MODELING TIMBER SUPPLY, FUEL-WOOD, AND ATMOSPHERIC CARBON MITIGATION

There is general agreement that global warming is occurring and that the main contributor to this probably is the buildup of green house gases, GHG, in the atmosphere. Two main contributors are the utilization of fossil fuels and the deforestation of many regions of the world. This paper examines a number of current issues related to mitigating the global warming problem through forestry. We use discrete time optimal control to model a simplified carbon cycle. The burning of fossil fuels increases atmospheric carbon while the burning of fuel-wood along with its forest source maintain an atmospheric carbon level. The standing timber in the forests is a carbon sink, as are wood buildings and structures, and fossil fuel in the ground. Through time the buildings and structures decay and release carbon to the atmosphere. We also present a numerical example to help illustrate the characteristics of the model. The conclusions are that the forest sector can have a significant impact.Environmental Economics and Policy,

VALUING WILDLIFE MANAGEMENT: A UTAH DEER HERD

Managers of public wildlife resources generally are concerned with enhancing the quality of recreation by increasing wildlife through habitat manipulation. However, current recreation valuation studies have focused upon variables that are inappropriate for use in these management decisions. The economic criterion for these decisions should be the value of a change in the stock of the wildlife population compared to its cost. An estimate of such a value was made for the Oak Creek deer herd in Utah, using a household production function approach in an optimal control framework. The value of an additional deer in the herd was estimated to be approximately $40.00.Resource /Energy Economics and Policy,

The Costate Variable in a Stochastic Renewable Resource Model

In this paper we discuss the costate variable in a stochastic optimal control model of a renewable natural resource, which we call a fishery. The role of the costate variable in deterministic control models has been discussed extensively in the literature. See, for example, Lyon (1999), Clark (1990, pp. 102-107), and Arrow and Kurz (1970, pp. 35-37); however, there is little discussion of this variable for stochastic models, even though the costate variable has similar roles in the two models. In both models the costate variable is a shadow value of the associated state variable, and as such has the role of rationing the use of the state variable. In addition, as has been shown in Lyon (1999), in natural resource problems the costate variable can be partitioned into a scarcity effect and a cost effect. We show that this same partitioning can be done in the stochastic renewable resource problem. We discuss and contrast the similarities and differences in these concepts for deterministic and stochastic models. In addition, we present a numerical example to help solidity the results

The Effects on Agriculture in Utah of Water Transfers to Oil Shale Development

In Part I the institutional factors affecting water distribution in the Upper Colorado River Basin in general and specifically the Uintah Basin are presented. The historical development of the appropriation doctrine of water allocation is outlined and Utah water policy is examined. These institutional factors are analyzed in light of the prototype oil shale development in the Uintah Basin and potential impact on the area\u27s agricultural sector. Oil shale water estimates are compared with Uintah Basin water availability and examined with regard to population projections and municipal water use. Lastly, Utah water policy and the appropriation doctrine are viewed as restraints to efficient water transfers. In Part II irrigation water is treated as a random variable. Its actual quantity is not known ahead of time. If transfers of water to oil shale production affect the variability of water used in agriculture then there will be impacts in agriculture even if the farmers receive the same average quantity of water as originally. These impacts are analyzed in the context of the expected utility maximization hypothesis, i.e., the farmers are hypothesized to maximize expected utility. The measure of an increase in variability is the mean preserving spread. The analyses seek to determine the impact upon expected (average) real income (utility), expected profits (net farm income), purchased inputs, the price of water, and the price of land. The analyses are conducted for both the case where the farmers are risk neutral and the case where they are risk averse

