Short sea shipping: a statistical analysis of influencing factors on SSS in European countries

van den Bos, Gertjan; Wiegmans, Bart W. Article Short sea shipping: a statistical analysis of influencing factors on SSS in European countries Journal of Shipping and Trade (JST) Provided in Cooperation with: Shipping Research Centre (SRC), The Hong Kong Polytechnic University Suggested Citation: van den Bos, Gertjan; Wiegmans, Bart W. (2018) : Short sea shipping: a statistical analysis of influencing factors on SSS in European countries, Journal of Shipping and Trade (JST), ISSN 2364-4575, SpringerOpen, London, Vol. 3, Iss. 6, pp. 1-20, https://doi.org/10.1186/s41072-018-0032-3 This Version is available at: https://hdl.handle.net/10419/217524 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ ORIGINAL ARTICLE Open Access Short sea shipping: a statistical analysis of influencing factors on SSS in European countries Gertjan van den Bos 1 and Bart Wiegmans 1,2* * Correspondence: [email protected] 1 TU Delft, Civil Engineering and Geosciences, Department of Transport & Planning, Stevinweg 1, 2628 CN Delft, P.O. 5048, 2600 Delft, GA, the Netherlands 2 Associate Transport Institute, Asper School of Business, University of Manitoba, Winnipeg, Canada Abstract Short sea shipping (SSS) is the maritime transport of goods over relatively short distances, as opposed to the intercontinental cross-ocean deep sea shipping. The goal of the current paper is to identify SSS growth potential and the univariate regression analysis indicates that the following variables influence total SSS volume in European countries: land area, coastline, total number of SSS ports, number of small SSS ports, number of large SSS ports, number of inhabitants, Gross Domestic Product (GDP), GDP per head, road length and rail length. An additional multivariate regression analysis indicates that more than 78% of the variance in the total SSS volume per country can be explained by variations in the number of large SSS ports and the GDP per head. Finally, future prospects for SSS indicate that most countries show (theoretical) potential to further increase their SSS volume calling for tailor-made policies to utilize this potential. Keywords: Short sea shipping, Regression analysis, Data envelopment analysis, Future prospects Introduction In European history, maritime transport (both deep sea and short sea) has always been a major catalyst of economic development and prosperity. Almost 75% of the EU external freight trade volume (or about 51% in value) is seaborne. Short Sea Shipping (SSS) represents approximately 33% of intra-EU exchanges in terms of ton kilometers (European Union 2017). An important part of European SSS policy is laid down in: ‘the concept of Marco Polo program’, in which subsidies for SSS are driven by the desire to move trucks from congested roads to SSS, and address sustainability issues at the same time. Overall, SSS is an important transport mode in Europe, and policymakers expect it to facilitate more freight transport in order to relieve congestion on European roads and to increase sustainability (European Union 2011). However, SSS is already transporting approximately 33% of intra-EU ton kilometers which makes it an important transport mode and this might indicate limited further growth potential. Furthermore, if policymakers address the claimed potential of SSS, often a clear goal such as a certain increase in the market share of SSS is lacking. In addition, SSS consists of many sub markets (e.g. bulk, containers, feeders, frozen products, etc.) and each sub market requires a dedicated approach in order to realize Journal of Shippin g and Trade © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 https://doi.org/10.1186/s41072-018-0032-3 potential improvements. It appears to be clear that SSS is able to deliver solutions to the congestion and sustainability problems in Europe. The interesting issue is, however, how large the solution potential of SSS is and also for which SSS sub markets this solution potential holds. The above sketched problems and challenges for SSS lead to a need to analyze the SSS market in much more detail in order to indicate its growth potential through a statistical analysis of influencing factors on SSS in European countries. Given this background on the SSS transport market and its challenges and problems, the central research question in this article is: ‘Which factors influence SSS in European countries?’The starting point for a study of SSS is to gain insight into markets, followed by an analysis of the ‘drivers’of successful short sea transport services. First, the article will give a short introduction into SSS, it describes the main definitions for SSS and analyses