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PROGRAMA DOCTORADO ECONOMÍA APLICADA

Tercer año de seguimiento, curso 2015-2016

DEPARTAMENTO ECONOMÍA APLICADA

PROYECTO

EL SECTOR DEL TRANSPORTE: CONSUMO ENERGÉTICO Y EMISIONES

Lidia Andrés Delgado

Emilio Padilla Rosa

DIRECTOR

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DRIVING FACTORS OF GREENHOUSE GAS EMISSIONS IN THE EU-28

TRANSPORT SECTOR. 1990-2012.

MOTIVATION

ABSTRAT

The aim of this research is to identify the driving factors of greenhouse gas emissions in the UE-

28 transport sector and the contribution of each one of them in their change during the period

1990–2012. The analysis is based on the STIRPAT model which is broadened in order to

investigate in-depth the impact on transport sector emissions caused by changes in the whole

economy and in the activity itself. Therefore, the study takes into account population, economic

activity, transport sector activity and its structural composition (passengers and freight activities,

energy efficiency, modes of transport, and energy sources). The use of panel data econometric

techniques allows to quantify the significance of each factor on emissions, as well as the effect

on them of a change in any key factor. A better knowledge of the key driving forces is crucial for

implementing environmental policies focused on successfully reducing emissions in the transport

sector.

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INTRODUCTION

Greenhouse gas emissions decreased by 19.7% in the EU-28 between 1990 and 2012. All

economic sectors contributed to this reduction with one exception, the transport sector. The

activity revealed a completely different behaviour as its emissions increased by 13.6% –from

785,891.1 to 893,042.9 thousand tonnes1- in the same period. Consequently, the contribution of

the activity to total emissions has increased considerably since 1990, being responsible for

19.6% in 2012. At present, the transport sector is the second most important source of

emissions after the energy sector in the EU.

The upward trend in emissions in the EU-28 transport sector is related to a 23.9% rise in its

energy consumption over the period, achieving a total of 351,967.7 thousands of tonnes of oil

equivalent in 2012, which represented 31.9% of total final energy consumption. These figures

explain the difficulty of diminishing the greenhouse gas emissions in the activity, as emissions

are the result of the volume of energy consumption and the mix of energy sources used in

transportation. Between 1990 and 2007, a scenario of growing activity, energy consumption in

the EU-28 transport sector came to reach an increase of 34.8% and its related emissions of

19.6%.

This trend in transport emissions needs to be reversed so as to satisfy the 2011 Transport White

Paper objective, which consists in reducing by 2050 the activity’s emissions in relation to 1990

by 60% (European Commission, 2011).

Profuse research has studied the role of the transport sector activity in greenhouse gas

emissions, from investigations such as Saboori et al. (2014), who examine the relationship

between emissions and energy use in the transport sector with economic growth, to

investigations more focused on the activity itself: taking it as a whole, paying attention to a

specific mode of transport, or disaggregating it by type of activity (passenger or freight). In

relation to this last research, two main methodologies are used: decomposition analysis, which is

based on the ASIF equation; and econometrics, which is based on the STIRPAT model.

Examples of studies which use decomposition analysis to investigate the emissions in transport

sector are the works of Laksmanan and Han (1997), who studied the underlying factors of the

changes in CO2 emissions in the USA transport sector during the period 1970–1991; Timilsina

and Shresta (2009), who investigated the same issue but referred to a group of selected Asian

countries for the period 1980–2005; Andreoni and Galmarini (2012), who explored the main

factors affecting the CO2 emissions of water and aviation transport activities in Europe between

1 CO2 equivalent emissions of the six gases covered by the Kyoto Protocol, European Commission (2016).

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2001 and 2008; Sobrino and Monzon (2014), who examined the main forces influencing road

transport emissions in Spain from 1990 to 2010; Scholl et al. (1996), who analysed the changes

in CO2 emissions and energy use of passenger transport sector in nine OECD countries

between 1973 and 1992; Steenhof et al. (2006), who studied the determinants of the increasing

greenhouse gas emissions in Canadian’s freight sector between 1990 and 2003 and explored

different scenarios to year 2012; or Fan and Lei (2016), who investigated the factors influencing

transport sector of Beijing between 1995 and 2012.

Among the investigations using econometrics techniques, some examples are the works of

Zhang and Nian (2013), who analysed CO2 emissions in transport sector in China between 1995

and 2010 and paid special attention to regional differences using panel data; Xu and Lin (2015),

who examined CO2 emissions in transport sector during the period 1980-2012 using a dynamic

VAR approach; or Ratanavaraha and Jomnonkwao (2015), who forecasted the CO2 emissions

from energy use in Thailand’s transport sector and their related factors by 2030 using four

different techniques.

