A Spatial Price Determination Model for Multi‐Market Heterogeneously Distributed Resources: An Application to Forestry Markets in Sweden

Abstract

The spatial dimension is a key paradigm in price determination, as attested by recent studies in the literature that highlighted the differential in market behavior between spatial and non‐spatial pricing settings. In this paper, we develop a model of spatial pricing for multi‐market heterogeneously distributed resources, with an application to the Swedish forestry sector. The focus of the model is to estimate the impact of spatial interaction on the demand for resources in terms of resource allocation, competition, and pricing. In its core, the pricing mechanism relies on a supply–demand framework. Using disaggregated data at the gridcell level for forest feedstock supply and harvesting costs in Sweden, we construct regional supply curves for each gridcell assuming a maximum transportation distance to delimit the potential market. Demand nodes are exogenously determined and are adjusted using a distance‐decay model to assess demand pressure across locations. We apply the model empirically to assess the impact on forest feedstock prices of a 20 TWh increase in biofuel production.

A spatial-explicit price impact analysis of increased biofuel production on forest feedstock markets: A scenario analysis for Sweden

“…In the first part, the optimal location and spatial forest feedstocks demand is determined under a set of exogenously given feedstock prices using the BeWhere-Sweden model. In the second part, the spatial-explicit feedstock demand changes are used in the spatial price determination model (SpPDM) in order to derive spatial price changes [37]. As such, the integrating framework relies on a "soft-link" between the SpPDM and the BeWhere Sweden models, which relies primarily on data exchange between the two models with respect to key variables of interest.…”

“…3 conceptually outlines the framework adopted in the SpPDM model. A detailed discussion of the SpPDM model is provided, along with a detailed technical discussion of the distancedecay framework for estimating demand pressure in [37].…”

“…A sensitivity analysis is made with respect to the distance-decay parameters that govern the spatial interaction across location. More specifically, the impact on the aggregate demand pressure from changing the values of the parameter determining the intensity of the spatial interaction (gamma) [37]. Fig.…”

Distance in Spatial Analysis: Measurement, Bias, and Alternatives

“…However, this issue has been largely neglected, except for a few studies. When polygons are involved, most studies provide no or minimal description of how the distance was computed and why the particular method was used (He, Páez, and Liu 2017; Ouraich and Lundmark 2019). In some location analysis where distance is explicit of interest, the effect of using the representative point of a polygon to measure the polygon‐to‐point distance has been studied.…”