Next Article in Journal
Evaluating Public Transit Reforms for Shrinking and Aging Populations: The Case of Takamatsu, Japan
Previous Article in Journal
The Space-Based Car-Following Model: Development and Application for Managed Motorway System Safety Evaluation
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Rail Freight Corridors in Low Population Regions

by
Juan Francisco Coloma
1,*,
Marta García
1 and
Eladio Pascual-Pedreño
2
1
Department of Construction, School of Technology, University of Extremadura, 06006 Badajoz, Spain
2
Department of Financial Economy and Accounting, Faculty of Business, Finance and Tourism, University of Extremadura, 06006 Badajoz, Spain
*
Author to whom correspondence should be addressed.
Future Transp. 2021, 1(3), 466-485; https://doi.org/10.3390/futuretransp1030025
Submission received: 11 August 2021 / Revised: 30 August 2021 / Accepted: 22 September 2021 / Published: 1 October 2021

Abstract

:
Impact analysis of the construction and commissioning of a rail freight infrastructure must contemplate the regional economic system in its entirety, since that impact extends beyond the direct contribution to the increase in production, and the consequent generation of income from capital and remuneration of labor incorporated in such activity. The objective of this research is to know the effects that a rail freight infrastructure produces in a region with a low population, such as Extremadura (Spain). For this purpose, the calculation of the total impact produced by the rail freight corridor on the regional economy of Extremadura has been made, which will correspond to the sum of the direct, indirect and induced impacts. For its determination, a simulation tool has been created from Input-Output tables that allow you to compare different impact levels depending on the intensity of the investments made, or the activity developed following the launch of the new rail corridor. The research concludes with the profitability of the action because for a planned investment of 160 M€ for 3 years in the freight rail corridor from Extremadura, every euro invested returns in more than 2 euros to the Extremadura’s productive system, creating more than 4100 jobs.

1. Introduction

Transportation is one of the key elements for the economic development of any society. The white paper of the European Commission [1] establishes a road map to achieve transport models that increase their freight volumes using more efficient and sustainable systems. Among its list of initiatives is to make investments to expand or improve the capacity of the rail network, since railway is the land transport system that achieves better performance and lower CO2 emissions into the atmosphere [2].

1.1. State of Art

In 2006, Givoni [3] conducted a study on the impact that different elements of the high-speed train have on the design and provision of the service. In 2016, Armstrong et al. [4] sets out a proposed framework for classifying the railway network by socio-economic importance and vulnerability to climate change. Subsequently, other authors have conducted studies that analyze the effects of high-speed rail on mobility [5,6] and urban structure [7,8].
However, this type of infrastructure requires high investments, so additional socio-economic impact studies that justify the profitability of its execution are necessary. In 2010, Monzon et al. [9] evaluate the territorial and economic impacts of the Transport Infrastructure Plan of Spain (2005–2020). Before, the Spanish Administrations had also carried out some impact studies for investments in their road network [10,11]. In 2008, De Rus [12] set the guidelines for the economic effects of investments in high-speed lines, having made a preliminary study [13] of the Madrid-Barcelona high-speed line. In 2014, Sánchez et al. [14] conducted a similar study for high-speed rail in Andalusia (Spain) and later in 2016, Hoyos and Bel [15] conducted an economic evaluation of the Spanish Basque rail infrastructure. Regarding freight research, Andreoli et al. [16] and Outwater et al. [17] study vehicle freight in Washington and Chicago. Samimi et al. [18] develope a Freight Activity Microsimulation Estimator (FAME), Kontelj y Jakomin [19] develope a model to know how investments in the railway infrastructure influence the whole railway system and finally, Camisón et al. [20,21] directs an economic impact study of the investments necessary for the construction of the Mediterranean rail corridor (passengers and freight) belonging to the Trans-European Transport Network.
Despite having these references, it must be taken into account that Extremadura is composed of the two largest provinces in Spain, and yet has a little over one million inhabitants. The unemployment rate is 29.23% [22] and the Gross Domestic Product (GDP) per capita is € 16,166 [23]. If we compare the existence of the almost 5 million inhabitants of the Valencian community, together with an unemployment rate below 20% and a GDP per capita of more than € 20,000, the results of this community and others with high population, are difficult to translate to the reality of the Extremaduran community.
Therefore, there is a research gap to know the effects that the construction and subsequent exploitation of a rail freight corridor will have on regions with a low population, such as Extremadura.

1.2. Case Study: Manchegan-Extremaduran Rail Freight Corridor (Cofemanex)

The Trans-European Transport Network (TEN-T) is formed by a compound of priority lines of transport which make the communication of people and freight easier throughout Europe. The 16th axis was a high-capacity rail freight corridor since 2003. It came from the ports of Sines and Algeciras, it went through the Iberian Peninsula and crossed the center of the Pyrenees through a low elevation tunnel to reach Paris. In December 2013 the European Parliament [24] establishes a radical change in the priority core ideas of the TEN-T reducing the 30 initial main axis to 9 in the 2014–2020 period. A part of the 16th axis, more specifically the Manchegan-Extremaduran rail freight corridor (Cofemanex), disappears as main idea of the Core Network, to become part of the Comprehensive Network, whose construction is not expected until 2050. Cofemanex is part of the Madrid-Ciudad Real-Badajoz railway line, it has 304 km of length and runs in a crosswise way (from east to west) by the communities of Castilla La Mancha (91 Km) and Extremadura (213 Km). Cofemanex starts in Puertollano (Castilla La Mancha) and finishes in Badajoz (Extremadura) at the Portuguese border (Figure 1).
Inauguration on 26 June 2016 of the expansion works of the Panama Canal has increased the traffic of large merchant ships coming from the main Asian ports, so the ports of Sines and Algeciras that serve as entry-exit to the Iberian Peninsula, are gaining lots of freight. The port of Sines has increased in 2016 its volume of TEUS by 13.58% having managed in 2016 a total of 1,513,083 TEU [25]. In order to guarantee the correct transport of the products from the port of Sines to Europe, it will be necessary that Extremadura has a high-performance rail freight infrastructure.

1.3. Scope and Objectives of the Research

The objective of the research will be, firstly, to evaluate the contribution of the high-performance rail freight corridor in its construction phase to the production and employment of Extremadura and, subsequently, the effects produced by the commissioning of this new infrastructure in the productivity and employment of Extremadura. The research will also include the study of the effects produced during the first 5 years by the operation of the new railway line in Extremadura.

