In response to global calls for climate change mitigation, many countries, especially in the developing world, have considered pursuing policies that can help reduce greenhouse gas (GHG) emissions and also ensure additional economic benefits. Accelerating the adoption of energy efficient technologies is one of the main options as it may help reduce consumers’ spending on energy besides reducing GHG emissions. Indeed, the International Energy Agency estimates that energy efficiency improvements could contribute half of the total abatement required to keep temperature increases below 2°C from the pre-industrialized level.
Marginal abatement cost curves (MACCs) are the principal analytical tool used to evaluate the cost effectiveness of climate change mitigation options. MACCs describe the relationship between the potential GHG reductions from a set of discrete technologies or policy options and their unit abatement costs. Such analysis is referred to as “bottom-up” or “partial equilibrium” because of its narrow focus on the specific measure (e.g., adoption energy efficiency electricity appliances) in isolation assuming that the measure would have no effect elsewhere in the economy. In reality, a GHG mitigation measure, such as large-scale energy efficiency improvement program, would likely impact relative prices and therefore multiple economic sectors, generating economy-wide consequences that might diverge from the predictions of partial equilibrium analysis. A key concern is that a bottom-up or partial equilibrium analysis might lead to overestimation of the GHG mitigation potential of energy efficiency options and/or underestimation of associated costs. One reason is that a bottom-up or partial equilibrium analysis does not account for “rebound effect” whereby energy efficiency improvements make consumption of energy services cheaper, which can induce firms and households to consume more energy, dampening net energy savings and GHG abatement.
A related effect is “leakage”, which occurs when a subset of the economy’s sectors are targeted for efficiency improvement and reduce their energy consumption, but this triggers a decline in energy prices that creates an incentive for non-targeted sectors to increase their fossil fuel use and emissions. Moreover, MACC analysis assumes that the entire GHG mitigation potential of each technology option is achievable at constant marginal cost. But energy users may not necessarily adopt efficient technologies to the extent shown by MACCs, even if the predicted energy cost savings exceed the incremental direct investment costs of adoption, because of opportunity costs not captured by the analysis or unintended increases in the prices of capital goods induced by policies to stimulate large-scale energy efficiency improvements. Thus, an energy efficiency improvement option attractive from the perspective of the MACC would not necessarily achieve the same level of GHG mitigation if evaluated from a broader or economy-wide perspective.
To overcome these limitations and avoid potentially misleading policy conclusions, a recent WB working study by Ian Sue Wing and Govinda Timilsina developed a technique to link the bottom-up MACC analysis to economy-wide top-down computable general equilibrium (CGE) analysis. The technique was then applied in two countries: Armenia and Georgia.
First, analysis by Govinda Timilsina, Anna Sikharulidze, Eduard Karapoghosyan and Suren Shatvoryan used engineering cost analysis to develop MACCs for energy efficiency improvements in the building sector in Armenia and Georgia. Technology options included were improved wall, window and roof insulation, energy efficient lighting, energy efficient heating, ventilation and air conditioning systems, energy efficient refrigerators. The key step involved to link the bottom-up MACC analysis with the top-down CGE model is to employ the MACC results to derive each option’s elasticity of energy saving with respect to its markup over the cost of the conventional technology that it was targeted to replace. The resulting elasticity parameters were combined with stylized representations of their corresponding technologies in a hybrid bottom-up/top-down CGE model, which was then used to simulate the economic consequences of technology mandates, represented by progressively stringent efficiency goals and concomitant markups.
A comparison of the findings of the two approaches reveals some interesting insights. The bottom-up MACC analysis generates only the volume of GHG mitigation from different GHG mitigation options and the corresponding costs at which these quantities of mitigation can be achieved, while the top-down CGE model generates more detailed information. First, it showed the actual abatement from technology mandates for GHG mitigation would be significantly smaller (up to 40 percent) than predicted by the bottom-up analysis, owing to intra-sectoral rebound and inter-sectoral leakage effects. Second, and surprisingly, technology mandates are economically beneficial up to certain threshold, beyond which further efficiency increases impose a drag on output and welfare. This key result arises because the broader productivity benefits of higher energy efficiency are progressively outweighed by the growing opportunity costs of diverting capital away from production toward efficiency improvements that are relatively more expensive. Moreover, because of how energy efficiency investments are distributed across economic sectors, some industries (construction, non-metallic minerals) expand but others, particularly energy industries, contract.
