Agroforestry and reforestation with the Gold Standard-Decision Analysis of a voluntary carbon offset label - Leonie Netter; Eike Luedeling; Cory Whitney (Mitigation and Adaptation Strategies for Global Change, 2022)
Voluntary standards can help to ensure the quality of projects eligible for carbon offsetting, i.e., selling carbon certificates. However, in deciding on whether to adopt such standards, the managers of carbon offset projects are faced with uncertainty regarding the costs and risks involved. Decision Analysis provides a helpful set of tools that can support such decisions by forecasting outcomes under different scenarios. We applied Decision Analysis methods to generate models for the decisions to certify two projects in Costa Rica with the voluntary carbon offset label "The Gold Standard". We evaluated certifying an additional site of a partially certified reforestation project, as well as the initial certification of an agroforestry project. We calibrated and interviewed decision-makers and stakeholders of the certification projects to identify important parameters and translate these into a decision model. We ran the final decision model as a Monte Carlo simulation to project plausible ranges of decision outcomes, expressed as Net Present Values and annual cash flows. We identified critical uncertainties and research priorities by using the Expected Value of Perfect Information. The results indicate that certification of the two projects would result in a positive Net Present Value. The partially low return on investment of the certification, however, shows the need for projects to undergo thorough evaluation and generate customized strategies before participating in a voluntary carbon offset scheme. The Decision Analysis approaches we describe can help to improve the process of decision making under uncertainty and should be widely adopted for evaluating the potential impacts of certification.
Keywords: 330; Decision Analysis; Monte Carlo simulation; Reforestation; Agroforestry; Carbon credits; Gold Standard