Appendix 1. Description of MERGE Model and Numerical Assumptions
MERGE is an inter-temporal optimization model with a top-down general actual representation of the economy and a bottom-up process representation of energy technologies. In each region, exogenous trajectories for population and reference economic growth are used to derive a growth scenario for labor productivity (equivalent to per capita income). A nested production function is used to describe how aggregate economic output depends upon inputs of capital, labor, and electric and non-electric energy. Energy prices are determined endogenously in the model as a result of resource scarcity, technological change, and policy constraints. The model’s solution represents a Pareto optimal outcome, where the discounted utility of each region’s consumption is maximized.
The rate of increase in energy demand relative to economic growth is determined both by price-induced shifts among inputs to production (as determined by elasticity parametersies in the production function) and by autonomous (i.e. non-price-induced) changes in energy intensity. Such changes can occur due to both technological progress (e.g. end-use efficiency) and structural changes in the economy (e.g. shifts away from manufactured goods toward services). All sources of non-price-induced changes in energy intensity are summarized in MERGE by a single “autonomous energy efficiency index” (AEEI) parameter, which operates as a scaling factor on the energy input to production. In choosing this parameter, we have attempted to take into account current trends as well as judgments about the relevant stage and patterns of development in different countries.
Growth Rates
Tables A1, A2 and A3 show the assumptions made about annual rates of economic growth and AEEI change for the regions used in this study. In the cases of aggregated regions, these rates are based on assumptions made at the level of individual component countries or smaller country groupings.
Table A1. Annual reference real growth rates in aggregate GDP
2010 - 2020 / 2020 - 2030 / 2030 - 2040 / 2040 - 2050 / 2050 - 2060 / 2060 - 2070 / 2070 - 2080 / 2080 - 2090 / 2090 - 2100USA / 2.4%[JM1] / 2.0% / 1.9% / 1.8% / 1.7% / 1.6% / 1.5% / 1.5% / 1.4%
Europe / 2.4% / 2.0% / 1.7% / 1.5% / 1.4% / 1.4% / 1.3% / 1.3% / 1.3%
Japan / 2.2% / 1.6% / 1.1% / 0.9% / 1.0% / 1.0% / 1.0% / 1.1% / 1.1%
Other OECD / 3.0% / 2.2% / 2.1% / 1.9% / 1.7% / 1.7% / 1.5% / 1.4% / 1.3%
ChinaRussia / 9.2% / 5.8% / 3.1% / 2.1% / 1.3% / 1.1% / 0.9% / 0.9% / 0.9%
IndiaChina / 8.8% / 7.7% / 5.6% / 4.1% / 3.0% / 2.4% / 1.8% / 1.5% / 1.3%
Rest of AsiaIndia / 5.1% / 4.8% / 4.8% / 4.3% / 3.5% / 2.9% / 2.4% / 2.0% / 1.8%
Group 3Rest of Asia / 5.0% / 4.0% / 3.2% / 2.5% / 2.2% / 2.1% / 1.8% / 1.7% / 1.7%
Rest of World / 5.2% / 4.7% / 4.2% / 3.7% / 3.3% / 3.0% / 2.8% / 2.5% / 2.4%
Table A2. Annual rates of decline in AEEI in reference case
2010 - 2020 / 2020 - 2030 / 2030 - 2040 / 2040 - 2050 / 2050 - 2060 / 2060 - 2070 / 2070 - 2080 / 2080 - 2090 / 2090 - 2100USA / 1.2% / 1.0% / 1.0% / 1.0% / 0.8% / 0.8% / 0.4% / 0.4% / 0.4%
Europe / 1.0% / 0.9% / 0.8% / 0.7% / 0.6% / 0.6% / 0.6% / 0.5% / 0.5%
Japan / 1.2% / 0.8% / 0.8% / 0.8% / 0.8% / 0.8% / 0.7% / 0.6% / 0.6%
Other OECD / 1.1% / 0.7% / 0.6% / 0.5% / 0.5% / 0.5% / 0.4% / 0.4% / 0.4%
ChinaRussia / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 0.8% / 0.7%
