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A Decision Support Tool for Evaluating GHG Mitigation Measures in Land Use Sectors

土地利用部門におけるGHG緩和策評価のための意思決定支援ツール (AI 翻訳)

Katerina Zeglova, Kristine Bilande, Una Diana Veipane, Irina Pilvere, Aleksejs Nipers

Land📚 査読済 / ジャーナル2026-04-29#政策Origin: EU
DOI: 10.3390/land15050758
原典: https://doi.org/10.3390/land15050758

🤖 gxceed AI 要約

日本語

本論文は、土地利用・林業（LULUCF）部門における温室効果ガス（GHG）緩和策を評価するための空間的な意思決定支援ツールを開発した。ラトビアを事例に、ユーザーが緩和策を選択し、空間的条件を指定することで、GHG削減ポテンシャルに加え、収益性、雇用、生息地の質への影響を定量的・空間的に評価できる。PythonとPostGISで構築され、ウェブインターフェースを通じて政策シナリオの比較を容易にする。

English

This paper presents a spatial decision-support tool for evaluating GHG mitigation measures in the Land Use, Land-Use Change, and Forestry (LULUCF) sector. Applied to Latvia, the tool allows users to select mitigation options, apply spatial criteria, and quantify impacts on GHG reduction, profitability, employment, and habitat quality. Built with Python and PostGIS, it provides a transparent, web-based framework for comparing policy scenarios and supporting evidence-based land-use climate planning.

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

日本では、森林・農地のLULUCF排出・吸収量の報告が重要であり、本ツールのような政策選択肢の多基準評価手法は、日本の地方自治体や農業政策における気候変動対策の検討に示唆を与える。特に、空間的に明示された評価は、日本の土地利用計画にも応用可能である。

In the global GX context

This tool contributes to the global need for integrated, spatially explicit evaluation of land-use mitigation measures. It aligns with IPCC guidelines and supports countries in designing effective LULUCF policies. The multi-criteria approach (GHG, profitability, employment, habitat) offers a model for balancing climate and socio-economic goals in land-use planning.

👥 読者別の含意

🔬研究者:Researchers in land-use modeling and decision support can examine the tool's methodological integration of biophysical and socio-economic indicators.

🏢実務担当者:Practitioners in environmental planning can adopt or adapt the tool for evaluating regional land-use mitigation scenarios.

🏛政策担当者:Policymakers can use the tool to transparently compare trade-offs and synergies of different land-use mitigation options.

📄 Abstract（原文）

Sustainable land use policy planning requires integrated approaches that account for environmental and socio-economic trade-offs of greenhouse gas (GHG) mitigation measures. This study presents a spatial decision-support tool developed to support the evaluation of policy scenarios in non-urban land-use sectors, with application to the land use, land-use change, and forestry (LULUCF) sector in Latvia. The tool enables users to select predefined mitigation measures, apply spatial selection criteria, and generate quantitative and spatially explicit outputs. In addition to estimating GHG mitigation potential, it evaluates impacts on profitability, employment, and habitat quality, allowing the assessment of trade-offs and synergies across multiple dimensions. Scenario results are reported as both absolute and relative impacts, improving transparency and comparability. Developed in Python 3.10 and supported by a PostgreSQL 17/PostGIS 3.5 database, the tool operates through a web-based interface and supports efficient scenario construction and evaluation. While results depend on underlying data and assumptions, the tool provides a transparent framework for exploring policy options and supports evidence-based decision-making in land-use and climate policy planning.

🔗 Provenance — このレコードを発見したソース

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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。