Renewable Resources and Value-Based Complex Forest Management

Renewable Resources and Value-Based Complex Forest Management

Yuri P. Pavlov (Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Bulgaria)
Copyright: © 2021 |Pages: 14
DOI: 10.4018/978-1-7998-3479-3.ch089


In the chapter the question of multifactor value-driven management of renewable resources of complex forest systems is investigated. Timber harvesting closely concerns local population from economic and ecological positions. The production is relevant only as means to achieve human values. Because of this, values are the focus of the decision making and the strategic management needs to clearly defining and structuring the fundamental values. In the paper value-oriented models, based on a multi-attribute utility function, which analytically represents human preferences, are discussed. Such value-based modeling permits mathematical description of complex multifactor processes and optimal control. The mathematical optimal control solutions define a well-founded ecologically, socially, and economically oriented strategy of forest resource management.
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The human society is an inseparable part of nature and if we perceive it as complex and difficult to describe, this is a result of the capabilities of our consciousness. The needs of the society and the vital resources required for its existence are completely embedded in the aspects of the surrounding reality. The nowadays use of natural resources demands a more complex assessment approach that takes into account factors such as economic efficiency, social impact, environmental impact, etc. The manner in which we conceptualize and utilize these resources depends on the level of the collective and personal individual consciousness and its perfection. Different social groups perceive and use the natural resources with different aspect of intensity and different awareness. This determines the multiple aspects of the production processes in the process of objectives’ multiattribute modeling and formation as well as in the control of these processes. Accounting for the social and group interests in the nature utilization processes have been analyzed by various contemporary authors (Farnsworth, 1983; Shukla & Dubey, 1997; Jungmeier, 2003).

In the literature are discussed different decisions making approaches, as the focus is on the different aspects of the group decision making as a response to the multiple aspects of the interests and needs of the various social strata representatives. In the considered approaches and methods it is sought to additionally account for the intensity of the social or group preferences and their combination in common relation. However, it is necessary to emphasize that measurement and comparison of preference intensities are mathematically permissible in the interval scale (Clark, 1990; Keeney & Raiffa, 1999; Jungmeier, 2003).

There are many investigations of the production processes and its modeling (Hotelling, 1931; Swallow, 1994, Clark, 1990). Widely known and discussed are the models for production management proposed and used by Canadian scientists Clark and Munro (Clark & Munro,1975). Both types – the exhaustive and renewable production were considered. Clark’s models aiming at optimal resource use have been applied to fishing industry in Canada and mining enterprise, but they can also be applied to other branches of the economy and human activity such as agriculture, mining, logging, ecology, etc (Clark, Clarke & Munro, 1979). As a leading scientist in the theory of non-smooth optimization, Clarke has made a brilliant analysis of the optimal control under such modeling and has determined a strategy for finding this control (Clarke, 1983). However, there remains the question of accounting for and including social, economic and other human factors as an extension to this modeling. In our view this can be achieved by using measurement theory and utility and multiattribute utility theory (Fishburn, 1970; Pfanzagl, 1971; Keeney & Raiffa, 1999).

In the present chapter we will consider value based control of logging process with jointly accounted for complex social, ecological and economic factors in the modeling and control. Cases of renewable natural resources are considered including economic efficiency and social effect for the population.

The new element is taking into account the social impact on the population, such as pay, ecological effects such as biodiversity and their combination with economic efficiency in logging. This is achieved by constructing a multifactor utility function which under the different control models is used as an objective function or as a part of the differential equation describing the production process. The utility function is constructed based on the preferences of biologist expert and reflects his attitudes with respect to the considered production – in this case logging (Lyubenova et al, 2015). Such utility function can be constructed for every stakeholder: government, business, ecologists, or all of them combined in a single multifactor utility function.

Key Terms in this Chapter

Utility Theory: A normative approach to the issue of how people should rationally choose in conditions of uncertainty.

Complexity: A condition of a system or situation integrated with some degree of order but with too many elements and relationships to be understood in a simple analytic or logical way. In the extreme, the complex system or situation is with multiple and diverse connections with dynamic and interdependent relationships, events, and processes.

Optimal Control: Is the process of determining control and state trajectories for a dynamic system over a period of time to minimize a performance index.

Utility Independence: An attribute A1 is utility-independent of attribute A2, if conditional preferences on lotteries on A1, given at a fixed value of A2, do not depend on that fixed point. The utility independence is not symmetrical.

Value-Driven Design: A system engineering strategy, which enables multidisciplinary design optimization. It creates an environment for optimization by providing designers with a value function as objective or as part of the mathematical model.

Model-Driven Decision-Making and Control: An emphasized access to and manipulation of a statistical, financial, optimization, or simulation model. It uses data and parameters provided by users to assist decision process in analyzing a situation.

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