Measuring the quality of port hinterland accessibility: The Ligurian case

Transport Policy 18 (2011) 382–391

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Measuring the quality of port hinterland accessibility: The Ligurian case C. Ferrari a,n, F. Parola b,1, E. Gattorna a a b

Italian Centre of Excellence for Integrated Logistics (CIELI), University of Genova, via Bensa 1, 16124 Genova, Italy Department of Business Studies, Faculty of Economics, University of Naples ‘‘Parthenope’’, via Medina 40, 80133 Napoli, Italy

a r t i c l e i n f o

a b s t r a c t

Available online 30 November 2010

Traditionally, distance was considered the parameter that could better reflect the economic influence of a seaport on land. Containerisation and intermodality progressively eroded such a paradigm and currently distance became only one of the factors across the overall ‘‘equation’’. In this respect, a fundamental role is played by the effectiveness of inland connections. The better the connection of a port to the various inland markets, the bigger the potential to enlarge its overall captive area. Furthermore, the higher the ‘‘frictions’’ (bottlenecks, delays, etc.) for reaching the hinterland, the lower the inland traffic flows. The major purpose of the paper is to measure container traffic diversion from Ligurian ports (Genoa, La Spezia and Savona) to the main Italian and European competitors. The application of a gravity model will reveal the current role of distance in drawing hinterland market share among the selected ports. Moreover, for evaluating the unexploited potentialities of Ligurian ports, we compared real traffic flows with the outcomes of a spatial interaction model, reassigning inland container flows to the different sampled ports. The calculation of the traffic delta through a gap analysis, allowed measuring the ‘‘frictions’’ thwarting the connectivity between the Ligurian ports and the sampled hinterland regions. Finally, the paper discusses the nature and the reasons for the above traffic diversion. & 2010 Elsevier Ltd. All rights reserved.

Keywords: Container port hinterland Competitiveness Spatial interaction Gravity model Traffic diversion

1. Port and hinterland: breakthrough of containerisation

1.1. From captive to contestable hinterlands

The concept of port hinterland deeply evolved over the years following the transformations that occurred in the maritime transport industry. A hinterland is the inland area from where a port produces the majority of its businesses. Concretely, the catchment area of a port is the scatter of inland points of cargo origin/destination generating the traffic flows passing through a specific port. In abstract terms, the traditional concept of hinterland conceives it as the area whose contour is a continuous line bounding the port economic influence on shore. Anyway, it is very complex or even not feasible to draw the hinterland’s shape as its extension can largely vary with respect to commodity (Blauwens and Van de Voorde, 1988) and transport mode. Moreover, the hinterland’s size can deeply change over time as a result of economic cycles, seasonality, technological breakthroughs, changes in carriers and MTO strategies, infrastructural bottlenecks, changes in transport policy, etc. Therefore, the concept of hinterland has to be conceived as a very dynamic one, and it is misleading to have a static concept of port hinterlands as being everlasting (Notteboom, 2008).

The development of global networks altered the relationship between the points on networks and their areas of influence. A few key factors have facilitated the rise of gateways competing for contestable hinterlands. Literature on gateway/hinterland relationships has studied the deep transformations affecting port catchment areas, in particular recognising that containerisation and intermodality have expanded the hinterland coverage and have thus highly intensified inter-port competition (Hayuth, 1981; Muller, 1999). The first revolution in the transport industry was brought by the invention of marine containers, which became the universal standard cargo unit, allowing the maritime transport system to expand on land by creating a door-to-door system. A second revolution, the development of intermodality (by rail and barge), further expanded land penetration of maritime containers by creating landbridges (Ashar, 1999). The intermodal revolution and the associated corridors largely widened port hinterlands, leading to a paradigm shift from captive hinterlands to shared or contestable hinterlands, and also changing the perception on port markets from being monopolistic or oligopolistic to competitive. Therefore, these changes have deeply affected the relationships among ports located in the same range (i.e. Hamburg–Le Havre) and even, to some extent, in opponent ranges (i.e. USWC vs. USEC), generating a much stronger on-shore competition for catching cargo. Such competition led to the ‘‘natural selection’’ of some gateways, emerging for operational

n

Corresponding author. Tel.: + 39 010 209 51931; fax: +39 010 209 51949. E-mail addresses: [email protected] (C. Ferrari), [email protected] (F. Parola). 1 Tel.: +39 081 547 4845; fax: +39 081 552 2313. 0967-070X/$ - see front matter & 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.tranpol.2010.11.002

