Bull - Carbon optimal insulation thesis - 2007

MSc Architecture:

Advanced Environmental and Energy Studies

What are the financial- and carbon-optimal points for return on investment in

insulation?

University of East London Jamie Bull School of Computing and Technology July 2007 Longbridge Road, Dagenham, RM8 2AS Tel: 020 8223 3215

What are the financial- and carbon-optimal points for return on investment in insulation?

Jamie Bull MSc Architecture Advanced Environmental and Energy Studies

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Abstract

This thesis looks at the financial and carbon case for insulation. It examines whether it is worthwhile to continue lowering the elemental U-values demanded by Building Regulations in light of the diminishing returns found when increasing insulation thickness. Also examined are the relative effects that low and high embodied carbon (ECO2), and lower and higher conductivity forms of insulation have on lifetime emissions. An assessment is made of the value of different methods for encouraging greater levels of insulation such as grants or carbon trading/taxation. Life cycle emissions and financial costs of insulation are found from published data and a building estimator. Sources of projected costs for gas and the social cost of carbon are assessed and a central value for each is found. The energy use of a typical semi-detached house is found by modelling in IES:VE. These data are then used as inputs for a computer model.

Optimal points are found through the use of the purpose-built Insulation Savings Model (ISM). This allows calculation of both optimal points and payback times. These points are calculated for three different types of insulation; polyurethane foam, mineral wool and cellulose fibre, representing a range of ECO2 and conductivity.

Parameters representing carbon pricing and grant schemes are varied in order to discover the relationships between these factors and financially-optimal levels of insulation.

Key results are that the carbon-optimal point is far beyond the financial-optimal point for all materials assessed. The carbon-optimal point is also beyond the requirements of Building Regulations. However the financially-optimal point (before accounting for co-benefits) is below Building Regulations. Therefore it is found that, at the margins, super-insulation is an expensive way of reducing lifetime CO2 emissions. Greater return on investment in insulation will be found by focusing on refurbishment of existing, poorly-performing homes than on increases in new build regulation. Lifetime emissions savings are greater for a given U-value when looking at lower ECO2 materials. However, they generally require a greater thickness of insulation.

Keywords: insulation, optimal points, return on investment, embodied carbon, carbon pricing.



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Contents

Abstract .......................................................................................................................3 Contents ......................................................................................................................4 Chapter 1 Introduction.................................................................................................6

1.1 Parameters of the question .................................................................................6 1.2 Method and methodology...................................................................................6 1.3 Context ..............................................................................................................7

Chapter 2 Thermal comfort, fuel poverty and CO2 emissions .......................................8 2.1 Introduction .......................................................................................................8 2.2 Definitions .........................................................................................................8 2.3 Interdependence .................................................................................................8 2.4 Effects ...............................................................................................................9 2.5 The role of increased insulation........................................................................10 2.6 Conclusions .....................................................................................................11

Chapter 3 Domestic heating and global warming .......................................................12 3.1 Introduction .....................................................................................................12 3.2 Space heating and CO2 emissions .....................................................................12 3.3 The effect of increased insulation.....................................................................13

Chapter 4 Optimal points ...........................................................................................14 4.1 Introduction .....................................................................................................14 4.2 Explanation of optimal points ..........................................................................14 4.4 The model........................................................................................................17

Chapter 5 Costs of insulation .....................................................................................18 5.1 Introduction .....................................................................................................18 5.2 Types of insulation covered .............................................................................18 5.3 Financial costs .................................................................................................18 5.4 Environmental costs.........................................................................................19

Chapter 6 Cost of space heating energy .....................................................................23 6.1 Introduction .....................................................................................................23 6.2 Forms of space heating considered ...................................................................23 6.3 The consultancy’s view – short-long term: Fuel Prophet – UKACE .................23 6.4 Issues affecting gas prices ................................................................................24 6.5 Analysis of price scenarios...............................................................................27 6.6 Conclusions .....................................................................................................29

Chapter 7 Cost of carbon ...........................................................................................31 7.1 Introduction .....................................................................................................31 7.3 Assessments of projections ..............................................................................33 7.4 Analysis...........................................................................................................35 7.5 Conclusions .....................................................................................................36

Chapter 8 Modelling..................................................................................................37 8.1 Introduction .....................................................................................................37 8.2 Uses of modelling ............................................................................................37 8.3 Limitations of modelling..................................................................................37 8.4 Discussion of building modelling software.......................................................38



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8.5 Conclusions .....................................................................................................38 Chapter 9 Calculation of fuel savings.........................................................................39

9.1 Introduction .....................................................................................................39 9.2 Heat losses .......................................................................................................39 9.3 Software choice ...............................................................................................40 9.4 The model........................................................................................................40 9.5 Coefficient of Energy Saving ...........................................................................40 9.6 Coefficient of energy saving in the model building ..........................................44

Chapter 10 The Model ...............................................................................................48 10.1 Introduction ...................................................................................................48 10.2 The interface ..................................................................................................48 10.3 The sheets ......................................................................................................48 10.4 Flow Chart .....................................................................................................50 10.5 The results .....................................................................................................51

Chapter 11 Results.....................................................................................................52 11.1 Introduction ...................................................................................................52 11.2 Optimal points and thickness required for Building Regulations.....................52 11.3 Payback time graphs ......................................................................................54 11.5 CO2 savings at U-value 0.16 W/m2 K and optimal points ...............................60

Chapter 12 Analysis...................................................................................................61 12.1 Introduction ...................................................................................................61 12.2 Insulation can save money and avoid CO2 emissions......................................61 12.3 The contribution of ECO2...............................................................................63 12.4 Sensitivity to parameters ................................................................................64 12.5 Analysis of sensitivity ....................................................................................67

Chapter 13 Conclusions and recommendations ..........................................................70 13.1 Introduction ...................................................................................................70 13.2 Implications for financial incentives...............................................................70 13.3 Building Regulations and CO2 emissions .......................................................72 13.4 Impacts on fuel poverty..................................................................................73 13.5 Impacts on thermal comfort............................................................................74 13.6 Implications for current orthodoxy .................................................................74 13.7 Limitations.....................................................................................................74 13.8 Further research .............................................................................................75

References and bibliography......................................................................................76 Glossary ....................................................................................................................80 Appendix 1 Insulation price estimates from Healey Associates, quantity surveyor .....82 Appendix 2 Carbon and energy prices over 50 years..................................................87 Appendix 3 Energy savings over 50 years..................................................................89



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Chapter 1 Introduction

“It is often more cost-effective to invest in end-use energy efficiency improvement than in increasing energy supply to satisfy demand for energy services” Intergovernmental Panel on Climate Change, Fourth Assessment Report, Working Group III, May 2007

1.1 Parameters of the question This thesis tries to answer the question of what is the most effective thickness of insulation to apply to a building element in terms of financial return and avoidance of carbon emissions. It uses the purpose-built Insulation Savings Model (ISM) to calculate optimal points, which are defined here as where the marginal return on investment (ROI) over 50 years is equal to one when the insulation level is increased by 10mm. In order to provide a manageable problem for modelling in this thesis, the scope of this study is limited solely to the case of roof insulation. This is because with other insulation options such as internal dry-lining there are a number of factors relevant to ROI1 such as the cost of lost floor area which are difficult to account for. Roof insulation still allows for several insulation types to be assessed as many are suitable in this application. The heating fuel assumed is gas. No assessment of how the heating market may move away from gas in the future is made. As domestic gas is studied in isolation, the fact that it is currently excluded from the EU emissions trading scheme (EU-ETS) allows a more meaningful comparison of costs, with and without carbon pricing. Of particular interest is the difference between using low ECO2 insulation such as Warmcel, and high ECO2 insulation such as Kingspan.

1.2 Method and methodology Initial modelling is carried out using the buildings modelling software, IES:VE (Integrated Environmental Solutions, 2006). A model of a standard semi-detached house is constructed and the insulation levels are varied to find the amount of energy saved by each increase. A review is carried out into projections of future energy prices and carbon pricing mechanisms. Assumptions made in the projections are analysed and assessed in order to find reasonable and reliable scenarios of future energy costs.

These results are then used as inputs for ISM using a projected natural gas price per kWh for the next 50 years, and the monetary cost of insulation in order to find the optimum depth of insulation in terms of financial payback.

1 Where not otherwise specified here, ROI is used to mean financial return on investment.



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Secondly, an assessment of the amount of carbon emissions avoided by insulation is compared with the ECO2 of the insulation used. This produces a measure referred to here as carbon return on carbon invested or CROCI. This is similar to energy return on energy invested (EROEI) and is of use in deciding the best way to maximise carbon return on investment. Finally, different ways of internalising the cost of carbon are assessed. This provides the measure of carbon return on financial investment (CROFI).

1.3 Context This question is of particular importance at present where a high importance is being placed on insulation. It should be assessed whether the levels of insulation demanded by the new Part-L of Building Regulations (elemental U-value of 0.16 W/m2 K for roofs) is financially- or carbon-optimal. Also, many in the environmentally conscious building world are proponents of super-insulation, insulation to very low U-values (<0.1W/m2 K for roof insulation to AECB Gold Standard) (AECB, 2007), however it is useful to question whether such high levels of insulation are providing sufficient additional benefit to justify their use.

Also assessed is whether higher ECO2 and less conductive insulants are preferable in terms of lifetime emissions, or whether low ECO2 but more conductive insulation should be preferred.

1.3.1 Carbon emissions from domestic heating One of the primary reasons why this question is important is the problem of global warming driven by CO2 emissions from the combustion of fossil fuels. A method of assessment of the optimal level of insulation in terms of avoided carbon emissions is one of the important outcomes of this thesis. Further examination of the issue of CO2 emissions can be found in chapters 2 and 3.

1.3.2 Fuel poverty The other issue which drives the question behind this thesis is one of financial costs. This is an important issue when thinking about the less well off members of society for whom the costs of heating their homes may be prohibitively high. Knowing the optimal level of insulation in terms of financial return is important when assessing the level of financial and regulatory incentives to improving the thermal performance of homes, particularly of lower income households. More on this issue can be found in chapter 2.



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Chapter 2 Thermal comfort, fuel poverty and CO2 emissions

2.1 Introduction This chapter examines why insulation is important in the context of thermal comfort, fuel poverty and CO2 emissions. They are briefly defined, the relationships between them are examined and some of their consequences explored. It is shown that insulation and other measures to directly affect thermal comfort and improvements in the emissions factor of fuel are the best ways of improving the all-round sustainability of a home’s thermal performance as opposed to financial measures such as the winter heating allowance.

2.2 Definitions Thermal comfort is a subjective measure of the fitness of a building to provide an acceptable living environment. It is necessarily subjective as people take different adaptive measures such as opening windows, or adding or removing layers of clothing in response to changes in the environment (Fanger, 1973).

Fuel poverty is defined as a household which needs to spend more than 10% of its combined disposable income on all fuel use necessary to maintain a satisfactory heating regime (DTI, 2001). It can be expected to become more common if fuel prices rise relative to income. CO2 emissions in this context are a consequence of heating homes with fossil fuels and of producing insulation products. CO2 is a greenhouse gas and responsible for a large majority (around 70%) of direct radiative forcing due to long-lived greenhouse gases (IPCC, 2007).

2.3 Interdependence These three factors, thermal comfort, fuel poverty and CO2 emissions are the main reasons for improving the thermal performance of a house through insulation and other measures. They are grouped together in this way because they are interdependent. It is interesting to note that these three areas fit well with the “three-legged stool” approach to sustainability. This is a way of looking at the requirements for sustainability of a community project on any scale from local to global (Kirby, Goodburn, Sinclair, 2000). If any of the three legs is not present the system becomes unstable. In this case, thermal comfort provides the social leg, avoidance of fuel poverty the economic leg and avoidance of CO2 emissions the environmental leg. The effects they have on each other are laid out below.

Excluding outside influence such as changes in fuel price or adding insulation and also assuming one cannot be too warm, where:

A = thermal comfort



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B = fuel poverty C = CO2 emissions

N+ = N gets better. N– = N gets worse.

1) A+ B– & C– 2) A– B+ & C+

If we rearrange the equation, in all cases A has the opposite sign to B and C. This is because when thermal comfort increases, that is seen as getting better whereas with the other two factors an increase is seen as getting worse.

Essentially what this means if we are not worried about thermal comfort we can reduce fuel poverty and emissions by not heating houses. And conversely, if we are not worried about fuel poverty or emissions we can improve thermal comfort by increasing heating. From this it follows that the only way to improve sustainability for all factors is to introduce an outside force acting on one of the three factors, emissions, fuel poverty or thermal comfort which decouples it from the others.

In practice only two of the options can work – A or C. On emissions (C) the force would be something that improved the CO2 emissions factor of heating such as the use of biofuels. On thermal comfort (A) an example would be insulation, whether of the occupant (more layers of clothing), or of the building fabric. Both of these also depend on cost, as if the measure was very expensive (greater than the savings) then it would fail the economic sustainability test.

However, a force which acts on fuel poverty (B) would not have the desired effect, as reducing fuel poverty directly (by reducing the cost of fuel or increasing income) would most likely result in the same or higher use of fuel. However, actions which reduce CO2 emissions, either directly by improving the emissions factor of fuel (a switch from coal to gas for example), or indirectly by increasing the thermal comfort derived from the same fuel (insulation, draught proofing, etc) may have a positive effect on fuel poverty.

