Estimates of marginal infrastructure costs for different modes of transport

One component of optimal prices for infrastructure use is the marginal cost of maintaining and operating infrastructure. While extensive studies on optimal congestion and environmental charges as well as the respective cost estimates are available much less attention has been paid to the estimation of marginal infrastructure costs probably due to the assumed lower quantitative importance for pricing compared to environmental and congestion costs. This paper presents results from a set of studies on marginal infrastructure costs for different modes of transport. It is based on research previously undertaken for the European Commission within the UNITE project (Unification of Marginal Costs and Accounts for Transport Efficiency in Europe). The studies employed different methodologies for estimating marginal costs ranging from econometric approaches up to engineering based methods. The focus of the analysis is on road and rail, however, the paper includes also results for an airport and for seaports. The main finding from the methodological point of view is that the "one" ideal methodological approach to estimate marginal infrastructure costs does not exist. Econometric approaches are based on observed behaviour of costs and cost drivers. It is obvious that the actual or observed costs do not always follow technical needs resulting from the use of infrastructure, i.e. do not necessarily reflect true marginal costs. In comparison, marginal costs derived with engineering-based methods are built on measured technical relationships, but which are not necessarily reflected in actual spending. Both econometric and engineering based approaches require a considerable amount of high-quality data with a demanding level of detail so that under this aspect one cannot express a preference for one of the two. The experience with econometric approaches was that in absence of data on axle-load km (for road and rail) the inclusion of transport performance indicators for different vehicle categories or types of transport as explanatory variables cause serious multicollinearity problems which can only be solved by constructing aggregate indicators with the consequence of restrictions to interpretation of results. Engineering based approaches which use axle-load km face vice versa the problem that marginal costs per axle-load km have to be translated back into marginal costs per vehicle type. The research presented in the paper provides evidence that for rail tracks and road infrastructure it is mainly the cost of maintenance, repair and renewal that vary with traffic volume. For terminal infrastructure such as ports and airports it is staff costs which varies in the short run with traffic. For rail tracks and road infrastructure the main cost drivers identified are traffic load, especially measured by weight indicators such as gross-tonne km and axle-load km, infrastructure characteristics such as number of bridges, tunnels, electrification etc., age of infrastructure and maintenance history. For terminal infrastructure where staff costs form the major category of marginal infrastructure costs the traffic load (measured as throughput in ports and as aircraft movements and departing/arriving passengers at airports) is again the main cost driver. In addition, the studies provided evidence that the season, the weekday and the salaries' arrangement have to be considered for analysing operation costs of terminal infrastructure. Both the econometric and the engineering based approaches mostly provided results which are consistent with the u-shaped marginal cost curve suggested by neoclassical economic theory. However, in many cases the detected non-linearities were rather weak in the relevant range of traffic variables. No uniform result was obtained with respect to the question which branch of the "u" describes marginal infrastructure cost behaviour. The quantitative results of the studies are widespread and indicate the need for further research in the field. This holds also true for airport infrastructure and waterborne infrastructure were so far only few studies are available.

An econometric analysis of motorway renewal costs in Germany

This paper analyses the cost bahaviour of motorway renewal costs with the aim to derive an estimate of marginal infrastructure costs per vehicle-km of trucks as part of optimal road user charges. The analysis is based on cross-sectional data of motorway renewal costs and traffic volume per motorway section in Germany during the period 1980-1999. The translog model estimated in this paper includes the factor input prices for labour, material and capital, the average annual daily traffic volume of trucks and passenger cars with the respective second-order terms. and a set of dummy variables for regions (the German länder) as well as for the type of material used for renewal as the most explanatory variables. In contrast to this, we could not find any significant influence of the age of motorway sections, the past renewal expenditures and the climate conditions measured as days with temperature fluctuations around zero. The cost elasticity, i.e. the relationship between marginal and average costs found in this analysis ranges from 0.05 up to 1.17 with a digressive increase of marginal costs.Cost functions, motorway renewal costs, marginal costs, infrastructure charging, road transport

A Two-Stage Efficiency Analysis of Rail Passenger Franchising in Germany

This paper analyses the differences in the efficiency of using subsidies for franchised regional rail services between the federal states in Germany, and provides evidence on the impact of procurement strategies and contractual design on the efficient use of funds. The analysis is based on a 15-year panel data set at the level of the federal states and employs a two-stage efficiency analysis, including a DEA approach and a Tobit panel model. The analysis shows that a higher share of tendering, a higher share of gross contracts, and longer and smaller contracts were efficiency-enhancing factors in the period of analysis