2. Methodology

The analysis of the impact of the construction and commissioning of a rail freight infrastructure must consider the regional economic system in its entirety, since this impact extends beyond the direct contribution to the increase of production, and the consequent generation of income from capital and remuneration for work incorporated in that activity. It is necessary to quantify the indirect effects that are generated with the purchases of goods and services, as well as the income generated, from both direct and indirect suppliers; that is, the drag effect of the investments made and the operation of the infrastructure [26]. The structure of intersectoral relations reflected in the Input-Output tables provides an adequate tool to determine the drag on the rest of the economic system [27]. The effects generated on the employed population and the wage incomes associated with these levels of employment (induced effects) can be quantified by the relationships between production levels and employment needs [28].
The input-output analysis has been a valuable tool for the study of productive systems, since it considers that the effects of demand in the productive system are not exhausted in its simple direct satisfaction but are spread and multiplied through the framework of the dominant intermediate relationships in the economic system [29]. These analyzes require the creation of a symmetric Input-Output table (TIO) that is able to represent the economic system as a whole, on which an accurate measurement of all its interrelationships and main variables will be established within the production and exploitation accounts of a certain economy. This matrix simulates all the intersectoral relations of a given economy, which allows implementing an input-output demand model, which quantifies the impacts on the vectors of production, added value and employment, associated with a certain volume of investment/billing in a temporary reference period. It is therefore the best method to quantify the socio-economic effects of the investments required in the adaptation of Cofemanex and its subsequent exploitation as a high-performance rail freight corridor, on the whole of the economic system of Extremadura.
In 1995, Macorra and Prudencio published the regional Input-Output tables (TIO-R) for Extremadura for the year 1990 [30]. De Miguel et al. published a social accounting matrix and accounting multipliers for Extremadura [31] and subsequently an Input-Output model of prices applied to Extremadura’s economy [32]. However, this information should not be used as a reference because it is a reflection of society at the end of the 20th century that has nothing to do with the situation in Extremadura in the 21st century. It has therefore been necessary to look for an autonomous community that has updated TIO-R tables and that has a productive structure similar to Extremadura as its starting point. Finally, among the 12 Spanish autonomous communities that do have TIOs, the 2008 Autonomous Community of Castilla la Mancha has been selected [33], which is the latest updated and has the structure of the closest Gross Value Added (GVA) to Extremadura. It is important to note that the impact model derived from the TIO tables operates on the basis of technical coefficients of production, GVA and employment in relative terms. That is why the use of these ratios can be transferred to the case of Extremadura. When absolute values are used, the impact model always work with updated statistical information of Extremadura.
The approach is based on the quantification of the performance indicators on which the I t e impact vectors are built, for each of the stages considered “e” (construction and exploitation), and each of the years contemplated in the time horizon of the analysis “t”. Being A the inverse matrix of the input-output matrix, the calculation of the total production necessary to supply this initial demand P t e is made through Equation (1) [34].
P t e = [ I A ] 1 × I t e
Tarancón [35] describes the way to obtain the total income generated ( G V A e t ), the employment necessary to carry out this production and the fiscal returns from the calculated production and the coefficients of added value and employment of the TIO-R tables.
The socio-economic impact indicators finally generated are:
(a)
Total value of regional production, total income (added value) and tax collection generated by the main tax figures (direct and indirect), differentiating between the construction and exploitation phases.
(b)
Total employment generated (direct, indirect and induced), differentiating the construction and exploitation phases.
To carry out this methodology it will be necessary:
(a)
Identify the flows of acquisition of goods and services during the stages of construction and operation of the high-performance railway network.
(b)
Calculate the corresponding macroeconomic effects direct, indirect and induced, in terms of production, added value and employment.
For the construction and commissioning phase, the flows to be considered will be formed by all those investments made for the adaptation of the rail freight corridor. For the exploitation stage, it will be necessary to identify the total amounts of billing generated by the different agents linked to the rail corridor, which must include, not only the strict activities of freight transport, but all those services generated around the infrastructure of reference (maintenance of infrastructure, hotel business, etc.). Additionally, other investments that help to boost the transport activity in the rail corridor (electrification, logistics platforms, execution of accesses to logistic nodes, etc.) must be included. The calculation of the direct effect will be determined by the added value and employment used in each of these activities, while the indirect effect will be calculated from the purchases of goods and services made to other suppliers. In addition to the calculation of direct and indirect effects generated as a consequence of the economic transactions originated, the induced effects are also included, which would be the effects caused by the income generated by the previous ones.

2.1. Construction and Commissioning: Investments and Assumptions Made

Table 1 includes an investment program for the adaptation of the logistics corridor of Extremadura, which includes not only the improvement works in the rail freight corridor between Puertollano and Badajoz, but also the actions associated with the development of regional logistics nodes.
Investment distribution in the period 2015–2017 is attached in Table 2.
The ballast is obtained in Extremadura, supplying sleepers and rails from other Spanish regions. Hence, 35% of the investment corresponding to manufactures are produced in Extremadura and the remaining 65% is imported from other regions.

2.2. Exploitation Phase: Billing, Investments and Assumptions Made

Five years are considered since the commissioning of the high-performance rail freight network in Extremadura. In this case the period 2018–2022. It is considered that the optimal exploitation of the railway infrastructure will generate returns/impacts in the following products/activity components:
(a)
Maintenance of infrastructure in the Badajoz-Puertollano section. The Spanish Railway Infrastructure Manager [37] values the average maintenance cost of one kilometer of conventional rail network per year in Spain at € 33,000/km-year (single track and Iberian gauge). The total cost of maintenance is distributed among the productive sectors of construction and manufacturing, at a ratio of 60 (maintenance of the track)/40 (maintenance of infrastructure and facilities).
(b)
Rail freight transport to/from intermodal terminals (increase in demand). Table 3 shows the potential demand that can be collected by the railway under two premises: Badajoz-Puertollano railway section is dedicated to the freight transport and the improvement of the Portuguese rail network in the Sines-Badajoz section, scheduled for 2020.
Based on this potential demand, the expectations for the increase of rail freight traffic in the reference logistic rail corridor are monetized, knowing that only the impacts linked to the import/export rail traffic with origin/destination in Extremadura will be taken into consideration. These impacts will be reflected in two levels: road transport to/from intermodal terminals in the region (the only level contemplated in the model), reduction of transport externalities and gains in competitiveness for the region’s production companies due to the reduction of their logistic costs. Freight transport distances by road within Extremadura are estimated at 120 km. The cost of one ton of merchandise transported by road in an articulated truck of 25 t is 1.23 €/Km [39]. Table 4 encloses the monetization of the demand for freight transport in the high-performance logistics rail corridor in Extremadura for the reference time horizon (2018–2022).
(c)
Related or auxiliary activities of the previous ones, such as training, consultancy, hotel services, etc. A billing estimate of 2.0% of the total invested is made.
(d)
Other investments that are complementary to the development of the high-performance railway corridor and the regional logistics system. Within this chapter are computed the investments destined to the enhancement of logistics and rail freight traffic, such as the actions linked to the line electrification and the rail connections (accesses) of the territory logistics nodes. The sectoral allocation of this investment will correspond entirely to the construction sector. For the period 2020–2022, Coloma and García [36] justify a complementary investment of € 79.538 million, divided into € 69.528 million for the electrification of the line and € 10.01 million for the execution of railway accesses. The total amount of the investments planned during the exploitation of Cofemanex is attached in Table 5.
Appendix A describes the design of the reference macroeconomic scenario and Appendix B describes the procedure followed to estimate the socioeconomic impact and its effects.

3. Results

3.1. Simulation Tool

The model designed is introduced in a simulation tool that allows comparing different levels of impact depending on the intensity of the investments made or the activity developed after the commissioning of the new rail freight corridor. In order to be easy to manage, it has been chosen to develop the tool in a standardized spreadsheet of Microsoft Excel.
Table 6 and Table 7 quantify the total and disaggregated economic impact, both in absolute and relative terms, of the investments for adapting the existing rail line into a high-performance rail freight corridor in Extremadura.