2.4 Effects

2.4.1 Thermal comfort Thermal comfort is one of the most important things people require from a home. They are prepared to spend money to raise the room temperature to the level they desire to feel comfortable. The normal range in the UK is defined as 21°C in main living areas and 18°C in other rooms (CIBSE, 1999a) though this has increased over time. People derive great psychological comfort from their homes. A lack of thermal comfort, and problems associated with this such as damp, can have a severe negative effect on this psychological comfort. This is illustrated by a study of 3,000 people in the central west of Scotland which found that;

“More than any other feature of either the occupant, the house or the neighbourhood, the presence of problems with the home such as dampness,



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[and] lack of warmth and space… detracts from the acquisition of psycho-social benefits from the home.” (Kearns et al, 2004)

2.4.2 Fuel poverty Fuel poverty is an issue connected to thermal comfort. It is lack of thermal comfort in a home which is the problem – fuel poverty is often the cause. Given adequate income any home could be made fairly comfortable by buying more heat in the winter (and possibly cooling in summer). The problem is that many members of society do not have access to a sufficient income to meet the fuel cost of this without compromising on other essentials such as food and rent. There is often a link suggested between excess winter deaths in the UK and homes in fuel poverty (Clinch and Healy, 2000, Healy, 2003). Such links are often couched in tentative terms as so many other factors affect excess seasonal mortality. Nevertheless, fuel poverty is seen by the Government and others as a real problem, and much is being done to try to alleviate it including various insulation grant schemes and the winter fuel allowance for pensioners. It is clear that, no matter what the truth is on excess winter deaths, attempts to raise homes out of fuel poverty are of benefit to society. This is due to the benefits to thermal comfort and hence psycho-social wellbeing discussed above. It also leaves more money in the pocket of the household which may be spent on other goods and services so stimulating the economy.

2.4.3 CO2 emissions Atmospheric CO2 is a forcing factor in global warming. The predicted effects of this include hotter, wetter summers and dryer winters in the UK. Globally, rising sea levels leading to mass migration from low-lying countries such as Bangladesh, increased incidence of hurricanes in the Mexican Gulf and many other effects are expected (IPCC, 2006, 2007, Stern, 2006). This is the environmental side-effect of the domestic heating and the scale of this is discussed further in chapter 3.

2.5 The role of increased insulation

2.5.1 Co-benefits When assessing the value of insulation, not only the cost of saved energy should be counted. There are also valuable effects for society to be had by improving thermal comfort and reducing CO2 emissions (Jakob, 2006). These provide financial benefit for which the value to society could be calculated. In this thesis only financial co-benefits based on the social cost of carbon have been used. In this thesis, improvements to thermal comfort and decrease in fuel poverty are seen as co-benefits. Another co-benefit touched upon but not fully assessed is that of increased energy security due to a lower requirement for imported gas.



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2.5.2 “Fuel proofing” Without some form of protection in the form of improving the fabric of hard to heat homes, fuel poverty will continue to be a social and possibly medical problem in the UK. The Association for Conservation of Energy (ACE) report on their online tool, Fuel Prophet2, explains how increased insulation can “insulate” against price rises. They term this effect “fuel proofing” (Smith, Wu, Pett, 2005, p.42). This is backed up by experiments in their tool in which the effects of rapidly increasing fuel prices are tested for variously insulated houses (see chapter 6 for more on the Fuel Prophet prices). It was found that fuel bills in properties where measures have been taken to reduce them become “more resistant to price fluctuations over time” (Smith, Wu, Pett, 2005, p. 42). Basically this states the obvious fact that a 10% rise in a £100 bill is harder to cope with than a 10% rise in a £50 bill.

2.5.3 Comfort factor Insulation allows people to affordably increase the temperature of their homes to a more comfortable level. This means that not all savings from insulation will automatically be translated into a decrease in fuel use. This is known as the “comfort factor” and is one of the reasons that predictions of energy savings through insulation are often greater than the savings achieved (Defra, 2007). It is an example of the rebound effect which is seen in many areas where energy is saved through efficiency. This comfort factor represents an increase in the social sustainability of heating. It may mean a smaller increase in environmental and economic sustainability but overall, the right level of insulation generally makes an improvement in all three aspects.

2.6 Conclusions This chapter has placed the issue of insulation in context. It has demonstrated the benefits to occupants of increased thermal comfort and reduced risk of fuel poverty. The benefit of reduced CO2 emissions is covered further in chapter 3.

2 On online tool which allows simple calculations of return on investment in insulation and other energy saving measures in the domestic sector.



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Chapter 3 Domestic heating and global warming

3.1 Introduction It is necessary for homes to be kept warm in order that they are comfortable and healthy places in which to live. In the UK this is generally achieved by heating the home with fossil fuels such as gas, coal or oil. This has implications in terms of CO2 emissions which are the major forcing gas in global warming (IPCC, 2006). The issue is discussed in this chapter in order to place the issue of insulation in its global context. Greater insulation is a way of avoiding CO2 emissions associated with heating from fossil fuels. Therefore higher levels of insulation can serve to reduce the emissions of a home and to bring it closer to an equitable global average.

3.2 Space heating and CO2 emissions According to the Digest of UK Energy Statistics (DUKES), total UK energy use in 2005 was 246.9 MTOE (DTI, 2006c). This is approximately 2,870 TWh. The Sustainable Development Commission state that 27% of UK energy is used by the domestic sector (2006). According to the Government’s figures, 61% of that (473 TWh) is spent on space heating (DTI, 2005).

Domestic energy use

Space heating61%

Water23%

Cooking3%

Lighting and appliances

13%

Figure 1, Domestic energy consumption by end use (Source, DTI, 2005)

83% of that (392 TWh) is from natural gas (ONS, 2005) which has a CO2 emissions factor of 0.19 kg/kWh (Defra, 2003). This translates into total CO2 emissions from UK domestic gas-fired central heating of 75 million tonnes. This is over a tonne a year each for every man, woman and child in the



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UK. To put that in a global context, average per capita annual emissions are around 1.1 tCO2 (Marland et al, 2006). Therefore, the average person in the UK is emitting far more than his or her equitable share of CO2 and, on average, 10-15% of those emissions are from domestic space heating.

3.3 The effect of increased insulation Some homes are worse than others. The worst performing homes are those with inadequate insulation and draught-proofing. A badly performing home would emit much more CO2 than a new home built to the current Part L of Building Regulations (Building Regulations, 2006) or one which had been refurbished with energy efficiency in mind. The concept of efficiency is an important one. If we are not to compromise our quality of life, whilst still reducing CO2 emissions, we must achieve greater returns from our energy supplies by not wasting them. In the case of energy efficient space heating this means a choice over whether more fuel or greater efficiency is a more cost-effective way of maintaining thermal comfort.

Often the ideal situation is to use very little heating and to keep it in rather than buying more heat. One long term solution is insulation. Increasing insulation such as the roof insulation looked at in this thesis will have the effect of reducing heat loss and hence in-use CO2 emissions immediately and continuing to do so for many years. It is accepted as one of the most cost-effective ways of decreasing the UK’s carbon emissions. The Government recognises this and for a long time successive updates to Building Regulations have stipulated increasing levels of insulation for new housing. This can be expected to continue in the light of the plans to make all new homes “zero carbon” by 2016 (CLG, 2007). For existing homes there are Government grants in place for loft insulation (WarmFront, etc.) as well as the energy industry’s Energy Efficiency Commitment (EEC) which requires domestic energy suppliers to make improvements to their customers’ efficiency. These are subsidised by the electricity and gas suppliers and include other carbon-saving measures such as draught-proofing, supplying energy-efficient bulbs and A-rated boilers as well as insulation (DTI, 2002).



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Chapter 4 Optimal points

4.1 Introduction Increasing levels of insulation to create homes with a very low heat loss also has the effect of increasing the environmental impact of some of the materials in it. There is a question to be answered about where the best trade-off comes between positive impacts (avoidance of in use emissions, decrease in fuel poverty, etc) and negative impacts (ECO2, etc).

Simply defined, the optimal point is that point where the marginal cost of investment is equal to the saving it produces. In the case of insulation as assessed here, this is where an additional 10mm added costs the same as it saves. The optimal point, if calculated correctly, is the most efficient configuration for insulation. It is important to know where the carbon-optimal point is in relation to the financial-optimal point so that cost-effective measures can be designed to encourage insulation to go nearer to the carbon-optimal point.

4.2 Explanation of optimal points The object of this thesis is to find the optimum levels of insulation for a roof. These are defined here:

• The point at which financial investment in 10mm more insulation is repaid financially in the 50 year life-span.

• And where the ECO2 of 10mm more insulation is matched by avoided emissions over the same life-span.

These optimal points exist because of the diminishing returns of increasing depth of insulation. Previous work on optimal points for insulation has been carried out by Lowe et al (1997) and Kalema (2001). In the Lowe paper, formulae are derived analytically in order to find the optimal points in terms of energy and lifetime CO2 emissions. Their formulae work on the fact that the optimal level of insulation is where ECO2 of a square metre of insulation is equal to energy lost through that square metre.

This perhaps seems counter-intuitive so it is worth demonstrating that it is the case.



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0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

800.0

0 0.5 1 1.5 2

Thickness of insulation

kg C

O2 Embodied CO2

CO2 emissionsTotal

Figure 2, Graph demonstrating one way of finding optimal points.

The simplest way to demonstrate why the optimal point is where in-use CO2 emissions = ECO2 is to draw the graph above. It can be seen that the lowest total CO2 must occur at the point where ECO2 and CO2 in-use are equal.

In fact, wherever one variable is falling and the other is rising, the sum will always be smallest where they are equal.

In the case of carbon, ISM uses the fact that the optimal level is where the ECO2 of a 10mm layer is equal to the CO2 saved by that layer, compared with an uninsulated baseline. The Lowe et al method and the ISM method for calculating carbon-optimal points both give the same end result.

A significant limitation of Lowe’s paper is that it assumes all insulation is equal in terms of ECO2 and conductivity (although it varies them in sensitivity analysis). This thesis models several different types of insulation in order to see how optimal points are affected by differences in conductivity, cost and ECO2. It also takes into account the financial costs and benefits of insulation.

The Kalema paper (2001) details the OPTIX model which optimises U-values for all building elements. It looks solely at energy costs and financial cost of insulation and makes no allowance for ECO2 or social costs of carbon. It also assumes electric heating. The optimal U-value found for roof insulation is 0.08-0.16 W/m2 K.

4.2.1 Lifetime In order to calculate a return on investment the duration of the investment must be specified. In this thesis the duration is set at 50 years. This is as it is a reasonable length of time to assume before refurbishment may be carried out. This is at variance with Lowe



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et al (1997) who used a lifetime of 100 years. This doubles the savings and therefore results in a greater optimal thickness of insulation. It can certainly be argued that 100 years is a reasonable lifespan for the type of cavity wall insulation modelled by Lowe, however roofs may be refurbished after less time.

4.2.2 Carbon-optimum Sought in this thesis is the point at which carbon emissions avoided by insulating are outweighed by carbon emissions embodied in the insulation. This is calculated for each 10mm layer. First the carbon return on carbon investment (CROCI) is calculated (Eq.1).

Where: S = savings

C = costs e = energy saved in that year

Pe = price of energy in that year

Pc = price of carbon in that year

Pi = price of insulation

EC02 = embodied CO2 of insulation

EFgas = emissions factor of fuel (gas) L = lifetime of building

Equation 1

e x EF gas x L ECO2

This means that the optimal point is where: Equation 2

e x EFgas x L = ECO2

4.3.1 Financial optimum The first formula required is the undiscounted ROI, the savings from insulation divided by the cost of insulation. Equation 3

L ∑ e x P e

t=1 Pi



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4.3.2 Net present value Financial-optimal points are affected by the idea of net present value (NPV). NPV is an accounting method used to quantify the idea that income (or savings) made now are worth more than those made in the future. It does so through a process known as discounting. The formula for calculating this is presented below. Equation 4

n ∑ C t – C0

t=1 (1 + r)t

Where: t = year of the cash flow

n = total duration of the project r = rate of discount

Ct = net cash flow in that year C0 = capital investment at the beginning of the investment

The discount rate chosen is 3.5% - the UK Government’s preferred rate for projects with social benefits (HM Treasury, 2004, ch. 5.49).

4.3.3 Financial-optimal points Putting Eq. 3 together with the Eq. 4 for undiscounted ROI gives Eq. 5. Equation 5

L S = ∑((e x P e) + (e x EF gas x P c x L)) C 1 Pi + (ECO2 x Pc) Here the optimal point is where: Equation 6

L

∑((e x Pe) + (e x EFgas x Pc x L) = Pi + (ECO2 x Pc) 1

4.4 The model The model created in Excel, ISM, uses the formulae above to identify the optimal point in terms of financial cost and carbon cost for each type of insulation in the application studied. The model is explained in detail in Chapter 10.



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Chapter 5 Costs of insulation

5.1 Introduction This chapter describes the types of insulation studied and examines their different costs, both financial and environmental.

5.2 Types of insulation covered The insulating materials chosen for study in this thesis have been selected primarily to look at what effect if any different values of ECO2 have on the payback time when the cost of carbon it taken into account. For this reason three types of insulation are examined; firstly cellulose as a very low ECO2 product; secondly a mineral wool; and thirdly a polyurethane foam as a high ECO2 material.

The cellulose product studied is Warmcel 500. Cellulose insulation a loose, fibrous material made from recycled newspaper with boron added as a fire retardant. It can also be purchased as insulated panels used in roof insulation sold as Tradis. The mineral wool in this study is Rockwool. Mineral wool is made by heating a mineral and blowing it into filaments rather like spun sugar. It can be supplied in rigid batts that fit between rafters.

Polyurethane foam is represented by Kingspan TP10. Rigid polyurethane foam insulation is a petrochemical product made by foaming polyurethane plastic, usually with pentane. It is supplied in rigid boards which can be fitted between rafters.

5.3 Financial costs These take two forms. There is a cost for the insulating material and also for the installation (except in the case of DIY fitting). The insulation batts looked at come in several thicknesses; however Warmcel can be fitted in any depth as it is supplied loose, either in bags or to be sprayed on. The data for this section were obtained from a quantity surveyor (see Appendix 1). A list of the required work was sent and the following prices were received. The data is broken down into material cost and installed cost and presented below.



