Author: Rabindra Nath BhattacharyyaPublisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 188
Book Description
Optimal Forest Rotation: Decisions Under Conditions of Certainty and Uncertainty
Author: Rabindra Nath BhattacharyyaPublisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 188
Book Description
The Optimal Forest Rotation
Author: David H. NewmanPublisher:
ISBN:
Category : Forest management
Languages : en
Pages : 56
Book Description
Consideration of Uncertainty in Forest Management Decision-making
Author: Emmett Frank ThompsonPublisher:
ISBN:
Category : Decision making
Languages : en
Pages : 258
Book Description
Management decisions are generally considered to be made under one of three categories of future knowledge: certainty, risk, or uncertainty. All three categories occur in forest management. However, forest management decisions whose outcomes are dependent upon future levels of timber yields, prices, utilization standards, or social and legal institutions are made under uncertainty. Forest managers have always recognized that uncertainty existed; however, they have not systematically included it in their decision- making process. The objectives of the study were to: (1) establish the importance of systematically considering uncertainty in forest management decision-making and (2) illustrate and evaluate a model or procedure, for the systematic consideration of uncertainty in forest management decision-making. A review of the present status of forest management decision-making constituted fulfillment of the first objective. Theoretical decision-making models which are currently used in forest management, e. g., present worth analysis, capital budgeting, financial maturity, and linear programming, while conceptually capable of considering uncertainty, imply certainty That is, forestry applications of these models have employed single-valued expectations. Fulfillment of the second objective consisted initially of a review of recent developments in the theory of decision-making under uncertainty. All decision-making problems have some common components. These components are: decision-alternatives, the actions which the decision-maker deems possible to take; states of nature, the future events which determine the outcome of the actions; and consequences, the result of taking a specific action and finding that a particular state occurs. The more popular theoretical models for decision-making under uncertainty were reviewed: minimax, minimax regret, Hurwicz index, and Laplace. While useful in some cases, each of these models has specific disadvantages. In addition, all the models have one common major disadvantage, they contain the implicit assumption that the decision-maker is completely ignorant about the states of nature which influence his problem. In reality, forest managers and other decision-makers usually possess some information, although it may be vague, about their problems. If a decision-maker is not willing to assume complete ignorance about the occurrence of the states of nature, he cannot apply any of the above models. There is a theoretical decision-making model which appears compatible with reality. The model, Bayesian decision theory, allows the decision-maker to arrive at a solution which is compatible with his opinions or judgements about the states of nature. Also, he can combine these opinions or judgements with experimental data to derive a solution using all available information, both subjective and objective. Fulfillment of the second objective was completed by illustrating the application of Bayesian decision theory to a hypothetical problem. The problem, optimal degree of land ownership for an industrial forestry firm, was defined within the Bayesian model and a solution derived. Since the problem was hypothetical, the actual solution is not the primary result of the study. The resulting implications for actual situations is the primary contribution. If forest managers are to make decisions which contain uncertainty, the uncertainty should be systematically recognized in the decision-making process. The Bayesian model is a logical procedure for such recognition. By adopting and applying such models, the efficiency of forest management decision-making will be increased.
Does Risk Aversion Accelerate Optimal Forest Rotation under Uncertainty?.
Author: General Technical Report NC.
Author: Publisher:
ISBN:
Category : Forests and forestry
Languages : en
Pages : 668
Book Description
Does Risk Aversion Accelerate Optimal Forest Rotation Under Uncertainty?
Author: Luis H. R. AlvarezPublisher:
ISBN:
Category :
Languages : en
Pages : 12
Book Description
We use a Wicksellian single rotation framework to analyze the impact of the intertemporally fluctuating and stochastic mean-reverting interest rate process on the optimal harvesting threshold and thereby the expected length of the rotation period, when forest value is also stochastic following geometric Brownian motion and landowners are risk-averse. We provide an explicit solution for the two-dimensional path-dependent rotation problem and demonstrate that higher interest rate volatility increases, while higher risk aversion decreases the optimal harvesting threshold. Moreover, under risk aversion increased forest value volatility decreases the optimal harvesting threshold, while it has no effect under risk neutrality. Numerical illustrations indicate that higher interest rate volatility will raise the expected rotation period at an increasing rate, while higher forest value volatility will decrease its sensitivity under risk aversion.
Encyclopedia of Energy, Natural Resource, and Environmental Economics
Author: Publisher: Newnes
ISBN: 0080964524
Category : Business & Economics
Languages : en
Pages : 1056
Book Description
Every decision about energy involves its price and cost. The price of gasoline and the cost of buying from foreign producers; the price of nuclear and hydroelectricity and the costs to our ecosystems; the price of electricity from coal-fired plants and the cost to the atmosphere. Giving life to inventions, lifestyle changes, geopolitical shifts, and things in-between, energy economics is of high interest to Academia, Corporations and Governments. For economists, energy economics is one of three subdisciplines which, taken together, compose an economic approach to the exploitation and preservation of natural resources: energy economics, which focuses on energy-related subjects such as renewable energy, hydropower, nuclear power, and the political economy of energy resource economics, which covers subjects in land and water use, such as mining, fisheries, agriculture, and forests environmental economics, which takes a broader view of natural resources through economic concepts such as risk, valuation, regulation, and distribution Although the three are closely related, they are not often presented as an integrated whole. This Encyclopedia has done just that by unifying these fields into a high-quality and unique overview. The only reference work that codifies the relationships among the three subdisciplines: energy economics, resource economics and environmental economics. Understanding these relationships just became simpler! Nobel Prize Winning Editor-in-Chief (joint recipient 2007 Peace Prize), Jason Shogren, has demonstrated excellent team work again, by coordinating and steering his Editorial Board to produce a cohesive work that guides the user seamlessly through the diverse topics This work contains in equal parts information from and about business, academic, and government perspectives and is intended to serve as a tool for unifying and systematizing research and analysis in business, universities, and government
Dissertation Abstracts International
Author: Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 596
Book Description
The Indian Economic Journal
Author: Risk Analysis and Timber Investments
Author: Carol A. HyldahlPublisher:
ISBN:
Category : Risk assessment
Languages : en
Pages : 36
Book Description