| Title: |
A globally relevant data-driven assessment of carbon leakage from forestry |
| Authors: |
Daigneault, Adam; Sohngen, Brent; Belair, Ethan; Ellis, Peter W |
| Contributors: |
Bezos Earth Fund; National Institute of Food and Agriculture; Beyond Alliance of the We Mean Business Coalition; McIntire-Stennis |
| Source: |
Environmental Research Letters ; volume 20, issue 11, page 114022 ; ISSN 1748-9326 |
| Publisher Information: |
IOP Publishing |
| Publication Year: |
2025 |
| Description: |
Climate smart forestry (CSF) practices are widely recognized as efficient natural climate solutions. However, leakage accounting for these practices often relies on limited analysis and ad hoc reasoning, leading to integrity concerns and underinvestment in CSF. This study proposes a data-intensive, dynamic economic-ecological modeling approach to estimating regional CSF leakage, with global applicability. Results show how leakage varies by CSF activity, location, forest type, timeframe, and implementation rate. Critically, we show that widely cited harvest leakage estimates ignore complex forest dynamics and are a poor proxy for the metric most applicable to CSF implementation: carbon leakage. While harvest leakage is nearly always positive, our results demonstrate that some project designs can result in beneficial carbon spillovers, or negative carbon leakage. These results improve the evidence base for robust leakage quantification in CSF-based projects, enabling more accurate accounting and thereby ensuring credible climate benefits. These results are relevant in a carbon markets context, where robust leakage accounting would help safeguard the credibility of ecosystem service payments, but are also applicable to traditional, non-carbon markets conservation projects seeking to quantify carbon mitigation impacts. |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| DOI: |
10.1088/1748-9326/ae0ce2 |
| DOI: |
10.1088/1748-9326/ae0ce2/pdf |
| Availability: |
https://doi.org/10.1088/1748-9326/ae0ce2; https://iopscience.iop.org/article/10.1088/1748-9326/ae0ce2; https://iopscience.iop.org/article/10.1088/1748-9326/ae0ce2/pdf |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ ; https://iopscience.iop.org/info/page/text-and-data-mining |
| Accession Number: |
edsbas.C7DE4FAF |
| Database: |
BASE |