Modelling the Initial Effects of the Climate Change Levy - Enagri

Modelling the Initial Effects of theClimate Change LevyA Report Submitted to HM Customs and Excise by Cambridge Econometrics,Department of Applied Economics, University of Cambridge and the Policy StudiesInstitute8 March 2005Cambridge EconometricsCovent Garden, Cambridge CB1 2HSTel +44 (0)1223 460760 Fax +44 (0)1223 464378Email sj@camecon.comWebsite www.camecon.comDepartment of Applied EconomicsUniversity of CambridgeSidgwick AvenueCambridgeCB3 9DEEmail terry.barker@econ.cam.ac.ukPolicy Studies Institute100 Park Village EastLondon NW1 3SREmail p.ekins@psi.org.ukPrefaceThis is the report for the study, Modelling the Initial Effects of the Climate ChangeLevy, produced by Cambridge Econometrics (CE), Department of Applied Economics(DAE), University of Cambridge and the Policy Studies Institute (PSI).All projections are the outcome of data analysis by Cambridge Econometrics usingversion 95 of the Multisectoral Dynamic Model of the UK economy (MDM) developedby Cambridge Econometrics and the Cambridge Growth Project. The energy andenvironment projections are based on the results of the energy and environmentalsub-models within MDM and use the data that were available in August 2004. Thefollowing have contributed to the simulation projections and the body of the report:Paolo Agnolucci (PSI); Dijon Anthony (CE); Terry Barker (DAE); Paul Ekins (PSI);Ben Etheridge (CE); Ben Gardiner (CE); Sudhir Junankar (CE); Ole Lofsnaes (CE);Jonathan Stenning (CE). The contribution of other Cambridge Econometrics staff isacknowledged.ConventionsInitials used:Page 1 of 116

CCAs: Climate Change AgreementsCCL: Climate Change LevyCCGT: Combined Cycle Gas TurbineCE: Cambridge Econometrics LimitedCHP: Combined Heat and PowerCO: Carbon monoxideCO2: Carbon dioxideDefra: UK Department for Environment, Food and Rural AffairsDES: DETR Digest of Environmental StatisticsDTI: UK Department of Trade and IndustryDAE: Department of Applied Economics, University of CambridgeDUKES: DTI Digest of UK Energy StatisticsEC: European CommissionETS: Emissions Trading SchemeEU: European UnionFGD: Flue-Gas DesulphurisationGHG: Greenhouse gasesGT: Gas TurbineGVA: Gross Value AddedHMT: Her Majesty’s TreasuryHMCE: HM Customs & ExciseMDM: Multisectoral Dynamic ModelMPP: Major Power ProducersNETA: New Electricity Trading ArrangementsNIC: National Insurance Contributions (Employers)NOX: Nitrogen oxidesNFH: Natural Flow HydroOfgem: Office of Gas and Electricity MarketsONS: Office of National StatisticsPM10: Particulate Matter with diameter of 10 micrometres or lessPSI: Policy Studies InstituteSIC: Standard Industrial ClassificationSO2: Sulphur dioxideVOCs: Volatile organic compoundsbn: billionGW: GigawattGwe: Gigawatt-electricGWh: Gigawatt-hoursm: millionmt: million tonesmtC: million tonnes of carbonmtC-eq: million tonnes of carbon equivalentmtoe: million tonnes of oil equivalentMwe: Megawatt of Electricitynes: not elsewhere specifiedpa: per annumpp: percentage pointt: tonneTWh: Terawatt-hoursPage 2 of 116

Executive SummaryProject background and objectiveThis report evaluates the initial effects of the Climate Change Levy (CCL). The focusof the evaluation is the environmental effectiveness of the CCL, specifically anyannouncement effects and the effects of induced price changes on energy marketsand greenhouse gas emissions (GHGs). The overall objective of the study is toinvestigate the effectiveness of the CCL in reducing energy use/carbon emissions,which was its principal purpose, and, where possible, its other (eg economic) effects.It has also been necessary to consider the Climate Change Agreements (CCAs) inthe modelling work, since the overall impact of the price signal provided by the CCLis necessarily influenced by the presence of the CCAs. The study uses theCambridge Econometrics MDM-E3 (energy-environment-economy) model of the UK,plus off-model analysis and other research methods where necessary. The reportcontains the following elements, which are addressed in turn:• Investigation of the CCL’s announcement effect (if any) onbusiness energy use, supported by a comprehensive technicalpaper proposing a ‘best practice’ method or economicevaluation. This has been the outcome of a literature review ofthe announcement effects of taxes and of ex-post evaluationstudies relating to carbon/energy taxes, mainly drawing uponexisting reviews.• The investigation of the CCL’s effects since its introduction onbusiness energy use from 2001 to 2004Q1.• The likely energy and carbon savings from the CCL by 2010 thathave been informed by our findings from the ex-post analysis.The approachWe report in Chapters 3 and 5 on:• The investigation of the announcement effects of the CCL onbusiness energy use.• Our estimate of the announcement effect.• Dummy-variable estimation as a means of testing for structuralchange.• The design and specification of a counterfactual ‘no CCL’reference case and alternative CCL scenarios and theirevaluation within MDM-E3, incorporating the announcementeffect estimated from quarterly equations.Page 3 of 116

• The results which are based on latest annual outturn energy(2003) and emissions (2001) data available in August 2004:MDM-E3 projections to 2010 were produced for the variousscenarios to permit an analysis of the economic andenvironmental effectiveness of the CCL and the CCAs.The key finding: a permanent announcement effectThe sectors considered in the formal econometric estimation were fuel users subjectto a further decline in energy use in 2003 by the broad sector other final users (iecommerce and the public sector). This followed a substantial fall in energy use in2002, which coincided with a decline in electricity prices over the preceding twoyears. It is a matter of econometric judgement as to how much of this fall in energydemand by other final users is to be attributed to the above-average temperatures of2002 and also in 2003, to the announcement effects of the CCL or some otherunrelated factor. Another judgement is related to whether the announcement effect istransitory or permanent. It has been modelled here as a permanent effect (in contrastto the initial model runs based on data to 2001 where it was modelled as a transitoryeffect).With the addition of historical observations to the sample, and more importantly theaddition of three extra quarter end-point observations (ie to 2004Q1), the final reporttests confirm the interim findings suggesting that the CCL announcement wasstronger in changing the long-term outcome than simply having a transitory effect(Note: Only for other final users was the dummy variable significant in the modelruns; thus all tests on the Announcement Effect were done using data and equationsfor this sector). The dummy variable was highly significant when included in the longtermcomponent of the equation, but not when included in the dynamic equation. Thisfinding was supported by the availability of more data (ie to 2004Q1) and by the otherjudgements that were developed and refined for the final model run.A key modification adopted in the final model run is the imposition of the equality ofthe short and long-run energy price elasticity. This judgment, which is consistent withHMCE advice, has been made because it sits more comfortably with a priorieconomic reasoning: this suggests that the long-run price elasticity should be equalto, if not greater than, the short-run price elasticity. Although the imposed equationresults in a reduction in the overall goodness-of-fit, when compared with theunconstrained equation (ie when the short-run price elasticity exceeds the long-runprice elasticity) specified for the interim model run, this deterioration in statisticalperformance is relatively small. The overall performance of the constrained other finalusers’ equation remains sufficiently robust to permit its use in the MDM scenariosimulations reported in this study.The inclusion of the CCL announcement effect as permanent implies the existence ofpath dependency or hysteresis in energy demand due to the possible institutionalfactors and irreversibility in the investment process.Method for testing for the general and announcement effects of the CCLPage 4 of 116

The underlying methodology involves the comparison of dynamic simulations of theUK economy 1998-2010, with the base case solution for the historical years matchingas close as possible to the latest data on the economy and energy system. Thescenarios are based on annual outturn data for energy use and energy prices to2003 and the latest emissions data to 2001 and the most recent assumptions fromthe CE’s July 2004 UK Energy and the Environment forecast report. However for thepurposes of analysing the CCL the European Union Emissions Trading Scheme isnot included in the solutions. The UK voluntary scheme is assumed to continue to2010 with an allowance price of £8/tC over 2003-2010; this allowance price is furtherassumed to be unaffected by the trading of emissions permits by those firms needingto meet CCAs. The scenarios outlined below are identified by letters, and the letteridentifies a particular scenario. The characteristics of the scenarios constructed are:• The counterfactual, reference case R which presumes that theCCL had never been announced or introduced and there are noCCAs.• B (the ‘base case’) then introduces the announcement effect asestimated and imposes the CCL at actual rates from April 2001to April 2004; thereafter rates are assumed to rise in line withinflation as measured by the RPI. However, since this is anuncalibrated solution of the model, which does not include anyeffects of the EU Emissions Trading Scheme, the projections inthis scenario should not in any way be regarded as a central ormost likely forecast of what we would expect to happen over theperiod to 2010.• C (the ‘reduced-rate case’) simply imposes the 20% CCL rate onthe CCA sectors (ie does not impose the CCL on the rest ofbusiness and commercial energy use), in order to see what the‘pure’ price effect of the reduced-rate CCL is on these sectors,with revenues used to reduce the rate of employers’ NICs. Thetreatment of the CCA target savings is the same as in ScenarioB, implying that the energy use by the CCA sectors is modeldeterminedand not adjusted to ensure that the energy efficiencytargets are achieved. This run includes any announcement effectfor 1999 and 2000 for these sectors and the rest of the economy,but this should not be too significant for the comparison that isbeing made (between sectoral energy use in Scenarios R and Cfor the CCA sectors).• F, FA and FB (the full-rate CCL cases) impose the full CCL on allenergy users including the CCA sectors (ie on all business andcommercial energy use) from April 2001. There are noadjustments to energy use in the CCA sectors to take account ofthe CCA targets: this means that the annual energy use prior tothe charging of the CCL is the same as under the ReferenceCase. All revenue raised, over and above the value of theoriginal 0.3 pp cut in employers’ NICs is therefore recycled in fullthrough additional reductions in the rate of NIC. Two variants ofthis scenario have been undertaken:Page 5 of 116

– Scenario FA is exactly the same as described above, except that norevenue is recycled, implying that the original 0.3 pp reduction inemployers’ NICs was never made.– Scenario FB is identical to Scenario F, except that the rates of CCLintroduced in April 2001 are set so as to achieve the same carbonreduction by 2010 as in Scenario B, with no adjustment to energy usein the CCA sectors to take account of the CCA targets. All extrarevenue above that collected in B is recycled through employers’ NICs.In the event that CCL revenues are lower than in B, NICs are cut by thesame amount (0.3 pp).Key resultsThe announcement effectThe announcement of the CCL in the 1999 Budget has its effect first appearing in theoutcome for 2000. This represents a change of -1.2% for other final users’ energydemand in 2000, and it grows rapidly thereafter (2003 is -13.8% rising to -14.6% by2010), but in combination with price effects.The effects of the CCL on fuel pricesIn 2002, the first full year of the Levy, the price of gas and electricity is estimated tobe higher in B compared to R across all business sectors. Gas prices are estimatedtoincrease more than electricity prices because of the differential in the CCL rates ongas and electricity, eg for 2002 it implies a rise in the price of gas relative to electricityof 2.2 percentage points for other industrial users. The proportionally higher increasein price of gas occurs despite higher effective p/kWh CCL rates for electricity, asabsolute gas prices are much lower than absolute electricity prices. Hence, themarginal increase in the gas price is higher than the marginal increase in theelectricity price, resulting in the proportionately higher rise in the price of gas. Theincrease in fuel prices in percentage terms as an effect of the CCL diminishes overthe period, due to the underlying projection of real increases in the levels of gas andelectricity prices to 2010 from the low levels in 2003 compared to the assumption thatCCL rates are held constant in real terms from 2005 using price inflation asmeasured by the RPI.The general effects of the CCL on fuel useThe announcement effect intensifies and combines with the price effects of the CCLfrom 2001 onwards; the total reduction in other final users’ demand for energy risesto 14.6% in 2010. It should be noted that most of the effects of the CCL are attributedto the ‘pure’ announcement effect, not to the price effect. The effect of the CCL onthe energy-intensive sectors is far less because most firms in these sectors do notpay the full rate of the Levy, and because no announcement effects are detected inthese sectors.Page 6 of 116

The CCL has different effects on demand for the individual fuels: coal, LPG, gas,electricity. These arise from the effect on relative prices, the type of fuel demand ofthe sectors most affected by the CCL, and the possibility for fuel switching in thesesectors. The reduction in demand is greatest for gas and electricity; other final usersaccount for a high share of their consumption, and this is the sector that is mostaffected by the CCL. Final demand for gas by other final users is reduced by 16.2%in 2010; demand for electricity by 14%. The declines, compared with the referencecase, for gas and electricity consumed in all final uses (ie industrial, commercial,transport and households) is 4.8% and 3.6 % respectively.The effects of the Climate Change AgreementsThe reduction in energy use of the industrial sectors over the period to 2010 in thebase case appears, with the exception of other industry, to be sufficient without anyfurther modification to the projections to achieve the CCA targets for both energysaving and energy efficiency. This energy use projected by energy demandequations includes substantial trends, estimated from historical data, in the long-termuse of energy to allow for improvements in energy efficiency, and which also reflectstructural change within the sector. These trends have been allowed to continuethroughout the projection period. A combination of technological change and relativedecline in UK energy-intensive subsectors of manufacturing (ie bulk chemicals asopposed to speciality chemicals), implies that the energy (and therefore carbon)saving and energy-efficiency targets would have been met without the CCAs. Thisresult is uncertain because the historical technical and structural-change trends maynot continue as in the past, and some firms in the broad groups are not covered byCCAs, especially in ‘other industry’ with around 50% coverage. Moreover, the CCAtargets are set in terms of improvements in energy efficiency, whereas the modelprojections have used energy intensity which means that the comparison is distortedby any structural change within the sectors. Only for one sector (other industry in2008) did we find that the CCA target would have been missed had no CCL everexisted. We also found that the price effect of the reduced-rate CCL was sufficient,on its own, for the target to be met (again with other industry in 2008 as anexception). However, it should not be interpreted from this that the CCAs wereineffective. The very significant over-achievement against the CCA targets at the endof the first target period (2002) has led Paul Ekins (2005) in his work on theenvironmental and economic impacts of the UK Climate Change Agreements tosuggest that the CCAs may have stimulated additional energy savings. Theargument is that the CCAs had an ‘awareness effect’ analogous to the CCL’sannouncement effect for the commercial (ie OFU) sector, that went beyond what theCCA targets would have achieved on their own. We would emphasise strongly thatthe treatment of the CCAs in this project is subject to the caveat that the scenariosimulations in MDM-E3 have necessarily required a more simplified and moreaggregated treatment of the 44 CCA sectors than was the case when the CCAs werenegotiated by a number of energy-intensive sectors. This issue is discussed in moredetail in Chapter 5.Estimated effects on electricity supplyPage 7 of 116

The main direct effect of the CCL on electricity supply is to encourage the installationof new renewables and CHP capacity, because generation from these sources isexempt from the Levy. Good Quality CHP capacity is increased by 1.2GWe by 2010,compared with the reference case, and contributes to progress towards theGovernment’s 10GWe target for that year.Revenues from the CCL and the effects of recyclingThe CCL revenues are calibrated to actual receipts of £831m in 2003 (the financialyear figure for 2003/04 was used in preference to the calendar year because thelatter showed an erratic quarterly profile). The CCL rates are assumed to be indexedwith inflation from the 2005 Budget and revenues rise to £933m by 2010. The NICreduction of 0.3 percentage points introduced in the 2001 Budget to offset the coststo business of the CCL appears to be worth more to business than the CCL revenuesthroughout the period. The reduction in NIC revenues is estimated to be £1,582m in2004 rising to £2,100m in 2010. The effects of the CCL and the NIC reductiontogether give very small effects on the main macro-economic variables; by 2010,GDP is only 0.06% higher than it would have been had the CCL never existed.Estimated effects on emissions of CO2 and greenhouse gases (GHGs)Total CO2 emissions are reduced by 3.1mtC (2.0%) from the reference case in 2002,and by 3.6mtC in 2003 rising to 3.7mtC (2.3%) by 2010. Other final users contributemost of the cut in emissions (1.8mtC in 2010), mainly because the reduction in totalenergy demand is greatest from this user due to the announcement effect, but otherindustry also delivers a cut in CO2 emissions of around 0.8mtC by 2010. Emissionsfrom power generation are also lower, due to the lower demand for electricity. TotalGHG emissions are reduced by 1.7% in 2002, rising to a reduction of 2.0% by 2010.The Reduced CCL scenarioThe reduced rate of 20% on the CCA sectors yields a CCL revenue of £127m in2002. This falls to £94m in 2010 as energy consumption by the CCA sectors falls.Nearly all the effects are in the four MDM industrial sectors, namely, basic metals,mineral products, chemicals and other industrial users, with about a 0.5-1.5% fall inenergy use by 2010 in each sector.The Full CCL Rate scenariosThe full-rate CCL with no CCAs and recycling of extra revenues raises, comparedwith the base case, an extra £366m in revenues by 2003 and total CCL receiptsreach £1292m by 2010. Fuel use in the main broad sectors with CCAs is reduced bya further 3% to 6%. CO2 emissions are reduced by around 0.5 mtC compared to thebase case. Lower emissions from the industrial sector are offset slightly by higheremissions from power generation due to the higher demand for electricity. Higherdemand for electricity in the full-rate scenarios is a result of the increase in the scopeof the CCL, which in turn strengthens the effect of relatively higher gas prices, againshifting demand towards electricity.Page 8 of 116

IntroductionBackground and ObjectivesThis report evaluates the initial effects of the Climate Change Levy (CCL). The focusof the evaluation is the environmental effectiveness of the CCL, specifically theeffects of induced price changes on energy markets and greenhouse gas emissions(GHGs).The background to the introduction of the CCL and the Climate Change Agreements(CCAs) is directly relevant to the study as it relates to the possible existence of anannouncement effect and the chronology can be summarised in the following terms.In Budget 1998 the Government announced its intention to explore the possibility ofintroducing a tax on the business (the term business is used throughout this report torefer to industrial, commercial and public sector users of energy) use of energy. It setup a Commission chaired by Lord Marshall to explore this issue. Lord Marshallreported later that year that there probably was a case for such a tax, carefullydesigned.The Government accepted this recommendation and over the next 18 monthsconsulted widely on the form and design of the tax, which it said it intended tointroduce in April 2001. The CCL was in fact introduced at that time, at the rates setout below, as part of a package of policy measures, which included CCAs with 44energy-intensive sectors, the establishment of an energy efficiency fund with some ofthe revenues (this fund now finances Action Energy administered by the CarbonTrust) and enhanced capital allowances for a number of low-carbon technologies. Asimultaneous cut in employers’ National Insurance Contributions (NICs) wasintended to return the balance of the revenues from the CCL to the business sector ina way that did not affect the CCL incentive to save energy.The CCL was introduced on 1st April 2001, but it was announced in the March 1999Budget to give businesses a full two years to adjust.The rates of the Levy are:• 0.15p/kWh for gas;• 1.17p/kg (equivalent to 0.15p/kWh) for coal;• 0.96p/kg (equivalent to 0.07p/kWh) for liquefied petroleum gas(LPG); and• 0.43p/kWh for electricity (a more detailed description of the taxcan be found at www.defra.gov.uk/environment/ccl/intro.htm).The Levy does not apply to:Page 9 of 116

