Browsing by Subject "CVaR"

Risk management is essential in forest management planning. However, decision making with risk analysis is rarely done in forestry. This study presents an example of the application of conditional value-at-risk (CVaR) as a decision tool and optimizes the management planning problem from a risk perspective. Stochastic programming is used to solve the problem. The model contains four different types of risk using an assumed probability distribution and quantifies these risks, namely, inventory errors, growth model errors, price uncertainty and policy uncertainty. The results suggest that forest owners’ risk tolerance, i.e., their willingness and ability to assume risk determines to the greatest extent the return potential. When the expected first period income is maximized, the subsequent period always experiences a loss that is the greatest of the entire management horizon. The proportion of carbon subsidy in the first period is also the highest. With this model it is possible to hedge some risks or to use it as means to assess the amount of insurance to purchase in order to transfer risks. The use of CVaR in forest management planning can be seen as a useful tool to manage risk and to assist in the decision making process to assess forest owners’ willingness and ability to tolerate risks.