Abstract
When government policy-makers propose new policies, they need to assess the costs and benefits of the proposed policy measures to compare them to existing and alternative policies and to rank them according to their effectiveness. In the case of equipment energy efficiency regulations, comparing the effects of a range of alternative policy measures requires evaluating their effects on consumers’ budgets, on national energy consumption and economics, and on the environment. A useful methodology to perform such policy analysis should represent in a single framework the characteristics of each policy measure and provide comparable results. This paper presents an integrated methodological framework for the prospective assessment of the energy, economic, and environmental impacts of a variety of equipment energy efficiency policy measures. The framework is a comparative assessment tool for energy efficiency policy measures that (a) relies on a common set of primary data and parameters; (b) follows a single functional approach to estimate the energy, economic, and emissions savings resulting from each assessed measure; and (c) summarizes results in a set of metrics to facilitate comparative assessments. It provides a general methodology useful for evaluating a broad range of policies to promote greater equipment energy efficiency and the capability to further compare the impacts of such market interventions. The paper concludes with a demonstration of the use of the framework to compare the estimated impacts of 12 policy measures focusing on increasing the energy efficiency of gas furnaces in the USA.
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Notes
Despite the importance of accounting for both benefits and costs of the policy measures under scrutiny, in some regulatory fora the assessment of costs is not required.
For the sake of simplicity, this paper uses the terms energy efficiency policy and energy efficiency policy measure to refer, respectively, to policies and policy measures targeting prescriptively the energy efficiency of equipment such as appliances, lighting, and other energy-consuming devices used in the residential, commercial, institutional, or industrial sectors (exclusive of industrial process equipment or transportation vehicles).
The framework elaborates particularly on the National Impact Analysis models (US DOE 2007a, c, 2009a, 2010a, 2011c, e) and Regulatory Impact Analysis models (US DOE 2007b, d, 2009b, 2010b, 2011d, f) developed by the Energy Efficiency Standards Group (efficiency.lbl.gov) in the Environmental Energy Technologies Division (eetd.lbl.gov) of Lawrence Berkeley National Laboratory (www.lbl.gov).
This may include greenhouse gas emissions and any other pollutant discharges to the environment.
Because the framework has no geographic or sectoral constraints, totals may refer to any combination of geographical entities and sectors. The geographical scope can range from a state, (sub-national) region, nation, group of nations, or the world. The sectoral scope can include one or more specific economic sectors or a whole economy.
sNPV builds upon cNPV, removing any financial incentive provided by the policy measure and including the monetized benefits from emissions reduction. sNPV does not account, however, for any government investments and operational costs necessary to support the policy. These have been considered very small when compared to the benefits of some policy measures. For example, a study of the impacts of the US appliance standards program estimated the cumulative (1988–2030) net present value of standards (in place or scheduled to take effect) to be about a thousand times greater than the amount of taxpayer funds used to support DOE’s residential appliance standards program over the prior 20 years (Meyers et al. 2008).
Energy efficiency design options refer to a set of representative technology options with similar consumer utility but different energy performance. This set may include both existing and potential technology options.
Not all energy-consuming devices use water.
Energy cost refers to the aggregated lifetime cost of all fuels necessary to run the equipment. Equipment costs encompass the purchase and installation costs, as well as the lifetime maintenance, repair, and any other non-energy related operating costs.
We assume the lifetime of a piece of equipment to span from the time it is purchased to the time it is taken out of service. Consequently, the survival probability function should include the warranty period and—when applicable—any delay time until the equipment starts to be operated. Annual energy (and water) consumption, as well as annual maintenance and repair costs, should be (exogenously) estimated accordingly.
For policy measures that do not provide any financial incentive, results from the economic analysis for the base case and the policy measure case scenarios will be the same.
Additional inputs may be required, depending on the policy measures to be assessed and on assumptions on consumers’ behavior. Financial incentive policy measures require the definition of the monetary incentive to be offered to consumers. Energy efficiency standards and financial incentives can be more accurately assessed if the price elasticity of demand is known. If the direct rebound effect is to be analyzed, an estimate of the percentage of additional energy service consumed due to this effect is necessary.