its respective important sub-market segment. Also the future prospects of SSS are discussed. Secondly, our dataset needs are described and the characteristics of the resulting actual dataset are given. Thirdly, a regression analysis is performed on the country level to analyze the SSS growth potential in the respective European countries. Several different regression analyses are performed, and also different segments are analyzed. In order to check the results, a DEA analysis is performed to see which countries are efficient in SSS and which countries are less efficient. Last of all, several hypothesis are tested. The paper closes with several conclusions. Short sea shipping in Europe Defining SSS: In several scientific papers, SSS is positioned as a solution to congestion problems and sustainability issues (Perakis and Denisis 2008; Medda and Trujillo 2010; Brooks and Frost 2004; Sambracos and Maniati 2012; Lopez-Navarro et al. 2011). There are a number of different definitions of SSS, and there is no single definition that is universally agreed upon. In-depth discussions on the definition of SSS can be found in Paixão-Casaca and Marlow (2007) and in Medda and Trujillo (2010). In particular, Paixão-Casaca and Marlow investigate a large number of different SSS definitions in great detail. Often used classification criteria for SSS are based on: 1) geography, 2) type of loads, 3) type of traffic, and 4) legal (port of origin and destination). But there is no consensus among scientists on the SSS definition, due to the broad and diverse SSS market (Douet and Cappuchilli 2011). In our paper, the definition of Eurostat is used as most data come from Eurostat, which in turn is derived from the Communication of the Commission COM (1999) 317 on the development of Short Sea Shipping in Europe: “‘Short sea shipping’means the movement of cargo and passengers by sea between ports situated in geographical Europe or between those ports and ports situated in non-European countries having a coastline on the enclosed seas bordering Europe” (European Commission 1999). The importance of SSS in Europe: In Europe, after road transport, SSS is the main transport mode in terms of ton kilometers in intra-EU transport. Never the less, since 1995, while the share of road transport has risen by 4 percentage points, the market share of SSS has remained relatively stable (see Fig. 1). It is regularly claimed that SSS has more potential to transport freight. When the data are analyzed (e.g. in terms of ton kilometers) it is found that SSS already has a market share of 33%. In order to understand the influencing factors on SSS in European countries also the SSS market segments are important. The SSS sector and market segments; The short sea market is diverse and complicated and can be divided into van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 2 of 20 several different classifications of market segments. Basically, there are six ways to distinguish market segments (adapted from European Commission 2015): 1. Type of products, 2. Volumes according to geography, 3. Vessels, 4. Type of contracts, 5. Transport distance, and 6. SSS suppliers. Precise data (e.g. origin-destination) about type of products being transported in Europe by SSS are lacking but it would be interesting to analyze data about dry bulk, liquid bulk, containers, neo-bulk, and ferries in much more detail. Secondly, the SSS sector can be viewed according to volumes transported with a geographical focus. Data about volumes could be distinguished according to sea regions or country (which is the basis for the data analysis that we perform on the influencing factors of SSS in countries in Section 4). Thirdly, transport means could be used to distinguish different market segments: the size of the SSS vessels, or roll-on-roll-off (RO-RO) versus load-on-load-off (LO-LO). The SSS market could also be distinguished according to contract type: voyage versus period charters. Fifthly, different market segments according to transport distance could be of interest for data analysis. According to Brooks and Trifts (2008), mode choice for distances under 700 kms is dominated by truck, and distances over 1400 kms by intermodal transport. Finally, the focus could be on the number and type of SSS companies. These different ways of distinguishing the different market segments in SSS serve to indicate the quite fragmented and complicated character of the SSS market (see also Medda and Trujillo 2010, p. 286). Paixão-Casaca and Marlow (2005) made a first analysis of the strengths and weaknesses of SSS. Medda and Trujillo (2010) analyzed the advantages, disadvantages, and goals of SSS. In this paper, we have extended these strengths and weaknesses (See