• fuels used by the domestic or transport sector, or fuels used forthe production of other forms of energy (eg electricity generation)or for non-energy purposes;• energy used by registered charities for non-business uses, andenergy used by very small firms, ie those using a de minimis(domestic) amount of energy; nor• oils, which are already subject to excise duty.There are also several exemptions from the Levy, including:• electricity generated from new renewable energy (eg solar andwind power);• fuel used by good quality combined heat and power schemes(“Good Quality CHP”- certified via the CHP Quality AssuranceProgramme CHPQA);• fuels used as a feedstock; and• electricity used in electrolysis processes, for example, the chloralkaliprocess, or primary aluminium smelting.The overall objective of the study, as mentioned above, is to investigate theeffectiveness of the CCL in reducing energy use/carbon emissions, which was itsprincipal purpose, and, where possible, its other (eg economic) effects. The mainfocuses of the study are the investigation of the nature and size of the‘announcement effects’ and the ‘price effects’ of the Levy, ie the effects of inducedprice changes on energy markets and greenhouse gas emissions. It has also beennecessary, albeit in a relatively simplified manner, to consider the CCAs in themodelling work, since the overall impact of the price signal provided by the CCL isnecessarily influenced by the presence of the CCAs. However a full-scale analysis ofthe impact of the CCAs was not within the remit of the study, which uses theCambridge Econometrics MDM-E3 (energy-environment-economy) model of the UK,plus off-model analysis and other research methods where necessary. Acomprehensive assessment of the CCAs would require either a detailed bottom-uptechnological approach or a top-down econometric model disaggregated at the 44CCA sector level. Neither approach is possible without extending MDM (which hasonly 4 broad energy-intensive industrial sectors out of the 50 industries in the model).The report contains the following elements, which are addressed in turn:Page 10 of 116

• Investigation of the CCL’s announcement effect (if any) onbusiness energy use, supported by a comprehensive technicalpaper proposing a ‘best practice’ method of economic evaluation.This has been the outcome of a literature review of theannouncement effects of taxes and of ex-post evaluation studiesrelating to carbon/energy taxes, mainly drawing upon existingreviews. Two papers entitled Ex-Post Evaluations of CO2-basedTaxes: A Survey (Agnolucci P, 2004) and The AnnouncementEffect and Environmental Taxation (Agnolucci P, Ekins P, 2004)have been published as Tyndall Centre Working Papers and arereferenced in Chapter 6 of this report.• The investigation of the CCL’s effects since its introduction onbusiness energy use so far.• The likely energy and carbon savings from the CCL by 2010 willbe informed by our findings from the ex post analysis.The exercise will also provide insights into the effectiveness of the CCAs inpromoting energy saving by key energy-using sectors. The project is especiallymotivated by the desirability of undertaking ex post evaluation of major publicpolicies, so that the comparison may be made with ex ante evaluations of theireffects and the policy may be amended to enhance its effectiveness if desired.CE undertook an initial analysis of the CCL following the Chancellor’s Budgetproposals in March 1999 (Cambridge Econometrics, 1999). The Government’sexpectations at the time were for CCL rates and exemptions that would raise a totalof £1.75bn in the first full year of the CCL from April 2001. The CE analysissuggested that such a levy would reduce annual CO2 emissions in the business andpublic sectors by 2.2 mtC by 2010, primarily as a result of lower electricity generationfrom coal. This ex post evaluation will provide a check on those estimates (which didnot allow for any announcement effects) as well as providing an interestingcomparison with the Government’s own ex ante evaluations.The Government’s own estimates published in the UK’s Third NationalCommunication under the UN Framework Convention on Climate Change (3NC;2001) suggested that the CCL including the exemption for renewables and CHPwould reduce CO2 emissions in the business sector by 2 mtC by 2010. The CCAs,meanwhile, were expected to save a further 2.5 mtC per year by 2010, with energyefficiency measures under the CCL package contributing an additional 0.5 mtCreduction. These projections were broadly unchanged from the estimates cited in theUK Climate Change Programme (UK CCP, 2000), published in November 2000:these also indicated that the CCL package as a whole would save at least 2 mtC peryear by 2010, with the first wave of the CCAs expected to deliver around 2.5 mtC ofcarbon saving.Page 11 of 116

The ApproachThe study consists of several key stages, defined by deliverables. However, anappropriate lag has been specified between the publication of outturn data and thedelivery of the modelling output to allow sufficient time for analysis. The studytherefore has consisted of various model runs undertaken at different points in timeover an 18 month period to allow more outturn data to be incorporated.It is important to recognise, that there is considerable uncertainty as to how the pastdata on energy use can be interpreted (as discussed in Chapter 3). This uncertaintyis reflected in qualifying statements in the report which provide some indication of therobustness of the findings, particularly in relation to those concerning the CCAs.We report here on:• The investigation of the announcement effects of the CCL onbusiness energy use.• Our estimate of the announcement effect.• Dummy-variable estimation as a means of testing for structuralchange.• The design and specification of a counterfactual ‘no CCL’reference case and alternative CCL scenarios and theirevaluation within MDM-E3, incorporating the announcementeffect estimated from annual equations.• The results which are based on latest annual outturndisaggregated energy (2003) and emissions (2001) dataavailable in August 2004: MDM-E3 projections to 2010 wereproduced for the various scenarios to permit an analysis of theeconomic and environmental effectiveness of the CCL.Page 12 of 116

Outline of this ReportChapter 2 of the report discusses the methodology employed to test, using quarterlyand annual data, for the announcement/direct effects and the general or the MDM-E3model-simulation effects of the CCL. Chapter 3 outlines the data sources used andthe required data processing; it then discusses the procedures followed to estimatethe energy demand equations on a quarterly and annual basis and the subsequentincorporation into the MDM-E3 equation system. Chapter 4 describes the design andspecification of the scenarios, the scenario analysis undertaken and the treatment ofthe CCAs within the study. Chapter 5 then discusses the projections for the period1998-2010, focusing upon the Reference (counterfactual) Case, the Base Case andthe reduced and full CCL rate scenarios. Appendix A provides a broad description ofthe structure of MDM-E3, excluding the work undertaken for this project. In particular,it outlines the energy-environment-economy linkages and how each of the parts ofthe model is constructed. The details of data and model classification used in MDM-E3 are given in m,Appendix B. Appendix C presents the results of the estimation ofthe quarterly and annual energy demand equations; the full results of the scenarioprojections (1998-2010) are given in Appendix D.MethodologyIntroductionThis chapter reports the approach adopted for the analysis of the general (GE) anddirect effect of the (CCL) to be reported in later chapters. The analysis is based upontwo datasets: the quarterly dataset covers the period 1973Q1 to 2004Q1; the annualdataset covers a broadly similar time period, 1972 to 2003, but it contains moredetailed information at the broad industry sector level.Definition of the Modelled Direct Effect and the GeneralEffect of the CCLThe modelling undertaken for this study distinguishes between the direct effect of theCCL determined from the single equation methodology reported and the generaleffect (GE) determined by an economy-wide simulation of the CCL using the MDM-E3 model as reported in this chapter.Page 13 of 116

The direct effect refers to the energy saving being achieved between 1999Q1 and2004Q1 from the announcement of the CCL and the ‘attention’ or ‘awareness’ impactreflecting institutional factors as the tax gradually becomes a key board-level issuefor companies. To assess the announcement effect (AE) (one of the components ofthe direct effect), we construct and include a dummy variable and run a regressionwith the dataset until 1998Q4 (1999Q1 as the breakdate). If there is a ‘structuralbreak’ (ie a significant dummy variable), then there is also an AE, whose magnitudecan be quantified; if there is no such break, then no AE has been identified. To allowfor the gradual impact of the AE, a ‘diffusion’ dummy variable is introduced asfollows: a low positive value in 1999Q1, building up over time to a value of one by2002Q2. This profile is consistent with the results from the estimation of annualenergy demand equations within MDM-E3. The CCL announcement ‘after’ effectcontinues at a value of 1 through to the end of the sample period (2004Q1) as thiswas found to be most significant and depicts the profile of a long run effect. For anoverall explanation of the effects, see Table 2.1:The main overall direct effect refers to the energy savings being achieved over theestimation period 1999Q1-2004Q1 inclusive, and projected using MDM-E3 to 2010.As there is a relationship between price and energy demand (or at least we havegood a priori reasons to presume so), the introduction of the tax is bound to havecaused a certain amount of energy savings (the price effect of the tax). However it ispossible that the announcement and the successive introduction of the tax has alsocaused a change in the value of the parameters and we shall test for this. If this hasbeen the case, then there will be further energy savings due to the change in thevalue of the parameters of the equation (ie a structural effect) after theannouncement of the tax (ie the period from 1999Q1 to 2001Q1).The method followed involves the introduction of a dummy variable, with its valuerising over time, in a regression run over a dataset ending in 2004Q1 and taking1999Q1 as a breakdate:• if there has been a structural break, the direct effect is composedof the AE (structural effect) and the price effect• if there has not been any such break, the direct effectcorresponds only to the price effect of the CCLPage 14 of 116

Overview of the ApproachEstimation of the AE in demand equations in quarterly and annual dataWhen analysing the effect of a tax, two issues have to be addressed. Firstly, while intheory the focus is on the responses of economic groups, at the empirical level onlythe outcome arising from those responses are normally observable. Secondly, theanalysis of the effect of a new tax also requires the construction of a counterfactual,or a ‘reference’ scenario, in which the tax is not introduced.The econometric approach adopted in this study enables both the construction of areference case and of indicators of the effect of the tax. This approach involvesregressing energy consumption on its determinants over time, by using the generalto-specificmethod of model construction. The parameters of the regression and theanalysis of the parameters’ stability enables us to measure the savings brought aboutby the announcement of the CCL (AE) and more generally by its introduction (ie thedirect effect).Econometric analysis of energy demand are usually based on log-linear specificationsuch as those used by Pesaran and Smith (1995).In the model above, the energy consumption in tonnes of oil equivalent, E, is afunction of the real Gross Domestic Product (GDP), Y, and of an aggregate index ofenergy prices relative to the GDP deflator, P; the subscript t indicates the period ofobservation. The difference between (1a) and (1b) is just in the inclusion of laggedindependent variables. A priori knowledge has led to the possible inclusion of atemperature variable (both short and long-term), which we will later illustrate as beingan important determinant in the demand for energy in certain sectors.Sectoral and total energy demandEquations analogous to equations (1a) and (1b) can be estimated both for the totalenergy consumption and at a more disaggregated level. As pointed out by Pesaranand Smith (1995), disaggregated analysis is helpful as residential, industrial andtransport demands for different types of energy differ systematically in ways likely tobias the aggregate estimates. Furthermore, as shown in Agnolucci and Ekins (2004),the announcement effect has generally been detected in the consumption of singlefuels and/or sectors of the economy.Page 15 of 116

In the case of the CCL, if analysis is conducted only for total energy, changes in thebusiness, commercial and public sector (ie the sectors paying the tax) could be easilyconcealed by the constant consumption pattern in the transport and householdsectors (ie the sectors not subject to the tax). Therefore, this study estimates, usingquarterly data, both the total UK energy demand and more importantly the demandfor energy used in (I) the industrial sector, and (ii) in the commercial, agricultural andpublic sector (ie ‘other final users’). In addition, the use of the annual data allows theestimation of energy demand for more detailed industrial sectors.The analysis of fuel-specific equations could be accommodated by using a modelwith an equation for each of the fuels used (eg a VAR model - a VAR model is atime-series model, in which a set of variables is regressed on the differences of theirlagged values). However, this approach is both data intensive and time consuming,due to the numerous cross-equation relationships to be estimated, and hence couldnot be accommodated given the scope and layout of the project.Identifying the Announcement DateLord Marshall’s report in November 1998 concluded that there was likely to be a casefor a tax on energy consumption. Soon after this in the annual Budget in March 1999the Levy became Government policy and it was projected to raise £1.75 billions (thiscosting subsequently changed in the Pre-Budget report 1999 to £1bn as the CCL’sscope and rates were revised after further consultation with industry) in its first year,2001/2 (Ends Report, March 1999). On 1 April 2001 the tax, called the CCL, wasintroduced.Using annual data, if 1 January 1999 is taken as the announcement date of CCL,only five annual observations (the dataset used in this study conforms to the practicerecommending that we leave at least 5% of observations between the breakdate andthe end of the sample (see Hansen, 2001)) are available for the analysis of structuralstability. Econometric estimation of energy demand is generally carried out usingannual data because of the seasonality of quarterly observations. However, thelimited number of annual data is likely to be a problem in the detection of the AE andtherefore energy demand equations have been estimated using quarterly data. Thismeans, if the beginning of the first quarter in 1999 is taken as the announcementdate, that twenty-one and ten observations are available to test for the direct effectand AE, respectively. The dataset for the study of the AE is 1973Q1-2001Q2, whilethe dataset for the analysis of the direct effect is 1973Q1-2004Q1.Demand RigidityPage 16 of 116

Time-series estimation of energy demand, as pointed out by Baltagi and Griffin(1984) omits, however, the long lags related to the time needed by economic groupsto adjust their demand to the long-term desired outcome (ie because of long-termcontracts signed by firms, the building of power stations etc). This issue is ofparticular concern here, especially for the AE, as the time needed by the policy toaffect the energy consumption blurs the temporal border of this effect. In thedefinition of AE given earlier on in this section, it is implied that the response of theeconomic groups is more or less immediate. However, if this response is constrainedby the rigidities in the energy system, the effects of the announcement of a policymight materialise after the policy is implemented. In the case of the direct effect ofthe CCL, the longer observation period makes this issue less of a problem. It turnedout that this “delayed” reaction to policy implementation did indeed appear to occurfrom the results shown within the regression testing, and consequently the CCLdummy was found to be most significant when it comes into complete effect (iedummy takes on a value of 1) in 2002Q2, a full year after the tax was introduced.Pesaran and Smith (1995) note that time-series estimation often underestimates thegeneral effect of the price change, as it works through the system, and therefore thetime-series estimates of price elasticities are normally biased downwards. It issometimes suggested that the price effect on energy demand can be better analysedusing a panel dataset, although it is also the case that these biases may be presentin dynamic panel-data studies. However, as a panel dataset is not readily available,the use of time series was the only approach that could be employed in this study.Implementation of the test for AE and Direct EffectThe AE and the direct effect is tested in an Error Correction Model (ECM)WhereEt is the energy consumption per capita in tonnes of oil equivalent, is an aggregateindex of energy prices, is an index of the general level of prices, is real output percapita, and tet is the variance of temperature from the 30 year mean. The ECMmodel has the advantage of being able to distinguish between the long-run ( and )and short-run parameters. This distinction is particularly important to the analysis ofCCL. After some preliminary testing, all term’s involving the lags of the differences(with parameters ) were dropped from the equation estimation. In testing for AE andthe direct effect, this study follows the approach suggested by Pesaran, Smith andShin (PSS) (2001) which tests the existence of a relationship between the levels ofvariables, irrespective of whether the regressors are stationary, cointegrated of order1 or mutually cointegrated (ie some are stationary, some are integrated of order 1).Page 17 of 116

At this point it is worth pointing out a substantial difference between AE and the directeffect. If there is a relationship between price and consumption of energy, as it isexpected, the CCL will have a direct effect irrespective of the structural stability ofequation (2). Indeed, as the tax causes a price increase there will a correspondingdecrease of consumption, whose magnitude is determined by the coefficient of themodel. Through the parameters of equation (2), it is therefore possible to computethe energy savings caused by the tax. Similarly, savings originating from other taxrates can be quantified under the assumption that different rates would have had thesame effect on the parameters’ stability.However, any price increase in the period of time between the announcement andthe enforcement of the CCL is unlikely to be due to the tax. An announcement effect(AE) on the energy consumption will therefore be detected, if and only if, theparameters of equation (2) have changed, and the proxy variable for theannouncement (ie CCL dummy) is found to be statistically significant. Clearly, if therehas been an AE, the direct effect will be composed by the “price effect” mentionedabove and of the announcement effect, and be attributable to the impact of theparameters’ structural change on energy consumption. In this case, the knowledge ofthe tax rate and the difference between the parameters pre- and post CCL enable thecomputation of the energy savings brought about by the tax. (In addition, when theannual estimated equations are included in MDM-E3, the energy, CO2 andgreenhouse gases (GHGs) savings can be calculated for all relevant scenarios,taking into account whole-economy interactions and feedbacks.)Page 18 of 116

When the model was first run, the CCL effect was treated and tested solely as ashort-term effect (ie a transitory effect). This was in line with economic theorysuggested in the literature and the general view on the behaviour of previousstructural-break events (Pesaran (personal communication): this point of view isconfirmed by the fact that empirical studies on structural breaks normally considerexceptional events, such as the Depression of the early 1930s or the oil price risesseen in the early 1970s and again in the late 1970s/early 1980s). However, due tothe addition of historical observations to the sample, and more importantly theaddition of an extra quarter end-point observation (ie in 2003Q2), further AE testssuggested that the CCL announcement was stronger in changing the long-termoutcome than simply having a transitory effect taking place (Note: Only for other finalusers was the dummy variable significant in the model runs, thus all tests on the AEwere done using data and equations suited to this sector). The dummy variable washighly significant when included in the long-term component of the equation, butwhen modelled together with a short-term variable in the dynamic equation, the latterwas in non-significant. The main finding then, is that there is a permanent effect, afterthe CCL’s announcement. The final model run, which added three extra quarters tothe estimation period (ie to 2004Q1), confirmed this result of a permanent effect forother final users. This long-term announcement effect, presumably resulting from theimplementation of cost-effective energy-saving measures before the CCL wasactually introduced, suggests that the commercial sector was not operating on itscost efficiency frontier. The nature of the implemented measures is not known, buttheir early implementation means that they are unlikely to have involved significantnew investment, but rather consisted of energy management measures, perhapsthrough the creation of new institutions within firms to reduce energy demand, andthrough changing employee behaviour to encourage energy saving. Such institutionalchanges are unlikely to be reversed over time, which could explain why the estimatedannouncement effect is permanent and not temporary.Thus, there appears to be significant path dependency or hysteresis (thephenomenon of hysteresis is well known in labour market analysis, where it has beenfound that the history of unemployment affects the employability of labour. Here it isfound in the energy market due to the fact that long-term changes in energy demandare determined by irreversible investment in appliances and buildings.) in energydemand.Given the ECM in equation (2) the testing of structural stability can be implementedin the following manner:1 Use Least Squares with the pre-announcement observations to estimate equation(2) for different values of the lags and select an appropriate order by using one of theinformation selection criteria2. For the selected model, test the existence of a long-run demand equation usingthe PSS test3. Assuming a long-run energy demand equation exists, estimate the long-runelasticities4. Run the error correction equations, with the error correction termPage 19 of 116

taken as given using pre- and post-announcement observations with announcementdummies (shown as LRDt ) included in the long-term component of the equation. Atest of no structural break corresponds to testing the null hypothesis that thecoefficient of the dummy is zero.In the event that a long-run energy demand is not identified on pre-announcementobservations, the above analysis (ie from point (3) above) is repeated with.For example, given if one wants to test whether or not the economy is more energyefficient, given the income and the price level, as a result of the announcement of theCCL, one has to test for the structural stability of the intercept a0 in equation (2) witha dataset until 2001 Q1. To implement this test, one needs to run the regressionwhere contains the long-term dummy (LRDt) variable and . For the CCL to have hadthe desired effect one would expect a statistically significant negative value for . Atest of no AE on the values of the intercept can be carried out by testing the nullhypothesis that = 0. The t-ratio of the OLS estimate of in the above regression is validfor this test.The announcement of the CCL can imply that the economy will react in a differentway to a change in the level of prices and income.In particular:• less energy might be needed to fuel a given change in income -a decrease in the short-run slope coefficients of the relationshipbetween income and energy consumption ;• a given increase in the price level might cause a bigger decreaseof consumption – an increase in the absolute value of the shortrunslope coefficient ;• the economy could have changed the speed of adjustment froma position of disequilibrium (ie a change in the short-termparameter could have occurred).Analogously to the example above, the AE is tested through a dummy variableapplied on the corresponding coefficient.As mentioned above, the structural stability of equation (2) determines thecomponents of the direct effect in case:Page 20 of 116