An Economic Appraisal of Reuse Concepts in Regional Water Supply Planning

Using a conceptual model of a water supply firm, the necessary conditions for production and market efficiency are derived when renovated wastewater is considered as a potential water resource. The nature and extent of the supply augmentation due to recycled reuse is demonstrated using classical optimization techniques. Three stages of short-run supply corresponding to no recycling, partial recycled reuse and complete recycling of all reclaimable water are identified through appropriate Lagrangian Multipliers as well as graphical techniques. A mathematical programming model is structured to determine the optimal water resource allocation and pricing policy for Salt Lake County. By maximizing the sum of consumer and producer surplus (the difference between total willingness-to-pay and total cost) economically efficient equilibria are derived. The feasibility of recycled reuse for municipal purposes is examined in a planning context. The impact of higher water quality discharge standards on the attractiveness of water recycling option is studies. To ensure social acceptability of renovated wastewater for culinary purposes, blending restrictions are imposed, which stipulate that the amount of water for reuse be less than a fixed percentage of the water from other sources. The effect of such a constraint on the prices and water allocation are delineated. The hydrologic uncertainty in water supply is treated using stochastic programming techniques. Application of the concepts of single and joint change-constrained programming are illustrated. The resulting changes in pricing and allocation policies are discussed

Laser vision : lidar as a transformative tool to advance critical zone science

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Hydrology and Earth System Sciences 19 (2015): 2881-2897, doi:10.5194/hess-19-2881-2015.Observation and quantification of the Earth's surface is undergoing a revolutionary change due to the increased spatial resolution and extent afforded by light detection and ranging (lidar) technology. As a consequence, lidar-derived information has led to fundamental discoveries within the individual disciplines of geomorphology, hydrology, and ecology. These disciplines form the cornerstones of critical zone (CZ) science, where researchers study how interactions among the geosphere, hydrosphere, and biosphere shape and maintain the "zone of life", which extends from the top of unweathered bedrock to the top of the vegetation canopy. Fundamental to CZ science is the development of transdisciplinary theories and tools that transcend disciplines and inform other's work, capture new levels of complexity, and create new intellectual outcomes and spaces. Researchers are just beginning to use lidar data sets to answer synergistic, transdisciplinary questions in CZ science, such as how CZ processes co-evolve over long timescales and interact over shorter timescales to create thresholds, shifts in states and fluxes of water, energy, and carbon. The objective of this review is to elucidate the transformative potential of lidar for CZ science to simultaneously allow for quantification of topographic, vegetative, and hydrological processes. A review of 147 peer-reviewed lidar studies highlights a lack of lidar applications for CZ studies as 38 % of the studies were focused in geomorphology, 18 % in hydrology, 32 % in ecology, and the remaining 12 % had an interdisciplinary focus. A handful of exemplar transdisciplinary studies demonstrate lidar data sets that are well-integrated with other observations can lead to fundamental advances in CZ science, such as identification of feedbacks between hydrological and ecological processes over hillslope scales and the synergistic co-evolution of landscape-scale CZ structure due to interactions amongst carbon, energy, and water cycles. We propose that using lidar to its full potential will require numerous advances, including new and more powerful open-source processing tools, exploiting new lidar acquisition technologies, and improved integration with physically based models and complementary in situ and remote-sensing observations. We provide a 5-year vision that advocates for the expanded use of lidar data sets and highlights subsequent potential to advance the state of CZ science.The workshop forming the impetus for this paper was funded by the National Science Foundation (EAR 1406031). Additional funding for the workshop and planning was provided to S. W. Lyon by the Swedish Foundation for International Cooperation in Research and Higher Education (STINT grant no. 2013-5261). A. A. Harpold was supported by an NSF fellowship (EAR 1144894)

Bringing 'place' back in: regional clusters, project governance, and new product outcomes

We examine new product outcomes in the context of regional clusters. Based on past research on marketing relationships, clusters, and social networks, we propose that the overall configuration of a cluster helps promote particular governance practices among its members. These practices have distinct value-creating properties, and when they are brought to bear on a specific new product development project within a cluster, they promote performance outcomes like product novelty and speed to market. Ultimately, these performance effects are reinforced by the configuration of the cluster itself. In general, we propose that new product outcomes follow from complex interactions between a cluster's macro-level configuration and its micro-level governance processes. More broadly, our framework points to the importance of geographical variables and to the role of “place” in marketing decision-making