Interlinking interregional economic models with infrastructure networks: Three essays

Interregional economic trade implies transportation of commodities across space. These physical flows are supported by infrastructures such as the transportation, electric power, and natural gas networks. Those infrastructures impose constraints on flows and therefore have an impact on the final outcome that arises in a multiregional economy. Physical laws impose complex constraints on flow circulation. On the other hand infrastructure networks are widely extended across space increasing the interdependence between consumers and firms situated at distant locations.; Traditional static multiregional multicommodity models can be extended to consider these influences. We extended a commodity flow model based in fixed coefficient technology to consider the effect of these constraints on economic flows. Infrastructures are not only indirectly interconnected through the economic system but also have direct input-output relationships as in the case of the electric and natural gas network.; In the implementation approaches such as entropy maximization may prove to be especially useful in order to capture spatial flow dispersion and overcome the specialization that arises in planning models based on cost minimization approaches. Likewise network constraints limit such spatial specialization of flows. Network simplification may cause significant distortions on spatial interdependence between agents as they affect constraints governing physical flows.; Handling interdependent networks in a multiregional economic endogenous price model poses serious challenges in analytical and operative terms. Instead of constructing a unitary integrated model we may opt by establishing informational input-output relationships between the models and building an integrated model under a modular approach. This approach permits to preserve at significant extent the structure, algorithms, analytical tools, calibration procedures, and software applications of each model (now considered a module) and provides an operative way to handle the complex relationships that arise in this context.; Dynamic models vary in complexity depending on the time horizon length. In the short term, where we can reasonably assume that technology and the network topology and capacity are fixed we can model dynamics as a sequence of static equilibriums where each period may be linked to the previous ones by storage decisions. Changing conditions are reflected by demand seasonalities and variations in the nodal supplying capacities. The same amount of gross demand or supply variation may cause very different effects depending on the way it is distributed across network nodes.