Challenges of decarbonizing global maritime container shipping toward net-zero emissions

Container shipping accounts for about 30% of global maritime CO2 emissions and contributes significantly to air pollution that can affect port cities and inland regions. Without new measures, total maritime shipping emissions could reach 2.5 Gt by 2050 (about 50% above 2018). In response, the International Maritime Organization (IMO) adopted a strategy to reduce total maritime GHG emissions by at least 50% by 2050 relative to 2008 and, as strengthened at MEPC 80, to peak GHG emissions from international shipping as soon as possible and reach net-zero GHG emissions by or around 2050, considering different national circumstances. Complementary initiatives include the Clydebank Declaration to establish green shipping corridors by 2025–2030 and carrier-level targets (e.g., Maersk net-zero by 2040). Despite various measures (tighter energy-efficiency rules for new ships, port collaborations on abatement, and multi-donor trust funds), concrete pathways aligned with climate targets remain unclear. Containerized trade is economically vital and has grown substantially (0.1 to 1.85 billion tons from 1980–2020; >50% of cargo value while ~16% of volume; container ship DWT grew from 1 to 225 million tonnes; port traffic to 840,635 billion TEU by 2021), implying rising fuel use and emissions without targeted mitigation. High-resolution emission inventories are challenging due to limitations of AIS data (discontinuities, errors/omissions, duplicates, difficulty distinguishing lines in narrow waters), restricting many studies to single years or regions. Liner shipping service data explicitly captures port-to-port service lines and frequencies, aligns strongly with seaborne trade, and can better apportion emissions embodied in trade flows. This paper addresses these gaps by developing an integrated framework (BEEPA) that combines bottom-up emission estimation based on liner service data with scenario-based decarbonization pathway analysis to identify high-potential regions for reductions, quantify key drivers across time horizons, and inform medium-to-long-term strategies and investment decisions toward decarbonizing global container shipping.

Prior work has built global shipping emission estimates and explored decarbonization through trade, ship design, and alternative fuels. AIS-based activity data enable high-resolution inventories but suffer from discontinuities, data errors, duplicates, and limited ability to distinguish services in narrow waters, leading to uncertainties and constrained temporal/geographic scope. Activity-based methodologies and models (e.g., STEAM, IMO GHG studies) provide foundational emission factors, engine characteristics, and operational profiles. Studies highlight potential from energy efficiency, cleaner fuels (LNG, methanol, ammonia, electricity, hydrogen), and negative emission technologies (NETs), but few integrate bottom-up container shipping emission accounting tightly with comprehensive socio-economic pathways. The paper positions BEEPA to fill this integration gap by combining detailed liner service activity data with SSP-based trade projections and policy/technology scenarios.

