Can Ghana's Cocoa Forest REDD+ program achieve its intended goals of promoting sustainable cocoa production and enhancing carbon stocks?

Abstract

There is a lack of spatially explicit landscape-level data to support the nuances of the potential implications of the Ghana Cocoa Forest REDD + Programme (GCFRP) on land-use changes and carbon stock dynamics, which is critical for effective long-term planning, monitoring, and evaluation of REDD + interventions. The aim of this study is to analyse the future dynamics of land cover and carbon stock under a business-as-usual (BAU) scenario and a REDD + scenario (GCFRP). First, land cover data for the epochs 2000, 2015, and 2020 were produced from Landsat Images. Next, two land cover data for 2050 under the BAU and GCFRP scenarios were predicted using the GIS-based Cellular Automata-Markov model and Multi-Layer Perceptron Neural Network. Finally, the InVEST carbon model was used to estimate total carbon stocks for each epoch across the different land cover types. The GCFRP scenario reveals a significant increase in forest cover, with closed-canopy forests rising by 4.77 % points and open-canopy forests increasing by 9.735 points. The GCFRP scenario also predicts a shift from monoculture cocoa farming, with a significant decrease of 13.89 % points. Other notable changes include increases in agroforestry cocoa (+3.64 % points) and decrease in oil palm (-1.4 % points), food crops (-1.57 % points), shrub lands/fallow (-0.49 % points), and mining (-0.48 % points). Under the BAU, the total carbon stock is projected to decline by 13.9 % from 12.46 Tg C in 2020 to 10.73 Tg C by 2050. In stark contrast, the GCFRP scenario forecasts a 3.4 % increase in total carbon, rising to 12.88 Tg C by 2050. The GCFRP intervention is projected to sequester 2.15 Tg C (20 %) more carbon than the BAU scenario over the next 30 years. These findings underscore the critical significance of GCFRP interventions in enhancing carbon sequestration, promoting sustainable land use practices, and mitigating climate change impacts.

Keywords: Carbon sequestration; Climate change; Emission reduction programs; Land use land cover prediction; REDD+; Remote sensing.