Monitoreo forestal nacional

Unlocking climate finance: Ghana’s Measurement Reporting and Verification best practice

27/02/2024

Ghana recently submitted forest-related emission reductions to ART-TREES, making it the first African country to do so, and the third worldwide. The achievement stems from consistent efforts to improve the quality of its forest data and Measurement, reporting and Verification (MRV) to meet ART-TREES requirements, unlocking valuable climate finance.

Ghana joined the World Bank’s Forest Carbon Partnership Facility (FCPF) in 2008 and submitted an Emission Reduction Program Document in November 2016 and its first National Forest Reference Level to the United Nations Framework Convention on Climate Change (UNFCCC) in 2017. At that time, deforestation was assessed by comparing different map classifications and improving the accuracy of the assessment was a condition for inclusion in the Carbon Fund (CF) pipeline. [1]

Technical support from FAO enabled Ghana to improve data accuracy, applying a stratified systematic grid for sample-based assessment. The plot data was assessed using Collect Earth, from FAO’s free and open-source technical solutions under the Open Foris initiative, that facilitate flexible and efficient data collection, analysis and reporting.

The improved data resulted in the submission of Ghana’s first CF Monitoring Report (containing assessed emission reductions) in 2021. Following a successful validation and verification process, in 2022, the Carbon Fund awarded Ghana USD 4.86 million for reducing 972 thousand tCO2.

 

Ghana’s Cocoa Forest REDD+ Program (GCFRP) is the world’s first commodity-based emissions reductions program at a jurisdictional scale. It directs 69% of the Emission Reduction (ER) payments to farmer groups and local communities.

For the data submitted to ART-TREES, Ghana has done further efforts to enhance data quality and cover a larger portion of the country.  Ghana enhanced data quality for ART-TREES submissions, implementing additional quality assurance and control, and using AI techniques for state-of-the-art ensemble approach.

Support to Ghana for data and MRV improvements was enabled by FAO’s new Accelerating Innovative Monitoring for Forests (AIM4Forests) program funded by the United Kingdom of Great Britain and Northern Ireland. Ongoing support to Ghana has also been provided by UN-REDD and Forest Carbon Partnership Facility (FCPF).

Ms. Roselyn Fosuah Adjei, Ghana’s REDD+ coordinator, agreed that FAO’s technical support has built capacity in our country to meet high-integrity MRV requirements for new carbon accounting standards like ART-TREES”. Due to its efforts in improving the quality of its data for monitoring and reporting, Ghana has become the first country to sign an Emission Reduction Purchase Agreement with a view to supply the Lowering Emissions by Accelerating Forest finance (LEAF) coalition (requiring TREES emission reductions). In total, 11.9 mln tCO2 of TREES credits are presented for the period 2017-2021, of which 90% are from reduced emissions from deforestation and forest degradation, and 10% from increased removals from restoration. The emission reduction payments can finance Ghana’s continued efforts to combat deforestation and restore forests in the country.

Technological innovation has been an enabler in improving forest data and unlocking climate finance in Ghana and in many other countries, and this will be highlighted during International Day of Forests 2024.

On March 21st, as part of the International Day of Forests celebration, the FAO Forestry Division is hosting a technical session on how "Innovation and technology have transformed countries’ ability to monitor and report on their forests." This session will showcase how technological innovation has revolutionized forest monitoring and reporting practices worldwide.

For more information, visit the IDF website and join us via webcast to learn about the latest advancements in forest management. 

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[1]Sandker et al 2021 describes why comparing map classifications is prone to errors and gives examples of the magnitude of bias associated with this methodology, including Ghana as one of the examples of data improvement discussed https://www.mdpi.com/1999-4907/12/1/99