options within the framework of smooth cost and expenditure functions customarily used to represent the envelope of firms’ and households’ substitution possibilities. This challenge has spawned the development of hybrid bottom-up/top-down modeling approaches which represent discrete mitigation options as production functions that generate GHG-free energy (abated emissions) from inputs of GHG-intensive fossil fuels (unabated emissions) and technology-specific abatement capital. Recent methodological advances by Kiulia and Rutherford (2013a,b) have developed procedures to derive the underlying abatement potentials and capital input requirements from MAC curves, but substantial calibration effort is required, and the resulting computational scheme does little to elucidate which options have the greatest potential for capital-fuel substitutability and market penetration, or the macroeconomic impacts of deploying different technology packages.

In this study the specific question we address is deceptively simple: can developing countries pursue GHG mitigation without slowing down their expected economic growth, and if so does significant abatement result? To provide answers we first develop a theoretical framework that unifies stage (i), (ii) and (iii) analyses by consistently tying together the underpinnings of MAC curves, bottom-up techno-economic simulation modeling, and CGE modeling. We then apply this framework to bottom-up engineering assessments of technology deployment for improving energy efficiency in Armenia and Georgia, drawing on detailed data developed by Sikharulidze et al (2015) and Timilsina et al (2015). We build on our theoretical framework to develop a methodology for incorporating bottom-up technology detail on energy-efficiency improvements into top-down CGE model, which allows us to estimate the economy-wide economic costs and mitigation benefits of technology deployment policies in our two case-study countries.

Surprisingly, we find that low-carbon growth trajectories are feasible in both Armenia and Georgia, and that it is possible for them to offset similar fractions of their baseline CO2 emissions (up to 4%) while enjoying slight increases in GDP, despite significant differences in the scope of technological opportunities for energy efficiency improvement. The major divergence in outcomes is the extent of the productivity and GDP increase over baseline levels, which can be as large as 1% in Armenia, but only 0.2% in Georgia. Our results demonstrate how the exogenous trajectories of efficiency penetration that underlie the construction of bottom-up MAC curves can paint a misleading picture of the true potential for both abatement and economic growth when these technological changes operate within a system of general equilibrium interactions. But they also highlight how the use of the data that underpins MAC curves— _not_ the curves themselves—to identify energy efficiency improvement options with high cost elasticities of energy productivity can facilitate assessments of the macroeconomic consequences of technology strategies for low-carbon growth.

The remainder of the paper is divided into four sections. Our theoretical framework is developed in section 2. Section 3 introduces our bottom-up Armenia and Georgia case studies. The results of numerical experiments with the CGE model are presented and discussed in section 4. Section 5 discusses caveats to the analysis and future research directions, and offers concluding remarks.

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