. Hence, only 13% of respondents felt that the universities environmental policy had made a significant impact on energy management. Table 3.3 Environmental policy, & management (% valid responses) Question Small Medium Large Total Does the university have an environmental policy? Does the university have an environmental manager? Does the university have an environmental committee? Has the universities environmental policy had a significant impact on energy management? Does the university have an accredited environmental management system? Does the university intend to develop such a system? ENERGY INFORMATION SYSTEMS A comprehensive data collection and data analysis system for energy consumption is a prerequisite for effective energy management. In the UK, such systems are generally referred to as Monitoring & Targeting, and are supported by a variety of software packages (Croner, 2000). Respondents were asked for the level at which energy use was metered and the frequency with which it was recorded. Submetering was well established for electricity, with 72% metering at the building level and 19% at the departmental level. The latter can be problematic if several departments are contained in a single building and/or frequent changes are made in building use. Submetering at the departmental level may therefore only be technically feasible for some organisations - notably where there is one department per building or floor. Submetering for heat consumption is more problematic and depends upon whether each building has its own boiler or whether there is a campus district heating system fed from a central boiler. In the former case, fuel consumption for each boiler can be recorded, while in the latter metering of hot water is required, which can be technically difficult. An average of 40

5 69% of respondents metered heat at the building level, with the remainder metering at the site level. Electricity consumption was recorded monthly by 78% of respondents, while 9% recorded weekly and 13% daily. Daily recording was entirely confined to medium and large sites. The frequency of fuel/heat recording was very similar. More frequent recording can be highly effective in allowing faults and other problems to be rapidly identified and dealt with. Table 3.4 Level that energy use is metered (% of valid replies) Energy small med large total Electricity Site level Building level Departmental level Heat Site level Building level Departmental level Table 3.5 Frequency that energy use is recorded (% of valid replies) 19 Energy small med large total Electricity Monthly Weekly Daily Heat Monthly Weekly Daily % of respondents monitored trends in energy consumption, with 71% of these adjusting figures for weather conditions through the use of degree days. A quarter of the small sites did not monitor trends and only half adjusted records for weather conditions. The extent to which more formal data analysis is used can be gauged from the reported use of M&T schemes. Here, 62% of respondents stated that they used such schemes, with the proportion varying from 45% in the small category to 78% in the large. A slightly smaller proportion, 57%, reported comparing energy use to targets for major buildings. The HEFCE benchmarks were used by 62% of respondents, including all those classified as large but only 27% of small. Energy information appears to be relatively centralised with only 41% of respondents advising performance to building users. The centralisation of information is matched by the centralisation of budgeting, with only 29% of respondents charging departments for the energy they consume. This would appear to undermine the incentives for energy efficiency, but it is necessary to ask: first, how much control could building users have over the energy they consume; and second, how much notice they would take of energy costs anyway if they form a small proportion of their total costs. Only 17 institutions (53%) reported their performance against benchmarks. The results are summarised in Table 3.7. Only two institutions (12%) reported very good performance, while non reported poor. For electricity, 70% reported either good or average performance, while 41

6 the corresponding proportion for heat was 54%. A third of institutions reported below average performance for heat. Table 3.6 Energy information systems (% of valid replies) Question Small Medium Large Total Do you monitor trends in energy consumption? Are consumption records adjusted for weather conditions? Is a monitoring and targeting scheme employed? Is energy use compared to targets for major buildings? Is energy performance advised to building users? Are departments charged for the energy they consume? Is consumption compared with sector benchmarks (e.g. HEFCE standards)? Table 3.7 Performance against benchmarks (% of valid replies) Measure small med large total Electricity Very good Good Average Below average Poor Heat Very good Good Average Below average Poor Note: Only 17 institutions (53%) responded to this question - and only 3 in the small category! 3.5 ENERGY MANAGEMENT SELF ASSESSMENT MATRIX Respondents were asked to complete an energy management self assessment matrix, based on the widely publicised BRECSU matrix, first published in 1993 (EEO, 1993d). This grades organisational performance from 0 (poor) to 4 (excellent) in five dimensions: policy, organisation, communication, information, awareness and investment. The matrix allows the overall performance of an organisation to be assessed by means of a numerical grade - albeit a highly subjective one. A common use for the matrix in energy management is to assess the relative progress of the organisation along different dimensions, and to identify any imbalances in the progress achieved. The full matrix is illustrated in Annex 1. 42

