代替燃料移行下における海運脱炭素経路の分数階・フラクタル動態特性

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:分数階・フラクタル動学をエネルギー転換モデルに応用する方法論的知見を提供。

🏢実務担当者:海運・造船業の長期燃料戦略や船隊更新投資の意思決定に活用可能。

🏛政策担当者:炭素価格と技術進歩の相互作用を考慮した海運脱炭素政策設計に示唆。

Maritime decarbonization is a complex evolutionary process characterized by nonlinear interactions, delayed responses, path dependence, and multi-scale feedback among economic growth, energy transition, technological innovation, and environmental regulation. Traditional dynamic models based on integer-order assumptions may not fully capture the long-memory effects and anomalous transition behaviors embedded in fleet renewal and alternative fuel diffusion. This study develops a fractal–fractional enhanced 3E (Economic–Energy–Environment) System Dynamics framework to investigate the nonlinear evolution of China’s shipping decarbonization pathways. The proposed model integrates fractional-order dynamic characteristics into the conventional stock–flow representation, allowing historical transition states to influence future fleet evolution through memory-dependent mechanisms. A total cost of ownership (TCO)-based technology selection mechanism is further incorporated to describe the endogenous competition between conventional fuel vessels and alternative-fuel vessels. The framework simultaneously considers trade demand evolution, fleet replacement dynamics, fuel price uncertainty, life-cycle carbon emissions, and carbon-pricing constraints. Historical data from 2013 to 2024 are employed for model calibration, while scenario simulations are conducted from 2025 to 2050 to explore the nonlinear transition characteristics under different technology and policy conditions. The results demonstrate that maritime decarbonization exhibits significant nonlinear and memory-dependent behavior, where the diffusion rate of alternative-fuel vessels is strongly influenced by technological maturity, carbon constraints, and accumulated investment decisions. Compared with conventional dynamic approaches, the fractal–fractional framework provides a more flexible representation of delayed fleet transformation and heterogeneous transition patterns. The findings indicate that deep decarbonization of China’s shipping sector is not only determined by carbon pricing intensity, but also by the interaction between technological progress, economic competitiveness, and long-term system memory. This study provides a novel complex-system perspective for understanding fractal and fractional characteristics in maritime energy transition and offers methodological insights for modeling sustainable transformation pathways in large-scale engineering systems.

• openalex https://doi.org/10.3390/fractalfract10100696first seen 2026-10-07 05:06:23
• crossref https://doi.org/10.3390/fractalfract10100696first seen 2026-10-07 06:01:13 · last seen 2026-10-11 05:40:23
• semanticscholar https://doi.org/10.3390/fractalfract10100696first seen 2026-10-08 05:35:46 · last seen 2026-10-11 05:18:57