A study on key issues in Korea's pig carbon-footprint and data construction for advanced calculation of domestic pig's carbon-footprint
韓国養豚のカーボンフットプリントにおける課題と高度化のためのデータ構築に関する研究 (AI 翻訳)
Yoo-Sung Park, Sung-Mo Yeon, K. J. Park
🤖 gxceed AI 要約
日本語
韓国での養豚生産における温室効果ガス排出量を正確に定量化するため、ゆりかごから農場ゲートまでのLCAを実施。その結果、総CFは1頭あたり993.21 kg CO2-eqで、飼料生産が76.31%を占めた。飼料原料の原産地によってCFが大きく変動するため、モンテカルロシミュレーションを用いた確率的アプローチを採用。標準的な飼料構成データベースの構築が不可欠であると結論づけた。
English
Conducted a cradle-to-farm gate LCA of pig production in Korea, finding total CF of 993.21 kg CO2-eq per head with feed production accounting for 76.31%. Used Monte Carlo simulation to address uncertainty from feed ingredient origin (corn CF varied from 0.23 to 1.86 kg CO2-eq/kg), resulting in 18% fluctuation in final CF. Calls for a standardized national feed composition database to improve GHG accounting reliability.
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
日本でも畜産部門のGHG算定精度の向上が求められており、特に輸入飼料由来の排出量の不確実性は共通課題。本論文の確率的アプローチとデータベース構築の提言は、日本における家畜CFP算定の高度化に参考となる。
In the global GX context
Global livestock carbon accounting faces similar feed data transparency issues. This study's stochastic approach quantifies the material impact of feed origin uncertainty, highlighting the need for better supply-chain data for Scope 3 inventory—relevant for international GHG protocol and LCA standards.
👥 読者別の含意
🔬研究者:Demonstrates the significance of feed sourcing uncertainty in livestock LCA and a Monte Carlo-based method to handle it.
🏢実務担当者:Highlights the need for detailed feed composition data to improve carbon footprint accuracy for pork products.
🏛政策担当者:Supports development of national feed databases to align livestock sector with 2050 carbon neutrality goals.
📄 Abstract（原文）
As Korea strives for carbon neutrality by 2050, accurate quantification of greenhouse gas (GHG) emissions in the livestock sector has become a critical priority. This study conducted a cradle-to-farm gate Life Cycle Assessment (LCA) of domestic pig production to identify key issues in carbon footprint (CF) calculation and address significant data gaps regarding feed composition. The functional unit was defined as 115.26 kg of live weight per pig head. The results revealed a total CF of 993.21 kg CO2-eqivalent (CO2-eq.) per head, with feed production accounting for a predominant 76.31% of total emissions. This share is notably higher than global averages, primarily due to the Korea’s heavy structural reliance on carbon-intensive imported feed grains. To address the inherent opacity and information asymmetry of commercial feed data, a stochastic approach utilizing Monte Carlo simulation (1,000 iterations) was employed. While variability in dietary formulations across growth stages was relatively stable (CV = 2.30%), the geographical origin of primary feed ingredients, specifically corn, introduced substantial uncertainty. The CF of corn varied from 0.23 to 1.86 kg CO2-eq./kg depending on its origin, leading to a 9.35% coefficient of variation (CV) in feed-related emissions and an 18.0% fluctuation in the final CF per unit of live weight. These findings demonstrate that ingredient sourcing is as critical as nutritional efficiency in determining the environmental impact of swine production. The study concludes that establishing a representative, standardized national pig feed composition database is imperative for enhancing the transparency and reliability of GHG accounting. Such a foundation will provide a robust baseline for developing effective mitigation strategies and ensuring the livestock industry’s alignment with national climate commitments.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5187/jast.2500214first seen 2026-06-18 05:27:43
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