C. Ferrari et al. / Transport Policy 18 (2011) 382–391

efficiency, marine strategic location and inland penetration effectiveness. These gateways are nodal points within global maritime networks, where deep-sea transport flows can penetrate wide continental areas and vice versa (Hayuth and Fleming, 1994). Containerisation and intermodality have progressively eroded traditional hinterland paradigms. The rise of hinterland contestability drove to transformations not only in hinterland size but also in its shape. Hinterlands, in fact, also became more discontinuous in nature, especially beyond the immediate hinterland of the port. Such a process can even lead to the formation of ‘‘islands’’ in the distant hinterland for which the load centre achieves a comparative cost and service advantage in the vicinities of rival seaports (Notteboom and Rodrigue, 2005). Therefore, conventional perspectives based on distance-decay are ill-fitted to address this new reality. In this respect, as mentioned before, a fundamental role is played by the effectiveness of inland connections. 1.2. Container ports as portion of overall ‘‘logistics equation’’ The development of corridors produced a change in the relationship between gateway ports and their hinterland. In fact, contrary to pre-containerisation paradigms, inland penetration aggressiveness has become a relevant side of maritime gateways strategy for increasing traffic volumes. Formerly, the traditional view on container port selection was basically used to consider standalone ‘‘physical’’ attributes of a port, such as assets endowment, nautical accessibility, quay performances, containers dwell time, etc. Nowadays, such focus on standalone attributes for addressing the notion of port competitiveness does not reflect the complexity of global supply chains anymore. This is an effect of the delocalisation of production plants and the dispersion of inputs for multinational manufacturing companies, which led to the adoption of flexible multi-firm organization structures and to the fragmentation on a global scale of companies’ supply chains. As a result, in fact, ports became only a portion of the overall logistics equation and, paradoxically, their competitiveness is mostly defined by the efficiency of operations outside the port domain and, in particular, by their inland connectivity and reliability. Ports are increasingly competing not as individual maritime nodes, simply loading and unloading cargo, but as crucial links within various logistics chains (Robinson, 2002; Carbone and Gouvernal, 2007). This requires a more supply-chain oriented approach to port selection by carriers and shippers, as more than ever, it has become the dominant viewpoint for evaluating port competitiveness (Guy and Urli, 2006). This also implies that port competitiveness becomes increasingly dependent on external co-ordination and control by ‘‘outside actors’’. The crucial role of such players is witnessed by the institutional problems and the bottlenecks that can be generated by the eventual lack of such integration among public bodies (municipality, region, central government, etc.) and private companies (Caballini et al., 2009). 1.3. Gravitational forces and frictions Given the above considerations, the degree of contestability over a hinterland as well as the port capacity of attracting cargo can be summarised by the contextual intervention of positive and negative ‘‘forces’’ (standalone and external factors) across the overall transport chain. In this respect, a port and hinterland can be seen as interconnected entities ‘‘calling’’ each other, in proportion to their own capacity of attracting cargo and in inverse relation to the frictions thwarting such mutual attractiveness. This approach takes into account the trend outlined earlier, where the quality of hinterland connections deeply affects the competition

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among ports as inserted in global supply chains. In fact, port choice has become more a function of the overall network cost and performance, as port selection criteria are related to the entire network in which the port is just one node (Magala and Sammons, 2008). The ‘‘gravitational forces’’ attracting ports and hinterland towards each other can be referred to those positive forces generating traffic flows between them. Among such ‘‘pushing’’ factors, we can cite the following: the effectiveness of port handling operations, the location of distriparks and inland terminals in and around the port area (Ferrari et al., 2006), the presence of an international business environment (headquarters location, English speaking workforce, etc.), a strong local cargo base (calling for transhipment operations as well as additional gateway cargo for long inland distances), fast vessel turnaround times, short container dwell times, insertion in efficient rail, road and barge networks, effective port governance, etc. These positive forces increase the port capacity of enlarging its hinterland coverage, even ‘‘contesting’’ the market area of faraway ports. Nevertheless, such attracting forces are hindered by the presence of negative ones, generating ‘‘frictions’’ to the transit of cargo flows and limiting hinterland contestability—lack of capacity in port and inland infrastructures, high port costs, inefficient, expensive and unreliable inland connections, etc. All these usually produce traffic deviations to other corridors and/or ports, thus reshaping port hinterlands. This because logistics players find alternative routes with a lower ‘‘resistance’’ in terms of costs and reliability. Ports that are located on inefficient or low-capacity corridors obviously are in a disadvantageous position. In summary, gravitational forces and frictions can be primarily referred to issues on costs (overall logistics cost), capacity (port handling, inland infrastructures, etc.) and reliability. However, besides such factors, it is worth mentioning that the competitive position of a port in relation to a specific hinterland region also depends on many other factors, i.e. historical, psychological, political, cultural, institutional, legislative and personal ones, which can produce a routing of container flows largely diverging from a ‘‘perfect’’ market-based division (Van Klink and Van Den Berg, 1998). In fact, bounded rationality, inertia and opportunistic behaviour are among the behavioural factors that could lead to a deviation from a ‘‘distance-decay’’ optimal solution (Notteboom, 2008).

