Carbon sequestration and land management under uncertainty
Uncertainty about the role of forestry and land-use change in mitigating global warming is addressed using a possibilistic linear programming model of forest and agricultural land management. The objective is to maximize the cumulative net discounted returns in the two sectors, while meeting specific carbon-uptake goals and maintaining stable flows of timber over the planning horizon. Because of ambiguity related to timber yield and carbon parameters, and vagueness of policy targets (economic returns, timber production and carbon-uptake), ordinal measures of uncertainty are applied. While ordinality entails loss of precision, it makes it possible to solve complex problems. This paper compares land-use policies in the boreal forest zone of Northeastern British Columbia under uncertainty with those from a more typical scenario that applies best-guess parameter values. Including uncertainty explicitly into the possibility analysis changes optimal land-use and forest management, and leads to different levels of projected timber supply, economic performance and carbon sequestration. The amount of carbon dioxide (CO2) removed from the atmosphere and the economic cost of carbon uptake are sensitive to how the decision-maker tackles uncertainty.
carbon sequestration land management uncertainty forestry climate change fuzzy set theory possibility theory