7 Table 3.8 summarises respondents performance in terms of overall weighted average scores. The overall average score was 2.0, which is reasonable but leaves considerable scope for improvement. A score of 2.0 in each category corresponds to: 1. Policy: an unadopted energy policy; 2. Organisation: an energy manager, but with authority unclear; 3. Communication: contact with users through an ad hoc committee; 4. Information: M&T reports based on supply meter data; 5. Awareness: some ad hoc staff awareness training 6. Investment: short term payback criteria The weighted average profile was relatively balanced with most scores in the range 1.5 to 2.5. Respondents scored the highest on policy, with an average of 2.5, and the lowest on communication and awareness with an average in both cases of 1.6. Self assessed performance clearly improved with the size of the institution, with a difference of 0.8 in the overall weighted average scores for small and large institutions. The difference was particularly marked for policy, while the score for investment was similar for all size ranges. Table 3.8 Self assessment matrix: overall weighted average scores Category Small Medium Large Total Policy Organisation Communication Information systems Awareness Investment Overall Table 3.9 summarises the percentage of respondents scoring very good (4) and very poor (0) in each category. On average, 3.3 respondents (12%) scored very good in each category, while half that number scored very poor. Eight respondents (28%) scored very good in the policy category, while only two (7%) scored very good for investment and none for awareness. The results are suggestive of a gap between policy statements and initiatives undertaken by Estates departments on the one hand, and the general level of energy awareness in the organisations on the other. 43

8 Table 3.9 Self assessment matrix: Proportion of very good and very poor performers Category Very poor (0) Very good (4) no. universities % of valid replies no. universities % of valid replies Policy Organisation Communication Information systems Awareness Investment Average The detailed results of the self assessment matrices are summarised in Tables 3.10 to Table 3.10 Self assessment matrix: Policy (percentage of valid replies) Score Description Small Medium Large Total 0 No explicit policy Unwritten guidelines Unadopted energy policy Formal energy policy, but no active commitment 4 Energy policy, action plan & review, and management commitment Weighted average score Table 3.11 Self assessment matrix: Organising (percentage of valid replies) Score Description Small Medium Large Total 0 No energy management Energy management part time Energy manager, but authority unclear 3 Energy manager accountable to committee 4 Energy manager integrated into management structure Weighted average score

9 Table 3.12 Self assessment matrix: Communication (percentage of valid replies) Score Description Small Medium Large Total 0 No contact with users Informal contacts Contact through ad hoc committee 3 Energy committee & direct contact with major users 4 Full exploitation of formal & informal channels of communication Weighted average score Table 3.13 Self assessment matrix: Information (percentage of valid replies) Score Description Small Medium Large Total 0 No information system Cost reporting based on invoice data 2 M&T reports based on supply meter data 3 M&T reports based on submeter data 4 Comprehensive system, sets targets, monitors consumption, identifies faults etc. Weighted average score Table 3.14 Self assessment matrix: Awareness (% of valid replies) Score Description Small Medium Large Total 0 No promotion of energy efficiency 1 Informal contacts used Some ad hoc staff awareness training 3 Program of staff awareness & regular publicity campaigns 4 Marketing the value of energy efficiency both within & outside the organisation Weighted average score

