全球智库信息集成服务系统

Tradable forest products are normally the focus of attention in forest management as opposed to non tradable products and non marketable benefits such as erosion control, aesthetic and traditional ceremonies values, biodiversity maintenance, wildlife conservation and other intangible benefits. This situation results from the fact that tradable goods are included in the national accounts in terms of their contribution to the GDP. Potential and still controversial proxies for analysis of the macroeconomic impacts of environmental variables include the creation of the Resource Environmental Accounts (REA) and the Environmental Adjusted Net Domestic Product (EDP) and other approaches such as change of macroeconomic policies and linear programming. Planning sustainable forest management demands an integration of all resources including potential uses by different social groups at the national and international level over medium or long term. The commitment of both the formal and informal sectors in the planning and implementation of measures to protect the environment at national level depends on the perceived value that each associate with the resources. However, the most applied method of valuation of environment, Contingent Valuation (CV), is still facing a great deal of scepticism concerning the reliability of its results. Therefore, methodological problems rise further when attempting to analyse the likely impact of alternative future environmental policy /strategies at macro level in a data scarce situation, where target groups have minimum or no information about environmental goods. Other difficulties include derivation of coefficients and selection of the appropriate aggregation level. In this research, a multiobjevtive mathematical programming model framework incorporating farmers’ preferences, regional and national environmental goals and priorities is developed to determine the impact of utilisation of marketable forest products on the non-market benefits of forests. The incorporation of the latter in the model provides the basis of a powerful decision support system for policy interventions.