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5.3.1 Material and labour costs Table 1, Insulation costs per m3

Insulation Kingspan TP10 Warmcel Rockwool Material cost/m2 £12.20 £4.50 £20.02 Labour cost/m2 £6.40 £3.50 £6.15 Total cost/m2 £18.60 £8.00 £26.17

5.3.2 Grant schemes There are grant schemes available for those on benefits for some types of insulation which reduces the financial cost of insulation. This is not the case for new build properties such as that modelled in this thesis. Some of the schemes are Warmfront, the Energy Efficiency Commitment (EEC), etc.

5.4 Environmental costs The primary environmental cost of insulation is the ECO2 generated in its manufacture. However there are other costs which are revealed in a full life-cycle analysis (LCA).

5.4.1.1 Carbon cost The total ECO2 of insulation is the CO2 emitted by manufacturing and transport of the product as well as its end of life costs.

Further to this, other green house gases (GHGs) should also be counted if one is to calculate the balance between global warming potential (GWP) embodied in the product and that which it helps to avoid. Several sources of data are available on this subject. One of the most comprehensive is the Inventory of Carbon and Energy (Hammond, Jones, 2006). This consists of a compilation of data from many sources and includes many types of insulation product. However, despite covering many products, it is hard to compare them as the source papers make very different assumptions. This includes those which are made explicit such as the boundaries in terms of transport (ie, to factory gate or to building site), but also many others which remain unstated. They also do not explicitly cover GHGs other than CO2. There may be associated maintenance cost and there will be CO2 emissions associated with disposal of the product at the end of its life. An assessment which covers all of this is known as a cradle to cradle LCA.

Availability of data has limited this thesis to a comparison of the few insulating products with full LCAs. These are Rockwool, Warmcel and Kingspan.

The sources of data for these LCAs are a paper produced for Rockwool and LCAs produced for Kingspan and Excel (the manufacturers of Warmcel. The Rockwool-sponsored paper covers both Warmcel and Rockwool allowing for some comparison of results.



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In the LCA commissioned by Rockwool (Schmidt et al, 2004) is a list of emissions to air which are converted to kg CO2e/kg below. GWP figures are from IPCC (1996, p. 22). Table 2, CO2e/kg of Rockwool and Warmcel (source, Schmidt et al, 2004)

Rockwool GHGs Cellulose GHGs

Gas g/kg GWP CO2e Gas g/kg GWP CO2e

CO2 1200 1 1200 CO2 629 1 629

CO 89 4 355.92 CO 0.88 4 3.52

N2O 0.02 310 6.2 N2O 0 310 0

CH4 0.88 21 18.48 CH4 0.44 21 9.24

total kg CO2e/kg 1.58 total kg CO2e/kg 0.64

Calculations are done differently for Kingspan as they have an LCA by BRE (Beedel, 2002). This assessment method gives a kg CO2e figure per functional unit, defined by them as 1m2 of insulation with a resistance of 1.45 m2 K/W. This is equivalent to 32mm of Kingspan Therma. The calculation for kg CO2e/kg is shown below.

volume of 1m2: 32mm x 1m2 = 0.032 m3 weight of 1m2: 0.032 m3 x 33 kg/m3 = 1.056 kg

kg CO2e/kg 7 kg/1.056 kg = 6.63 kg

Doing the same with the BRE LCA for Warmcel 500 (Beedel, 2003) gives a figure of 0.91 kg CO2e/kg. This is higher than the figure derived from Schmidt et al which could be due to the higher density of sprayed Warmcel 500 when compared with loose-fill insulation.

0 1 2 3 4 5 6 7

Kingspan TP10

Warmcel 500

Rockwool

kg CO2/kg

Figure 3, embodied CO2e/kg of insulation

Rockwool 1.58 kg CO2e/kg



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Kingspan Thermapitch TP10 6.63 kg CO2e/kg Warmcel 500 0.91 kg CO2e/kg

These figures should be treated with a degree of caution as they are in most cases sponsored by the manufacturer. Interestingly, in the one case which is not (Warmcel as assessed by Schmidt et al for Rockwool), a lower figure is given.

5.4.1.2 Comparison of conductivity of high and low ECO2 materials

ECO2/m3 against lambda

0

50

100

150

200

250

300

350

0.02 0.03 0.04 0.05 0.06Lambda

Series1Log. (Series1)

Figure 4, showing trend towards higher ECO2 with lower conductivity

The graph above plots the conductivity against ECO2 of various insulating materials. As can be seen, there is a trend towards increased ECO2 as thermal performance improves. However there is a high degree of variation involved. The greatest concentration is of low ECO2 insulation materials at conductivities of around 0.04 W/mK. These materials fall below the trend line and are therefore outperforming the average.

Furthest below the trend line is the Warmcel data point (green triangle) and furthest above is reinforced fibre blanket (green lozenge).

The details of this graph must be treated cautiously as figures taken from the ICE database (Hammond, Jones, 2006) have been normalised3 to take account of the fact that most data points only take into account CO2 emissions up to the factory gate or to the site and do not account for end of life costs such as methane emissions on decomposition. Caution is advised as the figure used when normalising is drawn from only two points of comparison. However, the conclusion that lower ECO2 insulation generally performs worse thermally is more robust. More data on other high performance materials such as polyisocyanurate would be useful in testing this conclusion as almost all the points currently refer to polyurethane of various forms. 3 By a factor of 1.73. Normalised data are represented by lozenges on the graph, LCA data are triangles.



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5.4.2.1 Other life-cycle costs All the insulation manufacturers studied have commissioned LCAs of their products. Both Warmcel and Kingspan have the analyses carried out by BRE and Rockwool has the one previously mentioned which was carried out by Force Technology/dk-TEKNIK (Schmidt et al, 2004). This thesis is primarily concerned with the financial and CO2 savings of insulation. An in-depth study of these LCAs is not considered necessary. Lifetime energy savings are seen as a far greater factor. It should however be noted that Warmcel 500 betters Kingspan Therma in all but one (mineral extraction) of the categories measured in the BRE method.

Ayres discusses the difficulties involved in accurate life cycle analysis and the setting of different boundaries in his critique of LCAs (1995).



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Chapter 6 Cost of space heating energy

6.1 Introduction Space heating energy costs are determined by the cost of fuel and the fuel type chosen. Gas is generally more expensive per unit of energy than coal at present; however, gas is normally used more efficiently when used directly for heating homes. Electric heaters are very efficient at point of use, but once one takes into account the losses in generation and transmission this apparent efficiency disappears. Section 6.2 looks at forms of space heating and explains the reasoning behind focusing on natural gas prices in this thesis. Sections 6.3 is an assessment of projections of fuel prices into the future from a consultancy. In section 6.4 the issues which affect the price of fuels, and particularly natural gas, are examined. In section 6.5 analyses of the projection scenarios are undertaken and in 6.6 a conclusion is drawn as to which of these scenarios is most reliable and useful in calculating financial savings on financial investment on insulation.

6.2 Forms of space heating considered The main fuels used for domestic space heating are listed below along with the amount used in 2004 in MTOE (million tonnes of oil equivalent) and the percentage of market share this represents: Table 3, market share of domestic heating fuels (source, ONS, 2005)

Fuel MTOE % share Natural gas 32.316 83 %

Burning oil 2.818 7 % Anthracite 1.110 3 %

Coal 0.976 2.5 %

In this chapter gas central heating costs are focused on gas, as it currently accounts for 83% of domestic fuel in the UK making it the market leader. It is likely to remain so in the short-medium term.

Other forms of heating are likely to come to greater prominence in the next 15-20 years. This is likely to include combined heat and power (CHP) from gas (Whispergen and successors) and potentially biomass. If renewable electricity achieves a significant market penetration it is likely to require demand management. The provision of electric storage heating can act to smooth the fluctuating demand and supply from variable renewable energy sources (Barrett, 2006). This is seen as too large a topic to cover within this thesis and so a study based on gas central heating is preferred.

6.3 The consultancy’s view – short-long term: Fuel Prophet – UKACE Fuel Prophet is a tool developed by the UK Association for the Conservation of Energy (ACE) supported by the EAGA Partnership (ACE, 2006). It is intended to help housing



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professionals and researchers to make decisions about the financial viability of refurbishment options, primarily as a means of easing fuel poverty.

As such, it requires assumptions to be made about likely changes in UK energy prices over the next 30 years. They have generated projections for electricity prices and for gas prices to feed into their Excel spreadsheet which is available free online. The ACE projections draw on DTI figures for the first 5 years (Smith, Wu, Pett, 2005) and can be updated to reflect the Government’s changing assessments. Beyond this they are expanded based on “well-established fuel and economic scenarios” (Smith, Wu, Pett, 2005, p. iv). There are six different scenarios which cover a range of future possibilities:

“1. base case – moderate increase in demand, rising prices 2. high prices – higher demand and prices than base case

3. very high prices (a) – fuel poverty eliminated 4. very high prices (b) – record levels of winter fuel poverty; summer mortality due to heat 5. low prices – similar price to base case in short term but access to cheap gas in longer term 6. very low prices – plentiful fuel and weak global markets; personal carbon allowances” (Smith, Wu, Pett, 2005, p. iv)

These scenarios in general are biased towards non-nuclear futures which may not be a reasonable assumption given the current political climate post-Energy White Paper (DTI, 2007).

Each of these scenarios, as may be expected, places a large emphasis on the effects of climate change and climate change policies on the cost of fuel as well as international political and economic issues. The differences between the causes of very high prices in Scenarios 3 and 4 illustrate this point. In Scenario 3 prices are high due to “energy conservation and cost internalisation policies” (ACE, 2006). The ACE argue this means that despite higher energy costs, on average UK households spend a similar amount as in 2005. However, in Scenario 4 the high cost is due to a disruption of supply due to global security problems and climate change effects leading to rising insurance premiums. In this scenario fuel poverty increases and both summer and winter mortality are predicted to rise.

6.4 Issues affecting gas prices The drivers of natural gas prices are discussed below, including the main factor – oil prices and the pressures on that including peak oil concerns. The likely course of world economy over the short-medium term is assessed. The possibility of replacement of natural gas by other fuels is discussed although it is not looked at in detail. Also addressed are the difficult to predict issues of geopolitical stability.



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6.4.1 Oil Short term increases in production This is agreed upon by the ACE research although they may be echoing the DTI.

Peak oil – long term slowing of production Many oil industry analysts believe that we are at or approaching peak oil, the point where half of oil resources have been extracted and the rate of recovery is predicted to slow (Campbell, 2004, Heinberg, 2003). However, there are conflicting reports which predict that peak is up to 30 years away and in any case will be followed by an “undulating plateau” in which new capacity comes on stream but is balanced by old fields becoming exhausted (CERA, Yergin et al, 2006). As reserves of easily extractable oil are depleted, more expensive and harder to extract forms become more viable. The cost of producing oil from these forms (tar sands, oil shale, etc) is higher than conventional drilling. This will keep the supply of oil up but will tend to increase the price. On the other hand, if new oil supplies are brought online at the same time as substitution away from oil occurs this will bring downward pressure on oil and gas prices. This means that competition between suppliers leads to price cutting in an attempt to retain market share meaning the price of fuel becomes much closer to the cost of production. Oil prices – UK natural gas price linked to Continental contracts

One of the main reasons for this is that continental European gas contracts are fixed to the market price of oil (EIUG, 2002, Smith, Wu, Pett, 2005). There is no real reason for this as they are “distinct products sold to different customers in different markets” as stated by the Energy Intensive Users Group in their response to a DTI consultation on gas prices (2002, p.5) but no one, including the EIUG, expects this relationship to change. In winter, gas from the continent is imported which means the UK pays the continental oil-linked price plus a transport differential. In summer when the UK exports gas to the continent the fact that gas suppliers can get a higher price for their gas there means that prices in the UK stay higher too (Smith, Wu, Pett, 2005). This means that despite the UK’s gas production capabilities the price of gas remains close to the continental oil-linked price year-round. Another impact of the link to oil prices is that wholesale gas prices exhibit a similar volatility. When wholesale prices remain high for a long time retail gas prices may follow, usually with a lag of around 6 – 12 months according to Ofgem, quoted in a BBC News article (BBC, 2006c). This volatility means that projections made are necessarily an average price with the actual price, particularly of wholesale gas varying greatly around that figure. To some extent this volatility is expected to be smoothed out by the retail gas suppliers.

6.4.2 Economy Growth/demand.

In the past, recessions (periods of negative growth) have tended to be short lived – much shorter than the 15 – 25 year scope of this model. In fact the trend is somewhere in the



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region of 2% growth of gross domestic product (GDP) per annum (Budget Report, 2006). Growth is something we can expect to continue at a greater or lesser rate.

World GDP is expected to continue to rise at 4½% of GDP each year in the short term (Budget Report, 2006, table 2.1). Strong growth and hence surges in demand for oil in China, the USA and India have led to recent oil and gas price rises (DTI, 2004).

6.4.3 EU deregulation and competition Current high retail gas prices, despite falling wholesale prices, are primarily due to the recent high prices prior to the opening of new import capacity. This includes the new Landgeled pipeline from Norway connecting to the Sleipner gas field and on to Easington (see fig.). This as well as increases in the capacity of the Bacton-Zeebrugge pipeline (Smith, Wu, Pett, 2005, BBC, 2006b) led to the unusual situation of lower gas prices towards the end of 2006. In fact in the week after the Langeled pipeline opened, for a short while gas was trading at -5p a therm, meaning traders were paying to move it on (BBC, 2006a).

Figure 5, the Landgeled gas pipeline

Source: BBC (2006)

Forthcoming EU energy policy may mean increased deregulation of the gas industry and also splitting of retail companies from transmission companies. There is a second suggestion that large, integrated energy companies may be made to hand over management of their transmission services to independent operators but be allowed to retain ownership. A decision on this is expected in summer 2007 (PricewaterhouseCoopers, 2007).

6.4.4 Substitution Fuel substitution in generation, renewables, etc



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Demand elasticity will only increase if alternatives to oil and natural gas are found and brought online. This may include a greater take-up of electrical and hybrid vehicles (and renewable forms of charging them).