The section Energy Efficiency Policy Measures below details, for selected energy efficiency policy measures, how to estimate the policy measure case shipments from the base case scenario.
“Appendix 2” provides a simplified version of the methodology.
The methodology assumes that a mandatory energy efficiency standard will apply to all models of the equipment targeted by the policy measure. Full compliance with appliance energy efficiency standards is also assumed by policy-makers in the USA. However, compliance with the standards could also be assumed to be partial, applying to certain products on the market or to an average of all the products in a given market or sold by a given manufacturer (IEA 2000).
The methodology assumes that consumers seeking to purchase a device with an energy efficiency level below the meps in the base case will not be willing to buy any model more expensive than the one with an energy efficiency level that just meets the meps.
Target level refers to a minimum efficiency level stipulated by a policy measure.
This accounts for the possibility that, even under a wide industry agreement, some manufacturers might not fully implement the negotiated meps.
Monetary incentives could also target installation costs or any other costs that would make the commodity covered by the policy measure less costly to consumers.
A policy measure strictly designed to promote early replacements restricts the effects to this one.
The methodology assumes, for each design option i, the availability of shipments data for a period of LF i − 1 years before the first year of the assessment period.
Although shipments from less efficient models and/or from other product classes are (potentially) likely to shift to any design option with energy efficiency equal to or greater than the target level, Eq. (3) presents a more conservative approach where all shifted shipments are concentrated in the energy efficiency design option corresponding to the target level (i.e., the design option with the lowest energy efficiency level eligible to receive the monetary incentive).
Ibid
Ibid
A market barrier, in this context, is a mechanism that deters investment in energy efficient equipment that appears to be economically rational (Sorrel 2004).
We consider only early replacements encouraged by a decrease in the cost of higher energy-efficient equipment. Early replacements motivated by any other reason without an adequate financial incentive might actually put upward demand pressure on the market and, in the short term, increase the price of more efficient models for consumers.
Endorsement labeling programs promote the most energy-efficient models to consumers—for example, the US ENERGY STAR (US EPA and US DOE 2011) and Brazil Selo PROCEL (PROCEL 2011) programs. Comparative labeling programs give consumers information about equipment energy use. Prime examples are the appliance energy-efficiency labeling programs in Australia (EEEP 2011), Brazil (Inmetro 2011), Canada (NRCan 2011), Europe (EU 2011), and the USA (US FTC 2008), among others. See CLASP (2011b) for more examples.
Although shipments from less efficient models are (potentially) likely to shift to any design option with energy efficiency equal to or greater than the target level, Eq. (9) presents a more conservative approach where all shifted shipments are concentrated in the energy efficiency design option corresponding to the target level (i.e., the design option with the lowest energy efficiency level covered by the informational incentive).
Such programs are referred to as bulk (or mass) government purchasing or government procurement programs.
See PEPS (2011) for an extensive list of international public sector energy-efficiency programs.
A trade-off chart can also be useful when comparing results from sets of policy measures (policy packages).
A trade-off chart based on relative values presents results as the ratio of the variable value and either the highest value of the variable or the value of the variable in the base case.
The baseline technology refers to the energy efficiency design option composed of commonly marketed equipment, usually with energy efficiency at the level mandated by the current efficiency standard.
Therefore, the only effect that the policy measures have on shipments is to change their distribution across design options.
Government purchases would shift annual shipments of around 69,000 units from furnaces with an AFUE of 0.80 to the level of AFUE targeted by the policy measure. These shipments refer to the annual number of units estimated for the natural renovation of the fleet of gas furnaces available in public housings in 2009.
Values refer to 2010 and do not change significantly across the analysis period. Percentage ranges are due to ranges in the efficiency levels targeted by consumer rebates policy measures.
The length of the assessment period may vary depending on the purposes of the study, but will typically cover at least one full equipment-lifetime period.