1.4. Reshaping port hinterland The literature started to face port–hinterland relationships since the 1960s, although some seminal studies date back even earlier in the past (Sargent, 1938). At the beginning, the concept of hinterland was mainly represented through zonal models, in which scarce relevance was given to inter-port inland competition (Schaffer, 1965; Von Schirach-Szmiegel, 1979; Vigarie´, 1979). As mentioned, containerisation and intermodality broke this framework of scarce competition between ports over a shared hinterland. The insertion of ports into global transport networks, the growing carriers’ role in selecting gateways and corridors and thus in drawing port hinterlands has led to a profound revisiting of the hinterland concept. Linked to global supply chains and remotely managed by international transport players, the hinterland is currently viewed as a notion cleared from traditional spatial logics. Its shape is not ‘‘regular’’ anymore and in function of the distance, but it seems to be affected by many discontinuities and ‘‘border effects’’. This would be generated by frictions hindering hinterland expansion and also by the contextual aggression of faraway ports, colonising the captive market of some competitors. In this respect, some scholars still support the explanatory power of distance in

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defining port hinterlands (Debrie and Guerrero, 2008), while most of them assert that the hard competitive game among the top players (Notteboom, 1997; Olivier and Slack, 2005) makes the definition of a hinterland as an a-spatial job, in which the port choice is not necessarily related to the inland distance. Although the above debate is not the primary focus of this paper, in further sections we will bring some considerations about this point. The paper is organized as follows. In the next section, we will introduce spatial interaction models and their application in the Ligurian case, also raising the major research questions of this work. Further, in Section 3, the main methodological sources and aspects used will be tackled; in Section 4 the peculiarities and the structure of the gravitational model that will be used will be explained and lastly, in Section 5 the main outputs of the model will be presented, together with comments and a discussion on their significance and interpretation (Section 6).

2. Container ports, hinterlands and spatial interactions: the Ligurian case The inter-port competition has intensified in Europe due to the development of railway and fluvial corridors that have consented some load centres (i.e. Rotterdam, Antwerp, etc.) to penetrate the captive market of other ports. Moreover, the shift of the barycentre of the European economic heart towards the East will create in perspective development opportunities for Black Sea ports. In fact, major contestable hinterlands are increasingly being served not only by the ports of one gateway region, but by some multi-port gateway regions. Within this framework a major role is played by Northern European ports, ranging from Le Havre to Hamburg, which still dominate the central European market and also cannibalise container volumes from North Italian regions. In the Northern part of the Mediterranean rim, Spanish ports (Barcelona and Valencia) are keeping pace with the world throughput growth, while North Italian gateway ports are experiencing a profound lack in competitiveness (Musso and Parola, 2007). This paper deals with the case of Ligurian ports, Genoa, La Spezia and Savona inserted in the European panorama. These ports fiercely compete each other in various shipping businesses, notably containers. They are located very closely and, as a consequence, insist on the same hinterland, roughly corresponding to the North of Italy. At the same time, these ports have to defend themselves from the penetration of Northern European ports, which, even if their share is smaller than in the recent past, still subtract them some maritime traffic volumes. In this context it really seems that the traditional paradigms that characterized the spatial definition of the port hinterland have been overcome. The common opinion of practitioners, policy makers and academics dealing with this subject is that the hinterland of Ligurian ports is geographically more limited than what can be expected on the basis of their favourable geographical position. This regards their location with respect to maritime routes to/from Asia and their closeness to important nations such as Switzerland and Austria. The incapacity of Ligurian ports to expand their hinterland beyond a certain distance threshold (i.e. ‘‘active colonization’’) with heavy ‘‘border effects’’ and the coexisting difficulty to defend their own ‘‘captive’’ in the North of Italy (i.e. ‘‘passive colonization’’) may induce one to assume that, at least in relation to the central European market, the paradigms of competition between ports have changed dramatically. The distance then would seem to have become only one of the different parameters that contribute to determine the share of the inland market of a port. The first aim of this paper is to test this hypothesis and then evaluate how big the explanatory power of the distance in the

determination of the hinterland of Ligurian ports tends to be. Moreover, the study also aims at measuring the real extension of the inland market of the selected ports as well as their market shares in the different areas. The outputs would allow us to carry out evaluations in terms of hinterland morphology, showing eventual discontinuity regions and barrier effects produced by frictions. 2.1. Measuring hinterland accessibility: spatial interaction models The selected topic ascribes itself to the stream of studies on spatial interaction and its modelling. Spatial interactions are realised through movements of freight (or people) between origins and destinations over a geographical space. In this respect, spatial interaction models are based on the assumption that flows are a function of the attributes of origin and destination locations as well as the frictions given by distance between such nodes. Therefore, it is possible to insert the phenomena of freight transport flows between ports and the various hinterland areas in spatial interaction studies. The containers handled in port j and destined to market i are a growing function of the mutual attractiveness between the two entities and in an inverse proportion to the distance among them (Fotheringham and O’Kelly, 1989; Baccaini, 1997). Within the constraints of the model, an increase in the distance, which represents the factor of spatial separation between two places, is translated then into the decrease in the spatial interaction probability, in parity of conditions (Haynes and Fotheringham, 1984; Fotheringham and O’Kelly, 1989; Bailey and Gatrell, 1995). After the model analysis, some discontinuities can appear (gaps, points of change/inversion, etc.). This reveals the presence of barriers offering resistance to spatial interaction and ‘‘deforming’’ the relations between the elements of the system (Batten and Fischer, 1993). As mentioned earlier, this can be the result of organizational, operational, infrastructural inefficiencies that actually require an additional generalised cost (monetary, time, psychological) to overcome them (Grasland, 1999). This mechanism leads to the decrease in the intensity of interactions in certain areas, also inducing the deviation of some traffic flows towards less-constraining routes. The models of spatial interaction based on distances and masses (attractiveness) allow one to compare the obtained outputs with the real distribution of flows and to measure the degree of friction within the system, also revealing if and how the distance has an explicative power in determining flows. Furthermore, and so as to highlight and measure probable ‘‘border effects’’, the models allow one to study the spatial distribution of the ‘‘residuals’’ that are the result of difference between the outputs of the model and the real system (Grasland, 1999). In this sense, the residual flows reveal the ‘‘border effects’’, in the case of overestimation of the model, and the existence of preferential routes for the goods, in the case of underestimation of the flows. Moreover, the residual of the gravitational models can be used to quantify the border effects, thanks to the construction of special permeability indexes (Mackay, 1958; Grasland, 1999). 2.2. Major aims of the paper As highlighted before, the paper deals with the study of the Ligurian ports hinterland through the application of a spatial interaction model. For our purpose yet, we will use a simplified hypothesis, i.e. we will not make distinctions between import and export flows. The impossibility of obtaining reliable data in this sense has driven us to this kind of approach that does not compromise the achievement of our goals. Therefore, we will consider the different areas of the hinterland to be ‘‘generators’’ of