“[Conventional fuel costs are related to] the development and cost of alternative production and generation technologies... The degree of cost internalisation imposed (e.g. through carbon taxes or emissions trading) and the degree of support granted to micro and renewable generation (e.g. via the renewables obligation, EEC or building codes) will affect fuel prices.” (Smith, Wu, Pett, 2005)

Coal price will stay high so long as gas and oil prices stay high as it will be required for electricity generation. If gas is cheaper then it will be preferred to coal in generation.

6.4.5 Geopolitics Issues of geopolitical instability have recently had some large effects on oil prices. These have included war in the Middle East, particularly Iraq. Also kidnappings in Nigeria and pipeline sabotage in Colombia have exerted upwards pressure on prices (Peterson, 2005).

Russia has a major role to play in current and future gas supply. Presently it supplies 25% of European gas and oil and this is expected to rise (BBC, 2006b). However, Russia is a state described in a recent Economist article as “chaotic, factional, corrupt and criminalised.” (The Economist, Nov 25th, 2006) It is hardly surprising that the IEA says there are “enormous uncertainties surrounding Russia’s energy future” (IEA, 2004). Furthermore, climate change-linked effects such as hurricanes in the Gulf of Mexico are likely to disrupt supply and increase insurance premiums leading to higher oil prices (Stern, 2006).

The relative likelihoods of these geopolitical influences on fuel prices coming to pass are hard to quantify, however in the short-medium term it is considered likely that the main pressure will be upwards unless peace comes to the Middle East.

6.5 Analysis of price scenarios Here the main assumptions made in the scenarios examined are assessed in the light of the issues described above. They are described as possible, probable or subject to oil price moves with oil price rises seen as probable and oil price falls as possible.

6.5.1 Fuel Prophet base scenario (1.9p/kWh retail in 2020) Moderate demand increase and moderate growth: Probable

Reduced North Sea production: Probable

Short-term restricted supply from the Continent: Probable

Medium-term shortage of generation capacity: Possible/probable

6.5.2 Fuel Prophet high prices (2.2 p/kWh retail in 2020) As in base case except demand rises faster: Possible/probable



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6.5.3 Fuel Prophet very high prices (a) (2.8 p/kWh retail in 2020) Moderate to high demand and growth: Possible/probable Assertive energy conservation and cost internalisation: Possible

Demanding ETS quotas (see Ch. 7): Possible/probable Personal carbon allowances (see Ch. 7): Possible

LZC technologies become cheaper: Probable Microgeneration mandatory for refurbishments by 2017: Possible/probable

6.5.4 Fuel Prophet very high prices (b) (2.9 p/kWh retail in 2020) Moderate ETS quotas: Possible/probable Gas price remains oil-linked: Probable

High global oil demand: Possible/probable Geopolitical instability: Possible/probable

Climate change-related disruption to offshore production: Possible

6.5.5 Fuel Prophet low prices (1.4 p/kWh retail in 2020) As base case plus deregulation of Continental gas market: Possible/probable Generation from “super CCGT” technology: Possible/probable

6.5.6 Fuel Prophet very low prices (1.2 p/kWh retail in 2020) Plentiful new reserves found in Russia and North Africa: Possible Asian economies decelerate: Possible

Developed economies remain steady state or growing: Possible



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Retail gas price range

0.00

0.50

1.00

1.50

2.00

2.50

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Year

Pric

e (p

/kW

h)

FP highFP baseFP low

Figure 6, Retail gas price projections to 2020 (source, ACE, 2005)

6.6 Conclusions From the sources consulted it is considered unlikely that peak oil will have a major effect on gas prices in the short-medium term (to 2020). Even if the peak is close or has already been reached there is likely to be a period in which new discoveries enable the extraction rate to be maintained in the medium term. Later in the period modelled, resource depletion may begin to have a noticeable effect; however no reliable long-term projection of by how much has been found.

From the categorisation of the main assumptions in both sets of scenarios, the most probable is, unsurprisingly, Fuel Prophet’s base scenario. After this the two “high” scenarios are seen as more likely than the “low” scenarios. Nevertheless, the low scenarios do not contain any very unlikely assumptions and must still be considered as possible outturns. The Fuel Prophet “very high (a)”, “very high (b)” and “very low” scenarios contain some more unlikely assumptions and are therefore considered unlikely to occur. These projections are necessarily far from certain. As the DTI state in the accompanying literature to their projections upon which Fuel Prophet draws,

“Econometric models are valuable in the medium to longer term but projecting beyond 2020 to 2050 becomes difficult as for example, technologies are expected to change, new processes and systems are introduced and the detailed econometric relationships built on past relationships are no longer valid in this time frame.”

(DTI, UK Energy and CO2 Emissions Projections, July 2006) For this reason the central gas price projected by ACE is used for the first 20 years. Thereafter the price is assumed to remain stable. The outlying projections are not used in



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sensitivity analysis; rather, the central price is doubled and halved to allow for the wide variation in possible outturn. This is a far greater range than any of the ACE projections.

The most likely outturn for retail prices is 1.4 – 2.2p/kWh in 2020, most likely around or higher than 1.9p/kWh.

See appendix 2 for tables showing the prices used to 2057.



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Chapter 7 Cost of carbon

7.1 Introduction In assessing a value for carbon to be used in modelling of financial incentives towards insulation there were two options. Firstly, use the social cost of carbon as assessed by many authors and analysed in Clarkson and Deyes (2002). Secondly, use the projected trading cost of carbon in emissions trading markets such as the EU-ETS.

The features of these two options are explored below, followed by an assessment of the arguments for and against using them in the thesis.

7.2.1 Social cost The social cost attempts to measure all economic externalities. This should include the cost of mitigation and adaptation. In this way it is concerned with finding the cost of carbon emissions.

According to Stern, the rational optimal price for carbon is where the marginal social cost of externalities (the balance of the positive and negative economic effects of climate change) is equal to the marginal cost of abatement (2006). Externalities are those economic effects of an action which are not primarily experienced by the agent doing the action. For example the cost of disposal of fly-tipped waste by the council is an externality of the business which dumped the waste. Negative externalities can be seen as a “free ride”. Looking at costs to society, if the marginal cost of externalities is greater than that of abatement then further abatement measures could be introduced at no net cost. If the marginal cost of abatement is greater than that of externalities then the additional economic benefit derived from further abatement efforts is less than that of doing nothing. Only if marginal costs of abatement and externalities are equal (the point where the lines cross in the figure below) is the maximum economic benefit being taken.

0.0

2.0

4.0

6.0

8.0

10.0

12.0

1 3 5 7 9 11 13 15Abatement effort

Cos

t

Cost of climatechangeCost of avoidingclimate changeRational optimalprice for carbon

Figure 7, Showing decrease in damage costs as abatement costs rise



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The externalities in the case of GHGs are particularly hard to calculate. The main features are laid out in the Stern Review (2006, Ch. 14.2) and paraphrased below:

• The damaging effect of GHG is roughly the same wherever it is emitted but the impacts are likely to be felt unevenly around the world.

• Impacts are not immediately felt and will continue to have an effect into the future.

• Uncertainty exists about when the effects will be felt and about how great the effects will be.

• The effects could possibly be huge. Taking these issues in turn they suggest that:

• The price should be equal across the world.

• The price should take account of future damage.

• The price is uncertain and hard to calculate.

• The price could be very high.

7.2.2 Trading price Trading-based systems attempt to keep the level of emissions below a set level by means of establishing a cap. Analysis carried out looks at the likely implications of setting different caps (represented by stabilisation levels). In this way it tries to find the price of carbon emissions. An emissions trading scheme is a mechanism by which the market is used to find the optimal price for a pollutant. The idea is that if a company can find a cheaper way to avoid emitting the pollutant it will do so; otherwise, assuming it sees no other benefits to reducing emissions, it will buy rights to emit from the trading market. It is important that a cap is set on emissions. If this is set too high then the price of buying rights will fall. This is important since if the price falls lower than the social cost of carbon, the full externality is not being internalised.

This has been the case in the EU ETS up to now although there are hopes that the situation will improve in the next phase and in potential subsequent phases.

Assuming a continuation of the EU ETS in its present form, prices are essentially dictated by the European Commission which approves the emissions rights allocations for each phase. These are reliant on each member state’s national allocation plan (NAP). However, if the Commission believes that a state’s self-set targets are not demanding enough they can cut the allocation. This was the case recently where Finland, Poland and the Czech Republic’s allocations were cut. Both the Czech Republic and Poland intend to take legal action over this (Point Carbon, 2007).



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7.3 Assessments of projections

7.3.1 Social cost A thorough review of existing literature was carried out by Clarkson & Deyes for Defra (2002). They compared the models used in a number of papers which aimed to calculate the social costs of carbon. The range of values found between 2001-2010 was from $9-197/tonne of carbon (tC) in 2000 prices. Having assessed the methodologies of the existing papers, the authors chose the “most sophisticated”. It produced an estimated marginal damage figure of £70/tC (2000 prices). The figure was predicted to increase by £1/tC per year. Recognising the uncertainties associated with modelling the impacts of climate change they recommend using an upper and lower value of £140/tC and £35/tC.

7.3.2 Trading price No meta-analysis was publicly available to compare the many models used in projections of future carbon prices (many have been carried out by consultancies such as Point Carbon, but these are commercially confidential for obvious reasons). For this reason a set of projections using different scenarios are compared here. They were carried out by Nakicenovic et al (2003), for the WBGU, the German Advisory Council on Global Change. The scenarios are for different GHG stabilisation scenarios ranging from 450 ppmv to 650 ppmv. This stabilisation level is the most important factor driving carbon prices in the medium term looked at in this thesis:

“[D]ue to technological inertia, costs of meeting stringent climate stabilisation goals are almost “pre-programmed” over the next couple of decades (i.e., are baseline scenario independent) and are essentially dependent on emissions constraints levels and existence (or absence) of flexibility mechanisms such as the international trade in carbon permits.” (WBGU, 2003, p. 39)

This is saying that due to length of time necessary to develop and introduce new low and zero carbon technology, any early efforts to avoid emissions must bear the fixed costs of today’s technologies.

This modelling work was carried out by Nakicenovic et al of the Institute of Applied Systems Analysis for WBGU. Three scenarios are used in the study. They are referred to by the Special Report on Emissions Scenarios (SRES) scenarios they are based upon, A1T, B1 and B2. These are baseline scenarios based on “storylines” and with no particular action on emissions reductions. These are combined with an assumed atmospheric GHG stabilisation level, the model is run and results are generated. Complete details of the assumptions behind these scenarios can be found in the SRES report (Nakicenovic et al, 2000) but an outline of the important factors is given below.

Baseline scenarios A1T and B1 focus on economic and social sustainability. In addition B1 also encompasses environmental sustainability. A1T is low- and zero-emission technology intensive but has a higher overall energy demand.



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While A1T and B1 represent baseline scenarios where the world is gradually moving towards sustainability, scenario B2 represents a more unfavourable world for climate change minimisation policies. It has a “more cautious geopolitical, economic and technologic outlook” than the other two scenarios (Nakicenovic, 2003, p. 17).

The basic assumptions of the analysis are that scenarios B1 and B2 assume a stabilisation figure of 400 ppmv while A1T assumes 450 ppmv, all under a “very limited and restricted number of mitigation measures and options” (Nakicenovic et al, 2003, p. 4). Nakicenovic et al are careful to point out that two of the baseline scenarios chosen, A1T and B1, already assume a fairly high level of climate change avoidance due to their sustainable “storylines”. B2 has a more “dynamics as usual” approach (2003, p. 1). In the study authors’ view the funder’s choice of primarily “sustainable development” scenarios leads to “an optimistic baseline projection of availability and costs of environmentally benign technology, easing subsequently the achievement of ambitious climate stabilization targets.” (2003, p. 2) This would mean an underestimate of the total additional social cost of carbon emissions. Nevertheless, due to the “technological inertia” mentioned previously, these issues appear to have little short – medium term effect on permit prices so are negligible as far as the scope of this thesis is concerned.

It should be noted that the finding of the authors was that; “The costs of meeting a particular climate stabilization target [are] more dependent on the type of base scenario analyzed (high- versus low-emission futures) and the range of mitigation technologies available (unconstrained versus – as in this study – constrained) than on the absolute level of emission reduction or the particular model of emission permit allocation chosen.” (Nakicenovic et al, 2003, p. 37)

This is the reason that the sustainable development baselines, A1T and B1, are seen to arrive at lower-priced assessments of the costs of climate change minimization, even with such challenging stabilization targets.

It is debatable whether this is a correct assessment. Stern for example claims that a target of 550 ppmv is economically feasible whereas a lower target such as that assessed by Nakicenovic et al is not feasible. This suggests that the stabilization scenario is a significant factor. It would appear clear that this is the case in any cap and trade system as scarcity acts to increase price of the commodity. It is also more meaningful in that governments may act to impose a cap on carbon whereas they have less control over which mitigation technologies come to market - though still some through measures such as market priming for technologies which are felt to be important such as streamed ROCs for offshore wind (Crown, 2006). The price projected for 2030 is around USD(1990)140/tC (£80/tC4). In today’s money this is around £134/tC5, close to the upper range in Clarkson and Deyes and equivalent to

4 Average 1990 exchange rate of USD 1 = GBP 0.57 (oanda.com) 5 Inflation figures from CPI, (ONS, 2007) available online at http://www.statistics.gov.uk/statbase/product.asp?vlnk=867



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£36.50/tCO2. By 2060 it is projected that the price of carbon will be around USD(1990)220-390/tC, which is £57-102/tCO2 in today’s money.

7.4 Analysis

7.4.1 Social costs The argument for using the social cost has moral authority in that it attempts to internalise the externality of climate change. It is debatable whether the cost to humanity as assessed by any of the researchers who have looked at this question would do so successfully.

Nevertheless, Clarkson and Deyes’ cost is a well-accepted figure and is used explicitly in Government modelling. This means that modelling using this cost is more easily comparable with other Government research.

