Container transport modelling
- This section aims at describing a model explaining the demand for container transport on the Traditional Rhine. Container transport evolution can be explained both by macroeconomic reasons and by environmental parameters. The objective is to identify key indicators that can explain this evolution.
- Several macroeconomic indicators (GDP, exchange rate) and sectoral indicators (rail traffic, port transhipment) will be used and statistical tests will enable us to identify the combination of indicators that are most appropriate to explain the evolution of container traffic on the Traditional Rhine.
- Both macroeconomic and sectorial variables are taken into account in order to identify the best way to explain the transport of containers on the Rhine, to see which variables have the best explanatory contribution and how we can interpret the relationship between the transport of containers on the Rhine and these explanatory variables. The variable to be explained (dependant variable) is the volume of container transported on the Traditional Rhine from 1997 to 2016 (quarterly), with twenty-foot equivalents (TEU) being the unit. The model is of the log-log type for a better interpretation of the coefficients as elasticities allocated to each explanatory variable, coefficients that are obtained from a regression by the ordinary least squares method (OLS). These variables were statistically tested for their significance and for their multi-collinearity.
|Tested variables||Sources||Included in the model?|
|European Gross Domestic Product (GDP)||Eurostat||Yes|
|Container transhipment in the port of Rotterdam||Eurostat||Yes|
|Transport of containers by German railways||Destatis||Yes|
|Effective exchange rates of the United States||Eurostat||Yes|
|Effective exchange rates of China||Eurostat||Yes|
|Industrial Production Index (IPI)||OCDE||No|
|Producer Price Indices (PPI)||OCDE||No|
|RWI/ISL Container Throughput Index||ISL||No|
|Container transhipment in the port of Antwerp||Eurostat||No|
Model regression equation
log(teu)= α+ β log(gdp)+ γ log(oil)+ δ log(rot)+ ε log(rail)+ ζ log(usa)+ η log(chi)
Explanatory variables of the model and coefficients obtained (OLS)
|European Gross Domestic Product (gdp)||1.05036||***|
|Oil price (oil)||0.07110||**|
|Container transhipment in the port of Rotterdam (rot)||0.74760||***|
|Transport of containers by German railways (rail)||-0.48218||***|
|Exchange rate with United States USD/EUR (usa)||0.78180||***|
|Exchange rate with China CNY/EUR (chi)||-0.59955||***|
*evaluated with the level of threshold: *** = 0.1%, ** = 1%, * = 5%
- The coefficient of determination (R²) is a measure of the quality of the prediction of a linear regression, evaluated between 0 and 1. The closer the coefficient is to 1, the better the prediction will be. Here, the R-squared equals 0.9586 which, added to the significances of the variables at the 1% threshold, attests to the quality of prediction of the regression.
- GDP is a strong macroeconomic indicator to represent the overall economy of European countries. It is strongly correlated with the transport of containers on the Rhine. The oil price is an indicator that represents the business stance of the economy. High oil prices are often an indicator for a strong business cycle and can indicate high container traffic both in maritime and in inland shipping.
World trade indicator
- Container transhipment in the port of Rotterdam serves as an indicator of world trade. The situation of the port of Rotterdam, the largest European port, at the mouth of the Rhine allows the exchange of goods between Europe and other countries in the world.
Market competition indicator
- The transport of containers by German railways acts as an indicator of market competition. Its negative coefficient attests to the modal shift that can occur between the two modes of transport, namely the river and the railways.
Macroeconomic competition indicators
- The effective exchange rates USD/EUR and CNY/EUR serve as indicators of macroeconomic competition and have an influence on global trade flows. The United States and China are the two main commercial partners of the European Union but their relationships with the EU are different. The USA-EU trade relationship is mainly composed of EU exports while the weight of EU imports is more important in the China-EU relationship. And this explains the different signs for the coefficients of exchange rates for USA and China. The appreciation or depreciation of each currency influences trade and, logically, container traffic. If the US Dollar gets stronger compared to the Euro, EU exports to USA are expected to increase and EU imports from USA are expected to decrease.
