Quantifying greenhouse gas emissions from wood fuel use by households
家庭での木質燃料使用による温室効果ガス排出量の定量化 (AI 翻訳)
Alessandro Flammini, Hanif Adzmir, Kevin Karl, Francesco N. Tubiello
🤖 gxceed AI 要約
日本語
本論文は、家庭での調理用木質燃料のうち非再生可能な伐採に起因するGHG排出量を、国別・世界規模で推計した。2019年の年間排出量は約7.45億トンCO2換算と推定され、不確実性は-63%から+64%と大きい。サハラ以南アフリカや南アジアで増加、東アジア・東南アジアで減少が見られた。FAOのAGRIDATAデータベースの一部として、AFOLUとエネルギー部門の重複を避けるIPCCガイドラインに基づく。
English
This paper estimates GHG emissions from non-renewable wood fuel harvesting for household cooking, by country and globally. In 2019, annual emissions were about 745 Mt CO2 eq, with uncertainty from -63% to +64%. Increases in sub-Saharan Africa, southern Asia, and Latin America were offset by decreases in eastern and South-East Asia. The work is part of FAO's agri-food GHG database, following IPCC guidelines to avoid double counting between AFOLU and energy.
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
日本では木質バイオマス発電の持続可能性基準が議論されており、家庭用木質燃料の排出算定は、カーボンニュートラルと見なす慣行の見直しに示唆を与える。SSBJ開示でのScope 1排出算定にも関連する。
In the global GX context
Globally, this paper challenges the assumption that wood fuel combustion is carbon-neutral, providing a methodology to account for non-renewable harvesting. It informs national GHG inventories and corporate Scope 1 accounting for bioenergy, relevant to ISSB and CSRD disclosure requirements.
👥 読者別の含意
🔬研究者:Provides a country-level dataset and method for attributing wood fuel emissions to non-renewable sources, useful for refining bioenergy carbon accounting.
🏢実務担当者:Highlights the need to assess the renewability of biomass feedstocks in corporate GHG inventories, especially for companies using wood fuel in operations or supply chains.
🏛政策担当者:Offers evidence for revising bioenergy carbon neutrality assumptions in national climate policies and reporting frameworks.
📄 Abstract(原文)
Abstract. The combustion of wood fuel for residential use is often not considered to be a source of greenhouse gas (GHG) emissions from households, as the emissions from wood fuel combustion can be offset by the CO2 absorbed by the growth of the forest (as a carbon sink) (IPCC, 2006). However, this only applies to wood that is harvested in a renewable way, i.e. at a rate not exceeding the regrowth rate of the forest from which it was harvested (Drigo et al., 2002). This paper estimates the share of GHG emissions attributable to non-renewable wood fuel harvesting for use in residential food activities, by country and with global coverage. It adds to a growing research base estimating GHG emissions from across the entire agri-food value chain, from the manufacture of farm inputs, through food supply chains, and finally to waste disposal (Tubiello et al., 2021). Country-level information is generated from United Nations Statistics Division (UNSD) and International Energy Agency (IEA) data on wood fuel use by households. We find that, in 2019, annual emissions from non-renewable wood fuel consumed for household food preparation were about 745×106 t (Mt CO2 eq. yr−1), with an uncertainty ranging from −63 % to +64 %. Overall, global trends were a result of counterbalancing effects: the emission increases were largely fuelled by countries in sub-Saharan Africa, southern Asia, and Latin America, whereas significant decreases were seen in countries in eastern Asia and South-East Asia. The Food and Agriculture Organization of the United Nations (FAO) has developed and regularly maintains a database covering GHG emissions from the various components of the agri-food sector, including pre- and post-production activities, by country and world regions. The dataset has been developed according to the International Panel on Climate Change guidelines (IPCC, 2006), which avoid overlaps between agriculture, forestry, and other land use (AFOLU) and energy components. The aforementioned dataset relies mainly on UNSD Energy Statistics data, which are used as activity data for the calculation of the GHG emissions (Tubiello et al., 2022). The information used in this work is available as open data at https://doi.org/10.5281/zenodo.7310932 (Flammini et al., 2022a).
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5194/essd-15-2179-2023first seen 2026-08-02 17:40:14
🔔 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。