Ireland recorded significant growth in energy-related carbon emissions from 1990 to 2007 as the country underwent rapid economic development. Using the LMDI decomposition analysis method, this paper aims to identify and analyse the driving forces of CO2 emissions in eleven final energy consuming sectors. This multi-sectoral analysis is based on four economic sectors, the residential sector and gives a detailed representation of transport in keeping with UNFCCC recommendations. Scale, structure and intensity effects are explored and substantial heterogeneity in sectoral performance is observed. Scale growth in economic and transport activity was considerable. Some improvements in energy intensity were recorded in the economic sectors. In transport, increases in intensity contributed to a significant increase in emissions, while energy intensity decreased in the residential sector. The declining emissions coefficient of electricity was important in limiting emissions but renewable energy has been slow to penetrate the demand side. The results have relevance in considering development paths and can aid in identifying policy measures required to address the key driving forces of emissions in the sectors. The rapid increase in transport emissions in particular raises concerns of future lock-in to a higher emissions trajectory.
Energy and CO2 emissions are integral to issues of development in all nations and have become a significant policy challenge in the Republic of Ireland. Through the Kyoto Protocol and the European Union (EU) Burden Sharing Agreement (Council Decision 2002/358/EC) the target agreed for Ireland was to limit the increase in greenhouse gas (GHG) emissions to +13% of 1990 levels by 2008–2012. This coincided with a period of rapid economic development, during which infrastructure was expanded, lifestyles changed and energy demand and CO2 emissions increased substantially. In 2007, total GHG emissions in Ireland were 25% higher than the 1990 level (McGettigan et al., 2009). Energy-related CO2emissions increased by 49.4% and accounted for two-thirds of all GHG emissions. Understanding driving forces of CO2 emissions is essential to formulating climate change mitigation policy and the fulfilment of applicable targets. Decomposition analysis of change in emissions provides a robust means of achieving this objective.
At the United Nations Framework Convention on Climate Change (UNFCCC) Dublin Workshop on Fourth National Communications from Annex I Parties (UNFCCC, 2004), index decomposition analysis (IDA) was recommended to quantify key drivers of emissions and separate effects such as energy efficiency and GDP growth. In the literature, applications of IDA have undergone substantial changes since the late 1970s. Recently, IDA methods particularly the logarithmic mean divisia index (LMDI) technique have been widely applied to track economy-wide energy efficiency trends by different countries/organisations (Ang et al., 2010). Sectoral analysis has been expanding from energy demand and CO2 emissions in industry and manufacturing sub-sectors to analysis such as UK road freight in Sorrell et al. (2009).
Insight into the driving forces underlying change in CO2 emissions in Irish sectors has been limited to the decomposition analysis of manufacturing. Given the large increase in emissions, particularly from the under-investigated transport sectors, the absence of appropriate enquiry could have consequences for policy. In the in-depth review (IDR) of Ireland’s third national communication to the UNFCCC, Rolle et al. (2005) highlighted this gap in knowledge. The reviewers recommended that changes in GHG emissions of Irish transport be linked to changes in modal split and changes in physical activity by passenger kilometres (p-km) and tonne kilometres (t-km). The objectives of this study were to identify and analyse trends in the historical driving forces of CO2 in all of the energy end-use sectors from 1990–2007. It also forms the first response to these UNFCCC recommendations. The sectors are analysed separately but inter-sectoral shifts in the shares of activity in the economic sectors and the transport modes are also analysed. Rather than develop policy recommendations per se, this study contributes to the discussion of appropriate mitigation measures by engaging with gaps in knowledge of trends and driving forces in the end-use sectors.
The results obtained are not only relevant to Irish policy-making but may provide useful insights for other countries experiencing a development transition. The analysis of the historical progression of key indicators, particularly energy intensity, also functioned as the first step in the development of scenarios of future CO2 emissions in O’ Mahony et al. (submitted). Similar to Wu et al. (2005) who analysed driving forces in China, the originality of this study lies in the use of a framework that gives a disaggregated multi-sectoral decomposition. Multi-sectoral decompositions in previous studies often deal with three or four sectors (Diakoulaki et al., 2006, Lise, 2006; Tunç et al., 2009) whereas this study disaggregates eleven final consumption sectors. Whereas the EU-ODEX has been used to track energy efficiency in some sectors (Dennehy et al., 2009), this is the first comprehensive index decomposition analysis of the Republic of Ireland, including of the non-manufacturing economic sectors, the residential sector and of particular importance, the transport modes. While this study decomposes all final consumption sectors, particular focus is accorded to the disaggregation of transport recognising both that it is under-investigated and also its importance in total emissions. This is as opposed to Oh et al. (2010) a multi-sectoral decomposition of South Korea, which concentrated on disaggregated manufacturing but aggregated transport. This multi-sectoral analysis may provide deeper insights than the macro level approach recommended by the UNFCCC (2004).