• if there has not been any change in the parameters, the savingbrought about by the tax will be due only to the price changecaused by the CCL• if there has been a change in the parameters, the direct effect iscomposed of the savings caused by the structural instability andby the increase of the energy priceFor the direct effect, the analysis of the parameters and the computation of thesavings is carried out with a dataset until 2004Q1.Independently from the outcome of these tests, the evolution of the coefficients overtime will be assessed by recursive estimation ie CUSUM and CUSUMQ tests.As already mentioned in the discussion of sectoral and energy demand, the modelabove (equation 3) is estimated for both the total energy demand and at a moredisaggregated level - (I) industrial sector, and (ii) the commercial, agricultural andpublic sectors combined (ie other final users).The model above is estimated initially using quarterly data. Available evidenceindicates that if seasonally-adjusted data are used in econometric modelling,estimated static and, in particular, dynamic relationships are distorted by theseasonal adjustment. Hylleberg (1992) points out that as the degree of distortionsvaries, the best advice for the researcher is to consider both the seasonally-adjustedand the seasonally-unadjusted series. However practical considerations, related todata availability, dictated that the model in equation 3 above be estimated using onlyseasonally-adjusted quarterly data.Definition of scenariosThe time period for the projections in the model run is from 1998 (when theGovernment first announced its intention to introduce the CCL) to 2010 on acalendar-year basis. The scenarios outlined below are identified by a letter.Assumptions which are applicable to all the scenarios are:• all other variables/assumptions (eg energy prices) are as of themost up-to-date data and most recent CE model runs;• the European Union Emissions Trading Scheme (EU ETS) is notincluded in the solutions. The UK voluntary scheme continues to2010 with an assumed allowance price of £8/tC over 2003-2010;this allowance price is assumed to be unaffected by the tradingof emissions permits by those firms needing to meet CCAs.Briefly Defining the ScenariosThere are effectively six different scenarios run, each producing results given variousassumptions and conditions:Page 21 of 116

• The Reference Case (R), also known as the counterfactual case,projects through to 2010 as if the CCL had never beenannounced or introduced.• The Base Case (B), introduces the CCL announcement andimposes the Climate Change Levy at actual rates for April 2001to April 2004, and at projected rates to 2010.• The Reduced Rate Scenario (C), implies a 20% CCL on the CCAsectors (ie not on the rest of business and commercial energyuse) in order to determine the ‘pure’ price effect.• The Full Rate Scenarios are broken up into 3 sub-components,namely F,FA and FB.• The F scenario imposes the full CCL on all energy usersincluding the CCA sectors (ie on all business and commercialenergy use) from April 2001. There are no adjustments to energyuse in CCA sectors, and all revenues are recycled throughadditional NIC reductions.• The FA case scenario is the same as above, except there is norevenue recycling taking place.• The FB case scenario is also the same as F, except the CCLrates introduced in April 2001 are set so as to meet the samecarbon reduction by 2010 as in the case of scenario B, with noadjustment to energy use in the CCA sectors.The above section only provides a brief description on each scenario. A more indepthdiscussion on the construction and results of the scenario cases is presentedin Chapter 4.Derivation of ‘best practice’ method/ model specification based on quarterlydata…The main concern in determining the ‘best fit’ model, amongst others, was thenumber of data points available for analysis. There are not many annual dataavailable for determining the presence and effect of the announcement (ie only 5observations available in the annual data - 1999 to 2003). A strategic decision wasmade to model a set of equations using quarterly data covering the same variablesover a similar sample period as found in the annual data, permitting greater analysisof the announcement effect.Page 22 of 116

The first step in the procedure of ‘best practice’ econometric method/modelspecification was to begin with a general econometric model, and start removing theinappropriate variables (either because they do not conform to economic theory ortheir coefficients prove to be statistical insignificant), and move towards obtaining amore specific quarterly equation for each of the three energy using sectors (iegeneral-to-specific model for industry, other final users, and the whole economy). Apriori knowledge suggests that the CCL effect may only be present within the otherfinal users sector, and thus rigorous econometric testing on the time-series data wasundertaken using Microfit econometric package; at each step of the process, theresults were thoroughly analysed to arrive at a ‘best fit’ quarterly equation - see TableC4, C6 and C7 in Appendix C....and its incorporation in annual equations for use within MDM-E3The next step involved estimating the annual equations in Microfit by imposingcertain long-term estimates gathered from the quarterly equation estimationpreviously discussed. There are, however, problems with directly replicating theresults from the quarterly equations in the annual ones, in that there are many moresectors for which annual data are available. Other final users is the only sector with aclosely matching definition both in the quarterly and the annual data, and the processof linking quarterly estimates with annual estimates was successful in providingsimilar results in the overall fit and coefficients for the freely estimating variables.Procedure for incorporating AE within MDM-E3The procedure for incorporating the announcement effect within MDM-E3 leads tothe final step of the derivation of ‘best practice’ econometric method. The procedureis simple: impose the relevant corresponding coefficients from the quarterlyequations found using Microfit (as suggested in the previous step), and essentiallyreplicate the equation specification for the annual equation Tables (at all timeschecking the results) found in Appendix C. As noted before, this transition wasaccomplished for the other final users sector.CCL dummy profileThe profile of the CCL dummy, which represents the presence of an AE, was derivedfrom econometric testing, using the t-ratio (test for significance) to determine the‘best fit’ equation - seven such output results can be seen in Table 3.1 in Chapter 3.Adopting this definition of the CCL dummy, equations were estimated for each of thequarterly-data energy sectors (industry, other final users, whole economy) to producethe best statistical results, while still conforming to economic theory. Various stabilitytests were included in the process, and can be seen in Appendix C as shown by thevariable deletion (PSS) test.Scenario projections to 2010Page 23 of 116

By incorporating the ‘best practice’ methods and model specification within the MDM-E3 model, projections for key figures such as CO2 emissions, energy demand bysectors, and various other macro-environment variables can be shown to 2010. Asstated before, for certain energy related variables, the model simulation begins in1998 (in order to capture any CCL announcement effects), and therefore theseprojections are not intended to reproduce exactly the outturn for period 1998-2003.Chapter 5 provides more detail on the scenario projections to 2010, and the fullresults are given in Appendix D.The method adopted in this study to determine the extent of carbon savingsattributable to the CCL was based on dynamic model simulations of MDM-E3 overthe period to 2010. The results showing the impact of the CCL, when compared withthe Reference Case R, are also discussed in Chapter 5.Scenario analysisThe analysis of the scenarios is an important as determining which equation best fitsthe data. They are compared to one another in order to obtain answers to various‘what if’ questions, as well as to evaluate the general effect of the CCL underdifferent assumptions and conditions. Chapters 4 and 5 discuss the scenariospecification and results in more detail.Testing for the Announcement Effect:Estimation Procedures for Energy DemandEquationsIntroductionThis section reports the estimation of the energy demand equations as set out inChapter 2. Section 3.2 contains a description of the quarterly equation and estimationresults. The data comprise quarterly observations for the industrial sector, other finalusers and the whole economy 1973Q1-2004Q1. Section 3.3 contains a description ofthe annual equation and estimation results. The annual observations’ data range is1972-2003 for the sectors comprising basic metals, chemicals, other industry,mineral products, and other final users. A non-technical discussion follows on theresults of the estimation for the above mentioned sectors. The main focus of thechapter is the reporting of the stand-alone energy demand equations (using thequarterly data) and of the sectoral energy demand equations (using the annual data),which have been used in the MDM-E3 model projections.Estimation of Quarterly EquationsData sources and processingPage 24 of 116

Data on energy consumption and prices are available in most detail on an annualbasis, as presented in the Digest of United Kingdom Energy Statistics (DUKES).However, following technical decisions on the econometric assessment ofattention/announcement effects of the introduction of the Climate Change Levy, itwas decided to estimate a quarterly model and, as a result, a new quarterly databankwas created.Data used in the estimation of the quarterly equationsMost of the data used in the quarterly model were collected from the following fourpublications: Monthly Digest of Statistics (ONS), UK Economic Accounts (ONS),Quarterly Energy Trends (DTI), and Quarterly Energy Prices (DTI). Data werecollected for the following time series: energy consumption, energy prices,temperature, output (GVA) and the stock of fixed capital. These were formed as bothunadjusted and seasonally-adjusted time series for three different sectors: industry(comprising mainly of manufacturing), other final users, and the whole economy. Theconsumption and output data were transformed into per capita figures, based onhistorical population data. Appendix C contains further details on the data sources.An important difference between the modelling which underpinned the interimfindings is that, because of further data collection, the sample period could beextended back to 1973Q1. This was then kept as the starting period observation forthis model run. Data for 2004Q1 have also become available for the final run; thishas allowed the extension of the sample size of the data by three observations.The sectors used in the quarterly estimation follow the classification reported inDUKES 2003 Table 1E. The industrial sector comprises the manufacturing sectorsexcluding fuel manufacture (SIC 1992 codes 17-22,24-37), together with construction(SIC 45), water supply (SIC 41) and mining and quarrying (SIC 13-14). Other finalusers comprises public administration (SIC 75, 80, 85), commerce (SIC 50-52, 55,64-67, 70-74), agriculture (SIC 01, 02, 05) and ‘miscellaneous’ (90-93, 99), butexcludes domestic use. The whole economy comprises all final users of energy: theindustrial and other final users sectors as defined above, together with transport andthe domestic sector.Problems with the classificationThe classification outlined above was adopted because it is used in energy statisticspublications, and the methodology adopted was restricted mainly by the availability ofquarterly energy data. The classification entails two main problems. First, theindustrial sector does not coincide exactly with those industrial sectors liable to theCCL. For example, consumption by electricity and gas suppliers (SIC 40) is taxable,but is excluded. This might distort the estimate of the announcement effect of theCCL.Second, this classification does not coincide exactly with the MDM-E3 fuel users. Forexample consumption by the construction industry is included in other final users inMDM rather than in industry. This problem slightly reduces the compatibility betweenthe quarterly model and MDM-E3.Energy consumption dataPage 25 of 116

Energy consumption data were collected from Quarterly Energy Trends from 1973Q1to 2004Q1. The format of this publication has changed several times over the past 30years, so the collection of data was limited to those time series that appeared overthe whole historical period. Data were gathered, first, from the table: ‘EnergyConsumption by Final Users’, then, after 1977, from the table: ‘Supply and Use ofFuels’ and from various tables after June 2000. Energy consumption by industry isthe sum of consumption by basic metals and ‘other industry’ (ie industrial sectorsother than basic metals). Consumption by other final users appears directly.Consumption by the whole-economy sector is total final consumption (ie excludesconsumption by energy industries and losses in transformation). The time serieswere translated from various units to a common basis expressed in terms ofthousand tonnes of oil equivalent and were then compared with the annual timeseries in DUKES 2004 to ensure consistency across time.Population data The quarterly model requires energy consumption per capita, so thevalues of output for the sectors involved were divided by the UK population. Mid-yearannual population estimates were collected from the Annual Abstract of Statistics(ONS). Linear interpolation was then used to produce the necessary quarterlyestimates.Energy price dataQuarterly Energy Prices contains detailed data on energy prices by fuel type and forvarious fuel users. It also publishes the quarterly UK GDP deflator in 2000 prices.From this source, a time series of relative energy price indices could be created foreach sector with and without the CCL. Quarterly Energy Prices contains data onaverage industrial fuel prices both including and excluding the CCL, collected from anindustry survey. The prices including the CCL are calculated using an estimate of theCCL paid, taking into account all discounts. The DTI does not publish quarterlyestimates of energy prices for other final users. Prices excluding the CCL for thissector were calculated by first assuming individual fuel prices are 50% more thanthose for the industrial sector (a broadly representative figure chosen by inspectionfrom annual data) and then calculating an average energy price weighted byconsumption of the different fuels. TheCCL was then added at the full rate from 2001Q2 onwards to obtain prices includingthe CCL.The prices for the whole economy were calculated by averaging prices across theindustrial, commercial, transport and domestic sectors weighted by fuel consumption.Domestic fuel prices are those from the fuel components of the retail price index. Fortransport sector, prices for the various grades of petroleum products are weightedthese according to their annual consumption by different transport modes (air, road,rail and water) from the annual data. A further simplifying assumption that railtransport pays the same price for electricity as industrial consumers has also beenmade.Problems with the price dataPage 26 of 116

The main problem with these data was the lack of price information for other finalusers. As this sector comprises a diverse array of activities it is likely there is a greatvariety of prices paid, hence our estimate is only a rough approximation. In addition,the method of averaging prices according to fuel consumption poses problems: a lotof the variation in prices is explained through swings in the relative consumption ofgas/electricity. There is also a lack of many observations for various transport prices.In most cases, the appropriate proxies to fill the series were used; for example, weextrapolated motor spirit prices back from 1991 using the growth rate of internationalcrude oil prices. This approximation omits the impact of changes in transporttaxation. Another problem may be the assumption that only motor spirit is consumedin road transport. As diesel and motor spirit prices are different, and consumption hasshifted to diesel in recent years, the price for the transport, and hence the wholeeconomy sectors, has been distorted slightly.Output dataData on output from the industrial sectors were collected from the Monthly Digest ofStatistics. Output data for other final users and the whole economy were collectedfrom UK Economic Accounts. From these two sources, the chain-linked outputindices were gathered (reference year 2000) for the exact sectors specified in theclassification above (see Appendix C for a list of the code names of the variablesused). Indices for the aggregate sectors were formed by weighting and summing thegrowth rates of the indices of the disaggregated sectors (this method is discussed bythe ONS atwww.statistics.gov.uk/about/Methodology_by_theme/chainlinking/methods.asp).These indices were then scaled to current-price output for the UK in 2000. The outputfor each sector was then divided by UK population to obtain output per capita.Problems with the output dataFor industrial and whole economy sectors, both unadjusted and seasonally adjusteddata were available. However, the ONS does not supply unadjusted data for theservice sector and was unwilling to aggregate its raw figures. It was therefore notpossible to form an unadjusted time series for other final users. Output data wereavailable for most series over 1948-2004Q1, and for all series over 1983-2004Q1.However, data on output of water supply were missing over 1973-1977, and data forbanking & finance and government services were missing over 1973-1982. Output ofwater supply was more or less constant from 1978 to privatisation in the late 1980s;we therefore assumed output to have been constant at the 1978Q1 level over themissing years. As water supply is a small proportion of industry output, thisassumption poses few problems. To replace the missing data for banking & financeand government services, the quarterly time series was extrapolated back usinggrowth rates from annual MDM-E3 data. Some of the quarterly fluctuation in outputfrom other final users has therefore been lost.TemperaturesPage 27 of 116

Quarterly Energy Trends contains data on the average temperatures suitable forestimating energy-demand affects for the UK over 1989-2003 in degrees Celsius,both on a statistical and a calendar monthly basis. Data over 1972-1988 wereavailable from the DTI on request. The calendar months were averaged to obtainquarterly temperatures.Problems with the temperature dataMost models of this kind use ‘degree days’ as a temperature indicator, as in contrastwith average temperatures, this variable has a linear relationship with energyconsumption. However, these data were out of the budget range for this project.Cumulated capital stockThe models required an index of technological progress. Cumulated capital stockdata, calculated by Cambridge Econometrics in the MDM-E3, was used. Thisvariable is formed from annual data for fixed investment by sector gathered from theONS and is calculated in MDM-E3 based on estimated depreciation rates. Thesedata were extracted from the MDM databank and we obtained quarterly estimates ofthe capital stock by linear interpolation.Seasonal adjustmentWhere possible, published seasonally-adjusted versions of the variables listed abovewere used. Where these were not available, the additive X11 procedure in E-Viewswas used to adjust the following variables: energy consumption, temperature, andenergy prices for the commercial sector and for the whole economy.Outline of general-to-specific model sector-by-sectorAppendix C contains a more detailed analysis of the equations mentioned below, butfor ease of reading, a brief description of each of the variables is repeated:• D = indicates a first difference• L = indicates a logarithm• E = energy consumption (equivalent to MDM’s FUJT)• Y= output (equivalent to MDM’s FUYO)• TE = degree-difference temperature from the 30-year mean• RP = relative prices• TREND indicates a time trend• (-1) = indicates a variable lagged one period• LRD = gradual long-run dummyPage 28 of 116

The estimation for energy demand equations using quarterly data is implementedaccording to the guidelines set out in Chapter 2. The key aspects of the methodologyadopted are:• first, an econometric model is estimated on thepreannouncement sample (from 1973Q1 until 1998Q4);• second, the existence of a cointegrating relationship is testedthrough the PSS test;• finally the model, with or without the long-term component,according to the outcome from the PSS test, is used tointerpolate the whole sample (until 2004Q1). The existence of amore general form of structural break is tested through CUSUMand CUSUMSQ tests.Determining the ‘best fit’ model specificationIn determining our ‘best fitting’ model specification, a general-to-specific method wasused. Literature, along with a priori knowledge, led us to begin with a specificationincluding the dependent variable - energy demand/consumption - and regressing thisagainst temperature, prices, output, investment (technological indicator), and a lineartrend. By dropping the insignificant variables throughout the testing process, andanalysing the results at each step of the procedure, our ‘best fit’ equation for eachsector is calculated. This follows the general-to-specific methodology of modelconstruction.Table 3.1 summarises an important process in estimating the quarterly equationestimates, and ultimately the annual MDM-E3 based equations. The quarterlyequation for other final uses (the only broad sector identified with a CCLannouncement effect) was picked up from the early model run specifications andadjusted to provide the best resulting estimations, which are consistent witheconomic reasoning as well as performing well with most statistical diagnostics. Thespecification of the CCL dummy variable is then tested with different profiles, whichare listed in order of statistical strength (ie largest negative t-ratio) in Table 3.1. Theannouncement effect, if any, should begin in 1999 and gradually increase through to2001 when the direct effect comes into play. It was, however, determined that theCCL does not reach full effect (ie dummy equal to 1) until 2002Q2 (where it wasfound to provide the most statistically significant coefficient). The ‘preferred’annualised choice of the CCL dummy profile is 0.10, 0.32, 0.66 and 1.0 for the years1999, 2000, 2001 and 2002 respectively. Although they remain statisticallysignificant, the two other options of including a zero-onePage 29 of 116

dummy (ie a full effect from the beginning of the announcement in 1999, or a fulleffect at a time when the Levy actually came into force in 2001) have the lowest t-ratios and this can be seen in the last two rows of Table 3.1 respectively. This tablelists the statistics for the category of other final users (OFU), as it became evidentthat the announcement effect was influential in this category.The ‘preferred choice’ dummy variable profile is then added to the long-runcomponent of the equations in order to test for the announcement effect. When themodel was first run, the initial presumption was that an announcement of the CCL in1999Q2 caused only a transitory (ie short run) effect, and was therefore only testedin such a manner. However, with the additional historical data available for the laterruns, the evidence from the regressions suggested that a far stronger long-run effecthad taken place as a result of the announcement, and we therefore decided to treateach equation as having a long-run announcement effect, and then to test thehypothesis as to whether or not it was statistically significant.Industrial Sector (no CCL announcement effect found)General modelModels with output, relative prices, temperature, and accumulated investments asindependent variables were fitted to the data for the industrial sector. However, nolog-linear model was found to simulate the data in the pre-announcement sample in away that conforms to the value of the parameters suggested a priori from economictheory. Therefore, models with the level of energy consumption (instead of thelogarithm) were fitted to the data.During the process of model selection it was found that:• the coefficient of accumulated investments assumed alwaysproduced an implausible value• dropping the temperature from the long-term component of themodel caused a substantial decrease in the fit of the regressionPage 30 of 116

