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Open AccessJournal ArticleDOI

Valuing environmental amenities through inverse optimization: Theory and case study

TLDR
In this paper, an innovative valuation approach integrating techniques of operations research and economic theory of pricing environmental goods was presented, where a forest planning problem was inversed through altering its reward function of timber values so that the observed harvesting behavior became optimal.
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This article is published in Journal of Environmental Economics and Management.The article was published on 2017-05-01 and is currently open access. It has received 9 citations till now. The article focuses on the topics: Forest management & Amenity.

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Citations
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Journal ArticleDOI

Biodiversity as a solution to mitigate climate change impacts on the functioning of forest ecosystems

TL;DR: Forest management strategies based on B→EF have strong potential for augmenting the effectiveness of the roles of forests in the mitigation of climate change impacts on ecosystem functioning.
Journal ArticleDOI

Parameterization of biodiversity–productivity relationship and its scale dependency using georeferenced tree‐level data

TL;DR: In this paper, a large-scale forest observational site in Northeastern China was used to study the scale dependency of the BPR relationship and quantified, for the first time, how biodiversity-productivity relationship scale up from 0.01 to 1.00.
Journal ArticleDOI

Continuous-time inverse quadratic optimal control problem

TL;DR: In this paper, the problem of finite horizon inverse optimal control (IOC) is investigated, where the quadratic cost function of a dynamic process is required to be recovered based on the observati...
Journal ArticleDOI

Economics of mixed-species forestry with ecosystem services

TL;DR: The Faustmann-Hartman setup is widely established for specifying the economics of forest values besides timber, but it is criticized as restrictive for capturing diversity values as discussed by the authors, and it has been shown that the Faustmann Hartman setup does not capture diversity values.
Journal ArticleDOI

Optimal forest management under financial risk aversion with discounted Markov decision process models

TL;DR: In this paper, the common assumption of risk neutrality in forest decision-making is generally inadequate because the stakeholders tend to be averse to fluctuations in the return criteria, and the risk neutrality assumption is generally insufficient.
References
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Journal ArticleDOI

Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition

TL;DR: In this article, a theory of hedonic prices is formulated as a problem in the economics of spatial equilibrium in which the entire set of implicit prices guides both consumer and producer locational decisions in characteristics space.
Book

Using surveys to value public goods : the contingent valuation method

TL;DR: Mitchell and Carson as discussed by the authors argue that at this time the contingent valuation (CV) method offers the most promising approach for determining public willingness to pay for many public goods, an approach likely to succeed, if used carefully, where other methods may fail.
Book

Inverse Problem Theory and Methods for Model Parameter Estimation

TL;DR: This chapter discusses Monte Carol methods, the least-absolute values criterion and the minimax criterion, and their applications to functional inverse problems.
Journal ArticleDOI

Rational Expectations and the Theory of Price Movements

John F. Muth
- 01 Jul 1961 - 
TL;DR: In this article, the Stockholm School hypothesis is used to explain how expectations are formed in the context of an isolated market with a fixed production lag, and commodity speculation is introduced into the system.
Proceedings ArticleDOI

Apprenticeship learning via inverse reinforcement learning

TL;DR: This work thinks of the expert as trying to maximize a reward function that is expressible as a linear combination of known features, and gives an algorithm for learning the task demonstrated by the expert, based on using "inverse reinforcement learning" to try to recover the unknown reward function.
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