The BEEPA framework integrates bottom-up activity-based emission estimation with scenario-based pathway analysis. Data: 11,011 liner shipping service line records (2015–2020) from Alphaliner (cover ~98% of container liner services), including port sequences, service duration/frequency, traffic capacity, and ship types. For each service line, voyages are segmented into operational modes: cruising, manoeuvring, and berthing, distinguishable by speed and distance to port. Annual emissions per service are the sum of emissions in these modes: E_service = E_cruising + E_manoeuvring + E_berthing. Emissions at sea refer to cruising; emissions at port include manoeuvring and berthing. Emissions for each mode are computed by engine type usage: propulsion and auxiliary operate at sea; auxiliary and boiler at berth; all three during manoeuvring. Propulsion emissions: E_propulsion = [Propulsion] × LF × e_p,pollutant × T_j, where [Propulsion] is installed main engine power (by capacity), LF is load factor (LF = V_a / MDS with average route speed V_a and maximum design speed MDS), e_p is ratio of MCR to initial power (~0.9), emission factors are mode- and pollutant-specific, and T_j is active time. Auxiliary and boiler emissions: E_auxiliary = P_auxiliary × EF_auxiliary,j × T_j (boiler analogous), with powers from IMO statistics. Working durations: T_cruising = D / V_a; T_berthing = T_total − T_cruising − T_manoeuvring; T_manoeuvring assumed 1 hour per port call. Emission factors and specific fuel consumption (SFC) by engine and mode are drawn from the Fourth IMO GHG Study (Supplementary Tables). Fuel and energy assumptions: containerships use HFO during cruising and MDO during manoeuvring/berthing; energy densities HFO = 42,000 kJ kg⁻¹, MDO = 42,700 kJ kg⁻¹; density adjustments applied as per IMO data. Total annual emissions are the sum across all service lines. Fuel consumption is estimated by substituting EF with SFC in the same structure. Trade projection: global trade-to-GDP ratio is modeled using a logistic function x = 1 / (1 + exp(a + b t)), with parameters estimated from UNCTAD trade and World Bank GDP (1960–2020). GDP projections under SSP1/SSP2/SSP3 are sourced from the IIASA database and interpolated annually to 2050 to derive trade volumes. Pathway analysis: Implemented in the Long-range Energy Alternatives Planning System (LEAP), enabling multi-scenario emission projections. Scenarios combine Shared Socioeconomic Pathways (SSPs) with mitigation effort tiers: BAU, ER (emissions reduction), EER (enhanced ER), and SER/ SERR (stringent). Mitigation levers include energy intensity reduction (efficiency), energy mix transformation (HFO, MDO, LNG, methanol, ammonia, electricity, hydrogen), and diffusion of negative emission technologies (NETs). Timeframes: short term (to 2030), medium (2031–2040), long (2041–2050). The 2020 base year is used due to COVID-19-related volatility and data constraints. Emission decomposition attributes changes to trade growth (TG), energy efficiency improvement (EEI), energy transition (ET), and NETs, with TE (total emissions) as the product of effects over time.

- Historical emissions and energy use: Global seaborne container CO2 emissions fluctuated 2015–2020, peaking at 264 Mt in 2017, dipping to a minimum of 226 Mt in 2018, and slightly increasing in 2020. Average annual energy consumption was 77.7 Mt (HFO-equivalent). The trend mirrors container traffic dynamics (e.g., a 6.4% demand increase in 2017 after a 2016 downturn). - Modal and port dynamics: Emissions during cruising (at sea) account for about 21% of total emissions; port-related operational inefficiencies significantly affect emissions. Hourly emissions during port operations averaged 1.08 t/h, rising from 2016–2019 and declining 2019–2020. A 16% increase in ship size is associated with a ~29% increase in berth time, exacerbating port emissions. Pandemic-related congestion increased anchorage emissions, partially offsetting overall reductions. - Geography of emissions: About 55% of port-operation emissions are at Asian ports, with hotspots in Northeast/Southeast Asia, the Mediterranean, Red Sea, Northern Sea, and the United States. Top-emitting ports include multiple Chinese ports (e.g., Shanghai, Shenzhen), with additions like Xiamen and Guangzhou by 2020; Colombo rose from rank 17 (2015) to 9 (2020). Emission flows are dominated by Asia–North America and Asia–Europe routes; post-2018 increases are seen for Middle East/South Asia-related lines, intra-Europe, and Asia–North America. Declines occurred for Australia/Oceania-related lines and some Europe–Far East and African lines, attributable to efficiency improvements, route consolidation, and changing trade patterns. - Regionalization and intensity: With growing intra-regional trade and shorter average transport distances, emissions per nautical mile during cruising increased, indicating the need to balance trade structure with ship efficiency. Emissions per unit transport distance rose from 0.41 t km⁻¹ (2015) to 0.43 t km⁻¹ (2020). - Scenario pathways: Under BAU across SSPs, emissions keep rising to about 2040; under SSP5-BAU, peak emissions reach ~4206.7 Mt. Greater early transition efforts advance peak timing and lower mid-century levels. Under SSP1 scenarios, 2050 emission intensities (per GDP) fall to 18.5% (ER), 11.2% (EER), and 3.56% (SER) of 2020. Under the most stringent scenario (SSP1-SER), emissions peak by 2025 and decline to ~19.6 Mt by 2050, approaching—but not fully achieving—net-zero. - Fuel mix contributions: Under BAU, HFO combustion dominates; in SSP5-BAU, HFO contributes ~46.4% (≈193.3 Mt) of 2050 emissions, with LNG and methanol contributing the remainder among fossil-derived fuels. Under SSP1-EER and SSP1-SER, HFO and MDO emissions in 2050 reduce to ~43.8 Mt and ~7.4 Mt, respectively; LNG and methanol shares remain low even under stringent scenarios. - Drivers of reductions: Decomposition shows trade growth as the dominant driver of emissions increases in the short term. Energy efficiency curbs growth (effective values ~0.65–0.70 across SSP1 ER/EER/SER). In the medium term, efficiency yields the largest reduction (effective value ~0.5), with growing contributions from energy transition and NETs. By the long term, clean energy transition is the primary mitigation contributor across targeted scenarios, with NETs also playing a significant role.

