PROGRAMA DOCTORADO ECONOMÍA APLICADA

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1 PROGRAMA DOCTORADO ECONOMÍA APLICADA Tercer año de seguimiento, curso DEPARTAMENTO ECONOMÍA APLICADA PROYECTO EL SECTOR DEL TRANSPORTE: CONSUMO ENERGÉTICO Y EMISIONES Lidia Andrés Delgado Emilio Padilla Rosa DIRECTOR 1

2 DRIVING FACTORS OF GREENHOUSE GAS EMISSIONS IN THE EU-28 TRANSPORT SECTOR 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 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. 2

3 INTRODUCTION Greenhouse gas emissions decreased by 19.7% in the EU-28 between 1990 and 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 tonnes 1 - in the same period. Consequently, the contribution of the activity to total emissions has increased considerably since 1990, being responsible for 19.6% in 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 CO 2 emissions in the USA transport sector during the period ; Timilsina and Shresta (2009), who investigated the same issue but referred to a group of selected Asian countries for the period ; Andreoni and Galmarini (2012), who explored the main factors affecting the CO2 emissions of water and aviation transport activities in Europe between 1 CO 2 equivalent emissions of the six gases covered by the Kyoto Protocol, European Commission (2016). 3

4 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 CO 2 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 Among the investigations using econometrics techniques, some examples are the works of Zhang and Nian (2013), who analysed CO 2 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 CO 2 emissions in transport sector during the period 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 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 4

5 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 CO 2 emissions. DATA In order to perform the analysis, annual data of the EU-28 countries have been collected from different sources for the period 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. 5

6 Table 1. Energy consumption by modes of transport and by energy sources in the EU-28 transport sector (thousand TOE) Energy consumption Share Total change (%) Total activity 284, , % 100.0% 100.0% Modes of transport Aviation 29, , % 10.4% 14.0% Navigation 6, , % 2.2% 1.4% Road 238, , % 83.8% 81.6% Rail 8, , % 2.9% 2.0% Pipelines , % 0.1% 0.4% Other 1, , % 0.6% 0.6% Source of energy Solid fuels % 0.1% 0.0% Petroleum products 278, , % 97.9% 93.5% Gas , % 0.1% 0.8% Renewable energies , ,848.4% 0.0% 4.1% Electrical energy 5, , % 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 Population 1.75e e e+07 Per capita GDP Energy intensity PKM TKM % Road % Rail % Aviation % Navigation % Pipelines % Oil products % Electricity % Renewable energies % Coal

7 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 β i are the estimated parameters. All variables are taken in log form, so that β i 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. GHG i,t are the greenhouse gas emissions in transport sector, P i,t is population; GDP i,t is per capita real GDP, EI i,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 α i collects all fixed factors which characterize each country and are time invariant. M j 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, S k is the share of source of energy k in total energy consumption of transport sector, 7

8 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 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 Energy intensity defined as total energy consumption of per million USD transport sector divided by real GDP PKM Gross tonnekilometres Passenger activity measured in passenger-kilometres TKM Gross tonnekilometres Freight activity measured in tonne-kilometres M j Percent Ratio of mode of transport j in total energy consumption of transport sector S k Percent Ratio of source of energy k in total energy consumption of transport sector One variable M j and one variable S k 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 8

9 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 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

10 CD stat p-value F stat p-value 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 (0.5699) (0.6702) Population *** *** (0.0347) (0.0415) Per capita GDP *** *** (0.0202) (0.0306) Energy intensity *** *** (0.0164) (0.0263) PKM * (0.0147) (0.0222) TKM *** *** (0.0093) (0.0113) % Rail ** *** (0.2014) (0.2178) % Aviation *** *** (0.0628) (0.0698) % Navigation (0.1595) (0.1243) % Pipelines *** *** (0.0569) (0.1639) % Electricity (0.3186) (0.6527) % Renewable *** *** (0.0915) (0.1224) % Coal

11 (0.9069) (0.8810) Fixed effects Yes Yes Year No No R N Groups 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 pipelines 2 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 CO 2 emission factor measured in tco 2 /TJ of Kerosene is 71.9 and of diesel ). 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 (2011) 11

12 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 (0.6651) (2.0992) Population *** *** (0.0403) (0.1342) Per capita GDP *** *** (0.0227) (0.0505) Energy intensity *** *** (0.0184) (0.0403) PKM ** (0.0181) (0.0401) TKM * ** (0.0093) (0.0229) % Rail *** (0.3909) (0.2554) % Aviation *** *** (0.0580) (0.1695) % Navigation (0.1386) (0.2813) % Pipelines * *** (0.2757) (0.1550) % Electricity *** (0.4012) (0.7899) % Renewable *** * (0.0912) (0.3186) 12

13 % Coal ( ) (0.9149) Fixed effects Yes Yes Year No No R N Groups 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 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 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, 13

14 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. REFERENCES Andreoni, V. and Galmarini, S. (2012) European CO 2 emissions trends: A decomposition analysis for water and aviation transport sectors. Energy, 45, 1, pp

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