10 Table 3.15 Self assessment matrix: Investment (% of valid replies) Score Description Small Medium Large Total 0 No investment Only low cost measures taken Short term payback criteria Same payback criteria as all other investment 4 Positive discrimination in favour of green schemes Total Weighted average score INVESTMENT IN ENERGY EFFICIENCY Survey participants were asked what investment criteria they used for energy efficiency. Only 50% gave a numerical response to this question. These reported a mean payback of 4.1 years (mode = 5 years). Three of the remainder reported using either simple payback or DCF depending on the nature of the investment. Two reported using simple payback, without stating the value, while the rest gave no reply. Survey participants were also asked whether they had an annual budget for energy efficiency investments (Table 3.16). Only 39% of institutions said they had, implying that, in the remainder, efficiency improvements were funded under other budgets such as new build, refurbishment or routine maintenance. Of those with a budget, the average ratio of this to the annual energy bill was 8%, which compares favourably with the EEO recommendation of 10%. However, this varied widely from 1% to 19%. Some 27% of institutions (and 50% of large institutions) stated that they reinvested energy cost savings in future projects. The difficulty here is that energy savings from efficiency improvements may be outweighed by other changes, such as increasing electrical loads from PCs and other equipment, and thereby be hard to isolate. The only unambiguous measure is the annual bill, but this is influenced by a multitude of factors. Participants were asked how much they agreed with the statement that a wide range of energy efficiency measures could be implemented that would yield paybacks of <4 years at current energy prices. A four year payback is likely to compare very favourably with the universities cost of capital 1. The results (Table 3.17) show that 79% of institutions either agree or strongly agree with this statement, with 14% neutral and only 7% disagreeing. The potential for energy efficiency improvement is therefore widely acknowledged. 1 For example, it gives a 20% internal rate of return for an investment with a 10 year lifetime. 46

11 Table 3.16 Energy efficiency investment (% of valid replies) Category Small Medium Large Total Use of annual budget for efficiency investment (% of sample) Energy cost savings reinvested to fund future projects (% of sample) Table 3.17 Lots of opportunities at <4 years payback? (% of valid replies) Category Small Medium Large Total Strongly agree Agree Neutral Disagree Strongly disagree Don t know Total INFORMATION ON ENERGY EFFICIENCY Table 3.18 summarises the respondents evaluations of the usefulness of various sources of information on energy efficiency. A notable feature of the results is that the DETR Best Practice Program is considered the most useful source, with 20% of respondents considering it excellent, 53% rating it as good, and none rating it as poor. Also rated highly were contacts in other universities and energy manager networks, where 59% and 65% respectively considered these contacts to be good or excellent sources of information. This demonstrates the value of personal contacts with individuals in similar situations. Such contacts have been facilitated through the DETR sponsored Higher Education Energy Share Fair network, which combines an list with regular workshops. Here, university energy managers can use a system of offers and requests to share problems and experiences with colleagues in other universities. Scoring good to average in the evaluations were equipment suppliers, technical journals and technical seminars. Colleagues within the university were most commonly rated average or poor, suggesting that technical expertise is concentrated in one or two individuals. The source considered least useful was the energy supply industry. 47

12 Table 3.18 Sources of information on energy efficiency (% of valid replies) Information source Excellent Good Avg. Poor Don t Weighted use score DETR Best Practice Program Energy manager networks Contacts in other universities Technical conferences/seminars Professional/technical journals Equipment suppliers Professional associations Energy supply industry Colleagues within the university Note: Weights are: excellent=3; good=2; average=1 3.8 CONTRACT ENERGY MANAGEMENT Only five institutions (14% of respondents) used some form of contract energy management (CEM). This result supports the interview findings that CEM, or energy services contracting, is not widespread in the sector and is treated with considerable suspicion by many Estates staff. The reported CEM contracts included: heat and electricity supply to halls of residence; running of the boiler house; provision of electrical and steam services to the university campus from a CHP facility shared with major teaching hospital; and monitoring, targeting and staff training. One institution reported that the boilerhouse CEM contract had not been reviewed as important investment opportunities had been missed. Table 3.19 Use of contract energy management (% of valid replies) Category Small Medium Large Total Use some form of CEM TECHNOLOGY ADOPTION Participants were asked to rate the extent to which they had adopted a range of energy efficient technologies, using a scale from 1 (not adopted) to 5 (extensively adopted). This allowed a quantitative score to be assigned for each technology. Around six technologies were listed for each of the following categories: space heating; lighting; plantroom; building fabric, and electrical. The selected technologies were based on the list developed in section 2.0 and included those most commonly mentioned in DETR Best Practice Program literature. 48