Appendix). The main conclusions that arise are: 1) the SSS strengths are mainly positive external effects, important to policy makers, and 2) the SSS weaknesses are mainly challenges in the price/quality ratio, important to companies. Therefore, the main challenge for SSS is to define options to improve its quality, reliability, speed, and price, in order to make it more attractive to customers. Fig. 1 Split of freight transport in the EU-27 in ton-kilometers (European Union 2017). Note: SSS = short sea shipping, IWW = inland waterways van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 3 of 20 Short sea shipping data requirements and availability In SSS, there are no publicly available commodity data, vessel data, and transport cost data to name all but a few. This limits the possibilities to build models that predict future freight flows and that optimize freight flows between multiple origins and destinations. In our research, we performed an extensive search in order to build a large SSS dataset. The data we have been able to find is concentrated on the country level and this thus leads us to this level of analysis for our paper. This necessarily means that a lot of the statistical analyses are related to the infrastructure and geography fields. It is important to stress here that there are more, and possibly more important, factors involved (such as commodity type, sales, vessels, etc.). However, publicly available data on SSS is limited. In the end, we were able to find data in the form of country-based SSS data for the main ports in 25 countries in Europe and this is the market segment division that we use for our quantitative analysis (http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=mar_sg_am_ cwk&lang=en). Because the data concerns total transport volumes (both inward and outward), there is a potential issue with “double counting”related to this data (http://ec.europa.eu/eurostat/cache/metadata/en/mar_esms.htm). For an overview of the country-based SSS data see Table 1. Data that have been used in the analysis of the influencing factors of SSS in European countries are: land area of the EU countries (https://www.cia.gov/library/publications/ the-world-factbook/fields/2147.html), the length of the coastline (https://www.cia.gov/ library/publications/the-world-factbook/fields/2060.html), the coastline to area ratio, the total number of SSS ports per EU country (ports handling more than 1 million tons per year) (http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database?_ piref458_1209540_458_211810_211810.node_code=mar_go_am), the number of small SSS ports per EU country (ports handling 1 to 10 million tons per year), the number of large SSS ports per EU country (ports handling more than 10 million tons per year), the number of inhabitants per country (http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table& init=1&plugin=1&language=en&pcode=tps00001), the GDP per country (http://epp.eurostat. ec.europa.eu/tgm/refreshTableAction.do?tab=table&plugin=1&pcode=tec00001&language=en), the GDP per head, the length of the road network (motorways and main or national roads) (http://ec.europa.eu/transport/facts-fundings/statistics/doc/2013/pocketbook2013. pdf), the length of the rail network (http://ec.europa.eu/transport/facts-fundings/statistics/ doc/2013/pocketbook2013.pdf), and the length of the inland waterway (IWW) network (http://ec.europa.eu/transport/facts-fundings/statistics/doc/2013/pocketbook2013.pdf). These data have been used in order to determine the relationship between these variables and the SSS volume per country. The variables have been selected based on their public availability and their expected correlation with the SSS volume of each country. For an overview of the data see Table 2. Based on the literature and this data we have been able to find, we have formulated four hypotheses about SSS: 1) a longer coastline leads to more SSS, 2) a higher GDP leads to more SSS, 3) more ports lead to more SSS, and 4) a large rail infrastructure leads to less SSS. A longer coastline would also most likely mean more ports and thus more possibilities for the usage of SSS. Also islands tend to have a longer coastline and by nature are more involved in SSS. In general, a higher GDP means more freight flows and more freight flows might also indicate more possibilities for SSS. If more ports are available and offering SSS services this might also result in more SSS for a country. van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 4 of 20 Finally, the interaction between freight transport networks, such as SSS and rail could be interesting to analyze. SSS and rail might compete on certain routes and if rail options are available this might harm