The data below tracks the total tonnage and value of commodities shipped on the Maritime Transportation System between 2012 to 2018. The Maritime Transportation System includes the Great Lakes, St. Lawrence Seaway, and St. Lawrence River downstream to Les Escoumins, QC. The figures below illustrate how this cargo tonnage and value was broken down by different commodities. Changes in commodity tonnage handled and value of cargo reflect changing global and regional economic forces. 

"Category"General CargoSteelOther Liquid BulkPetroleumGrainCementCoalIron OreSaltOther Dry BulkStone/Aggregate
2012404739233745098692371450432629308346477751094106978210202385055088774887834134922129118372.65724757874.1779000816.4
201337952699113323000737153686178425459798378866631995310497155965040131357580958154632671421707371003737493438
201454497178214374561409157798871224291661006992518648210756721345027189397627948312449152063805011683585681589
201549412129793190406563132637449915167200288783304133012363431563940056993537156264050404517689206033629363892
2016454995885624774771651117941231140479326437214678934120377938230113457925331684529203775360408517052515154293
2017404184253738480759431117518639171740780386617960678130567774529775472146085505453234401031430603547546051079
2018446612028846095464801447650542213329609397084535693110832811224876212906504331600256622111407045373504204548
"Category"General CargoSteelOther Liquid BulkPetroleumGrainCementCoalIron OreSaltOther Dry BulkStone/AggregateUnknown Commodity
2012252434738905581959768337837102013622410082703494205561181085441416172208673885045021842350764
201325912963369294221677728799577164695079882009503336771171289101257683221318745194508740544409
201437022264735753230564529552597233780749981193505499721187156621894327280349524950757739232415
2015375107941583152540666306531432088169010610312424343751041942071676734281325195327488636608898
2016416397639020602632155361072282201880010018612343886801098091666878302172411304839749636289785
2017372211451114652451757376909432095365810528759361406431181332547253499187609565306896639017150
201841985295657221299471939240360222117548821955307359411217553017845065181839595102787739803950

More information on commodity tonnage and value 

Bulk cargoes such as iron ore, stone, grain, and coal make up the majority of the tonnage of goods moving on the MTS. However, containers carried on the MTS make up the largest share of value. This difference in the shares of value and tonnage reflect the fact that bulk cargoes have a relatively low-value per ton, while containerized cargos are usually manufactured products that have a greater value per ton. A further discussion of MTS container traffic and trends is here.

While the value metric above presents the market value of cargo moving on the MTS, it is important to remember that the true value of this cargo is greater than its market value. This “hidden” value is due to the fact that many of the commodities moving on the MTS are crucial inputs to manufacturers producing higher-value goods. For example, the iron ore moved on the MTS has a relatively low value based on market price, but this iron ore is the critical component to higher-value US and Canadian steelmaking, and thus secondary manufacturing industries such as automotive, machinery, and appliance producers. Without access to the materials, many of the region’s manufacturers would not be able to operate.

Information about data

The tonnage and value estimates presented here are estimates, and their full accuracy is not guaranteed. There are no publicly-available, comprehensive sources of data on the tonnage or value of cargo carried on the MTS.  Estimates of tonnage and value must be created using multiple data sources, which often have differing reporting or collection methodologies, or restrictions on how data may be disclosed.

This estimate of tonnage and value was created using the following sources:

1. US Army Corps of Engineers Waterborne Commerce Statistics.

USACE WCSC data was used to identify tonnage handled at US ports on the Great Lakes and St. Lawrence River.

2. US Army Corps of Engineers Foreign Cargo Inbound and Outbound

USACE Foreign Cargo data was used to identify tonnage that was (1)handled at ports in Ontario that are not organized as Canada Port Authorities (CPA), and (2) that was moved to or from US Great Lakes ports.

3. Maritime Information System (MIS) Data

 MIS data was used to identify tonnage handled at the four Quebec CPAs that are included in the GLSLS. These five CPAs are Montreal, Trois-Riveres, Quebec City, Saguenay. Sept-Iles is not included because it lies outside of the system boundaries defined for this project. Due to confidentiality restrictions, the tonnages for these 4 ports are combined into one estimate, and information on tonnages at specific ports is unavailable for this project.

4. Ontario CPAs

Data from Ontario CPAs was used to improve the estimate of tonnage handled in Ontario. Data was collected from websites for Thunder Bay and Windsor.  Hamilton-Oshawa and Toronto Port Authorities also provided data in a format compatible with the commodity classifications used here. Due to confidentiality issues, port-level tonnage breakdowns are not provided, only provincial-level breakdowns with USACE  Foreign Cargo data added.

5. Martin Associates' Economic Impacts of Maritime Shipping in the Great Lakes St. Lawrence Region(2018).

This report contains a state/provincial-level estimate of tonnage handled, and was used to double-check tonnage estimates from USACE WCSC data, as well as improve our understanding of the share of tonnage that was unaccounted for in other data sources listed above. Martin's estimates of each commodity group's value per ton also serve as the foundation for this sheet's value calculations.

6. Shipping in Canada Reports

StatCan's Shipping in Canada reports were used to understand tonnage trends prior to 2011, and understand the share of tonnage

6. USA Trade Online Data

USA Trade Online data was used to calculate changes in the value per ton of general commodity groups over time.

Date range