metadata: authors: 0: author: Sue Wing,Ian Newel James
1: author: Timilsina,Govinda R.
count: World,Georgia
docna: 0: docna: Technology strategies for low-carbon
            economic growth : a general equilibrium assessment
docty: Policy Research Working Paper
subtopic: Climate Change Mitigation and Green House Gases,Economic Theory & Research,Industrial Economics,Energy Policies & Economics,Economic Growth,Energy Demand,Energy and Environment,Energy and Mining
teratopic: Energy,Environment
abstracts: cdata!: This paper investigates the potential
            for developing countries to mitigate greenhouse gas
            emissions without slowing their expected economic growth. A
            theoretical frame- work is developed that unifies bottom-up
            marginal abatement cost curves and partial equilibrium
            techno-economic simulation modeling with computational
            general equilibrium (CGE) modeling. The framework is then
            applied to engineering assessments of energy efficiency
            technology deployments in Armenia and Georgia. The results
            facilitate incorporation of bottom-up technology detail on
            energy-efficiency improvements into a CGE simulation of the
            economy-wide economic costs and mitigation benefits of
            technology deployment policies. Low-carbon growth
            trajectories are feasible in both countries, enabling
            reductions of up to 4 percent of baseline emissions while
            generating slight increases in GDP (1 percent in Armenia and
            0.2 percent in Georgia). The results demonstrate how MAC
            curves can paint a misleading picture of the true potential
            for both abatement and economic growth when technological
            improvements operate within a system of general equilibrium
            interactions, but also highlight how using their underlying
            data to identify technology options with high opportunity
            cost elasticities of productivity improvement can lead to
            more accurate assessments of the macroeconomic consequences
            of technology strategies for low-carbon growth.
geo_reg_key: 517191,517208,517198,82689
repnme: repnme: Technology strategies for low-carbon
            economic growth : a general equilibrium assessment
display_title: Technology strategies for low-carbon
            economic growth : a general equilibrium assessment
listing_relative_url: /research/2016/07/26561773/technology-strategies-low-carbon-economic-growth-general-equilibrium-assessment
topicv3: Economic Growth m1326241 471,Energy and Extractives m1326595 507,Climate Change Mitigation m1327079 579,Power and Electricity Sector m1327094 515,Iron m1503514 2931
publishtoextweb_dt: 2016-07-11T00:00:00Z
historic_topic: Energy,Environment
no_of_pages: 33
author_keys: 0: author_key: 1255693
1: author_key: 940446
model_extractions: WPS7742 Policy Research Working Paper 7742 Technology Strategies for Low-Carbon Economic Growth A General Equilibrium Assessment _Ian Sue Wing Govinda Timilsina_ Development Research Group Environment and Energy Team July 2016
Policy Research Working Paper 7742

# **Abstract**

This paper investigates the potential for developing countries to mitigate greenhouse gas emissions without slowing their expected economic growth. A theoretical frame- work is developed that unifies bottom-up marginal abatement cost curves and partial equilibrium techno-economic simulation modeling with computational general equilibrium (CGE) modeling. The framework is then applied to engineering assessments of energy efficiency technology deployments in Armenia and Georgia. The results facilitate incorporation of bottom-up technology detail on energy-efficiency improvements into a CGE simulation of the economywide economic costs and mitigation benefits of technology deployment policies. Low-carbon growth trajectories are feasible in both countries, enabling reductions of up to 4 percent of baseline emissions while generating slight increases in GDP (1 percent in Armenia and 0.2 percent in Georgia). The results demonstrate how MAC curves can paint a misleading picture of the true potential for both abatement and economic growth when technological improvements operate within a system of general equilibrium interactions, but also highlight how using their underlying data to identify technology options with high opportunity cost elasticities of productivity improvement can lead to more accurate assessments of the macroeconomic consequences of technology strategies for low-carbon growth.

This paper is a product of the Environment and Energy Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at gtimilsina@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Technology Strategies for Low-Carbon Economic Growth: A General Equilibrium Assessment<sup>_∗_</sup> Ian Sue Wing<sup>1</sup> and Govinda Timilsina<sup>2</sup>

> 1Dept. of Earth & Environment, Boston University

> 2Development Research Group, World Bank

JEL classification: C68, Q48, Q54, Q55, Q65 Keywords: Static Computable General Equilibrium, Green Growth and Sustainability, Low Carbon Development, Climate Change Mitigation, Energy Efficiency

> _∗_ Comments by Jan Gaska and Mun Ho are gratefully acknowledged. The authors also acknowledge the financial support from Knowledge for Change (KCP) Trust Fund of the World Bank.
# **1 Introduction**

Mitigating global emissions of greenhouse gases (GHGs) to levels that avert the threat of dangerous climate change requires the active participation of developing nations. Projected 21<sup>st</sup> century economic growth and population expansion in non-OECD countries are associated with substantial increases in GHG emissions and radiative forcing of climate which cannot be completely offset by GHG abatement in the developed world—even if the latter countries stop emitting entirely (Jacoby et al, 1997). This state of affairs has led to increasing calls for industrializing countries to take on binding emission reduction commitments. Thus far, developing nations have generally been reluctant to accept abatement targets, citing resource constraints that limit their ability to lift their burgeoning populations out of poverty, let alone incur the opportunity costs of curtailing emissions. These constraints, developing countries argue, mean that that they will only be able to pursue substantive emission reductions if the necessary technology and financial resources are provided by industrialized countries—an issue which is at the crux of ongoing international climate negotiations. A temporary compromise solution is for developing countries to identify and pursue activities that slow the growth rate of their emissions, but do not hinder their expected economic growth.

Assessing the viability of such “low-carbon economic growth” strategies is the objective of this paper. As noted by Jacoby et al (1998), Steward and Weiner (2001) and Aldy et al (2003) among others, the goal is to allow for a transition path in which developing countries have the “headroom” to continue economic expansion over the next two or three decades, but with declining rates of increase in emissions. For low-carbon growth to be feasible there must be activities which avoid GHG emissions but also provide “win-win” economic benefits, and large-scale deployment of energy efficient devices and processes are of particular interest in this regard (Hallegate et al, 2011; Rozenberg et al, 2013). The latter investments are typically more costly compared with conventional technology, but they enable firms and households to use less fossil energy, simultaneously lowering emissions and generating offsetting savings on energy and other operational costs. Crucially, since a key benefit of these cost savings is improved productivity at the micro-level, sufficiently widespread deployment of efficiency-enhancing technology within a country could in principle generate an aggregate gross productivity gain. And if, in turn, the resulting stimulus to the growth of output at the macro level is large enough that it exceeds the economy-wide opportunity costs of additional expenditure on energy-efficiency investments, the result will be a net increase in GDP.

How might such an auspicious outcome arise? Simple intuition suggests that the largest potential for win-wins should come from intensive investment in technology options which have a high elasticity of energy efficiency improvement with respect to the incremental cost of their acquisition. Engineering assessments (e.g., McKinsey & Co., 2010) are the key data source on technologies’ performance and direct cost characteristics which allow us to identify such options. But these assessments frequently uncover what appear to be large “energy efficiency gaps” in which technical potentials for efficiency remain unrealized due to factors such as imperfect information or institutional constraints, which cause economic actors to systematically overlook making energy efficiency investments that are privately 1
profitable.<sup>1</sup> By contrast, microeconomic analyses contend that the actual magnitude of the energy efficiency gap is small because engineering studies suffer from problems of potentially substantial unobserved or mis-measured ancillary costs and/or benefits, uncertainty in projections of the net present value of future energy cost savings, and heterogeneity among agents in the returns to energy efficiency investments (Allcott and Greenstone, 2012).

