The paper presents a decomposition analysis of the changes in carbon dioxide (CO2) emissions from passenger cars in Denmark and Greece, for the period 1990–2005. A time series analysis has been applied based on the logarithmic mean Divisia index I (LMDI I) methodology, which belongs to the wider family of index decomposition approaches. The particularity in road transport that justifies a profound analysis is its remarkably rapid growth during the last decades, followed by a respective increase in emissions. Denmark and Greece have been selected based on the challenging differences of specific socio-economic characteristics of these two small EU countries, as well as on the availability of detailed data used in the frame of the analysis. In both countries, passenger cars are responsible for half of the emissions from road transport as well as for their upward trend, which provokes the implementation of a decomposition analysis focusing exactly on this segment of road transport. The factors examined in the present decomposition analysis are related to vehicles ownership, fuel mix, annual mileage, engine capacity and technology of cars. The comparison of the results discloses the differences in the transportation profiles of the two countries and reveals how they affect the trend of CO2 emissions.
Decomposition analysis is one of the most effective and widely applied tools for investigating the mechanisms influencing energy consumption and its environmental side effects. In the early 1970s, the main concern was the analysis of the aggregated energy intensity in the manufacturing sector, through the decomposition of changes in the sectoral energy intensity and the product mix (Ang and Zhang, 2000). Over the next decades, the interest of the researchers also included the analysis of energy-related greenhouse gas (GHG) emissions because of the growing concern for climate change. To that end, other energy consuming sectors in addition to the industrial one have undergone a decomposition analysis, in order to better understand the driving forces behind carbon dioxide (CO2) emissions and accordingly establish the appropriate mitigation strategies.
Because of its continuously growing share in the overall energy consumption, the transport sector has been acknowledged as one of the most important contributors to global emissions. In the first 15 member countries of the European Union (EU15), transport accounts for more than 25% of total CO2 emissions, over 90% of which are attributed to road transport. There is already rich material among recent studies on transportation, focusing on the environmental aspects of fuel consumption and the examination of changes in transport energy intensity (Kiang and Shipper, 1996; Kwon, 2006; Michaelis and Davidson, 1996; Zachariadis, 2006), or analysing the link between transportation and socio-economic activity (Preston, 2001; Stead, 2001; Tapio et al., 2007). Furthermore, in the field of decomposition analysis, several variables affecting CO2 emissions or carbon intensity have been examined either for freight (Greening et al., 1999; Schipper et al., 1997), or for passenger transportation (Scholl et al., 1996; Schipper et al., 1992). Kwon (2005), in his study on the decomposition of CO2 emissions from privately owned vehicles, moved forward to an in depth examination of the effect that car driving distance per person had in the upward trend of emissions.
The present study proceeds to a thorough examination of a wide range of key factors that are expected to contribute to CO2 emissions produced by passenger cars. These interrelated factors provide an integrated view upon the cars transportation profile in order to detect the ultimate reasons explaining the upward trend of CO2 emissions. More specifically, the impact of activity (vehicles-km) has been broken down into population, vehicles ownership and annual mileage; the effect of fuel mix is given by the shares of cars by fuel; finally, changes in the shares of cars by engine size and engine technology address not only the changes in the fleet profile, but also in the energy intensity which varies according to each car sub-category. The availability of detailed data for both Denmark and Greece, along with challenging differences of specific socio-economic characteristics of these two small EU countries have been effectual reasons for the two-country comparison.
In Denmark, the increase in CO2 emissions from road transport between 1990 and 2005 lies very close to the EU15 average, while the corresponding increase in Greece is almost twofold. In both countries, passenger cars are responsible for approximately half of the emissions from road transport as well as for their upward trend, which provokes the implementation of a decomposition analysis focused exactly on this segment of road transport.
Both countries have small size economies relatively to their contribution to the EU15GDP. However, GDP per capita in Denmark is one of the highest in the EU15, while the Greek GDP is well below average, although with a higher average annual growth in the period 1990–2005 (United Nations Statistics Division, 2008). The upward trend of GDP in both countries has been closely followed by a similar trend in energy consumed in road transportation. In the case of Greece, the growth of passenger cars per capita in the period 1990–2005 has been remarkable, by far surpassing that of per capita GDP. Indicative of the excessive trend of cars sales in Greece is the fact that new registrations per capita have increased by 135% since 1990, when the respective increase for Denmark is about 73% (ACEA, 2007). Private gasoline cars in use have increased by 150% since 1990, while the corresponding increase in Denmark is 23%. On the other hand, the share of diesel cars in private transportation in Denmark grew to 12% in 2005, while in Greece it remains negligible, because of the interdiction of diesel-fueled private cars in urban centers. Additionally, energy statistics indicate that privately owned cars almost exclusively rely on petroleum, while the penetration of alternative or renewable fuels is still minimal. Recognizing therefore the importance of the abovementioned facts, the present study attempts a more profound comparative analysis of the determinants behind the trends of CO2 emissions from passenger cars in the two European countries.
The following sections present the methodological approach developed for the decomposition analysis, the trend of CO2 emissions and of the parameters related to passenger cars in Denmark and Greece from 1990 to 2005, the results of the decomposition analysis followed by a discussion and the concluding remarks.
The most widely applied index decomposition methodologies in recent decades are based on the Laspeyres and the Divisia indices, well known in the fields of economics and statistics (Ang and Zhang, 2000). The major problem encountered with conventional Laspeyres and Divisia index methods used in the past was the large residual term found in most applications, leaving a significant part of the examined changes unexplained. However, this problem has been effectively solved through the development of improved techniques leading to perfect decomposition. The refined Laspeyres extension proposed by Sun (1998)allocates the residuals on the basis of the “jointly created and equally distributed” principle. One basic drawback of the refined Laspeyres method is the complexity of the decomposition formula when the number of factors analyzed exceeds three (Ang et al., 2003). On the other hand, the logarithmic mean Divisia index method (Ang and Choi, 1997) is a refinement of the conventional arithmetic mean Divisia index approach and gives complete decomposition results. The only problem related to the logarithmic terms of the formula is the handling of zero values in the data set, which is solved by replacing zero values with a small positive number (Ang and Liu, 2007). In this study we use the logarithmic mean Divisia index I (LMDI I) method, introduced by Ang and Liu (2001), which has the most robust theoretical foundation, provides complete and more stable decomposition results (Zhang and Ang, 2001) and has the advantage of being very easily implemented. The LMDI I approach can be either multiplicative or additive, while the choice between the two methods depends mostly on the desired presentation of results. In a time series analysis the multiplicative approach has the advantage of better deploying the trends of the factorial effects over time. The additive methodology, however, is based on the use of the familiar concept of percentage changes facilitating comprehension and exploitation of results.
In the present study, the decomposition analysis, on the basis of the additive LMDI I method, focuses on several factors which constitute important driving forces behind the CO2 changes from passenger cars, namely the population, the vehicles ownership, the annual distance covered, the fuel mix, the engine size and the engine technology of cars.
If you are interested in this research article, you can download the full length article through the download page using the link below, both PDF file and Word file are provided.
No comments:
Post a Comment