A Deep Learning application towards transparent communication for Payment for Forest Environmental Services (PES) (Proposals Track)
Lan HOANG (IBM Research); Thuy Thu Phan (Center for International Forestry Research (CIFOR))
Deforestation accounts for more than 20% of global emission. Payments for Environmental Services (PES) is seen by both policy makers and practitioners as an effective market-based instrument to provide financial incentives for forest owners, particularly poor and indigenous households in developing countries. It is a critical instrument to protect forests, and ultimately to mitigate climate change and reduce emission from deforestation. However, previous studies have pointed out a key challenge for PES is to ensure transparent payment to local people, due to i) weak monitoring and evaluation and ii) indigenous inaccessibility to e-banking and complying with procedural and administrative paper works to receive payments. Specifically, the amount and the complexity of forms along with the language barriers is a key issue; and most transactions need several intermediaries and transaction costs which reduce the payments reaching landowners. To address these issues, we propose a communication platform that links across the stakeholders and processes. Our proposal will utilize Machine Learning techniques to lower the language barrier and provide technology solutions to help indigenous people to access payments. This would also help improve the effectiveness and transparency of PES schemes. Specifically, we propose the use of Natural Language Processing techniques in providing a speech-to-text and auto translation capability, and the use of Graph Neural Network to provide link predictions of transaction types, volumes and values. The pathway to impact will be forest protection and local livelihood through providing financial incentives, and subsequently contribution to more carbon sequestration and storage – a key issue in climate change mitigation.