This and other equipment-related costs are represented as time-varying variables due to effects such as learning by doing and economies of scale that may change their values over the analysis period.
Ibid
Ibid
Operating costs, as well as energy (and water) consumption and pollution emissions, are represented as a function of the operation year to support cases where these variables can change over the equipment lifetime.
See footnote 42
See footnote 45
Ibid
Ibid
Ibid
This equation includes consumers’ expenses with water as part of the equipment cost. This cost should be zero in the case of equipment that does not require water consumption. The equation can be extended to account for other non-energy-related operating costs.
A maximum lifetime can be estimated as the time necessary for the survival probability reach an arbitrary very small value (e.g., 0.1 %).
These are site consumptions and emissions. To estimate the corresponding amounts of primary energy and source water consumed, as well as to account for emissions across the whole supply chain of energy and water, Eqs. (22) to (24) should be evaluated as:
$$ {\text{cumEnergy}} = \sum\nolimits_{\text t} {\left( {{\text{Energy}}(\text t) \cdot {\sigma_e}(t)} \right)} $$(22a)$$ {\text{cumWater}} = \sum\nolimits_{\text t} {\left( {{\text{Water}}(\text t) \cdot {\sigma_w}(t)} \right)} $$(23a)$$ {\text{cumEmissio}}{{\text{n}}_g} = \sum\nolimits_t {{\text{Emissio}}{{\text{n}}_g}(t) + \sum\nolimits_t {\left( {{\text{Energy}}(t) \cdot \left( {{\sigma_e}(t) - 1} \right) \cdot {\theta_{{e,g}}}(t)} \right) + \sum\nolimits_t {\left( {{\text{Water}}(t) \cdot \left( {{\sigma_w}(t) - 1} \right) \cdot {\theta_{{w,g}}}(t)} \right)} } } $$(24a)where:
- σ e (t):
-
Dynamic energy end-use-to-primary conversion factor
- σ w (t):
-
Dynamic water site-to-source conversion factor
- θ e,g (t):
-
Dynamic emission factor of pollutant g in the energy supply chain
- θ w,g (t):
-
Dynamic emission factor of pollutant g in the water supply chain.
The direct rebound effect refers to the increase in the demand for an energy service motivated by the decrease in its per-unit cost. The effect may occur as a result of technological improvements that reduce the amount of energy necessary to provide the energy service.
One could add, respectively, to TESpol and TWSpol the energy savings resulting from water savings and the water savings associated with energy savings. Also, when emissions from different pollutants can be converted to a common unit of measurement, the various TERpol,g can be summarized into a single metric TERpol.
Results for TESpol, TWSpol, and TERpol,g refer to site savings. See footnote 54 on how to estimate savings of primary energy and source water, as well as to account for emissions across the whole energy and water supply chains.
Monetary incentives should not be accounted for in the sNPVpol metric (Eq. (31)) when the assessment includes the whole economy. This is because consumers benefit from the incentives at the expense of taxpayers. When the assessment covers only part of an economy, it is necessary to estimate the net monetary incentive from which consumers will benefit. This should consider the extent to which these consumers and the taxpayers for the same economic unit do not coincide. For more details on financial incentive policy measures, see the “Financial Incentives” section in the main text of the paper.
This refers to shipments of devices with energy efficiency below the target level that are shifted by the policy measure to the target level.
The effective useful life is the time at which half of the shipped equipment will be in service and half will not.
See footnote 41
When σ e (t) = 1 the cumulative energy consumption and emissions refer to site values.
See footnote 58
When in the base case the shipments forecast in t for the design option at the target level is zero (shp bc,i* (t) = 0), the increase in shipments due to the substitution of less efficient equipment by models eligible for the incentive should be estimated using a different approach than the one proposed in this section. Alternative methods for predicting consumer response to financial incentives offered toward the purchase of energy-efficient equipment estimate adoption rates from: payback period and equipment utility-based market shares (ICF 2007), incentive level as a percent of total project cost (Mosenthal and Wickenden 1999), market data (Richey 1998; Global 2010), and experts’ judgment (Kintner-Meyer et al. 2003).
