There is an urgent need to develop a framework for understanding and predicting the effect of opportunity costs of REDD+. We develop an approach comprising two components: distributed land use modeling for assessing the profitability gap between maintaining palm oil plantations and complying with REDD+ and a sensitivity analysis of the model’s predictions. First, a spatially explicit model is used to predict the future distribution of land use changes in central Kalimantan, Indonesia. This model predicts the change in carbon storage due to deforestation by linking business-as-usual baseline emissions scenario to historic data and using an improved cellular automaton system to predict land use changes. Input parameters include elevation, slope, aspect, soil types, distance to road, distance to river, etc. The so-called “ton-year approach” is combined with the future price of carbon to estimate compensation under the REDD+ mechanism. Potential revenues from palm oil plantation are calculated by multiplying yields from palm oil products with corresponding prices in the world market. Second, a sensitivity analysis is conducted to assess the robustness of the modeling results to alternative assumptions about palm oil price and carbon price. The palm oil price is shown to have the highest relative sensitivity. Further analysis indicates remarkable changes in the profitability gap depending on the price of palm oil; a change in palm oil price from $545.33 to $773.03 shows a large 155% increase in the profitability gap. Unfortunately, the most likely forecasts of palm oil prices continue to predict large differences in the profitability gap, favoring palm oil plantation over REDD+ projects. Thus, the effect of carbon pricing policies, as they currently stand, will remain limited.