3.2. Effects in Terms of Production

The total economic impact of the investment made in the construction and commissioning of the high-performance rail freight corridor in Extremadura, as well as in the development of the regional logistics system, exceeds 341 million euros in the period 2015–2018, equivalent to a return on regional production of 2.12 euros for each euro of investment. The disbursement of the planned investments will cause a drag effect (indirect more induced) in the regional economy of more than 205 million euros in the period 2015–2017, which represents a multiplier effect of 1.49. This means that for every euro of direct demand in the regional productive system a drag effect of 1.49 euros is generated in the economy of the territory. The impact of investments in terms of production is shown in Figure 2.
For annuities, it is worth highlighting the year 2017, where the total effects on production come to represent 1.10% of the regional GDP for that year.
During the exploitation phase, complementary actions in the regional logistics system mediate the results. Highlights the effect derived from investment flows in the regional productive system, before 2018 and 2020 onwards.

3.3. Effects in Terms of GVA (Income Generated)

The aggregate of investments for the period 2016–2018 comes to represent 1.19% of the autonomous GVA of 2015 [23]. Construction, industry and services are the main motor activities. With respect to the estimated regional GDP, the GVA generated in the reference time horizon represents up to 0.57% of the GDP of Extremadura in the year 2017. Figure 3 shows the impact of the investments on the GVA.

3.4. Effects in Terms of Employment (Unitary Needs)

The unitary employment needs for the period 2015–2017 (construction phase) are estimated at more than 4100 jobs. Figure 4 shows the impact of investments on employment.
For the exploitation phase, the annual employment detail associated with the operation of the high-performance rail freight corridor, offers employment generation figures ranging from practically 500 jobs per year for the first two years of operation of the improved line, to the 1300 from the period 2020–2022. This is due to the increase in transport demand and particularly from new investments to improve the regional logistics system.
In relative terms, the volume of employment generated during the construction phase of the Badajoz-Puertollano line and the development of the regional logistics system is equivalent to 1.24% of the total employed in Extremadura [22]. In addition, the employment ratio associated with the volume of investment made a unit cost of 39,000 euros for employment generated in the period 2015–2017.
The employment forecast in Extremadura is represented by the number of employed in the macroeconomic scenario of reference (Figure 5). During the construction phase, the volume of additional employment generated in 2017 represents 0.66% of the total number of employed persons in the region. In the exploitation phase, the relative impact falls to 0.38%.

3.5. Tax Purposes

The tax returns computed over the reference time horizon (2015–2022) amount to around 90 million euros, disaggregated as follows: 45% during the construction phase and 55% during the exploitation period. Taxes return ratio from the adaptation investments of the railway corridor and the regional logistics system (2015–2017) stands at 0.25; therefore, each euro invested in the improvement of Cofemanex generates a tax return of 25-euro cents, including in that calculation the direct and indirect taxes, and the social contributions of the jobs generated.

4. Discussion

These types of investment studies are usually carried out in the medium term (10–20 years). However, in this case, it has been decided to carry out a study of a first 3-year investment for the start-up of the high-performance rail freight corridor and a subsequent 5-year investment for its connection to the logistics nodes and supply electricity to the line, making investment more feasible for the administration.
The generated demand model allows to quantify, for any branch of activity contemplated in the Input-Output table of reference, two interesting economic phenomena:
(a)
How this activity diffuses or transmits the variations of its own final demand throughout the economic system, that is, the sensitivity of production, income or employment of the economic system to the situation of the final demand of that activity.
(b)
How this activity absorbs the variations of the final demand of other activities or of the whole economic system, that is, the sensitivity shown by the production, income or employment of the activity in the context of the final demand of other sectors of the economy.
However, since it is an estimative method, it also has some drawbacks:
(a)
A wealth of very detailed statistical information is needed on the intersectoral relationships of the industries that make up the structure of a certain region or country. All this information is collected in the TIO. The great number of resources necessary to be able to elaborate the TIO implies, in practice, that they are made every certain year. Therefore, in case of using the table to analyze a year that does not correspond to the elaboration of that TIO, it is necessary to suppose that the technical coefficients have not changed in time.
(b)
On the other hand, Barro [40] criticizes the use of multipliers to justify the profitability of a public investment. De Rus [41] recommends the use of Cost Benefit Analysis (CBA) to evaluate the impact of infrastructure investment. Boscá et al. [42] consider that the multiplier of the Spanish economy stands at 1.05, well below the 2.12 obtained in this study. This could show that the effect of investments in less developed regions produces a higher impact than in more developed regions.
In any case, the long tradition of studies based on input-output tables, their disaggregated nature, and availability, advise the use of this procedure for this research.

5. Conclusions and Future Research

This research analyzes the effects of the investment made to upgrade the conventional railway line Cofemanex in a high-performance rail freight line of a sparsely populated region such as Extremadura (Spain). The results obtained show the profitability of this investment since each euro that is invested in Cofemanex, returns in more than 2 euros in the production system of Extremadura. In addition, in a region such as Extremadura that suffers a high rate of unemployment (29.23%, [22]), it is very necessary to create more than 4100 jobs that are expected in the construction and commissioning of the freight corridor. In the first 5 years of operation of the corridor, the forecasts for job creation are also high, exceeding 1300 jobs/year. The simulated fiscal return also yields positive data since it is expected that in the construction phase of Cofemanex, 25 cents will be collected for each euro invested.
Finally, it is necessary to emphasize that the socioeconomic impact measured is limited to evaluating the consequences generated by the new infrastructure on the current economic structure. The growth of production, income and employment caused by the exploitation of the new business opportunities that the railway corridor will produce, have not been contemplated in the calculation. Nor is included in this quantification the evaluation of the effects derived from the increase of the productivity of the companies, thanks to the liberation of the time of journey in the displacements.
For future research will be interesting to evaluate the effects linked to the saving of external costs (energy, road accidents, emissions, etc.) as a result of the transfer of freight from the road to the railway in future scenarios. Furthermore, it would be of interest implementing a new cost-benefit analysis considering all the life stages of the railway line (not only 5 years), analyzing the positive and negative effects on populations, property values, land use and location of production activities.
The future of the freight transportation has lots of uncertainties, mainly due to two factors: vehicle automation and global pandemic. In terms of vehicle automation, the recent development of electric automated freight trucks is making truck-based freight transportation more and more appealing [43]. In terms of the global pandemic, regional and global trade, along with passenger travel behavior, have been shaped greatly [44,45]. Future research should consider these factors and study their effects.
This research shows that conventional rail lines can be adapted to high-performance freight lines with small investments (compared to high-speed infrastructure), making them more profitable and advantageous to operate. It can be concluded that the effects of rail freight corridors in low-population regions are positive, profitable, sustainable and very necessary for regions such as Extremadura.

Author Contributions

J.F.C. was the main researcher and wrote the manuscript. M.G. was a member of the academic team and created some of the tables and figures. E.P.-P. coordinated the research, reviewed the manuscript and proposed changes and improvements to its structure. All authors have read and agreed to the published version of the manuscript.

Funding

This paper has been the result of the Collaboration Agreement between the Ministry of Development of the Junta de Extremadura and the University of Extremadura for “The research on the adaptation of the existing railway Ciudad Real-Badajoz, to a high-performance rail freight line and the analysis of environmental and socioeconomic effects in the region of Extremadura (Spain)”. Junta de Extremadura. Reference: 026/14.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

In order to acquire data and more information of this research, please contact with Juan Francisco Coloma ([email protected]).