Installation and non-energy operational costs of energy-efficient devices may be higher than, equivalent to, or lower than those for the less-efficient devices.
In its rulemaking analyses, DOE has considered the market penetration due to rebate programs to be the same as the implementation rate resulting from the interpolation method (λ(t) = 1). For tax credit programs, DOE estimated that the market penetration of more efficient equipment induced by consumer tax credits will be 60 % of the market increase estimated from the interpolated curves (λ(t) = 0.6) and 30 % in the case of manufacturer tax credits (λ(t) = 0.3) (US DOE 2011c, d). The lower penetration rates for consumer tax credit programs occur because of the deferred nature of the discount to the consumer. For manufacturer tax credits, the lower implementation rate occurs because the consumer receives an indirect benefit via the assumed manufacturer pass-through of the financial incentive but receives no incentive of direct information at the consumer level.
The level of market barriers without the earlier program is an estimate that can be calculated either ex ante or ex post the earlier program.
The methodology assumes the existence of market barriers in the market targeted by the earlier program (\( b_{\text{wo}}^{\prime }\left( {{t^{\prime }}} \right) > 0 \)).
This refers to the benefit/cost ratio of models meeting the energy efficiency of the target level of the earlier program.
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Acknowledgments
This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State, and Community Programs, of the US Department of Energy under contract no. DE-AC02-05CH11231. We acknowledge Margaret Taylor, LBNL, and three other anonymous reviewers for their valuable comments on a draft version of this paper.
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Appendices
Appendix 1: Calculating the result metrics for a policy measure
This appendix presents the methodology used by the framework to:
-
Estimate the total lifetime consumption, costs, and emissions of all equipment shipped in a given year of the assessment period, either for the base-case or the policy measure-case scenario
-
Calculate the cumulative values of these totals over the assessment period for either of these scenarios
-
Calculate the metrics from the cumulative results evaluated for the base-case and the policy measure-case scenarios
A simplified version of these calculations is provided in “Appendix 2.”
Total lifetime values
Let dev i (i = 1,…,N) represent the ith energy efficiency design option of the equipment targeted by an energy efficiency policy (energy efficiency of dev i increases with i); t (t = 1,…,T) represent the sequence of years in a T-year assessment periodFootnote 41 and:
- prci (t):
-
End-user price of a unit represented by dev i shipped in t (the tth year of the assessment period)Footnote 42
- insti (t):
-
Total installation costs of a unit represented by dev i shipped in t Footnote 43
- mnti (t, j):
-
Maintenance costs in the jth year of operation of a unit represented by dev i shipped in t Footnote 44 , Footnote 45
- rpri (t, j):
-
Repair costs in the jth year of operation of a unit represented by dev i shipped in t Footnote 46 , Footnote 47
- energyi,f (j):
-
Energy consumption of fuel f in the jth year of operation of a unit represented by dev i Footnote 48
- wateri (j):
-
Water consumption in the jth year of operation of a unit represented by dev i Footnote 49
- emissioni,g (j):
-
Emissions of pollutant g in the jth year of operation of a unit represented by dev i Footnote 50
- shpi (t):
-
Shipments forecast of the units represented by dev i
The total lifetime energy and water consumption, emissions, and equipment and energy costs of units of all energy efficiency design options shipped in the tth year of the assessment period can be calculated from:
and:
where:
- LFi :
-
Maximum lifetime of a unit represented by dev i Footnote 52
- pSurvi (j):
-
Survival probability of a unit represented by dev i on its jth year of operationFootnote 53
- v (t):
-
Monetary incentive offered in t toward units at or above the target level
- mntLi (t):
-
Present value in t of the lifetime consumers’ expense with equipment maintenance for a unit shipped in t
- rprLi (t):
-
Present value in t of the lifetime consumers’ expense with equipment repair for a unit shipped in t
- wExpi (t):
-
Present value in t of the lifetime consumers’ expense with water for a unit shipped in t
- wPrice (t′):
-
Water price in the t′th year of the assessment period
- eExpi (t):
-
Present value in t of the lifetime consumers’ expense with energy for a unit shipped in t
- fPricef (t′):
-
Price of fuel f in the t′th year of the assessment period
- dRate:
-
Social discount rate.