With the purpose of achieving a sustainable transport as described in the 2011 Transport White

Paper, the increasing trend on greenhouse gas emissions in the EU-28 transport sector in the

last years needs to be analysed in-depth in order to identify its key driving factors and the impact

of each one of them in its evolution.

In this paper, we focus on identifying the driving factors of greenhouse gas emissions in the EU-

28 transport sector over the period 1990-2012 and on quantifying the effects of a change in any

of them over such emissions using the STIRPAT model, which is based on the IPAT identity.

The IPAT identity, founded on ecological principles (York et al., 2003), states that the

environmental impact (I) is the product of population (P), affluence (A), and technology (T)

(Ehlrich and Holdren, 1971, 1972). This identity has been widely used as a basis for analysing

the effect of economic activity on the environment. However, it is an accounting equation and

does not allow hypothesis testing, additionally, it assumes that the functional relationship

between factors is proportional (York et al., 2003). Due to its limitations, it is the STIRPAT model

proposed by Dietz and Rosa (1997), a reformulation of the IPAT identity into a stochastic model,

the method used in this research. The STIRPAT method overcomes the limitations of the IPAT

identity as it allows estimation and hypothesis testing using econometric techniques. We use an

extended STIRPAT model to identify the driving factors of the activity, where besides population

and affluence, technology has been decomposed into eleven factors so as to obtain more

detailed results focused on transport activity.

This paper is, then, different from prior research as it complements the STIRPAT model by

introducing the structural composition of transport sector by taking into account passenger and

freight activities and, additionally, total energy consumption disaggregated by all modes of

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transport, and by all sources of energy. The objective is to highlight that the effect of the activity

on its emissions relies not only on transport volume but also on its structural composition. In

order to quantify the impact of the different factors identified previously, panel data econometrics

is employed. The main purpose of the analysis is to inform the design of environmental policies

focused on diminishing environmental impacts besides promoting an efficient energy use and

energy savings in the transport sector. As Grazi and van den Bergh (2008) pointed out the

results of the environmental policies aimed at reducing emissions in transport sector depend on

in their effects in the modal split, energy efficiency, fuel type used and transport volume

(passenger-kilometres or tonnes-kilometre). Therefore, both the volume and the structural

composition of transport sector are important to explain the evolution of its emissions and to

design more accurate policies.

Moreover, this paper contributes, in particular, in providing information about the possible results

of the application of the measures suggested in the 2011 Transport White Paper in order to

achieve the objective of reducing the emissions of the transport activity.

An additional contribution of this paper is that the analysis is performed taking into account the

EU-28 as a whole and differentiating by regions (EU-15 and EU East). Besides, this paper differs

from previous researches as it focus the analysis on all greenhouse gas emissions of the

transport sector instead of only CO2 emissions.

DATA

In order to perform the analysis, annual data of the EU-28 countries have been collected from

different sources for the period 1990-2012. Data on greenhouse gas emissions in transport

sector (in Gg of CO2 equivalent) and real GDP (in constant 2005 million USD) are obtained from

UNFCCC, data on population (individuals) and energy consumption (in thousand tonnes of oil

equivalent) from EUROSTAT, and data on passenger-kilometres and tonne-kilometres (both in

gross tonne-kilometres) from the Odyssee database. So as to consider the structural

composition of the energy consumption in the EU-28 transport sector, data on energy

consumption are disaggregated by mode of transport and by source of energy (table 1).

To conduct the analysis, moreover, it is necessary to obtain data of energy intensity in transport

sector. In this research this variable is defined as energy consumption on transport sector per

unit of GDP, as the level of the activity and its evolution depend on the level and evolution of the

other economic sectors.

This research takes into account the whole transport activity, excluding international maritime

bunkers and international aviation, as they cannot be assigned to a country.

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Table 1. Energy consumption by modes of transport and by energy sources in the EU-28 transport sector (thousand TOE). 1990-2012.

