ID: 2203
Presenting Author: Man-Seok Shin
Session: 516 - Asian S3EA: Strategic, Spatial and Sustainable EA with effective information
Status: pending
This study proposes an assessment framework to enhance the co-benefits of biodiversity and carbon neutrality, utilizing a machine learning algorithm and the estimation of carbon sequestration capacity
Amid concurrent biodiversity loss and climate change, biodiversity and carbon-neutrality policies often conflict with each other. This study develops an integrated assessment to serve both policy goals by generating spatial grades for biodiversity and carbon sequestration potential. We first review relevant policies and methodologies, then construct an assessment system. This system models species richness using the Random Forest machine learning algorithm for 285 plant species and estimates carbon sequestration by combining species-level carbon uptake data from the Korea Forest Research Institute with land-cover data. Each result is classified into three grades. The cross-combinations of these grades (totaling nine classes) are then reclassified into six policy-ready categories. The results show weak alignment between biodiversity conservation and carbon sequestration, indicated by a low correlation (r = 0.01) between richness and sequestration. Across forest areas, areas exhibiting both high biodiversity and high carbon sequestration capacity constitute only 11.4%. The differing spatial compositions of the two factors increase the likelihood that biodiversity may be sacrificed to secure new carbon sinks. Therefore, this study proposes a strategy to implement carbon neutrality policies by targeting areas with low biodiversity and high carbon sequestration capacity, thereby maximizing the co-benefits of both policies.
Dr. Man-Seok Shin is a researcher at Korea’s National Institute of Ecology, developing biodiversity indicators and carbon–biodiversity maps for policy.
Coauthor 1: Chang Wan Seo