Acknowledgments

It is necessary to thank the work and advice of the Spanish company Consultrans, S.A.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

CBACost Benefit Analysis
CECoefficient of Employment
COFEMANEXManchegan-Extremaduran Rail Freight Corridor
CVACoefficient of Value Added
FAMEFreight Activity Microsimulation Estimator
GDPGross Domestic Product
GVAGross Value Added
PITPersonal Income Tax
RASRegional average salary
TIOInput-Output table
TIO-RRegiomal Input-Output table
TEN-TTrans-European Transport Network
TEUTwenty-foot Equivalent Unit
VATValue Added Tax

Appendix A. Design of The Reference Macroeconomic Scenario

A reference macroeconomic scenario is built in order to adequately contextualize the results of the impact analysis. This scenario extends throughout the time horizon considered for the analysis and includes the main indicators of impact selected: Production, GVA, Employment and Tax Return.

Projection of Variables in Extremadura: Keys and Assumptions

The fundamental variables to be projected in the reference time horizon will be:
(a)
Resident population. The Statistical Institute of Extremadura [46] provides the projected series of resident population in the region.
(b)
Nominal GDP. The National Institute of Statistics [23] provides the historical series of Regional Accounting for Spain and details the evolution of GDP in Extremadura at current prices (Nominal GDP) for the period 2000–2017. For the projection of 2017 and successive, the following criteria are followed:
  • Weak economic growth or stoppage of activity in the euro zone.
  • Level of indebtedness of the Autonomous Community that limits the ability to streamline actions.
  • Low diversification of productive activity, lack of competitive dynamism and human capital.
In this context, a growth between 1 and 2% interannual for the period 2017–2022 is considered.
(c)
GDP deflator. It is based on the cycle of the Spanish historical series since 2000. Forecast of the following years, the Bank of Spain’s economic bulletin [47] is used as a reference source, which points to a weak and sustained inflation over time. For practical purposes, a constant inflation of 0.4% year-on-year for the entire time horizon is considered, on which Real GDP (at constant prices) is determined for the reference time horizon.
(d)
Productivity per employee. Apparent productivity is expressed as the quotient between Real GDP and the number of employed. Therefore, from [46,47] the apparent productivity of Extremadura can be calculated in the historical series 2000–2017. In this sense, we can see how the performance of productivity per employee suffers increases in the years of crisis, because the adjustment of companies to changes in the situation of the economy as a whole is mainly through employment. This would explain the growth of productivity per employee, around the 1.4% year-on-year average since 2009. For calculations of the economic model, an interannual increase in apparent productivity is considered of 0.75–1.00% for the period 2018–2022, weaker than the one recorded in the historical series.
(e)
Average wage. The National Institute of Statistics [48] provides the 2014–2015 series of labor costs. For the projection of the average salary in the reference time horizon, the trend of the historical series is taken into account together with the macroeconomic context, estimating an interannual growth of the average wage of 1% for the period 2016–2017 and 1.5% for the rest of exercises. The reference macroeconomic scenario of the socioeconomic impact model is detailed in Table A1.
Table A1. Macroeconomic basic scenario.
Table A1. Macroeconomic basic scenario.
20082009201020112012201320142015
Resident population 1,101,3091,098,9091,096,327
Resident population interannual variation −0.22%−0.23%
Population of 16 years and over 931,767930,171928,466
Proportion of active employees 57%
Nominal GDP current prices (€ thousands)17,628,67717,144,18717,176,78916,954,36816,371,57016,199,82616,167,42616,506,942
Interannual variation Nominal GDP0−2.7%0.2%−1.3%−3.4%−1.0%−0.20%2.10%
Income per capita: Nominal GDP/Total population (€/people) 14,71014,71215,057
GDP deflator (base 2008 = 100)100100.1100.2100.2100.2100.8101.20101.60
Interannual variation Real GDP: Variation Nominal GDP-Deflator0.0%−2.8%0.1%−1.3%−3.4%−1.6%−0.60%1.70%
Real GDP constant prices (base 2008) in € thousands17,628,67717,127,06017,142,50416,920,52716,338,89216,071,25615,975,71816,246,990
Total Employed (in thousands of people)396.9374.6370.4361.2343.4332.4328.78332.70
Apparent productivity: Real GDP/Employed44,41645,72146,28146,84547,58048,34948,59148,834
Interannual variation Productivity02.94%1.23%1.22%1.57%1.62%0.50%0.50%
Average salary (euros)17,98318,82019,19819,47319,08418,41918,60318,789
Interannual variation of the average salary04.65%2.01%1.43%−2.00%−3.48%1.00%1.00%
2016201720182019202020212022
Resident population1,093,5731,090,6751,087,5971,084,3821,081,0401,077,6001,074,152
Resident population interannual variation−0.25%−0.27%−0.28%−0.30%−0.31%−0.32%−0.32%
Population of 16 years and over926,872925,315923,607921,818920,194918,528918,529
Proportion of active employees
Nominal GDP current prices (€ thousands)16,795,81417,047,75117,303,46717,563,01917,870,37218,227,78018,592,335
Interannual variation Nominal GDP1.75%1.50%1.50%1.50%1.75%2.00%2.00%
Income per capita: Nominal GDP/Total population (€/people)15,35915,63015,91016,19616,53116,91517,309
GDP deflator (base 2008 = 100)102.00102.40102.80103.20103.60104.00104.40
Interannual variation Real GDP: Variation Nominal GDP-Deflator1.35%1.10%1.10%1.10%1.35%1.60%1.60%
Real GDP constant prices (base 2008) in € thousands16,466,48416,648,19416,832,16717,018,43017,249,39417,526,71117,808,750
Total Employed (in thousands of people)335.52336.69337.88338.24339.43341.48343.53
Apparent productivity: Real GDP/Employed49,07849,44649,81750,31550,81851,32651,840
Interannual variation Productivity0.50%0.75%0.75%1.00%1.00%1.00%1.00%
Average salary (euros)18,97719,26219,55119,84420,14220,44420,750
Interannual variation of the average salary1.00%1.50%1.50%1.50%1.50%1.50%1.50%

Appendix B. Socioeconomic Impact. Estimation of Effects

This model provides an approximation to the direct, indirect and induced effects of an exogenous demand shock of the type contemplated in this study, triggered by investments in the railway corridor and in the regional logistics system.

Appendix B.1. Direct Effects

For the construction stage, direct effect will be formed by all those investments that are made for the adaptation of the railway corridor. For the exploitation phase, it will be necessary to identify the total billing amounts generated by the different agents linked to the logistics corridor, which must include, not only the strict activities of freight transport, but all those services generated around the railway axis (maintenance of infrastructure, hotel services, training, etc.).
For each one of the contemplated branches of activity, it’s been calculated the structure of purchases of intermediate consumption for each unit produced Ai,j, as well as the coefficients of Value Added (CVA) and Employment (CE), enclosing the unit needs for employment.
The values of the direct effect on value added and employment, as well as the impact vectors for the determination of indirect effects, are obtained starting from these coefficients and applying them to the estimated billing amounts for the different “s” activities linked to the railway corridor in year “t”. That is, D V A s t = I n v o i c i n g s t × CVAs, E M P s t = I n v o i c i n g s t × CEs and IMP_Vsi,j = I n v o i c i n g s t × Ai,j.
In addition to the direct effects, it is also necessary to quantify the indirect effects that are generated with the purchases of goods and services, as well as the income generated, both from direct and indirect suppliers, that is, the drag effect on the rest of the economic system (induced effects).

