BENEFICIOS DE APLICACIÓN DE LA METODOLOGIA

The BU TIMES_PT and the TD CGE GEM-E3_PT models were used to generate a baseline and three low carbon scenarios for Portugal up to 2050. Although the TIMES and GEM-E3 models have been widely used to assess the impact of the climate policies of the EU or its member states (Blesl et al., 2003; EC, 2008; Proost, et al., 2009; Russ et al., 2009), the mitigation options provided by these models have never been compared. To ensure that any divergence in the models’ results is not due to different reference states and assumptions, the models were initially calibrated to a common baseline scenario.

4.2.1 T

GEM-E3_PT

The GEM-E3 (General Equilibrium Model for Economy, Energy, Environment) is a multi-region, multi-sector, recursive, dynamic CGE model (E3M Lab, 2010). It adopts the quantitative application of the Arrow–Debreu paradigm, computing the equilibrium prices of goods, services, labour, and capital that simultaneously clear all markets, and optimizes the behaviour of economic agents.

GEM-E3’s production technology is represented by constant substitution elasticity (CES) production functions. The CES function combines primary factors with the intermediate consumption of materials, services, and energy in a four-level nested structure.

The GHG emissions abatement achieved by the production sectors results from three available strategies: (1) switching fuel to low-carbon options (i.e. decreasing carbon intensity, driven by substitution elasticities between energy carriers (e.g. electricity, oil, natural gas, and coal; E3M Lab, 2010); (2) increasing energy efficiency (i.e. decreasing energy intensity) by substituting energy for other production factors such as materials, labour, and capital (note that this can also translate into a shift to renewables, although this cannot be differentiated in the GEM-E3 model); (3) reducing activity levels (e.g. reducing domestic production). On the consumer side, the mitigation options are mainly driven by energy saving via energy demand price elasticities.

GEM-E3_PT corresponds to a single country version of GEM-E3 for Portugal and assumes 18 production sectors (see Appendix 4.7). The 2005 benchmark Social Accounting Matrix and transfers between sectors information were built from Use and Supply IO tables, published by Eurostat

respectively. Energy consumption was calibrated by crossing the national energy balances (DGGE, 2007) with the energy prices published by the IEA (2008). The GHG generated by each productive sector and category of consumption were computed through the use of aggregated CO², CH⁴, and N²O emissions factors for coal, oil, and natural gas, and the national energy balance, and then validated with the national GHG inventories (APA, 2012). The GEM-E3_PT CES nesting structure differs from the standard GEM-E3 (E3M LAB, 2010) model at the second level where the composite factors of labour, energy, and materials (LEM) are split by the three components. At the third level, CES functions define the substitution between materials types, as it defines (separately) energy substitution between electricity and a fossil fuel aggregate (coal, oil, and natural), which is divided at the succeeding level. The substitution possibilities are set through constant elasticities, from international econometric studies (for details, see E3M LAB, 2010). CES varies from 0.4 to 0.5 up to LEM factors, being the range of elasticities values between the energy sources higher (it is 0.5 between fossil fuels aggregated with electricity and from 0.4 to 0.9 between fossil types for transports and energy-intensive industries, respectively).

4.2.2 T

TIMES_PT

TIMES (The Integrated MARKAL-EFOM) system is a dynamic linear optimization model generator, which simulates regional or multi-regional energy systems (Loulou, et al., 2005). Based on a technology database and external constraints (e.g. GHG emissions caps, fossil fuel import prices, and energy sources potential), TIMES is used to compute the energy supply/demand equilibrium under conditions of perfect foresight. The ultimate goal of TIMES is to satisfy energy services demand at the minimum total system cost, making simultaneous decisions about equipment investment and operation, primary energy supply, and energy trade (Loulou et al., 2005). The TIMES_PT model represents the Portuguese energy system, in particular energy supply (e.g.

petroleum refining), power sector, and final energy consumption sectors (e.g. industry, residential, commercial, agricultural, and transportation, which in turn are divided into several subsectors) (see Appendix 4.7). The technology database in the model includes the characteristics of the existing and future energy technologies, namely efficiency, capacity factor, availability, technical lifetime, investment, and operation and maintenance costs. The original technology data was obtained from the European NEEDS Project and has been updated within the EU RES2020 project and from international literature, and validated by national stakeholders.