IndiaChina / 1.5% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0% / 1.0%
Rest of AsiaIndia / 0.7% / 1.1% / 1.5% / 1.8% / 1.5% / 1.0% / 1.0% / 0.8% / 0.6%
Group 3Rest of Asia / 2.0% / 1.9% / 1.9% / 1.1% / 0.9% / 0.8% / 0.7% / 0.6% / 0.5%
Rest of World / 0.7% / 1.0% / 1.2% / 1.3% / 1.3% / 1.3% / 1.2% / 1.0% / 0.9%
Table A3. Annual rates of decline in AEEI in optimistic case
2010 - 2020 / 2020 - 2030 / 2030 - 2040 / 2040 - 2050 / 2050 - 2060 / 2060 - 2070 / 2070 - 2080 / 2080 - 2090 / 2090 - 2100USA / 2.2% / 1.8% / 1.8% / 1.8% / 1.3% / 1.3% / 0.7% / 0.6% / 0.7%
Europe / 1.7% / 1.7% / 1.4% / 1.2% / 1.1% / 1.1% / 1.1% / 1.0% / 0.9%
Japan / 2.2% / 1.5% / 1.4% / 1.4% / 1.4% / 1.4% / 1.3% / 1.1% / 1.0%
Other OECD / 2.0% / 1.3% / 1.1% / 0.9% / 0.9% / 0.8% / 0.8% / 0.7% / 0.8%
ChinaRussia / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.5% / 1.3%
IndiaChina / 2.7% / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.8% / 1.7%
Rest of AsiaIndia / 1.3% / 2.0% / 2.7% / 3.3% / 2.6% / 1.8% / 1.8% / 1.5% / 1.1%
Group 3Rest of Asia / 3.6% / 3.5% / 3.4% / 2.0% / 1.7% / 1.4% / 1.3% / 1.1% / 1.0%
Rest of World / 1.3% / 1.8% / 2.2% / 2.4% / 2.4% / 2.4% / 2.1% / 1.8% / 1.5%
Energy System
MERGE includes a bottom-up representation of the energy supply sector with specific activities for the generation of electricity and for the supply of non-electric energy. Oil, gas, and uranium are viewed as exhaustible resources. Typically, the energy producing and consuming capital stock is long-lived. To simulate the transitional implications of capital “stickiness” in MERGE, introduction and decline constraints are placed on new vintage technologies.
The following description refers to all policies analyzed except for the 2.8 W/m2 policy. We assume that production from a new technology is constrained to 4%[1] of the region’s total production in the year in which it is initially introduced and can increase by 7% per year thereafter[2].[3].[4],[5] The decline rate is limited to 2% per year[6] for new technologies, but there is no decline rate limit for existing vintages to allow for the option of premature retirement of high-emitting capital stock in a policy scenario.
For the 2.8 W/m2 policy, .we assume that production from a new technology is constrained to 8%[7] of the region’s total production in the year in which it is initially introduced and can increase by 15% per year thereafter[8].[9] The decline rate is limited to 4% per year[10] for new technologies, but there is no decline rate limit for existing vintages to allow for the option of premature retirement of high-emitting capital stock in a policy scenario.
MERGE includes a number of electricity generation technologies that can be broadly categorized as renewables. These technologies include wind, solar and biomass. Two representations of solar technologies are included – an intermittent technology and a more expensive backstop technology (e.g. photovoltaics with associated energy storage).As the structure of the MERGE model cannot directly capture the variability of wind and photovoltaics, in addition to total regional resource constraints, these technologies are subject to constraints on the fraction of electricity demand they can meet, in addition to integration costs, ranging from 5-15 $/MWh, varying with the level of deployment.. In this paper, generally conservative assumptions are used in parameterizsing these constraints.
