Energy resource endowments - MESSAGE-GLOBIOM

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Model Documentation - MESSAGE-GLOBIOM

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Model information
Model link
Institution International Institute for Applied Systems Analysis (IIASA), Austria, http://data.ene.iiasa.ac.at.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

Fossil Fuel Reserves and Resources

The availability and costs of fossil fuels influences the future direction of the energy system, and therewith future mitigation challenges. Understanding the variations in fossil fuel availability and the underlying extraction cost assumptions across the SSPs is hence useful. Our fossil energy resource assumptions are derived from various sources (Rogner, 1997 1; Riahi et al., 2012 2) and are aligned with the storylines of the individual SSPs. While the physical resource base is identical across the SSPs, considerable differences are assumed regarding the technical and economic availability of overall resources, for example, of unconventional oil and gas. What ultimately determines the attractiveness of a particular type of resource is not just the cost at which it can be brought to the surface, but the cost at which it can be used to provide energy services. Assumptions on fossil energy resources should thus be considered together with those on related conversion technologies. In line with the narratives, technological change in fossil fuel extraction and conversion technologies is assumed to be slowest in SSP1, while comparatively faster technological change occurs in SSP3 thereby considerably enlarging the economic potentials of coal and unconventional hydrocarbons (Table 1, Figure 1). However, driven by tendency toward regional fragmentation the focus in SSP3 is assumed to be on developing coal technologies which in the longer term leads to a replacement of oil products by synthetic fuels based on coal-to-liquids technologies. In contrast, for SSP2 we assume a continuation of recent trends, focusing more on developing extraction technologies for unconventional hydrocarbon resources, thereby leading to higher potential cumulative oil extraction than in the other SSPs (Figure 1, middle panel).

Table 1 shows the assumed total quantities of fossil fuel resources in the MESSAGE model for the base year 2005. Figure 1 gives these resource estimates as supply curves. In addition, the assumptions are compared with estimates from the Global Energy Assessment (Rogner et al., 2012 3) as of the year 2009. Estimating fossil fuel reserves is built on both economic and technological assumptions. With an improvement in technology or a change in purchasing power, the amount that may be considered a “reserve” vs. a “resource” (generically referred to here as resources) can actually vary quite widely.

‘Reserves’ are generally defined as being those quantities for which geological and engineering information indicate with reasonable certainty that they can be recovered in the future from known reservoirs under existing economic and operating conditions. ‘Resources’ are detected quantities that cannot be profitably recovered with current technology, but might be recoverable in the future, as well as those quantities that are geologically possible, but yet to be found. The remainder are ‘Undiscovered resources’ and, by definition, one can only speculate on their existence. Definitions are based on Rogner et al. (2012) 3.

Table 1: Assumed global fossil fuel reserves and resources in the MESSAGE model. Estimates from the Global Energy Assessment also added for comparison
Source MESSAGE (Rogner et al., 1997 1) Rogner et al., 2012 2
Reserves+Resources [ZJ] Reserves [ZJ] Resources [ZJ]
Coal 259 17.3 – 21.0 291 – 435
Conventional Oil 9.8 4.0 – 7.6 4.2 – 6.2
Unconventional Oil 23.0 3.8 – 5.6 11.3 – 14.9
Conventional Gas 16.8 5.0 – 7.1 7.2 – 8.9
Unconventional Gas 23.0 20.1 – 67.1 40.2 – 122

The following table (Table 2) presents the fossil resource availability in ZJ (2010-2100) for coal, oil and gas, for SSP1, SSP2 and SSP3, respectively.

Table 2: Fossil resource availability for SSP1, SSP2, and SSP3
Type SSP1 [ZJ] SSP2 [ZJ] SSP3 [ZJ]
Coal 93 92 243
Oil 17 40 17
Gas 39 37 24

Coal is the largest resource among fossil fuels; it accounts for more than 50% of total fossil reserve plus resource estimates even at the higher end of the assumptions, which includes considerable amounts of unconventional hydrocarbons. Oil is the most vulnerable fossil fuel at less than 10 ZJ of conventional oil and possibly less than 10 ZJ of unconventional oil. Natural gas is more abundant in both the conventional and unconventional categories.

Figure 1 presents the cumulative global resource supply curves for coal, oil and gas in the IIASA IAM framework. Green shaded resources are technically and economically extractable in all SSPs, purple shaded resources are additionally available in SSP1 and SSP2 and blue shaded resources are additionally available in SSP2. Coloured vertical lines represent the cumulative use of each resource between 2010 and 2100 in the SSP baselines (see top panel for colour coding), and are thus the result of the combined effect of the assumptions on fossil resource availability and conversion technologies in the SSP baselines.