Policy makers thus face three challenges: (i) identifying putatively negative net cost—socalled “no-regret”—technology options whose savings exceed the corresponding investment cost premia at prevailing capital and energy prices, (ii) estimating these options’ market penetration potential and attendant fossil fuel/GHG emission savings based on economic as well as technical fundamentals, and (iii) assessing the implications for emissions, output and welfare at the level of the macroeconomy. Addressing these is a tall order, principally because of the difficulty reconciling often incommensurate results of engineering and economic calculations across three disparate sets of analyses. This is the essence of the “bottom-up” versus “top-down” divide in the climate policy literature.

The majority of analyses culminate at stage (i), developing marginal abatement cost (MAC) curves that rank, using engineering cost assessments and discounted cash flow analysis, various technology options or activities based on their net costs and avoided GHG emissions. MAC curves are attractive because they are easy to construct and bring to light activities that are profitable from the perspective of private actors investing in an individual technology. However, such analyses often fail to adequately account for the true opportunity cost of investments in technologies. They also ignore the potential substitutability or complementarity among different technologies which determines the latter’s market penetration. Of concern are unforeseen interdependencies among mitigation options’ cost or technical potential that render the shape of the MAC curve endogenous, where the width or height of one segment on the curve can influence the width or height of other segments, and their joint marginal-cost ranking.

Stage (ii) analyses are able to account for potential interdependencies through the use of partial equilibrium techno-economic process or activity analysis models. The latter incorporate data on the cost and energy use characteristics of a broad array of discrete technology options, and solve for the minimum cost vector activity-specific capacities and associated emission reductions in one or more economic sectors. But these models omit interactions between the specific technnologies and the broader economy—especially the effects of changes in demand on fuel prices and capital rental rates—and must often employ ad-hoc constraints to smooth activities’ time-paths of penetration or exogenously their technical potentials. It is here that the institutional or market barriers discussed above come in. The latter raise the issue of why a country might need to pursue low-carbon technology deployment policies in the first place—in other words, why the investment decisions of decentralized profit-maximizing firms would diverge from the command optimum solved for by the model, and how the resulting gap might be closed by policy interventions such as subsidies or mandates.

Systematic accounting for the web of spillover effects is the strength of computable general equilibrium (CGE) simulations, which are the principal tool for conducing stage (iii) analyses of the macroeconomic costs of GHG mitigation policies. However, these top-down economic models struggle to incorporate the detailed characteristics of discrete technology

> 1Allcott and Greenstone (2012) label these “investment inefficiencies”.

2
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.

3
~~ne~~ ye|
the world without mitigation. Now introduce a tilde over a variable to indicate the counterfactual imposition of emission mitigation, with new technology trajectories ( _x_ � _θ_ , _x_ �<sup>_∗_</sup> _θ_<sup>)such</sup> � � that _xθ ≤ xθ_ and _x_<sup>_∗_</sup> _θ_<sup>_>x∗_</sup> _θ_<sup>.Theimpactoftheresultingsequenceofinstantaneouscostand</sup> emissions can be summarized in the same way as (5) and (6): _C_<sup>�</sup> _θ_ =<sup>�</sup><sup>_T_</sup> _t_ =0<sup>_βtcθ_[</sup><sup>_x_�</sup><sup>_θ,t,_�</sup><sup>_x∗_</sup> _θ,t_<sup>] ≷</sup><sup>_Cθ_</sup> and _Z_<sup>�</sup> _θ_ =<sup>�</sup><sup>_T_</sup> _t_ =0<sup>_βtzθ_[</sup><sup>_x_�</sup><sup>_θ,t,_�</sup><sup>_x∗_</sup> _θ,t_<sup>]</sup><sup>_< Zθ_.ConstructingaMACcurvefromthebaselineandcoun-</sup> terfactual versions of (5) and (6) is a straightforward procedure. For each technology option, abatement ( _A_ ) and its average cost ( _τ_ ) are simply and all that is necessary is to rank-order elements of _θ_ from low to high values of _τ_ and plot their coordinates in _τ_ - _A_ space.

# **2.2 No Regret Potentials and Capital-Fuel Substitution**

Our exposition renders transparent how negative-cost MAC curve segments arise. These are called “no-regret” options because they are no opportunity costs to pursuing the requisite investments—at least in this partial equilibrium setting. The high-efficiency variety’s larger acquisition costs increase capital charges ( _v_ � _θ > vθ_ ) at the same time as its lower fuel intensities reduce fuel expenditures ( _f_<sup>�</sup> _e,θ < fe,θ_ ). When the latter outweighs the former, instantaneous total cost dips below its baseline level (� _cθ,t < cθ,t_ ); if such a divergence is sufficiently large and persistent the numerator of (8) will be negative. Such behavior emerges out of the parameter combinations in curly braces in eqs. (3) and (4), and their interaction with scenarios of energy-efficient technology penetration _⟨xθ_ , _x_<sup>_∗_</sup> _θ_<sup>_⟩_versus</sup><sup>_⟨x_�</sup><sup>_θ_,</sup><sup>_x_�</sup><sup>_∗_</sup> _θ_<sup>_⟩_.</sup> It should also be clear that the force driving the technology dynamics that give the MAC curve its shape is instantaneous substitution of capital for fuel. This is apparent from (3), which can be rearranged to yield Here, _π_<sup>_F_</sup> is the baseline share of fuel in the technology’s operating cost, _π_ denotes the implicit average price of emissions (i.e., the total value of fuel use divided by the quantity of emissions), and the terms on the right-hand side are the (increasing) capital and (declining) fuel components of cost.<sup>2</sup> Total costs decline if the second component exceeds the first, which occurs where the high-efficiency alternative’s capital cost premium relative to the conventional is small but its fuel use and emissions are much lower, and/or fuel constitutes a large share of total operating cost. Here, however, our interest is different. We focus not on overall cost, but on the ability of capital to substitute for energy, indicated by the elasticity

> 2Note that it is always the case that _π_ � _z_ � _< πz_ , making the second term _<_ 1.

5
# **2.3 Beyond Scenarios: Endogenous Energy Efficiency Improvement**

Two features of foregoing analysis highlight the need for transformation of the results of bottom-up assessments in order to evaluate mitigation options’ market penetration potential. Engineering cost calculations implicitly treat the quantity and character of output of the various options as constant, which may not be the case if high-efficiency alternatives are imperfect substitutes for conventional varieties. More problematic is that costs and abatement hinge on exogenous assumptions about the time-path of investment in the highefficiency alternative. Ceteris paribus, a technology with a more (less) aggressive trajectory will exhibit larger (smaller) discounted costs and total emissions, but due to the geometric character of discounting the result will vary in a nonlinear fashion, with implications for mitigation options’ average cost ranking and the overall shape of the MAC curve.

Following from these observations, endogenizing technologies’ abatement potential and cost requires one key additional ingredient: specifying output in the form of flows of services from the conventional and energy efficient varieties, indicated by _s_ ( _s_<sup>_∗_</sup> ). It is simplest to assume that production is Leontief with a fixed capital coefficient _κ_ ( _κ_<sup>_∗_</sup> ), and that both supplies are perfect substitutes that fulfill an assumed exogenous time-path of aggregate _S_ service demand _D_<sup>whichpresumablyincreaseswithprojectedgrowthoftheeconomy.In</sup> _θ,t_<sup>,</sup> the counterfactual scenario which is of interest here, the resulting supply-demand balance constraint is We may then solve for the capacity trajectories by minimizing system cost while satisfying demand: This capacity expansion problem is a linear program which is at the heart of virtually all bottom-up techno-economic models.

