Modification of GEM-E3 technological innovation module

By , Head of E3MLab at ICCS

Climate change is one of the greatest global policy challenges, as it is widely recognised that unabated climate change can have large impacts on human societies and economic development. Despite the global nature of climate change impacts, the outcome of the recent Conferences of the Parties (COP) to the UNFCCC1 held in Copenhagen (2009), Cancun (2010), Durban (2011), Doha (2012) and Warsaw (2013) suggest that the ideal of coordinated and stringent global climate policy action is highly unlikely to become a nearterm reality. As a result, emphasis has shifted from global cooperative action to regional climate policies and to the integration of other policy priorities, such as energy security, uptake of low carbon technologies, avoidance of external costs (air pollution, human health, etc.), innovation through R&D, industrial competitiveness and economic development policies.
General Equilibrium models have been extensively used to evaluate the macro-economic impacts of alternative energy and climate policies and to quantify the overall costs of climate change mitigation action. They represent comprehensively the articulation of production sectors in the macro-economy level and they account for the complex interactions between the energy system and the overall economy. However, they usually lack technological details for the representation of the energy system [1]. In order to overcome this limitation, in the context of SIMPATIC, the multi-sectoral, multi-regional global computable general equilibrium (CGE) model GEM-E3 model has been enhanced with a bottom-up representation of the energy system and emissions reduction options, especially with regard to power generation technologies, energy efficiency improvements, representation of the transport sector (including mobility electrification), energy demand for households and biofuels.
The uptake of RES and other clean energy technologies depends on their relative costs compared to fossil fuel alternatives. Thus, the evolution of costs of low and zero carbon technological options is considered an important factor for the restructuring of the global energy system towards decarbonisation. Strong climate policies (mainly in the form of carbon pricing) result in increased competitiveness of clean energy technologies compared to fossil fuel options leading to the massive uptake of these technologies. The increased technological deployment leads to reductions in the costs of low and zero carbon technologies enabled by economies of scale and other learning by doing effects.
“Experience” curves have been explicitly introduced in GEME3-RD, in which productivity improvements of clean energy technologies depend on accumulation variables (usually cumulative production or capacity are used to determine the cost improvements of power generation technologies).
The impact of innovation through R&D efforts can also be important especially for technologies that are in early stages of development and commercial uptake (such as electric vehicles and CCS2 technologies). The GEME3-RD model incorporates endogenous mechanisms to simulate technology dynamics for clean energy technologies. These mechanisms link innovations realised by production sectors to accumulation of R&D (“knowledge stock”), knowledge diffusion and spillovers (between production sectors and regions/countries) and to the profit maximisation behaviour of the representative firms. An endogenous demand-driven innovation mechanism is introduced in GEME3-RD according to which the demand addressed to a specific production sector boosts its R&D expenditure and thus its innovation (“learning by searching” effect). In the GEME3-RD model specification, demand-led innovation implies that economic demand patterns (i.e. how aggregate demand and overall economic output is distributed across production sectors) are both determined by and determine R&D expenditures realised by each production sector and thus the R&D driven innovation. The modelling of the interaction between sectoral demand (and production) and innovation through R&D expenditure constitutes an important challenge for the GEME3-RD model. Economies are interconnected through multiple channels, most important of which are: (a) the energy market channel (b) international trade of goods and relocation of industrial production and (c) endogenous technology learning and diffusion. The energy system partial equilibrium models and the optimal growth Integrated Assessment models usually characterise the energy market and the technology diffusion effect, but they neglect international trade and relocation of industrial activities [2]. In addition to the above, GEME3-RD is also able to quantify the international trade effects induced by alternative climate policies. Another effect that has not been explicitly captured by any energy economy model so far [2] concerns the potential domestic industry effects that being a global technology leader might bring about. In order to evaluate the possibility that a region (most importantly the EU which is considered as a first mover in climate policies) becomes a leader in clean energy technologies and sells them to the rest of the world, the GEME3-RD model has been enriched with a separate representation of the production of the most important clean energy producing industrial sectors and their global trade. Within the SIMPATIC framework, the GEME3-RD team has the specific task to study endogenous growth and technological innovation and to quantify the macro-economic impacts arising from policies and activities related to Greenhouse Gas (GHG) emission abatement in Europe and in other regions of the world. Towards this end, the model has been significantly enhanced in order to: i. incorporate a detailed bottom-up representation of the energy system ii. include endogenous technological change mechanisms through both learning by experience and by R&D innovation. Cost reductions for low and zero carbon technologies are assumed to be achieved as a result of their high deployment (“learning by doing”) and increased R&D expenditures (“learning by searching”) in clean energy producing sectors. iii. represent separately the clean energy producing industrial sectors and their global trade. The enhanced GEME3-RD model3 constitutes a robust tool for the quantitative assessment of technological innovation policies and for the consistent evaluation of macro-economic impacts of environmental and energy policies. The deliverable presents the most important changes that had to be undertaken in order to give the GEM-E3 model such capabilities. Section 2 describes the introduction of bottom-up representation of the energy system into the general equilibrium model. Section three discusses the implementation of endogenous technological learning mechanisms in GEME3-RD (learning by doing and learning by research), while the learning rates for low and zero carbon technologies used in the model are identified. Section four presents the methodology and the data employed in order to represent separately the production of new clean energy industrial sectors, their global trade, their R&D expenditures and the technological spillovers. Section 5 concludes.