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flows in both directions (inward and outward). The generated wealth in each modelled area can be read as a driver for exports, thanks to the industrial production (i.e. GDP), but also for imports because a high level of employment and high salaries can guarantee good purchasing power. Ports are then the nodes of the system trying to ‘‘attract’’ flows from the different areas of the hinterland (see Section 4). Starting from the case of Central-Southern Europe’s hinterland, the paper aims at measuring how containerisation and the mutation of the organizational–operational structures have broken the traditional paradigms on competition. According to those models, the logics of spatial interaction between port and hinterland see the distance take a curtail role, which generates polarizing effects around the main ports. Therefore it will be measured if the current morphology of the hinterland is different from the assumed one in terms of the mere distance, in which the ‘‘captive market’’ concept assumes a strong relevance. A second analysis ‘‘angle’’ will deal with the study of CentralSouthern Europe’s hinterland. The market share of Ligurian ports will be analysed across the sampled hinterland, evaluating its permeability degree in the different regions and also investigating the causes of possible ‘‘border effects’’ and frictions (mountaniousness of the territory, scarce infrastructural endowment, etc.).

3. Data and geographical scale: methodological notes For the goals of this study and the application of a spatial interaction model (Section 4) mainly the following two input matrixes are required: observed traffic flows matrix and distance matrix. In both cases the described relations concern sampled ports and hinterland regions. In this section therefore we will show the main steps followed from a methodological point of view, in the choice of the sample (ports and hinterland regions) and of the sources for the construction of the mentioned matrixes. 3.1. Geographical sample: ports and hinterland As this work is willing to investigate the (actual and potential) capacity of Ligurian ports of attracting cargo from the hinterland, our primary concern was to define the scale of the contestable inland market. The current captive area of Ligurian ports is basically represented by domestic provinces of Northern Italy. In order to test the real potentialities of these ports in expanding their hinterland, the geographical sample was also enriched by some foreign regions located across the Alps, such as Switzerland, Austria, Southern Germany and South-East France. In total, the sampled area includes a population of 58.6 million people of which about 30 live in Italy. This represents almost a fifth of the EU27 population (Eurostat, 2004 data). In GDP terms, the selected area generates around 23% of the overall EU27 output (Eurostat, 2006 data). As explained later in the methodological notes, this area was split into different zones to be able to appreciate regional peculiarities as well as to understand the contribution given by each zone to the gateway throughput of individual ports. For this purpose, we applied a widely recognised territorial unit of analysis, the Nomenclature of Units Territorial Statistical (NUTS). In the captive market of Ligurian ports (North Italy) we were able to carry out a very detailed level of analysis, applying the NUTS-3 classification (i.e. provinces), while in other countries we used the NUTS-1 scale (i.e. groups of regions), as the only available port data were much more aggregated. Therefore, in total we selected 10 NUTS-1 abroad and 57 NUTS-3 in Italy. This geographical sample represents the competing hinterland for many container ports. Our study selected two tiers of ports.

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The primary sample is composed by those ports on which this paper is basically focused, namely the three major Ligurian ports. In this relation, for obtaining more realistic results, the port of Genoa was split into two zones, old port (East) and new port (West). The secondary sample includes the most relevant competing ports, which are located both in the Mediterranean Sea and in the Northern range. These are Marseille and Le Havre in France, Livorno, Ravenna, Venice and Trieste in Italy, Antwerp and Zeebrugge in Belgium, Rotterdam in the Netherlands as well as Bremen/ Bremerhaven and Hamburg in Germany. Such ports are a very significant sample in relation to the selected hinterland, as they represent over 98% of the cargo generated in that region. In 2006, they globally produced over 29 million TEUs of container traffic (import and export), of which around 11 million exactly originated from the sampled area.