The reason that none of the research into social cost of carbon is likely to come up with an accurate cost for carbon is that none makes specific mention of the fact that, should the world reach a tipping point where positive feedback effects cause runaway climate change, the costs of adapting to the changing climate may become very high indeed. One can imagine this as taking the original social cost of a tonne of carbon then multiplying it by the additional radiative forcings caused by emission of that tonne. It is quite understandable that such a complicated calculation has not been carried out but it still remains a limitation (as is acknowledged by Clarkson and Deyes’ wide range of possible values).

Another possible argument against using the social cost of carbon in this modelling is that it is not presently a direct cost to the consumer. It is argued here that the effect on individuals is less important than that on humanity as a whole. For this reason, and to



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enable this research to have more meaning when deciding policy, social cost may be considered the better option in modelling.

7.4.2 Trading costs Perhaps the most powerful argument in favour of using a projection of the price of carbon permits in the UK is that it is the most likely direct cost to consumers. This will be the case whether it is levied on the gas supplier or directly on the fuel purchaser. If the aim of this modelling exercise is to find the financially optimal point to the consumer this would clearly be the correct choice. Unfortunately it is hard to assess whether the prices as assessed here will actually apply to domestic gas consumers. If the aim of this exercise is to find ways of encouraging insulation to the carbon-optimal level then it is more important to know what carbon should cost rather than what it will cost.

7.5 Conclusions The distinction between social costs and trading costs is perhaps a false one. This social cost is an important factor in the EU’s attempt to set a price in the EU-ETS. This price is driven by the level of allocations in each phase of the scheme. The fact that both are projecting a value early on of around £70/tC suggests that similar assumptions are being made.

The main use of the value of carbon in this thesis is to assess the impact of economic measures on optimal levels of insulation. This means that the aim is to find the optimal level in terms of climate change mitigation rather than in terms of financial benefit to the owner. For this reason, the baseline in the model is taken with no cost of carbon. Subsequently, in analysis of financial incentives to insulate, the social cost of carbon from Clarkson and Deyes (2002) is used.

It bears repeating that if we are approaching the point where positive feedbacks start to occur then the price of carbon should be much higher than has been modelled previously. Nevertheless, no reliable projections have been made of these and so the only advice that can be given is that the higher rather than lower figure used in sensitivity analysis should certainly not be considered unlikely.



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Chapter 8 Modelling

8.1 Introduction Two computer models were used in this thesis. First, the purpose-built Excel-based ISM which was used to predict the cost and savings of insulation. Secondly, IES:VE which was used to provide energy use data for ISM. The benefits and limitations of this are laid out below. 8.2 looks at the uses of computer modelling. 8.3 examines the drawbacks which must be considered. 8.4 looks specifically at the software chosen, IES:VE and Excel, and 8.5 draws conclusions about the use of modelling in this thesis.

8.2 Uses of modelling Computer models are useful in predicting things which may take too long to simulate experimentally. This is particularly true with IES:VE which performs dynamic thermal simulation of buildings. It is a commonly used tool in architecture and building design due to the number of different modifications that can be made and tested in a comparatively short time. With the continuing increase in computing power more and more complex situations can be modelled, including computational fluid dynamics (CFD) which was until recently only possible with super-computers.

One of the ways in which computer modelling is easier than real world testing is that it lends itself to simplified models. With a skilled designer and operator the relevant parameters can be modelled, leaving those which have little or no effect out of the experiment.

Finally, modelling is a powerful tool as it can be used to simulate almost anything from crowd movements to climate patterns to building performance. It allows for experimentation on parameters which may be impossible to change in the real world. For example, in this thesis predictions are made about emissions over the next 50 years.

8.3 Limitations of modelling Several problems can be encountered when using computer models.

• Most fundamentally there can be problems with the program used to build the model.

• Secondly there can be problems with imperfect model design. If the modeller has omitted important details believing them to have no effect then the results given can be far from what is found in real life. One possible source of this in this thesis is that no way has been found to account for comfort taking.

• Thirdly, where a model relies on data from elsewhere this can be a weak point. If a parameter is particularly sensitive then the results given can again be far removed from the real world. In this thesis such limitations come primarily from



38

the projections of future gas and carbon prices. They are controlled for by means of sensitivity analysis.

• In a very complex model it can be impossible for an inexperienced user to identify erroneous results which can come from something as basic as poor data entry.

Every model needs to be compared with results in the real world if one is to have confidence in its predictions.

8.4 Discussion of building modelling software IES:VE is a well-known, much used building modelling programme which has been in existence for over 15 years. ApacheSim, the thermal modelling element of IES:VE, has been tested using the ANSI/ASHRAE standard, 140-2001 (ANSI/ASHRAE, 2001). It performed well against benchmarks provided by other modelling software, returning results outside the range set by other programs in 13 of 326 tests (Gough, Rees, 2004).

The programme is accredited by CIBSE and has an entry in the CIBSE Applications Manual (CIBSE, 1998, Appendix B). The VE Compliance module is also an accepted program for calculating compliance with Part L2 of UK Building Regulations for non-domestic buildings.

9.3 looks at the benefits of IES:VE over other software tried.

8.5 Conclusions Modelling opens up numerous possibilities for experimenting with things that are impractical to test otherwise. It can provide the opportunity to see where something like building performance may be improved, without the problem of having to change the existing building to find out if the modification works. However, computer modelling should be just the first step. Modelling should not be used as a replacement for physical experimentation which is generally more reliable, and may uncover factors which were judged irrelevant in the model. Post-occupancy evaluation is also of great value in assessing whether a program is working as it should. This enables further development of more accurate modelling tools.

In order to try and avoid some of the pitfalls of modelling, some checks should be applied. Models should be checked carefully by using techniques such as sensitivity analysis – systematically changing values to see the effect this has on results. It is hard to get a sense of when a result is wrong as a number. Graphical output is useful as a form of checking. This graphical output can be used in sensitivity analysis. By changing values of inputs it can be seen which of the parameters are most sensitive to data errors. A spreadsheet model such as ISM must be carefully checked to ensure that every cell is acting as it should and that all formulae and data have been entered correctly.

The results of a model are not usually interesting in themselves. It is important to look back at what is being modelled and the effects that these results might have on the real world. It is also important to consider any other factors in the real world such as comfort taking which may affect the validity of results.



39

Chapter 9 Calculation of fuel savings

9.1 Introduction In this chapter the principles of housing heat losses are briefly introduced. A model house is described and the results of simulation in terms of annual saving on heating load are presented. A formula for calculating the savings from a known U-value (a coefficient of energy saving - CoES) is derived for this model house. A simple model is created to show how this method of finding a CoES can be applied to any building element. A final model for the experiment is then created and tables of predicted annual savings are presented for several types of roof insulation.

9.2 Heat losses The heat loss of a house is dependent on two main factors. One is the thermal transmittance of the building fabric and the other is the ventilation or air change rate. Air changes are normally measured in litres per second (l/s) or air changes per hour (ac/h) while the thermal transmittance is measured in W/m2 K and is known as a U-value. A lower U-value means lower heat loss. It decreases non-linearly with thickness as shown in the graph below.

CIBSE U-value of cellulose

00.20.40.60.8

11.21.41.61.8

0 100 200 300 400 500thickness mm

U-v

alue

W/m

2 K CIBSE u-value

Figure 8, U-value of cellulose calculated in IES:VE using CIBSE method.

This illustrates the reason for diminishing returns on investment in insulation. It should be noted that a U-value is not a precise figure. The tests which are carried out to determine the value are done in a hot box which drives out moisture. An allowance is



40

made for this, but nevertheless, varying moisture levels in a real world situation are likely to affect the U-value as water is a good conductor of heat.

Another factor which can affect the U-value of insulation is compression. Loose-fill insulation such as cellulose can be found to pack down over time which will reduce the U-value, both by increasing the conductivity and by decreasing the thickness. This has not been accounted for in this thesis.

9.3 Software choice The model house tested was built in IES:VE (IES, 2006). This was after preliminary comparative testing had been carried out in both IES and NHER Builder (NHER, 2005) which found disagreement between the two programmes. The decision was taken to use IES:VE. This had the effect of minimising the apparent benefits of insulation as the NHER figures showed greater energy savings. A further benefit is that IES allows for greater control of the parameters and quicker model construction.

Using IES allowed for the speedy development of the model house used in the study. Furthermore, it allows for the construction of highly simplified buildings in order to test a theory quickly and simply without needing to specify every building element. Further discussion of the software chosen can be found in chapter 8.

9.4 The model The model house is a semi-detached, two-storey property of 105 m2 plus a room in the roof. It has cavity walls with a U-value of 0.35 W/m2K. The floor has a U-value of 0.22 W/m2K. Windows and doors have a U-value of 2.02 W/m2K. The air change rate is 5.5 l/s. These data have an impact on the optimal level of insulation for the roof by modifying the necessary energy input to maintain a baseline internal temperature.

9.5 Coefficient of Energy Saving This section describes a simple method for calculating energy savings from a known U-value in a given space. A presentation of the experiment that led to this is given, followed by a demonstration for an idealised space.

9.5.1 Deriving annual fuel savings from U-value Simulation was carried out for insulation thicknesses up to 100mm in IES:VE. The graph below shows the tailing off of returns as the insulation thickness is increased.



41

IES model results:Savings against thickness

0.050.0

100.0150.0200.0250.0300.0350.0400.0450.0

0 10 20 30 40 50 60 70 80 90 100

thickness of insulation

kWh

savi

ngs

Figure 9, Initial model results showing savings against insulation thickness.

When this data was plotted against U-value rather than thickness the near-linear relationship was observed. It was decided to run one more model with a U-value of close to zero in order to see if this relationship continued.

IES model results:Savings against u-value

0.0

100.0

200.0

300.0

400.0

500.0

600.0

0 0.5 1 1.5 2u-value

KW

h sa

ving

s

Figure 10, Showing savings against U-value.

The graph remained essentially linear as is to be expected. This is because U-value is a measure of the amount of heat lost in Watts per m2 per degree difference. As the U-value decreases, the kWh saving grows proportionally. Therefore, in this case, the annual saving can be calculated thus:



42

Equation 7

y ≈ 280xins – 280x

Where: x = original U-value.

xins = insulated U-value y = annual savings

Projected and modelled graphs of savings

0.0

100.0

200.0

300.0

400.0

500.0

600.0

0 0.5 1 1.5 2u-value

kWh

savi

ngs

IES model results y = 280Xins -280X

Figure 11, Comparing IES-calculated savings and a simplified method of calculating savings.

This figure of 280 is important in that it gives us an annual heat loss figure throughout the whole model house rather than just through the roof, including losses due to infiltration and ventilation. This figure is the coefficient of energy saving (CoES) for the building by insulation of the chosen building element.

In the formula below, a is the coefficient of energy saving (CoES) for any building by insulation of any chosen building element. Equation 8

y ≈ axins – ax

Incidentally, the slight curve seen on the IES-simulated graph may be due to increasing mass of insulation acting as a thermal store. The failure to take account of this effect is a limitation of the CoES method and could perhaps be adjusted for in further development of the model.

9.5.2 Deriving savings of insulation for any space In order to show how this CoES can be found for any insulated element a simple box with a door on one side and a window on each of the others was built in IES:VE.



43

Figure 12, A simple box built to test the CoES method.

By running a simulation of the model with no insulation in the walls and another with wall U-value of approaching zero the graph approximating energy saving against U-value was created.

Energy saving against U-value

0

1000

2000

3000

4000

5000

6000

7000

8000

0 0.5 1 1.5 2U-value W/m2 K

savi

ng K

Wh

Figure 13, Energy saving against U-value using CoES method.

By substituting for a in Eq. 8, the CoES for wall insulation in this idealised box was found. This simple process can be followed for any space and any insulated element. There is also no need for the simulated building to be in IES:VE. The method would work equally well based on a building modelled in NHER or other software or with experimental data.



44

9.6 Coefficient of energy saving in the model building For the model building used in the experiment the uninsulated U-value of the roof was 2.4 W/m2 K and the building used 13,650 kWh. With a U-value of 0.01 W/m2 K the saving was 8,380 kWh. This converts to a CoES of 3,490. Below is a screenshot from the spreadsheet developed to calculate this.

Figure 14, Screenshot of CoES finder from ISM.

9.6.3 Fuel savings by type of insulation The actual relationship between thickness and energy savings is now calculated for the three types of insulation in this house to assess how much difference varying resistance of types of insulation makes. The insulations modelled are cellulose, polyurethane and Rock wool. Their characteristics are tabulated below: Table 4, Physical characterictics of insulation types

Insulation type Conductivity (W/m K) Density (kg/m3) Warmcel 500 0.036 45

Kingspan TP10 0.023 33



45

Rock wool 0.04 25

9.6.4 U-value calculations A U-value calculator (formula from McMullen, 2002) in Excel using the same construction as the IES model generates a U-value graph which shows good agreement with the IES-generated U-values which use the CIBSE admittance method (CIBSE, 1999b).

U -values for cellulose

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

10 20 30 40 50 60 70 80 90 100

thickness of insulation

W/m

2K CIBSE u-valueSimple calculation

Figure 15, Comparing CIBSE-calculated U-values with simple U-value calculation.

Since there is agreement, particularly at low U-values, it is considered reasonable to use this simple method rather than IES simulation. Graphs plotting thickness against U-value for three types of insulation are presented below.



46

U-values of insulation

0.00

0.50

1.00

1.50

2.00

2.50

0 20 40 60 80 100

120

140

160

180

200

220

240

260

280

300

thickness mm

U-v

alue

W/m

2 K

Kingspan TP10Warmcel 500Rockwool

Figure 16, U-values of three types of insulation by thickness.

9.6.5 Results for annual energy savings by insulation type These U-values can then finally be converted to annual energy savings by thickness for each type of insulation using Eq. 8 derived above and the CoES found in ISM.

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

0 70 140

210

280

350

420

490

560

630

700

770

840

910

980

thickness mm

kWh

savi

ng Kingspan kWhRockwool kWhWarmcel kWh

Figure 17, graph of energy saved by varying thickness of insulation

The graph below shows the savings each of the insulation types makes above Rockwool.



