# Maritime-Logistics Capacity

## **What this driver is**

Maritime-Logistics Capacity is a continuing, time-varying factor within trade, logistics, and industrial policy. It is modeled as a Driver because its intensity, capacity, or prevalence can change across reporting periods and can transmit the effects of multiple events, decisions, and institutions to later outcomes. It is not a dated event, a person, an organization, a welfare score, or an assertion that every movement in a correlated series was caused by the same mechanism. The relevant scope is the population, market, institution, infrastructure, or ecological system actually exposed to Maritime-Logistics Capacity; a national or sectoral observation must not be silently generalized to the whole world.

The boundary of Maritime-Logistics Capacity is narrower than its category and broader than one headline. It covers the durable condition named by the title, but excludes downstream welfare outcomes that must be measured separately. It also remains distinct from [Reliability of Shipping through Strategic Canals](/drivers/reliability-of-shipping-through-strategic-canals): the two may interact or share a proxy, yet they represent different causal questions. Maritime-Logistics Capacity is therefore a persistent analytical node, not a label for every adjacent development.

Maritime-Logistics Capacity must be interpreted through its own named mechanism and evidence rather than inferred from the category label. The selected proxy identifies one observable facet; it does not collapse the Driver into Liner shipping connectivity index (maximum value in 2004 = 100) or erase distinctions from Reliability of Shipping through Strategic Canals.

The temporal record attached to Maritime-Logistics Capacity uses Liner shipping connectivity index (maximum value in 2004 = 100) (IS.SHP.GCNW.XQ) as a disclosed proxy for 2020-2021. The proxy is an observable lens, not a complete operational definition. A higher normalized value means more of Maritime-Logistics Capacity, while a lower value means less; this measurement direction is not a welfare verdict. Current weight is derived from the latest real normalized observation rather than entered as an editorial score, probability, forecast, or confidence estimate.

## **Mechanism**

The mechanism for Maritime-Logistics Capacity begins with a change in the factor itself and then moves through input availability, transport costs, production continuity, market access, and the allocation of industrial capacity. The first step is exposure: the change must reach identifiable households, firms, public bodies, infrastructure, ecosystems, or security actors. The second step is transmission through prices, incentives, rules, information, physical constraints, organizational capacity, or behavior. The third step is adaptation: exposed actors may substitute, relocate, delay decisions, change compliance, invest, seek protection, or pass costs to others. The final welfare effect is the net result after those responses, not the initial movement alone.

For Maritime-Logistics Capacity, direct and indirect effects must be separated. A direct effect changes safety, access, income, health, legal protection, service continuity, or environmental exposure without a long chain of assumptions. An indirect effect passes through fiscal space, expectations, legitimacy, supply networks, knowledge, or capital formation. Each extra link makes timing and magnitude more conditional and raises the evidentiary burden for a graph relation.

Distribution is part of the mechanism. The same movement in Maritime-Logistics Capacity may help one group and harm another because exposure, geography, wealth, age, legal status, occupation, insurance, and institutional quality differ. Analysis must identify who is exposed, who can adapt, and who bears transition costs; an aggregate average can conceal opposing effects.

Timing also matters for Maritime-Logistics Capacity. Safety, availability, prices, or service interruption can move quickly; budgets, investment, reform, demography, diffusion, and capital replacement take longer. Feedback may reinforce the move, while substitution, policy response, learning, or resilience may offset it. The timeline records movement, but every DriverIndicatorImpact must separately state lag, direction, strength, and evidence.

## **Evidence base**

The primary quantitative record is the [Liner shipping connectivity index (maximum value in 2004 = 100) dataset](https://api.worldbank.org/v2/country/all/indicator/IS.SHP.GCNW.XQ?date=2020:2025&format=json&per_page=20000). It supplies the raw dated observations, provider unit, reporting scope, and aggregation note used for the 2020-2021 Driver series. Across that available record, Maritime-Logistics Capacity finished above its first normalized observation: 0.72600786 in 2020 versus 0.72674557 in 2021. The minimum was 0.72600786 in 2020, the maximum was 0.72674557 in 2021, and the observed sequence contained 1 increases and 0 decreases. These are descriptive facts about the selected proxy; they are not an estimated causal effect on welfare.

Adjacent real observations create a segment only when the value changes; direction follows the sign and strength is the absolute change. The latest point sets current weight. Normalization preserves reproducibility but neither makes unlike concepts interchangeable nor turns a proxy into a complete index.

For Maritime-Logistics Capacity, the provider describes the selected series in these terms: The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country's ports. For each component a country's value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. . The underlying data come from Containerisation International Online. The cited producer is Review of Maritime Transport 2010., UN Conference on Trade and Development (UNCTAD). That qualification controls interpretation: coverage gaps, aggregation choices, revisions, reporting incentives, and the distance between the series and the Driver concept can all limit inference. Where the exact requested metric lacked sufficient observations and a documented fallback was used, the fallback remains visibly identified in the research artifact rather than presented as an exact measurement.

Separate context comes from [World Trade Organization statistics methodology](https://www.wto.org/english/res_e/statis_e/statis_e.htm). It supports domain vocabulary and setting, not hidden annual values or a manufactured coefficient. Evidence is strongest for observed proxy movement, weaker for the claim that Maritime-Logistics Capacity caused an outcome, and weakest where the proxy captures a neighboring facet. Coincidence or association remains a hypothesis until analysis addresses confounding, reverse causality, selection, and measurement error.