Cumulative totals over the assessment period
The cumulative (energy and water) consumption and emissions over the assessment periodFootnote 54 and the present values of the total equipment and energy costs for the same period are given by:
Result metrics
Equations (22) to (26) should be evaluated for the base-case scenario and the policy measure-case scenario. These equations depend on Eqs. (10) to (14), where equipment costs and shipments are part of the calculation. These variables interact with each other and such interaction should be accounted for when evaluating Eqs. (22) to (26) for the base-case and the policy measure-case scenarios. Because a policy measure increases the shipments of higher-efficiency devices, the equipment costs of these devices are likely to be lower—and follow a different trend (see footnote 42)—in the policy measure-case than in the base-case scenario. Also, because equipment prices may affect shipments, when assuming non-zero price elasticity of demand or non-zero price elasticity of substitution in the shipments forecast for a policy measure-case scenario, the total shipments in the base case should be adjusted—for the sake of comparability—to be equivalent to the shipments in the policy measure-case scenario. Otherwise, the metrics calculated from Eqs. (22) to (26) will be distorted by these differences in shipments, even though these are the estimated effects of the policy measure. Alternatively, one can evaluate Eqs. (10) to (14) on a unitary basis, in which case the metrics calculated from Eqs. (22) to (26) will provide results at the (average) consumer level rather than as market totals (see footnote 5 in the main text for the meaning of totals).
Based on the results of Eqs. (22) to (26) for the base-case and the policy measure-case scenarios and on a potential direct rebound effect,Footnote 55 the assessment metrics for a policy measure pol is evaluated from the following equations:Footnote 56 , Footnote 57
where:
and:
- rbdEnergy (t):
-
Energy consumption in t due to the direct rebound effect
- rbdWater (t):
-
Water consumption in t due to the direct rebound effect
- rbdEmissiong (t):
-
Emissions of pollutant g in t due to the direct rebound effect
- vpol :
-
Present value of all monetary incentives provided by the policy measureFootnote 58
- mEmissiong :
-
Present value of the monetized annual emissions reduction of pollutant g provided by the policy measure
- sCostpol :
-
Present value of administrative costs incurred by the policy measure to economic agents in the supply chain of the targeted equipment
- \( {\text{sh}}{{{\text{p}}}_{{{{{\text{i}}}_{{\text{r}}}}}}}({\text{t}}) \) :
-
Shipments in the tth year of the assessment period of units represented by \( {\text{de}}{{\text{v}}_{{{i_r}}}} \)—the devices that contribute to the direct rebound effect in the policy measure-case scenarioFootnote 59
- \( {{\rho }_{{{{{\text{i}}}_{{\text{r}}}}}}} \) :
-
Percentage of additional energy service consumed by units represented by \( {\text{de}}{{\text{v}}_{{{i_r}}}} \) due to the direct rebound effect
- gPriceg(t):
-
Market value of reducing one unit of emission of pollutant g in t
Appendix 2: Calculating the result metrics for a policy measure (simplified version)
In the following, we provide a simplified version of the methodology detailed in “Appendix 1.” This may be useful for situations where data needed to develop the exogenous forecasts are limited and/or where computational capability is limited. For example, such may be the case in some developing countries that may otherwise find the framework methodology quite useful. The simplifications assumed are:
-
The targeted equipment operates on only one fuel and does not consume water