An unpleasant feature of (12) is that capacity may exhibit unrealistic “bang-bang” behavior, switching over completely from the conventional to the energy-efficient variety in the space of one or two time-steps. This stems from the fact that there is no meaningful constraint on technology-specific capital, which in this framework is perfectly elastically supplied at the exogenous cost of capital acquisition. A common means of ensuring smooth market penetration dynamics is to augment the problem with ad-hoc expansion and decline constraints, <u>(</u> _<u>γ, γ</u>_ <u>)</u> and <u>(</u> _<u>γ∗, γ</u>_<sup>_~~∗~~_</sup> ), based on technology options’ service lives, e.g. (cf Vogt-Schilb et al, 2014), Yet, the question this remedy raises is what are the economic processes out of which these constraints arise. Evidence abounds that the true opportunity cost of energy efficiency investments—even those clearing a net profit—can be substantial (Anderson and Newell, 2004), and can increase as more and more output is forgone with progressive diversion of 6
capital away from conventional production and toward lower-return energy saving activities. These additional costs are the true hurdle that the savings from energy efficiency improvements must clear, and the best way to account for them is a general equilibrium framework.

# **2.4 General Equilibrium Implications**

The main thrust of the paper is to model the outcome of eq. (12) without explicitly representing the procedural details of capacity adjustment. Accordingly, we develop a deliberate simple approach that does not distinguish conventional and energy efficient varieties, but instead seeks to bring the effect of penetration of the latter on efficiency improvement within the ambit of top-down production and cost functions.

Each technology is modeled as a monolithic, constant returns to scale technology that produces a flow of services (with quantity _QS_ and price _PS_ ) from inputs of a vector of fuels and technology-specific capital (with quantities _QFe_ and _QX_ , and prices _PFe_ and _PX_ , respectively). The top-down analogue of eq. (3) is then where _η_ is the overall improvement in energy efficiency experienced by each technology option as a consequence of increased efficiency penetration.

We treat production in (14) as Leontief, which implies that the relationship between output and input quantities admits a dual unit cost function that is linear in output and input prices: All else equal, efficiency improvement lowers the unit cost of service production. Following the complementarity format of general equilibrium, each technology’s unit cost function exhibits complementary slackness ( _⊥_ ) with its output quantity.<sup>3</sup> The latter satisfies a demand for services ( _D_<sup>_S_</sup> ) determined offstage in the rest of the economy

> 3Formally, for a variable _u_ and a function _F_ ( _u_ ), _F_ ( _u_ ) _⊥ u ⇔ u ≥_ 0 _, F_ ( _u_ ) _≥_ 0 _, u · F_ ( _u_ ) = 0. The economic logic of this formulation is easiest to grasp in the case of market clearance (cf eq. (17)), where, given a commodity with price _p_ and supply and demand functions _S_ ( _p_ ) and _D_ ( _p_ ),

7
where each service’s supply-demand balance exhibits complementary slackness with the corresponding technology options’ level of activity. Symmetrically, the supply of fuels ( _S_<sup>_F_</sup> ), which for our purposes also originates offstage, satisfies rest-of-economy demand ( _D_<sup>_F_</sup> ), and, in addition, aggregate fuel demand across discrete technology options, which declines with efficiency improvements: This expression shows that expansion in discrete technology options’ capacity is a source of additional demand for fuel, which, ceteris paribus, increases fuel prices. This effect is moderated by energy efficiency improvements.

Supply-demand balance for technology specific capital is given by In this model the policy lever is the exogenously-mandated unit cost of technology-specific capital, which determines the rate of improvement in technologies’ energy efficiency. The latter benefit comes at a cost, which we represent via a markup on conventional “jelly capital” ( _μ ≥_ 0). The resulting zero-profit condition is complementary to the endogenouslydetermined quantity of technology capital: We take pains to emphasize that _μθ_ is _not_ a tax. Rather, it is meant to capture the impact of mandates to purchase energy-efficient technology capital which is more expensive than conventional (i.e., jelly) capital. The general equilibrium opportunity costs of technologyspecific capital derive from the fact that the latter represents an additional claim on the aggregate capital endowment, and must therefore compete with aggregate conventional demands ( _D_<sup>_K_</sup> ). The upshot is a rise in the economy-wide cost of capital ( _PK_ ) and a drag on overall economic activity: We close our model with three equations at the core of every CGE simulation, namely, the definition of nominal income, the unit expenditure function ( _E_ ) and the market clearance condition for the aggregate welfare good, which are complementary to the levels of income and welfare ( _M_ and _W_ ) and the unit expenditure index or economy-wide price level ( _P_ ), respectively: The final elements are the linkage between energy efficiency improvement and increases in technologies’ capacity, which we specify using the analogue of eq. (10): 8
as well as an expression for economy-wide emissions, obtained by substituting (16) into the right-hand side of (17): We apply our stylized model (15)-(25) to elucidate the macroeconomic implications of mandating accelerated energy-efficiency investment. With a fixed jelly capital endowment, growth in _μθ_ induces a decline in the price of technology-specific service output ( _PSθ_ ) and an increase in the prices of jelly capital ( _PK_ ). Lower values of _PSθ_ depress the unit expenditure index, while higher values of _PK_ increase it, resulting in a change in the price level which is ambiguous. On the other hand, rising _PK_ unequivocally boosts nominal income. If the former change falls short of the expansion in income then the economy will experience a welfare gain. Eq. (23) indicates that this outcome requires _dW/W_ = _dM/M − dP/P >_ 0, where and, plugging (19) into (15) and then substituting the result into (22), in which _π_<sup>_K_</sup> is capital’s aggregate income share, _ψPK_ - _μθ >_ 0 is the elasticity of the price of jelly capital to technology markups, _πe,θ_<sup>_F_and</sup><sup>_π_</sup> _θ_<sup>_X_indicatetechnologies’fuelandcapi-</sup> tal cost shares, _ψPFe_ - _ηθ <_ 0 is the is the elasticity of fuel prices to energy efficiency, and _ψE_ - _PK, ψE_ - _PSθ, ψE_ - _PFe >_ 0 denote the elasticities of the aggregate price level with respect to the prices of jelly capital, technology capital, and fuels.

The first two terms in (27) are negative while the last two are positive. The second and fourth terms account for the effects of efficiency increase and markups on the price level transmitted through the prices of fuel and capital. The first and third terms trace the same effects through the channel of the price of technology-specific services. In particular, the first term captures efficiency’s direct productivity-enhancing effect as well as its indirect effect through lower fuel prices, which are jointly moderated by technologies’ fuel-service price ratio, and decline with the level of efficiency. The sign of _dP/P_ is ambiguous, and depends on the value of the parameters, but a welfare gain is more likely if it tilts in a negative direction. Conditions under which this tends to occur include when technologies are initially relatively fuel intensive ( _λ_<sup>_F_</sup> _e,θ_<sup>_>λX_</sup> _θ_<sup>)and/orhighlyinefficient(</sup><sup>_ηθ≪_1),their</sup> efficiency responds relatively elastically to markups or the portfolio of markups intensively 9
targets technologies with such elastic responses, and fuel prices decline relatively elastically in response to efficiency improvement.<sup>4</sup> It also occurs if the economy is relatively capitalintensive ( _π_<sup>_K_</sup> _> ψE_ - _PK_ ) and capital’s price is simultaneously relatively unresponsive to technology markups. A key implication of eq. (27)’s first term is that the win-win of _gross_ welfare improvement with emission reductions becomes progressively difficult to sustain. As the level of energy efficiency rises _dP/P_ inexorably shifts in the positive direction, sharply diminishing the macro-level productivity benefit of additional efficiency investment.

Of course, even with a welfare gain our primary interest is in the magnitude of the associated climate benefit. After plugging (19) into (15) and then substituting the result into (25), the concomitant change in the economy’s emissions is were _πe_<sup>_Z−S_</sup> and _πe,θ_<sup>_Z−F_</sup> are the discrete technology and rest-of-economy shares of CO2 in each fuel’s contribution to aggregate emissions, and _ψDeF_<sup>-</sup><sup>_PFe,ψD_</sup> _θ_<sup>_S_-</sup><sup>_PSθ<_0aretheown-price</sup> elasticities of aggregate (rest-of-economy) demand for fuels and technology-specific services.

