An AIS-Based approach for estimating greenhouse gas emissions from shipping activities in Indonesian waters
インドネシア海域における船舶活動からの温室効果ガス排出量推定のためのAISベースのアプローチ (AI 翻訳)
Nandya Rezky Utami, Dewi Krismawati, Markie Muryawan, Cherryl Chico, Setia Pramana
🤖 gxceed AI 要約
日本語
インドネシア海域の船舶からの温室効果ガス排出量をAISデータを用いて推定。2022年のデータで369百万件から181百万件に前処理し、CO2換算で3886万トンの排出量を算出。ばら積み船がCO2とN2Oの最大排出源、LNGタンカーがCH4の主要排出源。空間的・時間的排出インベントリを提供し、ブルーエコノミーと気候目標を支援。
English
This study uses AIS data to estimate GHG emissions from shipping in Indonesian waters, preprocessing 369 million records down to 181 million for 2022. Total emissions were 38.86 million tons CO2e. Bulk carriers dominate CO2 and N2O, while LNG tankers dominate CH4. The bottom-up approach provides spatial and temporal insights for maritime governance and decarbonization.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
インドネシア海域に特化した研究だが、AISデータを用いた船舶排出量のボトムアップ推定手法は、日本のSSBJやGHG排出量算定においても参照可能。特に、日本の海運業界や港湾管理における脱炭素戦略のベンチマークとして有用。
In the global GX context
While focused on Indonesia, this AIS-based bottom-up emission estimation methodology is relevant globally for improving maritime GHG inventories. It supports TCFD/ISSB climate disclosures by providing vessel-level, spatially explicit emission data, which is crucial for shipping companies reporting under emerging disclosure standards.
👥 読者別の含意
🔬研究者:Offers a robust bottom-up methodology for maritime emission estimation using AIS data, highlighting sensitivity to assumptions and emission factors.
🏢実務担当者:Shipping companies and port authorities can use the spatial emission patterns to identify hotspots and optimize decarbonization strategies.
🏛政策担当者:Provides actionable data for aligning maritime transport with Paris Agreement goals and national blue economy roadmaps.
📄 Abstract（原文）
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 CO 2 , CH 4 , and N 2 O, resulting in a total of 38.86 million tons of CO 2 -equivalent (CO 2 e) emissions in Indonesian waters. Bulk carriers are identified as the largest contributors to CO 2 and N 2 O emissions, while liquefied gas tankers dominate CH 4 emissions; Heavy Fuel Oil (HFO) generates the highest CO 2 and N 2 O emissions, whereas Liquefied Natural Gas (LNG) contributes most to CH 4 . 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.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1177/18747655261420815first seen 2026-06-29 06:11:42
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