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Navigating maritime transition pathways and energy infrastructure towards net zero (supporting data)

ネットゼロに向けた海運転換経路とエネルギーインフラのナビゲーション(データ添付) (AI 翻訳)

Fristed, Frederik

Zenodoプレプリント2026-05-04#エネルギー転換Origin: EU
DOI: 10.5281/zenodo.18862996
原典: https://zenodo.org/records/18862996

🤖 gxceed AI 要約

日本語

本リポジトリは、海事セクターのネットゼロ移行を分析するためのエネルギーシステムモデル(Balmorel)とデータを提供する。燃料生産、CO2除去、輸送を含むシナリオを評価し、24-72時間の計算時間を要する大規模モデル。結果はGDX形式で提供。

English

This repository provides the code, input data, and selected results for a study on maritime transition pathways to net zero using the Balmorel energy system model. It includes scenarios for fuel production, carbon dioxide removal, and transport. The model requires GAMS and HPC resources.

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

日本は海運大国であり、IMOの規制強化に対応するため、本モデルは日本の海運脱炭素計画に応用可能。ただしデータは北欧ベースのため、日本固有の文脈は薄いが、モデル手法は参考になる。

In the global GX context

This open-source model supports global maritime decarbonization research, relevant to IMO's net-zero targets and energy infrastructure planning. It provides a framework for analyzing fuel transitions and infrastructure needs.

👥 読者別の含意

🔬研究者:Useful for energy system modelers working on maritime decarbonization and infrastructure pathways.

🏢実務担当者:Shipping companies or fuel suppliers can leverage scenario insights for transition planning and investment decisions.

🏛政策担当者:IMO or national regulators can consider the modeling framework for designing effective decarbonization policies.

📄 Abstract(原文)

Overview This repository contains the code, input data and selected results supporting the manuscript “Navigating maritime transition pathways and energy infrastructure towards net zero”, currently submitted for review. It includes the applied energy system model ( Balmorel.zip ), the underlying input data ( data.zip ), and the results generated for the core maritime transition scenarios reported in the manuscript. This repository provides a snapshot of the model and data used in the study. The model builds on the Balmorel framework. The model code and data repositories are also available at the following locations: Model code: https://github.com/balmorelcommunity/Balmorel/tree/navigating_martime_transitions_paper-v1.0 Input data: https://github.com/balmorelcommunity/Balmorel_data/tree/navigating_martime_transitions_data-v1.0 The model and data are open source. The model is implemented in GAMS and therefore requires a GAMS licence to run. Due to its computational size, the model was solved on a high-performance computing (HPC) system using 16 cores (16 GB RAM per core), with a typical run taking approximately 24-72 hours. The results for the maritime transition scenarios are provided in .gdx format. .gdx is the standard data format used by the GAMS modelling framework to store model outputs. The files can be opened using the GAMS IDE or GDX Viewer. Instructions for running the base model Unzip Balmorel.zip . Unzip data.zip and place the data folder inside Balmorel/base/ . Run the model by executing the file Balmorel/base/model/Balmorel.gms . Instructions for running the transition scenarios from the study Unzip Scenarios.zip . Place the six scenario folders (e.g.  1-FEU-Fuel ) inside  Balmorel/ . Run each scenario by executing the relevant .gms file (e.g. Balmorel/1-FEU-Fuel/model/Balmorel.gms) . Note that any required files not explicitly defined within a scenario’s data or model folders are automatically inherited from the corresponding files in the base folder. Model results The base model results are stored in the files  Balmorel/base/model/MainResults.gdx and Balmorel/base/model/Optiflow_MainResults.gdx. Scenario results are stored in a similar way, e.g. Balmorel/1-FEU-Fuel/model/MainResults.gdx and Balmorel/1-FEU-Fuel/model/Optiflow_MainResults.gdx. MainResults.gdx contains the Balmorel energy system results, while  Optiflow_MainResults.gdx contains the results for fuel production, carbon dioxide removal and transport generated by the OptiFlow model. Output files are postprocessed and visualized in python.         

🔗 Provenance — このレコードを発見したソース

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