3.2. Major sources and previous studies Up until today there are no in-depth studies that tackle the containerised goods’ origin–destination topic of port matrix, both dealing with the Genoese port and other Italian ports. Referring to Northern Europe, there are some studies commissioned by port authorities that sometimes allow the separation of container traffic flows from the total. Furthermore, in most cases the sources and the methodological criteria are not clearly specified and therefore it is very difficult to compare these analyses or verify their reliability. In this study we relied, where possible, on primary and direct sources like the Italian customs. On a second basis we relied on pre¨ Guterverkehr, ¨ existing studies (among others, Bundesamt fur 2007), and on field interviews, on which re-elaborations and extrapolations have been carried out for our dataset. The customs’ database, specially for Ligurian ports, has represented our major and most reliable source. To complement that database, and due to the lack of reliable statistics from Italian port authorities, we have addressed the main terminal operators present in the selected ports. These companies have provided data about the origin/ destination of the containers transiting their terminal and, after being interviewed, they have allowed us to carry on qualitative evaluations to reconstruct, at least partially, even the flows for which there is no reliable information. Regarding port traffic data (2006), we have used some Drewry reports, the Containerisation International on-line database, as well as statistics directly provided by various port authorities. Macro-economic and demographic (GDP, added value, population, etc.) data have been obtained from ISTAT and Eurostat reports.

3.3. Methodology From a methodological viewpoint, for data collection and elaboration and yet for the construction of the two matrixes, a rather articulated procedure has been adapted, which is described below. Regarding the traffic flows matrix, the following steps have been performed: 1. preliminary delimitation of a wide geographical area of reference, ranging from France to Poland/Romania and representing the ‘‘core’’ of the European port market. This area was divided into NUTS-3 (Northern Italy) and NUTS-1 (foreign countries); it was decided to keep this ‘‘asymmetry’’ between Italy and foreign countries to appreciate more eventual differences within the captive market of Ligurian ports; 2. calculation of the overall maritime traffic (import and export flows) generated within this area; so far, a wider area has been

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3. 4.

5.

6.

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considered (over 29 million TEUs), just for estimating the traffic produced by each NUTS (see step 3); splitting of the overall maritime traffic among the various NUTS, on the basis of GDP (NUTS-1) and added value (NUTS-3); restriction of the geographical focus and selection of the ‘‘sampled area’’ (around 11 million TEUs), as defined in Section 3.1; aggregation of the inland traffic volumes of some ports (i.e. Belgian ports, Rotterdam and German ports), for a simpler application of the gravitational model, due to the short distance between some neighbouring ports and the scarce available statistics. Anyway, for our purposes, this approximation has not caused particular distortions; assignment of the traffic generated by each hinterland region (NUTS-1 and NUTS-3) to the various ports; thus, we obtain the observed traffic flows matrix Tij.

When it comes to the construction of the Euclidean distance matrix between the ports and the hinterland areas, we have used the centroid concept. In fact, the representation of origins and destinations commonly involves centroids. This is particularly relevant when the attributes of the movements considered are zonal (i.e. traffic), while the graphic representation and the modelling of these movements require specific origins and destinations. For this reason it is necessary to perform an abstraction, transforming a zone with a real spatial extension into a point. In our case, the distance between NUTS and ports has been calculated using the most highly populated city as the centroid of the area and not the simple geometrical barycentre. This because the selected inhabited centres can be reasonably considered to be the major generators of maritime traffic within their own NUTS. Finally we obtained two matrixes, one for the traffic flows and the other one for the Euclidean distances, both of 67 dimensions (NUTS) per 12 (ports and groups of ports), which we will use in our spatial interaction model.

Tij ¼ Ai Oi Bj Dj f ðdij Þ

4.1. Introduction: spatial distribution model In order to analyse the spatial interaction between the ith zone of origin (i.e. production) and the jth zone of destination, we define Tij as the number of travels between them and dij as the distance. In this respect, attribute Oi is the capacity of the ith zone of producing movements (emissiveness) while Dj is the capacity of the jth zone of attracting movements (attractiveness). With reference to the Newton gravity law, we have ð1Þ

The spatial interaction between zones i and j is then directly proportional to the emissiveness (i) and attractiveness (j) attributes, and inversely proportional to the square of distance. The distance is the Euclidean gap between the centroids of origin and destination (Dujardin, 2001). For our purposes, however, Eq. (1) shows a clear deficiency. In fact, if the capacity of an origin/destination doubles, in turn the number of movements among them quadruples, and not simply doubles, as we could reasonably expect. For ironing out this problem, other two constraints have to be settled, in relation to a generic move from the ith to the jth zone (Wilson, 1967) Oi ¼ Sj Tij

ð2Þ

Dj ¼ Si Tij

ð3Þ

ð4Þ

where Ai ¼ ½Sj Bj Dj f ðdij Þ1

ð5Þ

Bj ¼ ½Si Ai Oi f ðdij Þ1

ð6Þ

Eqs. (5) and (6) have to be solved iteratively. For the nature of constraints, such a model is usually called doubly constraint. In our study, for calculating the above equations, we used the SIMODEL (Spatial Interaction MODEL) software worked out by Williams and Fotheringham, allowing the calibration of spatial interactions models through the Maximum Likelihood (ML) estimation (Williams and Fotheringham, 1984). Input data are the observed origin–destination (OD) matrix, where generic element Tij represents the flows produced by ‘‘i’’ and attracted by ‘‘j’’, and the distances’ matrix where element Eij shows the Euclidean distance between the ith origin and the jth destination. The Spatial Interaction MODEL produces as output a new origin–destination matrix (predicted OD matrix), which represents the redistribution of the observed traffic flows. The software calculates the new element (Tuij ) of the predicted OD matrix, setting the following three alternative types of constraints:

 production constraints [Tuij ¼AiOiDjf(dij)]; the traffic generated

 

