デジタル化下におけるカーボンファーミング発展の障壁と機会
BARRIERS AND OPPORTUNITIES FOR THE DEVELOPMENT OF CARBON FARMING UNDER DIGITALIZATION (原題)
Пламен Драгнев
🤖 gxceed AI 要約
日本語
ブルガリアのカーボンファーミング（CF）普及に向けた機会・障壁・戦略的方向性を、デジタル化とMRVの役割に注目して分析した研究。精密農業、IoT、リモートセンシング、AI、ブロックチェーンはデータ収集・追跡・検証の基盤を提供し得るが、科学的に妥当なMRV手法や現場測定、独立検証を代替できないと指摘。デジタル基盤不足、人材・投資コスト、土壌有機炭素評価の方法論的不確実性、規制・市場の不確実性が主な障壁であり、共同スキームや共有インフラ、公的・民間資金の組合せが中小農家の経済性改善に有効と結論づける。
English
This study analyzes opportunities, barriers, and strategic directions for carbon farming (CF) in Bulgaria, focusing on digitalisation's role in MRV of carbon outcomes. Precision agriculture, IoT, remote sensing, AI, and blockchain can support data collection and verification but cannot replace scientifically valid MRV, field measurements, and independent verification. Key barriers include weak digital infrastructure, limited skills, high costs, soil organic carbon methodological uncertainty, and regulatory/market uncertainty; collective schemes, shared infrastructure, and blended finance can improve viability, especially for SMEs.
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
EU炭素農業イニシアチブやCRCF（炭素除去認証枠組み）の動向と直結し、日本のJ-クレジット制度における農業分野の炭素貯留・MRV高度化やデジタル技術活用を検討する際の参照枠となる。
In the global GX context
Directly relevant to the EU Carbon Farming Initiative and Carbon Removals Certification Framework (CRCF), offering lessons on MRV integrity and digital infrastructure that inform global soil-carbon crediting and disclosure debates.
👥 読者別の含意
🔬研究者:デジタル技術とMRV方法論の補完関係、および土壌有機炭素評価の不確実性に関する実証的知見を得られる。
🏢実務担当者:農業・食品企業が炭素クレジット創出やサプライチェーン排出削減にデジタルMRVを組み込む際の障壁と投資判断の参考になる。
🏛政策担当者:炭素農業の認証制度設計において、デジタル基盤整備と中小農家支援、公的・民間資金の組合せの必要性を示唆する。
📄 Abstract（原文）
This study aims to analyse the opportunities, barriers, and strategic directions for the development of carbon farming (CF) in Bulgaria, with particular emphasis on the role of digitalisation in supporting sustainable agricultural practices and monitoring, reporting, and verification (MRV) of carbon outcomes. The study applies a theoretical and comparative approach based on a review and critical analysis of scientific literature, European strategic and regulatory documents, and available examples from Bulgarian practice. The analysis focuses on the application of precision agriculture, Internet of Things (IoT) technologies, remote sensing, artificial intelligence (AI), digital farm-management systems, and blockchain technologies, as well as on economic, technological, methodological, regulatory, and organisational barriers to CF adoption. The findings indicate that digitalisation can provide essential infrastructure for data collection, monitoring, modelling, traceability, and verification of carbon outcomes. However, digital technologies cannot replace scientifically valid MRV methodologies, field measurements, reliable primary data, and independent verification. The main barriers identified include insufficient digital infrastructure, limited digital and interdisciplinary competences, high investment and transaction costs, methodological uncertainty in soil organic carbon assessment, regulatory and market uncertainty, concerns regarding data ownership and transparency, and significant pedoclimatic differences. The analysis indicates that collective schemes, shared infrastructure, demonstration farms, specialised training, and a combination of public and private financing can improve the accessibility and economic viability of CF, particularly for small and medium-sized farms.
🔗 Provenance — このレコードを発見したソース
- openalex https://doaj.org/article/2e1abccf16e04971846c83b8ff349cf5first seen 2026-10-25 04:54:07 · last seen 2026-10-31 05:05:39
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