Eq. (28) suggests that economy-wide abatement is likely to arise where the characteristics of the economy allow the fourth term to dominate. This occurs when the demands for fuels and technologies’ outputs are both highly inelastic, fuel prices are relatively unresponsive to efficiency improvements, technology options subject to efficiency mandates constitute a substantial share of aggregate emissions, and opportunity costs are large. As before, the factors 1 _/ηθ_ suggest that abatement becomes progressively less feasible as the level of energy efficiency increases.

# **3 Application: Energy-Efficiency Improvements in Georgia and Armenia**

## **3.1 Background**

Improving efficiency is central to energy policy in both Armenia and Georgia, which have developed national programs to implement energy efficiency measures in major energy consuming sectors such as buildings, industries and transportation.<sup>6</sup> Both countries depend on imports to meet domestic demand for petroleum and natural gas, which together account for more than two-thirds of their total primary energy supply, and are major sources of GHG emissions and local air pollution. Large-scale deployment of energy efficient technologies remains limited in the private sector and relies on an array of government supports, raising the question of how to allocate scarce funding across technologies and industries. Thus far, the basis for making these decisions has been engineering bottom-up analysis, in particular evaluation of technology options by Sikharulidze et al (2015) and Timilsina et al (2015). In the remainder of the paper we apply the framework developed in section 2 to introduce discrete technology options into a dynamic CGE model for the purpose of assessing the broader impacts on Armenian and Georgian economies.

## **3.2 Bottom-Up Analysis**

Table 1 summarizes the slate of technology options considered by Sikharulidze et al (2015) and Timilsina et al (2015) and gives their distribution across sectors and countries.<sup>7</sup> The industries identified therein (Food Products, Chemicals, Rubber & Plastics, Iron & Steel, Non-metallic Minerals, Other Industry, Services and Private Dwellings) are referred to as “technology-rich” to distinguish them from other sectors in which discrete technology options are not represented. MAC curves developed using eqs. (1)-(8) are shown in panel A of Figure 1. Their striking feature is the overwhelming preponderance of no-regrets options, primarily 6Armenia’s 2005 Renewable Energy and Energy Efficiency law and subsequent national program on energy saving and renewable energy led to the creation of the energy efficiency action plan, which includes measures such as new construction codes for building energy, provision of efficient public lighting, monitoring of building energy consumption and savings, and industrial heating systems improvement. Georgia is developing a low carbon strategy for economic development while promoting economic growth and prosperity, of which a key component is energy efficiency improvement.

> 7Data consist of ( _lθ, l∗θ_<sup>),(</sup><sup>_hθ, h∗_</sup> _θ_<sup>),(</sup><sup>_φe,θ, φ∗_</sup> _e,θ_<sup>)and</sup><sup>_εe,θ_andassumptionsabout</sup> _PF e_ , _⟨xθ, x_<sup>_∗_</sup> _θ_<sup>_⟩_andthe</sup> discount rate (7.5%), which is used to calculate _ρ_ .

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