Interestingly, a decline in aggregate emissions is not assured: as in the case of eq. (27), the sign of _dZ/Z_ depends on the parameter values, with the first two terms positive and the remainder negative. The latter respectively capture the drag on technologies’ service output and associated fuel use from the increase in the cost of capital, and the direct effect of efficiency improvement’s reductions in the fuel necessary to generate that output, which is the driver of abatement. The first term captures the offsetting intersectoral “rebound” effect of efficiency improvements on fuel use in the rest of the economy through the channel of fuel price changes.<sup>5</sup> This is the energy conservation analogue of emission leakage: the narrower the sectoral scope of technology mandates, the greater the potential for their direct fuel savings to “leak out” via increases in unregulated consumption in response to lower energy prices (see, e.g., the discussion in Broberg et al, 2015). The second term captures the intra-sectoral rebound effect of the offsetting influence of the productivity boost provided by higher energy efficiency. No-regrets options’ lower unit production costs and associated output prices stimulate increased demand for technology-specific services, resulting in an expansion of output that blunts energy savings and abatement.

> 4The latter might occur if the portfolio of technologies under consideration accounts for a substantial share of the economy’s energy use.

> 5Rebound refers to a situation where an improvement in energy efficiency causes the effective price of energy services to decline, which creates incentives economic actors to behave in ways that at least partially offset the expected beneficial impact of the efficiency gain (Allan et al, 2007, 2009).

10
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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Table 1: Technologies Considered in This Study

||Residential|Commercial||I|ndustrial|||
|---|---|---|---|---|---|---|---|
||_Private_<br>_Dwellings_|_Services_|_Food_<br>_Products_|_Chemicals,_<br>_Rubber_<br>_& Plastics_|_Iron_<br>_& Steel_|_Non-_<br>_Metallic_<br>_Minerals_|_Other_<br>_Industry_|
|_Building Shell_||||||||
|Windows|G|G||||||
|Roof Insulation|G|G||||||
|Wall Insulation|G|G||||||
|Insulation (general)|A||_←−−−−−_|_−−−−−−−−−−−_|_−_A<sup>_∗_</sup>_−−−_|_−−−−−−−−−_|_−−−−−→_|
|_Lighting_|A,G|A,G||||||
|_Appliances_||||||||
|Washing Machines|G|||||||
|Refrigerators|A,G|||||||
|Televisions|A,G|||||||
|_Air Conditioning_|A|A||||||
|_Mechanical_|||G|G|G|G|G|
|_High Temp. Processes_|||G|G|G|G|G|

Discrete technology options and their encompassing sectors analyzed by Sikharulidze et al (2015) and Timilsina et al (2015) shown in Roman text, aggregation to technology classes and industry groups in the present study shown in italics. A = Armenia, G = Georgia.<sup>_∗_</sup> Different industries considered as a single aggregate.

in the areas of residential appliances and insulation, which indicate potential for long-run discounted abatement in the amount of 0.67 MTCO2 in Armenia and 6 MTCO2 in Georgia at or below zero net cost. The underlying scenarios of energy-efficiency penetration assume trajectories of capital-energy substitution over the 2015-2034 policy horizon that are shown in Panel B. Importantly, a substantial share of the assumed total abatement emanates from improvements in technology options whose efficiency improvements can only be acquired at a significant capital cost premium (particularly residential lighting, and commercial lighting in Georgia). The key issue here is that, even controlling for the cost of abatement and the “profitability” of the options under consideration, the penetration of energy efficiency and the overall quantity of abatement both turn on assumptions about the size of the market for technologies’ services. We shall see that this question of market potential figures prominently in the subsequent numerical analysis in Section 4. Panel C provides details of technologies’ costs of fuel and capital inputs in the 2015 base year, along with capital-energy elasticities of substitution implied by the penetration of energy efficient technology relative to the baseline scenario.<sup>8</sup> The magnitude of the latter reflect the steepness of the loci in panel B, and indicate the potential for larger energy efficency improvement at lower opportunity cost.

> 8Elasticities are estimated from OLS fits of (10) to annual projections of technology costs and emissions under baseline and counterfactual scenarios.

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# Figure 1: Bottom-Up Analysis Results

# A. Marginal abatement cost curves

<!-- Start of picture text --> Armenia<br><!-- End of picture text --> <!-- Start of picture text --> Georgia<br><!-- End of picture text --> <!-- Start of picture text --> Residential Televisions<br>100 Household Televisions 100 Residential Washing Machines<br>Commercial Air Conditioning Residential Wall Insulation<br>50 Public Lighting 50 Commercial Wall Insulation<br>Household Air Conditioning 0<br>0<br>‐50<br>‐50 Residential Windows<br>‐100<br>Residential Roof Insulation<br>‐100 Commercial Windows<br>‐150<br>‐150 Household Insulation Commercial Roof Insulation<br>‐200 Public Lighting<br>‐200 Household RefrigeratorsIndustrial Insulation ‐250 Residential LightingResidential Refrigerators<br>‐250 Household Lighting ‐300 Commercial Lighting<br>0 100 200 300 400 500 600 700 800 0 1000 2000 3000 4000 5000 6000 7000<br>Discounted Potential ktCO2e saving Discounted Potential ktCO2e saving<br>Discounted Marginal Abatement Cost ($/tCO2e) Discounted Marginal Abatement Cost ($/tCO2e)<br><!-- End of picture text -->

# B. Capital-energy substitution

# Armenia Georgia

<!-- Start of picture text --> 1 1<br>HH Building Shell HH Building Shell<br>0.9 HH Lighting 0.9 HH Lighting<br>HH Appliances HH Appliances<br>0.8 HH HVAC 0.8 Com. Bldg Shell<br>Com. HVAC Com. Lighting<br>0.7 Com. Lighting 0.7 Ind. Mechanical<br>Ind. Insulation Ind. High Temp.<br>0.6 0.6<br>0.5 0.5<br>0.4 0.4<br>0.3 0.3<br>0.2 0.2<br>1 2 3 4 5 6 1 3 5 7 9 11 13<br>Fractional change in capital input from baseline ( ��/� ) Fractional change in capital input from baseline ( ��/� )<br>�̃/� )(Fractional change in energy input from baseline �̃/� )(<br>Fractional change in energy input from baseline<br><!-- End of picture text -->

# C. Base-year technology input costs ($ M) and implied capital-energy elasticities

||||Armen<br>|ia<br>|||||Georgi<br>|a<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|
||Gas|Coal|Elec.|Capital|_ξθ_||Gas|Coal|Elec.|Capital|_ξθ_|
|||||||Residential||||||
|Bldg. Shell|||376.1|79.0|-2.15|Bldg. Shell|300.7|||88.6|-0.77|
|Lighting|||97.0|2.5|-0.16|Lighting|||63.7|4.6|-0.11|
|Appliances|||114.5|73.4|-1.50|Appliances|||73.7|111.1|-1.28|
|HVAC|||22.4|9.8|-1.13|||||||
|||||||Commercial||||||
|HVAC|||8.0|3.9|-1.16|Bldg. Shell|||57.2|8.4|-0.76|
|Lighting|||9.1|8.6|-1.31|Lighting|||15.6|9.0|-0.24|
|||||||Industrial||||||
|Insulation|93.7|||0.4|-0.19|Mechanical|||69.2|4.8|-0.07|
|||||||High Temp. Proc.|57.1|66.3||38.1|-0.60|