• dropping the linear trend (TREND) from the regression causedother parameters to assume values that do not conform toeconomic theorySpecific modelAmong all the models tested, a model with the output, relative price, temperature anda linear trend in the long-term component fitted the data best while retaining value ofthe coefficients that conform to the theory. Sequentially dropping the insignificantvariables from the dynamic equations led to the following specification.• a0+a4*DTE+a6*DLY+a8*(E(-1)-a9*LY(-1)-a10*LRP(-1)-a11*TE(-1)-a13*TREND(-1)) (3.1)Table C1, in Appendix C, shows the estimated parameters and the usual diagnosticstatistics. However, the plotted and actual values of energy consumption – see ChartC5 - cast some doubts on the regression, as the plotted variable does not track verywell, for most of the sample, the actual values of energy consumption. However, asshown inTable C2, the PSS test confirms that there is a cointegrating relationship among thevariables.Qualification on the specific modelThe main concerns with this estimation are due to the relationship between price,energy and output. As shown in Chart C1-C3, energy consumption appears todecrease in a linear fashion – although there is a shift in 1980Q1 and again in1996Q4 - while the behaviour of both energy price and output shows a biggervariation over the same period.This can cause problems when the specific model is re-estimated on the wholesample period, as the regression is likely to be unstable. Furthermore, in the model(eq 3.1) – see Table C1 – the long-term price and temperature effects are notsignificant. However, when these variables are dropped from eq 3.1, some of theother coefficients assume values that do not conform to the theory. The value of thelong-term price coefficient is also dubious as it turns out this variable has an effect onenergy consumption which is minimal and almost identical to that of long-runtemperature.CCL EffectAs shown in Table C3, when the selected model is estimated on the whole samplewith the dummy variable added to the regression, the dummy is positive, althoughstatistically significant. Because of the incorrect sign, we cannot interpret thecoefficient of the announcement dummy as making any economic sense and thiswould therefore imply that no CCL announcement effect has taken place within thissector. The value of the dummy is 0.04.Qualification on the CCL effectPage 31 of 116

Surprisingly, adding a dummy and extending the sample has not caused anyremarkable changes in the other coefficients of the regression, and although thevalue of the R-bar-squared statistic was low to begin with, it has not changed muchfrom the pre- to post-announcement equation. The final graph of the plotted andactual values of energy consumption including a CCL effect - see Chart C6 - shows arelatively poor fit on the data; the deviations from the actual values are concerning. Inthe case of the industrial sector, the null hypothesis of structural stability is notrejected, as the CUSUM and CUSUMSQ tests confirms this in the results.Alternative Estimation StrategiesThe next section takes into account the comments and methodological suggestionsmade by HMCE and other stakeholders (Defra, DTI, Carbon Trust and HM Treasury)on the results of the interim findings for industry.Testing the delayed effects of the CCAs on the industry sectorAn alternative dummy variable (NEWLRD) was tested within equation 3.1 to detectpossible delayed energy-reduction effects as a result of CCA negotiations. Thehypothesis is that industry was mainly focused on securing CCAs and lower rates ofthe CCL, rather than responding to the announcement of the Levy by reducingenergy use (ie this is one possible reason as to why there was no announcementeffect detected for the industry sector). To test the delayed effect, we needed toextend the sample period to 2004Q1 and re-estimate equation 3.1 with an alternativedummy variable (which followed the profile: zero between 1973Q2 and 2001Q1,moving to a sudden value of 1 from 2001Q2 to the end of the sample period). Thereason behind this view is that industry’s response to the announcement of the Levymight have be delayed by the lobbying and time consuming negotiations up until thetime of the actual CCL introduction (April 2001).The estimation results from the above test proved to be inconclusive. The coefficientof the alternative dummy variable, although small and significant, was of the wrongsign (ie positive with a value of 0.03). This yields similar results found in the LRD (ieannouncement dummy variable) shown in Table C3. Based on a priori knowledge,we would expect a small, negative value for the alternative dummy coefficient. Theresults therefore do not support the hypothesis of a delayed effect of the CCL on theindustry sectors (ie we should reject the hypothesis based on a priori knowledge ofthe sign), and no further proceedings with the above dummy specification wereundertaken.ConclusionPage 32 of 116

According to this analysis, the CCL announcement is not likely to have caused aneffect in the industrial sector. This result is backed by the fact that all coefficients ofthe regression are relatively stable in the model that was estimated on the wholesample. However, this model does not track the observed values of the energyconsumption very well. This might be due to the fact that over the sample the energyconsumption is relatively stable, while the energy price and the output shows greatvariation. As the industrial sector defined here is comprised of several sectorsmaking differing uses of energy, a more specific, disaggregated analysis such as thatcarried out with annual observations, is considered to be more appropriate, but lackof detailed data prevented this.Other final users (CCL announcement effect found)General modelModels with output, relative prices, temperature, and accumulated investments asindependent variables were fitted to the data. During the process of model selection,it was found that:• the coefficient of accumulated investments always producedimplausible values• the temperature in the long-term component of the model madea significant contribution to the general fit and was very stable• a linear trend caused the long-term income elasticity to assumeimplausible values that do not conform to the values ofparameters from economic theory. The drop in the incomeelasticity was likely to be caused by the income and time trendbeing highly correlatedSpecific modelAmong all the models tested, a model with output, price and temperature in the longtermcomponent fitted the data best while retaining a value of the coefficients whichconforms to the economic theory. Sequentially dropping the non–statisticallysignificant parameters from the dynamic equations led to the following specification.DLE = a0+a4*DTE+a5*DLRP+a8*(LE(-1)-a9*LY(-1)-a10*LRP(-1)-a11*TE(-1)) (3.2)Page 33 of 116

• As shown in Table C4, the level of the temperature in thedynamic equation and the long run component were found to behighly significant alongside the first difference of the relative price(ie the growth rate of the price). The value of energyconsumption estimated using the equation 3.2 can reasonablytrack, with the exception of the drop in 1978Q2-Q4 and in1994Q1, all the major changes in the actual values - see ChartC11. As shown in Table C5, the PSS test for the null hypothesisof no cointegration is firmly rejected.Alternative Estimation StrategiesThis section takes into account the comments and methodological suggestions madeby HMCE and other stakeholders (Defra, DTI, Carbon Trust and HM Treasury) on theresults of the interim findings for other final users. The announcement effect remainsa permanent effect throughout these tests.The energy-efficiency dummy (EED)To test for the effect of the Government’s Energy Efficiency Best PracticeProgramme (EEBPP) that started in the early 1990s, a dummy variable wasintroduced with a zero value until 1990Q1 and then was applied as a constant timetrend to the end of the sample period to reflect the greater awareness and practice inenergy efficiency. The EEBPP is now known as Action Energy, run by the CarbonTrust. The hypothesis was that the effect of the EED dummy would be small andnegative. The early tests showed that the dummy parameter had an unexpectedpositive value of around 0.002 and was statistically significant over the 1973-1998period. However, when the dataset was extended to 1998-2004Q1, covering theperiod of the announcement and implementation of the CCL, the value of the EEDfell to 0.001 and became insignificant - as shown in Table C30. If we were to ignorethe insignificance of the EED, its positive sign could be attributed to the growth indemand from the spread of air conditioning and the increased investment in ITequipment in the commercial sector, which possibly offset the impact of early energyefficiencyschemes in decreasing energy demand, resulting in the small net increase.However, because of the insignificance of the dummy, as well as the decrease insignificance of the long run output variable by including the dummy in the equation,we have decided not to keep this variable in the final specification of the OFUquarterly equation. There is not enough convincing evidence from our analysis forthis project to indicate that these energy-efficiency schemes have made a significantnet reduction in energy demand during the 1990s.The distinctive CCL tax effect via a separate long-run dummy variable (TAX)Page 34 of 116

• A test for the distinctive CCL tax effect via a separate dummyvariable in the long-run component of the OFU equation wasundertaken: the tax variable tested (called TAX) was calculatedas the ratio between two price variables (ie price with the CCLincluded and the other without). This test was carried out as anattempt to see if the price change as a result of CCL had adistinctive effect for other final users. The tax dummy profile wasspecified as follows:• over 1973Q1-2001Q1 it equalled one;• over 2001Q2-2004Q1 it took a value above 1 as given by thecomputed ratio of the price including the CCL to the priceexcluding the CCL.The inclusion of the TAX dummy variable in the long run component within the ‘bestfit’ equation (ie equation 3.2), led to the CCL dummy becoming insignificant. The TAXdummy itself was highly insignificant, and its inclusion increased the problem of serialcorrelation within the equation - Table C31. The conclusion is that the two variablescannot be accounted for in the same long run component of the OFU equation. Ourjudgment is that there is a clear mis-specification error arising from the inclusion ofboth the announcement effect and the actual CCL tax effect within the sameequation. There is also a ‘double counting’ error occurring, whereby the tax effect ispresent in the price variable as well as being separately estimated as a dummy,along with the CCL announcement effect in place.Separation of the PRICE and TAX effectsFollowing that, a test involving the separation of the ‘price’ and ‘tax’ effects wasperformed. This was undertaken to see whether or not it cast any light on a counterintuitiveresult found in interim model runs of the short-run price elasticity exceedingthe long-run elasticity. For this test, two variables were used in the equation:• the log of the relative price excluding the tax (called LRPE);• and the log of the tax effect itself (called LTAX - derived fromvariable TAX above).When the two effects were separated, but were forced to have the same coefficient(ie a5 in the short run and a10 in the long run), the results showed a similar picture tothe results seen in Table C6. This would have been expected. However, the mainpoint of this test was to split the price and tax effects apart and have each of the twovariables (both in the short and the long run) freely estimated. When this was thecase, the short and long-run price parameters remained stable (a5 and a10), but thetax effect in the short run (a6) became largely positive (ie >1.0) and barely significant,and the long run tax effect (a13) was also positive and highly insignificant. The CCLdummy was also included in the equation - Table C32 - and this became negativelylarger (from -0.14 to -0.25) but remained significant. An attempt was made to dropyears off the latest data and with the equation being re-estimated. The followingresults occurred:Page 35 of 116

• the LRD became larger and more significant;• and the ‘LTAX’ effect became less insignificant as the years weredropped.Looking at this from the other direction, the results show that the ‘tax’ effect via theprice effect (or as a price effect) is becoming weaker as time goes on (ie the TAXeffect becomes more insignificant as we add observations), but still remainsinsignificant at our latest available period (2004Q1). Due to the lack of furtherobservations, we cannot conclude with certainty anything about the separation of theeffects. Following these experiments and similar others, we decided not to not pursuethis approach any further, having exhausted many options. We would, therefore,continue to use the ‘best fit’ equation with a single price variable (LRP) whichincludes the tax effect, and continue to include an announcement effect in the form ofa CCL Dummy (LRD).Separation of the gas and electricity pricesTesting for the separation of the gas and electricity prices was undertaken, asopposed to using the overall relative price variable (LRP). This test was carried out tosee whether or not the presence of aggregation bias was driving the short- and longrunprice elasticity result in the interim findings, combined with the fact that in therecent past gas and electricity prices movements have diverged markedly. This, too,was considered to be a possible reason for the short-run price elasticity being greaterthan the long- run price elasticity; these two energy sources are in principlesubstitutes for each other (eg in space and water heating, and cooking uses indomestic sector). But electricity is expected to be more ‘price inelastic’ because ofthe fact that in many uses (eg lighting, motive power, domestic appliances) it cannotbe substituted by gas if the price rises. In contrast, in several uses (eg space andwater heating, heat/steam raising in industrial processes) gas can be readilysubstituted for electricity in response to movements in the gas-electricity pricedifferential. Extensive testing on this issue showed poor statistical results, withincorrect signs, many insignificant pricing variables, and structural changes takingplace within the ‘best fit’ equation to variables that had otherwise been quite robust(eg output, CCL dummy). It was therefore decided that this ‘substitution’ effect forprices of different primary sources did not warrant, given the project timetable, anyfurther testing, and, again, we judge that it is appropriate to revert to using our ‘bestfit’ OFU equation (ie equation 3.2).Constraining the short and long-run price elasticities to be equalPage 36 of 116

Lastly, and most importantly, a test for the alternative specification of the short-runand long-run price elasticity of energy demand in the quarterly equations wasrequired. A restriction would be imposed on the coefficients of the short- and long-runprice elasticities to ensure their equality; if this test proved successful, the aim wasthen to look at the effect of the conventional, a priori restriction of SR elasticity < LRelasticity. The value of the price elasticities fell from -0.47 for short run elasticity and -0.13 for long run (Table C6), to a common -0.146 (Table C7) when they were forcedto be equal. The coefficients of all the other variables remained robust andsignificant, but a noticeable change between the unconstrained and constrained(Table C6 and C7) was the decline in the goodness-of-fit (eg R-bar-squared droppedfrom around 0.63 to about 0.57 when the constraint was imposed). Because of theimportance of correcting this price elasticity issue in order to make the equations fitbetter to economic theory and reasoning, it was thought sensible to perform a similartest to the annual equations (discussed in section 3.3), which, if they bore positiveresults, would eventually feed into the MDM scenarios simulations.Conclusion on Alternative Estimation SpecificationsAfter having performed the tests above on the quarterly data for other final users, theoverall conclusion was that there was one significant finding which warranted achange in specification from the ‘best fit’ equation found in the interim model runs.Due to strong a priori economic reasons that suggest the short-run price elasticityshould be smaller, or at least equal to, the long-run price elasticity, we have movedtowards a preference of using the constrained equations in the regression for otherfinal users.Qualification on the specific modelThe main concern with this estimation is due to the nature of the variables and to thevalue of the short-term price elasticity. By adding the new historic data to the sample,the logarithm of the energy consumption in this sector is now found to be stationary –in the sense that the null hypothesis of having a unit root is rejected – and its averageremains relatively stable across time. However, over the same period the logarithm ofoutput is strongly trended, with both relative prices and output variables are nonstationary- null hypothesis of having a unit root is not rejected. This may cause theregression to be slightly unstable. Furthermore, the value of the short-term priceelasticity in the unconstrained equation is much larger than the long-term value – seeTable C4. While it is understood that the short-term elasticity should have a valuesmaller or equal to the long-term one, the goodness-of-fit of the regression and someinformation criteria decreased when this constraint was imposed (compare Tables C6and C7). It was thought however, that in keeping with a priori economic reasoning, itwould be best to use the constrained price elasticity equation for use in the annualMicrofit equations (section 3.3) and to progress through to the MDM scenariosimulations. As suggested in the section on alternative estimation strategies, thisinvolved constraining the long and short-run pricing variable elasticities in thequarterly equations to have them equal each other. The coefficient of the long-runCCL dummy variable is the parameter we were interested in for each equation, whichgave a value of -0.141 and -0.15 in the unconstrained and constrained equationsrespectively. In addition to this, the coefficient of the error correction term -comprising energy, relative price and income - was always highly significant.Page 37 of 116

CCL EffectAs shown in the Table C6, when the selected model is estimated on the wholesample with the dummy variable added to the regression, the coefficient of thedummy is negative and significant. This implies a long-run CCL announcement effectwith a long-run reduction in energy demand by other final users of around 14%.However, as seen in Table C7, when the long and short run price elasticities areconstrained to be equal, the reduction in energy demand brought about by theannouncement of the CCL for other final users increases to about 15%. However, inboth cases, there are lags between the decision to install new methods andequipment, and their actual operation. Confirmation of the length of these lags isprovided by a survey of the lags between the decision to install and the operation ofthe CHP plant, conducted by the CHP Association and reported by CambridgeEconometrics (2003). The survey found that for small-scale CCGT plants, of the typeinstalled by other final users, 25% came on stream after 1-2 years, and 95% after 2-3years (ie if the decision to install was taken in 1999, most of the effects would appearin 2001 and 2002).Qualification on the CCL effectThe plotted values obtained from the model estimated on the whole sample (with adummy added to the long-run component) track the actual values of energyconsumption fairly well, and is a slightly better fit (statistically) compared with interimmodel runs. This could be due to a number of reasons: the extension of the databack to 1973Q1 has allowed for better estimation results to be produced; theinclusion of a stable, significant long-run temperature variable has added to theoverall fit; and the fact that there is a more predominant long-run CCL effect has ledus to adjust the form of the equation, and has thus resulted in a better fit.. Thecoefficients of the ECM and long-run output are significant and conform to economictheory in the direction of their effects. In contrast with the result from the use of thedummy, the outcome from the CUSUM and CUSUMSQ test is ambiguous: the nullhypothesis of structural stability is accepted by the CUSUM, but rejected by theCUSUMSQ, although this is at the 5% level. As the latter can detect sudden changesof the parameters more easily than the former, this might suggest that the energydemand adjusted rapidly to the CCL. The fact that outcome from the dummy and theCUSUM and CUSUMSQ tests are not in complete agreement is not a surprise. Whilethe dummy variable tests for a specific type of structural break, the nature of theinstability is not a priori specified in the CUSUM and CUSUMSQ tests. Cases wherethe break occurring in one parameter (in this case in the long-term component) isobscured by the behaviour of other parameters are therefore not implausible.Further testing of the OFU equationIn order to test whether or not the inclusion (and acceptance) of a long-term CCLdummy is spurious, a linear trend variable (TREND) is added to the OFU equation -see Table C28 in Appendix C. The results show the trend is extremely small andinsignificant, and the long-term output variable becomes unstable, resulting in thewrong sign and high insignificance. It is encouraging though, to see that the CCLDummy remains robust and the value of its coefficient did not change much.Page 38 of 116

There may also be cause for concern about the inclusion of a short-term temperatureeffect, mainly because of the method of seasonal adjustment used. The serialcorrelation problem shown in the quarterly data may be as a result of this adjustment,but when attempting to change the method of seasonal adjustment (by either usingadditive as opposed to multiplicative, or performing seasonal adjustment on thenatural logs as opposed to the levels), there were no improvements in the serialcorrelation of the errors. Adding additional lags of the independent variables alsobore no significant positive results, and our equation therefore remains unchanged.As mentioned in Chapter 2, experimenting with unadjusted data could provide fruitfulresults, but due to the lack of such data for output from sources, it is not possible toperform such test within the scope of this project.Removing all temperature effects from the equation will again test the robustness ofthe long-term dummy. The resulting outputs can be seen in Table C29 in AppendixC, where the dummy variable remains around its previous value of about -0.14. It isimportant to note, however, that there is a huge loss of goodness-of-fit in theequation where temperature effects have been removed, as well as a drop insignificance of many other diagnostics, suggesting that they should not be droppedfrom the OFU equation.ConclusionAccording to this analysis, a CCL announcement effect in the other final users sectorappears to have a permanent long-run effect on the demand for energy. It alsoshows resilience to changes in the specification of the model. In terms of theoutcome of the model, it is worth pointing out that the CCL dummy variable in theannual estimations originates from quarterly estimates where the long-run priceelasticity is constrained to equal the short-term price elasticity.Whole Economy (no CCL announcement effect found)General modelModels with output, relative prices, temperature, and accumulated investments asindependent variables were fitted to the data. During the process to select the modelit was found that:• the coefficient of accumulated investments always producedimplausible values• the temperature in the long-term component made a significantcontribution to the overall fit of the equation• removing the trend caused certain other variables to becomeinsignificant and not conform to economic theory, thus it remainsa part of the long-term componentSpecific modelPage 39 of 116