The integrated BEEPA framework reveals that container shipping emissions track trade demand and operational conditions rather than following a monotonic trend. Rising emissions per unit of transport distance amid regionalization highlight trade structure and efficiency trade-offs. Spatial analysis identifies major reduction opportunities on Asia–Europe, Asia–North America, and intra-Asia routes, and underscores the importance of port operational improvements in Asia and Europe. Scenario analysis indicates that without additional measures, emissions likely peak around 2040. Accelerated efficiency upgrades can slow near-term growth, but deep decarbonization depends on a fuel transition toward zero or near-zero carbon energy carriers and the deployment of NETs. Even under stringent efforts (SSP1-SER), 2050 emissions remain above true net-zero given sectoral technological and infrastructural constraints. Nonetheless, the pathway narrows the gap substantially and aligns with strengthened IMO ambitions, showing the complementary roles of efficiency (short-to-medium term), fuel switching (medium-to-long term), and NETs (supporting role) in meeting decarbonization milestones.

This study introduces BEEPA, a bottom-up, liner service-based accounting combined with scenario pathway analysis for global container shipping emissions. From 2015–2020, emissions fluctuated with a 2017 peak and strong geographic concentration in Asian ports and major intercontinental routes. Under BAU, emissions continue rising to ~2040; under stringent transitions, emissions can peak by 2025 and decline markedly by 2050, nearing IMO’s net-zero ambition but not fully attaining sectoral net-zero absent further breakthroughs. Key contributions include: (1) a high-coverage activity-based inventory leveraging liner service data; (2) integrated SSP-consistent scenario modeling in LEAP; and (3) decomposition of drivers across time horizons clarifying when efficiency, fuel transition, and NETs are most impactful. Policy and industry implications point to coordinated actions: enhance vessel and port efficiency, optimize trade structures and routing, accelerate investment in zero/near-zero carbon fuels (ammonia, electricity, hydrogen), and plan for NETs deployment. Future research should refine operational data resolution (e.g., harmonizing AIS with liner data), assess technology adoption timing and costs, quantify lifecycle emissions of alternative fuels, and evaluate policy instruments and financing mechanisms to de-risk the transition.

- Data and methods: AIS-related issues (discontinuities, errors, duplicates) challenge validation; distinguishing services in narrow waters is difficult. The study mitigates some issues by using liner service data but still assumes standardized operational modes and fixed manoeuvring time (1 hour per port call). Emission factors and SFC values are drawn from the Fourth IMO GHG Study, introducing parameter uncertainty. - Scenario assumptions: 2020 is used as the base year due to pandemic disruptions; short-term shocks and subsequent rebounds may bias projections. The logistic trade-to-GDP model and SSP GDP trajectories carry uncertainty. LEAP-based scenarios simplify technology adoption pathways and fuel availability. - Scope: Cost-effectiveness, technology adoption timing, infrastructure readiness, and policy enforcement are not fully explored. NETs are included as a mitigation lever without detailed deployment constraints. Net-zero is defined without external offsets, making the target more stringent and harder to achieve. - Reporting inconsistencies: Some reported figures in the narrative exhibit inconsistencies across sections, reflecting uncertainty and data limitations; overall trends and comparative insights remain robust.