Beyond slow steaming: a predictive framework for green shipping

A shipping company slows down its fleet to reduce emissions. The fuel bill falls, but the net‑zero target distant. This is not an isolated dilemma but a common challenge across the container shipping industry. Over the past few years, operational emission reduction measures adopted in the sector have shared a common pattern: they deliver quick results but quickly reach their limits. What the industry now needs is a systematic solution that aligns short‑term operations with long‑term investment.

In a study published in Transportation Research Part E: Logistics and Transportation Review, Dr Jianghang Chen, Associate Professor at International Business School Suzhou (IBSS), Xi’an Jiaotong-Liverpool University, and his collaborators provide a clear, systematic answer. The “prediction–optimisation”" framework they propose integrates four main emission reduction measures – slow steaming, maintenance optimisation, fuel switching, and capital investment in ship retrofitting – and demonstrates that a  67%  reduction in carbon emissions could be achieved by 2040 in a case study.

The core breakthrough of this research lies in building a decision logic that balances techno‑economic trade‑offs. Slow steaming is effective in the short term, but its  potential is limited under net-zero targets. Shipping companies face not just a single option, but the challenge of making optimal, long‑term combinations of slow steaming, maintenance planning, alternative fuel adoption, and vessel retrofitting.

The prediction–optimisation framework proposed by the team is designed precisely for this purpose. The prediction layer first establishes ship‑specific fuel consumption models to forecast future emission trajectories, rather than relying on industry averages. On this basis, the optimisation layer uses these projections within  a long‑term strategic model, while accounting  for Carbon Intensity Indicator (CII) regulatory constraints and net‑zero goals, to determine the timing and scale of each emission reduction measure.

The study finds that relying solely on operational efficiency improvements quickly reaches a bottleneck. Alternative fuels (e.g., biofuels or ammonia) require high capital investment but are essential for achieving net‑zero. The optimal path is a phased approach: first use low‑cost operational measures such as slow steaming to capture short‑term carbon reduction gains, while simultaneously planning the timeline for fuel technology transition and vessel retrofitting, spreading high‑cost investments over a longer planning horizon. The sensitivity analysis also shows that changes in interest rates and regulatory stringency influence  the cost‑effectiveness of different strategies, further highlighting the need for dynamic decision‑making.

For container shipping companies, this means that green transformation should not be a binary choice between "saving money" and "cutting emissions". A truly effective strategy uses a data‑driven insights to link short‑term operational optimisation with long‑term capital investment, creating a pathway that is both practical and financially viable.

Dr Jianghang Chen is an Associate Professor at International Business School Suzhou (IBSS), Xi’an Jiaotong-Liverpool University. His research focuses on mathematical modelling of complex systems such as supply chains, logistics and manufacturing, the development of intelligent decision‑support tools, and the application of optimisation and simulation techniques.

Transportation Research Part E: Logistics and Transportation Review publishes high‑quality, interdisciplinary research covering traditional areas of logistics (transportation, warehousing, inventory, etc.) as well as emerging topics that integrate operations management, supply chain management, sustainability, and artificial intelligence.