Energy consumption Share

1990 2012 Total change

(%) 1990 2012

Total activity 284,182.8 351,967.7 23.9% 100.0% 100.0%

Modes of transport

Aviation 29,628.4 49,137.9 65.8% 10.4% 14.0%

Navigation 6,390.4 4,906.9 -23.2% 2.2% 1.4%

Road 238,020.6 287,248.5 20.7% 83.8% 81.6%

Rail 8,278.6 7,009.3 -15.3% 2.9% 2.0%

Pipelines 194.7 1,484.5 662.5% 0.1% 0.4%

Other 1,669.9 2,180.8 30.6% 0.6% 0.6%

Source of energy

Solid fuels 213.5 9.7 -95.5% 0.1% 0.0%

Petroleum products 278,156.9 329,156.5 18.3% 97.9% 93.5%

Gas 337.7 2,832.4 738.7% 0.1% 0.8%

Renewable energies 18.8 14,466.3 76,848.4% 0.0% 4.1%

Electrical energy 5,455.7 5,503.1 0.9% 1.9% 1.6%

Source: Prepared by the author with data from EUROSTAT (2016)

The statistical description of the variables used in the analysis are shown in table 2.

Table 2. Statistical description of the variables

Variable Mean Std. Dev. Min Max

-------------------------------------------------------------------

GHG 32317.85 46330.45 349.4967 187062.1

Population 1.75e+07 2.21e+07 352430 8.25e+07

Per capita GDP .0231114 .0160943 .0023458 .0884178

Energy intensity .0404269 .0184087 .0184933 .1024503

PKM 217.8131 299.0942 4.4 1103.53

TKM 89.34148 119.752 .83 637.31

% Road .8256987 .0758432 .5255048 .9733585

% Rail .0307442 .0279337 0 .164859

% Aviation .1163119 .0862229 0 .4750266

% Navigation .0152930 .0210313 0 .1459083

% Pipelines .0076593 .0286657 0 .2717684

% Oil products .9738079 .022704 .8863409 1

% Electricity .0174560 .0164623 0 .0996536

% Renewable energies .0085311 .0151462 0 .0851297

% Coal .0002043 .0013428 0 .0236173

-------------------------------------------------------------------

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METHODOLOGY

The STIRPAT model for transport sector

With the aim of identifying the driving factors of greenhouse gas emissions in the EU-28

transport sector and quantifying its impacts on them, an extended version of the STIRPAT model

is used.

The STIRPAT model is a reformulation of the IPAT identity into a stochastic model, as follows:

where I is the environmental impact, is a constant, P is the population, A is the affluence –per

capita activity-, T is the technology –impact per unit of activity-, is the error term and βi are the

estimated parameters. All variables are taken in log form, so that βi can be interpreted as

“ecological elasticities” (York et al., 2013). The ecological elasticity is referred to the sensitivity of

environmental impacts to a change in any driving factor.

In our case the STIRPAT model disaggregates T in order to take account not only energy

intensity but also the structural composition of transport sector in terms of activity –passenger

and freight activities- and of energy. This last is possible by introducing into the model the share

on energy consumption of the activity of each energy source, on one hand, and of each mode of

transport, on the other hand. Therefore, it is stressed that the effect on emissions of energy

consumption in transport sector depends on both volume and composition.

Then, the model takes the next form:

where i 1,…,28; t 1990,…,2012; j 1,…,5; and k 1,…,5.

GHGi,t are the greenhouse gas emissions in transport sector, Pi,t is population; GDPi,t is per capita

real GDP, EIi,t is energy intensity which is measured as energy consumption on transport sector

related to real GDP, PKM is passenger transport activity measured as passenger-kilometres,

TKM is freight activity and it is measured in tonne-kilometres. All these variables are taken in log

form which implies that the estimated coefficients, β, denote the ecological elasticity of each

driving factor with respect to greenhouse gas emissions. The unobserved country-specific term

αi collects all fixed factors which characterize each country and are time invariant. Mj is the share

of modal transport j in total energy consumption of transport sector, where J = 5 given that the

modes of transport are road, rail, aviation, navigation and pipelines, with 1, , . In

like manner, Sk is the share of source of energy k in total energy consumption of transport sector,

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where k = 5 since the sources of energy of the activity are oil, electricity, renewable, coal and

gas, with 1, , .

Table 3. Definition of data used in the model

One variable Mj and one variable Sk are omitted in estimating the above equation so as to avoid

multicollinearity problems. For mode of transport, road transport is the modal transport which is

omitted, meaning that remaining parameter estimates of are referred to the energy

consumption share of road transport. In the same way, the source of energy omitted is oil, which

means that parameter estimates are referred to the oil energy consumption share. Finally,

is the error term.

Estimation method

Given the unobserved country-specific heterogeneity, the use of Fixed Effect Model is advised to

estimate the panel data model.