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# **3.3 CGE Modeling**

We conduct a top-down analysis using a three-region, 14-sector recursive-dynamic CGE simulation of the world economy. Consumers in each region are modeled as a representative agent who owns the factors of production (labor and jelly capital) and rents them out to producers in exchange for income which is used to finance consumption of commodities. Producers in each region’s sectors are modeled as a representative firm which combines inputs of intermediate commodities and factors to produce a sector-specific good which is sold to the households, other sectors, the government, and other regions. The government is modeled as a passive entity which produces a government good from purchases of commodity inputs that are financed by revenue collected from taxes on production, imports and exports. Inter-regional trade in commodities is modeled using the Armington formulation in which a region’s uses of a given commodity are a constant elasticity of substitution (CES) composite of domestic and imported varieties, and the region’s aggregate imports of each commodity are a CES composite of quantities exported by its trade partners. The dimensions of this static general equilibrium structure are summarized in Figure 2.A. Following the complementarity format of equilibrium, the structure is posed algebraically as a square system of zero profit, market clearance and income balance conditions. The technical coefficients of the constituent cost and demand functions are numerically calibrated on the Global Trade Analysis Project (GTAP) version 8 database (Narayanan et al, 2012) using the MPSGE subsystem (Rutherford, 1999) for GAMS (Brooke et al, 1998), and the resulting numerical problem is expressed and solved as a mixed complementarity problem using the PATH solver (Dirkse and Ferris, 1995; Ferris and Munson, 2000).

The static sub-model described above is embedded within a dynamic process which updates economies’ savings/investment demands, and endowments of labor, capital and natural resources, over the 2015-2035 simulation horizon on a five-year time step. Regions’ labor endowments are assumed to increase exogenously with population, following projections in World Bank (2015). Regions’ representative agents are assumed to exhibit constant marginal propensity to save and invest out of factor income, and investment drives interperiod jelly capital accumulation according to the standard perpetual inventory formulation. The model’s dynamics are tuned to produce baseline trajectories of GDP and CO2 emissions are consistent with DOE/EIA (2014) projections. We do this by adjusting the growth rates of exogenous aggregate labor productivity and autonomous energy efficiency improvement (AEEI) parameters. Additional modeling details are given in an appendix to the paper.

Two challenges attend implementation of the general equilibrium model of section 2.4. The first is specifying the demand for technology-specific services in eq. (16). In technologyrich sectors (denoted by the index _j_<sup>_′_</sup> ) discrete technology options’ demand for fuel and capital ( _QFe,θ,j′_ and _QXθ,j′_ ) make up a portion of the total sectoral use of these inputs. From a modeling perspective, the key unknown is how precisely the services thus produced ( _QSθ,j′_ ) combine with the broader set of inputs to generate the sector’s output. Our approach is to specify a “technology services composite” which is a constant elasticity of substitution (CES) aggregation of the outputs of the technologies in that sector, and allow this bundle of services to substitute for capital, labor energy and materials. As shown in Figure 2.B, the practical consequence is to split the target sectors into technology and non-technology components (indicated by the dashed and dotted areas), where, starting at the top of the 14
Figure 2: Structure of the CGE Model

|||A. Regional and sectoral structu|re||
|---|---|---|---|---|
|Regions||Sectors|||
||Fuels (_e_)|Technology-Rich (_j_<sup>_′_</sup>)|Other|Final Demand|
|Armenia|Coal*|Food Products|Agriculture|Consumption|
|Georgia|Crude Oil|Chemicals, Rubber & Plastics|Rest of Economy|Investment|
|Rest of World|Natural Gas*|Iron & Steel||Government|
||Refned Fuels*<br>Electricity|Non-metallic Minerals<br>Other Industry|||
|||Services|||
|||Private dwellings|||

* Sectors whose output commodities are associated with CO2 emissions.

<!-- Start of picture text --> B. Nesting Structure in technology-rich production sectors<br>Domestic<br>Production<br>� �<br>Technology Services Energy + Materials<br>Composite + Value Added<br>� �� � �<br>Discrete<br>Technology<br>Options Energy Value Added<br>( ��� ) Composite + Materials<br>� ���<br>��0 � �<br>Technology Fuel Coal Crude Gas Ref. Elec. Value‐ Intermediate<br>Capital ( ��� ) Oil Fuel Added Materials<br>( ��� )<br>� �� � �<br>Armington Armington Armington<br>Energy Energy Labor Capital Non‐energy<br>Commodities Commodities Commodities<br>� �� � �� � ��<br>Domestic Imported Domestic Imported Domestic Imported<br>Inputs Inputs Inputs Inputs Inputs Inputs<br>Technology Component Non‐Technology Component<br><!-- End of picture text --> Elasticities of substitution: _σ_<sup>_D_</sup> = 0 _._ 2, _σ_<sup>_T S_</sup> = 0 _._ 5, _σ_<sup>_Y_</sup> = 0 _._ 8, _σ_<sup>_E_</sup> = 0 _._ 5, _σ_<sup>_NV A_</sup> = 0 _._ 75, _σ_<sup>_N_</sup> = 1 _._ 5, _σ_<sup>_V A_</sup> = 0 _._ 93 _−_ 1 _._ 43, _σ_<sup>_DM_</sup> = 1 _._ 9 _−_ 11 15
production hierarchy, a composite of technology-specific services substitutes for the component of sectoral output produced from inputs of labor, capital and intermediate energy and material commodities. A key issue we confront in numerically calibrating this structure is a pervasive lack of empirical estimates on the substitutability of technology services for other inputs to production. Absent evidence to the contrary, our assumption is that the relationship between technology services and sectoral output is complementary, though not strictly so. We therefore assign the elasticity of substitution between technology and non-technology components ( _σ_<sup>_D_</sup> ) a default value of 0.2. Although the services produced by individual technology options are necessary inputs, it is plausible to assume that they are fungible to a limited degree, which we capture by setting the substitution elasticity _σ_<sup>_TS_</sup> to a default value of 0.5. Following eq. (15), each technology’s output is a Leontief combination of technology-specific capital and Armington energy commodities, with input coefficients determined by the benchmark cost shares in Table 1.C.

We model the rest of the sector using a familiar nested CES formulation, in which a composite of residual intermediate energy inputs substitute for a composite of residual intermediate material inputs and value added. One level lower in the hierarchy, the energy (materials) composite is a CES aggregation of intermediate (non-) fuel Armington commodities, while the value-added composite is a Cobb-Douglas aggregation of labor and capital. One caution is in order regarding interpretation of Figure 2.B, however. It may be tempting to think of the dashed area as representing energy efficient technologies and the remainder of the sector as representing conventional technologies, but such an interpretation is not correct. Crucially, as in the analytical model in section 2.4, each technology option represents an average of conventional and energy efficient varieties. The effect of low-carbon growth policies is to shift toward the latter, which results in an increase in the quantity of technology-specific capital and a decline in technologies’ use of energy. We implement this by scaling the coefficient on _QXθ,j′_ upward while simultaneously scaling the coefficient on _QFe,θ,j′_ downward.<sup>9</sup> The second challenge is representing the opportunity cost of increased investment in energy efficient varieties of capital. Perhaps the closest CGE analogue of the capacity expansion problem in eq. (12) is the forward-looking model of Timilsina and Landis (2014), in which distinct technology-specific capital stocks are used to model individual technologies’ capacities, and investment is allocated among the various options according to expectations of their future rates of return. But application of this formulation in the present setting is beyond our capability, for several reasons. Our model’s myopic character means that investment decisions would need to be based on current as opposed to future prices. Another issue is the multiple-capital-stocks model’s lack of _intra_ -temporal flexibility: technology supports have to be expressed either as mandated shares of investment in _all_ technology options as a share of aggregate new capital formation, or as a vector of _θ_ investment subsidies, otherwise their prescribed acceleration in targeted options’ investment and capital accumulation rules out substitution where some technologies expand endogenously at the expense of others. But by far the biggest hurdle is the algebraic strictures of CGE models’ computational im-

> 9 Of course other production structures are possible—e.g., where technology-specific and rest-of-sector inputs of capital substitute for one other within the same nest, and the same for fuel—but their limitation is that by separating inputs across sub-nests they do not allow for the production of technology services within the sector.