Appendix B.2. Indirect Effects

Pulido [49] describes the way to obtain the total effect generated on the set of the economic system X, measured in terms of production. Starts from an initial demand W, which is called the impact vector, and from the corresponding matrices of technical coefficients A, that collect the unitary requirements of intermediate consumption for each unit produced (Equation (A1)).
X = [ I A ] 1 × W
In this way, it will be necessary to generate an impact vector W10x1 that reflects the direct demand received by each of the 10 branches of activity considered and in each of the years included in the analysis (2015–2022).
This impact vector W will be constituted by the direct demand received by each of the branches in the corresponding year expressed in homogeneous monetary units. In our case, thousands of euros in 2008, so the values in current terms of each year will have to be applied the value of the GDP deflator in base 2008 in that year. Its value is obtained from Equation (A2).
Wt2008 = Wt/([DefGDP]t2008) × 100
These impact vectors during the construction stage (2015–2017) will be constituted by the total purchases made to each sector as a result of the investment executed. In the operation stage (2018–2022) they will be constituted by the purchases of goods and intermediate services performed by the different agents to the rest of the productive system, calculated from the technical coefficients Ai,j. This matrix of intermediate consumption by sectors is attached in Table A2.
Table A2. Intermediate consumption matrix by sector.
Table A2. Intermediate consumption matrix by sector.
12345678910
Agriculture, forestry and fishing0.10301990.10863590.00019960.0087910.0000082380.00041960.0001570.00033570.00033490.0037791
Extractive industries; manufacturing industry; electric power supply0.2155150.21569950.18329460.12641620.05643390.00873290.02323690.09482470.03173640.0660325
Construction0.01490050.01556630.27533250.01051620.006430.00774810.08800720.01218350.01201420.0116284
Wholesale and Retail; repair of motor vehicles and motorcycles; transportation and storage; hotel services0.04026210.08911090.05321760.12283130.03056430.00809980.01625380.03423510.0351170.039765
Information and communications0.00091830.00635370.00374360.00605130.08758930.00483140.00195220.01765370.00883130.0045536
Financial and insurance activities0.0143290.00706790.01610520.01291750.00601880.0346430.04849910.0134130.00582840.0039154
Real estate activities0.00354860.01827090.01543480.05851130.01943040.01392560.00962680.0351360.01192750.0424071
Professional, scientific and technical activities; administrative activities and auxiliary services0.0120510.04522450.02155290.01899140.05838130.02261630.04825060.0345740.01913860.0218412
Public administration and defense; compulsory social security; education; health and social services activities0.00282490.00668210.0002420.00653620.00542570.00181350.00301170.00368740.05543650.0059839
Artistic, recreational activities and entertainment; repair of household items and other services0.00016120.00209670.00010170.00594190.00363710.00093610.0002040.03663840.01077190.1806384
If the billing estimated by activity “s” in period “t” is denominated as B A C s t , the corresponding impact vector W s t would be calculated multiplying that billing by the technical coefficients of the sector s and expressed in homogeneous monetary units; that is, thousands of euros in 2008, according to the expression of Equation (A3).
W t s = [ B A C t s D e f G D P t 2008 × 100 ] × a i , s
Bearing in mind that there may be different activities linked to the operation of the railway corridor, the final impact vector for each period “t”, will be formed by the sum of the impact vectors originated in each activity “s”, according to the expression of Equation (A4).
W t = s W t s
From the values obtained in terms of the total production generated in each year “t” (Xt) the added value generated can be estimated, as well as the total employment linked to this production according to the expressions GVAt = Xt × CVA and EMPt = Xt × CE, where the CVA (value added coefficients), and the CE (employment coefficients) are used.
The added value coefficients are calculated as the quotient between the total production and the added value of each branch of activity and represent the amount of added value generated by each branch of activity for each unit produced. On the other hand, the employment coefficients are calculated by quotient between the total number of jobs used by each productive branch and the total production of that branch and represent the unit labor requirements for each unit produced. Tables available refer to the year 2008, so the employment coefficients have been slightly modified to collect the possible productivity gains that are generated over time, and they are collected through apparent productivity per employee, as described in Equation (A5).
C E t = C E t 1 Δ P r o d u c t i v i t y t = C E t 1 P r o d u c t i v i t y t / P r o d u c t i v i t y t 1
Based on the estimated employment levels, the salary incomes generated SIs are calculated by multiplying that employment by the average salary in each of the ASs productive branches, as shown in Equations (A6) and (A7). Similar to the employment coefficients, average salaries by sectors have been revitalized using the projections made for the regional average salary (RAS):
S I t s = E M P t s × A S t s
A S t s = A S t 1 s × Δ R A S t = A S t 1 s × ( R A S t R A S t 1 )

Appendix B.3. Induced Effects

The economic impact analysis in addition to the so-called direct and indirect effects, generated “Induced Effects” as a result of the economic transactions originated in the analyzed activity. These effects are caused by the income generated from the previous ones.
Monchon and Beker [50] identify two types of induced effects, those strictly denominated as “Income Effects” and those known as “Fiscal Effects”. Income effects include all production, added value and employment, which is generated from direct and indirect wage income and its subsequent application to consumption. The fiscal effects, collect all the tax collection that would be generated from production, added value and employment, both direct, indirect and induced.
For the determination of the income effects, it would be taken from the total income generated (ISt) to which it would be deducted, both direct taxes ( T A X d t ) and social contributions (CSt), in order to obtain a figure of disposable income IDt according to the expression IDt = ISt T A X d t   CSt. To this disposable income, the average propensity to consumption (apc) would be applied, obtaining, finally, a total consumption figure linked to said direct and indirect jobs, according to the expression CONt = IDt × apc. From this CONt consumption figure and using the average sectoral distribution structure obtained from the TIOs, a new induced impact vector Wit would be calculated. A similar treatment is applied to the calculation of indirect effects, including, once again, a correction by price evolution collected through the GDP deflator, and using the structure of final consumption to each branch “i” derived from table ci, composed of the quotients of the household consumption vector in each branch of activity “i” among the total, according to described in Equation (A8).
W I t = [ C O N t D e f G D P t 2008 × 100 ] × c i
In the case of tax effects, the main tax figures (direct and indirect taxes) linked to production, added value and employment, both direct and indirect, are identified and, the total tax induced would be obtained through the application of average tax rates.
These tax figures include both the indirect tax burden (VAT and special taxes) calculated as a percentage of GDP, as well as social contributions and direct personal income taxes (PIT) and companies (Corporation Tax). Table A3 includes the tax rates with which the impact model operates.
Table A3. Tax rates applied in the impact model.
Table A3. Tax rates applied in the impact model.
Average rate PIT% over Income14%
Average rate Social contributions% over Salaries6%
Medium type Business surplus% over Surplus19%
Average type Indirect Taxes% over GDP10%
Marginal propensity to consumption% over Available income70%
Real tax rate (PIT + Quotes)20%

