Abstract: This paper builds a benchmark framework to study optimal land use, encompassing land use activities and environmental degradation. We focus on the spatial externalities of land use as drivers of spatial patterns: land is immobile by nature, but local actions affect the whole space since pollution flows across locations resulting in both local and global damages. We prove that the decision maker problem has a solution, and characterize the corresponding social optimum trajectories by means of the Pontryagin conditions. We also show that the existence and uniqueness of time-invariant solutions are not in general guaranteed. Finally, a global dynamic algorithm is proposed in order to illustrate the spatial-dynamic richness of the model. We find that our simple set-up already reproduces a great variety of spatial patterns related to the interaction between land use activities and the environment. In particular, abatement technology turns out to play a central role as pollution stabilizer, allowing the economy to reach a time-invariant equilibrium that can be spatially heterogeneous. (C) 2014 Elsevier B.V. All rights reserved.

Abstract: The main aim of the paper is to show how, and how many, simulations carried out using the Land Surface Model UTOPIA (University of TOrino model of land Process Interaction with Atmosphere) are representative of the micro-meteorological conditions and exchange processes at the atmosphere/biosphere interface, with a particular focus on heat and hydrologic transfers, over an area of the Piemonte (Piedmont) region, NW Italy, which is characterized by the presence of many vineyards. Another equally important aim is to understand how much the quality of the simulation outputs was influenced by the input data, whose measurements are often unavailable for long periods over country areas at an hourly basis. Three types of forcing data were used: observations from an experimental campaign carried out during the 2008, 2009, and 2010 vegetative seasons in three vineyards, and values extracted from the freely available Global Land Data Assimilation System (GLDAS, versions 2.0 and 2.1). Since GLDAS also contains the outputs of the simulations performed using the Land Surface Model NOAH, an additional intercomparison between the two models, UTOPIA and NOAH, both driven by the same GLDAS datasets, was performed. The intercomparisons were performed on the following micro-meteorological variables: net radiation, sensible and latent turbulent heat fluxes, and temperature and humidity of soil. The results of this study indicate that the methodology of employing land surface models driven by a gridded database to evaluate variables of micro-meteorological and agronomic interest in the absence of observations is suitable and gives satisfactory results, with uncertainties comparable to measurement errors, thus, allowing us to also evaluate some time trends. The comparison between GLDAS2.0 and GLDAS2.1 indicates that the latter generally produces simulation outputs more similar to the observations than the former, using both UTOPIA and NOAH models.