13 The overall average score in each category is summarised in Table The overall average was 3.1 and there was remarkably little difference between small and large institutions. With the technologies selected, efficiency options for space heating were the most commonly adopted and those for electrical equipment the least. Table 3.20 Overall technology implementation (average score) Technology Small Medium Large Total Space heating Lighting Plantroom Building fabric Electrical Overall mean score Note: 1 (not implemented) to 5 (extensively implemented) Table 3.21 summarises the results for technology implementation, showing: a) the percentage of sites that had extensively implemented the technology; b) the percentage that had not implemented; and c) the overall average score. The technologies are listed in descending order of score within each technology subcategory. This table provides a useful picture of the extent of take-up of common energy efficient technologies within one third of UK universities. Tables 3.22 to 3.24 give a more detailed breakdown by size of institution The following observations may be made: The technology with the widest reported take up is building energy management systems (BEMS), with over half of institutions reporting that they has extensively implemented this. BEMS are largely confined to heating, ventilation and air conditioning (HVAC), as evidenced by the fact that the integration of lighting controls into BEMS was the lowest scoring technology. Technologies that all institutions had implemented to some extent included programming of heating and ventilation controls; use of high frequency electronic ballasts; use of boiler sequencing controls; and insulation of pipes, valves and flanges. Additional technologies that all but one institution had implemented to some extent included compact fluorescents and 28mm fluorescents. Technologies with relatively limited take-up (scoring 2.5 or less) included condensing boilers, CHP, draught proofing, high efficiency office equipment and high efficiency catering equipment. Technologies where large institutions performed significantly better than small (a difference of 0.7 or more) included BEMS, replacement of oversized boiler plant, CHP, power factor correction and specification of high efficiency office equipment. Of these, the largest difference in score was for CHP, at 1.7. Small institutions were proportionally more likely to have not implemented energy efficient technologies. On average, the technologies listed had not been implemented by 19% of small institutions compared to 6% of large. Technologies that were poorly represented in small institutions include all those listed above plus variable speed drives (VSDs). CHP had only been adopted by two small institutions compared to two thirds of the large. 49

14 Small institutions were also proportionately more likely to have extensively implemented energy efficient technologies, although the difference is not statistically significant. On average, the technologies listed had been extensively implemented by 16% of small institutions compared to 10% of large. Technologies that scored high in this respect included restricting use of portable heaters, fitting door closers to external doors and retrofitting double glazing. As elsewhere, the limitations of self assessment and the small sample size must be borne in mind when interpreting these results. 50

15 Table 3.21 Summary of technology implementation Area Technology/technique % extensively implemente d % not implemente d Average score Space Use of Building Energy Management heating System? Programming heating and ventilation controls to match occupancy patterns and/or temperature? Ensuring thermostats and temperature sensors are located in the correct place? Installation of thermostatic radiator valves? Improvements to the zoning of heating areas? Restricting use of portable electric heaters? Lighting Use of compact fluorescents? Replacement of 38mm fluorescents with 26mm? Use of high frequency electronic ballasts? Use of photocell, acoustic or movement sensors? Lighting controls integrated into Building Energy Management System? Plantroom Use of boiler sequencing controls? Insulation of pipes, valves and flanges? Replacement of oversized boiler plant? Replacement of central generation of hot water with point of use applications? Installation of condensing boilers? Installation of CHP? Building Fitting door closers to external doors? fabric Specification of insulation standards in new buildings that exceed the building regulations? Installation of secondary or double glazing on refurbishment? Retrofitting insulation to walls and roofs?

16 Draught proofing of windows and doors?

17 Electrical Power factor correction Use of variable speed drives (VSD) in pumps, fans and other applications Automatic switch off of fans & pumps when the equipment they serve is not in use Specification of high efficiency motors on motor replacement Specification of high efficiency office equipment (e.g. power down facilities on computers)? Specification of high efficiency catering equipment? 53

