An AIS-Based approach for estimating greenhouse gas emissions from shipping activities in Indonesian waters

Abstract

Indonesia's vast maritime sector plays a pivotal role in trade and economic growth but also contributes substantially to greenhouse gas (GHG) emissions from shipping activities. This study applies Automatic Identification System (AIS) data as a form of high-resolution, near-real-time transformative informatics to enable precise, vessel-level, and spatially explicit emission accounting that is fundamental to effective maritime governance. Using a bottom-up approach recommended by the International Maritime Organization, AIS data from 2022 were preprocessed from 369 million to 181 million valid records (49.17%) and used to estimate emissions of CO₂, CH₄, and N₂O, resulting in a total of 38.86 million tons of CO₂-equivalent (CO₂e) emissions in Indonesian waters. Bulk carriers are identified as the largest contributors to CO₂ and N₂O emissions, while liquefied gas tankers dominate CH₄ emissions; Heavy Fuel Oil (HFO) generates the highest CO₂ and N₂O emissions, whereas Liquefied Natural Gas (LNG) contributes most to CH₄. Spatial benchmarking and comparisons with alternative approaches reveal both consistencies and uncertainties, highlighting the sensitivity of emission estimates to methodological assumptions, AIS coverage, and emission factors. Despite these limitations, the results demonstrate that AIS-based emission inventories provide critical spatial and temporal insights to support Indonesia's Blue Economy Roadmap, inform decarbonization strategies, and align maritime economic growth with climate commitments under SDG 13 and the Paris Agreement.