-
All design options have the same effective useful life (EUL)Footnote 60
-
End-user price and installation costs are combined in a single variable, as are annual maintenance and repair costs
-
All equipment related costs, as well as annual energy consumption and emissions are constant both over the equipment lifetime and the assessment period
-
Energy price is constant over the assessment period
-
Any monetary incentive provided by a financial incentive policy measure is constant over the assessment period
-
There is no direct rebound effect
-
The social value of reducing the emissions of any pollutant is constant
-
The policy measures do not result in additional administrative costs to economic agents in the supply chain of the targeted equipment
-
Annual total shipments for the base-case and all policy measure-case scenarios are identical and differ only in their distribution across design options
Total lifetime values
Let dev i (i = 1,…,N) represent the ith energy efficiency design option of the equipment targeted by an energy efficiency policy (energy efficiency of dev i increases with i); t (t = 1,…,T) represent the sequence of years in a T-year assessment periodFootnote 61 and:
- tici :
-
Total installed cost of a unit represented by dev i
- mri :
-
Annual maintenance and repair costs of a unit represented by dev i
- energyi :
-
Annual energy consumption of a unit represented by dev i
- emissioni,g :
-
Annual emissions of pollutant g of a unit represented by dev i
- shpi(t):
-
Shipments forecast of the units represented by dev i
The total lifetime energy consumption, emissions, and equipment- and energy costs of units of all energy efficiency design options shipped in the tth year of the assessment period can be calculated from:
where:
- EUL:
-
Estimated useful lifetime of any design option represented by dev i
- v:
-
Monetary incentive offered toward design options with energy efficiency equal or greater than the target level of the policy measure
- fPrice:
-
Energy price
- dRate:
-
Social discount rate
Cumulative totals over the assessment period
The cumulative energy consumption and emissions and the present values of the total equipment and energy costs over the assessment period are given by:
where:
- σ e(t):
-
Energy end-use-to-primary conversion factor \( \left( {{\sigma_e}(t) \geqslant 1} \right) \) Footnote 62
- θ e,g(t):
-
Emission factor of pollutant g in the energy supply chain.
Result metrics
Equations (36) to (39) should be evaluated for the base-case scenario and the policy measure-case scenario. Using solutions to these equations, the assessment metrics for a policy measure pol are evaluated from the following equations:
where:
and:
- vpol :
-
Present value of all monetary incentives provided by the policy measureFootnote 63
- mEmissiong :
-
Present value of the monetized annual emissions reduction of pollutant g provided by the policy measure
Appendix 3: Estimating market penetration from financial incentives
This appendix details how to use the interpolated market penetration curves described by Blum et al. (2011, Appendix A) to estimate the increase in market penetration resulting from a monetary incentive according to the market barrier level and the benefit/cost ratio provided by the incentive. For a given pair of market barrier level and benefit/cost ratio (\( {\text{b}}_{\text{c}}^{{*}},{\text{b}}{{\text{c}}^{{*}}} \)). the market implementation \( {{imp}}\left( {{\text{b}}_{\text{c}}^{{*}},{\text{b}}{{\text{c}}^{{*}}}} \right) \) is given by:
where r c , fit c , mid c , and max c are piecewise linear functions that interpolate these parameters between their corresponding values in the five reference curves developed by Rufo and Coito (2002).