Estimates of marginal infrastructure costs for different modes of transport

One component of optimal prices for infrastructure use is the marginal cost of maintaining and operating infrastructure. While extensive studies on optimal congestion and environmental charges as well as the respective cost estimates are available much less attention has been paid to the estimation of marginal infrastructure costs probably due to the assumed lower quantitative importance for pricing compared to environmental and congestion costs. This paper presents results from a set of studies on marginal infrastructure costs for different modes of transport. It is based on research previously undertaken for the European Commission within the UNITE project (Unification of Marginal Costs and Accounts for Transport Efficiency in Europe). The studies employed different methodologies for estimating marginal costs ranging from econometric approaches up to engineering based methods. The focus of the analysis is on road and rail, however, the paper includes also results for an airport and for seaports. The main finding from the methodological point of view is that the "one" ideal methodological approach to estimate marginal infrastructure costs does not exist. Econometric approaches are based on observed behaviour of costs and cost drivers. It is obvious that the actual or observed costs do not always follow technical needs resulting from the use of infrastructure, i.e. do not necessarily reflect true marginal costs. In comparison, marginal costs derived with engineering-based methods are built on measured technical relationships, but which are not necessarily reflected in actual spending. Both econometric and engineering based approaches require a considerable amount of high-quality data with a demanding level of detail so that under this aspect one cannot express a preference for one of the two. The experience with econometric approaches was that in absence of data on axle-load km (for road and rail) the inclusion of transport performance indicators for different vehicle categories or types of transport as explanatory variables cause serious multicollinearity problems which can only be solved by constructing aggregate indicators with the consequence of restrictions to interpretation of results. Engineering based approaches which use axle-load km face vice versa the problem that marginal costs per axle-load km have to be translated back into marginal costs per vehicle type. The research presented in the paper provides evidence that for rail tracks and road infrastructure it is mainly the cost of maintenance, repair and renewal that vary with traffic volume. For terminal infrastructure such as ports and airports it is staff costs which varies in the short run with traffic. For rail tracks and road infrastructure the main cost drivers identified are traffic load, especially measured by weight indicators such as gross-tonne km and axle-load km, infrastructure characteristics such as number of bridges, tunnels, electrification etc., age of infrastructure and maintenance history. For terminal infrastructure where staff costs form the major category of marginal infrastructure costs the traffic load (measured as throughput in ports and as aircraft movements and departing/arriving passengers at airports) is again the main cost driver. In addition, the studies provided evidence that the season, the weekday and the salaries' arrangement have to be considered for analysing operation costs of terminal infrastructure. Both the econometric and the engineering based approaches mostly provided results which are consistent with the u-shaped marginal cost curve suggested by neoclassical economic theory. However, in many cases the detected non-linearities were rather weak in the relevant range of traffic variables. No uniform result was obtained with respect to the question which branch of the "u" describes marginal infrastructure cost behaviour. The quantitative results of the studies are widespread and indicate the need for further research in the field. This holds also true for airport infrastructure and waterborne infrastructure were so far only few studies are available

Transport infrastructure: Higher investments needed to preserve assets

A quantitatively and qualitatively efficient transport infrastructure is a fundamental requirement for the success and prosperity of the German economy, with its high degree of labor division, its many exchange relationships, and its central European location. The transport infrastructure represents a considerable economic capital stock with gross fixed assets of 778 billion euros. This corresponds to six percent of the gross fixed assets of all economic sectors in Germany. Despite the importance of this sector for the economy, there is a serious lack of investment in the maintenance and quality assurance of the transport infrastructure. Against this backdrop, a brief survey on the transport sector has been developed for this article based on an ex-post comparison of replacement demand and replacement investment made from 2006 to 2011. The analysis shows that, in the past, there has been an investment shortfall of almost four billion euros for the maintenance of the transport infrastructure. Assuming that this investment gap will need to be closed in order to maintain the transport infrastructure in coming years, and if the cumulative result of years of neglect is also taken into account, the additional annual investment requirement is likely to be at least 6.5 billion euros. There are also additional investment requirements for vehicles and selective network and capacity expansion that are difficult to estimate

