Assessing the Broader Value of Planted Forests to Inform Forest Management Decisions

This study highlights the importance of incorporating objectively quantified, non-market environmental values (such as avoided erosion and carbon sequestration) into land use decision making for sustainable forest management. A continuously developing approach that has facilitated discussions between researchers, industries, and governments on the quantification of non-market values is the ecosystem services (ES) framework. Using a spatial economic tool, called Forest Investment Framework, this study is, to the best of our knowledge, the first assessment of the market (timber) and non-market (carbon sequestration, avoided nitrogen leaching and avoided erosion) ES values of the 1.75 million-hectare New Zealand planted forest estate. To collect the views of key planted forest industry representatives on ES assessment/quantification, we interviewed 14 forest managers representing 60% of the planted forest area. Results from the spatial economic analysis indicated that the non-market ES values can be more than four times the timber profit nationally, and up to 12 times higher in New Zealand's most erosion-prone region. These estimated values are indicative and should be treated with caution. From a sensitivity analysis, we found that different discount rates significantly impact ES values, ratios, and distributions. Results from the interviews indicated that ES quantification helped inform decision making by supporting license to operate, while also signaling the development of a reward system for sustaining ES. Sixty-four percent of survey respondents identified the importance of quantifying ES in ecological terms and describing other non-market ES in spatial, qualitative, or binary forms. Overall, this study provided evidence of how estimated non-market ES values compare with market values and highlighted the importance of including them in decision making processes. Future cost benefit analyses that incorporate these non-market monetary ES values would complement multi-criteria analysis that integrate additional dimensions and allow decision makers to rank options based on their particular criteria.