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排出上限・電力部門脱炭素化・産業電化下におけるCCUのシステムレベル価値：日本を対象とした空間最適化分析のデータとコード

Data and code for: System-level value of carbon capture and utilization under emission caps, power-sector decarbonization, and industrial electrification: A spatial optimization analysis for Japan (原題)

Jundai Koketsu

Mendeley Dataデータセット2026-11-06#CCUSOrigin: JP対象セクター: power
DOI: 10.17632/mwr22m6h78
原典: https://doi.org/10.17632/mwr22m6h78

🤖 gxceed AI 要約

日本語

日本46都道府県を対象に、電力・燃料・CO2の流れを同時最適化する空間モデルでCCUのシステム価値を評価した研究のデータ・コード公開パッケージ。排出上限・電力脱炭素・産業電化の3条件を変動させた1,331シナリオをCCUあり/なしで解き、CCUが正の価値を持つのは98シナリオ（7.4%）に限られると示す。正の価値には電力部門の燃料原単位0.0396 t-Fuel/MWh未満（炭素強度約0.109 t-CO2/MWh）が必要で、合成燃料製造の電力効率20%改善でも正値領域は約0.7ポイントしか拡大しない。

English

Companion data-and-code package for a spatial optimization study of CCU across 46 Japanese prefectures, co-optimizing electricity, fuel, and CO2 flows under a net emission cap. Solving 1,331 scenarios with and without CCU, it finds positive system-level value in only 98 cases (7.4%), up to ~1.66 trillion USD when a stringent cap meets low-carbon power. Positive value requires power-sector fuel intensity below 0.0396 t-Fuel/MWh (~0.109 t-CO2/MWh); a 20% efficiency gain in synthetic-fuel electricity use expands the positive region by only ~0.7 points.

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

日本のGX政策（GX推進法・カーボンプライシング・CCS/CCU事業化）に対し、CCUが成立する条件を定量化した点は政策立案・企業投資判断に直結する。再現可能なモデル・コード公開は国内GX研究の透明性向上にも寄与する。

In the global GX context

Adds reproducible, spatially explicit evidence on when CCU creates system value—directly relevant to transition-finance and industrial-decarbonization debates under ISSB/CSRD disclosure of climate strategy and capex. The open code/data also models good practice for transparency in climate-transition modeling.

👥 読者別の含意

🔬研究者:CCU・エネルギーシステム最適化の再現可能なモデルと大規模シナリオ設計の枠組みを参照できる。

🏢実務担当者:CCU/合成燃料事業への投資判断において、電力脱炭素水準など成立条件の閾値を把握できる。

🏛政策担当者:CCU支援策の費用対効果を検討する際、正の価値が生じる排出上限・電力炭素強度の条件を政策設計に反映できる。

📄 Abstract（原文）

This package accompanies the paper "System-level value of carbon capture and utilization under emission caps, power-sector decarbonization, and industrial electrification: A spatial optimization analysis for Japan" (accepted for publication in Energy Reports). It contains the optimization model, the processed inputs, the processed scenario-level outputs, and the scripts that regenerate every figure and computed table of the paper. The study asks which external conditions (the relative net-emission cap, power-sector decarbonization, and industrial electrification) determine the optimal deployment of carbon capture and utilization (CCU), under which combinations CCU creates positive system-level value, and how far CCU technology improvement expands that range. A spatial optimization model of 46 of Japan's 47 prefectures co-optimizes electricity, fuel, and CO2 flows, including CO2 capture, transport, utilization for synthetic fuel, and storage, with price-responsive industrial demand under a net CO2 emission cap. Each of 1,331 scenarios (11 levels each of the three conditions) is solved with and without CCU; the system-level value of CCU is the difference between the two optimal objective values. Contents: AMPL model files; processed inputs for the adopted calibration and five alternative calibrations; 54 scenario grids (base case, technology improvement, one-at-a-time sensitivity, structural proxies, CO2-storage sweep); value decompositions; the outer boundary of the positive-value region; Sobol sensitivity results; baseline validation against FY2020 statistics; and optimality checks. Key results: CCU has positive system-level value in 98 of 1,331 scenarios (7.4%), up to about 1.66 trillion USD where a stringent cap coincides with low-carbon electricity. Positive value requires a power-sector fuel intensity below 0.0396 t-Fuel/MWh (a grid carbon intensity of about 0.109 t-CO2/MWh). A 20% improvement in the electricity consumption of synthetic-fuel production enlarges the positive-value region by only about 0.7 percentage points. Inputs were compiled from public statistics, literature values, and author calculations; original third-party statistical files are not redistributed (sources under Related links and in Appendix A of the paper). Data are licensed CC BY 4.0 and code MIT (LICENSE-CODE.txt).

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