47

0

200

400

600

800

1000

1200

1400

0 70 140

210

280

350

420

490

560

630

700

770

840

910

980

thickness mm

kWh

savi

ng Kingspan kWhRockwool kWhWarmcel kWh

Figure 18, graph showing extra saving above that of Rockwool by thickness

It can be seen that the difference in energy saving is greatest between around 10mm and 100mm in thickness. Before and after this the lines converge. By somewhere around 1000mm the difference is almost negligible – around 60 kWh/yr at most. The data are presented in Appendix 3.



48

Chapter 10 The Model

10.1 Introduction The Insulation Savings Model (ISM) is the software created for calculation of optimal points for this research. It is described below including a description of the interface and a flow chart showing its operation.

10.2 The interface The main user interface of the ISM is a drop-down list on the sheet “Front page”. The user selects an insulation type, enters the area of insulation and enters the desired discount rate.

Figure 19, Screenshot from ISM showing data entered on the front page.

On the second page, “CoES” the user inputs four pieces of data from the building element being modelled. This is two U-values and the energy use associated with them. They then vary the value in the “CoES” cell until the green line covers the red to find the coefficient of energy saving of insulating their building element as described in section 9.5.2.

10.3 The sheets The model is a tool which contains the following datasets:

• Projected natural gas price for 30 years. • Physical and cost data for the four insulation types. • Projected carbon price for 30 years.



49

Eight pieces of data are entered by the user: four about a building element’s thermal performance found either through modelling or from measurement; a selection of the insulation type; the insulation area; the discount rate; and the level of grant funding available.

10.4

Flo

w C

hart

Th

e pr

oces

s fol

low

ed b

y th

e to

ol is

laid

out

in th

e fo

llow

ing

flow

cha

rt.

Fi

gure

20,

ISM

flow

cha

rt

10.5 The results From the data which the user enters, the ISM model calculates the optimum thickness of insulation for the building element being modelled. This is where the ROI or CROCI is equal to 1. Graphs are plotted to show this, and also the net carbon saving at the two optimal points.

Optimal points for insulation

0.0

1.0

2.0

10 110

210

310

410

510

610

710

810

910

Thickness mm

RO

I/CR

OC

I

ROI of layerCROCI of layer

kg CO2 saving at optimal point for ROI of layer and CROCI of layer

0.0

1.0

2.0

35,000 40,000 45,000 50,000 55,000

Saving

RO

I/CR

OC

I

Savings atfinancial trade-off pointSavings atcarbon trade-offpoint

Figure 21, ISM sample results

Finally, graphs are plotted of the financial and carbon payback times.

CO2 marginal payback time of insulation

-

10

20

30

40

50

60

70

80

90

100

10 50 90 130

170

210

250

290

330

370

410

450

490

530

thickness mm

year

s

Marginal financial payback time of insulation

-

10

20

30

40

50

60

70

80

90

100

10 50 90 130

170

210

250

290

330

370

410

450

490

530

thickness mm

year

s

Figure 22, ISM sample results Figure 23, ISM sample results



52

Chapter 11 Results

11.1 Introduction The results presented here are for 66 m2 of roof insulation on a medium-sized, semi-detached house. Some of the findings may apply in other situations but this will not always be the case. The base case does not include any grant towards the cost of insulation or a price for carbon.

11.2 Optimal points and thickness required for Building Regulations Table 5, Thickness in mm required for Building Regulations and optimal points

Financial-optimal

point

U-value = 0.16

Carbon-optimal point

Kingspan TP10

40 130 190

Warmcel 100 210 790

Rockwool 70 235 730

This table shows that the CO2 returns for the products in this sample set and construction continue to increase after the insulation is no longer cost-effective and after the level required by Building Regulations. The effect is less pronounced for Kingspan than for the lower embodied CO2 materials but still significant. On the other hand Kingspan achieves the same U-value from a much thinner layer than the other two insulations.

0

100

200

300

400

500

600

700

800

900

Financial optimalpoint

U-value = 0.16 Carbon optimalpoint

Thic

knes

s m

m

Kingspan TP10WarmcelRockwool

Figure 24, showing optimal depths of insulation and required level for Building Regulations



53

Regulations are set closer to the financial-optimal point than carbon-optimal except in the case of Kingspan.

Regulations could be set much higher for lower ECO2 materials and so avoid more emissions. This would only apply where space is not critical. It may also not be the most cost effective way of reducing emissions. This is further examined in the sensitivity analysis.



54

11.3 Payback time graphs

11.3.1 Kingspan TP10


-

10

20

30

40

50

60

70

80

90

100

10 60 110

160

210

260

310

360

410

460

510

560

610

660

thickness mm

year

s

Figure 25: Marginal financial payback of Kingspan TP10


-

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100.0

10 60 110

160

210

260

310

360

410

460

510

560

610

660

thickness mm

year

s

Figure 26, Marginal CO2 payback of Kingspan TP10



55

11.3.2 Warmcel


-

10

20

30

40

50

60

70

80

90

100

10 60 110

160

210

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310

360

410

460

510

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660

thickness mm

year

s

Figure 27, Marginal financial payback of Warmcel


-

10.0

20.0

30.0

40.0

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90.0

100.0

10 60 110

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thickness mm

year

s

Figure 28, Marginal CO2 payback time of Warmcel



56

11.3.3 Rockwool


-

10

20

30

40

50

60

70

80

90

100

10 60 110

160

210

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360

410

460

510

560

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660

thickness mm

year

s

Figure 29, Marginal financial payback time of Rockwool


-

10.0

20.0

30.0

40.0

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60.0

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90.0

100.0

10 60 110

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660

thickness mm

year

s

Figure 30, Marginal CO2 payback time of Rockwool



57

11.3.4 Analysis of payback time graphs Firstly, it should be restated that these payback time graphs are looking at the marginal return on investment. The thickness of installed insulation will pay back far more quickly when considered as a complete unit. For example, Rockwool to 380mm will pay back the total financial investment after 50 years. The marginal 10mm however (370-380mm) would take 1,088 years to pay back. Obviously all of the insulation types have a payback of 50 yrs at the optimal point measured against, as this is the duration of the investment. In other words the marginal financial payback time on the financial payback graph is 50 years and the marginal CO2 payback time on the CO2 payback graph is also 50 years. The marginal financial payback time for a given thickness of Kingspan is high (ie, the curve is steep) both in terms of financial and of CO2 payback. This is because it is both expensive and high in embodied carbon. This is a skewed measure however as Kingspan performs better for a given thickness due to a lower conductivity. This means that the payback time for a given U-value is the more meaningful result. Table 6, Financial payback times of financially marginal 10mm.

U-value = 0.16

Carbon-optimal point

Kingspan TP10 423 yrs 488 yrs

Warmcel 185 yrs 1,474 yrs

Rockwool 454 yrs 2,441 yrs

This shows that, in the case of marginal return of the final 10mm added, none of these types of insulation starts to save money at Building Regulations levels within in the lifespan used here. Even in the best case, going to the carbon-optimal point would take almost 500 years to repay the financial investment. This is in the case of Kingspan, not because it performs well, but because it is so high in ECO2. This skews the CO2 optimal point downwards in comparison to the lower ECO2 materials. Table 7, CO2 payback times of CO2 marginal 10mm.

Financial-optimal point

U-value = 0.16

Kingspan TP10 2.5 yrs 23.1 yrs

Warmcel 0.9 yrs 3.8 yrs

Rockwool 0.6 yrs 5.6 yrs

Warmcel provides the best financial investment when going to the required level of insulation, paying back the investment twice as fast as the other two materials assessed.



58

The two low ECO2 insulation types, Rockwool and Warmcel, both have a shorter payback at Building Regulations level in this configuration. All the insulation types pay back their ECO2 very quickly at the financially optimal point. This shows that it is correct in terms of CROCI to go beyond the financial-optimal point and therefore that, if this is the priority, regulations should be employed to force this (as they are). The fact that the carbon payback time is still very short at a U-value of 0.016 W/m2K, at least in the case of the lower ECO2 materials, means that a lower U-value could be insisted upon. However, this may not be the most cost-effective way of reducing emissions, particularly as returns per mm added continue to fall.



59

11.4 Financial savings at U-value = 0.16 W/m2 K and optimal points Table 8, Financial savings at U-value=16 W/m2 K and optimal points

Financial-optimal

point

U-value = 0.16

Carbon-optimal

point Kingspan TP10

£1,830 £1,017 £193

Warmcel £2,200 £2,017 £212 Rockwool £1,847 £1,017 -£2,499

Warmcel saves the most money at the financially optimal point while Rockwool and Kingspan save less. Kingspan saves the most at the CO2 optimal point, Warmcel saves a little less and Rockwool loses a lot of money. When insulating using these materials, there is no marginal financial benefit to be had by going over regulations to the CO2 optimal point. In fact even going to Building Regulations level shows a sub-optimal financial return over 50 years for all materials. This makes the point that if it is desired that carbon-optimal levels of insulation be installed, either further lowering of legally required U-values, or some financial incentive is necessary.

-£3,000

-£2,000

-£1,000

£0

£1,000

£2,000

£3,000

Financialoptimal point

U-value = 0.16 Carbon optimalpoint

£ sa

ving Kingspan TP10

WarmcelRockwool

Figure 31, Financial savings at U-value = 0.16 and optimal points



60

11.5 CO2 savings at U-value 0.16 W/m2 K and optimal points Table 9, kg CO2 savings at U-value=0.16 W/m2 K and optimal points

Financial-optimal

point

U-value = 0.16

Carbon-optimal

point Kingspan TP10

67,048

75,680

76,182

Warmcel 72,960

78,107

80,999

Rockwool 67,773

77,953

80,375

At the carbon-optimal point all of these types of insulation save between 76 and 81 tonnes of CO2 over the 50 year period assessed. The greatest difference between carbon and financial-optimal points is for Rockwool which saves an additional 12.6 tCO2 over 50 years at the carbon-optimal point.

A very small carbon saving can be had from Kingspan when going over regulations. With Rockwool and Warmcel the extra savings are more significant – over two tonnes for each. This suggests that regulations are set at the optimal level for Kingspan or similar high ECO2, low conductivity products. Again, if a low ECO2 product is used, the optimal U-value in regulations would be lower.

65,000

67,000

69,000

71,000

73,000

75,000

77,000

79,000

81,000

83,000

Financialoptimal point

U-value =0.16

Carbonoptimal point

kg C

O2

savi

ng

Kingspan TP10WarmcelRockwool

Figure 32, CO2 savings at Building Regulations and optimal points



61

Chapter 12 Analysis

12.1 Introduction

12.2 Insulation can save money and avoid CO2 emissions First, the obvious point should be made that generally insulating a home will save the occupant money and prevent the emission of a certain amount of CO2 into the atmosphere. Building Regulations require an elemental U-value of 0.16 W/m2K in pitched roofs. At this U-value each of the insulation types assessed is still producing marginal carbon savings. All but Rockwool still produce a marginal financial saving and all produce overall financial savings.

All of the insulation types looked at here will provide the occupant with at least £1,000 of NPV and 75.5 tCO2 saving over a 50 year lifespan at the depth required by Building Regulations. At the carbon-optimal point Rockwool will lose £2,500 while the other two types make a saving of around £200. However all will provide 76-80 tCO2 saving. These results reinforce the commonplace advice to insulate homes.

12.2.1 Optimal strategies There is a mismatch between financial and carbon-optimal insulation strategies. Under the conditions used in this modelling exercise it is not financially optimal to put in enough insulation to maximise avoided CO2 emissions. This shows that either measures such as grants, a higher cost for carbon emissions, or more stringent regulations must be put in place if the aim is to incentivise insulation closer to the carbon-optimal point. This is further explored in section 12.4 on sensitivity to grant level and sensitivity to carbon prices.

The U-value at the carbon-optimal point is 0.12 W/m2 K for Kingspan and 0.05 W/m2 K for Warmcel and Rockwool. This compares with an elemental U-value for pitched roofs of 0.16 W/m2 K from Building Regulations. This suggests that even for high ECO2 insulation, the elemental U-value could be set a little lower. This conclusion needs to be tested in other building types through modelling and physical data collection. It is also important to consider whether this provides optimal CROFI as returns become very low when approaching the carbon-optimal point.



62

-£5,000

-£4,000

-£3,000

-£2,000

-£1,000

£0

£1,000

£2,000

£3,000

0 100 200 300 400 500 600

Thickness mm

Savi

ng

Saving

Figure 33, ROI curve for Kingspan

It is interesting to note that the return on investment curve is asymmetric in the case of insulation. This means that when designing an optimal strategy for insulation it is better to over-specify than to under-specify. This has previously been noted by Lowe, et al (1997) in the case of energy and carbon-optimal points although not for financial-optimal points. In fact the effect is much more pronounced in the case of CROCI (see fig 34).

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

0 200 400 600

Thickness mm

Savi

ng k

g C

O2

Carbon saving

Figure 34, CROCI curve for Kingspan

This asymmetricality means that, as it is better to over-specify, where insulation comes in standard thicknesses it is generally optimal to add one more layer in order to go beyond the optimal point than to stay below it. For example, Rockwool generally comes in



63

150mm batts. Given that the two optimal thicknesses in this application are found to be 90mm and 560mm the optimum level of insulation using Rockwool is either 150mm for financial optimum (although in a new build this alone would be insufficient to satisfy Building Regulations) or 600mm for carbon optimum.

With loose-fill insulation like Warmcel this is not necessarily the case as the calculated optimal thickness can often be installed. There may be restrictions placed by the size of timbers used in constructing the roof. It is therefore still reasonable to add a small margin to allow for error in the calculation.

In a situation of uncertainty over any of the parameters used, over-specification is the better strategy.