The rest of this paper is organised as follows. Section 2 presents sectoral emissions from 1990–2007 and the classification of the sectors in Ireland that leads to the decomposition scheme. Section 3 introduces our decomposition framework and Section 4 describes data sources. In Section 5, we present the decomposition analysis results from each sector and an aggregate analysis. Section 6 concludes this study.
2. Sectoral CO2 emissions in Ireland
Fig. 1 shows the evolution of the sectoral contribution to total energy CO2 emissions in Ireland from 1990 to 2007. The classification of final energy end-use sectors was established in the Ireland’s energy balance sheets communicated to the European Commission and the International Energy Agency. It can be observed from Fig. 1 that there was an increasing trend in the total CO2 emissions and a concentration of growth occurred from 1993–2001. The residential and industry sectors are the major contributors but their shares have declined over time. All transport modes experienced substantial growth in emissions with the exception of rail.1
Fig. 1
The decomposition framework used in this study is based on the sectoral classification previously described. This sectoral disaggregation is similar to that used in Agnolucci et al. (2009), Oh et al. (2010) and to a lesser degree that of Wu et al. (2005). Full coverage of the main final consumption sectors is achieved using data by sector and fuel type. The four economic sectors include agriculture, industry, commercial services and public services, but does not separate construction as disaggregated was unavailable. Agnolucci et al. (2009)split industry into energy intensive/extensive branches but gross value added (GVA) data in Ireland is not disaggregated along these lines pre-1995.2 Industry remains aggregated to facilitate a full analysis from 1990 to 2007. Agriculture and public services are given their own characterisation as agriculture is unique in its use of energy and public services are unique in the instruments necessary to reduce CO2 emissions due to state control.
In our analysis, a methodological challenge arises in attempting to understand the increase in emissions from transport since 1990. A response is required to the recommendations of Rolle et al. (2005), that changes in emissions be linked to changes in modal split and activity by p-km and t-km. Ang and Zhang (2000) specifically suggested the use of LMDI I to measure the physical efficiency of transport. As discussed by Timilsina and Shrestha (2009)given a lack of data, a common approach in energy literature is to measure modal shift by changes in modal fuel consumption in total transport fuels (EIA, 2007, IEA, 2004). This proxy method assumes the same intensity across the different modes and weakens results. The approach adopted in this study retains the physical measure of modal shift and also intensity. As shown in Fig. 1, transport in the decomposition framework is split into six sub-sectors or modes corresponding to energy and CO2 data. This reflects the considerable modal differences in the provision of transport services while responding to the recommendations of Rolle et al. (2005). International aviation and maritime transport are not considered as both are memo items in national inventories and excluded from national totals and quantitative targets (IPCC, 1999). It should be pointed out that a similar limitation to Agnolucci et al. (2009) arose in the case of rail since the data on energy use by passenger and freight components cannot be separated. As such, we aggregate the total activities performed by rail passenger transport and rail freight using the approach suggested by Diakoulaki et al. (2006). Since the ratio of rail p-km to t-km was not stable, the results of aggregate intensity for rail are interpreted with this in mind. The road public passenger sector is also an aggregation of bus and taxi transport modes. It is worth noting that the approach of Timilsina and Shrestha (2009) aggregates road transport and also aggregates rail transport. The activity of the unspecified and fuel tourism sectors cannot be measured. These sectors are aggregated to complete the analysis but effects are not measured.
In keeping with Ekins and Barker (2001) it is recognised that energy demand is more related to energy services (heat, light, power, mobility, etc.) than for energy itself per se. This has implications for the drivers that lead to change in energy CO2 emissions, but it is difficult to accommodate given the huge variety of energy services required in each sector. In order to overcome this, energy services are represented by either monetary or physical indicators based on the characteristics of different sectors. Usually, the use of physical indicators is considered to be more accurate but this may only be applicable for particular sectors such as transport (Diakoulaki et al., 2006, Freeman et al., 1997). In the case of the economic sectors, both physical and monetary indicators of output can be used, but both present with potential limitations. Physical indicators can create difficulties in aggregating disparate physical outputs across different products, commodities or service groups, while monetary indicators can mislead due to changes in unit prices. Similar multi-sectoral studies have used monetary indicators of economic output (Wu et al., 2005, Oh et al., 2010, Lise, 2006, Diakoulaki et al., 2006). In this study, using the monetary indicator GVA facilitates a commonality in the method of analysis across the sectors and also the analysis of structural shifts. For the residential sector, the number of households is taken as the activity indicator. Transport activity is represented by mobility rather than vehicle distance as mobility is the primary energy service sought (Ekins and Barker, 2001). Therefore, p-km and t-km are, respectively, taken as the activity indicators for passenger and freight transport modes.