Equation (44) provides an estimate of the market penetration of devices with a certain efficiency level according to the level of market barriers to their dissemination (b*) and their benefit/cost ratio to consumers (bc).Footnote 64 In the benefit/cost ratio, benefit refers to the energy cost savings that consumers will enjoy from using an energy-efficient device, and cost includes the likely greater purchase cost and possible differencesFootnote 65 in installation and non-energy-related operational costs associated with the energy-efficient equipment. A financial incentive will reduce the higher costs of the more efficient technology and thus increase the benefit/cost ratios of models covered by the policy measure. With a greater benefit/cost ratio, the market penetration of those devices will increase. The dynamic increase in market penetration m(t) of models at the target level can be estimated from:
where:
and:
- m(t):
-
Market penetration in the policy measure-case scenario of models at the target level shipped in t
- λ(t):
-
Market penetration adjusting factorFootnote 66 (\( 0 < \lambda (t) \leqslant 1 \))
- b*:
-
Market barriers level, estimated such that\( \text{\text{imp}}\left( {{b^{*}},{\text{b}}{{\text{c}}_{\text{bc}}}(t)} \right) = \frac{{{\text{sh}}{{\text{p}}_{{{\text{bc}},{i^{*}}}}}(t)}}{{\mathop{\sum }\nolimits_i {\text{sh}}{{\text{p}}_{{{\text{bc}},i}}}(t)}} \)
- bcpol(t):
-
Benefit/cost ratio of dev i* shipped in t in the policy measure-case scenario
- bcbc(t):
-
Benefit/cost ratio of dev i* shipped in t in the base-case scenario
- incB(t):
-
Incremental energy costs savings of dev i* shipped in t
- incCbc(t):
-
Incremental equipment costs, in the base-case scenario, of dev i* shipped in t
- eCosti(t):
-
Lifetime energy cost of dev i shipped in t
- qCosti(t):
-
Lifetime equipment costs of dev i shipped in t
Appendix 4: estimating market penetration from informational incentives
In the following, we detail how to use the interpolated market penetration curves proposed by Blum et al. (2011, Appendix A) to estimate the dynamic increase in market penetration resulting from information incentives. The estimate relies on the benefit/cost ratio provided by the design options targeted by the incentives and in the change in the market barriers level promoted by the measure. It also relies on historical data from earlier informational incentive programs focusing the targeted or any similar equipment. For each year y′ for which historical market penetration data are available, the relative change in market barrier level \( b_{{{\text{w/wo}}}}^{\prime }\left( {{y^{\prime }}} \right) \) can be estimated using the ratio between the levels of market barriers with and without Footnote 67 the earlier program:
Footnote 68where \( \text b_{\text{wo}}^{\prime }\left( {{y^{\prime }}} \right) \) and \( \text b_{\text{w}}^{\prime }\left( {{y^{\prime }}} \right) \) are such that:
and:
- \( \text b_{{{\text{w/wo}}}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Relative change in the level of market barriers in year y′ due to the earlier program
- \( \text b_{\text{wo}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Market barriers level in year y′ without the earlier program
- \( \text b_{\text{w}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Market barriers level in year y′ with the earlier program in place
- \( \text{\text{b}}{{\text{c}}^{\prime }}\left( {{y^{\prime }}} \right) \) :
-
Benefit/cost ratio in year y′ of models covered by the earlier programFootnote 69
- \( \text{\text{shp}}_{\text{wo}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Shipments in year y′ of models covered by the earlier program without the program
- \( \text{\text{shp}}_{\text{w}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Shipments in year y′ of models covered by the earlier program with the program in place
- \( \text{\text{shp}}_{\text{tot}}^{\prime }\left( {{y^{\prime }}} \right) \) :
-
Total shipments in year y′ of units from the product classes covered by earlier program
The historical change in market barriers expressed by \( b_{{{\text{w/wo}}}}^{\prime }\left( {{y^{\prime }}} \right) \) can be used to estimate a pattern in the market transformation that the policy measure under study is likely to promote. Let \( M(t) \) be a function that transforms the historical market barrier changes stimulated by the earlier program into market barrier changes forecast for the policy measure under study. The increase in market penetration for the new policy measure can then be estimated from:
where:
- m′(t):
-
Market penetration increase fostered by policy measure
- bc(t):
-
Average benefit/cost ratio of models covered by policy measure and shipped in t
- bbc(t):
-
Base-case scenario market barriers level in t
- bpol(t):
-
Policy measure-case scenario market barriers level in t.
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Blum, H., Atkinson, B. & Lekov, A.B. A methodological framework for comparative assessments of equipment energy efficiency policy measures. Energy Efficiency 6, 65–90 (2013). https://doi.org/10.1007/s12053-012-9162-x
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DOI: https://doi.org/10.1007/s12053-012-9162-x