By Amaka Benson
A better understanding of potential factors driving CO2 emissions growth can be obtained through a decomposition analysis13. Most of the studies have focused on the decomposition of national CO2 emissions and emission intensities e.g. Serap et al (2009) for Turkey, Ebohon and Ikeme (2006) for Sub-Saharan African countries, Paul and Bhattacharya (2004) for India and Lee and Oh (2004) for APEC countries. Other studies have focused on the decomposition of power and manufacturing sectors CO2 emissions and emission intensities. For instance, Bhattacharyya and Ussanarassamee (2004) focused on emission intensity of the Thai industry, whilst Liaskas et al (2000) similarly did for EU industry and Shrestha et al (2009) for the power sector of selected countries in Asia and the Pacific.
Nonetheless, a few studies also analysed the factors affecting transport sector CO2 emissions growth. Lakshmanan and Han (1997) attributed the increase in the transport sector CO2 emissions in the U.S.A. between 1970 and 1991 to the growth in people's propensity to travel, population, and gross domestic product (GDP). In the same vein, Timilsina and Shrestha (2009) decomposed the changes in the transport sector CO2 emissions in Asia during 1980-2005 into changes in per capita GDP, population growth, fuel mix, modal shift, emission coefficients and transportation energy intensity. They found that population growth, increase in per capita GDP and transportation energy intensity were the main factors driving emissions growth.
intensities affected CO2 emissions from passenger transport in nine OECD countries between 1973 and 1992. They concluded that increased activity and modal shifts increased CO2 emissions. Finally, Lu et al (2007) calculated how vehicle fuel intensity, vehicle ownership, population intensity and economic growth affected the total carbon dioxide emissions from highway vehicles in Germany, Japan, South Korea and Taiwan during 1990–2002. They came to the conclusion that economic growth and vehicle ownership were the most significant factors for the increased CO2 emissions, whereas population intensity contributed substantially to CO2 emissions reduction.
While decomposition analysis has been used in the study of energy intensity and CO2 emissions in the French economy with regards to energy demand (Mairet and Decellas, 2009), no such analysis has been carried out specifically for CO2 emissions in the transport sector, which is why this study may be considered as an original contribution to France’s climate change debates. Studies such as Lin et al (2008), Ebohon and Ikeme (2006), and Diakoulaki and Mandaraka (2007) used the refined Laspeyres method, while others like Liu et al. (2007), Hatzigeorgiou et al. (2008) and Bhattacharyya and Ussanarassamee (2004) used the Arithmetic Mean Divisia Index (AMDI) and the Logarithmic Mean Divisia Index (LMDI) techniques. Like Wang et al (2005) the present study utilises the LMDI method because this method has several desirable advantages including time independence, ability to handle zero values and consistency in aggregation14. Furthermore, the additive version of this method is utilized in which the change in one variable is decomposed as summation of changes in the components of that variable. The LMDI unlike the AMDI technique, gives perfect decomposition, i.e. the results do not contain an unexplained residual term, and can accommodate the occurrence of zero values in the data set15. Even though the refined Laspeyres methods also have these merits, it becomes quite complex when the number of factors surpasses three, and the linkages between the additive and multiplicative forms are not as straightforward16.
Given the current challenge of energy security and heightened global awareness of climate change and the dangers of global warming coupled with importance of the transportation sector as a major consumer of fossil fuel as well a leading producer of CO2 emissions, there exists a large number of pertinent research studies that have attempted to forecast both the short-term and long-term energy demand for this sector. In recent years, researchers have used many methods to forecast energy demand, each one having its own methodological strengths and weaknesses17. Such methods include Time Series analysis, Partial Adjustment Model (PAM), Grey Relative analysis, Partial Least Square Regression (PLSR), Multiple Linear Regression (MLR), and Input-Output approach.
Studies such as Gonzales et al (1999) used the Univariate Box- Jenkins time- series analyses (AutoRegressive Integrated Moving Average models) for modelling and forecasting future energy consumption in Asturias with monthly historic data from 1980 to 1996. Similarly, Mu et al (2004) employed the method of grey relative analysis to analyze the relative relationship between rural household biofuels18 consumption and affecting factors19 in China through period 1991 - 1999. Now based on the analytical results of the relative degrees and relative polarities, forecast models of future consumption were proposed and in addition, future consumption of biofuels up to the year 2020 was forecasted. Finally, using the PLSR method, Zhang et al (2009) projected the transport energy demand in China for 2010, 2015 and 2020 under two scenarios (baseline scenario and policy scenario). Based on the method and the two scenarios considered, they came to the conclusion that by 2020 transport energy demand will reach a level of 416 Mtce for policy scenario and 460 Mtce for baseline scenario.
However, this present study utilises computer based software called the Long-Range Energy Alternatives Planning System (LEAP) to forecast the energy demand and CO2 emissions in the transport sector of France. A key benefit of LEAP and one of the reasons why it is chosen over the other forecasting methods is its ability to address at a more detailed sectoral and sub-sectoral level the implications of energy policies on energy system.
Another key advantage of LEAP is its initial low data requirement because LEAP relies on simpler accounting principles, and because many aspects of LEAP are optional, its initial data requirements are thus relatively low. LEAP has had a significant impact in shaping energy and environmental policies worldwide20. Applications of LEAP include a 2009 study by the Asia Pacific Energy Research Centre (APERC) to develop the 4th edition of the APEC Energy Demand and Supply outlook. This report contains energy demand and supply forecasts for the 21 member countries of APEC to the year 2030. In Jamaica, LEAP was used to assess the country’s GHG mitigation strategy covering projections of selected GHGs for the period 2009 to 2035. The LEAP model was used to make projections for four emissions-related categories: Energy demand, Transformation, Energy resources and Non- energy sector effects.
With regards to urban transportation, a research was conducted by the Institute for Global Environmental Strategies (IGES) in Kathmandu Valley in Nepal in 2005. The report analysed the emission of air pollutant, CO2 and energy use in the Kathmandu Valley. The results of the study indicate that by 2025, energy consumption will increase only about two fold as more fuel-efficient vehicles penetrate the system. However, while there was a decrease in the emission of PM10 from its 2004 value, CO2 emissions from passenger transport is expected to double from its 2004 value.
Unfortunately, many of the existing studies relating to the transport sector address a single mode of transport such as only passenger (road) travel and do not include any comprehensive data collection or analysis for other modes of transportation such as Passenger (rail) travel or freight transport. Moreover, France lacks research on the implications of various transportation policy options and such a study would assist the French policy makers in formulating effective policies to improve the energy and environmental situation in the country.
This is the second instalment in an analysis of on Decarbonising France Transport Sector by Amaka Benson. Her study aims to fill an important gap in literature by developing a database on all transport modes in the French transportation system including passenger transport (private cars, rails and buses) and freight transport in order to facilitate the development of reasonable and meaningful alternative energy demand scenarios. This will allow us to simulate and assess the impacts of alternative energy policies and, to project the energy demand and emissions from the French transportation sector. For more information about this article and to view Amaka's professional profile, Click here -->