SSS. Statistical analysis of influencing factors on SSS SSS in European countries: Univariate linear regression analysis Most scientific studies on predictions of volumes have been mainly based on long-term forecasting (Peng and Chu 2009). One of the most widely-used methods in forecasting is the regression analysis that identifies causal relationships between variables. In addition to the regression analysis performed in this study, also four hypothesis are tested: 1) a longer coastline leads to more SSS, 2) a higher GDP leads to more SSS, 3) more ports lead to more SSS, and 4) a large rail infrastructure leads to less SSS. The initial univariate linear regression analysis started with six aspects of SSS volume: 1) total SSS, 2) liquid bulk SSS, 3) Table 1 SSS volume per European country and per segment in 2012 (http://appsso.eurostat.ec.europa. eu/nui/show.do?dataset=mar_sg_am_cwk&lang=en) Country Total SSS volume Liquid bulk SSS volume Dry bulk SSS volume RO-RO units SSS volume Containers SSS volume Other SSS volume Million tonnes in 2012 Belgium 123,9 35,5 22,3 17,7 41,3 7,2 Bulgaria 22,1 10,6 7,4 0,2 1,8 2,1 Croatia 12,1 6,1 3,1 0,8 0,8 1,4 Cyprus 5,7 2,3 1,2 0,1 1,8 0,2 Denmark 66,1 19,4 18,2 20,2 4,4 4,0 Estonia 25,5 13,5 3,3 3,8 1,6 3,2 Finland 88,0 29,1 21,8 16,4 10,0 10,7 France 171,0 95,2 33,6 22,2 9,1 10,9 Germany 170,4 43,7 37,3 31,8 47,9 9,6 Greece 90,0 39,9 13,8 12,3 20,1 4,0 Iceland 2,2 0,3 0,1 0,0 0,3 1,4 Ireland 37,0 10,3 8,2 11,6 6,5 0,4 Italy 285,5 141,9 32,2 53,0 37,3 21,1 Latvia 61,0 19,1 30,6 2,7 3,7 4,9 Lithuania 32,4 17,5 6,7 2,9 3,6 1,7 Malta 3,0 1,5 0,5 0,5 0,6 0,1 Netherlands 253,5 155,3 41,0 11,5 26,1 19,6 Norway 147,4 70,0 53,5 5,9 5,0 12,9 Poland 48,7 13,4 17,9 6,2 8,4 2,8 Portugal 34,7 14,5 7,8 0,2 8,7 3,5 Romania 23,9 8,1 10,0 0,4 1,8 3,6 Slovenia 8,8 2,5 2,2 0,5 2,7 0,9 Spain 191,4 82,1 37,6 12,2 42,8 16,8 Sweden 142,1 54,0 21,2 42,2 11,3 13,3 Turkey 254,6 84,1 89,6 8,4 55,8 16,6 United Kingdom 311,0 129,9 61,7 82,9 22,7 13,8 Data from main ports only (ports handling more than 1 million tons per year) van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 5 of 20 Table 2 Variables used as potential influencing factors of SSS in European countries (https://www.cia.gov/library/publications/the-world-factbook/fields/2147.html,https://www.cia.gov/ library/publications/the-world-factbook/fields/2060.html,http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database?_piref458_1209540_458_211810_211810.node_ code=mar_go_am,http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tps00001,http://epp.eurostat.ec.europa.eu/tgm/refreshTableAction. do?tab=table&plugin=1&pcode=tec00001&language=en,http://ec.europa.eu/transport/facts-fundings/statistics/doc/2013/pocketbook2013.pdf) Country Land area Coastline Coast/ Area rtio Total number of SSS ports in 2012 Number of small SSS ports in 2012 Number of Large SSS Ports in 2012 Number of Inhabitants in 2012 GDP in 2012 GDP per Head in 2012 Length of road network in 2010 Length of rail network in 2011 Length of inland waterway network in 2010 km 2 km m/km 2 Port port Port Inhabitant Million euro Euro/Inhabitant km km km Belgium 30.278 67 2,2 4 1 3 11.094.850 375.881 33.879 14.992 3.558 1.516 Bulgaria 108.489 354 3,3 2 0 2 7.327.224 39.668 5.414 3.407 3.947 470 Croatia 55.974 6.268 112,0 5 5 0 4.275.984 43.682 10.216 8.055 2.722 805 Cyprus 9.241 648 70,1 2 2 0 862.011 17.887 20.750 2.443 0 0 Denmark 42.434 7.314 172,4 23 22 1 5.580.516 245.252 43.948 3.835 2.629 0 Estonia 42.388 3.794 89,5 5 4 1 1.333.788 17.415 13.057 4.118 792 335 Finland 303.815 1.250 4,1 19 16 3 5.401.267 192.350 35.612 13.329 5.944 8.006 France 640.427 4.853 7,6 18 12 6 65.327.724 2.032.297 31.109 21.146 30.884 5.110 Germany 348.672 2.389 6,9 16 10 6 80.327.900 2.666.400 33.194 52.529 33.576 7.728 Greece 130.647 13.676 104,7 22 18 4 11.123.034 193.749 17.419 10.490 2.554 0 Iceland 100.250 4.970 49,6 0 0 0 319.575 10.567 33.066 4.941 0 0 Ireland 68.883 1.448 21,0 6 4 2 4.582.707 163.938 35.773 5.680 1.919 0 Italy 294.140 7.600 25,8 45 28 17 59.394.207 1.567.010 26.383 27.524 17.045 1.562 Latvia 62.249 498 8,0 3 1 2 2.044.813 22.257 10.885 1.653 1.865 0 Lithuania 62.680 90 1,4 2 1 1 3.003.641 32.940 10.967 6.675 1.767 448 Malta 316 253 800,0 2 2 0 417.546 6.851 16.407 184 0 0 Netherlands 33.893 451 13,3 9 4 5 16.730.348 599.338 35.823 5.121 3.016 6.104 Norway 304.282 25.148 82,6 23 19 4 4.985.870 389.149 78.050 10.877 4.154 0 Poland 304.255 440 1,4 5 2 3 38.538.447 381.204 9.892 18.608 19.725 3.659 van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 6 of 20 Table 2 Variables used as potential influencing factors of SSS in European countries (https://www.cia.gov/library/publications/the-world-factbook/fields/2147.html,https://www.cia.gov/ library/publications/the-world-factbook/fields/2060.html,http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database?