Processed analysis dataset and reproducible code for: Carbon Capture and Storage Impact on Hydrogen Production Scalability
炭素回収・貯留が水素製造のスケーラビリティに与える影響：加工済み分析データセットと再現可能なコード (AI 翻訳)
Joseph Junior NKOU NKOU
🤖 gxceed AI 要約
日本語
本データセットとPythonパイプラインは、CCUS導入が水素プロジェクトの展開容量に与える因果効果を分析するためのものです。IEAの公開データベースから調和化された分析セットを提供し、全ての因果推定、感度分析、図表を再現可能にします。
English
This deposit provides the harmonized analysis dataset and a fully reproducible Python pipeline for studying the causal effect of CCUS adoption on hydrogen project deployable capacity. Using IEA data, it enables reproduction of all causal estimates, sensitivity tests, and figures in the associated manuscript.
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
日本は水素基本戦略でCCUS付き水素を重視しており、本データセットは政策効果の検証やGXリーグでの投資判断に活用できる。
In the global GX context
Globally, the interplay between CCUS and hydrogen scalability is central to net-zero pathways. This reproducible dataset supports evidence-based policy and investment decisions in the hydrogen transition.
👥 読者別の含意
🔬研究者:Provides a clean, reproducible dataset and code pipeline for causal inference on CCUS and hydrogen scalability, enabling verification and extension.
🏢実務担当者:Use the dataset to benchmark project scalability under CCUS adoption, informing investment and technology selection.
🏛政策担当者:The causal estimates can inform subsidy design and infrastructure planning for CCUS-enabled hydrogen.
📄 Abstract（原文）
This deposit contains the derived analysis dataset, supporting tables, and a fully reproducible Python pipeline accompanying the iScience manuscript on the causal effect of CCUS adoption on hydrogen project deployable capacity. The raw source data (the IEA Hydrogen Production and Infrastructure Projects Database 2025 and the IEA CCUS Projects Database 2025) are publicly available from the International Energy Agency under a CC BY 4.0 licence and are not redistributed here. The deposited files are the harmonised analysis set (with CCUS exposure indicators, MWel imputations, regional and technology aggregations) and the numpy/pandas-only revision-analyses pipeline used to generate every causal estimate, sensitivity test, and figure in the revised manuscript. Together they allow any reader with Python 3.10 to reproduce the headline results, the changepoint analysis, the Rosenbaum / E-value sensitivity, the technology-stratified ATEs, the placebo and region-exclusion robustness, and the exposure-validation cross-check against the IEA CCUS Projects Database.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.17632/tymx4h7b5f.1first seen 2026-07-30 05:32:21 · last seen 2026-07-30 05:32:23
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。