Carbon reduction assessment of PV-assisted mine land reclamation using deep learning from remote sensing images
深層学習とリモートセンシング画像を用いたPV支援鉱山跡地再生の炭素削減評価 (AI 翻訳)
Ming Hao, Zhen Zhang, Weiqiang Luo, Min Tan
🤖 gxceed AI 要約
日本語
中国東部の高地下水位地域における鉱山跡地の土地利用転換と温室効果ガス削減を目的に、深層学習（YOLOv7＋DeepLabv3+）を用いて太陽光発電（PV）の適地を高精度にマッピングし、ライフサイクルアセスメント(LCA)により炭素削減効果を定量化。5省で検証し、1481.85km²のPV展開可能面積を特定。年間発電ポテンシャルは6193.74GWh〜24,118.06GWhで、炭素回収期間は7.05〜8.29年。
English
This study proposes a deep learning framework (YOLOv7 + DeepLabv3+) to map photovoltaic (PV) suitability on reclaimed mine lands in high groundwater table regions of central-east China, combined with lifecycle assessment (LCA) to quantify carbon reduction. Validated across five provinces, it identifies 1,491.85 km² of deployable area with 98.11% localization and 94.74% segmentation accuracy. Annual PV potential ranges from 6,193.74 to 24,118.06 GWh, with carbon payback periods of 7.05–8.29 years. The framework supports China's carbon neutrality goals.
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
中国の炭素中和目標に直結する研究だが、日本の鉱山跡地や褐地再生にも応用可能な方法論を提供する点で参考となる。日本では再生可能エネルギー導入拡大と土地利用の競合が課題であり、本手法は適地選定の効率化に寄与しうる。
In the global GX context
This paper demonstrates a novel integration of deep learning and LCA for renewable energy planning in post-mining landscapes, relevant globally for energy transition and land remediation. The methodology can be adapted for other countries, including Japan, where brownfield redevelopment for solar PV is gaining attention.
👥 読者別の含意
🔬研究者:Provides a validated deep learning pipeline for PV mapping from remote sensing, with transferable potential for other regions.
🏢実務担当者:Offers a decision-support tool for assessing PV potential and carbon payback on reclaimed land, useful for energy and mining companies.
🏛政策担当者:Highlights the carbon reduction potential of PV on degraded mine lands, supporting integrated land use and energy policy.
📄 Abstract（原文）
Abstract Coal mining in high groundwater table regions (HGTRs) of central-east China has caused severe land degradation and greenhouse gas emissions, necessitating sustainable energy solutions. Photovoltaic (PV) technology offers significant potential for carbon reduction in these areas, yet existing studies face limitations in PV mapping (e.g., reliance on single-sensor data and coarse spatial resolutions) and lifecycle carbon assessments (e.g., incomplete stage-specific emission analysis). To address these gaps, this study proposes a multi-scale two-stage deep learning framework integrating YOLOv7 for preliminary PV localization and DeepLabv3 + for refined segmentation, combined with a lifecycle assessment (LCA) to quantify carbon reduction benefits for assessing carbon reduction of PV in high groundwater table regions (HGTRPVs) during land reclamation. Experiments across five HGTR provinces demonstrate high accuracy (98.11% localization, 94.74% segmentation) and identify 1,491.85 km 2 of PV-deployable areas. Results reveal that over 45% ofche lifecycle emissions originate from PV manufacturing, The annual PV potential ranges from 6193.74 GWh to 24,118.06 GWh, with carbon payback periods ranging from 7.05 to 8.29 years. This framework provides actionable insights for optimizing PV deployment in ecologically sensitive mining regions, supporting China’s carbon neutrality goals.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s44212-026-00105-2first seen 2026-05-21 04:28:41
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