It can be concluded from the two studies that GHG mitigation analysis based on bottom-up models needs to be supplemented by analysis with top-down models. The results coming from a hybrid model that combines both bottom-up and top-down approaches does not only help to precisely estimate the GHG mitigation potential of a measure, but it also brings much more information that would be helpful to policy makers to prioritize different measures and options to combat climate change.
Marginal abatement cost curves (MACCs) are the principal analytical tool used to evaluate the cost effectiveness of climate change mitigation options. MACCs describe the relationship between the potential GHG reductions from a set of discrete technologies or policy options and their unit abatement costs. Such analysis is referred to as “bottom-up” or “partial equilibrium” because of its narrow focus on the specific measure (e.g., adoption energy efficiency electricity appliances) in isolation assuming that the measure would have no effect elsewhere in the economy. In reality, a GHG mitigation measure, such as large-scale energy efficiency improvement program, would likely impact relative prices and therefore multiple economic sectors, generating economy-wide consequences that might diverge from the predictions of partial equilibrium analysis. A key concern is that a bottom-up or partial equilibrium analysis might lead to overestimation of the GHG mitigation potential of energy efficiency options and/or underestimation of associated costs. One reason is that a bottom-up or partial equilibrium analysis does not account for “rebound effect” whereby energy efficiency improvements make consumption of energy services cheaper, which can induce firms and households to consume more energy, dampening net energy savings and GHG abatement.
A related effect is “leakage”, which occurs when a subset of the economy’s sectors are targeted for efficiency improvement and reduce their energy consumption, but this triggers a decline in energy prices that creates an incentive for non-targeted sectors to increase their fossil fuel use and emissions. Moreover, MACC analysis assumes that the entire GHG mitigation potential of each technology option is achievable at constant marginal cost. But energy users may not necessarily adopt efficient technologies to the extent shown by MACCs, even if the predicted energy cost savings exceed the incremental direct investment costs of adoption, because of opportunity costs not captured by the analysis or unintended increases in the prices of capital goods induced by policies to stimulate large-scale energy efficiency improvements. Thus, an energy efficiency improvement option attractive from the perspective of the MACC would not necessarily achieve the same level of GHG mitigation if evaluated from a broader or economy-wide perspective.
To overcome these limitations and avoid potentially misleading policy conclusions, a recent WB working study by Ian Sue Wing and Govinda Timilsina developed a technique to link the bottom-up MACC analysis to economy-wide top-down computable general equilibrium (CGE) analysis. The technique was then applied in two countries: Armenia and Georgia.
First, analysis by Govinda Timilsina, Anna Sikharulidze, Eduard Karapoghosyan and Suren Shatvoryan used engineering cost analysis to develop MACCs for energy efficiency improvements in the building sector in Armenia and Georgia. Technology options included were improved wall, window and roof insulation, energy efficient lighting, energy efficient heating, ventilation and air conditioning systems, energy efficient refrigerators. The key step involved to link the bottom-up MACC analysis with the top-down CGE model is to employ the MACC results to derive each option’s elasticity of energy saving with respect to its markup over the cost of the conventional technology that it was targeted to replace. The resulting elasticity parameters were combined with stylized representations of their corresponding technologies in a hybrid bottom-up/top-down CGE model, which was then used to simulate the economic consequences of technology mandates, represented by progressively stringent efficiency goals and concomitant markups.
A comparison of the findings of the two approaches reveals some interesting insights. The bottom-up MACC analysis generates only the volume of GHG mitigation from different GHG mitigation options and the corresponding costs at which these quantities of mitigation can be achieved, while the top-down CGE model generates more detailed information. First, it showed the actual abatement from technology mandates for GHG mitigation would be significantly smaller (up to 40 percent) than predicted by the bottom-up analysis, owing to intra-sectoral rebound and inter-sectoral leakage effects. Second, and surprisingly, technology mandates are economically beneficial up to certain threshold, beyond which further efficiency increases impose a drag on output and welfare. This key result arises because the broader productivity benefits of higher energy efficiency are progressively outweighed by the growing opportunity costs of diverting capital away from production toward efficiency improvements that are relatively more expensive. Moreover, because of how energy efficiency investments are distributed across economic sectors, some industries (construction, non-metallic minerals) expand but others, particularly energy industries, contract.
It can be concluded from the two studies that GHG mitigation analysis based on bottom-up models needs to be supplemented by analysis with top-down models. The results coming from a hybrid model that combines both bottom-up and top-down approaches does not only help to precisely estimate the GHG mitigation potential of a measure, but it also brings much more information that would be helpful to policy makers to prioritize different measures and options to combat climate change.
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