Strategies to remove carbon dioxide from the atmosphere, known as carbon dioxide removal (CDR), are being pursued given the urgency of climate change. However, CDR measures may unintentionally increase emissions of other climate forcers. If emissions of potent short-lived climate forcers (like methane) are increased, the CDR mechanism could potentially worsen climate change in the near-term despite benefiting the climate in the long-term. This temporal trade-off can be easily overlooked when employing the standard climate metric used for assessments— carbon dioxide equivalent (CO2e) using a 100 year global warming potential (GWP)—because it solely conveys the long-term warming impacts of a pulse of emissions. A more sophisticated assessment method is needed to reveal potential temporal trade-offs in climate benefits—important information for effective decision making. In this study, we compare three climate impact assessment approaches of increasing complexity to evaluate temporal trade-offs in climate benefits from CDR strategies: (1) the standard CO2e using GWP approach with both 20 and 100 year time horizons (GWP20 and GWP100, respectively, or dual-valued CO2e); (2) a variation of GWP that considers the climate impact of continuous emissions over time (known as technology warming potential (TWP); and (3) reduced complexity climate models. We use wetland restoration as a case study because studies have shown that it may remove carbon dioxide from the atmosphere, while also increasing methane emissions. Analyzing eleven rewetting scenarios, we find that all approaches identify temporal tradeoffs. The TWP and reduced complexity climate model results are largely consistent and reveal drawbacks of the dual-valued CO2e that considers pulse rather than continuous emissions. Given the accessibility of the TWP approach relative to reduced complexity climate models, we recommend its use for most stakeholders. Overall, our findings emphasize the need to thoroughly assess potential climate trade-offs of CDR measures across all timescales.

Read original article

Read original article