_piref458_1209540_458_211810_211810.node_ code=mar_go_am,http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tps00001,http://epp.eurostat.ec.europa.eu/tgm/refreshTableAction. do?tab=table&plugin=1&pcode=tec00001&language=en,http://ec.europa.eu/transport/facts-fundings/statistics/doc/2013/pocketbook2013.pdf)(Continued) Country Land area Coastline Coast/ Area rtio Total number of SSS ports in 2012 Number of small SSS ports in 2012 Number of Large SSS Ports in 2012 Number of Inhabitants in 2012 GDP in 2012 GDP per Head in 2012 Length of road network in 2010 Length of rail network in 2011 Length of inland waterway network in 2010 km 2 km m/km 2 Port port Port Inhabitant Million euro Euro/Inhabitant km km km Portugal 91.470 1.793 19,6 7 4 3 10.542.398 165.108 15.661 8.703 2.793 0 Romania 229.891 225 1,0 3 2 1 20.095.996 131.579 6.548 16.552 10.777 1.779 Slovenia 20.151 47 2,3 1 0 1 2.055.496 35.319 17.183 1.588 1.209 0 Spain 498.980 4.964 9,9 27 14 13 46.818.219 1.029.002 21.979 29.365 15.932 0 Sweden 410.335 3.218 7,8 27 25 2 9.482.855 407.820 43.006 15.434 11.213 0 Turkey 769.632 7.200 9,4 22 14 8 74.724.269 611.967 8.190 33.475 9.642 0 United Kingdom 241.930 12.429 51,4 41 25 16 63.495.303 1.932.702 30.439 52.697 16.134 1.050 van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 7 of 20 dry bulk SSS, 4) RO-RO SSS, 5) container SSS, and 6) other SSS (http://appsso.eurostat.ec. europa.eu/nui/show.do?dataset=mar_sg_am_cwk&lang=en). ‘Other SSS’is not taken into account in the final results because in the analysis the category ‘other SSS’shows results which are mainly consistent with the total SSS volume, its relatively small volume, and the largely unknown composition of ‘other SSS’. The results of the initial univariate regression analysis are presented in Table 3. The univariate regression analysis is executed to be able to select the variables that show a sufficiently strong relationship with the SSS volume. The software that has been used for the regression analysis is the open source programming language R, using the generic lm() function from the stats package (R Core Team 2017). The results indicate that the following variables correlate (at least to some extent) with total SSS volume: land area, coastline, total number of SSS ports (ports handling more than 1 million tons per year), number of small SSS ports (ports handling 1 to 10 million tons per year), number of large SSS ports (ports handling more than 10 million tons per year), number of inhabitants, GDP, GDP per head, road length and rail length. The coast/area ratio and the inland waterway (IWW) length show only limited correlation with total SSS volume. The coast and area variables are still included in the analysis when the coast/area ratio is disregarded. For the IWW length this might be simply because only a limited number of countries have sufficient IWW infrastructure leading to a low correlation. A comparison of the different SSS freight-type sectors shows that both liquid and dry bulk have similar results compared with the total SSS volume, which is no surprise because the total SSS volume is largely made up of liquid bulk (42.1%) and dry bulk (22.3%). The results of the SSS RO-RO units sector are also largely in accordance with the overall results for the total SSS volume, except for a lower relevance of both land area and coastline. However, the SSS container sector has some different results, with overall lower correlations and explanatory power (R 40 ) for the different variables. The lower explanatory power of both the total number of SSS ports and the number of small SSS ports is most notable, but may be explained by the high level of concentration of container flows around only a couple of main ports (e.g. Antwerp, Hamburg, and Rotterdam). SSS in European countries: Multivariate linear regression analysis The number of variables considered in the multivariate linear regression analysis has been reduced by removing both the coast/area ratio and the waterway length, due to their limited correlation with the total SSS volume as well as with each of the four SSS segments considered in the analysis. Therefore, the variables that are used for the multivariate linear regression analysis are: land area, coastline, total number of SSS ports (SSS ports), number of small SSS ports (sSSS ports), number of large SSS ports (lSSS ports), number of inhabitants, GDP, GDP per head, road length and rail length. As several of these (independent) variables are expected to explain the same part of the variance in the total SSS volume between countries (dependent variable), a stepwise model