Among all the models fitted to the data, a model with output, price, temperature and alinear trend in the long-term component gave the best fit while retaining coefficientswith values conforming to economic theory:• = DLE = a0+a4*DTE+a5*DLRP+a6*DLY+a8*(LE(-1)-a9*LY(-1)-a10*LRP(-1) -a11*TE(-1)-a13*TREND(-1)) (3.3)As in the case of other final users, the level of the temperature in the whole economyequation was found to be a very significant variable in the dynamic equationalongside the relative price – see Table C8. Long-run output however, has becomeslightly insignificant since the interim runs (through small revisions at the beginningand end of the sample periods), although it still showed to be a significant variable inthe overall regression and would therefore not be left out (the fit of the regression - R-bar-squared - dropped when the variable was removed). In terms of estimated andactual values, with the exception in 1979Q3 and 1996Q2-Q3, all the major changesin the data are tracked – see Chart C17. Finally, as shown in Table C9, the PSSconfirms the existence of a cointegrating relationship among the long-term variables.Qualification on the specific modelThe main concern with this regression is due to the fact that energy consumption inthe whole economy fulfils many different functions in different sectors liketransportation, household and industry. Each of them is expected to have differentvalues of the parameters and pay different prices for the same fuel. Putting all thistogether could cancel out the differences and systematically bias the estimation ofcoefficients. Regarding the estimated variables, as shown in Chart C13-C15, thereappears to be a similar relationship between energy consumption and output:although the former is more volatile, they both have an upward trend. Therelationship between price on one side, and energy consumption and income on theother appears to be more complicated. Quite surprisingly, wide variation of the priceshas not had a big influence on the GDP growth rate, whose average has beenremarkably stable for the period after 1994. This seems to deny the claims thatincreasing energy taxation would put a break on the growth of the economy. On theother hand, price seems to have a constraining effect on energy demand in thesense that a drop in consumption always occurred in periods with high price ofenergy, as is clearly illustrated over the period 1978 to 1985, when energy pricesrose due to two World oil crises and the energy consumption over the same perioddeclined.CCL EffectAs shown in the Table C10, when the selected model is estimated on the wholesample with the dummy variable added to the regression, the coefficient of thedummy is positive and highly insignificant. This implies no CCL announcement effect,which is no surprise given the vast array of sectors making up the whole economy.The value of the dummy is 0.0047.Qualification on the CCL effectPage 40 of 116

Adding a long-term dummy variable and extending the sample causes a substantialdecrease in the fit of the regression, as measured by the R-bar-squared statistic. Thisis confirmed by inspecting the graph of the fitted and actual values of energyconsumption - see Chart C18. Furthermore, the value of the long-term coefficientsestimated on the whole sample are quite different from the estimates in thepreannouncement one. Finally, as in the case of the other final users, the nullhypothesis of structural stability is accepted by the CUSUM, but rejected by theCUSUMSQ test.ConclusionAccording to this analysis, the CCL is not likely to have caused an announcementeffect in the whole economy. However, many parameters in the regression arerelatively unstable. The fact that energy consumption comprises different fuels usedby different users for different purposes is more likely to be the main reason why therelationship between energy, income and price appears so unstable.Estimation of the Annual MDM-E3 Based EquationsAppendix C contains a more detailed analysis of the annual equations mentionedbelow, but for ease of reading, a brief description of each of the variables is repeated:• D = indicates a first difference• L = indicates a logarithm• E = energy consumption (equivalent to MDM’s FUJT)• Y= output (equivalent to MDM’s FUYO)• TE = degree-difference temperature from the 30-year mean• PRICE = nominal prices• HUC = Home unit cost (price deflator)• COMBO = indicates a subtraction between PRICE and HUC (thiswas necessary in order to perform the variable deletion - PSS -test for cointegration within Microfit)• TREND indicates a time trend• (-1) = indicates a variable lagged one period• LRD = gradual long-run dummyData sources and estimationPage 41 of 116

The annual equations had a similar structure to the quarterly equations.Consequently, the data came from the same or similar original sources as thequarterly data. However, in contrast with the quarterly data, the annual data wereextracted from CE’s databanks; they were originally gathered and processed as partof the normal maintenance of MDM-E3.Energy DataBoth energy consumption and price datasets were collected from DUKES. Data aredisaggregated in detail, and are aggregated for use in MDM-E3 into four industrialfinal users: basic metals, mineral products, chemicals, and other industry, as well asfour transport final users, the domestic sector and other final users. The definitions ofthese sectors are based on Table 1E in DUKES 2004, although expenditure andconsumption of energy by construction is included in other final users. Thisclassification is broadly consistent with that of the quarterly data.Output and other dataAnnual data for gross output are obtained from the ONS. These are first processed atthe level of the 49 MDM industries, then summed to form output for the five fuel usersfor which equations were estimated. Temperatures are obtained from the DTI fromDUKES, while the formation of the time series of the cumulative capital stock, whichis used as an index of technological progress, is described in section 3.2 above.MDM-E3 requires annual estimated equations for 10 broad groups of energy users,including those sectors directly affected by the CCL, namely 4 energy-intensivesectors, (including other industrial users) and other final users. Estimation of theannual equations is informed by the results for the quarterly equations, ie the samespecification is adopted, with the same variables and restrictions. Appendix Cpresents the various equation estimates for both the quarterly and the annual data.The main purpose of these tables is to show the results that determine whether ornot there is a structural break or significant CCL dummy variable effect, thus servingto either confirm or refute a CCL announcement effect.Outline of general-to specific model sector-by-sectorThe annual equations did not need as much rigorous testing as in the quarterlyestimation, because the overall idea is to build the annual equations around thevarious findings from the quarterly equations. However, following a similarmethodology as noted in section 3.2, the annual equations are estimated for thepreannouncement period first (1973 - 1998). The PSS test (variable deletion) is thencalculated to establish the existence of a cointegrating relationship. The model, withor without the long-term component (determined by the PSS test) is used tointerpolate the full period of the sample (1973 -2003). The gradual dummy variable isadded and its significance tested on a 95% confidence level (probability < 0.05).Page 42 of 116

There is one fundamental difference between the price variable used in the quarterlyestimation, and those used in the annual estimation. In the quarterly equation, theprice variable (RP) is the relative price of energy for any given sector, whereas theprice variable used in the annual estimation (PRICE) is the nominal price of energy.The reason being, that within the annual estimation, a ‘home unit cost’ variable(HUC), which is a price deflator, is included as one of the regressors to capture theeffect of real prices via these two inter-linking variables (PRICE and HUC). This isconsistent with the model specification found in MDM-E3.Industrial SectorsThe section below discusses the four sub-sectors of the industrial users and theirrespective annual energy demand equations. In section 3.2 of this report, weconcluded that there was no CCL announcement effect for the broad industrial sectorin the quarterly data. The annual data confirms this finding in the tables listed inAppendix C section 3. The parameter of the dummy is either insignificant or of thewrong sign (ie positive). One possible reason for there being no announcement effectis that these industries are energy-intensive users and already pay great attention tofuel consumption and efficiency, thus leading to little or no change in energy demandwhen the prospective CCL was announced. Another plausible reason is that beforethe Levy came into effect, the Government noted that there would be exemptions orlarge discounts if industry could meet the targets relating to emissions and/or theirenergy ratio (energy used per unit of output). This may have reduced the urgency tosave energy. However, it must also be borne in mind that the lack of quarterly databy industry sub-sector means that we may fail to detect announcement effects asthese may not be picked up using the annual equations.Basic metalsThe short-term parameters for annual energy demand in the basic metals equation(eq 3.4) consists of an intercept, a differenced log of output, a differenced log ofprice, and the differenced log of the price deflator. The parameter ‘a8’ is the ErrorCorrection Mechanism (ECM) which is used to tie the short-term dynamic behaviourof a variable to its long-term value. The ECM is therefore influenced by the laggedenergy demand, output and price. The values -0.75 and 0.35 have been imposed onthe long-term output and price variables respectively (ie an activity elasticity of 0.75and a price elasticity of -0.35), which are in keeping with economic theory and a prioriknowledge based on previous use of the MDM-E3 model. A lagged trend variablehas also been included as it was seen to be significant in the energy demandequation. The statistical output table for the specific model can be seen in AppendixC, Table C11.• DLE=a0+a3*DLY+a5*DLPRICE+a6*DLHUC+a8*(LE(-1)-0.75*LY(-1) +0.35*LPRICE(-1)-0.35*LHUC(-1)-a13*TREND(-1))(3.4)Summary of results/findings with respect to AEPage 43 of 116

An important point to note is that in equation 3.4, the variable deletion test (PSS test)fails, as shown in Table C12 - the p-value of obtaining the observed F-statistic is0.148. This would normally suggest the long-term component of the equation is notcointegrating over time, which can cause problems with forecasting. However, byimposing the relevant coefficients within the MDM-E3 model, and using separateequations for both the short- and long-run estimates, we assume cointegration andthus efficiency and accuracy in the forecasts.Table C13 illustrates the inclusion of a CCL announcement effect in the form of adummy variable (parameter ‘a12’). As can be seen from the table, it is insignificantand carries the wrong sign (ie positive). Therefore it is not likely that a CCLannouncement effect occurred within the basic metals sector.Mineral productsThe short-term parameters for annual energy demand in the mineral productsequation (eq 3.5) consists of an intercept, a differenced log of output, a differencedlog of price and the differenced log of the price deflator. The parameter ‘a8’ is theECM and is influenced by the lagged energy demand, output and price. The values -0.2 and 0.5 have been imposed on the long-term output and price variablesrespectively. A lagged trend variable has also been included as it was seen to behighly significant in the energy demand equation. Demand for energy also appearedto be significantly influenced by technological progress (LI) when tested within theMDM-E3 model, and therefore is included in equation 3.5 and has an imposedcoefficient value of 0.35. The statistical output tables can be seen in Appendix C,Table C14.• DLE=a0+a3*DLY+a5*DLPRICE+a6*DLHUC+a8*(LE(-1)-0.2*LY(-1) +0.5*LPRICE(-1)-0.5*LHUC(-1)-a13*TREND(-1)+0.35*LI(-1))(3.5)Summary of results/findings with respect to AETable C16 illustrates the inclusion of a CCL announcement effect in the form of adummy variable (parameter ‘a12’). Again it was estimated and the result shows it ishighly insignificant and of the wrong sign (ie positive). The short-run output variablebecomes insignificant with the inclusion of the CCL dummy, and the general fitdecreases slightly. Therefore it can be said that it is not likely that a CCLannouncement effect exists in the mineral products sector.ChemicalsThe short-term parameters for annual energy demand in the chemicals equation (eq3.6) consists of an intercept and a differenced log of output, price and a pricedeflator. The lagged energy demand, and output (imposed coefficients of -0.2) andprice (imposed coefficients of 0.65), form the long-term component of the equation. Alagged technological indicator (LI) has also been included as it carried highsignificance when included in the MDM-E3 annual equation, and is thus shown inequation 3.6 as having a coefficient of 0.35. The statistical output tables can be seenin Appendix C, Table C17.Page 44 of 116

• DLE = a0+a3*DLY+a5*DLPRICE+a6*DLHUC+a8*(LE(-1)-0.2*LY(-1) +0.65*LPRICE(-1)-0.65*LHUC(-1)+0.35*LI(-1)) (3.6)Summary of results/findings with respect to AEAs in the basic metals sector, the equation 3.6 does not pass the PSS test forcointegration within the long-term component. The p-value of the F-statistic in TableC18 is 0.227, suggesting we reject the null hypothesis of cointegration within thelong-term component. This would normally lead to dropping the long-term componentall together. However, because of the imposition of all the long-term variables andthe separate estimation of both short- and long-run equations in MDM-E3,cointegration is assumed to hold.Table C19 illustrates the inclusion of a CCL announcement effect in the form of adummy variable (parameter ‘a12’). Again it was estimated and the result shows it isinsignificant on a 95% confidence interval, although it is of the correct sign (ienegative). It is, however, extremely large. The ECM coefficient ‘a8’ also becomesmore insignificant than when estimated during the preannouncement period (TableC17) and thus renders the long-term component unstable. Therefore it can be saidthat it is not likely that a CCL announcement effect exists in the chemical sector.Other industryThe short-term parameters for annual energy demand in the other industry equation(eq 3.7) consists of an intercept and a differenced log of output, nominal price and aprice deflator. The coefficient ‘a8’ is the ECM, therefore influences the long-termparameters, namely lagged energy demand, output (imposed values of -0.2) andprice (imposed value of 0.65). Such imposed values are in keeping with economictheory and originate from a priori testing done on the sector using MDM-E3 model. Alagged trend variable has also been included as it showed high significance indetermining energy demand in the sector. The statistical output tables can be seen inAppendix C, Table C20.• DLE = a0+a3*DLY+a5*DLPRICE+a6*DLHUC+a8*(LE(-1)-0.2*LY(-1) +0.65*LPRICE(-1)-0.65*LHUC(-1)+0.35*LI(-1)-a13*TREND(-1)) (3.7)Summary of results/findings with respect to AEThe short-term output variable remains insignificant, and the general statistical fit ofthe equation (ie R-bar-squared) is reduced from 0.510 in the preannouncementperiod, to 0.400 in the post-announcement period.Table C22 illustrates the inclusion of a CCL announcement effect in the form of adummy variable (parameter ‘a12’). It was estimated by the model and the resultshows it is insignificant and of the wrong sign (ie positive). The conclusion can bedrawn that a CCL announcement effect is not likely to exist in the other industrysector.Other final usersPage 45 of 116

This sector follows a different layout to the previous four industrial users above.Following the same methodology performed in the interim model runs, the Microfitestimation for the final run still produced the counterintuitive result in the other finalusers (OFU); the short-run energy price elasticity consistently exceeded the long-runelasticity. This resulting output tables can be seen in Table C24 and Table C27 inAppendix C (referred to as the ‘unconstrained’ price elasticity equation). Thealternative (and incidentally, the ‘preferred’ specification) was one in which theelasticities of the short-run and long-run prices were forced to be equal (which wasthen freely estimated), thus conforming better to economic theory. The resultingoutput tables can be seen in Table C23 and Table C26 in Appendix C (referred to asthe ‘constrained’ price elasticity equation).The dynamic component for the constrained annual energy demand equation for theother final users (eq 3.8) is composed of an intercept, a differenced log oftemperature and a differenced log of nominal price and price deflator. When dealingwith the constrained price elasticity equations (see Table C23 and C26), the ‘a5’coefficient is common to both the short-run and long-run price variables, henceillustrating the forced equality between the short and long-run price elasticities. In theunconstrained annual energy demand equation, the imposed values of long-termoutput and price (-0.139 and 0.127 respectively) come from the estimates of thequarterly equations, while the dynamic price and price deflator is left to be estimatedfreely. As noted in Chapter 2, the results from extensive empirical analysis on thequarterly data for other final users concluded that this was the only sector containingan AE from the Climate Change Levy. Due to the fact that the MDM-E3 model runsannual demand equations, it was necessary to impose the coefficients of the longtermvariables (found in the quarterly equation in Table C6) onto the annual dataequation. The reasons for not estimating freely the coefficients of the annualequations are:• the lack of data representing the announcement period;• structural stability in the quarterly equations outweighing that ofthe annual;• temperature effects (which were presumed, a priori, as having aninfluence on• energy demand) being better tested and captured usingseasonally-adjusted quarterly data. The statistical output tablesfor the annual equation can thus be seen in Appendix C, TableC23 (constrained) and Table C24 (unconstrained).• DLE=a0+a4*DTE+a5*DLPRICE-a5*DLHUC+a8*(LE(-1)+a9*LY(-1) -a5*LPRICE(-1)+a5*LHUC(-1)+0.037*TE(-1)) (3.8)Summary of results/findings with respect to AEPage 46 of 116

The other final users energy demand equation gives different results to those shownin the other four industrial sectors. The CCL announcement appears to have asignificant and influential effect on energy demand for this sector. Table C6(Appendix C) shows the parameter of the CCL dummy (-0.141) using the quarterlydata in the unconstrained equation (discussed in section 3.2 of this report) to bestatistically significant and of the correct sign (ie negative). This value can thereforebe confidently imposed in the annual equation, in much the same way as theimposed output, temperature and price elasticities described in the sectors above.Therefore this implies that the announcement of the CCL was effective in loweringthe demand for energy consumption of other final users from the year 1999. Anoticeable difference between the interim model runs lies in the type of CCLannouncement effect. As discussed in Section 2.3 of this report, econometricanalysis on the extended quarterly data suggested that the AE was strongest incausing an irreversible change in energy demand behaviour (ie a long-run effect).This effect begins in 1999Q1 and reaches full effect in 2002Q2, with the dummyvariable remaining at a value of 1 (ie full effect) through to the end of the sample.The inclusion of a significant short-term temperature effect also differentiates theOFU equation from the four industrial equations. This variable increases the overallfit of the equation to a noticeable degree, and proves robust and stable to structuralchanges within the equation. It has a significant inverse relationship with energydemand.Alternative Estimation StrategiesAs stated in section 3.2, an alternative to be tested within the scope of this projectwas the reaction of the annual estimates once a constraint of equality on the longand short-run price variables had been imposed. This constraint was performedusing least squares method of estimation in the annual equations, and the calculatedprice coefficient ‘a5’ seen in Table C23 and C26 (which is freely estimated, subject tothe constraint of the short and long-run price elasticities being equal) would then beincorporated into the MDM-E3 scenario simulations. The value of the constrainedprice elasticity was estimated to be -0.122.Overall Annual Equation ConclusionTherefore, in agreement with the findings in section 3.2 of this report (ie the quarterlyequation estimates), the CCL is not likely to have caused an announcement effect inthe four industrial sub-sectors (basic metals, mineral products, chemicals and otherindustry). When freely estimated, the coefficient for the dummy in these sectors ofthe annual equations was either insignificant or of the wrong sign, or a combination ofthe two.In the other final users sector, however, the estimation does find the CCL effect to besignificant and of the correct sign. Thus, the full effect of the CCL announcement inthe unconstrained equation results in an elasticity of -0.14, and in the preferred priceconstrainedequation, an elasticity of -0.15. In each case, this causes a long-termeffect on energy demand for other final users, which can be seen from 2002 onwardswhen the value of the dummy variable is equal to 1. Following that analogy, the effectfor 1999, 2000, and 2001 will be 0.10, 0.32 and 0.66 of the full 15% effectrespectively (in the constrained case).Page 47 of 116

The results from the constrained elasticity between the short and long run pricingvariables showed the overall R-bar-squared (goodness-of-fit) of the regressiondropped from around 0.78 to 0.63 after imposing the restriction in the annualequations. Although this decline should not be overlooked, by doing so we are drivingthe equation to conform better to a priori economic reasoning, thus providing a soundrationale for our view that this constraint is necessary and appropriate in thisinstance. We have therefore taken this constrained equation to be our final ‘best fit’equation, and it provides the basis for the scenario analysis discussed in Chapter 5and in Appendix D.Design and Specification of ScenariosIntroductionThis chapter outlines the principles underlying the design and construction of thescenarios used in the preparation of the model runs for this study. The results in thisreport are based on the latest outturn UK data for energy (2003) and emissions(2001) and current other assumptions that were available in August 2004 from themost recent CE model runs. The energy price assumptions used in the final report,presented in Table 4.1, are based on CE’s July 2004 UK Energy and theEnvironment report; they are compared with the DTI’s update published in May 2004of EP 68 Energy Projections in Chapter 5. The July 2004 report also, as Table 4.2shows, provided the basis for the macroeconomic projections, such as the rate ofeconomic and manufacturing output growth and for judgements relating to the mainexogenous variables including inflation rates in the UK’s trading partners, exchangerates, interest rates, and UK tax rates and government expenditure.The time period for the projections in the model run is from 1998 (the year before theGovernment first announced its intention to introduce the CCL) to 2010 on acalendar-year basis. The scenarios outlined below are identified by letters.Page 48 of 116