18 Table 3.22 Adoption of technologies by size of institution (average score) Technology/technique Small Medium Large Total Restricting use of portable electric heaters? Ensuring thermostats and temperature sensors are located in the correct place? Installation of thermostatic radiator valves? Programming heating and ventilation controls to match occupancy patterns and/or temperature? Improvements to the zoning of heating areas? Use of Building Energy Management System? Replacement of 38mm fluorescents with mm? Use of high frequency electronic ballasts? Use of compact fluorescents? Use of photocell, acoustic or movement sensors? Lighting controls integrated into Building Energy Management System? Insulation of pipes, valves and flanges? Use of boiler sequencing controls? Replacement of oversized boiler plant? Installation of condensing boilers? Replacement of central generation of hot water with point of use applications? Installation of CHP? Draught proofing of windows and doors? Fitting door closers to external doors? Retrofitting insulation to walls and roofs? Installation of secondary or double glazing on refurbishment? Specification of insulation standards in new buildings that exceed the building regulations? Power factor correction Specification of high efficiency office equipment? Specification of high efficiency catering equipment? Specification of high efficiency motors on motor replacement Use of variable speed drives (VSD) in pumps, fans and other applications Automatic switch off of fans & pumps

19 Table 3.23 Proportion of sites not implementing technology (% valid replies) Technology/technique Small Medium Large Total Restricting use of portable electric heaters? Improvements to the zoning of heating areas? Installation of thermostatic radiator valves? Ensuring thermostats and temperature sensors are located in the correct place? Use of Building Energy Management System? Programming heating and ventilation controls to match occupancy patterns and/or temperature? Lighting controls integrated into Building Energy Management System? Use of photocell, acoustic or movement sensors? Replacement of 38mm fluorescents with mm? Use of compact fluorescents? Use of high frequency electronic ballasts? Installation of CHP? Installation of condensing boilers? Replacement of oversized boiler plant? Replacement of central generation of hot water with point of use applications? Insulation of pipes, valves and flanges? Use of boiler sequencing controls? Specification of insulation standards in new buildings that exceed the building regulations? Retrofitting insulation to walls and roofs? Draught proofing of windows and doors? Installation of secondary or double glazing on refurbishment? Fitting door closers to external doors? Specification of high efficiency catering equipment? Use of variable speed drives (VSD) in pumps, fans and other applications Power factor correction Specification of high efficiency office equipment? Automatic switch off of fans & pumps Specification of high efficiency motors on motor replacement Average

20 Table 3.24 Proportion of sites extensively implementing technologies (% valid replies) Technology/technique Small Medium Large Total Use of Building Energy Management System? Programming heating and ventilation controls to match occupancy patterns and/or temperature? Restricting use of portable electric heaters? Improvements to the zoning of heating areas? Ensuring thermostats and temperature sensors are located in the correct place? Installation of thermostatic radiator valves? Use of compact fluorescents? Replacement of 38mm fluorescents with mm? Use of high frequency electronic ballasts? Use of photocell, acoustic or movement sensors? Lighting controls integrated into Building Energy Management System? Use of boiler sequencing controls? Insulation of pipes, valves and flanges? Installation of CHP? Replacement of oversized boiler plant? Installation of condensing boilers? Replacement of central generation of hot water with point of use applications? Fitting door closers to external doors? Installation of secondary or double glazing on refurbishment? Specification of insulation standards in new buildings that exceed the building regulations? Retrofitting insulation to walls and roofs? Draught proofing of windows and doors? Power factor correction Specification of high efficiency motors on motor replacement Use of variable speed drives (VSD) in pumps, fans and other applications Specification of high efficiency office equipment? Specification of high efficiency catering equipment? Automatic switch off of fans & pumps Average