Moreover, in order to detect problems of autocorrelation, heteroskedasticity or cross-sectional

dependence, several recommended test are applied. These tests will give information about which

must be the consistent estimation method if some of the classic assumptions were violated. The

Wooldridge test for serial correlation is used to test for autocorrelation, i.e., if the errors of each

country are or not temporally correlated (first-order autocorrelation). The modified Wald test for

heteroskedasticity is used to test for heteroskedasticity, i.e., if the variance of the errors of each

Variable Units of measure Definition

GHG Gg of CO2 equivalent

Total greenhouse gas emissions in transport sector

P Number of people Population

GDP constant 2005 million USD

Real per capita Gross Domestic Product

EI Thousand TOE per million USD

Energy intensity defined as total energy consumption of transport sector divided by real GDP

PKM Gross tonne-kilometres

Passenger activity measured in passenger-kilometres

TKM Gross tonne-kilometres

Freight activity measured in tonne-kilometres

Mj Percent Ratio of mode of transport j in total energy consumption of transport sector

Sk Percent Ratio of source of energy k in total energy consumption of transport sector

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country is or not constant. And the Pesaran CD test is used to test for contemporaneous correlation,

i.e., if the residuals are or not correlated across countries.

In order to solve the detected problems and due to the characteristics of our panel data, where N

> T, the Panel Corrected Standard Errors Model (PCSE) is used to estimate the above equation.

The PCSE model can be applied to models with problems of heteroskedasticity and/or

contemporaneous correlation, with or not autocorrelation.

The analysis is performed considering the EU-28 as a whole and differentiating by regions. The

regions, which are the EU-15 and the EU-East, have been defined considering its economic

development level and its geographic position. These groupings allows to enrich the analysis.

EMPIRICAL RESULTS

The first step is to check if fixed effects are suitable or not for estimating the panel data or, on

the contrary, if it would be better to estimate them using the random effects model. The use of

the Hausman specification test (table 4), so as to compare fixed and random effects models,

determines that there are fixed effects, given that the individual effects are correlated with a

regressor in the model. Then, the fixed effects model is chosen in order to estimate the extended

STIRPAT model for transport sector.

Table 4. Hausman test ----------------------------------

Chi square stat p-value

38.56 0.0001

-----------------------------------

The second step consists on testing if some assumptions –homoscedasticity, cross-sectional

independence and non-autocorrelation- are or not violated. The results are shown in table 5. The

null hypothesis of the modified Wald test for group-wise heteroskedasticity in fixed effects

regression model is of no heteroskedasticity, the results reject the null hypothesis and we

conclude there is heteroskedasticity. The null hypothesis of the Pesaran’s test of cross sectional

independence is sectional independence, the results fail to reject the null hypothesis. Finally, the

null hypothesis of no first-order autocorrelation of the Wooldridge test for autocorrelation in panel

data is rejected and we conclude there is serial correlation.

Table 5. Group-wise heteroskedasticity, cross-sectional independence and autocorrelation test -------------------------------

Wald stat p-value

4132.23 0.0000

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CD stat p-value

-0.312 1.2449

F stat p-value

9.886 0.0041

-------------------------------

The above results plus the fact that our panel data are such that N > T leads us to estimate the

extended STIRPAT model for transport sector using the Panel Corrected Standard Errors Model

(PCSE), which can handle heteroskedasticity and autocorrelation problems. The results of

estimating the model using fixed effects and PCSE are shown in table 6.

The results point out that the ecological elasticities of the next driving factors: population, per

capita GDP, energy intensity and freight activity are significant and positive, which means that

an increase of any of these factors would lead to an increase in greenhouse gas emissions in

EU-28 transport sector. It is worth to note that the ecological elasticity of population is the only

one that is higher than one, i.e. an increase of one percent in population would lead to an

increase higher than one percent in greenhouse gas emissions in the EU-28 transport sector.

Meaning that population is the driving factor with higher impact on emissions. On the other hand,

passenger activity seems not to affect transport sector emissions.