The Reference Case: Scenario RScenario R (the ‘reference case’) projects through to 2010 as if the CCL had neverbeen announced or introduced. Scenario R, which is a counterfactual case, thereforeneeds to have the following characteristics:• An assumption that there are no announcement effects for 1998-2000. Anyeffects estimated in the equations explaining energy demand will have to beremoved, eg by setting any dummy variables to zero.• No CCL implemented in 2001 and no adjustments to energy use by CCA sectorsto ensure that they meet their targets.• No reduction in employers’ National Insurance Contributions rates (NIC) ascompensation for the CCL.• All other variables/assumptions (eg energy prices) are as of the most up-to-datedata and most recent CE model runs as in scenario B, except for the CCLassumptions. However it has been assumed for this and the other scenarios inthe study that the European Union Emissions Trading Scheme (EU ETS) is notincluded in the solutions. The UK voluntary scheme is assumed to continue to2010 with an allowance price of £8/tC over 2003-2010. This allowance price isfurther assumed to be the same in all scenarios; it is assumed to be unaffected bythe trading of emissions permits by those firms needing to meet CCAs.Page 49 of 116

The Base Case: Scenario BScenario B (the ‘base case’) then introduces the announcement effects as estimatedand imposes the CCL at actual rates from April 2001 to April 2004; thereafter ratesare assumed to rise in line with RPI (all items). Table 4.3 shows, in nominal terms,the rates of CCL as levied by fuel over the period 2001-10. All assumptions (egenergy prices) reflect the most recent assumptions incorporating the most up-to-datedata from the CE’s July 2004 UK Energy and the Environment forecast report.Scenario B uses annual outturn data for energy use and energy prices from DUKES2004 edition, energy prices from the DTI’s quarterly Energy Prices (ie to 2003) andthe latest disaggregated emissions data to 2001 from the National AtmosphericEmissions Inventory. It should be noted that this scenario solution is a simulation ofthe economy from 1998 to 2010 and is not calibrated, so that it does not reproduceexactly the outturn over the historical period. This implies that the projections in thisscenario should not in any way be regarded as a central or most likely forecast ofwhat we would expect to happen over the period to 2010.The Reduced-Rate Scenario: Scenario CScenario C (the reduced-rate CCL case) simply imposes the 20% CCL rate on theCCA sectors (ie does not impose the CCL on the rest of business and commercialenergy use), in order to see what the ‘pure’ price effect of the reduced-rate CCL is onthese sectors, with revenues used to reduce the rate of employers’ NICs. Thetreatment of the CCA target savings is the same as in Scenario B, implying that theenergy use by the CCA sectors is model-determined and not adjusted to ensure thatthe efficiency targets are achieved. This run includes any announcement effect for1999 and 2000 for these sectors and the rest of the economy, but this should not betoo significant for the comparison that is being made (between sectoral energy use inScenarios R and C for the CCA sectors). This run also includes the recycling ofrevenues through a reduction in NICs. As the reduced-rate CCL raises around onetenthof the revenues of the full-rate CCL, it was decided to reduce NICs by one-tenthof the reduction in the base case, ie by 0.03 pp. The reduction is somewhat arbitrary,but was chosen to simulate plausibly a political decision.Page 50 of 116

The Full-Rate Scenario: Scenarios F, FA, FBScenarios F, FA and FB (the full-rate CCL cases) impose the full CCL on all energyusers including the CCA sectors (ie on all business and commercial energy use) fromApril 2001. There are no adjustments to energy use in the CCA sectors to takeaccount of the CCA targets: this means that the annual energy use prior to thecharging of the CCL is the same as under the Reference Case. All revenue raised,over and above the value of the original 0.3 pp cut in employers’ NICS is thereforerecycled in full through additional reductions in the rate of NIC. Two variants of thisrun have been undertaken:• Scenario FA is exactly the same as described above, except thatno revenue is recycled, implying that the original 0.3 pp reductionin employers’ NICs was never made.• Scenario FB is identical to Scenario F, except that the rates ofCCL introduced in April 2001 are set so as to achieve the samecarbon reduction by 2010 as in Scenario B, with no adjustment toenergy use in the CCA sectors to take account of the CCAtargets. All extra revenue above that collected in B is recycledthrough employers’ NICs. In the event that CCL revenues arelower than in B, NICs are cut by the same amount (0.3 pp).Scenario FB addresses the following questions: – first how highwould the CCL rates have to be to achieve the same reduction incarbon emissions that would have been achieved with CCAs,and the lower CCL rate (if in fact the savings with CCAs arelower than without), while we also need to consider theenvironmental effects from the CCAs themselves; and second,Scenario Analysis– what are the economic consequences incurred in reaching the samecarbon saving with the higher CCL rate on CCA sectors?In the model runs the following comparisons are broadly taken into account when theresults are discussed in Chapter 5:• By comparing the R and B scenario run outcomes, we canevaluate the general effect of the CCL as implemented.• By comparing the R and C scenario run outcomes, we are ableto estimate the effect of the 20% CCL rate levied on the CCAsectors.Furthermore, by comparing the CCA (primary energy) targets with the C scenariooutcomes, we can assess the extent to which the targets were necessary (over andabove the 20% CCL rate which was implemented in the C scenario runs) to achievegreater energy and emission reductions than would otherwise have been deliveredby the price mechanism alone.Page 51 of 116

• By comparing the B and F scenario outcomes, we can assessthe different economic and environmental impacts of the CCLwith and without the CCA package. By comparing F and FAscenario run outcomes, we can assess the economic andenvironmental impact of recycling the tax revenues; in this case,through a reduction in NIC.• By comparing B and FB scenario run outcomes, we can assessthe cost-effectiveness of the CCAs and reduced CCL rates. Inother words, are the economic impacts of B more or lessfavourable than those produced under scenario FB, given thatthey are both designed to deliver the same carbon saving by2010? However, a full analysis would also need to consider theenvironmental effects of the CCAs themselves in order to cometo an overall conclusion as to which is the best option from bothan economic and environmental perspective.The Treatment of the CCAsBackgroundThe implementation of the CCL included CCAs with 44 energy-intensive sectors,whereby they would qualify for an 80% reduction in their CCL liability on qualifyingenergy use provided that they met certain energy efficiency targets. The performanceof these sectors relative to targets is monitored by the Department for Environment,Food and Rural Affairs (Defra). The results of the first target period, relating to the2002 milestone, were announced by Defra in 2003 and are summarised below.The great majority of the Target Units (TUs) met their targets. Even those that did notdo so through their own use of energy were usually re-certified for the next targetperiod because of their use of the ETS. In fact the results of the first target periodshowed a very considerable over-achievement by most sectors compared to their2002 milestone targets. The results showed that overall 221PJ less energy had beenconsumed in the CCA sectors compared to the Base Years, which amounts to anabsolute saving of 4.3 mtC (in the UK Climate Change Programme, it was envisagedthat the CCAs would only save 2.5mtC by 2010). In relative terms, the calculationshowed that the sectors consumed 171PJ less energy (and emitted 2.8 mtC less)than they would have done had their specific energy use remained at the level oftheir Base Year. The fact that the relative reductions in energy and carbon are lessthan the absolute reductions reflects the fact that a number of sectors experiencedsteep declines in output over the target period.In particular, a saving of 2.6 mtC came from the steel sector alone, arising largelyfrom a 27.5% decrease in output over the target period.Modelling the CCAs in the scenario analysisPage 52 of 116

The treatment of the CCAs in the scenario analysis evolved from the modellingstrategy that was adopted during the early model runs. It addressed the issue of theextent to which the CCA targets could be viewed as representing additionalemissions cuts from the baseline emissions that the sectors would have achievedanyway under the Reference Scenario R. The CCAs are modelled in the followingway:The energy demand projections for the sectors with CCAs are adjusted in the BaseCase B, so that all CCA targets are met. The changes to the projections required arethen stored, and compared to the ‘pure’ announcement and price effects of the Levy.However, this proved to be unnecessary because the CCA targets are almost all metin R. We would however attach an important caveat to the modelling of the CCAsundertaken for this report. This study is not intended in any way to be acomprehensive evaluation of the CCAs, as this was not within the study’s remit; wewould therefore not wish to attach undue weight to the findings reported here. Theneed to model CCAs only arises when looking at the reduced (20%) CCL rates andthe analysis is necessarily simplified. A comprehensive assessment of the CCAswould require either a detailed bottom-up technological approach or a top-downeconometric model disaggregated at the 44 CCA sector level. Neither approach ispossible within MDM which has only 4 broad energy-intensive industrial sectors outof the 50 industries covered by the overall model. This issue is further discussed inChapter 5.Results and AnalysisIntroductionThis chapter describes and comments on the scenarios that quantify the generaleffects of the announcement and introduction of the UK Climate Change Levy (CCL),using the Cambridge MDM-E3 model. The underlying methodology is the comparisonof dynamic simulations of the UK economy 1998-2010, with the base case (B)solution for the historical years matching as closely as possible to the latest data onthe economy and energy system. This chapter provides commentary on estimates ofthe CCL’s effects on energy prices and demand, energy supplies and the broadereconomy.Page 53 of 116

The final report takes into account the comments and methodological suggestionsmade by HMCE and other stakeholders (Defra, DTI, Carbon Trust and HM Treasury)on the results of the interim findings. The underlying OFU energy demandparameters in the final model run therefore differ slightly from those used in theinterim model runs. Section 3.3 in Chapter 3 of this report discusses the importanttransition from the earlier equation specification and results to those seen in the finalmodel run, and illustrates specifically the switch to using the constrained priceelasticity equations in the final runs’ scenario simulations. One of the most notablechanges between the final and interim runs is the increased CCL dummy parameter,which has increased from 12.6%, to about 15% in the final run. A key implication ofthis adjustment is an increase in the emissions savings between the reference caseand the base case. Some of the reasons for this and many other key differencesbetween the runs can be attributed to the fact that we now have further outturn datain the final run; that the profile of the CCL dummy has altered slightly (see Table 3.1);and that a priori reasoning suggests we constrain the long and short run priceelasticities to be equal.The main underlying assumptions taken in the final model run are briefly discussedhere. The announcement of the CCL has a long-term permanent effect on the energydemand for other final users (as found in the interim model runs). A distinctdifference, however, between the interim and final OFU equation is the constraintplaced on the price elasticity as discussed in Chapter 3, whereby the long-run priceelasticity was set to equal the short-run price elasticity according to a prioriknowledge of the literature. The short term assumptions on oil prices have also beenrevised upwards, although the follow-through rate (to 2010) remains the same (this isconsistent with CE’s July 2004 UK Energy and the Environment report). We assumethe average oil price in 2004 to be $32 per barrel, and $31 per barrel in 2005. Theseassumptions are drawn on factors such as a weakening US dollar, threats toshortfalls in the supply, and strong demand from developed and developing countriesalike (North America and China’s fast-growing economy). Many of the othermacroeconomic assumptions are stated in the tables in Chapter 4 of this report.Table 5.1 shows how the price assumptions compare to those in the DTI’s EnergyPaper 68 and Updated Energy Projections (UEP).Page 54 of 116

The scenarios are all simulated counterfactual solutions 1998-2003 and projections2004-2010. The base case does not exactly match the historical data 1998-2003,mainly because the electricity sub-model overestimates the use of coal stationsduring the period. It is important to note that all scenarios (including B - the basecase) are intended purely for comparison with each other rather than to the historicaloutcome or any forecasts. Specifically, the projections shown for the levels of CO2and GHG over 1999-2010 in B should not be taken as forecasts (they include thesimulation mismatches for 1999-2001 (which are common to all the scenarios) andthey do not include any effects of the European Union Emissions Trading Scheme(ETS)).It should also be noted that the precision with which the results are reported does notindicate the reliability of the results. The uncertainties underlying the analysis areindicated in the text and in the scenario analysis.The Reference/Counterfactual Case, Scenario RScenario R (the ‘reference case’) projects through to 2010 as if the CCL had neverbeen announced or introduced. The energy demand, fuel shares, fuel prices,emissions, and electricity sub-models have all been run with the solution variablesendogenous from 1998 rather than set to historical values.The Effects of the CCL on the UK Energy SystemThe Base Case, Scenario BPage 55 of 116

Scenario B (the ‘base case’) introduces the announcement or ‘diffusion’ effects asestimated and imposes the CCL at actual or ‘after’ rates from April 2001. The solutionhas been adjusted to match the actual receipts of the CCL in 2003 of £831m asreported in ONS September 2004 Financial Statistics. (The financial year figure for2003/04 was used in preference to the calendar year because the latter showed anerratic quarterly profile.)Imposition of the Announcement EffectsThe aggregate energy demand equations by fuel-using sector have been estimatedto test for the CCL announcement ‘diffusion’ effect using a CCL dummy variabletaking the values of 0 in 1998, 0.10 in 1999, 0.32 in 2000, 0.66 in 2001 and 1.0 after2001. These values were chosen on the basis of the estimated announcementeffects for other final users of energy. No significant announcement effects using thisdummy variable were found in the equations for other energy users (the annualequations for basic metals, chemicals, mineral products and other industry, and thequarterly equations for total industrial use). The dummy variable is included in theequations for other final users for all scenarios other than the reference case R andthe C scenario.Scenario B compared to RThe announcement effectThe announcement of the CCL in the 1999 Budget has its effect appearing in theoutcomes for 2000 and all later years. Although the CCL dummy variable is given avalue of 0.1 in 1999, the variable is included in the long-run explanation of other finalusers demand for energy. This means that it is included as a lagged component inthe difference equation so that its effects come through one year later in 2000. Theeffect is -1.2% for other final users energy demand in 2000 and it grows rapidlythereafter, but in combination with price effects once the CCL affects energy pricesdirectly in 2001.The announcement effect on its own (ie without price effects from the imposition ofthe CCL) causes a reduction in energy demand from other final users of 4.0% in2001, then 8.4% in 2002, rising to 13.8% in 2010. However, this includes thefeedback effect of lower demand causing lower electricity prices, which limits theannouncement effect’s impact.The effects of theCCL on fuel pricesPage 56 of 116

The immediate effect of the CCL is to raise fuel prices to energy users. The extent ofthe increase in prices for each fuel user depends on its consumption of coal, gas andelectricity, the different rates of the CCL on these fuels and the coverage of the fueluser by CCAs. In 2002, the first full year of the Levy, the price of gas is estimated tobe 12.3% higher in B compared to R for other industry, 15.2% for other final usersand less for other sectors. The price of electricity is estimated to be 10% higher forboth other industry and other final users. The higher gas price increase is due to thedifferential in the CCL rates on gas and electricity, eg for 2002 it implies a rise in theprice of gas relative to electricity of 2.2 pp for other industrial users. Theproportionally higher increase in price of gas occurs despite higher effective p/kWhCCL rates for electricity, as absolute gas prices are much lower than absoluteelectricity prices. Hence, the marginal increase in the gas price is higher than themarginal increase in the electricity price, resulting in the proportionately higher rise inthe price of gas.The increase in energy prices in percentage terms (as an effect of the CCL)diminishes over the period. This is because energy prices are assumed to increase inreal terms to 2010 while the CCL falls or is held constant in real terms. By 2010, gasprices are expected to be 13.8% higher in the B scenario for other final users and11.1% higher for other industry; electricity prices are expected to be 5.1% higher forother final users and 4.6% higher for other industry. The average fuel price in 2010(ie the price weighted by fuel consumption) is expected to be 6.2% higher in B forother final users, and 9.4% higher for other industry.The general effects of the CCL on fuel useThe announcement effect intensifies and combines with the price effects of the CCLfrom 2001 onwards; the total reduction in other final users’ demand for energy risesto 4.9% in 2001 and 9.5% in 2002, then to 14.6% in 2010. Note that most of theeffects of the CCL are attributed to the ‘pure’ announcement effect, not to the priceeffect. The effect of the CCL on the energy-intensive sectors is far less because mostfirms in these sectors do not pay the full rate of the levy, and because noannouncement effect was detected in these sectors. For basic metals and chemicals,the effect of the CCL reaches its peak soon after its introduction and diminishes by2010, because energy prices are expected to grow faster than the rate of the CCL.However, the reduction in demand from mineral products continues to grow, reaching3.8% in 2010. Although the mineral sector does not use electricity in its mainprocesses, the CCL causes a minor switching to electricity in other areas. Thiscontributes to an overall reduction in energy demand, mainly because electricity ismore expensive.The effects on total final energy demand are reductions of 0.2% in 2000, 1.0% 2001and 1.8% 2002, rising to 2.9% in 2010 (see Table 5.2).Page 57 of 116

The CCL has different effects on demand for the individual fuels: coal, LPG, gas,electricity. These arise from the effect on relative prices, the type of fuel demand ofthe sectors most affected by the CCL, and the possibility for fuel switching in thesesectors. Final coal demand is reduced by 0.8% in 2002 and by 1.6% in 2010compared to R. Demand for oil products is reduced by less (0.4% in 2002 and by1.1% in 2010), as use in road transport is not subject to the CCL. The effect on gasand electricity use is greater as a far higher share of their consumption is by otherfinal users, the sector most affected by the Levy. Total final demand for gas is 2.8%less in 2002 and 4.8% less in 2010; demand for electricity is reduced by 3.2% in2002 and by 3.6% in 2010. In the early model runs, we projected a slight increase inthe demand for electricity by 2010 as the price increase for gas relative to electricityin B compared to R caused a shift in fuel demand. However, in the further interimruns, the announcement effect of the CCL for other final users was estimated to be apermanent effect; this reduces demand for all fuels in the long run irrespective ofcost. The permanent CCL effect remains in the model run for this report, and thuscontinues to reduce energy demand for all fuels in the long run. As other final usersconsume a greater share of electricity, the fall in total demand of electricity is nowonly slightly less than that of gas.The effects of the Climate Change Agreements (CCAs)Page 58 of 116

The reduction in energy use of the industrial sectors over the period to 2010 appearsto be sufficient without any further modification to the projection to achieve the CCAtargets for both energy saving and energy efficiency (see Section 5.6 for a furtherdiscussion of these targets and the limitations of MDM-E3 in the detailed modelling ofthe CCAs). This energy use is projected for broad sectors by energy demandequations that usually include substantial downward trends, estimated from historicaldata, in the long-term use of energy to allow for improvements in energy efficiencyand which reflect structural change within the sector. These trends have beenallowed to continue throughout the projection period. A combination of technologicalchange and relative decline in UK energy-intensive subsectors of manufacturing (iebulk chemicals as opposed to speciality chemicals), implies that the energy (andtherefore carbon) saving and energy-efficiency targets would have been met withoutthe CCAs. This result is uncertain because the historical technical and structuralchangetrends may not continue as in the past, and some firms in the broad groupsare not covered by CCAs, especially in ‘other industry’ with around 50% coverage.Moreover, the CCA targets are set in terms of improvements in energy efficiency,whereas the model projections have used energy intensity which means that thecomparison is distorted by any structural change within the sectors. The aggregatefuel-using industrial and commercial sectors in the model MDM-E3 are five broadgroups, and the CCAs cover 44 sectors within those groups, with fairly full coverageof the basic metals, mineral products and chemicals fuel-using sectors, but lowercoverage of the MDM other industry sector, and especially low coverage of other finalusers. The aggregation of the CCA sectors into the MDM-E3 sectors means thatassumptions must be made before conclusions can be drawn aboutHowever, we would stress that our analysis does not of itself mean that the CCAshave been ineffective. Indeed, we are aware of a separate analysis of the CCAs(Reference: Ekins P. and Etheridge B. (2005, forthcoming), The Environmental andEconomic Impacts of the UK Climate Change Agreements, Energy Policy, in press)which suggests that they induced an ‘awareness effect’ perhaps analogously, to theCCL’s announcement effect for the commercial sector, which has led to a substantialover-achievement of the targets. It is, however, too early for the econometricevidence to be able to detect this effect.The effects on electricity supplyThere is a small increase in capacity of major power producers in scenario Bcompared to R, mainly in CHP capacity. The reduction in electricity demandattributed to the CCL is not sufficient either to foreclose new CCGT stations beingbuilt, or to require the early retirement of coal-fired, nuclear or CCGT stations. Themain direct effect of the CCL on electricity supply is to encourage the installation ofnew renewables and CHP capacity, because generation from these sources isexempt from the Levy.Page 59 of 116