estimation procedure has been used. The stepwise model estimation procedure is chosen because the total number of potential models is computationally prohibitive. The procedure starts with an empty model, to which variables are added in order of their statistical significance (using a significance boundary of α= 0.05). The resulting model is then ‘pruned’by removing any variables that are no longer statistically significant after the inclusion of other variables (using a significance boundary of α= 0.05). In van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 8 of 20 is that, if a given country is capable of producing X (output) with Y (inputs), then other countries should be able to produce exactly the same. However, for the countries in this analysis, the difference in inputs (ports and GDP) is quite fixed and thus important. DEA assumes that outputs can be fully explained from the inputs (i.e., as well as the potential inefficiency and there are no random fluctuations in the output). Any deviation from the efficiency frontier is stated as inefficient. DEA’s distinguishing factor is the absence of assumptions regarding the underlying functional form relating the (in) dependent variables (Charnes et al. 1994). For a full methodological explanation of DEA we refer to Cullinane et al. (2006). In order to verify the results of the analysis of the residuals, a DEA was performed for the group of 25 European SSS countries and the DEA model was built in Excel. The same two variables (number of large SSS ports and GDP per head (Fig. 2)) were used as inputs for the DEA, while the output in the DEA is made up of the total SSS volume. The outcomes of the analysis should be treated with caution and in relation to the analyses above as discussed before (the limited number of inputs and outputs and the possible correlation between number of ports and SSS volume). The DEA generates an efficiency value for each of the SSS countries, with a low value for countries that have a smaller total SSS volume than might be expected based on their number of large SSS ports and GDP per head (Fig. 2), and vice versa. The DEA efficiency for all countries are presented in Table 10. The countries with low efficiency values are mostly the same countries as those that were indicated in the analysis of residuals to have smaller than expected SSS volumes, but which do have significant SSS volumes, e.g. Spain, Italy, Norway, Finland, Poland, Ireland, Portugal and Estonia. The only exception is Denmark, which is shown to already be quite efficient in the DEA even though the analysis of the residuals indicated it as a country with a smaller observed than estimated SSS volume. This last difference in results might be because Denmark has quite a significant SSS volume but only one large SSS port (Fredericia) and a relatively high GDP per head, which are accounted for somewhat differently in the DEA as compared with the regression analysis. Countries for which the estimated SSS volume was similar to the observed SSS volume in the analysis of the residuals, but that are shown to be somewhat inefficient in the DEA include Greece, Romania and to some extend the United Kingdom. In the end, DEA provides a way to “assess”the effects of “scale economies”in the performance and volumes going beyond the strictly linear relationship obtained through the multivariate analysis. Hypotheses testing based on the multivariate linear regression model Based on the literature and the data we have been able to find, we have formulated four hypotheses about SSS: 1) a longer coastline leads to more SSS, 2) a higher GDP leads to more SSS, 3) more ports lead to more SSS, and 4) a large rail infrastructure leads to less SSS. First, a longer coastline does not necessarily imply a larger SSS volume. Although the coastline was shown in Table 2to correlate significantly with total SSS volume, coastline was not included as a factor in any of the multivariate linear regression models. The reason for this is that the explanatory power of coastline is limited and other (related) factors, such as the total number of SSS ports or the number of large SSS ports show a much stronger relationship with the total SSS volume. van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 15 of 20 Second, even though GDP was shown to correlate strongly with total SSS volume, it is not included in any of the multivariate linear regression models. However, the GDP per head is included as a factor in the multivariate linear regression model for total SSS volume, liquid bulk SSS volume and dry bulk SSS volume. This indicates that it is not so much a high absolute value of the GDP of a country that leads to more SSS, but especially a high value of the GDP relative to