Table 6. Panel estimation of greenhouse gas emissions of the EU-28 transport sector --------------------------------------------

FE PCSE

--------------------------------------------

Constant -0.9887 -1.2228

(0.5699) (0.6702)

Population 1.0673*** 1.0620***

(0.0347) (0.0415)

Per capita GDP 0.8769*** 0.8385***

(0.0202) (0.0306)

Energy intensity 0.9322*** 0.8927***

(0.0164) (0.0263)

PKM 0.0289* 0.0373

(0.0147) (0.0222)

TKM 0.0514*** 0.0619***

(0.0093) (0.0113)

% Rail -0.5577** -0.9172***

(0.2014) (0.2178)

% Aviation -1.0487*** -1.0319***

(0.0628) (0.0698)

% Navigation -0.1106 -0.0517

(0.1595) (0.1243)

% Pipelines -0.9756*** -0.7520***

(0.0569) (0.1639)

% Electricity 0.0759 0.6523

(0.3186) (0.6527)

% Renewable -1.0894*** -1.0432***

(0.0915) (0.1224)

% Coal -0.8414 -0.5735

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(0.9069) (0.8810)

Fixed effects Yes Yes

Year No No

R2 0.9801 0.9997 N 523 523

Groups 27 27

--------------------------------------------

Note: Standard errors in parentheses

* p<0.05, ** p<0.01, *** p<0.001

With respect to the structural composition of the EU-28 transport sector in terms of energy the

results indicate that a higher share of rail, aviation or pipelines2 instead of road transport would

result in a decrease of emissions in the EU-28 transport sector. Although at first sight the

aviation result could be surprising, i.e. the substitution of road for aviation transport would result

in a decrease of emissions even though aviation is the most pollutant mode of transport, it is

really not. Given the energy consumption of transport sector switching from road to aviation

could diminish pollution since kerosene pollutes less than diesel (the CO2 emission factor

measured in tCO2/TJ of Kerosene is 71.9 and of diesel 74.13). Navigation, by the way, seems

not to affect transport sector emissions. Finally, and related to sources of energy, the results

show that the substitution of oil products by renewable energies would produce a decrease in

emissions, whereas the replacement of oil products by electricity or coal would not affect

transport emissions.

The results related to regional analysis, EU-15 and EU-East, are shown in table 7.

Similitudes and differences in the driving factors of greenhouse gas emissions on transport

sector are found in the case of the EU-15 with respect to the EU-East. Regarding to similarities,

it can be said that the driving factors whose growth leads to a negative impact on transport

emissions in both regions are population, per capita GDP, energy intensity and freight transport.

Among these driving factors, the main difference between the two regions analysed is found in

population, given that its ecological elasticity is lower than one for the EU-15 but higher than one

for EU-East. This result means that an increase of population in the EU-East would have a

relative higher negative impact in transport emissions than this same increase of population in

the EU-15. On the other hand, passenger activity is statistically significant only in the EU-15,

where an increase in the activity of passengers produce a negative impact in greenhouse

emissions of transport sector. In the EU-East passenger activity seems not to affect transport

emissions.

The results related to energy composition by modes of transport indicate that a substitution of

road transport by aviation or pipelines would result in a decrease in greenhouse gas emissions

2 It is evident that pipelines transport is related to gas transport. Thus, this result means that is better for

environment transport gas through pipelines than road transport. 3 España, Informe Inventarios GEI 1990-2009 (2011)

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of the EU transport sector. Meanwhile, the replacement of road transport by rail would only

reduce transport emissions in the EU-East, given that the result for the EU-15 is statistically not

significant. The outcomes for navigation are statistically no significant in both regions, which

means that the substitution of road transport by navigation would not have any impact on

transport emissions in the EU. Finally, the outcomes of energy composition by sources of energy

show that a change of oil products by renewable energies would result in a reduction of transport

emissions in both regions, although its impact would be higher in the EU-15 than in the EU-East.

On the other hand, the replacement of oil products by electricity would only have a positive

impact on transport emissions in the EU-15 as they would be reduced significantly in this region.

For the EU-East the change of oil products by electricity is statistically no significant. With

respect to the change of oil products by coal –the most pollutant source of energy in transport

sector- the results are statistically not significant.

Table 7. PCSE model estimation of greenhouse gas emissions of the EU-15 and the EU-East transport sector --------------------------------------------

EU-15 EU-East

--------------------------------------------

Constant 0.6638 -3.7229 (0.6651) (2.0992)

Population 0.9771*** 1.2146***

(0.0403) (0.1342)

Per capita GDP 0.9054*** 0.8445***

(0.0227) (0.0505)

Energy intensity 0.9447*** 0.8733***

(0.0184) (0.0403)

PKM 0.0533** 0.0172

(0.0181) (0.0401)

TKM 0.0228* 0.0744**

(0.0093) (0.0229)

% Rail 0.2099 -1.0950***

(0.3909) (0.2554)