4. Gravity model

Tij ¼ Oi Dj =d2ij

Eq. (2) defines the constraints on productions, while Eq. (3) those on destinations. The respect of such constraints is ensured by the introduction of some balancing factors, Ai and Bj, associated to production and destination zones, respectively. Therefore, generalising Eq. (1) and introducing the balancing factors, the gravitational model changes as follows:

by each origin remains constant both in the observed (Tij) and the predicted (Tuij ) matrix, while the traffic attracted by each destination is redistributed among them; destination constraints [Tuij ¼OiBjDjf(dij)]; the overall traffic is redistributed among the different origins, while the traffic flows assigned to each destination in both matrices are constant; doubly constraint [Tuij ¼AiOiBjDjf(dij)]; both the traffic of the ith origin and the flows assigned to the jth destination are kept constant.

4.2. Formulation of model: constraints and distances For our purposes, we applied two different sets of constraints. For measuring the frictions within the sampled area and therefore testing the explanatory power of distances in shaping container hinterlands, we first use a doubly constraint model. In this respect, as shown later, the calculation of the b parameter will bring some interesting indications. Furthermore, we applied the production constrained model. The re-assigning of traffic flows within the sampled hinterland, ironing out the frictions from the system and keeping the traffic generated by each NUTS constant. As known, the redistribution of traffic volumes among the sampled ports is basically performed on the basis of distances (inverse proportion) and masses (traffic) of each port and NUTS (direct proportion). This also allows one to evaluate the traffic delta (‘‘gaps’’) between the real data and the flows predicted by the gravitational model. Starting from the general equation (4), we introduce the distance function f(dij), choosing between two options: the power function (dbij) and the exponential function (ebdij). The mainstream literature (Wilson, 1971; Fotheringham and O’Kelly, 1989; Dujardin, 2001) suggests the former is preferable in case of long distances. In our case, the distances between ports and hinterland

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zones are often relevant and therefore we selected the power function. Applying it, the Tuij element of the predicted OD matrix can be calculated as follows: Tuij ¼ Ai Oi Dj dbij

ð7Þ

where all the parameters and variables have the same meanings shown earlier (see for e.g. Eqs. (4) and (6)). The interpretation of the b parameter will be discussed in the validation phase. 4.3. Calibration and goodness-of-fit statistics The calibration of a model is a process leading to the b parameter value ensuring that the estimated results are similar to the observed flows. This parameter also brings some useful insights for a deeper understanding of the reality. In mathematical terms, in fact, the b outcoming value basically maximises the probability that the flows estimated by the model are similar to the observed flows. Going further, a more concrete interpretation of the b value also drives to various meanings, in accordance with the selected constraint. For instance, applying the doubly constraint model, the b parameter measures the real deterrent power of the distance (see dbij ) in thwarting the attracting forces between ports and their hinterland. Therefore, b shows sensitiveness to the distance and reflects all those frictions that in the real world cause traffic flow deviations towards low-resistance pathways, thus expressing the relevance of transport costs (Clarke et al., 1986), infrastructural bottlenecks, operational inefficiencies, etc. Reasonably, we can assert that port attractiveness is driven by a distance-decay framework. So the b parameter can assume any negative value (b r0). More specifically, if 0 r b o  1 friction is less than proportional to the distance b¼ 1 friction is directly proportional to the distance bo 1 friction is more than proportional to the distance In particular, in the extreme cases system has no frictions at all, therefore the hinterland is ‘‘perfectly contestable’’ b- N frictions in the system are ‘‘infinite’’ and the hinterland has an extension virtually equal to zero.

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inter-range terms. In other words, as will be tackled further in (Section 5), this figure could also mask strong overlapping effects between the hinterland of different ports and the colonization of some captive markets by major competitors (i.e. ‘‘islands’’ formation). Therefore, a more in-depth analysis becomes necessary and this further investigation could even lead to a slightly different judgement on the real explanatory capacity of distance in shaping hinterlands. Besides b, two other parameters have been calculated for interpreting the results of the model: Absolute Entropy Difference (AED), a statistics index on the system’s entropy, and ‘‘statistic deviation’’ (d), a goodness-of-fit statistics. In this case, such parameters are calculated with respect to the ‘‘production constraint’’ model, because it allows the evaluation of the degree of redistribution becoming necessary to eliminate the frictions of the real system and it also assigns the flows on the basis of the mere distance of the masses. AED is the difference, in absolute value, between the Shannon entropy statistics AED ¼ 9Hr Hs 9

ð8Þ

where Hr and Hs are the variances of observed and predicted probability distributions, respectively. Therefore: Hr ¼ Si Sj rij lnðrij Þ