Figure 1: Cumulative global resource supply curves for coal (top), oil (middle), and gas (bottom) in the IIASA IAM framework

Conventional oil and gas are distributed unevenly throughout the world, with only a few regions dominating the reserves. Nearly half of the reserves of conventional oil is found in Middle East and North Africa, and close to 40% of conventional gas is found in Russia and the former Soviet Union states. The situation is somewhat different for unconventional oil of which North and Latin America potentially possess significantly higher global shares. Unconventional gas in turn is distributed quite well throughout the world, with North America holding most (roughly 25% of global resources). The distribution of coal reserves shows the highest geographical diversity which in the more fragmented SSP3 world contributes to increased overall reliance on this resource. Russia and the former Soviet Union states, Pacific OECD, North America, and Centrally Planned Asia and China all possess more than 10 ZJ of reserves.


Nuclear Resources

Estimates of available uranium resources in the literature vary considerably, which could become relevant if advanced nuclear fuel cycles (e.g., the plutonium cycle including fast breeder reactors, the thorium cycle) are not available. In MESSAGE advanced nuclear cycles such as the plutonium cycle and nuclear fuel reprocessing are in principle represented, but their availability varies following the scenario narrative. Figure 2 below shows the levels of uranium resources assumed available in MESSAGE scenarios, building upon the Global Energy Assessment scenarios (see Riahi et al., 2012 2). These span a considerable range of the estimates in the literature, but at the same time none of them fall at the extreme ends of the spectrum (see Rogner et al., 2012 3, Section 7.5.2 for a more detailed discussion of uranium resources). Nuclear resources and fuel cycle are modeled at the global level.

Figure 2: Global uranium resources in the MESSAGE interpretation of the SSPs compared to seven supply curves from a literature review

Figure 2 presents the global uranium resources in the MESSAGE interpretation of the SSPs compared to seven supply curves from a literature review (Schneider and Sailor, 2008 4). Conservative Crustal and Optimistic Crustal refer to simple crustal models of uranium distribution in the crust and the of extraction costs on the concentration. Pure-KCR refers to a fit of a simple crustal model to known conventional resources (KCR) as estimated by the Red Book 2003 (OECD/NEA 2004, 5). PPM-Cost over the simple crustal models include a relationship between uranium grade and extraction costs. FCCCG(1) and (2) as well as DANESS refer to estimats from more complicated models of the dependency of extraction costs on uranium concentration (and therefore resource grade).


Non-Biomass Renewable Resources

Table 3 shows the assumed total potentials of non-biomass renewable energy deployment (by resource type) in the MESSAGE model. In addition, the assumptions are compared with technical potential estimates from the Global Energy Assessment (Rogner et al., 2012 3). In this context, it is important to note that typical MESSAGE scenarios do not consider the full technical potential of renewable energy resources, but rather only a subset of those potentials, owing to additional constraints (e.g., sustainability criteria, technology diffusion and systems integration issues, and other economic considerations) that may not be fully captured within the model. These constraints may lead to a significant reduction of the technical potential.

Table 3: Assumed global non-biomass renewable energy deployment potentials in the MESSAGE-GLOBIOM model. Estimates from the Global Energy Assessment (Rogner et al., 2012 1) also added for comparison
Source MESSAGE Rogner et al., 2012 1
Deployment Potential [EJ/yr] Technical Potential [EJ/yr]
Hydro 38 50 - 60
Wind (on-/offshore) 689/287 1250 - 2250
Solar PV 6064 62,000 - 280,000
CSP 2132 same as Solar PV above
Geothermal 23 810 - 1400

Notes: MESSAGE-GLOBIOM renewable energy potentials are based on Pietzcker et al. (2014) 6, Eurek et al. (in review) 7, Christiansson (1995) 8, and Rogner et al (2012) 3. The potentials for non-combustible renewable energy sources are specified in terms of the electricity or heat that can be produced by specific technologies (i.e., from a secondary energy perspective). By contrast, the technical potentials from Rogner et al (2012) 3 refer to the flows of energy that could become available as inputs for technology conversion. So for example, the technical potential for wind is given as the kinetic energy available for wind power generation, whereas the deployment potential would only be the electricity that could be generated by the wind turbines.