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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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mandates that are targeted to differentiate among technologies. We do this by simulating the penetration scenario that underpins the MAC curves in Figure 1.A, specifying (1 + _μθ_ ) as evolving according to the trajectories in Figure 1.B. In each case, we draw inferences about the economic consequences of such a policy by comparing the resulting trajectories of economic variables against their counterparts in the baseline model solution.

# **4 Numerical Results**

## **4.1 Baseline Projections**

We begin by giving a sense of the technologies’ importance in a general equilibrium setting. In our 2015 base year the levelized cost of discrete technology options makes up a significant proportion of total production costs in only a handful of industries: building shell technologies in private dwellings (Armenia 43%; Georgia 36%), high-temperature process technologies in non-metallic minerals and mechanical equipment in iron & steel (29% and 11%, respectively, both in Georgia), appliances in private dwellings (15% in both countries), with the remaining technologies each accounting for less than ten percent of the total cost of their encompassing sectors.<sup>11</sup> All technologies exhibit quite stable output shares over the course of the baseline simulation.

Regarding technologies’ environmental performance, half of the technologies use electric power as an energy source. This is significant because improvement in these options’ energy efficiency generates emission reductions only indirectly, by reducing the demand for—and, crucially, domestic production of—electric power generated from fossil fuels. The quantity of abatement thus produced depends on the extent to which the consequent reduction in electric power demand depresses electricity prices, inducing those parts of the economy outside the scope of technology mandates to increase their consumption, and the CO2 emission intensity of power ultimately conserved as a result. Therefore, to account for abatement from electricity savings we impute emissions embodied in electric power using the average CO2 intensity of each country’s domestic generation.<sup>12</sup> This accounting indicates that, overall, technologies account for 15%-17% of Armenia’s and Georgia’s 2015 emissions, amounting to some 860 thousand tons (kT) and 1.2 million tons (MT) of CO2, respectively. Their contribution grows slightly over the course of the baseline simulation, by 2035 reaching 18%-20%, or 1.5 MT and 1.8 MT.<sup>13</sup> And with a few notable exceptions, these totals are broadly distributed across the slate of technologies. In Armenia, the largest emitters are building shell technologies in the private dwellings and other industry sectors (3%-4% of aggregate CO2). In Georgia, they are mechanical technologies in iron & steel (1.4% of total emissions), and high-temperature process technologies

> 11Noteworthy among the rest are lighting in private dwellings (Armenia 9%; Georgia 6%) and hightemperature process technologies in Georgia’s iron & steel sector (7%).

> 12Although the fraction of electric power consumption satisfied by imports, and how much CO2 is embodied in the kWhs generated by each electricity-exporting trade partner, bias this calculation, the fact that Armenia’s and Georgia’s electricity imports are each only 3% of production makes this a reasonable approximation.

> 13This is generally in line with the expansion of projected aggregate emissions over 2015-2035 from 5.6 MT to 8 MT in Armenia and 6.9 MT to 8.8 MT in Georgia.

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in iron & steel, non-metallic materials and food products (3%, 3.4% and 4%, respectively). These proportions are also stable over time.

Energy-efficiency mandates tend to have larger beneficial spillover effects on sectors’ productivity, and larger abatement potential, when the technologies that they target make up a large fraction of sectors’ energy consumption. In Armenia, building shell technologies generate 25% of the CO2 emitted by industrial sectors, while in Georgia high-temperature process technologies account for more than 40% of emissions from iron & steel and 65% from food products and non-metallic materials. Discrete technologies account for all of the CO2 emitted by private dwellings, but countries vary in the distribution of this total across different options.<sup>14</sup> In Armenia two-thirds of residential CO2 is generated by building shell technologies, with the remainder split evenly between lighting and appliances, By contrast, half of Georgia’s residential emissions emanate from appliances and a further 42% come from lighting. The temporal stability of these shares is lower, but they tend to vary over the simulation by only a few percentage points.

As our theoretical analysis indicates, realizing the economic benefits essential to lowcarbon growth strategies necessitates identification of technology options which possess both substantial abatement potential and elastic efficiency improvement responses to technology deployment mandates. Unfortunately, in this regard silver bullets may be rare on nonexistent; here we see only two: residential building shell technologies in Armenia, and, to a lesser extent, industrial high-temperature process technologies in Georgia. The key implication is that small quantities of abatement may have to accumulated across a broad portfolio of technologies, with attendant uncertainty in technology strategies’ ultimate economic and environmental outcomes.

It is also likely that such potential efficiency benefits may be limited by rebound effects. The latter are more likely when the targeted technologies comprise a substantial share of the demand for energy commodities that are widely used across the economy. This is also apparent from the model’s solution. Electricity-using technologies accounted for 21% of the CO2 emitted by Armenia’s generators and 16% of similar emissions in Georgia. In both counties electricity consumption is dominated by residential technologies: building shell (Armenia 12%; Georgia 4.5%), and lighting and appliances (each 2%-3.5%).

# **4.2 Counterfactual Scenarios**

The macroeconomic implications of our counterfactual simulations are illustrated in Figure 3. The main result of the paper is in Panel A, which shows the percentage change in CO2 emissions relative to the baseline against the percentage change in real GDP from the baseline. Despite the two charts having different scales, the scenarios trace out qualitatively similar loci in the shape of a backwards “C” that begins at the top (0,0) in the year 2015 and ends at the bottom in 2035. The surprising result is that low carbon growth strategies are feasible for both Armenia and Georgia. Energy-efficiency investments’ productivity benefits outweigh their opportunity costs over an unexpectedly broad range, enabling both countries to reduce their emissions by up to 4% from baseline levels while enjoying higher real GDP.

> 14This is by construction. In the benchmark social accounting matrix the private dwellings sector account does not record intermediate energy commodity purchases; these are tabulated in final consumption.

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Figure 3: Energy Efficiency Penetration: Aggregate Impacts, 2015-2035 <!-- Start of picture text --> Armenia Georgia<br>A. Real GDP and CO2 Emissions<br>0 0<br>-0.5<br>-1<br>-5<br>-1.5<br>-2<br>-2.5 -10<br>-3<br>-3.5<br>-15<br>-4<br>-4.5<br>-5 -20<br>-2 -1.5 -1 -0.5 0 0.5 1 1.5 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5<br>% Change in Real GDP from baseline % Change in Real GDP from baseline<br>B. Welfare<br>1.4 1.4<br>1.2 1.2<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br>-0.2 -0.2<br>2015 2020 2025 2030 2035 2015 2020 2025 2030 2035<br>C. Sectoral CO2 Abatement and Leakage<br>0 400<br>350<br>-50 300<br>250<br>-100<br>200<br>150<br>-150<br>100<br>-200 50<br>0<br>-250 -50<br>0 100 200 300 400 500 600 700 0 200 400 600 800 1000 1200 1400 1600<br>Abatement by Discrete Technologies (kTCO2) Abatement by Discrete Technologies (kTCO2)<br>Annual rate of increase in μ : 1% 2% 3% 5% 7% 10% Bottom-Up Analysis<br> emissions from baseline% Change in CO2 emissions from baseline% Change in CO2<br>% Change in Welfare from Baseline % Change in Welfare from Baseline<br>)Abatement by Rest of Sector (kTCO2 )Abatement by Rest of Sector (kTCO2<br><!-- End of picture text --> 20
Both countries exhibit similar potential for reducing emission without penalizing economic growth, but differ in the elasticity of their GDP and abatement responses to increasing technology-specific capital markups. Even with an aggressive 5% annual rate of increase in the technology markup, Armenia’s GDP remains above its baseline trajectory by 2035. By contrast, for Georgia’s GDP to exceed its baseline trajectory by 2035, markups must be much smaller—equivalent to a growth rate of efficiency penetration of 2% per annum— otherwise the opportunity costs of technology investment quickly outweigh the productivity benefits. Allied with this, the maximal increase in GDP above baseline experienced by Armenia (about 1%) is over four times as large as that in Georgia. This difference has its roots in the uniformly smaller magnitudes of the capital cost elasticity of efficiency improvement for equivalent classes of technologies in Georgia as compared to Armenia, which simply results in Georgia experiencing smaller productivity and abatement benefits for the same opportunity cost of abatement capital.

