10 years of REDD+! An outlook on the performance of the world’s REDD+ projects
The aim of REDD+ projects (short for Reducing Emissions from Deforestation and Forest Degradation) is to address one of the primary drivers of climate change by working to protect key areas of forest and wildlife from deforestation. Yet what sets REDD+ apart from similar climate mitigation projects is the focus on providing important co-benefits for surrounding populations including improved infrastructure, employment opportunities, and other community development projects.
As we pass the 10-year mark of active, credit-issuing REDD+ projects, HAMERKOP has collected and analysed data from active projects certified to the Verified Carbon Standard (VCS), the largest “voluntary” carbon credit certification organisation, to assess project prevalence and effectiveness across 6 key metrics.
All the projects reviewed had completed at least one monitoring cycle. The completion of the first monitoring cycle marks an important milestone in the certification process whereby the project is able to issue carbon credits for greenhouse gas emissions reduced or avoided. The data analysis performed omits projects that are in development but have not yet issued any carbon credits.
1. Monitoring Period Length
A monitoring period corresponds to the passage of time during which project developers record their project impacts before they undergo a verification audit. Successful completion of this leads to the issuance of carbon credits for the corresponding monitoring period. With most carbon certification standards, project developers are free to choose how often they want to materialise and monetise their project’s performance.
REDD+ projects are complex to implement and monitor and this partly explains the reason project developers tend to have longer monitoring period than for other project types. The monitoring periods of VCS-issued REDD+ projects range from one to ten years long, with an average period of 3.25 years (39.4 months).
The first monitoring period is often the longest, since a broad range project implementation and certification processes need to be set in place.
The Ecomapua Amazon REDD+ Project [1] in Brazil, the first REDD+ project by activity history, began monitoring its performance in 2003. However, most REDD+ project start dates are concentrated within a nine-year period between 2008 and 2016 (inclusive). The first REDD+ project to issue credits, however, was the Kasigau Corridor Project [2] in Kenya, which, in addition to preventing deforestation, works towards sustainably resolving local human-wildlife conflicts that have been prevalent in the area in the past.
2. Geographical Scope
In the decade since the Kasigau project issued its first credits in 2011, the field has exploded to 55 projects that are currently issuing carbon credits. These projects are dispersed throughout the developing world.
Among those already issuing credits, all except 5 projects are located in South America or Africa. In South America, they are concentrated in Brazil and Colombia, with a few additional projects in Peru. While Peru and Colombia have enabling environments, the case of Brazil is more contrasted and complex. African projects, by contrast, are spread more evenly throughout the continent.
3. Project Size
The size of a project can impact the ability to deploy activities to counteract deforestation and forest degradation. The size of REDD+ projects that have issued credits range from just 18,000 ha (e.g., the Amazon Rio REDD+ IFM [3]) to over 1 million ha (e.g., the REDD+ Project Resguardo Indígena Unificado Selva de Matavén [4] in Colombia and the Cordillera Azul National Park REDD project [5] in Peru).
Projects were categorised as small (under 100,000 ha), medium (100,000 ha to 500,000 ha), and large (500,000 ha and over). There are significantly more small and medium projects than large ones. It can be challenging in many countries to find areas that can be aggregated and managed under a single project entity and where the agents of deforestation and degradation can be addressed effectively. The figure to the left gives a more detailed breakdown of project sizes.
Furthermore, the figure to the right shows the relationship between project area and their resulting emission reductions. Project performance here is based on the total emission reductions included in monitoring reports thus far, measured in tCO2e reduced/avoided per hectare and per year. The results suggest that mid-sized projects have the largest variety in performance. The emissions reduction performance of small and large projects, by contrast, vary less. Note, that the analysis does not take into account the number of monitoring periods conducted so far into account into the analysis, meaning these projects are likely to be at different stages of implementation and performance, which could explain the high variability.
Overall, the highest project performance was found to be 77.06 tCO2e per hectare and per year for the Rimba Raya Biodiversity Reserve Project [6], over 5 monitoring periods covering 2009 to 2019. This is especially notable considering the second highest performance was over 30 tCO2e per hectare less annually [7]. The lowest project performance was 0.2 tCO2e per hectare and per year for the Biocorredor Martin Sagrado REDD+ project [8] in Peru over 2 monitoring periods covering 2010 to 2020.
4. Certification Methodology
The next metric examined was the choice of certification methodology to determine if there were differences in project performance based on different methodologies. Each methodology provides a slightly different framework used to develop and monitor projects [9]. This usually depends on the type of ecosystem as well as the drivers and patterns of deforestation. For example, methodology VM0007 is “applicable to forest lands, forested wetlands, forested peatlands, and tidal wetlands”[10] and cannot be used for projects where the deforestation is caused by illegal timber harvesting.