References

  1. European Comission. White Paper. Roadmap to a Single European Transport Area–Towards a Competitive and Resource Efficient Transport System, 2011. Available online: https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex%3A52011DC0144 (accessed on 11 August 2021).
  2. Blaskovic, J.; Niksic, M.; Kovacevic, D. Railway development from the aspect of environmental protection. Promet-Traffic Transp. 2002, 14, 72–80. Available online: http://traffic.fpz.hr/index.php/PROMTT/article/view/1037 (accessed on 11 August 2021).
  3. Givoni, M. Development and Impact of the Modern High-speed Train: A Review. Transp. Rev. 2006, 26, 593–611. [Google Scholar] [CrossRef]
  4. Armstrong, J.; Preston, J.; Hood, I. Adapting railways to provide resilience and sustainability. In Proceedings of the Institution of Civil Engineers-Engineering Sustainability; Thomas Telford Ltd.: London, UK, 2016; Volume 170, pp. 225–234. [Google Scholar]
  5. Martín, J.C.; Nombela, G. Impacto de los nuevos trenes AVE sobre la movilidad. Rev. Econ. Apl. 2008, 16, 5–23. Available online: https://www.redalyc.org/pdf/969/96915824001.pdf (accessed on 11 August 2021).
  6. Cascetta, E.; Papola, A.; Pagliara, F.; Marzano, V. Analysis of mobility impacts of the high speed Rome–Naples rail link using withinday dynamic mode service choice models. J. Transp. Geogr. 2011, 19, 635–643. [Google Scholar] [CrossRef]
  7. Bellet, C.; Alonso, P.; Casellas, A. Infraestructuras de transporte y territorio. Los efectos estructurantes de la llegada del tren de alta velocidad en España. Boletín de la Asociación de Geógrafos Españoles 2010, 52, 143–163. Available online: http://hdl.handle.net/10459.1/46533 (accessed on 11 August 2021).
  8. Bellet-Sanfeliu, C.; Gutiérrez-Palomero, A. Ciudad y ferrocarril en la España del siglo XXI. La integración de la alta velocidad ferroviaria en el medio urbano. Boletín de la Asociación de Geógrafos Españoles 2011, 55, 251–279. Available online: https://dialnet.unirioja.es/descarga/articulo/3606926/1.pdf (accessed on 11 August 2021).
  9. Monzón, A.; Ortega, E.; López, E. Evaluación de los Impactos Territoriales y Económicos de Planes de Infraestructuras del Transporte. PEIT 2005-2020. In Cuadernos de Investigación del Transporte; Edición Iarriccio Artes Gráficas: Madrid, Spain, 2010; Volume 10, ISBN 978-84-96398-11-5. [Google Scholar]
  10. Consultrans. Estudio Socioeconómico del Sector del Transporte por Carretera, Ministerio de Fomento, Madrid, 2005. Available online: https://www.fomento.gob.es/nr/rdonlyres/0d1a2a6a-7b07-483c-ba0e-97121413e7b8/16838/estudiosocioeconomicosectortteporcarreterav2.pdf (accessed on 11 August 2021).
  11. Junta de Andalucía. Consejería de Obras Públicas y Transportes. Gestión de Infraestructuras de Andalucía, S.A. GIASA. La A-92 y el Crecimiento Económico de Andalucía, Sevilla, 2002. Available online: http://www.aopandalucia.es/inetfiles/publicaciones_agencia/Libros_y_folletos/2002_Libro_La-A92_y_el_crecimiento_en_Andalucia/A_92_crecimiento.pdf (accessed on 11 August 2021).
  12. De Rus, G. Análisis Coste-Beneficio: Evaluación Económica de Políticas y Proyectos de Inversión, 3rd ed.; Ariel: Barcelona, Spain, 2008; ISBN 9788434445475. [Google Scholar]
  13. De Rus, G.; Román, C. Análisis económico de la línea de alta velocidad Madrid-Barcelona. Rev. Econ. Apl. 2006, 14, 35–79. Available online: http://www.revecap.com/revista/numeros/42/pdf/rus_roman.pdf (accessed on 11 August 2021).
  14. Sánchez-Ollero, J.L.; García-Pozo, A.; Marchante-Mera, A.J. Una aproximación al impacto socioeconómico de alta velocidad ferroviaria en Andalucía. Boletín de la Asociación de Geógrafos Españoles 2014, 64, 341–356. Available online: http://bage.age-geografia.es/ojs/index.php/bage/article/download/1701/1618 (accessed on 11 August 2021). [CrossRef]
  15. Hoyos, D.; Bel, G. Evaluación económica del proyecto de la Y vasca. Cuad. Trab./Lan-Koad. Hegoa 2016, 71, 17–25. Available online: http://hdl.handle.net/2445/105986 (accessed on 11 August 2021).
  16. Andreoli, D.; Goodchild, A.; Jessup, E. Estimating truck trips with product specific data: A disruption case study in Washington potatoes. Transp. Lett. 2012, 4, 153–166. [Google Scholar] [CrossRef]
  17. Outwater, M.; Smith, C.; Wies, K.; Yoder, S.; Sana, B.; Chen, J. Tour based and supply chain modeling for freight: Integrated model demonstration in Chicago. Transp. Lett. 2013, 5, 55–66. [Google Scholar] [CrossRef]
  18. Samimi, A.; Mohammadian, A.; Kawamura, K.; Pourabdollahi, Z. An activity-based freight mode choice microsimulation model. Transp. Lett. 2014, 6, 142–151. [Google Scholar] [CrossRef]
  19. Kontelj, M.; Jakomin, I. Transport Modelling of Freight Flows in accordance with Investments: Case Study of Slovenian Railways. Promet-Traffic Transp. 2014, 26, 29–436. [Google Scholar] [CrossRef]
  20. Camisón, C.; Fabra, E.; Forés, B.; Muro, J.D.; Prado, J.; Puig, A.; Villar, A. Estudio del Impacto de las Inversiones del Corredor Ferroviario Mediterráneo en la Comunidad Valenciana, Asociación Valenciana de Empresarios. Valencia, 2011. Available online: http://www.ave.org.es/adjuntos/documentos_ave/impacto_economico_inversiones_corredor_cv.pdf (accessed on 11 August 2021).
  21. Camisón, C.; Fabra, E.; Forés, B.; Moreno, J.J.; Muro, J.D.; Prado, J.; Puig, A. Efectos del Corredor Mediterráneo Sobre la Competitividad de la Economía de la Comunidad Valenciana, Asociación Valenciana de Empresarios. Valencia, 2012. Available online: http://www.ave.org.es/adjuntos/documentos_ave/efectos_corredor_competitividad_economia_cv.pdf (accessed on 11 August 2021).
  22. Instituto de Estadística de Extremadura. Encuesta de Población Activa Primer Trimestre 2017; Junta de Extremadura: Badajoz, Spain, 2017; Available online: https://ciudadano.gobex.es/web/ieex/tablas/-/tabla/ficha/9300834 (accessed on 11 August 2021).
  23. Instituto Nacional de Estadística. Enfoque funcional. PIB y Sus Componentes. Contabilidad Regional 2000–2017, Madrid, Spain, 2018. Available online: http://www.ine.es/dyngs/INEbase/es/operacion.htm?c=Estadistica_C&cid=1254736167628&menu=resultados&idp=1254735576581 (accessed on 11 August 2021).