Regional resource potentials for solar and wind are classified according to resource quality (annual capacity factor) based on Pietzcker et al. (2014, 6) and Eurek et al. (in review, 7). Regional resource potentials as implemented into MESSAGE-GLOBIOM are provided by region and capacity factor for solar PV, concentrating solar power (CSP), and onshore/offshore wind in Johnson et al. (in review, 9). The physical potential of these sources is assumed to be the same across all SSPs. Table 4, Table 5, Table 6, Table 8 show the resource potential for solar PV, CSP, on- and offshore wind respectively. For wind, Table 7 and Table 9 list the capacity factors corresponding to the wind classes used in the resource tables. It is important to note that part of the resource that is useable at economically competitive costs is assumed to differ widely (see Section Energy Conversion of MESSAGE-GLOBIOM).

Table 4: Resource potential (EJ) by region and capacity factor for solar photovoltaic (PV) technology (Johnson et al. in review, 1). For a description of each of the regions represented in the table, see Spatial dimension of MESSAGE-GLOBIOM
Capacity Factor (fraction per year)
0.28 0.21 0.20 0.19 0.18 0.17 0.15 0.14
AFR 0.0 1.1 46.5 176.6 233.4 218.2 169.9 61.9
CPA 0.0 0.0 0.0 10.3 194.3 315.5 159.4 41.9
EEU 0.0 0.0 0.0 0.0 0.0 0.0 0.1 1.0
FSU 0.0 0.0 0.0 0.2 2.8 23.6 94.9 116.6
LAM 0.1 4.9 49.4 165.6 157.5 167.4 81.4 48.5
MEA 0.2 3.1 100.8 533.6 621.8 310.1 75.3 14.5
NAM 0.0 0.3 24.3 140.4 131.0 116.3 155.7 106.4
PAO 0.0 0.0 0.1 2.2 53.1 226.4 311.2 158.9
PAS 0.0 0.0 0.0 0.2 0.8 17.0 31.2 12.8
SAS 0.0 0.0 6.1 42.7 67.2 82.3 23.7 4.1
WEU 0.0 0.1 0.2 3.0 12.8 39.4 58.3 33.3
Global 0.3 9.6 227.4 1074.7 1474.6 1516.3 1160.9 600.0

Table 5: Resource potential (EJ) by region and capacity factor for concentrating solar power (CSP) technologies with solar multiples (SM) of 1 and 3 (Johnson et al. in review, 1)
Capacity Factor (fraction of year)
SM1 0.27 0.25 0.23 0.22 0.20 0.18 0.17 0.15
SM3 0.75 0.68 0.64 0.59 0.55 0.50 0.46 0.41
AFR 0.0 3.6 19.0 81.6 106.7 62.8 59.6 37.8
CPA 0.0 0.0 0.0 0.0 0.0 0.3 11.5 53.0
EEU 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
FSU 0.0 0.0 0.0 0.0 0.0 0.1 0.4 6.1
LAM 0.0 2.0 7.0 11.8 29.3 57.1 56.8 53.5
MEA 0.1 3.7 24.8 122.4 155.3 144.5 68.4 34.0
NAM 0.0 0.0 0.0 6.3 19.7 20.2 29.6 43.2
PAO 0.0 3.0 75.1 326.9 158.3 140.4 40.2 10.2
PAS 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.6
SAS 0.0 0.0 0.0 0.1 3.9 8.7 16.1 9.8
WEU 0.0 0.0 0.0 0.0 0.2 0.7 2.4 3.0
Global 0.1 12.3 126.0 549.2 473.3 434.8 285.0 251.3

Table 6: Resource potential (EJ) by region and wind class for onshore wind (Johnson et al. in review, 1)
Wind Class
3 4 5 6 7 8+
AFR 38.2 21.3 13.4 6.8 2.6 2.1
CPA 24.7 11.4 5.4 2.6 0.3 0.0
EEU 6.1 5.7 0.3 0.0 0.0 0.0
FSU 52.3 83.8 5.8 0.8 0.0 0.0
LAM 33.5 15.9 9.6 5.7 3.9 3.7
MEA 56.1 22.2 6.0 2.1 0.9 0.3
NAM 28.6 66.4 23.7 1.5 0.4 0.0
PAO 18.9 18.8 3.6 1.4 1.8 0.5
PAS 5.2 2.9 0.8 0.2 0.0 0.0
SAS 12.3 7.9 2.4 1.6 0.9 0.3
WEU 16.1 10.5 6.6 8.2 3.7 0.6
World 292.1 266.8 77.5 30.9 14.3 7.5

