Synergistic Optimization of Yangshan Port’s Collection-Distribution Network with Application of Electric Autonomous Container Truck Configuration Under Carbon Constraints
炭素制約下における電動自律コンテナトラック導入を考慮した洋山港集配ネットワークの相乗的最適化 (AI 翻訳)
You Kong, Ling Xu, Qi Wu, Zhihong Yao
🤖 gxceed AI 要約
日本語
本研究は、炭素取引メカニズムと電動自律コンテナトラック（EACT）の導入が港湾集配ネットワークに与える影響を分析するため、多目的二層計画モデルを構築。NSGA-IIを用いてパレート最適解を求め、基準炭素価格70元/トンでEACT導入率25.03%～33.87%を達成。感度分析では、炭素価格90元/トンで導入率32.76%～45.38%に上昇し、CO2排出6.98%削減、運用コスト12.75%削減を確認。厳格な炭素枠（3000トン）でEACT利用率が35.08%～46.71%に達し、モーダルシフト促進効果を定量化した。
English
This study develops a multi-objective bi-level programming model to analyze the impact of carbon trading mechanisms and electric autonomous container trucks (EACTs) on port collection-distribution networks. Using NSGA-II, the model generates Pareto-optimal solutions. At a baseline carbon price of 70 CNY/ton, EACT deployment ranges from 25.03% to 33.87%. Sensitivity analysis shows that increasing the carbon price to 90 CNY/ton boosts EACT adoption to 32.76%-45.38%, reducing carbon emissions by 6.98% and operational costs by 12.75%. Strict carbon quotas of 3000 tons further push EACT usage to 35.08%-46.71%, demonstrating modal shift effectiveness.
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
中国の港湾（洋山港）を対象としたケーススタディだが、日本の港湾脱炭素化やカーボンプライシング政策（例えば、東京都の炭素取引など）にも示唆を与える。特に、EACT導入と炭素価格の非線形効果は、日本のSSBJやカーボンプライシング議論において参考になる。
In the global GX context
This case study on Yangshan Port provides quantitative evidence for the effectiveness of carbon pricing and electric vehicle deployment in port logistics. The non-linear threshold effects observed (e.g., at 90 CNY/ton) are relevant for global policymakers designing carbon trading systems. The multi-objective optimization approach can inform ISSB-aligned climate transition plans for transport infrastructure.
👥 読者別の含意
🔬研究者:Provides a rigorous multi-objective optimization framework and quantitative evidence on carbon pricing thresholds for port decarbonization.
🏢実務担当者:Offers actionable insights on optimal deployment rates of electric autonomous trucks under varying carbon prices and quotas.
🏛政策担当者:Demonstrates the effectiveness of carbon pricing and quota mechanisms in driving modal shift and emissions reduction in port logistics.
📄 Abstract（原文）
Decarbonization has emerged as a crucial objective in the optimization of port collection and distribution networks. To investigate the synergistic effects of carbon trading mechanisms and the implementation of electric autonomous container trucks (EACTs), this study develops a multi-objective bi-level programming model that simultaneously minimizes transportation cost, carbon trading cost, and transportation time. The model is solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), generating a Pareto-optimal solution set, from which the optimal solution is selected using a normalized ideal point method. Simulation-based case studies validate the feasibility and practical applicability of the proposed model. The results show that the optimized network significantly outperforms the traditional road-dominant mode. Under the baseline carbon price of 70 CNY/ton, the optimal deployment rate of EACTs reaches 25.03% and 33.87%. Sensitivity analysis reveals a distinct non-linear threshold effect: increasing the carbon price to 90 CNY/ton drives the EACT adoption rate to 32.76% and 45.38%, resulting in a 6.98% reduction in carbon emissions and a 12.75% decrease in total operational costs compared to the baseline scenario. Additionally, strict carbon quotas (e.g., 3000 tons) are found to further compel a modal shift, peaking EACT usage at 35.08% and 46.71%. These quantitative findings offer actionable insights for optimizing multimodal transport structures and refining carbon trading policies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/app16042155first seen 2026-05-15 17:18:30
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