The results also highlight the need for caution when attempting to use MAC curves to assess the prospects for low-carbon economic growth. The dashed line shows the results of simulating the bottom-up technology penetration scenario from Section 3.2 in which residential space conditioning and appliances, as well as residential and commercial lighting, are intensively targeted. Under this policy stimulus the locus of the economy’s response has a similar shape, but in both regions the peak GDP increase is sharply curtailed, as is the total reduction in emissions. The explanation is that mandated increases in capital costs are concentrated in technologies whose elasticity of energy efficiency improvement is relatively small, thus dissipating the policy’s gross environmental, and net economic, benefits.<sup>15</sup> We consider this result in more detail below.

Figure 3.B indicates that an improvement in aggregate consumption and gross welfare that for the most part parallels the increase in GDP. For annual rates of increase in the technology capital markup of up to 5% these benefits continue to increase over the simulation horizon, but with more rapid penetration diminishing returns become evident, as the marginal benefits of improved productivity fail to keep pace with the marginal opportunity cost of ever more expensive technology-specific capital. Additional longer-run simulations indicate that this phenomenon is pervasive, in the sense that even with a comparatively slow pace of cumulative technology deployment, the point of diminishing returns will eventually be reached—albeit well after the 30-year horizon of interest here. These results are also a reminder of how misleading GDP can be as measure of aggregate well-being. Section 3.2’s bottom-up technology penetration scenario, even though associated with comparatively small increase in GDP that quickly peters out, nonetheless generates a sustained improvement in welfare due to the effects of the policy on commodity prices and household consumption.

Figure 3.C illustrates the policy’s impacts on abatement by the technology and nontechnology components of technology-rich sectors. The two economies exhibit markedly different patterns of intra-sectoral emission leakage. In Armenia, the direct effect of increased energy efficiency is to reduce CO2 by up to 500 kT annually, but expansion of fuel use by the non-technology component of technology-rich sectors dissipates one half to one third of this

> 15A salutary example is commercial lighting in Georgia, which has one of the lowest capital-energy elasticities and is among the most capital intensive technologies, yet is subject to one of the most stringent penetration mandates—by 2035 (1 + _μ_ ) _>_ 6!

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abatement, with the rate at which such “internal” leakage occurs declining with increasing technology deployment. By contrast, in Georgia the technology and non-technology components’ demands for fuel appear to be largely complementary, with a much larger quantity of primary emission reductions (1.2 GT) coinciding with _negative_ leakage that enhances abatement by 10%-30% over most of its range. Abatement and leakage responses in the bottom-up scenario are qualitatively similar but much smaller in magnitude.

We also test the extent to which the bottom-up analysis’ assumptions of options’ technical potential might be biased by recalculating the discounted value of CO2 directly emitted by our slate of technologies over the 2015-2034 period. For the bottom-up scenario, total discounted abatement is 689 kT in Armenia, slightly underestimating Figure 1.A. However, in Georgia we find less than 1.2 MT of discounted abatement as compared to 7 MT predicted by engineering assessments, suggesting that the latter are wildly optimistic. Our results also capture the joint consequences for emissions of general equilibrium effects such as rebound and increasing capital prices’ drag on broader economic activity by repeating these calculations for total economy-wide abatement. Armenia’s discounted abatement shrinks by almost 40% to 421 kT, while Georgia’s rises slightly to 1.3 MT.

These results can be understood by examining the responses of sectors under a 2% annual energy efficiency improvement scenario, shown in Figure 4. Panel A indicates that a key contributor to Armenia’s increase in welfare (and, by implication, household consumption) is a rapid increase in output of the private dwellings sector (1% per annum on average), which is in turn propelled by productivity benefits of the concentration of elastic residential technologies. Over the simulation horizon, the chemicals, rubber & plastics sector is a a distant second, expanding by only 8% relative to the baseline, while the majority of industries exhibit increases, but of less than half this magnitude. By contrast, Georgia’s sectors are evenly split between small output increases and outright losses from their baseline levels, which accounts for its smaller improvement in GDP and welfare. At the the root of this outcome are sectors’ relatively inelastic energy efficiency and productivity responses to the technology mandate.

Sectoral dynamics also help explain the two countries’ similar abatement performance. By 2035 Armenia experiences an 8% drop in electricity output and associated emissions, while Georgia’s electricity and coal sectors shrink by 4% and 10%, respectively. Figure 4.B illustrates the abatement and leakage consequences of changes in the outputs of technology rich sectors. In Armenia, several sectors exhibit “internal” leakage rates greater than 100%, with relative prices changes inducing increases fuel use in their non-technology components that swamp the reduction in fuel use in their technology components. Chief among these are sectors which undergo intermediate expansion, such as chemicals, rubber & plastics and other industry. Crucially, emission leakage by the private dwellings sector is small relative to the large reduction in CO2 that it directly generates. This compensates for the leakage from other parts of the economy, and also accounts for the sharp divergence between the emission reductions attributable to technology options and the overall quantity of abatement identified earlier. Georgia’s experience is very different, however. Technology-rich sectors’ abatement responses tend to be larger and more evenly distributed. Intra-sectoral emission leakage is far smaller, with sectors such as chemicals, rubber & plastics, services and other industry exhibiting negative leakage that drives the responses in Figure 3.C.

Figure 5 elucidates the microeconomic underpinnings of our sectoral results by interpolat22
Figure 4: 2% Annual Energy Efficiency Improvement: Sectoral Impacts, 2015-2035 <!-- Start of picture text --> Armenia Georgia<br>A. Output<br>25 25<br>20 20<br>15 15<br>10 10<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>2015 2020 2025 2030 2035 2015 2020 2025 2030 2035<br>B. CO 2 A batement an d Intra-Sectoral Le ak age (Technol og y-Rich Sector s O nly)<br>5 5<br>0 0<br>-5 -5<br>-10 -10<br>-15 -15<br>-20 -20<br>-25 -25<br>-30 -30<br>-35 -35<br>-40 -40<br>-20 0 20 40 60 80 100 120 140 0 20 40 60 80 100 120 140<br>Abatement by Discrete Technologies (kTCO2) Abatement by Discrete Technologies (kTCO2)<br>Agriculture Food Products Chemicals, Rubber & Plastics<br>Non-metallic Minerals Iron & Steel Other Industry<br>Services Dwellings Rest of Economy<br>Gas Petroleum Electricity<br>Coal<br>% Change in Output from Baseline % Change in Output from Baseline<br>)Abatement by Rest of Sector (kTCO2 )Abatement by Rest of Sector (kTCO2<br><!-- End of picture text --> 23
ing across scenarios to trace out the locus of technologies’ responses to the policy stimulus.<sup>16</sup> In general, fewer technologies show declining trajectories in Armenia as compared to Georgia, reflecting the latter’s smaller efficiency improvement elasticity values. The impact of the markup and concomitant productivity boost on the change in the technology-specific capital price and level of output relative to the baseline solution is shown in Figure 5.A, where technology substitution dynamics lead to three types of trajectories. First, a handful of options are economically unattractive candidates for efficiency improvement, leading their output to decline relative to baseline levels (e.g., commercial lighting in both countries; residential appliances and mechanical devices in Georgia’s food products sector). Symmetrically, other options are competitive, and continue to expand their output over the entire period of the simulation (e.g., building shell insulation in both countries, and residential lighting and commercial space conditioning in Armenia). Most options fall between these two extremes, exhibiting increased output relative to the baseline for one or more periods before declining, and then eventually falling below baseline levels. The steepest drop-offs occur in residential appliances and space conditioning in Armenia, and industrial process technologies in Georgia’s iron & steel and non-metallic materials sectors. The fact that these technologies’ efficiency responses are relatively elastic demonstrates the critical importance of the saturation in efficiency’s direct productivity-enhancing effect (i.e., the 1 _/ηθ_ factor in the first term of (27)). The consequent non-monotonic response highlights the shifting balance between the diminishing marginal productivity benefit of efficiency improvement and the increasing marginal opportunity cost of the mandate as _ηθ_ increases. This is the key driver of aggregate output response.

