Emission Accounting and Mitigation Strategies for Landfill Gas: Quantifying Methane and Carbon Dioxide in Sustainable Waste Management Systems
埋立地ガスの排出算定と緩和戦略：持続可能な廃棄物管理システムにおけるメタンと二酸化炭素の定量化 (AI 翻訳)
Priyanka Dwivedi, Devendra Singh Rathore, Chitra Bhatangar and Vaibhav Purohit
🤖 gxceed AI 要約
日本語
本論文は、埋立地からのGHG排出（特にメタンとCO2）を算定するための理論モデル（デフォルト法、LandGEM、MTM、FOD）を比較し、MRVフレームワークの重要性を強調する。さらに、TROPOMIなどの衛星リモートセンシング技術と従来の推定モデルを統合し、モンテカルロ法による不確実性評価とLCAによる緩和策を提案する。循環経済における埋立地の環境フットプリント削減のロードマップを提供する。
English
This paper compares theoretical models (Default, LandGEM, MTM, FOD) for estimating landfill GHG emissions, emphasizing the MRV framework. It integrates satellite remote sensing (e.g., TROPOMI) with conventional models, uses Monte Carlo for uncertainty, and applies LCA for mitigation. It provides a roadmap to reduce landfill footprints in a circular economy.
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、廃棄物処理施設からのメタン排出は温暖化対策計画の対象であり、SSBJ開示においてもScope 1排出量の算定が求められる。本論文のMRVと衛星技術の統合は、日本の自治体や廃棄物処理企業が排出量の正確な算定・報告を行う上で参考になる。
In the global GX context
Globally, landfill methane is a key target for climate action (Global Methane Pledge). This paper's integration of MRV with satellite data aligns with international reporting standards (IPCC guidelines) and supports companies in meeting disclosure requirements under ISSB/CSRD by improving accuracy of Scope 1 emissions.
👥 読者別の含意
🔬研究者:Provides a comparative analysis of landfill gas estimation models and highlights the potential of satellite data for improving accuracy.
🏢実務担当者:Offers a framework for selecting appropriate estimation methods and integrating MRV for better emissions reporting.
🏛政策担当者:Supports the case for stringent regulations and technological integration in waste management to meet climate targets.
📄 Abstract（原文）
The sustainable approach to waste management has been a critical frontier in climate mitigation action. Comprehensive accounting tools are used for the mitigation of green house gases (GHGs) emissions in the landfill context. A number of analytical models for GHGs are considered, specifically for the estimation of methane (CH4) and carbon dioxide (CO2). The theoretical models range from the Default method to the Landfill Gas Emissions Model (LandGEM) standard industrial method, the Modified Triangular Method (MTM), and the First Order Decay Method (FOD), which is a complex kinetic approach. A robust integrated approach, known as Measurement, Reporting, and Verification (MRV), is needed for the validation of climate data and reduction of GHG. The MRV is bolstered by comparative analysis among more advanced high-precision satellite technologies and conventional theoretical estimation models. The study explores remote sensing techniques for GHGs accounting, such as TROPOMI data, hyper spectral and thermal sensors, for the accurate detection and measurement CH4 emissions. Conversely, conventional methods based on waste composition data, decay rates, waste age, and other relevant factors ascertain the model’s predictability and reliability. Although inherent uncertainties in these parameters are overcome by using the Monte Carlo model, enhances reliability. Life cycle assessments (LCA) provide a mitigation framework. LCA is also used to identify hot spots in the value of the waste-to-energy system. The chapter’s findings advocate the integration of stringent regulatory standards with technologically advanced approaches. This research offers an extensive roadmap to reduce the footprints of landfills in a circular economy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5772/intechopen.1015239first seen 2026-08-08 04:48:13
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