Table 7: Capacity factor by region and wind class for onshore wind (Johnson et al. in review, 1)
Wind Class
3 4 5 6 7 8+
AFR 0.24 0.28 0.32 0.36 0.40 0.45
CPA 0.24 0.28 0.32 0.36 0.38 0.45
EEU 0.24 0.27 0.31 0.36 0.38 0.45
FSU 0.24 0.28 0.31 0.35 0.38 0.45
LAM 0.24 0.28 0.32 0.36 0.39 0.46
MEA 0.24 0.27 0.32 0.35 0.39 0.45
NAM 0.24 0.28 0.31 0.36 0.39 0.45
PAO 0.24 0.28 0.32 0.36 0.40 0.43
PAS 0.24 0.27 0.32 0.35 0.40 0.45
SAS 0.24 0.27 0.32 0.36 0.39 0.42
WEU 0.24 0.28 0.32 0.36 0.39 0.43

Table 8: Resource potential (EJ) by region and wind class for offshore wind (Johnson et al. in review, 1)
Wind Class
3 4 5 6 7 8+
AFR 3.1 2.4 2.0 2.0 1.1 1.7
CPA 3.5 4.3 2.6 0.9 1.3 0.1
EEU 0.7 0.6 1.0 0.0 0.0 0.0
FSU 1.8 4.6 14.2 13.3 4.3 0.7
LAM 7.1 7.3 5.3 2.7 2.6 5.9
MEA 3.2 0.9 0.8 0.9 0.6 0.9
NAM 4.5 18.2 24.0 16.0 7.3 2.1
PAO 5.8 11.2 15.3 9.8 2.6 2.5
PAS 5.3 6.6 4.7 1.5 0.1 0.0
SAS 1.9 0.9 0.6 0.5 0.0 0.0
WEU 3.5 4.7 8.8 12.9 10.3 0.9
World 40.4 61.5 79.4 60.5 30.3 14.8

Table 9: Capacity factor by region and wind class for offshore wind (Johnson et al. in review, 1)
Wind Class
3 4 5 6 7 8+
AFR 0.24 0.28 0.32 0.36 0.41 0.47
CPA 0.24 0.28 0.32 0.36 0.40 0.42
EEU 0.24 0.29 0.32 0.34 0.40 0.42
FSU 0.25 0.28 0.32 0.35 0.39 0.43
LAM 0.24 0.28 0.32 0.36 0.40 0.49
MEA 0.24 0.28 0.32 0.36 0.40 0.45
NAM 0.25 0.28 0.32 0.36 0.40 0.43
PAO 0.24 0.28 0.32 0.36 0.40 0.47
PAS 0.24 0.28 0.32 0.35 0.39 0.42
SAS 0.24 0.27 0.32 0.36 0.40 0.42
WEU 0.24 0.28 0.32 0.36 0.40 0.42


Biomass Resources

Biomass energy is another potentially important renewable energy resource in the MESSAGE-GLOBIOM model. This includes both commercial and non-commercial use. Commercial refers to the use of bioenergy in, for example, power plants or biofuel refineries, while non-commercial refers to the use of bioenergy for residential heating and cooking, primarily in rural households of today’s developing countries. Bioenergy potentials differ across SSPs as a result of different levels of competition over land for food and fibre, but ultimately only vary to a limited degree (Figure 3). The drivers underlying this competition are different land-use developments in the SSPs, which are determined by agricultural productivity and global demand for food consumption. (Fricko et al., 2016 10)

Figure 3: Global bioenergy potential

Availability of bioenergy is presented in Figure 3 at different price levels in the MESSAGE-GLOBIOM model for the three SSPs (Fricko et al., 2016 10). Typically non-commercial biomass is not traded or sold, however in some cases there is a market – prices range from 0.1-1.5$/GJ (Pachauri et al., 2013 11) ($ equals 2005 USD).

References

  1. ^  |  H Rogner, Roberto F Aguilera, Christina Archer, Ruggero Bertani, S Bhattacharya, M Dusseault, Luc Gagnon, H Harbel, Monique Hoogwijk, Arthur Johnson (2012). Chapter 7 - Energy resources and potentials. In Global Energy Assessment - Toward a Sustainable Future(pp. 423--512). Cambridge University Press, Cambridge, UK and New York, NY, USA and the International Institute for Applied Systems Analysis, Laxenburg, Austria.
  2. a b c d e f  |  Nils Johnson, Manfred Strubegger, Madleine McPherson, Simon Parkinson, Volker Krey, Patrick Sullivan (2016). A reduced-form approach for representing the impacts of wind and solar PV deployment on the structure and operation of the electricity system. Energy Economics, In Review ().

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