ð9Þ

Hs ¼ Si Sj sij lnðsij Þ

ð10Þ

where rij ¼ Tij =ðSi Sj Tij Þ

ð11Þ

sij ¼ Tuij =ðSi Sj Tuij Þ

ð12Þ

If AED¼0 (lower bound) the system is fully predictable. On the contrary, the maximum entropy is associated with the maximum uncertainty, where all events are equiprobable. This is equal to ln(n), where n is the maximum size of the system given by the product number of origins (12) by destinations (67). Therefore, in our case, we have

b ¼0

0 r AEDr 6:690

The closer to zero the b value, the lower the explanatory power of the distance in shaping the port hinterland. The lower the b value, the higher the role of the distance in drawing the hinterland. In our case, the calibration achieved through the Maximum Likelihood (ML) estimation drove to the results summarised in Table 1. The value of b in the doubly constraint model shows a fair sensitiveness to distance, suggesting a good grade of ‘‘captivity’’ of inland port markets. This means that, in average, the distance has a relatively important role in the sampled area, even due to the probable presence of some important ‘‘barrier effects’’. This value, however, provides only an ‘‘average’’ view of the overall picture, as some compensation effects hiding the real degree of permeability of their respective captive markets may occur, in intra-range and

AED is therefore a parameter showing how much the variance of the predicted model is similar to the variance of the real system. The closer to zero the AED, the closer the two variances, while an AED value close to the upper bound means the predicted model totally ill-fits the reality (Fotheringham and Knudsen, 1986). The choice of a production constraint model leads to a rather high AED value (1.018). This is simply because, in our case, the parameter does not have a goodness-of-fit aim, but it is useful to reveal the profound redistribution effect generated by the predictive model, which, ironing out the frictions of the real world, redrew traffic flows on the basis of distances and masses only. In other words, this AED value mainly derives from the current unbalance between northern and southern port ranges in serving central European regions (i.e. Austria, Switzerland, etc.). Similar outcomes arise calculating the ‘‘statistic deviation’’, which measures the difference between predicted value Tuij and observed value Tij d ¼ fSi Sj 9Tij Tuij 9=Si Sj Tij g  100

Table 1 Outcomes of model in production and doubly constrained hypotheses. Distance function b

f(d) ¼ dij

ð13Þ

where

Constraint

b

AED

d (%)

Production Doubly

 1.380  3.090

1.018 0.307

87.4 44.3

0 r d r100% The d value is extremely high (87.4%), confirming the deep redistribution effect performed by the production constrained model.

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5. Major outcomes of the model The results of the model have been exploited for answering the two research questions. The first, which is not yet the major one within this paper, concerns the explanatory power of distance in shaping container ports hinterlands. The b value in the doubly constrained model showed a fair role of the distance. Nevertheless, further investigation was necessary in order to avoid entrusting just an average value. The sampled area, in fact, is strongly nonhomogeneous from a geographical and economic-productive viewpoint. Hence, additional analysis was performed. In this sense, it was useful to further investigate the observed values revealing the presence of overlapping hinterlands, barriers and cannibalisation effects from distant ports. In fact, this led to different evaluations on the role of distance. The actual flows revealed a diverse degree of hinterlands overlapping, considering the competition between the Ligurian ports and their closest competitors (a; intra-range) and the main ports in Northern Europe (b; inter-range). Indeed, analysing Northern Italy, the relative market shares of domestic ports (a) revealed significant hinterland overlapping with only some minor barrier effects. Fig. 1 shows the port traffic share held by the Ligurian ports in any geographical area. In Northern Italy, the connections with the provinces of Milan and Turin are rather effective for all the three ports; moreover, Genoa holds relevant flows not only with North-Western Italy, i.e. its captive market, but also with a large part of North-Eastern Italy. The La Spezia port, on the other hand, has good connections with the provinces of Tuscany and Emilia Romagna. Looking at inter-range competition (b), the outcomes highlighted that beyond the Alps the commercial power of Ligurian ports to attract traffic is really negligible. Therefore, the distance has a scarce explanatory power in describing the aggressiveness of northern range ports in the Italian market, while it brings discrete insights into intra-range competition among Italian domestic ports. Moreover, the friction given by distance effectively shows the weakness of Italian ports in attracting cargo from/to abroad. Finally, the distance appears as a rather

‘‘unstable’’ factor, whose explanatory power is not closely correlated to a specific group of ports or to a hinterland region. The second and major goal of this paper was to measure the ‘‘barrier effects’’ (particularly for Ligurian ports), by investigating the gaps between observed and predicted flows through the application of the production constraint model. As shown, we have two matrices: one with real observed values (Tij) and another one with the values estimated by the model (Tuij ). Therefore, the following two indexes have been calculated:

 ‘‘gap index’’ ( N oGij o + N) given by the difference (i.e. ‘‘residuals’’) between the observed and predicted flows between the ith NUTS and the jth port; if Gij 40 the relation ith NUTS–jth port is stronger than the spatial distance and the opposite if Gij o0; Gij ¼0 is the neutral value Gij ¼ Tij Tuij

ð14Þ

 ‘‘permeability index’’ (Pij Z0) given by the ratio between observed and predicted flows between the ith NUTS and the jth port; if Pij 41 the jth port has a strong capacity of penetrating the ith hinterland region; on the contrary, if 0rPij o1 the jth port is weak in serving the ith area; Pij ¼1 is the neutral value Pij ¼ Tij =Tuij