21 3.10 STATISTICAL TESTS The score for technology adoption can be considered a measure of energy efficiency performance and hence a dependent variable. Factors such as the existence of an energy policy can be considered independent variables. Statistical tests can then the conducted to determine whether there is any relationship between the two. Table 3.25 summarises total scores and t-test results for a number of such tests. In all cases, the observed scores are very similar and in some cases the difference is in the opposite direction to that predicted (e.g. institutions with an environmental policy score lower on technology adoption than those without). In no cases is the difference in the observed scores statistically significant. This suggests small or, in some cases, non-existent effects, but the results are also affected by the small sample size. A wide range of factors, such as the technical scope for retrofits, may act to offset and swamp any observed relationship. Table 3.25 Statistical tests Independent variable Overall score for No Overall score for Yes Presence of full time energy manager? Presence of energy committee? Existence of energy policy Existence of environmental policy Use of energy audits Charging departments for energy use Use of M&T system Using benchmarks for major buildings Annual budget for efficiency investment Tests were also made to determine whether there was any correlation between technology adoption and: the size of institution (measured by annual energy costs); the performance self assessment score; and the extent of integration of energy policy into other areas. A small positive correlation was found in the first two tests, but in neither case was it statistically significant (R 2 of 0.07 and 0.02). No relationship was found in the third test. t Sig 3.11 BARRIERS TO ENERGY EFFICIENCY Participants were asked to rate the importance of various barriers to energy efficiency using a scale from 1 (not important) to 5 (very important). The suggested barriers were derived directly from the analysis in Sorrell (1998b). The average scores for each suggested barrier are summarised in Table In addition, each suggested barrier can be assigned to a more general category, such as risk or information, based on the discussion in Sorrell (1998b). The average scores for these general categories are summarised in Table 3.27, while the codes 57

22 cross reference the two tables. In both cases, the results are organised in descending order of perceived importance. The following observations may be made: The overall mean score was 3.3, with the average for all sites ranging from 2.4 to 4.3. Hence the majority of the suggested barriers were considered of at least average importance and several were considered very important. Very few respondents rated any of the barriers as not important. Problems related to capital availability were clearly considered the most important obstacle to energy efficiency investment. Top of the list was other priorities for capital investment (4.3), followed by strict adherence to capital budgets (4.0) and general lack of capital (3.9). Lack of time was the next most commonly reported obstacle, with an overall score of 3.8. This was closely followed by departments being unaccountable for energy costs, with a score of 3.7. The factor considered least important was lack of technical skills by university staff, which had an overall score of 2.4. It is likely, however, that individuals would be reluctant to admit that they had insufficient skills. Barriers related to risk were also rated relatively low. Business and market uncertainty had an overall score of 2.6, while technical risk scored 2.8. Again, the Energy manager may not be the best placed individual to judge the former, and the consequences of business uncertainty may be manifest largely in the strict payback criteria required by financial staff. 2 Barriers related to heterogeneity (e.g. technology inappropriate) and hidden costs (e.g. hassle, inconvenience) were considered relatively unimportant, scoring less than average at 2.8 and 2.9 respectively. However, salary overheads may be considered a form of hidden cost. Perceptions of the importance of barriers were marginally higher for smaller sites than for large sites, with overall scores of 3.5 and 3.2 respectively. Areas where this difference was particularly important include i) capital availability ii) energy manager lacks influence; and iii) level of awareness. The results are referred to again in the discussion on barriers in section As noted in Sorrell (1998), strict payback criteria may also result from other factors. 58

23 Table 3.26 Barriers to energy efficiency (average scores) Code Reason Small Mediu Large Total m C Other priorities for capital investment C Strict adherence to capital budgets C Lack of capital T Lack of time/other priorities A Departments not accountable for energy costs O Low priority given to energy management P Conflicts of interest within the organisation O Energy objectives not integrated into operating, maintenance or purchasing procedures O Low priority given to environmental performance P Energy manager lacks influence I Lack of staff awareness H Cost of disruptions /hassle /inconvenience H Poor technology performance (e.g unreliable) I Lack of information/poor quality information on energy efficiency opportunities G Technology inappropriate at this site H Cost of identifying opportunities, analysing cost effectiveness and tendering H Cost of staff replacement, retirement, retraining R Technical risk I Difficulty/cost of obtaining information on the energy consumption of purchased equipment R Business/market uncertainty S Lack of technical skills Mean score Note: Scoring from 1 (not important) to 5 (very important). 59

24 Table 3.27 Barriers to energy efficiency grouped by general type Code Reason Small Medium Large Total C Capital T Time A Accountability P Power O Other I Information H Hidden costs G Heterogeneity R Risk S Skills Mean Note: Scoring from 1 (not important) to 5 (very important). 60