PLANtoACT Task 2.1: Costs database of the PLANtoACT project
PLANtoACT タスク2.1: PLANtoACTプロジェクトのコストデータベース (AI 翻訳)
Prina, Matteo Giacomo
🤖 gxceed AI 要約
日本語
PLANtoACTプロジェクト（LIFEプログラム助成）の一環として、欧州の地域・自治体のクリーンエネルギー移行計画を支援するため、2020年から2050年までの主要クリーンエネルギー技術のターンキー設置コスト（CAPEX）の学習曲線をまとめたデータベース。IEA、IRENA、NRELなど複数の文献・モデルソースからのコスト推定値を比較し、平均値を提供する。技術経済評価やシナリオモデリングに活用可能。
English
This repository provides a technology cost database compiled within the PLANtoACT project, a LIFE-funded initiative supporting European local and regional authorities in clean energy transition planning. It offers CAPEX learning curves for key clean energy technologies from 2020 to 2050, drawing on multiple sources like IEA, IRENA, and NREL, with averaged values to represent cost declines. The data supports techno-economic assessments and scenario modeling for pilot regions.
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
日本では、再生可能エネルギー導入拡大に伴い、技術コストの将来見通しが重要。本データベースは、日本の自治体や企業がエネルギー計画を策定する際の参照点となり得る。ただし、欧州中心のデータであり、日本の事情に合わせた調整が必要。
In the global GX context
This database aligns with global efforts to track clean energy technology cost trends, supporting energy transition planning and investment decisions. It complements international datasets like IEA and IRENA, offering a consolidated view of CAPEX projections. Useful for policymakers and practitioners in Europe and beyond.
👥 読者別の含意
🔬研究者:Techno-economic modelers can use the CAPEX learning curves for scenario analysis and cost projections.
🏢実務担当者:Energy planners in local authorities can leverage the cost data to inform investment decisions and transition strategies.
🏛政策担当者:Policymakers can reference the cost trends to design support mechanisms and assess feasibility of clean energy targets.
📄 Abstract（原文）
This repository presents the technology cost database compiled within the PLANtoACT project. PLANtoACT is a LIFE Programme–funded project (October 2025–September 2028), coordinated by EURAC with FEDARENE and partners from France, Italy, Romania, Germany and Portugal, that develops, tests and promotes a stakeholder-driven, spatially detailed integrated energy planning approach to help European Local and Regional Authorities move from clean energy transition targets to coordinated, financed and implementable action. The dataset provides capital expenditure (CAPEX) learning curves for turnkey installations of different clean energy technologies , projected annually from 2020 to 2050. For each technology, cost estimates from multiple literature/model sources (e.g. IEA, IRENA, NREL, JRC, ETIP, SETIS) are collected side by side, together with an averaged value, to represent the expected decline in turnkey investment cost as deployed capacity and manufacturing experience increase over time. The data are intended to support techno-economic assessments and scenario modelling for the clean energy transition strategies developed in the PLANtoACT pilot regions. Structure README (first sheet): describes each technology tab, its full name, category, and a short technical description. One sheet per technology (e.g. PV rooftop, PV utility-scale, AgriPV, BIPV, Li-ion batteries residential/utility-scale, electrolyzer, fuel cell, onshore wind, offshore wind), each containing: year : 2020–2050, one column per literature/model source, and Average column used as the representative learning-curve value. Detailed descriptions of each technology are provided in the README sheet of the workbook and are not repeated here.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/21820254first seen 2026-08-07 04:18:23
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