ð15Þ

The former is an absolute index while the latter is a relative one; both are useful to highlight the areas where each port is greatly competitive with respect to the other sampled ports. The two indexes give rise to two matrixes, with the j-ports sampled in the rows and the i-NUTS sampled in the columns. The row vectors referred to the Ligurian ports, are mapped in Fig. 2 (gap index) and Fig. 3 (permeability index). The outcomes of the model reveal the following:

 none of the Ligurian ports serve users beyond the Alps, while Northern European ports cannibalise a portion of the Italian

Fig. 1. Market share of Ligurian, Italian and foreign ports in sampled hinterland.

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Fig. 2. Gap index values for ports of Savona, Genoa-Voltri, Genoa-old port and La Spezia.







traffic flows even in ‘‘local captive markets’’; this refers to the ‘‘passive colonization’’ mentioned in Section 2; currently the port of Savona mainly serves the North-West of Italy, mostly because of low container volumes; this picture is expected to change once the new container terminal managed by APM Terminals will be operational in the future; the port of Genoa holds a dominant share in most Northern Italian provinces, except in some Italian NUTS-3, due to the competition of other local ports such as La Spezia, Livorno, Trieste, Venice and Ravenna; the following two major factors affect the hinterland’s shape: (i) the effectiveness of the infrastructural network, i.e. highways and rail alpine crossings, since it defines the directions of the hinterland development and (ii) the location of inland terminals (i.e. intermodal platforms), as suggested in Section 1. For instance the figures revealed a high value of the permeability index (Pij 41) for the linkages between the province of Verona and the ports of Genoa Voltri (1.46) and La Spezia (1.42). This is probably due to the location of the ‘‘Quadrante Europa’’ intermodal platform, which also contributes to enhance the attractiveness of the surrounding provinces.

Ligurian ports inland traffic area, through the distribution analysis of containerised flows in the Southern-Central European market. The real distribution flows have been compared with the output of a double constrained gravitational model based merely on the geographical distances. Finally, the results allowed us to calculate proper indices for measuring the gaps between the real data and the outputs of the model. The hinterlands of Ligurian ports are mostly overlapped, even if some captive areas still remain due to the structure of road and rail networks. For instance, La Spezia is the only Ligurian ports serving Tuscany largely. The only container flows of some significance between the Ligurian ports and some foreign NUTS-1 are those linking Genoa with Switzerland and Savona with Rhoˆ ne Alpes. All the other flows are negligible. The major results of this study may be interpreted as follows:

 the b value estimated by the doubly constrained model indi-

6. Conclusions

 A crucial factor in inter-port competition turned out to be the penetration capacity in the hinterland. This research has allowed us to set up the basis for a better understanding of the boundaries of

cates that the geographical distance partially explains the traffic flows’ distribution; there are clearly other kinds of conflicts in our opinion, more in the sphere of the organization of the transport industry and the logistics services than the characteristics of the infrastructural network, which should be further investigated; the Alps act as an ‘‘intermodal divide’’ for Italian ports. This is mostly due to current infrastructural weakness (congestion, delays, etc.), insufficient capacity of alpine crossings and city beltways, the only partial interoperability among various

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Fig. 3. Permeability index values for ports of Savona, Genoa-Voltri, Genoa-old port and La Spezia.





national rail networks, and the delay of Italy in the development of a logistics freight market; yet, the Alps act as an asymmetric barrier. In fact, the substantial penetration of North European ports in Northern Italy reveals that the major infrastructural bottlenecks mainly concern road and rail sections from the Ligurian ports to the Swiss/Austrian borders. In this sense, the outcomes clearly support the necessity of a new railway line connecting Genoa with Milan and Turin provinces; inland terminals confirm their primary role in enlarging port market areas, as their strategic location may represent an attractive gravitational factor, reducing the ‘‘frictions’’ generated by the distance.

This research demonstrated that the potential market area of Ligurian ports may be larger than the regions currently served. As a consequence, besides any possible future increase in the demand of port services, a mere redistribution of the actual flows more in line with the distance as leading factor would drive to a gateway traffic increase of almost 60%, i.e. approximately 2 million TEUs. These results represent an incentive for further research willing to point out the major causes of this picture as well as to outline some possible actions for inverting this trend. Such actions should include not only infrastructural interventions, but also new forms of regulations of the sector (where and if necessary), for pursuing an effective liberalization of services across the overall transport chain. In other words, only the combination of infrastructural,

organizational (rail interoperability, intra-modal partnerships, etc.) and regulating measures affecting the major transport players may lead to the enlargement of the Ligurian ports hinterland.

Acknowledgements This paper results from the close cooperation among the authors. However, Sections 1–3, 4.2 and 4.3 are by F. Parola Section 4.1 is by E. Gattorna and Sections 5 and 6 are by C. Ferrari. The authors are grateful to Dr. David Guerrero (INRETS— SPLOTT, Paris) for his useful support during the writing of this paper and to the anonymous referees for their valuable comments and suggestions.

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