element using gravity modeling or random-utility-maximization-based models. These are called multi-regional I/O models (MRIO). The discrete choice variant of this type of model is also known as a random utility-based multi-regional input-output model (RUMBRIO). Bachmann et al. (2014) provide extensive examples of the application of RUMBRIO models in transport economics.

# **4.1.3 (Spatial) Computable General Equilibrium models**

Among different economic trade models, CGE models have gained prominence due to their advantages in analyzing the wider economic impacts of a policy or exogenous trends (Robson et al., 2018). Specifically, a CGE approach simulates the whole economy by analyzing supply and demand for every market and their interactions. In this way, a CGE modeling framework allows the extraction of various economic indicators, including GDP, prices, demand elasticity, among others.

A CGE model is a nexus of mathematical equations that represents the evolution of a whole economy and takes into account its macroeconomic constraints and the microeconomic behavior of individual economic agents and their interactions. The CGE framework is built on modern micro-economic theory, which explains the conditions for economic equilibrium for all economic agents given certain demand and supply transactions. In CGE models, aggregate agents are used to represent the behavior of the whole population or of an industrial sector as a single economic agent. These agents are modeled based on the assumption that they follow a cost-minimizing behavior in performing their trade transactions. The price equilibrium conditions follow the basic market condition in which there has to be a balance in the demand and supply levels. The equilibrium is solved on a yearly basis and the model provides forecasted annual trade between economic agents as its output.

A particular extension of the CGE modeling approach is the incorporation of a spatial dimension in its model specifications, which gives birth to the Spatial Computable General Equilibrium (SCGE) model. SCGE models are able to account for price differentiations between regions and specify transport costs as one of the determinants of trade flows between regions. Examples of this modeling approach can be found in Bachmann (2017), who models the impacts of free trade agreements on transport costs and trade flows from and into Canada. Johansen and Hansen (2016) proposed a model to analyze the impact of transport infrastructure development programs on wider economic impacts in Norway. Lee et al. (2013) studied the impact of a carbon tax on the global economy by focusing on containerized commodities using an SCGE modeling approach from the Global Trade Analysis Project-Environment (GTAP-E). The model encompasses 129 countries and 29 sectors and is calibrated on the 2007 base year of the GTAP version 8 database. Sheng et al. (2018) studied the impact of a global carbon tax applied to international bunker emissions on several economic indicators using an SCGE model. The economic indicators studied include import and export volumes, GDP growth, and real GDP of countries and the countries’ market share of their export commodities.<sup>6</sup> Shahrokhi Shahraki and Bachmann (2018) provided a comprehensive review of SCGE models that have been applied to the domain of transportation. According to their study, the representation of transportation networks in SCGE models could influence the accuracy of model results, especially when the analysis requires a high level of disaggregation. Furthermore, Tavasszy et al. (2002) also noted the pitfalls of the applications of SCGE models in transport, which include oversimplification in the specification of transport costs of sectoral productions. Specifically, aggregate statistics on the share of transport costs in product values are commonly used in CGE models while,

> 6 SCGE models do not produce GDP as its direct output, but rather a variation of a welfare indicator that can be translated into a GDP prediction with additional assumptions.

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