Abatement Technologies and their Social Costs in a Hybrid General Equilibrium Framework.

AuthorMiess, Michael

    Depending on the fuel-mix to generate electricity and the modal split, individual motor vehicle transportation is responsible for up to 40% of total greenhouse gas (GHG) emissions, and for up to 30% of total emissions causing acidification and smog formation (Kerkhof et al., 2009; Mach et al., 2018). In numerous countries around the globe, the total share of emissions attributed to individual transportation is still growing, despite increasing efforts worldwide to reduce GHG emissions to meet targets designed to strengthen the global response to climate change. Electromobility is generally viewed as a feasible solution to the problem of growing GHG emissions in the transport sector, which at the same time allows societies to maintain a potentially desired high share of individual transportation.

    A number of policy measures have already been implemented to address this challenge. At the EU level, these include the EU-wide climate-energy targets set at 20-20-20 by 2020 and at 40-32-32.5 by 2030 for GHG emissions reduction, the share of renewable energy, and improvements in energy efficiency. (1) Moreover, and especially relevant for electromobility, the EU has defined a set of technology and emission standards for producers of vehicles--in particular EU Regulation 333/2014 setting the average emission target for the new vehicle fleet at 95g C[O.sub.2] per km in 2020. (2) This policy background calls for a systematic evaluation of potential economic costs connected with different abatement technologies for the transport sector, and different policy instruments that might foster their introduction. An economic assessment of different measures to mitigate emissions from transportation can support policy-makers by providing insights into potential economic costs, and thus help in selecting the most suitable policy instrument to reach certain targets. One major stepping stone in such an endeavor is an estimation of the total social costs and benefits of a large-scale introduction of electric vehicles as an abatement technology. For this purpose, a suitable modelling framework is essential to inform the scientific community, the general public and, in particular, policy-makers to what extent and under what conditions electric vehicles can be a viable policy option to reduce GHG emissions as only one possibility among several abatement technologies (Hourcade et al., 2006).

    Existing approaches to modeling abatement technologies have their respective strengths and weaknesses. Partial equilibrium bottom-up (BU) models such as TIMES (Recka and Scasny, 2016) are suitable to determine direct abatement costs, and they can capture interactions between energy carriers, energy technologies, and other features of the energy sector, including restrictions on fuel availability or technology deployment. Exogenous demand for electricity or energy services is satisfied via least-cost optimization of a technology-mix. However, energy system models generally disregard the economy-wide impacts of technological change and consider behavioral aspects and decisions only in a very limited way (Rivers and Jaccard, 2005; Bohringer and Rutherford, 2008). Therefore, a general equilibrium top-down (TD) approach, often represented by a computable general equilibrium (CGE) model, has increasingly been used in the literature, in particular to asses indirect abatement costs (Kiuila and Rutherford, 2013). While these TD models might have obvious advantages when compared to BU models, Andersen et al. (2019) argue that they lack the technological details underlying the generation of energy by different technologies, and that they usually abstract from disagreggated energy demand and supply by heterogeneous consumers and producers. According to Kiuila and Rutherford (2013), abatement technologies are either not incorporated in TD models due to lack of technology detail or, if they are modeled, their cost is disproportionately higher in comparison to substitution of fuels, which then becomes the preferred abatement channel. This considerably biases the economic cost of environmental policies upwards if estimated by TD models (Rivers and Jaccard, 2005; Nestor and Pasurka, 1995).

    The complementarity of the properties of TD and BU models makes them suitable to create interlinked structures. Such linked top-down bottom-up models feature both a rich and detailed technology sector, as well as theory-based realistic micro-founded behavior of agents that interact with macroeconomic feedback structures, such as economic sectors in general equilibrium. These features emphasize the different models' strengths and limit their weaknesses at the same time (Bataille et al., 2006). TD and BU models can be linked by iteratively transmitting information from one model to the other (Helgesen and Tomasgard, 2018). Such efforts require the identification of common measuring points (Wene, 1996), i.e. overlaps of TD and BU models, and the determination of appropriate ways these modelling paradigms can be reconciled in one common framework.