## **Effect on welfare indicators**

The following indicators are the concrete welfare-sensitive endpoints against which Maritime-Logistics Capacity should be evaluated. Their links resolve to existing Factrail Indicator records. They do not create a causal graph edge by themselves: an active DriverIndicatorImpact is warranted only when a source supports the specific pathway, direction, lag, and scope.

- [Global consumer price inflation](/indicators/econ-global-consumer-price-inflation). For Maritime-Logistics Capacity, expected first-order direction is **lower**. The channel to test is freight costs, delivery times, tariffs, input shortages, inventories, and substitution. Horizon: weeks to two years. Confidence: medium where affected inputs have large expenditure shares. A same-period correlation would not prove this impact.
- [Under-five mortality rate](/indicators/ph-under-five-mortality-global). For Maritime-Logistics Capacity, expected first-order direction is **lower**. The channel to test is availability and affordability of food, medicines, fuel, water-treatment inputs, and clinical supplies. Horizon: months to years. Confidence: low globally and higher in import-dependent exposed populations. A same-period correlation would not prove this impact.
- [Access to electricity](/indicators/energy-electricity-access-global). For Maritime-Logistics Capacity, expected first-order direction is **higher**. The channel to test is availability of generation equipment, grid components, finance, fuel, and maintenance capacity. Horizon: one to several years. Confidence: low to medium. A same-period correlation would not prove this impact.

First-order effects for Maritime-Logistics Capacity follow directly through named channels. Second-order effects depend on responses by governments, markets, organizations, communities, or households and may arrive later or with the opposite sign. Missing coefficients must not be replaced with round numbers; the relation stays draft or absent until evidence narrows the range.

## **Interactions and contested points**

Maritime-Logistics Capacity can reinforce or offset other Drivers in trade, logistics, and industrial policy, including Reliability of Shipping through Strategic Canals, but shared timing is not enough to establish an interaction. Reinforcement is plausible when both factors act on the same bottleneck, exposed population, institutional rule, price, or infrastructure network. Offsetting is plausible when adaptation, substitution, redundancy, legal safeguards, fiscal support, or technological learning weakens the pathway. Conditional interaction is the default where one Driver changes the exposure or response to another rather than moving the welfare indicator independently.

The strongest skeptical interpretation is that the selected series is too remote from Maritime-Logistics Capacity, that observed movement is driven by omitted factors, and that the broad causal narrative cannot identify a stable sign or magnitude across countries and periods. That objection is especially important when multiple Drivers share the same public dataset or when the series measures an outcome adjacent to the concept rather than the concept itself. The strongest competing interpretation is that a transparent proxy, clear boundaries, and explicit uncertainty are still more useful than an unmeasured label, provided the proxy is never mistaken for proof and the graph does not fabricate unsupported edges.

The record therefore separates what is established from what remains contested. Established here are the English canonical identity, the sourced 2020-2021 proxy observations, the disclosed normalization, and the distinction between Driver movement and welfare impact. Plausible but not automatically established are the indicator pathways listed above. Unresolved are universal causal magnitudes, responsibility, forecasts, and any claim outside the source scope. Publication or verification must not erase those boundaries; later evidence should update the relations and content through the governed pipeline rather than silently rewriting the history of Maritime-Logistics Capacity.

## Dated facts

- 2024: Clarksons: 2023 world fleet reached 2.3bn dwt; containerships %, tankers .9%. https://factrail.ai/facts/clarksons-2023-world-fleet-2-3bn-dwt-containership-up-8pct.
- 2024: Shanghai Port handled 49.158 million TEUs in 2023, 14th year as busiest. https://factrail.ai/facts/shanghai-port-49158-million-teu-throughput-2023.
- LA port chief warned 29,000 rail boxes clogged docks in July 2022. https://factrail.ai/facts/la-rail-hinterland-29000-containers-july-2022.
- Shanghai COVID lockdown left import boxes dwelling about 12 days in April 2022. https://factrail.ai/facts/shanghai-covid-lockdown-port-congestion-april-2022.
- 2022: Clarksons: record 4.2 million TEU of containerships ordered in 2021. https://factrail.ai/facts/clarksons-record-42-million-teu-ordered-2021.
- Los Angeles/Long Beach offshore ship queue peaked at 109 vessels in January 2022. https://factrail.ai/facts/la-lb-container-ship-queue-peak-109-january-2022.
- 2022: Clarksons: record 4.2 million TEU of containerships ordered in 2021. https://factrail.ai/facts/clarksons-record-4-2-million-teu-containerships-ordered-2021.
- Record 60 container ships waited off Los Angeles/Long Beach in September 2021. https://factrail.ai/facts/la-long-beach-record-60-ships-waiting-september-2021.
- Freightos Baltic Global Index peaked near $11,109 per FEU in September 2021. https://factrail.ai/facts/fbx-global-container-index-peak-11109-september-2021.
- Yantian COVID controls drove port congestion and mass blank sailings in June 2021. https://factrail.ai/facts/yantian-port-covid-congestion-blank-sailings-june-2021.
- Blank sailings cancelled nearly 4 million TEU of capacity by June 2020. https://factrail.ai/facts/blank-sailings-near-4-million-teu-june-2020.
- COVID blank sailings idled 2.04 million TEU—8.8% of container fleet—in February 2020. https://factrail.ai/facts/alphaliner-inactive-container-fleet-2m-teu-february-2020.

## Methodology

https://factrail.ai/methodology

Canonical: https://factrail.ai/drivers/maritime-logistics-capacity