On the importance of baseline setting in carbon offsets markets

Abstract

Incorporating carbon offsets in the design of cap-and-trade programs remains a controversial issue because of its potential unintended impacts on emissions. At the heart of this discussion is the issue of crediting of emissions reductions. Projects can be correctly, over-or under-credited for their actual emissions reductions. We develop a unified framework that considers the supply of offsets within a cap-and-trade program that allows us to compare the relative impact of over-credited offsets and under-credited emissions reductions on overall emissions under different levels of baseline stringency and carbon prices. In the context of a national carbon pricing scheme that includes offsets, we find that the emissions impacts of over-credited offsets can be fully balanced out by under-credited emissions reductions without sacrificing a significant portion of the overall supply of offsets, provided emissions baselines are stringent enough. In the presence of high predicted business-asusual (BAU) emissions uncertainty or low carbon prices, to maintain the environmental integrity of the program, baselines need to be set at stringent levels, in some cases below 50 percent of predicted BAU emissions. As predicted BAU emissions uncertainty declines or as the carbon market achieves higher equilibrium prices, however, less stringent baselines can balance out the emissions impacts of over-credited offsets and under-credited emissions reductions. These results imply that to maintain environmental integrity of offsets programs, baseline stringency should be tailored to project characteristics and market conditions that influence the proportion of over-credited offsets to under-credited emissions reductions. Electronic supplementary materialThe online version of this article (

Systematic over-crediting in California’s forest carbon offsets program

“…While the precision and uncertainty in our alternative estimate of common practice varies according to the rarity of forest types and prevalence of FIA data, the fact that our analysis accounts for variance in estimated carbon stocks across both species and space makes it more accurate and ecologically robust than the approach used in California's program. Invoking the use of FIA data to assure the quality of a forest offsets program is not enough; a reliable protocol must also show how sampling density and statistical uncertainty are managed through rigorous protocol design (7).…”

“…We then filtered the FIA data to meet the following criteria: (i) classified as accessible forestland (COND_STATUS_CD == 1); (ii) that were measured between 2001 and 2015; and (iii) fell on privately owned land. Using publicly reported (e.g., fuzzed and swapped) plot latitude and longitude, we assigned each condition a mean temperature and mean precipitation based on 30-year climate normals from PRISM (7). PRISM data were first regridded to a 4km Albers Equal Area Projection using area weighted resampling.…”

“…Offsets are also controversial. Because compliance offsets enable higher emissions within legally binding policy regimes, they must reflect "additional" climate benefits that go beyond what is expected under counterfactual business-as-usual conditions (7). Compliance offsets' additionality is therefore a fundamental prerequisite to their successful inclusion in climate policy, but this standard is not always achieved in practice.…”

Forest offsets partner climate‐change mitigation with conservation

“…At the program level, some projects may be under-credited because of strict project discounting (described below), and others may be over-credited by having non-additional credits. But with all projects evaluated by the same standards, the overall program should achieve program-level additionality (Bento et al 2016).…”

“…These data increase confidence in the program's climate benefits and additionality of emissions reductions. Forest offset projects in other jurisdictions have struggled to establish similarly reliable and standardized baselines (Bento et al 2016), and the California program has benefited greatly from having long-established regional baseline data. To address this challenge in programs outside of the US, we recommend considering different levels of discounting for uncertainty; offset programs that use data from sources with higher uncertainty could discount a greater portion of their credits.…”