Improving national greenhouse gas inventories for forestry and land use change using open-source software

摘要

National greenhouse gas (GHG) inventories are essential for public policy planning to mitigate GHG emissions. They provide critical information and enhance environmental integrity in planning and development of GHG mitigation policy. The characteristics of a high quality inventory are that it follows good practice according to the IPCC guidelines. The key components of good practice are that the inventory is transparent to others, has accurate and complete emissions estimates for all gases, sources and sinks; has consistent application of methods across time; and is comparable to inventories from other countries [1]. While developed countries struggle with implementation of good practice, most can produce reasonably high quality inventories [2]. However, the task is more challenging in low lying pacific island countries. Some of the specific challenges include: lack of clarity on roles and responsibilities of agencies in preparing inventories; small teams with limited resources; difficulty in retaining expertise; incomplete or non-existent activity data; inconsistent data between inventories; absence of an archiving system for previous inventories; no QA/QC plan [3]. As part of the UN-REDD Programme project on “Strengthening Regional Support to National Forest Monitoring Systems for REDD+ in the Pacific” a Regional Monitoring System has been developed to support low lying pacific island countries (Republic of Palau, Tuvalu, Cook Islands, Tonga, Republic of Kiribati, and Federated States of Micronesia). Using the Republic of Palau as a case study, we present this system and focus on four main challenges; 1) improving activity data compilation through systematic land use assessment 2) application of simple land use mapping algorithms 3) application of a regional geo-spatial portal to support improved data management 4) application of open-source land use analysis systems to help manage and analyze inventory data.