We close by examining the consequences for technologies’ direct and embodied CO2 emissions in Figure 5.B. Abatement is pervasive: there is only one technology for which output expansion increases emissions above its baseline level. Notwithstanding this, technologies that experience large expansions in output generally abate less.<sup>17</sup> As a group, abatement by electricity-using technologies is more vigorous than that by non-electric technologies in Armenia, but the reverse is true for Georgia. This can be traced to the larger (smaller) values of _ξθ_ for Armenia’s (non-) electric technologies compared to Georgia’s. Increasing activity reflects declining unit production costs as the productivity benefit of energy efficiency outstrips the rising cost of capital acquisition. Technologies for which this is the case experience a decoupling of output from fuel use that ends up causing their emissions to decline relative to the baseline. Crucially, however, the same emission trajectory can result if productivity falls short of opportunity costs, production costs and output prices rise, and activity and the demand for fuel both decline. In both cases the abatement benefit is the same—the key difference lies in the economic consequences. Examples are space conditioning and lighting in Armenia’s service sector, and Armenia’s residential building shell technologies juxtaposed with Georgia’s residential appliances.

> 16The loci are nonparametric lowess regression fits to the pooled datapoints from all simulations.

> 17Building shell technologies in Armenia’s residential and Georgia’s commercial sectors are exceptions.

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# Figure 5: Technology Dynamics

<!-- Start of picture text --> Armenia Georgia<br>A. Output<br>(i) Electricity-Using Technologies<br>40 40<br>20 20<br>0 0<br>−20 −20<br>−40 −40<br>0 100 200 300 400 0 100 200 300 400<br>(ii) Non-Electric Technologies<br>40 40<br>20 20<br>0 0<br>−20 −20<br>−40 −40<br>0 100 200 300 400 0 100 200 300 400<br>% Change in technology−specific capital price % Change in technology−specific capital price<br>B. CO2 Emissions<br>(i) Electricity-Using Technologies<br>0 0<br>−25 −25<br>−50 −50<br>−75 −75<br>−100 −100<br>0 100 200 300 400 0 100 200 300 400<br>(ii) N on -E lectric Technolo g ies<br>0 0<br>−25 − 25<br>−50 −50<br>−75 −75<br>−100 −100<br>0 100 200 300 400 0 100 200 300 400<br>% Chan ge in technology −sp ecific capital price % Change in technology−specific capital price<br>Appliances High−Temp. Proc. Mechanical Chemicals Food Prod. Non−Metallic Mat. Services<br>Technologies Sectors<br>Bldg. Shell Lighting HVAC Dwellings Iron & Steel Other Ind.<br>% Change in technology output % Change in technology output<br>% Change in technology output % Change in technology output<br>% Change in CO emissions2 % Change in CO emissions2<br> emissions2 O emissions2<br>C<br>% Change in CO % Change in<br><!-- End of picture text --> 25
# **5 Discussion and Conclusions**

A recent paper by Fowlie et al (2014) notes that “Widely publicized engineering estimates [ _. . ._ ] suggest that consumers are systematically bypassing opportunities to invest in costeffective energy efficiency improvements that lower their energy expenditures and reduce externalities associated with energy production. As a result, many policymakers are championing programs designed to encourage energy efficiency as a cost-effective strategy to confront climate change.” Like these authors, we find that bottom-up estimates tend to overstate the benefits of energy efficiency mandates in terms of energy and emissions savings. But our analysis comes at this issue from the opposite direction, examining the aggregatelevel environmental and economic effects of technology deployment strategies for low-carbon economic economic growth. Building on the fundamentals of engineering cost assessments, we develop a stylized bottom-up/top-down general equilibrium model which accounts for the several influences: the opportunity costs of increasing the installed base of energy efficient technology-specific capital, the direct productivity benefits to economic sectors of the resulting increases in energy efficiency, and the broader impact of rebound effects. We then implement this model as a CGE simulation which integrates energy technology data into the system of open-economy input-output accounts for Armenia and Georgia.

Our main finding is that mandated increases in the penetration of bottom-up energy efficiency technology options can mitigate CO2 up to around 4 % of baseline emissions without adversely impacting real GDP or welfare. Even so, as capacity in these more efficient but relatively expensive technologies expands, the productivity benefits to technology-rich sectors are progressively diminished by increasing opportunity costs. The good news for transition economies such as Armenia and Georgia is that even modest increases in energy efficiency (e.g., our 1% or 2% per annum deployment rates) appear to generate both welfare improvements and reductions in the rate of growth of emissions—albeit small in magnitude. The bad news is that, depending on the structure of the economy, the direct abatement realized by higher energy efficiency tends to be undermined by substantial emission leakage—due to increased fossil fuel use by less efficient conventional technologies in both target and non-target sectors. Moreover, despite the usefulness of our methodology for using abatement costs and potentials to identify technology options whose energy productivity response is elastic relative to opportunity costs, these options’ potential contributions to abatement or national income growth are unknown ex ante and remain an emergent property of the economy-wide simulation. Overall outcomes depend critically on the structure of the economy (particularly the fuel- and capital-intensities of sectors targeted for deployment) as well as the abatement technology portfolio, which together shape the general equilibrium effects that determine technologies’ macroeconomic costs, output demand, and leakage. All these considerations suggest that our findings, rather than being viewed as the definitive conclusion, should be seen as marking the start of a research program to rigorously assess the potential of a range of template low-carbon economic growth strategies in different country settings.

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# **Appendix A. Further Details of the Modeling Approach**

Our focus is on elucidating the tradeoff between the abatement and productivity benefits of improved energy efficiency and the opportunity costs of acquiring expensive relatively expensive technology-specific capital. To that end, we strive to keep the analysis as transparent as possible by choosing to represent the remainder of the economy in a simple and straightforward fashion, following the canonical model in Sue Wing and Balistreri (2014). In sectors where discrete technology options are not represented, the production function is specified according to the nested CES structure of the non-technology component identified by the dotted area in Figure 2.B. In each region the representative agent has CES preferences denominated over the 14 commodities, and a consumption elasticity of substitution of 0.5. Production of the government good has a similarly parameterized CES structure. Distortions are specified in terms of ad-valorem equivalents and are maintained at their benchmark levels given by the GTAP database. We do not impose government budgetary balance, or constrain revenue and expenditure in the counterfactual scenario to match their baseline trajectories. Similarly, we simulate the economy’s full-employment equilibrium, eschewing labor market closures such as labor-leisure choice that capture the potential impacts of productivity improvements on, and introduce the additional general equilibrium feedbacks of, elastic labor supply. To be sure, results such as the employment consequences of energy productivity gains will be of interest to policy makers, but they are secondary to our purposes here, and as such we leave their elaboration to future research.

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