    Bohringer and Rutherford (2008) identify three broad categories of linking techniques: 1) Soft-linking is based on the iterative convergence of central parameters that is fully dependent on users' inputs, and on the decision whether and how to interchange the information and to adjust model inputs (Kumbaroglu and Madlener, 2003). An example of a soft link between a BU model (MARKAL) and a TD Input-Output model (LIFT) is provided by Steckley et al. (2011). In this procedure, the scenario outputs provided by the MARKAL model in the form of fuel mixes and efficiency changes are manually transferred to the LIFT model in order to run the scenario again with LIFT. (3) However, soft-linking usually suffers from the methodological and structural differences between the two modeling approaches, which may induce unsuccessful convergence of parameters (Bohringer and Rutherford, 2009). 2) Hard-linking, on the other hand, relies on formalized and algorithmically automated model processing and information transmitting, generating one unique result for each set of data and assumptions (Helgesen and Tomasgard, 2018). Another important advantage of hard-linking is that it decreases the bias that occurs due to different results produced by distinct model types and due to the (potentially arbitrary) judgement by users of how to best link these different model types. Despite its drawbacks, the soft-linking approach has been prevalent with many practical applications in the literature, for example see the MARKAL-EPPA hybrid model by Schafer and Jacoby (2006). Other examples are listed in Andersen et al. (2019). 3) Integrated modeling, as developed by Bohringer and Rutherford (2008), integrates TD and BU models via mixed complementarity problem (MCP) algorithms, and the models are run inseparably in one common framework. Although Bohringer and Rutherford (2009) provide improved MCP solution algorithms, there are very few implementations (Helgesen, 2013); see Sue Wing (2008) or Tapia-Ahumada et al. (2015) for some of the few available examples. On a larger scale, there seems to be only very few instances in which the MCP approach was used. One example is Bohringer and Loschel (2006), in which a hybrid CGE model is constructed that investigates the effects of renewable energy promotion in Europe. A fully integrated hybrid model, though not solved via the MCP, is available in Kim et al. (2006), in which an Integrated Assessment Model is linked to a bottom-up representation of the transport sector.

    This paper describes a different and novel integrated hybrid approach for modelling abatement technologies. It contributes to advancing the knowledge in this area of research by recognizing consumer choice as a central driver of endogenous technological change. We focus on individual motor vehicle transportation, and build our hybrid model on the direct integration of BU into a TD CGE framework according to the methodology proposed by Bohringer and Rutherford (2008). However, following Truong and Hensher (2012), we move this methodology several steps forward with our approach. In particular, we couple the dynamic CGE model to the endogenous demand for a low-emission, energy efficient technology according to heterogeneous consumer preferences derived from a discrete choice (DC) model that is estimated using survey data specifically designed for this purpose (Bahamonde-Birke and Hanappi, 2016). Consumers, represented by nine types of households defined by level of education and residence area by degree of urbanisation, decide between public and individual transport, as well as between transportation and non-transportation goods on the consumption side of the CGE model. In doing so, we address several points of the critique of more traditional BU technology modelling as discussed in Rivers and Jaccard (2005) relating to, among others, the heterogeneity of consumer preferences for technology, imperfect substitutability of technologies, and imperfect information on these technologies.

    In our model, individual transport technologies include conventional vehicles (CV) and three alternative fuel vehicles (AFVs) comprising hybrid (HEV), plug-in hybrid (PHEV), and battery electric (BEV) vehicles. (4) Besides the monetary variables, we include charging station availability and driving range as non-monetary variables in the DC model, since the literature shows the importance of their inclusion for a proper estimation of consumer demand for EVs (Pernollet et al., 2019; Hamed and Al-Eideh, 2018). The consumer choice then translates to household demand for the new durables, determining their supply on the production side of the CGE model and the capital stock (the vehicle fleet), consequently affecting the use of related non-durable goods that are needed to operate the vehicle fleet (fuel and electricity use, vehicle service and maintenance). Endogenously...

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