  24. European Comission. Regulation (EU) Nº 1316/2013 of The European Parliament and of the Council of 11 December 2013 Establishing the Connecting Europe Facility, Amending Regulation (EU) Nº 913/2010 and Repealing Regulations (EC) Nº 680/2007 and (EC) Nº 67/2010. 2013. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A32013R1316 (accessed on 11 August 2021).
  25. APSA (Administração dos Portos de Sines e do Algarve). Estatística do Porto de Sines 2016–2015. 2017. Available online: http://www.apsinesalgarve.pt/media/2446/estat%C3%ADstica-do-porto-de-sines-jandez20162015.pdf (accessed on 11 August 2021).
  26. Pricewaterhouse Coopers, S.L. Estudios de Impacto Económico. Cómo Valorar la Repercusión y el Retorno de Iniciativas e Inversiones Públicas. 2012. Available online: https://www.pwc.es/es/sector-publico/assets/brochure-estudios-impacto-economico.pdf (accessed on 11 August 2021).
  27. Pulido, A.; Fontela, E. Análisis Input-Output Modelos, Datos y Aplicaciones; Pirámide: Madrid, Spain, 1993; ISBN 9788436807561. [Google Scholar]
  28. Cardenete, M.; Sancho, F. Análisis del impacto económico sobre sectores industriales a partir de matrices de contabilidad social. Econ. Ind. 2006, 359, 211–219. Available online: https://eco.mdp.edu.ar/cendocu/repositorio/01041.pdf (accessed on 11 August 2021).
  29. Miller, R.E.; Blair, P.D. Input-Output Analysis: Foundations and Extensions, 2nd ed.; Cambridge University Press: New York, NY, USA, 2009; ISBN 978-0-521-51713-3. [Google Scholar]
  30. Macorra, L.F.; Prudencio, C.A. Tablas Input-Output y Contabilidad Regional de Extremadura 1990; Consejería de Economía y Hacienda. Junta de Extremadura: Badajoz, Spain, 1995; Volume 1, ISBN 84-7671-292-8. Volume 2, ISBN 84-7671-293-6. [Google Scholar]
  31. De Miguel, F.J.; Manresa, A.; Ramajo, J. Matriz de contabilidad social y multiplicadores contables: Una aplicación para Extremadura. Estadística Española 1998, 40, 195–232. Available online: https://dehesa.unex.es/bitstream/10662/340/1/TDUEX_8477236763.pdf (accessed on 11 August 2021).
  32. De Miguel, F.J.; Manresa, A. Un Modelo Input-Output de Precios Aplicado a la Economía Extremeña. Documento de Trabajo, 19. Fundación Centro de Estudios Andaluces. 2004. Available online: https://www.researchgate.net/publication/4927747_Un_modelo_input-output_de_precios_aplicado_a_la_economia_extremena (accessed on 11 August 2021).
  33. Servicio de Estadística de Castilla-La Mancha. Marco Input-Output Actualizado a 2008, Gobierno de Casilla la Mancha Toledo, España, 2008. Available online: http://difusion.ies.jccm.es/wds/ReportFolders/reportFolders.aspx (accessed on 11 August 2021).
  34. Leontief, W. An Alternative to Aggregation in Input-Output Analysis. Input-Output Economics, 2nd ed.; Oxford University Press: New York, NY, USA, 1986; pp. 41–54. ISBN 0-19-503525-9. [Google Scholar]
  35. Tarancón, M.A. Análisis Input-Output: Enfoques y técnicas. In Técnicas de Análisis Económico Input-Output; Club Universitario: Alicante, Spain, 2003; pp. 37–48. ISBN 84-8454-255-6. Available online: https://www.researchgate.net/publication/299341017_Tecnicas_de_Analisis_Economico_Input-Output (accessed on 11 August 2021).
  36. Coloma, J.F.; García, M. Adaptation of Conventional Railway Lines to Upgraded Freight Rail Corridor. Application to the Manchegan-Extremaduran Corridor. Transp. Res. Procedia 2016, 18, 148–155. [Google Scholar] [CrossRef]
  37. ADIF (Administrador de Infraestructuras Ferroviarias). Cobertura de la Red Convencional. Informe de Fiscalización de la Financiación de las Infraestructuras Ferroviarias en el Periodo 2011–2013; Ministerio de Fomento, Gobierno de España: Madrid, Spain, 2015; p. 73. Available online: http://www.congreso.es/l10p/e19/e_0194066_n_000_m.pdf (accessed on 11 August 2021).
  38. Coloma, J.F.; García, M. Freight Potential Demand of Extremaduran Cross-Border Rail Corridor. Dyna 2017, 92, 507–512. Available online: https://recyt.fecyt.es/index.php/DY/article/view/59831 (accessed on 11 August 2021).
  39. Ministerio de Fomento. Acotram 2.4. Asistente para el Cálculo de Costes del Transporte de Mercancías por Carretera, Gobierno de España, 2015. Available online: https://www.fomento.gob.es/mfom/lang_castellano/direcciones_generales/transporte_terrestre/servicios_transportista/descarga_software/acotram.htm (accessed on 11 August 2021).
  40. Barro, R.J. Macroeconomics: A Modern Approach; Thomson Learning, Inc.: Mason, OH, USA, 2007; ISBN 978-0-324-17810-4. [Google Scholar]
  41. De Rus, G. The Economic Effects of High Speed Rail Investment. Discussion Paper nº 2008–2016; Joint Transport Research Centre: Paris, France, 2008. [Google Scholar] [CrossRef]
  42. Boscá, J.; Escribá, J.; Murgui, M.J. Efectos Macroeconómicos de las Inversiones en Infraestructuras Públicas; Universidad de Valencia: Valencia, Spain, 2004; Available online: https://acortar.link/8TjR7r (accessed on 11 August 2021).
  43. Wu, X.; Hu, X.; Yin, X.; Peng, Y.; Pickert, V. Convex programming improved online power management in a range extended fuel cell electric truck. J. Power Sources 2020, 476, 228642. [Google Scholar] [CrossRef]
  44. Guo, Y.; Yu, H.; Zhang, G.; Ma, D.T. Exploring the impacts of travel-implied policy factors on COVID-19 spread within communities based on multi-source data interpretations. Health Place 2021, 69, 102538. [Google Scholar] [CrossRef] [PubMed]
  45. Loske, D. The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics. Transp. Res. Interdiscip. Perspect. 2020, 6, 100165. [Google Scholar] [CrossRef] [PubMed]
  46. Instituto de Estadística de Extremadura. Proyecciones de Población de Extremadura. Gobierno de Extremadura, Badajoz, Spain, 2016. Available online: https://ciudadano.gobex.es/web/ieex/proyecciones-2016-2031 (accessed on 11 August 2021).
  47. Banco de España. Boletín Económico de Diciembre 2016. Informe Trimestral de la Economía Española, Madrid, 2016. Available online: https://acortar.link/EDcAGt (accessed on 11 August 2021).
  48. Instituto Nacional de Estadística. Encuesta de Costes Laborales, Gobierno de España, Madrid, 2017. Available online: http://www.ine.es/dyngs/INEbase/es/categoria.htm?c=Estadistica_P&cid=1254735976596 (accessed on 11 August 2021).
  49. Pulido, A. Input-Output Regional: Posibilidades y Limitaciones. XXII Reunión de Estudios Regionales. Asociación Española de Ciencia Regional, Pamplona, Spain, 1996. Available online: http://www.antoniopulido.es/documentos/con9606.pdf (accessed on 11 August 2021).