Renewable generation and capacity are imposed in MDM-E3. Therefore, we havenot been able to measure the effect on renewables of the exemption from the CCL.The competitive price of renewables generation is presently around 4.5 p/KWh; withthe wholesale price of electricity around 2.2 p/KWh in the summer of 2004 (Source:Renewables data from Argus Global Emissions), and because there is a lack ofrenewables capacity, Renewables Obligation Certificates (ROCs) effectively providea subsidy of 3 p/KWh. Exemption from the CCL provides a further subsidy torenewables generators of 0.43 p/kWh, worth roughly 8-10% of the generation price.Therefore, it would seem that the exemption from the CCL should have an impact oninvestment in renewables. However, the value of the exemption is probably smallcompared to other uncertainties in the market (for example, the long term price of theROCs and availability of planning permission), which are of far greater importance indecisions on investment in renewables.The effect of the CCL exemption on both fuel inputs to CHP, and electricity exportsfrom CHP, is modelled through a CHP submodel (See:www.dti.gov.uk/energy/environment/energy_efficiency/chpreport.pdf). With theintroduction of the CCL, CHP becomes more competitive compared to conventionalprovision of heat from a gas boiler and power from the national grid. Good QualityCHP capacity is increased by 1.2GWe by 2010, contributing towards theGovernment’s 10GWe target for that year. The new capacity displaces mostly CCGTgeneration using natural gas, but also some coal-fired generation.Revenues from the CCL and the effects of recyclingThe CCL revenues are calibrated to actual receipts of £831m in 2003. These thenincrease slightly to £834m in 2004 as the rates were frozen at 2003 levels in the2004 Budget, while the exemption of CHP exports came into force in 2003 and theenergy consumption by fuel users liable to the CCL declined. The CCL rates areassumed to be indexed with inflation from the 2005 Budget and revenues rise to£933m by 2010.The NIC reduction of 0.3 pp introduced in the 2001 Budget to offset the costs tobusiness of the CCL appear to be worth more to business than the CCL revenuesthroughout the period. The reduction in NIC revenues attributable to the reduction inrates combined with small effects from the CCL on employment and wage rates isestimated to be £1,582m in 2004 rising to £2,100 in 2010. The main reason for thedifference in trend with CCL receipts is the faster rise in the NIC base on which ratesare calculated, with nominal wages rising much faster than the use of coal, gas andelectricity.The effects on emissions of CO2 and greenhouse gases (GHGs)Page 60 of 116

The purpose of the CCL is to reduce emissions of CO2 and other GHGs. Thereductions in these emissions come from reductions in the use of coal and gas in theindustrial installations covered by the CCL. In addition the reduction of electricityconsumption in these installations causes a decline in the burning of fossil fuels forpower generation. Table 5.3 shows that total CO2 emissions are reduced by 3.1mtC(2.0%) from the reference case in 2002, rising to 3.7mtC (2.3%) by 2010. Other finalusers contribute most of the cut in emissions, mainly because the reduction in totalenergy demand is greatest from this user, but other industry delivers a cut in CO2emissions of around 0.8mtC by 2010. Emissions from power generation are alsolower, due to the lower demand for electricity. Total GHG emissions for the wholeeconomy are reduced by 1.7% from the reference case in 2002 rising to a reductionof 2.0% by 2010.The 3.7mtC CO2 reduction by 2010 is about 2.3% of the 1990 emissions level. Thereduction in CO2 is less than that for final energy demand because the carbonintensity of energy consumption is raised. Although the CCL encourages theinstallation of lower-emissions CHP and renewables for electricity generation, this isoutweighed by two factors (I) coal-fired generation takes a higher share of electricitysupply, and (ii) gas loses share of final energy demand to electricity, coal and oil.Effects on macro-economic variablesPage 61 of 116

The combination of the CCL and the NIC reduction together has little effect on themain macro variables. By 2010, GDP is only 0.06% above the reference case;meanwhile, the GDP deflator is 0.13% below the reference case. Although the CCLhas a direct positive effect on prices, this is outweighed by the negative feedbackeffect from lower industrial costs caused by the reduction in employers’ NICs.Despite these lower industrial costs, the CCL leads to a slight deterioration of theUK’s trade performance, as the net effect of the change in tax regime is to raise costsslightly for those industries producing tradeable goods.The Effects of the Reduced-rate CCL on the CCA SectorsScenario C compared to Scenario RThis section discusses the effects of the reduced-rate CCL on the CCA sectors. Itdescribes the external effects of imposing a levy of 20% of the CCL rates on thesectors covered by CCAs. Table 5.4 shows the estimated coverage of the CCAs byMDM fuel user. In view of the small coverage of other final users, no announcementeffects are included in this scenario.Another interesting feature of this scenario is a comparison of the pure price effect ofthe reduced-rate CCL on the energy intensity of those sectors for which the coverageof CCAs is fairly full: basic metals, mineral products, and chemicals. (This is bestshown in the Key Macro Variables table for C3; see Appendix D.) Energy intensity(measured as primary energy input per unit of gross output) is reduced most forchemicals and basic metals, whereas energy intensity for mineral products is littlechanged. As electricity becomes cheaper compared to gas, mineral productsconsume slightly more electricity, which requires greater primary fuel input per unit ofuseable energy. Note that in this scenario it is only the CCA component of eachMDM broad sector that is affected andPage 62 of 116

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there is an implicit assumption in the reference case that behaviour in the CCAcoveredpart of each sector is the same as behaviour in the sector as a whole.The effect of the reduced-rate CCL on emissionsCO2 and GHG emissions are relatively unchanged in C compared to R. Althoughenergy demand is reduced slightly, this has no effect on emissions, because relativefuel prices cause a switch to electricity, which creates more CO2 per unit of energyconsumed.The Effects of a CCL Without Lower Rates and WithoutCCAsThe Full CCL-Rate Scenarios: F,FA,FB Compared with other ScenariosFull-rate CCL with recycling of extra revenues (F compared to B)Page 64 of 116

The interest of the F scenario is that it shows the effects of the full rate of CCL onCCA sectors through a comparison with Scenario B. Scenario F raises an extra£366m revenues for the CCL by 2003 and this extra revenue has been used tofurther reduce payments of employers’ NICs. The total value of CCL receipts rises to£1292m by 2010.Fuel demand by mineral products is reduced by a further 5% in 2010 compared withthat in B. Fuel demand by chemicals is reduced by 4.4%, basic metals by 3.5% andother industry, 2.8%. Most of the overall reduction in fuel use can be attributed to areduction in gas demand. While there are further small falls in the use of electricity bybasic metals and chemicals, there are small increases for mineral products and otherindustry, as these sectors are more sensitive to changes in relative fuel prices. Totalfinal demand of gas across the whole economy falls by around 1.4% from B, totaldemand for oil falls by around 0.3% (although only a small proportion of theconsumption of oil products is taxed), while electricity consumption is slightly higher.CO2 emissions are reduced by around 0.5 mtC in the F scenario compared to B in2010. Lower emissions from the industrial sector are offset slightly by higheremissions from power generation due to the higher demand for electricity. Higherdemand for electricity in the full-rate scenarios is a result of the increase in the scopeof the CCL, which in turn strengthens the effect of relatively higher gas prices, againshifting demand towards electricity.Full-rate CCL scenarios without recycling of CCL revenues: FA compared to FScenario FA shows the full rate CCL with no revenue recycling. The effect of the NICreduction on macro-economic variables can be seen by comparing F (with the NICreduction) with FA (without) in Table 5.7. As expected, GDP and employment areboth lower in FA; they are in fact similar to the levels in the reference case, indicatingthat the economy is no worse off in this scenario than it would have been had theCCL never existed.There are contrasting feedback effects of the NIC reduction on energy demand: whilethe reduction in NICs causes a slight increase in output from most sectors, andhence an increase in the demand for energy, it also lowers labour costs, which raisesthe price of energy relative to the general price level (as production and supply ofenergy products is not labour intensive); this has a negative effect on energydemand. Overall, total energy demand is practically equal in FA and in F, while CO2emissions are almost identical over 2002-2010.Page 65 of 116

Meeting the CO2 reduction in B by levying the same rate across the whole tax base:scenario FBPage 66 of 116

Scenario FB changes the CCL rates in F so that by 2010 this scenario shows thesame reductions in CO2 as in the base case, scenario B, with the same reduction inNICs (0.3 percentage points). This scenario shows the effect on CCL rates and onmacroeconomic variables of adopting this different taxation regime. CCL revenues inthis scenario are £630m in 2010, around 70% of the revenue in B. Receipts arehigher from the consumption of coal and LPG, similar from gas consumption, butsharply lower from electricity, the consumption of which provides the majority of therevenues in B. GDP and employment are very slightly higher in FB than in B.However, GDP and employment are lower than in F, due to the greater reduction inNICs in that scenario. Energy demand is a fraction lower in FB compared to Bbecause this tax regime places a lower burden on other final users; there isconsequently a higher demand for electricity, so there can be less final energyconsumed to emit the same quantity of CO2.Treatment of the CCA Targets for the MDM Fuel UsersUsing MDM-E3 to forecast the attainment of targets in 2010As discussed briefly in section 5.3, page 45, there are CCAs covering 44 sectors witha range of types of target. These targets cover only five MDM fuel users, so we hadto make various assumptions on how to include these in the modelling and how tointerpret results. The estimated targets formed for the MDM fuel users are shown inTable 5.8. The projections discussed below are based on a top-down econometricmodelling and include the effects of both structural change and changes in energyefficiency, while the CCA targets themselves were derived by ETSU from a bottomup,technologically-based method that only considered energy efficiencyimprovements. The estimates of reductions in energy use or improved efficiency canbe expected to differ in part due to this difference in approach. It should be noted thata comprehensive assessment of the CCAs would require either a detailed bottom-uptechnological approach or a top-down model disaggregated at the 44 CCA sectorlevel. Neither approach is possible within the current structure of MDM (which hasonly four broad energy-intensive industrial sectors out of the 50 industries coveringthe whole economy).Advantages of bottom-up modellingThe bottom-up approach used to set the CCA targets has a number of strengths,namely:• it uses detailed micro data on energy use and physicalproduction at the individual-firm level that has been subject to acareful audit• it provides a sound understanding of the process of how energysavings are realized in practice at the sector and sub-sector level• it offers an invaluable data source for the energy-savingtechnologies that can be implemented by industrial firms and thefuel-switching techniques and processes that are available at thesub-sector levelPage 67 of 116

Limitations of bottom-up modellingTypically the engineering-type approach, used to form the CCA targets also has anumber of limitations, namely:• it is difficult to validate as there are no comparable modelsagainst which accurate validation is possible• the interaction between sectors is difficult to assess as sectorsare considered individually• the output measures are in physical units, making it difficult toperform transparent economic analysis, which requiresmeasurement in standardised economic units (eg gross output inconstant prices)• the uncertainty faced by an industry when implementing energyefficiencymeasures to meet a CCA target. The marketpenetration of a given technology could, for example, be linked toan industry’s growth profile such that faster growth is associatedwith the faster penetration of an energy-saving technologyAdvantages of top-down modellingThe top-down, MDM-E3 based approach adopted in this study has a number ofadvantages, providing:• a systematic analysis of the economic feedback effects arisingfrom the CCA targets on a broad sector level• an analysis of the effects on structural change, if any, of energyefficiencyor CO2 abatement policies in a macro-economiccontext• a measure of the adjustment costs in the economy as a result ofthe implementation of the targetsLimitations of top-down modellingPage 68 of 116

Long-term technological behavioural change in industry is difficult to address in theapproach adopted in this study, though this is not expected to be a serious limitationgiven the relatively short time horizon of the study. More seriously, the top-downapproach does not describe or model detailed energy supply technologies or energyefficiencytechnologies at the highly disaggregated level which underlie the bottomupspecification of the CCA targets. Moreover, the derivation of the CCA targets forMDM fuel-user sectors have to be carried out on the basis of projections of economicoutput, whereas the CCA targets themselves are usually denominated in terms ofsome physical characteristic of the product, such as weight. The derived targets forthe MDM fuel-user sectors therefore implicitly assume that the relationship between,say, weight and economic value remain the same over the period in question. Thesecaveats should be borne in mind when interpreting the results reported below.The targets for basic metalsWe could not estimate a CCA target for basic metals MDM fuel-user sector becauseapproximately two-thirds of the sector (by output) comprises iron & steel, which hasan absolute energy target in its CCA, while the other industries in the sector haveenergy ratio targets. However, as noted in section 4.7, the steel sector appears to beconsuming significantly less energy, which subsequently prompts the steel sector tomeet its target. The output in the steel sector further improves over the periodbetween 2002 and 2010. We forecast a fall in energy demand for basic metals ofover 20% from present levels to 2010 in both the base and the reference cases.However, basic metals other than steel are all included in the MDM fuel user and theMDM sector ‘basic metals’. As we can separate projections neither for output nor fueluse, we cannot use the results as an indication of whether or not these industries arelikely to meet their targets in 2010.The targets for mineral productsPage 69 of 116

The target for mineral products was formed by weighting the energy-efficiencytargets for the various subsectors according to gross output (monetary units) andenergy consumption. The subsector targets were based in different years, so werebased these to a common year, 1998. The target derived for mineral productsmakes the broad assumption that output will grow at a constant rate for allsubsectors. Hence the target is only indicative of the performance of the sector as awhole. The judgements on the attainability of these targets assume that thecompanies and industries not covered by the CCAs will make roughly the sameenergy savings as companies covered by the CCAs. Table 5.9 shows that in theReference Case (R) the sector is projected to reduce its energy intensity in 2010 by36% from its 1998 level, while Table 5.8 shows that the sector’s derived CCA targetfor 2010 is only a 10.9% reduction. Even bearing in mind the caveats about possiblestructural change and the target derivation process noted above, this very large overachievementof the sector against the derived CCA target suggests that the CCAtargets for the sub-sectors would have been met had no CCL existed.The targets for chemicals industriesThe target for chemicals coincides with the actual defined industry target. All targetunits have just been recertified in the sector following the 2002 intermediate review.Table 5.9 shows that in the Reference Case (R) the chemicals sector is projected toreduce its energy intensity in 2010 by 46% from its 1998 level, while Table 5.8 showsthat the sector’s CCA target for 2010 is only a 18.3% reduction in energy efficiency.The fall in chemicals use of fuels from 2002 is partly the result if increases in realprices leading to further structural change towards low-energy intensive chemicals.Even bearing in mind the caveats about possible structural change (likely to berelevant to this sector because of different relative growth of basic chemicals andpharmaceuticals), as for the mineral products sector this very large over-achievementof the sector against the derived CCA target suggests that the chemicals sectorwould have met its target had no CCL existed.The targets for other industryTable 5.9 shows that energy intensity has risen for other industry since 1998. Thatmost CCA subsectors met their interim targets in 2002 suggests that those industriesnotPage 70 of 116

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covered by a CCA must have increased their energy intensity. It also shows that theperformance of the sector as a whole is a poor indication of the performance of thesubsectors. Nevertheless, we have calculated a target for the sector as a whole, asshown in Table 5.8. We calculated this target by following the same methodology asdiscussed above for mineral products. We made the simplifying assumption that if asubsector has negotiated a CCA, then the whole subsector must meet this target,even if there are firms not covered by the CCA. Furthermore we assumed thatsectors not covered by agreements would hold their energy intensity constant at1998 levels. Although energy intensity has in fact not been constant for these sectorsover 1998-2002, this is the best assumption to make for the purposes of formingtargets to 2010.Table 5.9 shows that in the Reference Case (R), the other industry sector isprojected to see a significant reduction in its energy intensity, such that by 2010 it is10% below its 1998 level, and that in the Base Case (B) it is 11.5% below its 1998level. Table 5.8 shows that its derived target for 2010 was a comparable 11.4%. Inthis sector, there seems to be little reason to expect that structural change wouldreduce, rather than increase the energy intensity of the non-CCA subsectors, so thatthis near-achievement of the CCA target for the sector, without the CCL package,suggests that, as for mineral products, the CCA targets for the subsectors wouldhave been met had no CCL existed.The targets for other final usersAs only 4.3% of fuel use by other final users is covered by an agreement, we cannotuse the results as an indication of whether or not the targets in this sector will be met.Conclusions on CCA targetsPage 72 of 116

The reduction in energy use of the industrial sectors over the period to 2010 in thereference and base cases appears, with the exception of other industry in theintermediate target years to 2010, to be sufficient without any further modification tothe projection to achieve the CCA targets for both energy saving and energyefficiency. (The other industry sector consistently failed to meet its target for 2008,but did meet the target for 2010). As noted in Section 5.3, this energy use projectedby energy demand equations includes substantial trends in the long-term use ofenergy to allow for structural change and improvements in energy efficiency, whichhave been allowed to continue throughout the projection period. A combination oftechnological change and relative decline in UK energy-intensive subsectors ofmanufacturing (ie bulk chemicals as opposed to speciality chemicals) implies that theenergy (and therefore carbon) saving and energy-efficiency targets would have beenmet without the CCAs. This result is uncertain because the historical technical andstructural-change trends may not continue and for the other reasons discussedearlier. Only for one sector (other industry in 2008) did we find that a CCA targetwould have been missed had no CCL ever existed. We also found that the priceeffect of the reduced-rate CCL was sufficient, on its own, for these targets to be met(again with other industry in 2008 as an exception). However, again as noted in 5.3, itshould not be interpreted from this that the CCAs were ineffective. The verysignificant over-achievement against the CCA targets at the end of the first period(2002), which we noted in Section 4.7 page 39, have led Ekins and Etheridge (2005)to suggest that the CCAs may have stimulated energy savings, perhaps through an‘awareness effect’, that went beyond what the target effects of the CCAs would haveachieved on their own. While this seems plausible, it is, in our judgment, perhaps tooearly for such an awareness effect to be apparent in the econometric evidence.Finally, we would again emphasise strongly that the treatment of the CCAs in thisproject is subject to the caveat that the scenario simulations in MDM-E3 havenecessarily required a more simplified and more aggregated treatment of the 44 CCAsectors than was the case when the CCAs were negotiated by a number of energyintensivesectors.ReferencesAgnolucci P. (2004), Ex-Post Evaluations of CO2-based Taxes: A Survey, PolicyStudies Institute, London, (TYP 51,http://www.tyndall.ac.uk/publications/working_papers/wp51_summary.shtml).Agnolucci P. and Ekins P. (2004), The Announcement Effect and EnvironmentalTaxation, Policy Studies Institute, London, (TYP 53,http://www.tyndall.ac.uk/publications/working_papers/wp53_summary.shtml).Agnolucci P., Barker TS., Ekins P. (2004), Hysteresis and Energy Demand: theAnnouncement Effects and the Effects of the UK Climate Change Levy; (TYP 51,http://www.tyndall.ac.uk/publications/working_papers/wp51_summary.shtml).Baltagi, B. H. and Griffin, J. M.(1984), Short and Long Run Effects in Pooled Models,International Economic Review, 255, 631-45.Page 73 of 116