the number of inhabitants (GDP per head), which relates to a larger SSS volume. Third, the number of SSS ports (in total as well as only small or large SSS ports) was shown to correlate strongly with total SSS volume. Especially the number of large SSS ports displayed a very high correlation with total SSS volume and was also included in the multivariate linear regression model for total SSS volume and liquid bulk SSS. This indicates that it is not so much a higher total number of SSS ports that leads to a larger SSS volume, but more specifically a higher number of large SSS ports which each handle more than 10 million tons per year. For the RO-RO SSS volume on the other hand, the total number of SSS ports is more relevant as indicated by its inclusion in the multivariate linear regression model for RO-RO SSS volume. Either case confirms the third hypotheses that, in general: a higher number of ports relates to a larger SSS volume. The final hypothesis states that a large rail infrastructure relates to a smaller SSS volume. The factor rail length was shown to Table 10 DEA results Country Efficiency value Belgium 0.730 Bulgaria 0.299 Croatia 1.000 Cyprus 0.232 Denmark 0.677 Estonia 0.432 Finland 0.511 France 0.639 Germany 0.622 Greece 0.535 Ireland 0.281 Italy 0.502 Latvia 0.672 Lithuania 0.578 Malta 0.154 Netherlands 1.000 Norway 0.540 Poland 0.418 Portugal 0.259 Romania 0.490 Slovenia 0.136 Spain 0.435 Sweden 1.000 Turkey 1.000 United Kingdom 0.563 van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 16 of 20 correlate strongly with SSS volume, but was not included in the multivariate linear regression model for the total SSS volume. However, it was included in the multivariate linear regression models for dry bulk SSS volume and container SSS volume. In both of these models the coefficient of rail length was negative, which confirms the hypothesis that a higher rail length relates to a smaller SSS volume, but only for these two SSS segments. This indicates that countries with a large rail network in general have a smaller dry bulk and container SSS volume. Conclusions In this paper, the focus has been on the position of the SSS sector in Europe. The central research question in this article was: ‘Which factors influence SSS in European countries?’ The univariate regression analysis indicates that the following variables influence total SSS volume in European countries: land area, coastline, total number of SSS ports, number of small SSS ports, number of large SSS ports, number of inhabitants, GDP, GDP per head, road length and rail length. The multivariate regression analysis indicates that more than 78% of the variance in the total SSS volume per country can be explained by variations in the number of large SSS ports and the GDP per head. This should, however, be treated with care as this concerns only two variables and much more detailed variables on terminal level could be added to the analysis if data were available. Analysis of liquid bulk SSS leads to comparable conclusions as for the overall SSS sector. For dry bulk SSS it is interesting to note that in general the volume decreases for countries with a greater length of railway, which could be explained by the fact that rail transportation might be able to act as a substitute for dry bulk SSS, but only if sufficient rail infrastructure is available. In RO-RO SSS, it can be observed that large countries in terms of land area correspond to smaller RO-RO SSS volumes. This is interesting for countries such as France and Germany and might be explained by the competition of both rail and barge transport with truck transport on longer inland distances. These relations between different transport networks is also an interesting issue that could be explored in further research. For container SSS, it can be observed that the results are mixed and the reliability and performance of the estimated model (R 2 of 0.60) is not very high. Future prospects for SSS indicate that based on the influencing factors found in the respective analysis, most countries show (theoretical) potential to further increase their SSS volume. Four countries –the Netherlands, Turkey, Sweden and Belgium –have a larger SSS volume than might be expected based on their number of large SSS ports and GDP per head (Fig. 2). Four hypotheses were tested in order to further analyze the influencing factors on SSS. First, a longer coastline does not necessarily imply a larger SSS volume. Second, it is not so much a large absolute value of the GDP of a country that leads to more SSS, but especially a large value of the GDP relative to the number of inhabitants (GDP per head), which relates to a larger SSS volume. Third, the number of SSS ports (in total as well as