Improving uncertainty in forest carbon accounting for REDD+ mitigation efforts

Abstract

Reductions in atmospheric concentrations of greenhouse gases are urgently needed to avoid the most catastrophic consequences of warming. Reducing deforestation and forest degradation presents a climate change mitigation opportunity critical to meeting Paris Agreement goals. One strategy for decreasing carbon emissions from forests is to provide developing countries with results-based financial incentives for reducing deforestation: nearly two billion dollars are currently committed to finance such programs, referred to as REDD+ (Reducing Emissions from Deforestation and forest Degradation, conservation, sustainable management of forests, and enhancement of forest carbon stocks). Countries participating in these programs must document the uncertainty in their estimates of emissions and emission reductions, and payments are reduced if uncertainties are high. Our examination of documentation submitted to date to the United Nations Framework Convention on Climate Change (UNFCCC) and the Forest Carbon Partnership Facility (FCPF) reveals that uncertainties are commonly underestimated, both by omitting important sources of uncertainty and by incorrectly combining uncertainties. Here, we offer recommendations for addressing common problems in estimating uncertainty in emissions and emission reductions. Better uncertainty estimates will enable countries to improve forest carbon accounting, contribute to better informed forest management, and support efforts to track global greenhouse gas emissions. It will also strengthen confidence in markets for climate mitigation efforts. Demand by companies for nature-based carbon credits is growing and if such credits are used for offsets, in exchange for fossil fuel emissions, it is essential that they represent accurately quantified emissions reductions.

Keywords

• REDD+
• carbon credits
• tropical deforestation
• forest carbon
• Monte Carlo
• uncertainty estimation