plementation, which dramatically complicates the specification of efficiency improvements as endogenous relationships between additional current investment (the intertemporal analogue of _μ_ ) and declines in the values of different technologies’ coefficients on energy inputs in subsequent periods (the intertemporal analogue of _η_ ).

For all of these reasons we opt for the simpler approach of specifying opportunity costs not through the investment channel, but through intra-period endogenous transformation of the economy-wide stock of jelly capital into quantities of technology-specific capital input, following eq. (19). In each period the economy is endowed with a fixed quantity of jelly capital which is capable of being re-allocated across the various non-energy sectors as the relative prices of their inputs and outputs change. By Shephard’s Lemma, the derivative of the cost function represented by Figure 2.B with respect to the price of technology-specific capital determines the unconditional demand for this input, as well as the extent of transformation of jelly capital into technology capital that must generate the supply necessary for market clearance. It is this transformation process which mandates to improve energy efficiency are assumed to distort. Additional technology deployment policies such as subsidies may be introduced here as well, but for the purposes of this study we narrow the focus to mandates because their ultimate economic and environmental impacts are transparent, and uncontaminated by tax interaction effects associated with pre-existing distortions that tend to be more common in developing countries.

The model is spun up from the GTAP database’s year-2008 benchmark to 2015 by scaling regions’ endowments of labor and capital to generate increases in GDP consistent with IMF (2014) growth forecasts. In the 2015 base year technology options are introduced as “backstop” activities by recalibrating the model to accommodate both their demands for capital and fuels given in Figure 1.C, and corresponding demands for their outputs by the remainder of their encompassing sectors, per Figure 2.A. The upshot of this modeling choice is that the levels of base-year demand and activity solved for by the model diverge from the bottom-up values in Figure 1.C, with some options exhibiting large increases (e.g., mechanical processes in Georgia’s food products sector: +42%) or decreases (appliances in private dwellings in Armenia: -66%), but most coming in at between 80% and 110% of their target values.

From 2015 onward the model is simulated with discrete technologies active. In the baseline solution, technology options have no cost premium over jelly capital ( _μθ_ = 0), and capacity is allocated endogenously, in line with the capital intensities and relative rates of growth of the output of the various technology-rich sectors. Apropos the discussion in section 2.3, the model is not capable of selecting the optimal portfolio of energy-efficiency investments by endogenously adjusting the vector _μθ_ in each period. Rather, the markups are prescribed by the analyst.<sup>10</sup> We conduct several counterfactual numerical experiments in which we model technology mandates as an exogenously-imposed increase in technologies’ capital cost markups. Specifically, we assume that (1 + _μθ_ ) increases from a value of unity in 2015 at 1%, 2%, 3%, 5%, 7% and 10% per year for all technologies simultaneously. In addition to treating technology options with equanimity, we also investigate the impact of

> 10Note that if it were possible to solve for the optimal baseline portfolio, GDP improvement would be impossible unless there were additional new technologies that were somehow only available in the counterfactual scenario.

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