Reprint of "Governing knowledge transfer for deforestation monitoring: Insights from REDD+ projects in the Congo Basin region"

• Published in: Forest policy and economics Vol. 114; p. 102105
• Main Authors: Sufo Kankeu, Richard, Tsayem Demaze, Moise, Krott, Max, Sonwa, Denis Jean, Ongolo, Symphorien
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2020; Elsevier Science Ltd; Elsevier
• Subjects: Case studies; Coordination; Deforestation; Developing countries; Emissions control; Empirical analysis; Forest policy; Integration; Knowledge; Knowledge management; Knowledge transfer; Knowledge utilization; LDCs; Life Sciences; Monitoring; Monitoring verification and reporting (MRV); REDD; RIU model; Scientific knowledge; Scientists; Utilization; Verification; Forest policy; RIU model; REDD; Monitoring verification and reporting (MRV); Knowledge transfer
• ISSN: 1389-9341; 1872-7050
• DOI: 10.1016/j.forpol.2020.102105

Since the end of 2000s, many forest-rich countries have engaged into results-based deforestation reduction monitoring under REDD+ mechanisms. A set of methods and tools designed at international level is expected to be transferred to the domestic level in many developing countries, in order to generate information on how these countries contribute to global emissions reduction through local forest landscapes. Using the monitoring, reporting and verification (MRV) device as an example, this paper sets out to analyze this knowledge transfer by identifying bottlenecks. It proposes an original analysis that will help to better understand how such knowledge transfers could be improved at the domestic level. Based on empirical case studies related to REDD+ projects in Cameroon, the Democratic Republic of Congo and Rwanda, it assesses knowledge transfer and the role of stakeholders involved at multiple levels (global, regional, national and local). For this purpose, we used the Research Integration and Utilization (RIU) model, which is an analytical framework allowing analyses showing how scientific results can be owned, integrated and disseminated to meet specific needs. Results show that there is a weakness in MRV knowledge transfer from global to local levels and back. The MRV knowledge has a strong research background, a weak MRV knowledge integration and a mitigated direct utilization. The RIU model allows us to identify significant weaknesses in the transfer of MRV knowledge, including institutional dysfunction, weak institutional coordination, a lack of integration and reduced utilization of the scientific knowledge produced, despite the creation of coordinating institutions. These weaknesses are due partly to the absence of a common platform between exogenous and endogenous knowledge. To overcome these obstacles, synergies between scientists and indigenous actors should be explored and developed. •The complexity of multiple stakeholders at the multiscale level can slow down the Knowledge transfer process•Existing platforms and means for MRV Knowledge transfer are not sufficient for successful MRV implementation•The MRV knowledge transfer framework fit with the RIU model but demand some adjustment that integrate new actors.•Weakness of REDD+ tropical countries are due to the limited implication of local actors and indigenous knowledge•The lack of coordination between actors shows poor integration and a plausible failure of MRV knowledge transfer