Verkehrsinfrastruktur: Substanzerhaltung erfordert deutlich höhere Investitionen

Eine quantitativ und qualitativ leistungsfähige Verkehrsinfrastruktur ist für die deutsche Volkswirtschaft mit ihrem hohen Grad an Arbeitsteilung, ihren vielfältigen Austauschbeziehungen und ihrer zentraleuropäischen Lage eine grundlegende Voraussetzung für wirtschaftlichen Erfolg und Wohlstand. Die Verkehrsinfrastruktur repräsentiert mit einem Bruttoanlagevermögen von 778 Milliarden Euro einen beachtlichen volkswirtschaftlichen Kapitalstock. Dies sind sechs Prozent des Bruttoanlagevermögens aller Wirtschaftsbereiche in Deutschland. Dieser Bedeutung steht eine substantielle Vernachlässigung der Investitionen in die Erhaltung und Qualitätssicherung der Verkehrsinfrastruktur gegenüber. Vor diesem Hintergrund wurde für diesen Wochenbericht eine Kurzexpertise zum Verkehrssektor erarbeitet, die auf einem Ex-post-Vergleich zwischen Ersatzbedarf und getätigten Ersatzinvestitionen für den Zeitraum von 2006 bis 2011 basiert. Die Analyse zeigt, dass in der Vergangenheit jährlich knapp vier Milliarden Euro zu wenig für die Substanzerhaltung der Verkehrsinfrastruktur aufgewendet wurden. Geht man von mindestens dieser Investitionslücke für die Substanzerhaltung der Verkehrsinfrastruktur auch in den kommenden Jahren aus und berücksichtigt man darüber hinaus den aufgrund der jahrelangen Vernachlässigung aufgelaufenen Nachholbedarf, so dürfte der zusätzliche jährliche Investitionsbedarf bei mindestens 6,5 Milliarden Euro liegen. Hinzu kommen schwerer abschätzbare zusätzliche Investitionserfordernisse in Fahrzeuge sowie punktuelle Netz- und Kapazitätserweiterungen.A quantitatively and qualitatively efficient transport infrastructure is a fundamental requirement for the success and prosperity of the German economy, with its high degree of labor division, its many exchange relationships, and its location in central Europe. The transport infrastructure represents a considerable economic capital stock with gross fixed assets of 778 billion euros. This is six percent of the gross fixed assets of all economic sectors in Germany. This importance is offset by a substantial neglect of investment in the maintenance and quality assurance of the transport infrastructure. Against this background, a brief expertise on the transport sector has been developed for this Wochenbericht report based on an ex-post comparison of replacement demand and replacement investments made from 2006 to 2011. The analysis shows that in the past there has been an investment shortfall of almost four billion euros for the maintenance of the transport infrastructure. Assuming that at least this investment gap is required in order to maintain the transport infrastructure in coming years, and if the cumulative result of years of neglect is also taken into account, the additional annual investment requirement should be at least 6.5 billion euros. In addition, there are also investment requirements for vehicles that are difficult to estimate as well as selective network and capacity expansion

Are Hotspots Always Hotspots? The Relationship between Diversity, Resource and Ecosystem Functions in the Arctic

The diversity-ecosystem function relationship is an important topic in ecology but has not received much attention in Arctic environments, and has rarely been tested for its stability in time. We studied the temporal variability of benthic ecosystem functioning at hotspots (sites with high benthic boundary fluxes) and coldspots (sites with lower fluxes) across two years in the Canadian Arctic. Benthic remineralisation function was measured as fluxes of oxygen, silicic acid, phosphate, nitrate and nitrite at the sediment-water interface. In addition we determined sediment pigment concentration and taxonomic and functional macrobenthic diversity. To separate temporal from spatial variability, we sampled the same nine sites from the Mackenzie Shelf to Baffin Bay during the same season (summer or fall) in 2008 and 2009. We observed that temporal variability of benthic remineralisation function at hotspots is higher than at coldspots and that taxonomic and functional macrobenthic diversity did not change significantly between years. Temporal variability of food availability (i.e., sediment surface pigment concentration) seemed higher at coldspot than at hotspot areas. Sediment chlorophyll a (Chl a) concentration, taxonomic richness, total abundance, water depth and abundance of the largest gallery-burrowing polychaete Lumbrineris tetraura together explained 42% of the total variation in fluxes. Food supply proxies (i.e., sediment Chl a and depth) split hot- from coldspot stations and explained variation on the axis of temporal variability, and macrofaunal community parameters explained variation mostly along the axis separating eastern from western sites with hot- or coldspot regimes. We conclude that variability in benthic remineralisation function, food supply and diversity will react to climate change on different time scales, and that their interactive effects may hide the detection of progressive change, particularly at hotspots. Time-series of benthic functions and its related parameters should be conducted at both hot- and coldspots to produce reliable predictive models