12.3 The contribution of ECO2 The ECO2 of the insulation put into the building modelled does not make a huge contribution to the lifetime CO2 emissions of the house, or even to the total embodied CO2. Nevertheless it is significant. The average new build 3-bed semi-detached house as assessed in research to be published by the Empty Homes Agency has embodied CO2 of around 67 tonnes (Bull, forthcoming). Roof insulation to the carbon-optimal level for the three insulation types assessed gives embodied CO2 figures of between 1 and 2 tonnes. It should be noted that these numbers will be overstated when compared with the EHA research so cannot be converted to percentages. This is because the EHA research used figures from ICE (Hammond, Jones, 2006) which generally sets the boundaries at cradle to factory gate or site whereas this thesis uses cradle to grave emissions figures from full LCAs which are higher. If using cradle to grave or cradle to cradle figures the average ECO2 of a house would be greater than 67 tonnes. Table 10, Comparison of ECO2 data from ICE and from life cycle analysis

When constructing to Building Regulations in this type of property, the use of Warmcel instead of Kingspan would save an additional 2.5 tCO2 per house. This is equal to the additional ECO2 in Kingspan to provide the same returns.

12.3.1 Carbon-optimal U-values The carbon-optimal U-values for these materials are 0.05 W/m2K for Warmcel, and Rockwool, and 0.12 W/m2K for Kingspan. This shows that the lower ECO2 materials allow for greater savings over a given lifespan at the optimal point. Marginal CO2 benefits of insulation to beyond the U-value required are higher for lower ECO2 materials.

Material LCA Embodied Carbonkg CO2/kg

ICE Embodied Carbon kg CO2/kg

Cellulose 0.91 - Rock wool 1.58 1.05

PU foam 6.63 2.41



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This reveals the difficulty of achieving “zero-carbon homes” without coming up against the carbon barrier (where more carbon is embodied than will be saved) when using high specification materials such as Kingspan. Such materials will have greater heat losses at the optimal points than low ECO2 materials. This will then require more renewable heat input to offset those losses. On the other hand, the high ECO2, low conductivity materials allow a much thinner layer of insulation to be applied. This smaller footprint (when insulating walls) can be of significant financial benefit.

12.3.2 ECO2 more important as buildings waste less ECO2 will become proportionally more important factor as homes become more air-tight and well insulated. At present, according to the limited prior research on this topic, buildings like the Gallions Ecopark development take approximately 15 years to emit the same amount in-use as is embodied in their materials (Killip, 2007a). Given a nominal lifespan of 60 years, this means the building lifecycle emissions are 20% ECO2 and 80% in-use emissions. This is changing further as buildings approach very low or zero CO2 in-use emissions.

12.4 Sensitivity to parameters In a modelling exercise like this it is important to check how sensitive the results are to changes in some of the values. Here, some of the parameters are systematically varied in order to find the sensitivity to each of them.

The values for Rockwool are used as the baseline as it is closest to the centre of the range of products assessed. Also, for the purposes of this analysis the baseline was calculated using a 25% discount on insulation materials (equivalent to financial measures such as a grant or tax break), and the medium price of carbon (£70/tC) from Clarkson and Deyes discussed earlier. Each of the parameters was then doubled and halved and the change in optimum levels of insulation recorded.

12.4.1 Conductivity Change in conductivity affects the U-value so affects both optimal thicknesses.

• Doubling causes a 30% change in financial optimum. • Halving causes a -30% change in financial optimum.

• Doubling causes 37% change in carbon optimum. • Halving causes a -29% change in carbon optimum.

12.4.2 Density Increase will decrease the carbon-optimal thickness. The insulation will save less carbon at that optimal point. It would not affect the financial-optimal point in reality unless the material is sold by weight. As ISM calculates financial cost by weight it is affected. With this limitation, these are the results of sensitivity analysis.

• Doubling causes a -30% change in financial optimum. • Halving causes a 50% change in financial optimum.



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• Doubling causes -30% change in carbon optimum. • Halving causes a 37% change in carbon optimum.

12.4.3 Embodied CO2

Increase decreases the carbon-optimal thickness. Therefore less money and carbon is saved at that optimal point.

• No change in financial optimum.

• Doubling causes a -30% change in carbon optimum. • Halving causes a 37% change in carbon optimum.

12.4.4 Cost of insulation • Doubling causes a -30% change in financial optimum. • Halving causes a 50% change in financial optimum.

• No change in carbon optimum.

12.4.5 Carbon prices The carbon price does not make a large difference to the optimum level of insulation at the prices looked at in this thesis. The price of £70 is taken as a mid-point.

This lack of sensitivity indicates that they are either too simplistic, too low or that the true value of going beyond the financial-optimal point is low.

• Doubling causes a 20% change in financial optimum.

• Halving causes a -10% change in financial optimum. • No change on carbon optimum.

12.4.6 Emissions factor • Doubling causes a 20% change in carbon optimum. • Halving causes a -10% change in carbon optimum.

• Doubling causes 37% change in carbon optimum. • Halving causes a -30% change in carbon optimum.

12.4.7 Grant level This factor seems important. There should be an assessment of the best level of insulation, of the best type of insulation and the best ones should be encouraged with grants/tax breaks/soft loans.

• Doubling causes a 30% change in financial optimum. • Halving causes a -10% change in financial optimum.




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12.4.8 Area • Doubling causes a -30% change in financial optimum. • Halving causes a 50% change in financial optimum.

• Doubling causes a -30% change in carbon optimum. • Halving causes a -37% change in carbon optimum.

12.4.9 Coefficient of energy saving Situations with a higher CoES would get greater ROI and CROCI. This might be the case in a less well insulated building. In order to guard against this Lowe et al in their assessment of optimal levels of wall insulation used very low U-values for the building elements assessed (1997). This included a value of 0.085 W/m2 K for floors, ceilings and doors and 0.6 W/m2 K for glazed elements. This is one of the more sensitive variables for CO2 optimisation and so Lowe’s assumptions would have served to lower the EROEI and CROCI found.

• Doubling this causes a 50% change in financial optimum. • Halving causes a -30% change in financial optimum.

• Doubling causes a 37% change in carbon optimum. • Halving causes a -30% change in carbon optimum.

12.4.10 Discount rate This affects the financial-optimal point. The variation here is low since the original discount rate is very low.

• Doubling this causes a -20% change in financial optimum. • Halving causes a 20% change in financial optimum.


12.4.11 Energy price This affects the financial-optimal point. The variation here is fairly high, as with some other financial measures. See Conclusions 13.2.2 for more discussion on this.

• Doubling this causes a 30% change in financial optimum. • Halving causes a -20% change in financial optimum.




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12.5 Analysis of sensitivity

Sensitivity of financial-optimal points

-40%

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Figure 35, Sensitivity of financial-optimal points

Excluding density due to the reason mentioned above, the financial-optimal point is sensitive to variation in nine of the eleven variables. The sensitivity to doubling/halving is of -30% to 50%. The greatest sensitivity is to cost and area. The least is to emissions factor and carbon price.

12.5.1 Implications for financial incentives This insensitivity to carbon price suggests that, assuming the price given to carbon is fair, only a small amount of additional insulation is worthwhile. In the case of Rockwool, and given a 25% reduction in the price of insulation, the additional thickness is only 10mm when doubling the price of carbon. This means that after adding that additional 10mm of Rockwool, there are more cost-effective ways of reducing carbon emissions.

In order to minimise emissions the Government would have to use a higher (or rapidly rising) social cost of CO2 in their modelling. This would reflect the possibly very large cost of the hypothetical final kg of emissions which tips the climate system beyond a level where positive feedbacks lead to runaway climate change.

On the other hand, there are more cost-effective ways to reduce carbon emissions than super-insulating homes to the carbon-optimal level. Such measures might involve draught-proofing, fitting low energy light bulbs, or investment in renewable energy. The sensitivity to grants or tax rebates towards the cost of insulation is higher. This is perhaps due to the discount factor which mimics how many people prefer £10 today to £20 in five years’ time. An incentive which can be felt immediately is more likely to affect behaviour than one for which the benefit is cumulative over a number of years. It can also be seen in the graph below that the optimal point is more sensitive to energy price than to carbon price (around the baseline values). This will always be the case



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unless the carbon price is equal to the energy price. This would be equivalent to a 100% tax on energy – likely to be a difficult proposition politically.

This sensitivity underlines the importance of “fuel proofing”, particularly as fuel prices are one of the more uncertain parameters used in this modelling.

Sensitivity of financial-optimal points to financial incentives

80859095

100105110115120125130

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%age variation

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Figure 36, Sensitivity of financial-optimal points

12.5.1 Implications for embodied carbon

Sensitivity of carbon-optimal points

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Figure 37, sensitivity of carbon-optimal points



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Six of the eleven variables affect the carbon-optimal point with a -30% to 37% sensitivity to doubling/halving. All have similar effects with conductivity producing slightly less than the others. It should be pointed out that a 37% increase was the maximum allowed for in ISM (giving 1000mm of insulation). As CROCI of layer was only 1.1 at this point, the true optimal thickness would have been slightly greater. These results show that any way of changing the lifespan carbon emissions per functional unit of insulation will be almost equally sensitive. In Lowe’s analysis of sensitivity in his 1997 paper found that “optimum insulation thicknesses appear to vary more slowly than the square root of… CO2, and that predicted thicknesses are therefore reasonably robust to changing assumptions.”

Since all of the parameters which have an impact on carbon-optimal points are similarly sensitive, analysis is robust to changed assumptions about any one of them.

This conclusion is true for each parameter in isolation. Better sensitivity analysis could be carried out by varying more than one parameter at a time to uncover the relationships between them. A more precise analysis of sensitivity to parameters would use a finer scale than 10mm increments.



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Chapter 13 Conclusions and recommendations

13.1 Introduction The main conclusions of this thesis are:

• Insulation thickness in UK domestic roof construction to Building Regulations repays the carbon produced in its manufacture.

• Financially optimal insulation thicknesses are 40 – 90mm; carbon-optimal thicknesses are 190 – 790mm using the materials and construction specified here.

• Carbon-optimal U-values for roof insulation here are between 0.12 W/m2 K (Kingspan) and 0.05 W/m2 K (Warmcel and Rockwool).

• Low ECO2 materials in this situation save more CO2 than high ECO2 materials. The difference at a given U-value is the difference between the two ECO2 values.

• Therefore, if space is not an issue, low ECO2 materials should be preferred as they provide greater CROCI.

• Conversely there will be an issue with the larger footprint (or higher walls) of buildings where lower spec, lower ECO2 materials are used.

• Marginal CO2 benefits of insulation to beyond the level required are higher for lower embodied CO2 materials.

• CO2 prices are less of an incentive to insulate than grants. • Carbon-optimal levels of insulation are a cost-ineffective way of reducing carbon

emissions. • Lifetime penalties for non-optimality are higher for thicknesses less than optimal

than for thicknesses greater. Therefore in cases of uncertainty one ought to specify a greater thickness. This is the case, no matter what the source of uncertainty.

• If fuel prices rise then a greater level of insulation will be financially optimal. • Either domestic gas should be included in the next phase of EU-ETS or the EEC

(or similar measures) should be expanded. This is necessary to internalise the social cost of a large percentage of UK emissions.

These conclusions and their implications are further examined below.

13.2 Implications for financial incentives

13.2.1 Implications for grant schemes New build roof insulation to Building Regulations level is sub-optimal in terms of lifetime carbon emissions in the UK. This effect is more pronounced for low ECO2 insulating products.

Although this thesis looked at a specific building element – roof insulation in a semi-detached house – some general conclusions can still be drawn about the environmental economics of insulation. For example, grant schemes for loft insulation might consider whether they are deriving maximum return on investment from the scheme. More



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insulation could certainly be fitted than is provided for under WarmFront before hitting the carbon-optimal point.

However, when approaching the margins, the return on investment will become very low and therefore a better ROI (and CROCI/CROFI) may be had by focusing on air-tightness improvements, fitting low-energy light bulbs and other measures. This is shown by the fact that, even at Building Regulations-required levels, the marginal cost of a saved tonne of CO2 is £100-£480.

Implied cost of carbon by U-value

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Figure 38, Marginal cost of a saved tCO2.

This is a very expensive way to avoid emissions and is equivalent to a cost per tC of £370-£1,760. This is far higher than the Government’s social cost of carbon at £70/tC (or £75, allowing for a £1 increase per year since 2002) (Clarkson, Deyes, 2002). This means that, barring co-benefits of insulation such as decreased fuel poverty and reduced requirement for imported gas, Building Regulations are set higher than is rational. The WarmFront grant scheme generally allows for 10 inches (250mm) of mineral wool insulation (although fitted as loft insulation, not roof). In the construction used in this model (assuming Rockwool is used) this would give a U-value of 0.15 W/m2 K and so put it in the range of £300/tCO2 at the margin. Recommendation: It is imperative that co-benefits are quantified, in order to find whether best value is being obtained from providing grants for insulation to the levels currently allowed.



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13.2.2 Financial incentives today are more effective than a carbon price Financial incentives such as grants which act now are more effective than a carbon cost. This is partially to do with cost of carbon being comparatively low. Even a social cost of £700/tC, ten times that recommended by the UK Government, does not incentivise insulation to the carbon-optimal point for any of the insulation types looked at. A grant of over 95% towards the cost of insulation would be required to make insulation to the financial- and carbon-optimal level equal. Recommendation: Grant schemes (or other ways of reducing the cost of insulation such a zero rating for VAT) are the most effective way of encouraging insulation from a low starting point. Schemes such as WarmFront and the EEC are therefore should be continued. The amounts spent on insulation should be reviewed in the light of further quantification of co-benefits.

13.3 Building Regulations and CO2 emissions

13.3.1 New build This high implied cost of carbon reveals the difficulty of achieving “zero-carbon homes” without coming up against the cost barrier (the marginal point where more money is spent than the carbon saved is worth). This will be particularly true when using high specification materials such as Kingspan. The point may soon come where renewable heat (biomass or “green” electricity) or CHP is preferable in terms of providing cheaper carbon savings.