  50. Mochón, F.; Beker, V. Crecimiento Económico y el Desarrollo. In Economía: Principios y Aplicaciones, 4th ed.; McGraw-Hill: Santa Fe, Mexico, 2005; pp. 535–564. ISBN 978-970-10-6794-9. [Google Scholar]
Figure 1. Location of Cofemanex.
Figure 1. Location of Cofemanex.
Futuretransp 01 00025 g001
Figure 2. Impact of investments in Production (2015–2022).
Figure 2. Impact of investments in Production (2015–2022).
Futuretransp 01 00025 g002
Figure 3. Impact of investments on GVA (2015–2022).
Figure 3. Impact of investments on GVA (2015–2022).
Futuretransp 01 00025 g003
Figure 4. Impact of investments on employment (2015–2022).
Figure 4. Impact of investments on employment (2015–2022).
Futuretransp 01 00025 g004
Figure 5. Additional generation of employment on basic scenario (2015–2022).
Figure 5. Additional generation of employment on basic scenario (2015–2022).
Futuretransp 01 00025 g005
Table 1. Construction and Commissioning phase. Investment program in the Cofemanex [36].
Table 1. Construction and Commissioning phase. Investment program in the Cofemanex [36].
Cofemanex. Investment 1st Phase. Period 2015–2017
1Renewal of rail track€ 83,216,412.89
2Signaling, security installations and block system€ 18,200,000.00
3Rail track electrification€ 20,372,330.60
4Logistics facilities. Intermodal Logistics Platform in Southwestern Europe€ 24,950,000.00
5Logistics facilities. Railway connections € 14,454,810.52
Total Investment Cofemanex. 1st PHASE€ 161,193,554.01
Table 2. Construction and Commissioning phase. Investment distribution and sectoral allocation of demand.
Table 2. Construction and Commissioning phase. Investment distribution and sectoral allocation of demand.
InvestmentMillion €161.194201520162017
1%45%54%
Sectoral allocation of demandManufacturing activity distribution
Manufactures25%Interior production35%
Civil construction65%Import65%
Studies/assistance8%
Others (planning, insurance, …)2%Transportation (import)8% (*)
(*) Imports will generate returns in the activity of road transport that are estimated at 8% of the costs assigned to them [10] (Consultrans, 2005).
Table 3. Potential freight demand from Cofemanex. Period 2018–2022 [38].
Table 3. Potential freight demand from Cofemanex. Period 2018–2022 [38].
Tons Transported20182019202020212022
Origin-Destination Extremadura1,163,9401,190,7111,220,4781,263,1951,326,355
Transit1,225,5881,253,7761,931,7451,999,3562,099,324
Total2,389,5282,444,4873,152,2233,262,5513,425,679
Table 4. Monetization of the demand for freight transport in Extremadura. Period 2018–2022.
Table 4. Monetization of the demand for freight transport in Extremadura. Period 2018–2022.
Monetization of the Demand
Road Transport
Units20182019202020212022
Thousands t·km139,673142,885146,457151,583159,163
Thousands €68727030720674587831
Table 5. Exploitation Phase. Annual investments planned.
Table 5. Exploitation Phase. Annual investments planned.
20182019202020212022
Maintenance (€ thousands) 98679867986798679867
Construction (track)60%59205920592059205920
Manufactures (facilities)40%39473947394739473947
Conventional network cost33,000€/km-year
Length of the section299km.
Annual cost9867€ thousands
Complementary actions (€ thousands)79,538 20182019202020212022
Line electrification69,528 23,17623,17623,176
Railway access10,010 333733373337
Total year (€ thousands) 0026,51326,51326,513
Monetization of the Demand (€ thousands) 20182019202020212022
68727030720674587831
Related activities (€ thousands) 20182019202020212022
2% Operating amount335338872877884
Table 6. Impact in absolute terms of investments (2015–2022).
Table 6. Impact in absolute terms of investments (2015–2022).
Effects in terms of Production
€ thousands20152016201720182019202020212022
Total4952153,725182,97140,43140,815111,957112,546113,407
Direct198861,69373,41517,27617,43944,98045,23845,621
Indirect195660,71072,24514,49214,59343,84344,00444,243
Induced100831,32237,3128663878423,13423,30423,543
Effects in terms of GVA (income generated)
€ thousands20152016201720182019202020212022
Total261381,12496,56222,55422,79159,05559,41859,949
Direct94329,27934,8429058915921,16121,32221,561
Indirect104432,41438,5738122818323,54223,63923,783
Induced62519,43123,1475374544914,35214,45714,605
Effects in terms of Employment (unitary needs)
Jobs20152016201720182019202020212022
Total6118742210518518131513051300
Direct24738870229230526524524
Indirect23703829171170483478474
Induced14433511117118305303302
Tax purposes
€ thousands20152016201720182019202020212022
Total58818,23821,7045078512613,22013,29713,410
Family Income Tax1564858578713441362358836143651
Companies Income Tax1655129609514411446362436383660
Social contributions6720822480576584153815491565
Indirect taxes1996169734117181734447044964534
Table 7. Impact in relative terms of investments (2015–2022).
Table 7. Impact in relative terms of investments (2015–2022).
Effects in terms of Production
Expressed as % of GDP20152016201720182019202020212022
Total0.03%0.93%1.09%0.24%0.24%0.64%0.63%0.62%
Direct0.01%0.37%0.44%0.10%0.10%0.26%0.25%0.25%
Indirect0.01%0.37%0.43%0.09%0.08%0.25%0.25%0.24%
Induced0.01%0.19%0.22%0.05%0.05%0.13%0.13%0.13%
Effects in terms of GVA (income generated)
% GDP20152016201720182019202020212022
Total0.02%0.49%0.57%0.13%0.13%0.34%0.33%0.33%
Effects in terms of Employment (unitary needs)
% Employees20152016201720182019202020212022
Total0.02%0.56%0.66%0.15%0.15%0.39%0.38%0.38%
Tax purposes
% GDP20152016201720182019202020212022
Total0.004%0.110%0.129%0.030%0.030%0.075%0.074%0.074%
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Coloma, J.F.; García, M.; Pascual-Pedreño, E. Effects of Rail Freight Corridors in Low Population Regions. Future Transp. 2021, 1, 466-485. https://doi.org/10.3390/futuretransp1030025

AMA Style

Coloma JF, García M, Pascual-Pedreño E. Effects of Rail Freight Corridors in Low Population Regions. Future Transportation. 2021; 1(3):466-485. https://doi.org/10.3390/futuretransp1030025

Chicago/Turabian Style

Coloma, Juan Francisco, Marta García, and Eladio Pascual-Pedreño. 2021. "Effects of Rail Freight Corridors in Low Population Regions" Future Transportation 1, no. 3: 466-485. https://doi.org/10.3390/futuretransp1030025

Article Metrics

Back to TopTop