In the preparation of this work, a new electricity generation technology has been added to MERGE, Biomass with Carbon Storage (BECS). This technology represents a conventional bioelectricity facility, with the additional feature of CO2 capture from the emissions stack. As the upstream bio-residue and bio-crop feedstock inputs In MERGE are designed to represent carbon neutrality, the capture of emissions from a becs facility can lead to a negative emissions credit for electricity generation from this technology. As will be discussed later in this paper, considerable uncertainty exists about this technology – from land availability for feedstock to broader concerns about storing CO2 underground.
The electricity sector also has the option of Biomass with CCS. This is a new electricity generation technology that has been introduced into MERGE. This technology represents a conventional dedicated bioelectricity facility, with the additional feature of CO2 capture from the emissions stack. The facility is assigned a negative emissions factor as biomass combustion emissions are assumed to be offset by biomass feedstock growth, and regional biomass feedstocks are limited to biomass supplies that satisfy sustainability criteria[11]. The sustainability criteria assumes residue extraction preserves soil for forest health, dedicated energy crops occur on surplus agricultural lands, and additional deforestation is prevented. Also, food/feed/timber demands are paramount with residues a by-product. Overall, the set of assumptions minimizes land competition and commodity market effects in acknowledgement of non-climate social concerns regarding increased biomass for energy supplies. We have derived regional biomass feedstock constraints based on Smeets et al. (2007) that are consistent with MERGE’s structure and projected socioeconomic development. As will be discussed later, Cconsiderable uncertainty exists about this technology, especially regarding social acceptance –from land availability for feedstocks to concerns about storing CO2 underground.
Tables A4 and A5 outline technological options in the electric sector. It is important to note that these tables contain a snapshot, and that these costs vary by region and over time. For Annex B countries, coal with capture and new nuclear plants are first available in 2020, with improvement beginning in subsequent decades through 2050. In other regions, we assume the same technologies become available, lagged by one decade in the case of China and other mid-income countries, and two decades in the case of India and other low-income countries. Table A7 shows sample reference cost assumptions for non-electric energy technologies.
Table A4. Electric Generation Technology Assumptions for Existing Vintages* in USA in 2020
Technology / Market Cost($/ MWh) / Non-Market Cost
($/ MWh) / Carbon Emission Coefficiencyicients
(Million tons C/TWh)
Coal / 47 / 264
Natural Gas# / 55 / 112
Oil / 474 / 193
Nuclear# / 22 / 10
Hydroelectric / 20
Other Renewables† / 10
* Capital costs are assumed to be fully recovered for existing generation assets and hence are omitted from the levelized cost calculation.
† This category includes all non-hydro renewables in place in the base year, predominantly wind
Table A5. Electric Generation Technology Assumptions for New Vintages in USA in 2020
Technology / Model Year Available / Market Cost($/ MWh) / Non-Market Cost
($/ MWh) / Carbon Emission Coefficiencyicients
(Million tons C/TWh)
Coal (without CCS) / 74 / 207
Coal (with CCS) ‡ / 2020 / 113 / 10 / 26
Natural Gas (without CCS) # / 56 / 92
Natural Gas (with CCS) # / 2020 / 81 / 10 / 13
Nuclear#‡ / 65 / 10
Nuclear - Advanced#‡ / 2050 / 97 / 10
Wind^ / 76 / 5-15
Biomass# / 87
Biomass with CCS / 2020 / 104 / 10 / -205
Photovoltaics^ / 162 / 5-15
Backstop solar technology / 203
# The market cost for these generation technologies includes fuel prices that are displayed at 2020 BAU prices. Fuel prices faced by these technologies are a function of the market for each respective fuel.
.
‡ We assume that the cost of nuclear and ccs generation has a market and non-market component. The latter, which is calibrated to current usage in the case of nuclear, rises proportionally to market share and is intended to represent public concerns about environmental risks in the technology. (In the case of nuclear, the represented concern can extend to security risks and risks associated with the nuclear fuel cycle.)
^ Wind and solar technologies are subject to grid integration costs when providing greater than 5% of a region’s electricity; $5/MWh when providing 5%-10% of a region’s electricity, $7.5 $/MWh when providing 10-20%, and $15/MWh when providing greater than 20% of a region’s electricity.