Many see U-values continuing to fall with successive updates to Building Regulations, perhaps going so far as creating Passive Houses. Perhaps the most important thing about Passive House standards is the very low air-change rate required. It is likely that further regulation to control this factor would provide greater value than increasing insulation.

Recommendation: Continue with changes in Building Regulations to ensure lower domestic CO2 emissions in the most cost effective manner. This should include air-tightness testing to increasingly tight levels. U-values appear to be set close to the optimal level in terms of CROFI.

13.3.2 Refurbishment The Government’s recommended price for carbon emissions is £35-£140/tC. This is equivalent to £9.50-£38/tCO2. This means that, if it were not for the co-benefits, the optimal level of loft insulation would be 30-80mm of Rockwool. The financially optimal point for the house-holder, with a 50% WarmFront grant, is 100mm. With a 50% grant and a high cost of carbon the optimal level is 150mm. With all of the above plus a high energy price (doubled) the optimal level is 190mm.

As all of the above are not unreasonable assumptions, and as in the case of uncertainty it is optimal to over-specify, it is found that the level of insulation installed in WarmFront-funded refurbishment is set in the right region at 10 inches (250mm). This also allows room for co-benefits although it is important that they be quantified.



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There are proposals to make thermal refurbishment compulsory through consequential changes. This is where say 10% of the total cost of refurbishment must be spent on energy efficiency measures. There is also a stipulation that this need only happen if the measure in question is financially viable (Killip, 2007b). Dependent on the interpretation of this final point, this may mean insulation to the financially optimal point. Alternatively it may be interpreted as meaning insulation to the point where overall ROI is 1. In the case of Rockwool with the baseline assumptions, this could mean either 70mm for ROI of the marginal layer, or 360mm for overall ROI, a great difference. This should be clarified.

Recommendation: Ensure consequential changes happen by regulating that a set percentage of refurbishment budgets be spent on efficiency measures. This should not necessarily be financially viable unless the social cost of carbon has been included in calculations. Recommendation: Focus on regulating insulation of poorly-performing existing homes over further increases in new build regulation. This is since the greatest ROI, and lowest cost of carbon, is found from the first layer of insulation added and returns rapidly become smaller.

13.3.3 Higher ECO2 means lower CO2 savings It is shown by some of the results that where the insulation type specified is high in ECO2, as exemplified by Kingspan, the level of insulation required by Building Regulations is close to the carbon-optimal level. However, it does not save as much CO2 over a lifespan.

This suggests that the current preference for mineral wool insulation in lofts under the WarmFront grant scheme is reasonable. However it might be explicitly extended to cover natural and recycled insulation products such as Warmcel as the lower ECO2 leads to a greater lifetime CO2 saving. High ECO2 materials should be excluded from the scheme as, when insulating a loft, saving space is not normally so important. Recommendation: Explicitly include other low ECO2 forms of loft insulation in WarmFront.

13.4 Impacts on fuel poverty Going beyond the requirements of Building Regulations in this new build situation would actually save less money (assuming the occupier pays for the additional insulation through an increase in house price). This shows that the financial effects of increasing insulation are not a reason to go to the carbon-optimal point in new build. On the other hand, when insulating a previously uninsulated roof the effect of decreased sensitivity to fuel price rises has a major mitigating effect on fuel poverty (Wu, Smith, Pett, 2005).

Recommendation: Extend the focus of Building Regulations from new build to thermal refurbishment. Increase the EEC and ensure that it continues to focus on the poorest members of society, most at risk of fuel poverty.



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13.5 Impacts on thermal comfort There are also few impacts on thermal comfort to be had from going above current Building Regulations.

Adequate thermal comfort can be achieved at Building Regulations levels of insulation, only with slightly greater emissions and a lower cost. Again, however, a large increase in thermal comfort may be found when insulating a previously uninsulated property. Recommendation: Again, focus on improvement of poorly performing existing stock over increases in new build regulation.

13.6 Implications for current orthodoxy Many of these conclusions are counter to current orthodoxy, at least within environmental circles. The finding that roof insulation much beyond Building Regulations levels is not a cost-effective way of avoiding emissions appears to be at variance with PassivHaus design, MINERGIE requirements and the AECB standards. However, the findings are robust and follow from the physics which dictates that there are diminishing returns when increasing thickness of insulation. Generally people think in terms of saving space. This is not necessarily the case when looking at roof or loft insulation, particularly in the case of new build. Many people will insulate very highly in lofts as they see that space is not always such an issue. Where people have already recognised this it is important that they realise there is still a limit to what is worthwhile imposed by CROCI and CROFI.

Reasons given for using natural insulation are often not entirely based on thermal performance or CO2 saving. Other benefits quoted include the potential for breathable construction, low toxicity, better indoor air quality due to lower levels of off-gassing, and lower levels of resource depletion. The implications of this thesis mean that the marginal value of the insulation’s effects (including the value of lost performance and the value of the co-benefits mentioned) should be positive before they are specified. Assessment of these co-benefits will necessarily often be subjective as people place differing values on benefits such as indoor air quality.

Natural insulation may often be preferable when there is sufficient space. However, it is unlikely to be appropriate in situations such as urban wall insulation where footprint can be very valuable. A final implication is that refurbishment of uninsulated existing buildings is the most cost-effective way of using insulation. This is necessarily true, due to the fact that the greatest returns are found from the first insulation fitted to a building element. This means that the Government’s focus on zero carbon new build may be misguided.

13.7 Limitations Life-cycle analyses are generally paid for by the manufacturer. This means that they may not be published until the company is happy with it. There are also many different methodologies for calculating ECO2, which can and do return different results. This makes precise comparison of products particularly difficult.



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Fuel types are likely to change over the lifetime of the insulation so CO2 saving figures are likely to be different as emissions factors change. This is most likely to lower the carbon-optimal level of insulation as the emissions factor of heating decreases. When modelling an uncertain future, lack of complete confidence about prices and other projected inputs is to be expected. This is an inherent limitation of projection-based work. More time could have been spent on finding results had the Lowe paper on calculating optimal points been found earlier. This would perhaps have negated the need to build such a complex model as ISM.

More could have been done to assess the impact of the comfort factor. It is not really a factor in looking at increase over current Building Regulations, but may be more so when applying the findings of this thesis to grant schemes for thermal refurbishment. Other costs associated with increased insulation thickness including the requirement for higher walls have not been taken into account. This is a limitation and could be addressed in further work.

This model created for this thesis focused solely on roof insulation. Conclusions are drawn for loft insulation and may not necessarily hold.

13.8 Further research The largest area of further work identified is on co-benefits. This thesis has shown that the implied cost of a tonne of carbon when insulating to Building Regulations is very high – between £500 and £2,000 for these materials. The co-benefits (including energy security) must be immense if the Government has taken this into consideration when setting thermal regulations. This should be assessed. Experiments could be carried out using the model with different initial inputs from IES:VE or physical data to see if the findings hold true in other situations such as wall insulation.

A whole house optimisation to assess the difficulty of working with changing feedbacks should be carried out and the model design improved to take account of the results. ISM could be expanded to work out CROCI, CROFI and ROI for things other than insulation. Later versions may include add-ons to assess draught-proofing, changed heating systems and other measures. This would then enable whole-house optimisation. Lowe’s differential calculations (1997) would simplify the modelling.

It would be useful to look at integrating a cost of lost floor area into the model so it can handle other configurations than just roof insulation.

As more LCA data becomes available, new insulating materials can be assessed in ISM.

All of these areas of further research would assist in optimising returns, particularly CROFI, in buildings



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Glossary

ACE Association for Conservation of Energy ach Air changes per hour, a unit of air change rate AECB Association of Environmentally Conscious Builders Air change rate Measure of ventilation rate (ach or l/s) BRE Buildings Research Establishment C Carbon CHP Combined heat and power CIBSE Chartered Institute of Building Services Engineers Consequential changes Efficiency measures which must be carried out when

refurbishing a property. Currently only applies to non-domestic property

CO2 Carbon dioxide CROCI Carbon return on carbon investment CROFI Carbon return on financial investment Defra Department of Environment, Food and Rural Affairs DTI Department of Trade and Industry (now renamed BERR) DUKES Digest of UK Energy Statistics ECO2 Unit of embodied carbon dioxide (kg CO2/kg) EEC Energy Efficiency Commitment, a scheme whereby energy

supply companies must spend a proportion of their turnover on energy efficiency measures

EIUG Energy Intensive Users Group EROEI Energy return on energy invested EU-ETS European Union emissions trading scheme GDP Gross domestic product GHG Greenhouse gas IPCC Intergovernmental Panel on Climate Change IES:VE Modelling software, Integrated Environmental Solutions:

Virtual Environment ISM Insulation Savings Model kWh Kilowatt hour, a unit of energy Lambda (λ) Unit of conductivity (W/m K) LCA Life cycle analysis l/s Litres per second, a unit of air change rate Marginal cost Defined here as the cost (financial or carbon) of adding 10mm

of insulation MTOE Million tonnes of oil equivalent NAP National Allocation Plan NHER National Home Energy Rating ONS Office of National Statistics Part-L Section of UK Building Regulations which deals with thermal

performance of building fabric



81

PassivHaus A standard of house-building which insists on high levels of insulation and very low air-change rates

ROI Return on investment SAP Standard Assessment Procedure, the official method of

calculating compliance with Part-L of UK Building Regulations

t Tonne tC Tonne of carbon tCO2 Tonne of carbon dioxide U-value Unit of thermal transmittance (W/m2 K) WarmFront Grant scheme to encourage insulation



82

Appendix 1 Insulation price estimates from Healey Associates, quantity surveyor



83



84



85



86



87

Appendix 2 Carbon and energy prices over 50 years Year £/KWh Year £/kgC02

1 0.02 1 0.02 2 0.02 2 0.02

3 0.02 3 0.02 4 0.02 4 0.03

5 0.02 5 0.03 6 0.02 6 0.03

7 0.02 7 0.04 8 0.02 8 0.04

9 0.02 9 0.04 10 0.02 10 0.04

11 0.02 11 0.05 12 0.02 12 0.05

13 0.02 13 0.05 14 0.02 14 0.05

15 0.02 15 0.06

16 0.02 16 0.06 17 0.02 17 0.06

18 0.02 18 0.06 19 0.02 19 0.07 20 0.02 20 0.07

21 0.02 21 0.07 22 0.02 22 0.08

23 0.02 23 0.08 24 0.02 24 0.08

25 0.02 25 0.08 26 0.02 26 0.09

27 0.02 27 0.09 28 0.02 28 0.09

29 0.02 29 0.09 30 0.02 30 0.10

31 0.02 31 0.10 32 0.02 32 0.10

33 0.02 33 0.11 34 0.02 34 0.11

35 0.02 35 0.11 36 0.02 36 0.11

37 0.02 37 0.12 38 0.02 38 0.12

39 0.02 39 0.12 40 0.02 40 0.12

41 0.02 41 0.13



88

42 0.02 42 0.13 43 0.02 43 0.13 44 0.02 44 0.14

45 0.02 45 0.14 46 0.02 46 0.14

47 0.02 47 0.14 48 0.02 48 0.15

49 0.02 49 0.15 50 0.02 50 0.15



89

Appendix 3 Energy savings over 50 years Kingspan Rockwool Warmcel

mm kWh kWh kWh 0 0 0 0

10 4522 3321 3542

20 5987 4831 5061

30 6712 5693 5904

40 7144 6251 6441

50 7432 6642 6812

60 7636 6931 7085

70 7790 7153 7293

80 7909 7329 7458

90 8004 7472 7591

100 8082 7591 7701

110 8146 7691 7793

120 8201 7776 7872

130 8248 7850 7940

140 8289 7914 7999

150 8324 7970 8051

160 8356 8021 8097

170 8383 8065 8138

180 8408 8105 8175

190 8431 8142 8208

200 8451 8175 8238

210 8469 8205 8266

220 8486 8232 8291

230 8502 8258 8314

240 8516 8281 8335

250 8529 8303 8355

260 8541 8323 8373

270 8552 8341 8390

280 8563 8358 8406

290 8573 8375 8420

300 8582 8390 8434

310 8590 8404 8447

320 8598 8418 8459

330 8606 8430 8471

340 8613 8442 8482

350 8620 8453 8492

360 8626 8464 8502

370 8632 8474 8511

380 8638 8484 8520

390 8644 8493 8528



90

400 8649 8502 8536

410 8654 8510 8543

420 8658 8518 8551

430 8663 8526 8557

440 8667 8533 8564

450 8671 8540 8570

460 8675 8546 8576

470 8679 8553 8582

480 8683 8559 8588

490 8686 8565 8593

500 8689 8570 8598

510 8693 8576 8603

520 8696 8581 8608

530 8699 8586 8612

540 8702 8591 8617

550 8704 8596 8621

560 8707 8600 8625

570 8710 8604 8629

580 8712 8609 8633

590 8714 8613 8636

600 8717 8617 8640

610 8719 8620 8643

620 8721 8624 8647

630 8723 8628 8650

640 8725 8631 8653

650 8727 8635 8656

660 8729 8638 8659

670 8731 8641 8662

680 8733 8644 8665

690 8735 8647 8668

700 8736 8650 8670

710 8738 8653 8673

720 8740 8656 8675

730 8741 8658 8678

740 8743 8661 8680

750 8744 8663 8682

760 8746 8666 8685

770 8747 8668 8687

780 8749 8671 8689

790 8750 8673 8691

800 8751 8675 8693

810 8752 8677 8695

820 8754 8680 8697

830 8755 8682 8699

840 8756 8684 8701



91

850 8757 8686 8702

860 8758 8688 8704

870 8760 8690 8706

880 8761 8691 8708

890 8762 8693 8709

900 8763 8695 8711

910 8764 8697 8712

920 8765 8698 8714

930 8766 8700 8715

940 8767 8702 8717

950 8768 8703 8718

960 8768 8705 8720

970 8769 8706 8721

980 8770 8708 8722

990 8771 8709 8724

1000 8772 8711 8725

Documents

Bull - Carbon optimal insulation thesis - 2007