% Aviation -1.0126*** -1.0462***

(0.0580) (0.1695)

% Navigation -0.0199 -0.0846

(0.1386) (0.2813)

% Pipelines -0.6326* -0.7607***

(0.2757) (0.1550)

% Electricity -1.3218*** 1.0293

(0.4012) (0.7899)

% Renewable -1.0637*** -0.7939*

(0.0912) (0.3186)

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% Coal 4.6015 -0.0614

(12.2600) (0.9149)

Fixed effects Yes Yes

Year No No

R2 0.9999 0.9988 N 334 189

Groups 16 11

--------------------------------------------

Note: Standard errors in parentheses

* p<0.05, ** p<0.01, *** p<0.001

CONCLUSIONS

The greenhouse gas emissions of the EU-28 transport sector increased by 13.6% in the period

1990-2012 being, at present, the second largest source of emissions after the energy sector.

This trend in transport emissions needs to be reversed so as to satisfy the 2011 Transport White

Paper objective, which consists in reducing by 2050 the activity’s emissions in relation to 1990

by 60% (European Commission, 2011).

Taking into account the above, the identification of the driving factors of greenhouse gas

emissions in the UE-28 transport sector and the contribution of each one of them in their change

during the period 1990–2012 is crucial for implementing environmental policies focused on

successfully reducing emissions in the activity. Additionally, the design of environmental policies

focused on diminishing environmental impacts would promote an efficient energy use and

energy savings in the transport sector.

The use of an extended STIRPAT model allows to identify the driving factors of the transport

sector emissions. Thus, the analysis includes as driving factors: population, economic activity

and transport sector activity. In particular, transport activity takes into account its volume, given

that passenger and freight activities are counted in, its energy intensity and its structural

composition, given that total energy consumption of the activity is disaggregated by all modes of

transport and by all sources of energy. In the same way, the use of panel data econometric

techniques allows to quantify the significance of each factor on emissions, as well as the effect

on them of a change in any key factor.

The results obtained for the EU-28 transport sector show that a change in population, in per

capita GDP, in energy intensity and in freight activity would result in a change of the same sign

in greenhouse gas emissions. In the same way, a change in the energy consumption of road

transport towards any other alternative mode of transport would favour a reduction in emissions,

although navigation apparently does not result in a significant effect. Finally, a change in the use

of oil products towards any other alternative source of energy, with the exception of electricity,

would favour a reduction in emissions. Here, two important aspects must be stressed. First,

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although the substitution of oil products by electricity does not have the expected result, it is

worth to note that it is not statistically significant. Second, the replacement of oil products by coal

–the most pollutant source of energy in transport sector- it is not statistically significant either.

The results by regions show that a change in population, per capita GDP, energy intensity and

freight activity would produce a change of the same sign in greenhouse gas emissions

independently of the region analysed. Likewise, the substitution of road transport by aviation

and/or pipelines would lead to a decrease in emissions independently of the region investigated.

It is important to emphasise that the surprising result related to aviation- the most pollutant mode

of transport- it is not, because, given the energy consumption of transport sector switching from

road to aviation could diminish pollution since kerosene pollutes less than diesel. Finally, the

replacement of oil products by renewable energies would mean a reduction of transport

emissions independently of the region studied.

The factors showing different results in greenhouse gas emissions depending on region are

related to passenger activity and shares of rail and electricity in total energy consumption on

transport sector. Passenger activity is a driving factor but only in the EU-15, so that a change in

the activity would produce a change of the same sign on transport emissions. In the same way,

the replacement of oil products by electricity would reduce transport emissions but only in the

EU-15 region. And, finally, the substitution of road transport by rail would result in a decrease of

transport emissions but only in the UE-East region.

The results obtained in this research can give information about the measures suggested in the

2011 Transport White Paper in order to achieve the objective of reducing the emissions of the

transport activity. These measures consist on an increasing use of rail or waterbone at expense

of road in medium distance intercity journeys, the use of sustainable low carbon fuels in aviation

and the elimination of traditional fuel cars in cities (European Commission, 2011). Given the

results obtained in this research it is expected that an increase in the use of rail at the expense

of road transport will contribute to a reduction in the environmental pollution. In the same way, it

is expected that an increase in the use of low carbon fuels in aviation and the reduction of

traditional fuel cars will produce a positive impact in the environment. However, it seems that the

impact on environment of an increase in the use of navigation at the expense of road transport

apparently will not result in a significant effect.

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