インドネシアの重機サービス施設における簡略化ライフサイクル影響評価とホットスポット分析：ゲート・トゥ・ゲートのPythonベースアプローチ
STREAMLINED LIFE CYCLE IMPACT ASSESSMENT AND HOTSPOT ANALYSIS OF A HEAVY EQUIPMENT SERVICE FACILITY IN INDONESIA: A GATE-TO-GATE PYTHON-BASED APPROACH (原題)
Josua Aditya Manuel, Haryono Setiyo Huboyo, Ika Bagus Priyambada
🤖 gxceed AI 要約
日本語
本研究は、インドネシアの重機サービス施設を対象に、ゲート・トゥ・ゲート境界で簡略化したライフサイクル影響評価（LCIA）をPythonで実装した。2025年の実運用データを用い、気候変動スコアは年間176,203.80 kg CO2-eqで、市場ベースのScope 2では電力が60.92%を占め、ワークショップと配送が主要ホットスポットである。商業LCAソフト不要の低コストで再現可能な枠組みを提案する。
English
This study develops a streamlined gate-to-gate Life Cycle Impact Assessment (LCIA) using Python for a heavy equipment service facility in Indonesia. Using 2025 operational data, the climate change score is 176,203.80 kg CO2-eq/year, with electricity contributing 60.92% under market-based Scope 2. Workshop and delivery are key hotspots. The framework is low-cost and replicable without commercial LCA software.
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
日本企業の海外子会社（インドネシア等）での排出量算定に参考となる。SSBJ開示ではScope 2の算定が求められ、簡易なPythonベースのLCIAは中小企業や新興国拠点での実践的ツールとなり得る。
In the global GX context
This paper provides a replicable, code-based LCIA framework that is relevant for global Scope 2 accounting, especially in emerging economies where commercial LCA tools are less accessible. It aligns with ISSB/CSRD requirements for transparent and auditable emissions reporting.
👥 読者別の含意
🔬研究者:Provides a transparent, low-cost LCIA method that can be adapted for service industries in emerging markets.
🏢実務担当者:Offers a practical approach for internal carbon footprint assessment without expensive software, useful for subsidiaries in developing countries.
🏛政策担当者:Highlights the need for accessible tools to support SME emissions reporting in emerging economies.
📄 Abstract（原文）
This study develops and applies a streamlined Life Cycle Impact Assessment (LCIA) approach to evaluate the operational environmental performance of a heavy equipment service facility under a gate-to-gate system boundary. The case study uses 2025 operational data from PT United Tractors Tbk, Semarang Branch, including electricity, fuel, water, and non-hazardous waste consumption, and processes the inventory through a transparent, fully auditable Python-based implementation rather than through commercial LCA software. The results show that the Climate Change (GWP100) score reaches 176,203.80 kg CO2-eq per year under market-based Scope 2 accounting, with grid electricity from the Java-Madura-Bali (JAMALI) interconnection contributing 60.92% of the total impact, well above the gasoline- and diesel-type fuel flows that follow it. At the process level, Workshop and Service and Delivery and Mobilization emerge as the dominant hotspots, jointly contributing 59.05% to the annual climate footprint, while a supplementary hazardous-waste inventory further identifies 4.913 tonnes per year of regulated waste, of which 99.0% is verifiably managed through a licensed third party. The findings indicate that a streamlined, code-based LCIA can deliver sufficiently robust and decision-relevant environmental information for internal management purposes in organizations that face limited access to commercial LCA software and characterization databases. The study contributes a replicable, methodologically transparent, and low-cost assessment framework for service-oriented industrial operations in emerging economies and offers a template that other branch-scale or small-and-medium enterprise operations can adapt with modest technical resources.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/22662958first seen 2026-09-09 03:57:42 · last seen 2026-09-19 04:33:56
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