農業土地利用効率と貿易政策友好度を体化炭素移転に結びつける：中国・ASEAN農業貿易からのエビデンス
Linking agricultural land use efficiency and trade policy friendliness to embodied carbon transfers:Evidence from China-ASEAN agricultural trade (原題)
Xianlu Ao, Liang Dong, Chenxi Zhou, Chuyu Xia
🤖 gxceed AI 要約
日本語
中国・ASEAN農業貿易（2002-2021年）を対象に、農業土地利用効率（ALUE）と二国間貿易政策友好度（TPF）が純体化炭素移転（NECT）に与える影響をDouble Machine Learningで分析。ALUE改善は輸出側の体化炭素を通じてNECTを増加させ、1期ラグのTPFがこの正の関係を弱めることを示した。効率性向上だけでは炭素移転は減らず、友好な貿易政策が不可欠だと結論づける。
English
Using China-ASEAN agricultural trade data (2002-2021), this study applies Double Machine Learning to examine how agricultural land use efficiency (ALUE) and bilateral trade policy friendliness (TPF) shape net embodied carbon transfer (NECT). ALUE gains raise NECT via export-side embodied carbon, while lagged TPF weakens this link. Efficiency alone cannot cut trade-related carbon leakage; friendly trade policy is a necessary condition.
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
日本企業のScope 3・サプライチェーン排出管理や、農産物調達における輸入由来排出の把握に示唆を与える。SSBJ開示でScope 3上流の評価を迫られる日本企業にとって、貿易相手国の政策・効率性が排出量を左右する点は実務的示唆が大きい。
In the global GX context
Speaks to global debates on consumption-based vs. production-based emissions accounting and carbon leakage under CBAM-style border measures. Relevant to ISSB/CSRD Scope 3 upstream disclosure, showing trade policy as a lever on embodied carbon in agri-food supply chains.
👥 読者別の含意
🔬研究者:貿易・環境フレームワークと因果推論MLを組み合わせ、効率性と政策の交互作用を体化炭素に結びつけた実証手法が参考になる。
🏢実務担当者:農産物を輸入する企業は、調達先の土地利用効率と貿易政策がScope 3上流排出に影響しうる点をサプライチェーン評価に組み込むべき。
🏛政策担当者:効率性向上のみでは炭素移転が減らないため、貿易円滑化政策と低炭素化政策を連動させる必要がある。
📄 Abstract（原文）
Agricultural land use efficiency (ALUE) shapes global trade-related carbon transfer. However, whether ALUE improvements translate into reduced net embodied carbon transfer (NECT) in bilateral trade, and how bilateral trade policy friendliness (TPF) between trading partners reshapes this relationship, remain unclear. Based on the trade-environment framework, New Institutional Economics, and Trade Facilitation Theory, this study applies a Double Machine Learning approach with moderation analysis to explore the ALUE-TPF-NECT relationship. ALUE is measured using the DEA-Malmquist index, and a dynamic TPF index is constructed from 90 agricultural trade policy events through textual analysis. Using China-ASEAN agricultural trade data from 2002 to 2021, we find that: (1) The growth of NECT to China and improvements in bilateral TPF between ASEAN countries and China exhibit a clear spatial mismatch: Vietnam and Myanmar account for 94.8% of NECT growth, whereas Cambodia, Laos, and Thailand contribute 43.5% of TPF improvements; (2) Improvements in ALUE are significantly and positively associated with NECT from ASEAN to China, with this association mainly driven by export-side embodied carbon rather than import-side changes; and (3) One-period lagged TPF significantly weakens the positive ALUE-NECT relationship, indicating that friendly trade policies may reduce efficiency-driven NECT. Our findings reveal that efficiency gains alone cannot reduce agricultural trade-related embodied carbon transfer, and that bilateral TPF is a necessary condition for avoiding efficiency-driven carbon leakage and achieving green trade development. ASEAN countries should jointly improve ALUE, optimize bilateral TPF with China, and strengthen the low-carbon management of export-oriented agricultural supply chains.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.jenvman.2026.130958first seen 2026-09-23 04:57:58
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