Annex II: Methodology for the Global Scenarios
To explore the uncertainties and equity issues associated with fossil fuel production and use under different climate targets, the modelling in this paper used the TIMES Integrated Assessment Model at University College London (TIAM-UCL).163 This model represents the global energy system, capturing primary energy sources (oil, gas, coal, nuclear, biomass, and renewables) from resource production through to their conversion (electricity production), their transport and distribution, and their eventual use to meet energy demands across a range of economic sectors.
The model has a 16 region representation, allowing for more detailed characterization of regional energy sectors, and the trade flows between regions. Upstream sectors within regions that contain members of OPEC are modelled separately, for both OPEC and non-OPEC groups of countries. Regional coal, oil and gas prices are generated within the model. These incorporate the marginal cost of production, scarcity rents, rents arising from other imposed constraints, and transportation costs.
A key strength of TIAM-UCL is the characterization of the regional fossil resource base.164 For oil reserves and resources, these are categorized into current conventional proved and probable (2P) reserves in fields that are in production or are scheduled to be developed, reserve growth, undiscovered oil, Arctic oil, light tight oil, natural gas liquids, natural bitumen, extra-heavy oil, and kerogen oil. The latter three of these are all unconventional oil categories. For gas, these resources are categorized into current conventional 2P reserves that are in fields in production or are scheduled to be developed, reserve growth, undiscovered gas, Arctic gas, associated gas, tight gas, coal-bed methane, and shale gas. For oil and gas, individual supply cost curves for each of the categories are estimated for each region.
In the model, future demands for energy services (mobility, lighting, industrial heat etc.) drive the evolution of the system so that, an energy system in 2050 meets the energy services required, which have increased through the growth in population and the global economy. Decisions around what energy sector investments to make across regions are determined on the basis of the most cost-effective investments, taking into account the existing system in 2015, energy resource potential, technology availability, and crucially policy constraints such as emissions reduction targets. The model time horizon runs to 2100, as this is the timescale often used for climate stabilization. A climate module is also integrated into the model framework, allowing for a simple representation of the climate system. It ensures that any future energy system is consistent with a given temperature objective, such as limiting warming to 2°C by 2100.
Other important characteristics of the model include:
- The objective to minimize cumulative discounted costs of the energy system (investment, O&M, fuels) over the time horizon, based on a discount rate of 3.5 per cent.
- The assumption of perfect foresight, for example any investment decision made in 2020 is made with an understanding of future system requirements out to 2100.
- Energy service demands that are responsive (elastic) to changes in price; so if prices increase, demands can reduce based on elasticity.
- BECCS is included in the model, and provides ‘negative emissions’. This allows CO2 emissions to be emitted at one point in time and then removed from the atmosphere later. In the case of BECCS, the removal is by combusting bioenergy and storing the CO2 underground. This generates negative emissions based on the assumption that the bioenergy CO2 would have been sequestered in the biosphere by regrowth.
Climate scenarios in TIAM-UCL
The 2°C scenarios in this paper are based on the TIAM-UCL modelling and are normative in nature. They consider a future temperature stabilization target as given, and assess cost-optimal scenarios for meeting that target. One example is the rapid phase-out of coal under the 2°C scenario; while this may not happen, the insight for decision-maker is that phasing out coal as rapidly as possible offers the least-cost pathway to 2°C, as it is the most carbon-intensive fossil fuel and as there are viable alternatives in the near term (at least in power generation).
Equity scenarios in TIAM-UCL
Modelling was undertaken using TIAM-UCL to explore the impacts of allocating extractive rights based on development need using the HDI rankings,165 as described in Box 9 of the paper. The criteria for equity-based allocation were first proposed by Caney.166 The regions of the model were first allocated to a group, based on the HDI index values of the countries within those regions (see Table 4). For regions that were a specific country e.g. China, this was straightforward. However, for some of the highly aggregated regions e.g. Africa and ‘Other Developing Asia’, this was problematic. It was therefore decided to use population-weighted scores to help determine what score to allocate to each given TIAM-UCL region.