- The positive coefficient means that this has a positive impact on container transport which is consistent with the EU trade flow between USA and EU that gives more weight to EU exports. The opposite mechanism is at stake with China, which explains in this case the negative coefficient.
teu = e-9.7735.gdp1.05036.oil0.0711.rot0.7476.rail-0.48218.usa0.7818.chi-0.59955
Volume of containers transported on the Traditional Rhine (in TEU) and econometric model
Source : CCNR
RWI/ISL Container Throughput Index
Source : ISL
- The RWI/ISL Container Throughput Index is based on data from 82 international ports covering more than 60% of global container management in the world. It is a monthly index for the global container flow to provide reliable conclusions on short-term trends in global economic activity. It is an early indicator for world trade and container shipping.
- Calculated since 2007, the index is very closely linked to world trade. It offers similar results and provided reliable data for the 2008 financial crisis for example. The index has been steadily growing since 2009 and the recovery of the global economy.
- In 2016, the trend-cycle component of the index showed an upward trend in response to the 2015 decline, giving credit to the positive outlook for container transport. The first figures for the year 2017 are encouraging regarding the continuation of this trend.
Trends in demand for inland waterways transport in 2017 & 2018 in Europe
|Main driver(s)||Trends in demand for transport in 2017|
| Agricultural products|| Harvest results|| Stable after catch-up|
| Iron ores|| Steel production|| Stable|
| Metals|| Steel production|| Stable|
| Coal|| Weather & energy policy, partly steel production|| Decrease|
| Sand, soil & building materials|| Construction activity|| Increase|
| Containers|| World trade|| Increase|
| Mineral oil products|| Oil prices & refinery output|| Stable|
| Chemicals|| Chemical production|| Stable|
Source : CCNR analysis based on macroeconomic and sectorial data
- After the catch-up process for agricultural products transport that will occur during the year 2017, a stable evolution is expected for the end of 2017 and in 2018, assuming that harvest results will be on a multi-annual average. This outlook is aligned with the EU agricultural products output which is expected to increase by 0.5% in 2017 and 0.7% in 2018.
- Steel production in Germany increased only slightly (+2 %) during the first six months of 2017 compared to the same period in 2016. But the number of new orders has decreased a bit compared to the previous year. Therefore, the slight increase observed during the first semester of 2017 is not expected to announce further steel production increase in 2017 and 2018. And the outlook for the steel segment is rather oriented towards a stable evolution or a slight increase due to the reinforcement of the competitive position of the EU.
- Coal is faced with declining demand in the energy sector. In Germany, the consumption of coal decreased by 5 % in 2016 compared to 2015. Strongly rising prices for steam coal in the 2nd half of 2016 also contributed to this decline. The present trends are supposed to continue. The outlook for coal remains on a decreasing trend.
- The transport of sands, stones and building materials is promoted by rising construction activity in Western Europe, especially in the Netherlands and in France. Large new infrastructure projects contribute to this evolution that will benefit the inland navigation sector in the next two years.
- The world trade indicator (RWI/ISL index) followed a stable upward trend during 2016 and the 1st quarter of 2017, reaching a growth rate of 5 % between the first four months of 2017 and 2016. Consequently, maritime container traffic growth is robust, which lays the basis for a continuation of further growth for container transport on inland waterways. As analysed in the forecasting models, container waterways transport might nonetheless be impacted by environmental conditions and in particular by water level conditions.
- The oil price has shown rather strong fluctuations in 2017, but has been on a downward trend overall, reflecting the growing oil supply from non-OPEC countries. Although a declining oil price can stimulate the transport demand for mineral oil products, the long run trend in this segment is rather downward orientated. The long-term domestic oil demand in the EU is expected to decrease and this demand is expected to decrease respectively by 0.3% and 0.4% already in 2017 and in 2018 (Source: Oxford Economics). The impact should be limited for inland navigation transport in 2017, and in 2018 and a stable evolution is foreseen.
- It is expected that chemical production will remain overall stable in 2017, or grow only very modestly (+1 % forecast in Germany). Therefore, the outlook for chemical transport is stable as well, with the possibility of a slight increase.
- The transport of waste, boosted by the emergence and growth of the recycling and circular economy can be a chance for inland navigation in general and inland ports in particular. Concerned goods could for example be scrap metals, household waste and regenerated building materials, meaning that several inland navigation transport segments could benefit from new economic opportunities offered by current trends.
10. Perspective | CCNR
10. Ausblick | CCNR
10. Vooruitzichten | CCNR
10. Outlook | CCNR