Cambridge Econometrics (2003), Modelling Good Quality Combined Heat and PowerCapacity to 2010: Revised Projections, submitted to UK DTI, London,www.dti.gov.uk/energy/environment/energy_efficiency/chpreport.pdf.Ekins P. and Etheridge B. (2005), The Environmental and Economic Impacts of theUK Climate Change Agreements, Energy Policy (Forthcoming).Hansen H. (2001), The New Econometrics of Structural Change: Dating Changes inU.S. Labor Productivity, Journal of Economic Perspectives, 15, 117-128.Hylleberg, S. (eds.) (1992). Modelling Seasonality, Oxford University Press, NewYork, New York.Pesaran, M.H. and Shin Y.,(1999), An autoregressive distributed lag modellingapproach to cointegration analysis, in (ed) S Strom, Econometrics and EconomicTheory in the 20 th Century: The Ragnar Frisch Centennial Symposium, Chapter 11,Cambridge, Cambridge University Press.Pesaran M. H. and Smith R.,(1995), Alternative Approaches To Estimating Long-RunEnergy Demand Elasticities, in Barker, T. S., Ekins P., Johnstone N. (eds.), GlobalWarming and Energy Demand, Routledge, London, United Kingdom.Pesaran, M.H., Shin Y. and Smith R.J.,(2001), Bounds testing approaches to theanalysis of level relationships, Journal of Applied Econometrics, Special issue inhonour of J D Sargan on the theme “Studies in Empirical Macroeconometrics”, (eds)D.F. Hendry and M.H. Pesaran, V. UK DETR (2000) Climate Change: UKProgramme, DETR.UK DEFRA (2001) 3NC: the UK’s Third National Communication under the UnitedNations Framework Convention on Climate Change, DEFRA.UK DTI (2004) Digest of UK Energy Statistics, National Statistics.UK DTI (2004), Department of Trade and Industry: Updated Energy Projections,UK DTI (http://www.dti.gov.uk/energy/sepn/uep.pdf).Appendix A Basic Structure of MDM-E3A1 MDM-E3 as an Energy-Environment-Economy ModelThe Cambridge Multisectoral Dynamic Model of the UK economy (MDM-E3) is theUK’s most detailed energy-environment-economy (E3) model, designed to analyseand forecast changes in economic structure, energy demand and resultingenvironmental emissions.Page 74 of 116

The version of MDM-E3 used for this report is based on the 1992 Standard IndustrialClassification (SIC92), with 1995 as the price-base year, and uses input-output tablesfor 1995. A comprehensive account of an earlier version of the economic model isgiven in Barker and Peterson (1987). The model has since become a regionalizedenergy-environment-economy model and most of the equations have beenrespecified, but the basic structure of the model has remained unchanged.Flows in the economic model are generally in constant prices, while the energyenvironmentmodelling is done in physical units. This modelling is described inBarker et al (1995). Energy-environment characteristics are represented bysubmodels within MDM-E3, and at present the coverage includes energy demand(primary and final), environmental emissions, the electricity supply industry anddomestic energy appliances. The energy industries are included within the basicinput-output structure, and MDM-E3 is a fully-integrated single model, allowingextensiveeconomy-energy-environment interaction. Figure A1 summarises the energyenvironment-economylinkages within MDM-E3.Page 75 of 116

The ability to look at interactions and feedback effects between different sectors -industries, consumers, government - and the overall macroeconomy is essential forassessing the impact of government policy on energy inputs and environmentalemissions. The alternative, multi-model approach, in which macroeconomic modelsare combined with detailed industry or energy models, cannot adequately tackle thesimulation of ‘bottom-up’ policies. Normally these systems are first solved at themacroeconomic level, and then the results for the macroeconomic variables aredisaggregated by an industry model. However if the policy is directed at the levelofindustrial variables, it is very difficult (without substantial intervention by the modeloperator) to ensure that the implicit results for macroeconomic variables from theindustry model are consistent with the explicit results from the macro model. As anexample, it is very difficult to use a macro-industry, two-model system to simulate theeffect of exempting selected energy-intensive industries from a carbon or energy tax.A2 The Economic ModelThe economic model is designed to analyse and forecast changes in economicstructure. To do this, it disaggregates industries, commodities, consumers’expenditure and government expenditure, as well as foreign trade and investment(see Appendix B Table B1 for the main classifications); in fact it disaggregates all ofthe main variables that are treated as aggregates in most macroeconomic models.The detailed variables are linked together in an accounting framework based on thesystem of UK National Accounts consistent with the European System of Accounts(ESA95) (see Section 5.5 in the June 1999 edition of Cambridge Econometrics’Industry and the British Economy for a description of the framework). This frameworkensures consistency and correct accounting balances in the model’s projections andforecasts. The version used for this report incorporates the 1995 price base and theinput-output table for 1995 estimated from official data and uses the data from the2003 National Accounts and associated data from the ONS.The model is a combination of orthodox time-series econometric relationships andcross-section, input-output relationships. Aggregate demand is estimated using aconsumption function and investment equations. The supply side comes in throughthe export and import equations, in which innovation and capacity utilisation affecttrade performance, as well as a set of employment equations which allow relativewage rates and interest rates to affect employment and therefore industry-levelproductivity growth.A3 The Energy SubmodelThe energy submodel determines total secondary energy demand, fuel use by userand prices of fuel use, and also provides the feedback to the main economicframework of MDM-E3. This econometric ‘top-down’ treatment is supplemented byan engineering ‘bottom-up’ approach in a number of submodels, including that of theESI. The linksPage 76 of 116

within the energy submodel and the inputs from the main model are shown in FigureA2, and the feedback to the main model from the energy submodel in Figure A3.All the main equation sets in MDM-E3, including the energy equations, are estimatedusing a standard cointegrating technique. The equations for final energy demand areestimated on data from the Digest of UK Energy Statistics (DUKES), publishedannually by the DTI, supplemented by more up-to-date data published monthly inEnergy Trends.Page 77 of 116

The data are available in mtoe, original units and, in some cases, monetary unitsdisaggregated by major energy user. Prices are calculated as the ratio of themonetary unit and demand data.The energy user and energy type classifications used in the energy-environmentmodel are based on the classifications used in DUKES. They are listed in AppendixB, Table B2, which also shows the correspondence with the industries andcommodities in the economic model.On the supply side, coal, oil and gas price data are available from the OPEC Bulletin,DUKES, Energy Trends and the Financial Times. These are exogenous variablesduring the forecast period. Assumptions for oil and gas production are based ongovernment expectations given in the DTI’s Energy Report Volume 2 (formerlyknown as the Brown Book) up to 2007 and then extrapolated to 2010 using DTIprojections cited in the PIU Energy Review (2002). The same source is used for UKcoal output projections, augmented by Cambridge Econometrics’ views.Power generation energy demand is calculated by the ESI submodel, as describedbelow, and passed to the energy submodel. The aggregate demand for energy bythe other fuel users is dependent on:• the activity of the fuel user, usually taken to be gross output ofthe sector, but, in the case of road transport, total output plusconsumer demand is used and in the case of households,household expenditure is used• technological progress in energy use, which reflects both energysavingtechnical progress and the elimination of inefficienttechnologies (at present in MDM-E3 this is represented by a timetrend pending further research on the use of the technologymeasure)• the cost of energy relative to other inputs• changes in temperatureThis aggregate demand is then shared out among the fuel types. It is assumed thatfuel users adopt a hierarchy in their choice of fuels, choosing first electricity forpremium uses (light, electrical appliances, motive power, special heatingapplications), then sharing out non-electric demand for energy between three fossilfuels (coal and coal products, oil products and gas). The specification of theseequations follows similar lines to the aggregate energy equations, except that thedependent variable is the fuel share, and the variables are:• activity• technology measure (time trend at present)• three price terms - the price of the fuel type in question, the priceindex of its nearest competitor, and the general price index of allfuel usePage 78 of 116

• temperatureThe fossil fuel prices faced by the fuel user are based on the assumptions for oil, gasand coal production prices. Electricity prices are calculated by the ESI submodelbased on the cost of generation, transmission, distribution and supply. MDM-E3allows such measures as the fossil fuel levy, VAT on domestic fuels, the escalator inpetrol and derv excise duty, and a carbon and/or energy tax to be modelled.Revenues from any taxes on energy may be used in the main model, depending onthe assumptions made, to reduce the Government’s borrowing requirement, or toreduce the indirect or direct tax burden or for public investment in, for example,renewable energy sources or energy efficiency technologies.Feedback to the economic modelThe main feedback from the energy submodel (including the ESI submodel asdescribed below) is to the matrix of input-output coefficients, which are ratios of theinput of a commodity to an industry to the output of that industry, both measured inmonetary units. The input-output coefficients that are updated are those thatcorrespond to the fuel commodities: coal, manufactured fuels (petroleum products),electricity, and gas supply. Fuel use and prices on the energy-environment modelbasis are converted back to demand for and prices of MDM-E3 commodities, andfuel users back to MDM-E3 industries (see Appendix B, Table B2 for thecorrespondences). In the case where several industries have been aggregated intoone fuel user, such as ‘other industry’, there is the option to calculate the deviationfrom the fuel user mean of the different responses of each industry to fuel pricechanges. The energy submodel also calculates consumers’ expenditure on fuels andpetrol.A4 The Electricity Supply Industry SubmodelThis section describes the basic structure and operation of the ESI submodel. MDMhas recently been developed to incorporate a fuller treatment of CHP in a new CHPsubmodel and the detailed results arising out of this CHP submodel (See:www.dti.gov.uk/energy/environment/energy_efficiency/chpreport.pdf for CHP report)have been aggregated and fed back to the ESI submodel (see UK Energy and theEnvironment, July 2002, Appendix C).The ESI submodel is a simple treatment of the three electricity generation systems inEngland and Wales, Scotland and Northern Ireland. Its main purpose is to calculatethe annual fuel use by the UK ESI. It does not attempt to forecast plant despatch orthe traded price. That is, it is a simulation model rather than an optimisation model.Page 79 of 116

The submodel requires data on the capacity, efficiency and load factor of each powerstation in the UK. Existing and new station capacities for England and Wales areavailable in the National Grid’s Seven Year Statement, published annually. NorthernIreland Electricity’s Seven Year Capacity Statement, also published annually, is usedfor the capacities of existing stations and the proposed interconnector with Scotland.The annual reports of Scottish Power, Scottish Hydro-Electric and British Energy(Scottish Nuclear) are the data sources for Scotland. DUKES contains data by typeof fuel burnt aggregated over the whole UK electricity supply industry, and the stationand environmental performance reports produced by the generating companiescontain some capacity, generation and fuel use data by station. Load factorassumptions are augmented by the Environment Agency’s regulations on emissionsfrom coal and oil-fired power stations: within a single company these require the loadfactors of non-FGD plants to be restricted and FGD plants to operate at a higher loadfactor than non-FGD plants according to the so-called 2:1 rule.The demands for plant capacity and generation are at present assumed to grow withelectricity demand. The submodel aims to satisfy peak load plus plant margin bybuilding the type of new capacity which is found to have the cheapest overall cost perunit. However, assumptions may be made about expected new build: for example,renewables under the Renewables Obligation or the new CCGTs with planningpermission in England and Wales. There are variables for the commissioning year,lifetime, and assumed load factor and efficiency of each existing station and newstation type. Plant is not automatically retired early if there is surplus capacity, butstation lifetimes may be reduced or increased.The submodel fulfils the requirement for generation by adjusting the load factors ofthe stations. If there is a surplus of generation, the load factors of the most expensivestations are adjusted down. Conversely, if there is a deficit, the load factors of thecheapest stations are adjusted up to a maximum of 85%. The costs of generationand capacity are dependent on the fuel and non-fuel costs of the different stationtypes. The latter are calculated in the prices of fuels routine and passed to the ESIsubmodel. The submodel then calculates the thermal input to each station, and sumsto give the thermal requirements of the ESI by fuel type.A5 The Emissions SubmodelThe emissions classification in MDM-E3 is shown in Appendix B Table B4. This isbased on the availability of data, which are obtained from the National AtmosphericEmissions Inventory (NAEI). Environmental reporting by the ESI has increasinglymade data available on a station-by-station basis for emissions such as CO2, SO2,nitrogen oxides, hydrochloric acid and dust. Data for ESI emissions are also availablefrom the Environment Agency and the Scottish Environmental Protection Agency.At present emissions are related to energy demand, and MDM-E3 contains a set ofvariables (coefficients) which convert between fuel use and environmental emissions.Emissions from alternative (including renewable) sources are treated as a specialcase.Page 80 of 116

For the most part at present, the emission coefficients are fixed in the forecastperiod, and therefore do not take account of changing technologies. However, thetreatment of the sulphur coefficients takes into account legislation on the sulphurcontent of fuels and the introduction of emissions abatement technologies, such asflue-gas desulphurisation (FGD) or catalytic converters, which will reduce theemission of sulphur per unit of energy consumed.A6 The Reliability of Projections Using MDM-E3The reliability of the projections made using MDM-E3 partly reflects the reliability ofthe available data. There is great potential for inconsistencies between datasetswhich are collected by different government departments, by different methods, andwith different disaggregations. Data are improved through periodic revisions.Aside from the data, there are many other contributors to uncertainty surrounding theprojections. While it is not possible to quantify the extent of the uncertainty, it ispossible to comment on the validity of the methodology adopted. Compared to othermethods, MDM-E3 provides both a very detailed and a comprehensive framework forexploring the prospects for the economy and energy-environment linkages. Themodel is fully integrated, with feedback occurring between the economy, fuel pricesand energy demand. It also contains a high degree of detail, ie 49 industries; it iscomprehensive, ie covers all aspects of economic activity from government spendingand taxation to consumers’ expenditure and industrial energy demand; and it ispossible to moderate unsustainable historical trends to give credible outcomes for theprojections.Page 81 of 116

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Appendix C Estimation Results for theQuarterly and Annual Energy DemandequationsPage 84 of 116

C1 IntroductionAppendix C consists of this introduction and three sections:• Section C2 shows the quarterly energy demand equationscalculated for two sectors as well as the overall whole economy,and illustrates the estimated coefficients, t-ratios and variousother diagnostic statistics used to determine the most viablequarterly estimates. The sectors modelled for energy demandare industrial, other final users and the whole economy. Thereare three tables of results per sector, along with various chartsshowing the timeline of each parameter and two separate chartsshowing the actual data plotted against the fitted. The first tablefor each sector is calculated on the period prior to the CCLannouncement (ie 1973Q2 - 1998Q4 and excludes the CCLdummy variable), the second illustrates the PSS test, which is atest for a structural break (described in Chapter 3 of this report),and the third includes the full period sample (1973Q2 - 2004Q1)with the effect of the CCL announcement, starting in 1999Q1,and reaching full effect (ie 1) in 2002Q1. There is, in the case ofother final users, an extra table (Table C7) which illustrates theprogression of the quarterly regression to force an equal priceelasticity constrain into the equation. This would therefore resultin slightly different long-run parameters, and hence there are twoimportant values for the CCL dummy variable (-0.14 as in theunconstrained case shown in Table C6 and -0.15 as in theconstrain case shown in Table C7). Incidentally, the constrainedprice elasticities equation is most ‘preferred’, and this is what ourscenario results are based on in Appendix D.Page 85 of 116

• Section C3 illustrates the annual energy demand equationscalculated by industrial sector, showing the estimatedcoefficients, t-ratios and various other diagnostic statistics usedto determine the most viable annual estimates. The followingequations are estimated from annual data for five relevantsectors of the MDM-E3 model: basic metals, mineral products,chemicals, other industry and other final users. Similar to thesetup in Section C2, there are three tables of annual equationresults per sector, illustrating firstly, the period prior to the CCLannouncement (ie 1972 - 1998 and excludes the CCL dummyvariable), secondly the PSS (variable deletion) test, which is atest for a structural break and cointegration within the long-termcomponent, and finally the full period sample (1972 - 2003) (1Our annual data stretches from 1972-2003, but as we use laggedvariables in the estimation, results are given from 1973 andonwards) with the effect of the CCL announcement, starting in1999. However, the layout of other final users equations isdifferent in that we wished to show two sets of results tables;firstly tables including the ‘preferred’ constrained short and longrunprice elasticities (Table C23 and C26), in which the short andlong-run price elasticities have been forced to be equal, toconform with economic theory; and secondly tables including the“unconstrained” short and long-run price elasticities (Table C24and C27), in which the short-run price elasticity is freelyestimated, and is greater than the long-run price elasticity, whichis imposed only from quarterly equations, as it was done ininterim Model Runs.• Section C4 contains a brief explanation of the diagnostic teststatistics, so that the tables can be read and understood moreclearly.• Section C5 shows the precise sources for the data used in thequarterly and annual energy demand equations.C2 Quarterly Equations by IndustryThe following equations are the results from regression analysis on the quarterlydata. The equations illustrate whether or not there exists a structural break in thedata from 1999Q1 (ie announcement effect), by calculating the estimates for eachvariable and performing statistical significance test on these parameters andequations as a whole. A chart showing the actual and fitted observations is providedfor each sector to visually assist the equations and the text set out in section C2.• D = indicates a first difference;• L = indicates a logarithm;Page 86 of 116

• E = energy consumption (equivalent to MDM’s FUJT);• Y= output (equivalent to MDM’s FUYO);• TE = degree-difference temperature from the 30-year mean;• RP = relative prices;• Trend = time trend;• (-1) = indicates a lag;• LRD = Long-term CCL Dummy.Page 87 of 116

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C4: Explanation of Diagnostic Test StatisticsThe values of the parameters and key diagnostic statistics are shown in each of thetables in section C2 and C3. In particular, the number in brackets (to the right of thestatistics) indicates the P-value associated with the null hypothesis. The P-valueshows the probability of the null hypothesis being rejected when it is true - in otherwords being wrongly rejected. Ideally, this value should be as small as possible;however, in applied studies a level of 0.05 or below is considered acceptable. Morespecifically, the t-ratio tests the null hypothesis of a single parameter being equal tozero, and the F-statistic tests the joint hypothesis that all the estimates combined areequal to zero, thus a probability below 0.05 (ie reject null hypothesis) again would bedesirable. The serial correlation statistic tests for autocorrelation in the residuals ofthe regression. A P-value bigger than 0.05 implies that the null hypothesis ofabsence of serial correlation cannot be rejected.The heteroscedasticity statistic tests for heteroscedasticity (changing variances)within the residuals of the regression. A P-value bigger than 0.05 implies that the nullhypothesis of absence of heteroscedasticity (ie the residuals have the samevariance) cannot be rejected. Finally the R-squared is a measure of how well thevariability of the dependent variable is explained by the variability of the regressors.As the number of regressors increases, artificially the greater the goodness of the fitbecomes. Therefore, adjusted R-squared (or R-bar-squared) takes this influence intoaccount and the number of regressors does not influence its value. In regression withnumerous variables the adjusted R2 - and not the R2 - should be used to judge thegoodness of the fit. A value of around 0.70 or greater is generally considered a goodfit. The above explanation of the diagnostics are not hard and fast rules, and are onlymeant to provide a guideline for understanding the tables.Page 112 of 116

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C5: Data Sources for Estimation of Quarterly and AnnualEnergy Demand EquationsThis appendix describes the precise sources for the data used to estimate thequarterly equations. Chapter 3 contains a full description of how these data wereused.Output dataTable C33 shows that time series variables obtained from the ONS for use inconstructing the quarterly dataset.Energy consumption and temperature dataConsumption data over 1973-1988 were obtained from Table 2/Table 3 of QuarterlyEnergy Trends (originally Energy Trends ) over June 1975 - June 2000. Thereafter,data were taken from the DTI ’s website, from Tables 2.1, 2.2, 2.3, 3.2, 4.1, 5.2. (see:http://www.dti.gov.uk/energy/inform/energy_stats/index.shtml). This website was alsothe source for the temperature data.Energy price data and the GDP deflatorPrices were taken from the DTI’s website (see:http://www.dti.gov.uk/energy/inform/energy_prices/index.shtml) from Tables 2.1.1,3.3.1, 3.3.2 and 4.1.1. The GDP deflator was taken from Table 2.1.2.Page 115 of 116

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