Table 4: Allocation of TIAM-UCL regions to Human Development Index (HDI) groups
|
HDI group |
HDI level (0–1) |
TIAM-UCL regions |
|---|---|---|
|
Low-medium human development (LMHD) |
<0.7 |
Africa, India, Other Developing Asia |
|
High human development (HHD) |
0.7–0.8 |
Middle East, Mexico, South and Central America, China, Former Soviet Union |
|
Very high human development (VHHD) |
>0.8 |
Western Europe, Eastern Europe, UK, Canada, USA, Australia, Japan, South Korea |
Source: Compiled by UCL (2018), based on TIAM-UCL regions and UNDP HDI rankings.
There are a few issues to note with the above categorization. Russia, which is in the HHD group, has a score at the upper end of the range at 0.798. Similarly, a number of the oil-rich Gulf states have HDI scores in the VHHD range but are included in the HHD group because of larger population countries in the Middle East having scores in the HHD range. This is important for such oil exporting countries that would otherwise have a lower extraction right allocation if they were in the VHHD group. Other Developing Asia also has a high diversity of HDI scores; however, the most populous countries such as Indonesia, Bangladesh and Pakistan have HDIs of less than 0.7. Finally, Eastern European countries all have scores in the VHHD range, except for Bulgaria and Romania, which have scores of 0.782 and 0.793, respectively.
Production levels determined under the 2°C scenario were then allocated to the HDI groups, with HDI1 allocated an increased production quota and HDI3 a lower production quota. The quota levels were originally determined based on a differentiated carbon tax on extraction, with a higher tax applied to HDI3 (thereby reducing production), with HDI2 incurring a tax level of 60 per cent of that for HDI3, and HDI1 only incurring a tax level of 10 per cent of the HDI3 tax level. The use of carbon tax mechanism is not intended as a politically viable mechanism with which to redistribute the remaining ‘burnable’ carbon budget, but rather as a means of allowing the model to endogenously determine the redistribution, without prescribing which type of fossil fuel production would be allocated where. This resulted in two scenarios: a low-quota case leading to a lower level of redistribution to HDI1, and a high-quota case with a higher level of redistribution to HDI1.
163 Further information on TIAM-UCL can be found on the ADVANCE project website at http://themasites.pbl.nl/models/advance/index.php/Model_Documentation_-_TIAM-UCL (accessed 31 May 2018).
164 McGlade, C. and Ekins, P. (2015), ‘The geographical distribution of fossil fuels unused when limiting global warming to 2°C’, Nature, 517, pp. 187–190, (accessed 31 May 2018); McGlade, C. and Ekins, P. (2014), ‘Un-burnable oil: An examination of oil resource utilisation in a decarbonised energy system’, Energy Policy, 64, pp. 102–112, (accessed 31 May 2018); McGlade, C., Bradshaw, M., Anandarajah, G., Watson, J. and Ekins, P. (2014), ‘A Bridge to a Low-Carbon Future? Modelling the Long-Term Global Potential of Natural Gas, Research Report, London: UKERC, (accessed 31 May 2018); Bauer, N., McGlade, C., Hilaire, J., and Ekins, P. (2018), ‘Divestment prevails over the green paradox when anticipating strong future climate policies’, Nature Climate Change, 8, pp. 130–134 (accessed 31 May 2018); Pye, S., McGlade, C., Bataille, C., Anandarajah, G., Denis-Ryan, A., & Potashnikov, V. (2016), Exploring national decarbonization pathways and global energy trade flows: a multi-scale analysis, Climate Policy, 16 (sup1), S92-S109 (2016), (accessed 31 May 2018).
165 UNDP, Human Development Index (HDI) rankings, available http://hdr.undp.org/en/composite/HDI (accessed 31 May 2018).
166 Caney, S. (2016), ‘Climate change, equity, and stranded assets’, Oxfam America Backgrounder, https://www.oxfamamerica.org/static/media/files/climate_change_equity_a… (accessed 31 May 2018).