Table A6. Electric Generation Technology Assumptions in Pessimistic Renewables case
Technology / Model Year Available / Market Cost($/ MWh) / Non-Market Cost
($/ MWh) / Carbon Emission Coefficiencyicients
(Million tons C/TWh)
Wind^ / 76 / 5-15
Biomass# / 87
Biomass with CCS / 2020 / 104 / 10 / -205
Photovoltaics^ / 162 / 5-15
Backstop solar technology / 203
∆In the pessimistic case, these solar costs do not improve over time
[JM2]Table A766. Non-Electric Energy Technology Assumptions for USA in 2020
Fuel / Cost($/GJ) / Carbon Emissions
(kg per GJ)
Coal (for direct use) / 2.5 / 24.1
Petroleum (cost rises with extraction and depends on region) + / 8 / 16
Natural Gas (cost rises with extraction and depends on region) + / 6 / 13.7
Synthetic Liquids / 7.25 / 30
Biofuels+ / 6.5 / 0
Non-Electric Backstop / 25 / 0
+ The costs of these fuels are outcomes from their associated endogenous markets. These numbers represent costs in the reference case
Climate System
MERGE includes a reduced-form model of the climate system that relates emissions of greenhouse gases to atmospheric concentrations, atmospheric concentrations to radiative forcing, and radiative forcing to temperature change. [12] [13]Outcomes from a reduced form model of such a complex physical system exhibit are naturally subject to considerable uncertainty. One Pparticularly influential parameters includeis the climate sensitivity parameter that reflects the long-term equilibrium temperature response to sustained radiative forcing consistent with a doubling of atmospheric CO2 concentrations. For the temperature results reported in this study, a value of 3 degrees was used for climate sensitivity. In addition, the dynamic lag parameter in the one-box temperature model is chosen so that the assumed climate sensitivity is consistent with current observed increase in the global average surface temperature and historical estimates of radiative forcing. relates the change in radiative forcing to the change in actual surface temperature. For this study a value of 0.8 is used. This translates to an increase in actual temperature of just under 3 degrees Celsius for a doubling of atmospheric CO2 concentrations.
More details on the climate model formulation and calibration can be found in:
Manne, A., R. Mendelsohn, and R. Richels (1995). MERGE: A model for evaluating regional and global effects of GHG reduction policies, Energy Policy 23:1 pp 17-34.
Richels, R.G., A.S. Manne AS, and T.M.L. Wigley (2007). Moving beyond concentrations: the challenge of limiting temperature change. In: Schlesinger, M., F.C. de la Chesnaye, H. Kheshgi, C.D. Kolstad, J. Reilly, J.B. Smith, and T. Wilson (eds), Human induced climate change: an interdisciplinary assessment, Cambridge University Press, Cambridge, UK, pp 387–402.
1
[1] 1% in ROW and ‘Rest of Asia’ and ‘Rest of World’, and 8% for CCS
[2] 15% in China
[3] For CCS and nuclear electricity generation, deployment can increase by a factor of 3 per decade.
[4] 15% in China
[5] For CCS and nuclear electricity generation, deployment can increase by a factor of 3 per decade.
[6] 5% per year after 2050
[7] 2% in ROW and ‘Rest of Asia’
[8] 23% in China
[9] For CCS and nuclear electricity generation, can increase by a factor of 3 per decade
[10] 10% per year after 2050
[11]Smeets, E.M.W., A.P.C. Faaij, I.M. Lewandowski, and W.C. Turkenburg (2007). “A bottom-up assessment and review of global bio-energy potentials to 2050.” Progress in Energy and Combustion Science 33 pp 56-106.Smeets et al., 2007
[12] Are there appropriate references to point to regards the MERGE climate model?
[13] Manne, A. et al (1996)
[JM1]Note this column is the ‘2010’ column in the grow parameter in the GDX file. Should this column then be labeled 2000-2010
[JM2]No longer in paper