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Implications of future climatic uncertainty on payments for forest ecosystem services: The case of the East Coast of New Zealand

Juan J. Monge, Adam Daigneault (), Leslie J. Dowling, Duncan R. Harrison, Shaun Awatere and Anne-Gaelle Ausseil

Ecosystem Services, 2018, vol. 33, issue PB, 199-212

Abstract: Forestry’s long-term nature and associated uncertainties play against its consideration in land-use decisions. This study’s objective was assessing the implications of uncertainty on the feasibility of afforesting erodible land with natives. The New Zealand example has global relevance enabling: innovative statistical representation of climatic uncertainty, economic uncertainty analysis due to existing payments for services, and the profitability of long-rotation species due to MÄ ori’s socio-cultural aspirations. We complemented a deterministic optimisation approach with Monte-Carlo methods to identify optimal afforestation areas representing uncertainty probabilistically. With stochastic dominance/efficiency tests, relevant to landowners, we identified that the uncertainty and profitability of long-rotation species increase at low discount rates, plausible due to low opportunity costs and inter-generational aspirations (not possible in competitive circumstances). At low discount rates, the high profitability odds of the long-rotation alternative compensate its high uncertainty and make it the preferred one even for highly risk-averse owners. With a probabilistic cost-benefit analysis, relevant to policymakers, we identified that the benefits necessary to cover erosion-reduction investments would need to be higher than expected to hedge against climatic uncertainty. These results were obtained through the uncertainty analysis. The methods/results covered in this study are relevant to land-use studies/models seeking to measure uncertainty impacts simply.

Keywords: Stochastic dominance; Land-use change; Climate change; Benefit-cost analysis; Spatial optimisation; Forest ecosystem services (search for similar items in EconPapers)
Date: 2018
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