only small or large SSS ports) was shown to correlate strongly with total SSS volume. Especially the number of large SSS ports displayed a very high correlation with total SSS volume and was also included in the multivariate linear regression model for total SSS volume and liquid van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 17 of 20 bulk SSS. This indicates that it is not so much a higher total number of SSS ports that leads to a larger SSS volume, but more specifically a higher number of large SSS ports which each handle more than 10 million tons per year. The final hypothesis states that a large rail infrastructure relates to a smaller SSS volume. The results indicate that countries with a large rail network in general have a smaller dry bulk and container SSS volume. Due to the relatively small number of observations (n= 25 countries) used in our model estimations, the outcomes should be interpreted as a rough estimate. It is important to stress here that there are more, and possibly more important, factors involved besides the (geographical) influencing factors employed in this article. In further research, also relative values to for example inhabitants could be analyzed in greater detail. Further research could also incorporate variables such as overall maritime traffic per country, peripherality of the country, and proximity to major sea routes. Detailed data search could be done into other “physical” characteristics to describe the port (yard area, berth lengths with a given draught, equipment), (capacity) of the terminals for each kind of SSS traffic. In addition, also the current research methodologies (DEA especially) could benefit from more extensive data leading to the availability of panel data. Appendix Table 11 Strengths and weaknesses of SSS Strengths Weaknesses - SSS can solve congestion problems - SSS can play a significant role in curbing transport growth, rebalancing the modal split, and bypassing land bottlenecks - SSS is more energy-efficient (than road transport) - SSS is safer (than road transport) - SSS is environmentally-friendly SSS removes dangerous goods from the roads - SSS is green for bulk shipping (not for RO-RO). - The sea has an unlimited capacity (i.e. it does not require a huge land-take) - Port investments and port maintenance are low - SSS can offer services at lower freight rates due to inherent economies of scale and distance - It has no time restrictions, and has the ability to use the oceans 7 days per week, 52 weeks per year - Shippers perceptions of SSS are favorable (on the East coast of North America) - SSS is flexible: increase in volume does not require infrastructure improvement - Depending on load factors of the vessel and the trailers, the effective load factor may vary between 25% and 40% making RO-RO much less efficient and green than is claimed - Regulatory safety and security frameworks are required that contribute to increasing industry costs - Costs associated with improving or increasing the port infrastructure (SSS terminals) are considerable - SSS is a capital-intensive industry (both vessels and terminals) - Capacity-filling due to high fixed costs is necessary - There is a lack of port capacity - SSS can hardly offer a door-to-door transport service: part of a broken chain - SSS has old/traditional organizational cultures - SSS has low vessel speed - There is a lack of information technology/ information systems compatibility - SSS port operations are at low speed - There are low levels of port reliability - SSS has a poor image - There is port congestion at the land-side of large container ports - SSS has additional handling costs - The door-to-door price by sea would have to be 35% less if door-to-door transport were to switch to SSS - Market suffers from overcapacity, and is obliged to lower rates to be competitive - There is a lack of service differentiation - There is a higher risk of damage to goods Sources: based on Perakis and Denisis 2008; Paixão-Casaca and Marlow 2005; Brooks and Trifts 2008; Medda and Trujillo 2010; Hjelle 2010;Paixão-CasacaandMarlow2007; Paixão-Casaca and Marlow 2009; Garcia-Menendez and Feo-Valero 2009; Gouvernal et al. 2010; Bendall and Brooks 2011; Morales-Fusco et al. 2013; Baindur and Viegas 2011; Martell et al. 2013 van den Bos and Wiegmans Journal of Shipping and Trade (2018) 3:6 Page 18 of 20 Abbreviations DEA: Data envelopment analysis; GDP: Gross domestic product; lSSS ports: Large SSS ports; SSS: Short sea shipping; sSSS ports: Small SSS ports; VIF: Variance inflation factor Acknowledgements The authors would like to acknowledge the worthwhile and constructive comments and suggestions of the anonymous reviewers. 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