The CO2OL Tropical Mix project has planted 9M trees on 13,000 hectares of degraded pasture land across 45 sites in Panama since 1995; 40% teak, 60% native species; to produce sustainable hardwoods, especially for furniture, on 25-year rotations; while 20-30% of the land is reserved for conservation and bio-diversity. The project achieved a relatively high score of 88/100 on our usual assessment framework. CO2 credits are priced at $38/ton. We contributed $1,900 to the project and offset 50 tons of CO2.
Panama’s CO2OL Project is a reforestation project, established in 1995, and managed by German environmental services company Forliance. As part of our ongoing work into nature based solutions to climate change, and to practice what we preach in offsetting our own CO2, we have scored the project on our usual 100-point scorecard (others linked here).
Real. The CO2OL project is certified by Gold Standard, independently audited, and has high quality imagery and offtakers. Our score was mildly marked down for a complex corporate structure, which has been modified over time.
Incremental. Reforesting former cattle pasture in a country losing 0.4% of its primary forests per year. Minor “leakage” has been considered and deducted from CO2 calculations.
Measurable. The calculation methodology is clear, realistic, includes “give-backs” and baselines, is backed up by satellite/GIS data, has been audited, and de-risked by diversification.
Permanence. After harvest, hardwoods will lock away carbon in furniture, which has good carbon credentials as a wood usage. Score is marked down due to country transparency, long-term future and complex corporate structure.
Bio-diverse. The project is 40% teak, 60% spread across 5-20 native species, creating animal habitat and up to 200 jobs, although we marked down mildly as rotations are short at 25-years.
Full details on the CO2OL Reforestation project, which led us to the scores above are given in the data-file. Despite recent media scandal-mongering into nature-based solutions, we were happy to find a project that scores relatively well on our framework.