D5 Annex report WP 3: ETIS Database methodology ... - ETIS plus

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D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND independently estimated of the port distribution in Germany, port distribution in Germany is independently estimated of the port distribution in Spain). After this method has been applied origin/destination regions (on NUTS2 level), transhipment regions (on NUTS2 level) and hinterland modes are available for all maritime transport leaving or entering Spain and Germany. Since the method has been applied for Spain and Germany, the complete maritime transport chain including all information on the required level of detail results from this method. Annual seaborne transport data Eurostat New Cronos from Eurostat contains (amongst others) the following maritime data: Annual seaborne transport between reporting countries/MCA (Main Coastal Area) and partner countries/MCA, by direction, type of cargo and nationality of vessel (in 1000 tonnes), year 2000. Once all maritime transport chains have been estimated, the results will be validated (and eventually modified) using the seaborne transport data from Eurostat. Porttoport transport data Eurostat Data about maritime transport with information on porttoport level is collected by Eurostat (see ”Council directive 95/64/EC of 8 December 1995 on statistical returns in respect of carriage of goods and passengers by sea”). When this data becomes available (has already been requested by DGTREN), the method will be modified in order to include use of this data in order to produce maritime transport results that are more reliable (in the current method the port distribution on the origin/destination side is being estimated independently of the port distribution on the destination/origin side, the porttoport data from Eurostat can be used to determine the exact porttoport relations). When the porttoport data becomes available, the validation with the seaborne transport data from Eurostat (described above) becomes redundant. With the porttoport data a distinction can be made between an active mode and a passive mode for maritime transport. Method 3: Estimation of a regiontoregion matrix Although Eurostat’s COMEXT database is limited in its spatial scope to country to country transactions, many European countries do calculate regional versions of their own trade statistics. The Concerted Action on Short Sea Shipping (CASSS) for DGTREN, 1998/9, led by NTUA and ISL and in which NEA and MDS have been working on the statistical part, developed techniques for employing this existing regional data to build regional O/D matrices. These methods are being transferred to ETIS. The problem is a familiar one. The object is to construct an O/D matrix, where the cells represent the traffic ‘T’ between pairs of regions. The available data allows the row and column totals of the matrix to be measured, but an estimation procedure is required to fill the cells, for example: D 1 D 2 D 3 Total O 1 ΣO 1 52 Document2 27 May 2004
D5 Annex WP 3: DATABASE METHODOLOGY AND DATABASE USER MANUAL – FREIGHT TRANSPORT DEMAND O 2 ΣO 2 Total ΣD 1 ΣD 2 ΣD 3 ΣD 1..3 (= ΣO 1..2 ) In this example, the sets of ‘O’s and ‘D’s belong to specific countries. Country to country totals are known, as are regions to/from country totals for a significant proportion of EU and accession countries. The solution attempted within CASSS was to derive a set of gravity models, one for each SITC commodity relating the volume of traffic in a given O/D cell to the distance between the origin and destination, and the size of the regions, measured by the total volumes of the commodity produced and consumed. Other measures of region ‘mass’ could be hypothesised, but in the CASSS study most were found to be closely correlated to the total import and export volumes. The functional form used was: T p = d n e md E p I p Where: T p d E p I p n,m = tonnes of product ‘p’ lifted = distance (kms) = Exports of product ‘p’ = Imports of product ‘p’ are the parameters to be estimated The use of a function that combines the exponential and powern (where n is negative) curves provides greater control in circumstances where distance is close to zero, than would be the case if only the powern curve were used. This is important if the parameters are being estimated for zone pairs where the distances are relatively high and then transferred to zone pairs where the distances could be small. The intention was to develop a technique that could estimate parameters for, potentially, a highly disaggregated set of commodities, and in which a number of nonlinear functions could be tested and compared. The parameters would be estimated at the national level, for which large volumes of data are available, using regional information where possible. The same parameters would then be transferred to compute values at a regionregion scale. So, for example, if at a national scale, the propensity to trade ( T p /E p I p ) for Portugal and Spain is higher than for Portugal and Finland, for a given product, it seems sensible to conclude that the propensity to trade for adjacent Portuguese and Spanish regions may be higher than for distant Portuguese and Spanish regions. It also appears sensible to distinguish between product groups, as, for example, any distance decay effects may be more significant for low value, high volume goods than for higher value densities. This is exemplified using results from the 1996/7 study, for two contrasting commodities. Document2 27 May 2004 53
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