TIMES_PT is calibrated to 2005 national energy balances (DGGE, 2007) and includes CO², CH⁴, and N²O combustion and process emissions, which are calculated and calibrated using emissions factors per energy carrier and/or sector from national GHG inventories (APA, 2012). As with GEM-E3_PT,

the same strategies to reduce GHG abatement are assumed in TIMES_PT: (1) decreasing carbon intensity, by choosing technologies that provide the same service with a less carbon intensive fuel (e.g. substitution of a coal boiler to a gas boiler); (2) reducing energy intensity through more efficient technologies; (3) decreasing activity levels (reduction of energy services demand or mobility) through exogenous demand-price elasticities. A price elasticity of –0.3 for almost all demands categories is assumed in TIME_PT (as supplied by the Katholieke Universiteit Leuven). This is assumed to be a generic value for the EU countries. Although the TIMES model can also reduce GHG emissions through carbon capture and storage (CCS) technologies, this mechanism was not considered (see Section 4.2.3).

4.2.3 C

The GEM-E3 and TIMES models have been applied together in several European projects (e.g.

NEEDS, RES2020, REALISEGRID) in the following way: GEM-E3 is first used to compute the demand drivers, such as GDP and sector domestic production growth; these are then used to determine the evolution of energy services and materials demand (Step I of the calibration process, described later in this section), used as TIMES inputs (Step II of the calibration process). No further feedback of any kind has so far been considered.

To guarantee that both models are benchmarked to a common baseline scenario, and before considering particular climate mitigation targets, a calibration process was developed based on a soft link between the two models. The link follows an approach close to the one used by Labriet et al. (2010), such that prices and quantity variables (e.g. energy, emissions) are exchanged between the models, which are iteratively solved until similar results (i.e. less than a 10% difference) are reached. The overall calibration framework is depicted in Figure 4.1.

The main assumptions of the baseline scenario (BS) in both models include (1) an annual real interest rate of 4%; (2) fossil fuel import prices (adopted from the Current Policies Scenario in IEA (2010) up to 2035 and extended till 2050), of US$²⁰⁰⁹162.0/barrel for crude oil, US$²⁰⁰⁹176.9/BTU for natural gas, and US$²⁰⁰⁹124.5/tonne for coal in 2050; (3) a socio-economic scenario that assumes an annual average GDP and population growth of 2.3% and 0.3%, respectively, between 2010 and 2050 (Seixas et al., 2010); (4) a ban on nuclear electricity generation; and (5) the unavailability of CCS technologies (this is justified by the absence of this option in GEM-E3_PT).

Figure 4.1 | GEM-E3_PT and TIMES_PT calibration framework (Notes: The dotted grey lines represent Baseline scenario assumptions (e.g. economic and population growth, fossil fuel import prices) or calibration parameters (e.g. real interest rate, emission factor); the black lines represent the iteration process: full black lines are direct inputs/outputs and the black dashed lines represent indirect inputs).

The calibration process between the two models, as depicted in Figure 4.1 proceeded as follows:

- In Step I, the economic drivers and total energy prices by sector, generated by GEM-E3_PT through the optimization behaviour of the economic agents, were used to produce the evolution of energy services and materials demand according to the demand generation equation from Van Regemorter and Kanudia (2006) (as cited in Simões, Cleto, Fortes, Seixas, &

Huppes, 2008). The equation assumes that the evolution of the energy services demand is a product of the economic drivers and the total energy prices evolution and autonomous efficiency improvement in industry.

- In Step II, the energy service and materials demand generated in Step I were used as inputs for TIMES_PT, which was then run to compute the least-cost technological profile of the energy system (given in terms of energy consumption, i.e. quantities per sector per energy source, and the corresponding GHG emissions).

- Finally, in Step III, the sectoral energy profile given by TIMES_PT (in terms of both final and primary energy) for the time horizon was included in GEM-E3_PT, where the evolution of energy efficiency is associated with an exogenous AEEI. The goal of this step is to align, in the BS, the models energy consumption and GHG emissions per sector. In most uses of the general GEM-E3 model, AEEI is a fixed value, which is derived from the literature and is identical for all

-Sectoral energy

energy carriers and end-use energy sectors. However, in the present GEM-E3_PT, this parameter was disaggregated per energy carrier and sector and adjusted according to TIMES_PT technological choices in order to ensure consistency between the two models. (See the Appendix 4.7, which presents the 10 sectors considered in the calibration process and its correspondence with the models categories).

Modifications in sector energy consumption can induce changes in domestic production and, consequently, on energy services and materials demand. The three steps were therefore repeated until the energy consumption per energy carrier and calibration sector obtained from TIMES_PT and GEME3_PT models converged, i.e. until there was only a minor difference between the models (less than 10% or 1PJ), relative to the previous iterative process.