Abstract
Salt marsh restoration has the potential to reduce greenhouse gas emissions thereby providing an opportunity for blue carbon crediting, but implementation has been limited to date because of insufficient data and validation. In this paper, we demonstrate the potential scale of methane emissions that could be avoided if salinity-reducing impairments are mitigated by applying findings from six salt marsh restoration sites in Massachusetts combined with a previously demonstrated application of the salt marsh salinity-methane relationship. We used calculations of these avoided emissions to estimate the social benefit of salt marsh restoration by calculating avoided costs. Of the six sites selected, restorations at two sites were successful in improving salinity which we used as a methane proxy. Our approach and findings demonstrate the potential benefits in developing consistent accounting methodologies to better track, prioritize, and implement wetlands restoration strategies to mitigate methane emissions and contribute toward state-level emissions reduction targets across some of the 475 Massachusetts salt marches with an existing tidal restriction. We found the potential for $12 -$26M in added social benefit from 176 tons of avoided methane across 932 hectares of degraded salt marsh in Massachusetts. A significant limitation in estimating benefits, however, is the lack of coordinated, widespread monitoring strategies to infer methane and other GhGs at scale. While not insurmountable, these challenges will need to be addressed for GhG emissions reduction and/or sequestration through salt marsh restoration to be accepted as an effective strategy. We conclude that while carbon crediting may offer benefits to marsh restoration and state greenhouse gas emissions reduction targets, there remain significant limitations because of a lack of project monitoring and data validation. In the worst case, this could result in the offsetting of actual greenhouse gas emissions with credits that are supported by indirect and less-than-rigorous monitoring data.