The figure to the left shows that the VM0009 Methodology for Ecosystem Conversion performed consistently high, with a median of 5.1 tCO2e per hectare and per year, three times higher than a median of 1.74 tCO2e per hectare and per year from the VM0011 Methodology for Calculating GHG Benefits from Preventing Planned Degradation which had the lowest performance. These determinations were made through assessing the distribution of the available data. Both VM0011 and VM0009 showed little variation in the results with relatively few outliers. While other methodologies have similar ranges, they show greater variation and emission reductions cluster towards the lower end of the spectrum. There are more significant outliers in the VM0007 and VM0004 methodologies, but this mainly derived from Indonesian REDD+ projects, which had abnormally high annual averages of monitored emissions reduction per hectare.
5. Type of Forest Damage
We also attempted to analyse project performance based on the type of damage that was avoided (deforestation or degradation) and the various external drivers of deforestation.
The figure shows that project performances are not significantly dependent on the type of destruction prevented when looking at the total distribution. However, it can be challenging to differentiate both, one (degradation) often leading to another (deforestation) and to analyse these parameters taking a more granular approach to differentiate the way in which projects operate and perform.
Examining the primary drivers of deforestation, our analysis found that it was difficult to attribute project performance based on specific factors, due to the complexity and specificity of each situation, deforestation and degradation being due to a range of complex direct and indirect agents.
6. Predicted vs. Actual Emission Reductions
Another important aspect to take into consideration is how the project held up to predictions that were made upon the initial conception of the project. These predictions, formally called ex-ante emission reductions, provide an estimate of carbon credits the project proponents expect to generate from the project, and determine its financial viability.
Examining the difference between the predictions and the actual measurements of emission reductions can reveal the difference between expectations, planning and the field reality.
In the case of the 53 projects examined, the average difference between the predictions and measurements was found to be close to zero — just 1.02 tCO2e per hectare per year.
However, this does not mean that predictions were accurate. On the contrary, we found a wide variation in differences for each project. They ranged from producing 39.48 tCO2e less than expected for the Katingan Peatland Restoration and Conservation Project [11] to 33.33 tCO2e more than expected for the Cikel Brazilian Amazon REDD APD Project Avoiding Planned Deforestation [12]; and ranging from – 80% to + 380%.
Only 12 projects out of 55 (less than a quarter) managed to predict emissions reductions generated by their activities with an accuracy of plus or minus 10%. Moreover, 23 projects out of 55 (around half), predicted the potential of the project with an accuracy inferior to 50%, which shows how difficult it can be for a project developer and for potential upfront investors to estimate the financial potential of a project to cover its costs.
CONCLUSION
Through this analysis, we aimed to shed some light on results from REDD+ projects that are currently issuing carbon credits and provide a resource that collects and displays the data in one place. With new projects being approved and launched every year, we hope that additional reporting and data collection will further refine our findings and help inform project investments in the future.
HAMERKOP’s experts have more than a decade of experience supporting project developers, designing climate change mitigation interventions, carrying out technical feasibility studies and getting projects through the certification process to issue carbon assets.
Whether you are an international organization, a landowner, a project developer, or an NGO looking to benefit from carbon finance to financially support long-term and impactful climate change mitigation intervention, we can help, reach out to us.
Sources:
[1] Ecomapua Amazon REDD project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/1094
[2] Kasigau Corridor REDD project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/562
[3] Amazon Rio REDD+ project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/1147
[4] REDD+ Project Resguardo Indigena Unificado Selva de Mataven project VCS page: https://registry.verra.org/app/projectDetail/VCS/1566
[5] Cordillera Azul National Park REDD project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/985
[6] Rimba Raya Biodiversity Reserve Project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/674
[7] Cikel Brazilian Amazon REDD project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/832
[8] Biocorredor Martin Sagrado REDD+ project VCS registry page: https://registry.verra.org/app/projectDetail/VCS/958
[9] VCS methodologies: https://verra.org/methodologies/
[10] “VM0007 REDD+ Methodology Framework (REDD+MF), v1.6,” Verra, March 29, 2021, https://verra.org/methodology/vm0007-redd-methodology-framework-redd-mf-v1-6/#:~:text=This%20methodology%20provides%20a%20set,planned%20deforestation%20and%20forest%20degradation.
[11] Katingan Peatland Restoration and Conservation Project: https://registry.verra.org/app/projectDetail/VCS/1477
[12] Cikel Brazilian Amazon REDD APD Project Avoiding Planned Deforestation: https://registry.verra.org/app/projectDetail/VCS/832