露天金属鉱山における炭素排出予測と緩和シナリオ評価のためのプロセスパラメータ駆動型システムダイナミクスモデル
System dynamics model driven by process parameters for carbon emission prediction and mitigation scenario evaluation in open-pit metal mines (原題)
Bai B, Li GQ, Hou J
🤖 gxceed AI 要約
日本語
露天金属鉱山の排出量を、生産計画・運搬距離・エネルギー使用・排出係数などの工程パラメータから月次で予測するシステムダイナミクスモデルを開発。2024年データで検証し相対誤差3%未満、2027年計画に適用した。選鉱・尾鉱処理が排出の91%を占め、選鉱効率改善・電力脱炭素・運搬電化の組合せで排出を約22%削減、炭素コストも低減できると示した。
English
This study develops a process-parameter-driven system dynamics model that predicts monthly carbon emissions from open-pit metal mines across drilling, blasting, haulage, beneficiation and tailings handling. Validated on 2024 data (relative errors under 3%), it was applied to a 2027 mine plan where beneficiation and tailings accounted for 91% of emissions. Combined mitigation measures cut emissions by ~22% and reduced potential carbon costs.
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
日本企業が海外鉱山権益や資源調達に関わる際、Scope 3上流排出の算定・削減計画に直結する。SSBJ/Scope 3開示やサプライチェーン脱炭素の実務に応用可能。
In the global GX context
Adds a process-level, planning-integrated emissions model for mining, a hard-to-abate sector central to Scope 3 upstream accounting under ISSB/CSRD. Supports transition planning and carbon-cost scenario analysis for extractive supply chains.
👥 読者別の含意
🔬研究者:工程パラメータと排出量を結ぶ動的モデリング手法として、鉱山・重工業の排出予測研究に応用できる。
🏢実務担当者:資源調達や海外鉱山を持つ企業が、Scope 3上流排出の算定と削減シナリオ検討に活用できる。
🏛政策担当者:炭素価格導入時の鉱山セクターのコスト影響評価や脱炭素政策設計の参考になる。
📄 Abstract（原文）
<title>Abstract</title> <p> Carbon emissions from open-pit metal mines vary with production plans, haulage distances, energy use, energy mix, and emission factors, but conventional inventories provide limited support for planning. This study developed a process parameter driven system dynamics model that links mine plans to monthly emissions from drilling, blasting, loading, haulage, crushing, beneficiation, tailings handling, and land disturbance carbon sink loss. The model was validated using 2024 data and applied to the 2027 production plan of a case study mine. Relative errors in electricity use, diesel use, explosive consumption, and whole-mine emissions were below 3%, with a mean absolute percentage error of 0.99%. The 2027 baseline emissions were 1762.53 kt CO <sub>2</sub> eq; beneficiation and tailings handling accounted for 91%, and monthly emissions were strongly correlated with mining stripping quantity (r = 0.9993). Beneficiation energy efficiency improvement, electricity decarbonization, and haulage electrification reduced whole mine emissions by 7.83%, 14.06%, and 0.85%, respectively. Combined implementation reduced emissions to 1381.05 kt CO <sub>2</sub> eq and potential carbon cost from 176.67 to 138.42 million CNY. Haulage electrification reduced haulage emissions by 18.74%, but its whole mine effect was limited by haulage's small baseline contribution. The model supports identification of key sources and comparison of mitigation pathways during mine design and production planning. </p>
🔗 Provenance — このレコードを発見したソース
- Research Square https://doi.org/10.21203/rs.3.rs-10951758/v1first seen 2026-09-29 04:21:25
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
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。