This paper deals with the decomposition analysis of energy-related CO2 emissions in Greece from 1990 to 2002. The Arithmetic Mean Divisia Index (AMDI) and the Logarithmic Mean Divisia Index (LMDI) techniques are applied and changes in CO2 emissions are decomposed into four factors: income effect, energy intensity effect, fuel share effect and population effect. The period-wise and time series analyses show that the biggest contributor to the rise in CO2 emissions in Greece is the income effect; on the contrary, the energy intensity effect is mainly responsible for the decrease in CO2 emissions. A comparison of the results of the two techniques gave an insight in the intricacies of energy decomposition. Finally, conclusions and future areas of research are presented.
During the past decades, the acceleration of environmental degradation in combination with an ever increasing energy demand have raised the concern of energy analysts and policy makers regarding the adverse effects of energy use. The reduction of emitted greenhouse gases and atmospheric pollutants constitutes a foremost objective of contemporary energy and environmental policy. In particular, the findings of the scientific community with respect to the rising of energy-related CO2 emissions raised international awareness.
The increase in the concentration of CO2 in the atmosphere, caused mainly by the combustion of fossil fuels, is the main factor responsible for the intensification of the greenhouse effect and the resulting climate change. The study of the driving forces governing CO2 emission levels and their evolution, has therefore been of considerable interest to researchers and policymakers. Many factors influence these emissions, such as economic and demographic developments, technological change, institutional frameworks, lifestyle and international trade.
Several methodologies have been developed to depict temporal and cross country/region variations in energy and environmental factors. Decomposition Analysis (DA) methodologies, i.e. Index Decomposition Analysis (IDA) and Structural Decomposition Analysis (SDA), have been applied to identify parameters that trigger energy demand and related CO2 emissions [1]. These two fields of DA have a long history of independent development, leading to different approaches and techniques. In general, the literature on IDA has reported extensively on the implications of index theory and the specification of the decomposition, whereas the SDA literature has focused attention on distinguishing from a large number of specific determinant effects [2].
Furthermore, these methods use historical data, in order to assess how the driving forces of energy demand are connected with the change in particular indicators. An advantage of IDA over SDA methods is that they require less data. Regarding the period of examination, SDA methods often use time spans of 3–10 years; this is due to the fact that for many countries, Greece included, input–output tables are not constructed annually. On the other hand, IDA studies often use annual time steps [2]. In addition, IDA and SDA methods use different types of indicators; while IDA literature has developed and utilized all three indicator forms (absolute, intensity and elasticity), SDA literature generally restricts itself to the investigation of absolute changes in variables [2]. Due to these comparative advantages, IDA methods constitute a widely accepted analytical tool for policymaking in national energy and environmental issues.
IDA methods use national or sectoral data, and include, among others, the Arithmetic Mean Divisia Index (AMDI) and the Logarithmic Mean Divisia Index (LMDI) techniques. The properties of the AMDI and the LMDI techniques have been explored in Ref. [3], while for a review of IDA methods one may refer to Ang and Zhang [4] and Ang [1]. SDA methods use the Input–Output model. An overview of SDA methods is provided in Ref. [5], while Rose [6], Hoekstra and van den Bergh [7] and Hoekstra [8] provide overviews of environmental SDA. On the other hand, the survey by Ang and Zhand [4] lists a total of 124 related publications up to 1999.
Published studies have dealt with OECD countries, most Eastern European countries and Russia, and a large number of developing countries including Korea, China, India, Namibia, Brazil and Mexico. Main application areas include energy demand and supply, energy-related gas emissions, national energy efficiency trend monitoring, and cross-country comparisons [3]. Thereafter, extensions of IDA methods have been applied [9] and several researchers have made comparisons between different IDA methods [10], [11], [12].
Regarding Greece, a country not widely studied in the energy and environmental literature, a number of studies report on CO2 emissions and economic development: Vlachou et al. [13]developed long-run strategies for reducing the CO2 emissions by the electric power industry, using an econometric approach and an economic–engineering approach; Hondroyiannis et al. [14] established the empirical relationship between energy consumption, real GDP and price developments, employing the vector error–correction model estimation for the 1960–1996 period; Floros and Vlachou [15] studied the demand for energy in the manufacturing sectors and evaluated the impact of a carbon tax on energy-related CO2 emissions with time series data over the period 1982–1998; Paravantis and Georgakellos [16] applied appropriate regression models to forecast and compare fuel consumption and CO2emissions from passenger cars and buses until the year 2010; Zervas et al. [17] analyzed the effect of replacing gasoline passenger cars by diesel ones using scenarios that take into account current and future new car registrations and fuel consumption; Diakoulaki et al. [18]applied Park's extended decomposition method in the Greek manufacturing sector and its sub-sectors for the period 1985–1995 to identify the major factors that influenced changes in the industrial CO2 emissions. Recently, Diakoulaki et al. [19] proposed a bottom–up DA of energy-related CO2 emissions in Greece, based on the refined Laspeyres model.
This paper is a fairly robust application of DA to data for the Greek economy which has not been examined exhaustively up to now; Greece has ratified the Kyoto Protocol and is in the process of assessing its course regarding greenhouse gas emissions and this work contributes towards this goal. The period under investigation, 1990 to 2002, is examined in order to deduce the most important factors to the evolution of CO2 emissions. During this period a Community Support Framework (CSF II) was initiated to modernize the Greek Economy [20], [21], natural gas was introduced into the energy system [22] and an increase of construction activities due to the coming Olympic Games (Athens 2004) was evidenced. The introduction of natural gas signifies a change in the existing national fuel mix and its quantitative effect has not been assessed. The AMDI and the LMDI techniques were employed for ease of formulation and simplicity sake; the two techniques, widely used by energy agencies, are highly recommended by researchers [23], [24], [25], [26]. Furthermore, they are easy to implement, they take the same general functional form (based on Divisia Index) and they allow for different weighting schemes [2], [27].
The remainder of the paper is organized as follows: In Section 2 we introduce the issue of DA and we present the methodology for the case of Greece's CO2 emissions. Section 3presents a period-wise decomposition and a time series analysis for the 1990–2002 time-span for Greece and comparison of results. Finally, conclusions are summarized in Section 4.
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