3. Methodology
Index decomposition analysis (IDA) has been widely accepted as an analytical tool for supporting policymaking on national energy and environmental issues (Ang, 2004b). The decomposition of the change in an aggregate indicator into a pre-defined set of factors helps to understand the progression of driving forces, the impact of major processes occurring and policy dimensions tied to these processes (Steenhof et al., 2006). The results of an IDA application study have direct policy implications such as evaluation of energy conservation programs (Ang, 2004b, Ang and Liu, 2007). They may also provide a basis for forecasting (Ang, 2004a) or scenario analysis of future evolution. The results of this study are used as the basis for scenarios presented in O’ Mahony et al. (submitted).
A range of techniques have been established under the umbrella of IDA, among which the LMDI I technique has been identified as the preferred approach by Ang (2004b). The mathematical properties of the technique suggest its suitability for this study including: perfect decomposition, consistency in aggregation and ability to handle zero values. In the methodological literature (Ang, 2004b) recommends the multiplicative and additive LMDI I methods for their theoretical foundation, adaptability, ease of use and ease of result interpretation. LMDI I has both additive and multiplicative forms. In this study, it is applied in multiplicative form chain-linked annually accommodating separate decomposition of the sectors and subsequent aggregation to total change. The basic mathematical formulae for IDA and LMDI I can be found in Ang (2004b) developed from work by Ang and Liu (2001). The work of Ang and Liu (2001) was extended by Wu et al. (2005) as a three-level decomposition for China disaggregated by sector and province. In contrast, this study applies a two-level decomposition for Ireland without provincial disaggregation but for a greater number of sectors. The approach used facilitates the elaboration of sector-specific insights. The decomposition schemes applied to each of the sectors are detailed in Eqs. (1), (2), (3) where index i=1, 2,…,6 respectively denote coal, oil, peat, gas, renewables and electricity and index t the year from 0 (base year) to t (target year). Eq. (1) is applied to each of the economic sectors for j=1,2,3,4 denoting industry, commercial services, public services and agriculture:
(1)
In Eq. (2) applied to each of the transport sectors, j indexes sector, for j=5,6,…,10 for private car transport, road public passenger transport (bus and taxi), road freight transport, rail transport (passenger and freight), domestic aviation and aggregated unspecified and fuel tourism:
(2)
Eq. (3) applies to j=11 the residential sector:
(3)
Table 1
| Item | Meaning | Item | Meaning |
|---|---|---|---|
| Ctij | CO2 emissions fossil fuel i sector j year t | Yt | Total economic output year t |
| FFtij | Consumption fossil fuel i sector j year t | TDtj | Passenger/ Freight Distance sector j year t |
| FFtj | Total consumption fossil fuels sector j year t | TTDt | Total Transport Distance year t |
| Etj | Total energy consumption sector j year t | THNt | Total Household Number year t |
| Ytj | Economic output sector j year t |
Assume that CEtij=Ctij/FFtij is the carbon emissions coefficient for fuel i in sector j for year t; FStij=FFtij/FFtj is the ratio of fossil fuel i to total fossil fuels in sector j for year t; REtj=FFtj/Etj is the share of total fossil fuels in total energy consumption in sector j for year t; EIEtj=Etj/Ytj is the energy intensity of economic sector j ( j=1,2,3,4) for year t; EITtj=Etj/TDtj is the energy intensity of each transport sector (mode) j ( j=5,6,7,8,9,10) for year t; EIRtj=Etj/HNtj is the energy intensity of the residential sector for j=11 for year t; EStj=Ytj/Yt is the share of economic output in sector j (j=1,2,3,4) in total economic output for year t; ETt=Yt/Y0 is the change in total economic output for year t; TStj=TDtj/TTDt is the share of transport distance in sector (mode) j in total transport distance (j=5,6,7,8,9,10) for year t; TTt=TTDt/TTD0 is the change in total transport distance for year t; HNt=THNt/THN0 is the change in the total number of households for year t; where 0 is the base year and t the target year. Eqs. (1), (2), (3) can then be re-written as
(4)
The steps required to develop Eqs. (4), (5), (6) as LMDI I are detailed in Ang and Liu (2001). The detailed decomposition formulae applied in this study are presented in the Appendix. These give the determinant effects in each of the sectors described in Table 2 along with the nomenclature used for results. These effects can be categorised into three groups: the intensity effects Cemc, Cinte, Cintt and Cintr, the structure effects Cffse, Crepe, Ces and Cts, and the scale effects Cet, Ctt and Chn. The Cemc is the ratio of CO2 per unit of energy for each fuel type in each sector. It analyses fuel quality and the installation of abatement technologies. As electricity is included as a fuel type in the consuming sectors, this effect also shows the change in the CO2 coefficient of electricity due to fuel switching and renewables in power generation. The Cinte, Cintt, Cintr effects measure the change in CO2from the change in the intensity of energy use in each sector and can represent the push and pull of both technological efficiency and socio-economic behaviour. They can also subsume intra-sectoral structural changes and energy price effects. In the economic sectors Cinte measures change based on the energy consumption per unit of GVA. Cintt measures change in CO2 based on the energy consumption per unit of travel activity (p-km and t-km), while Cintr measures change through the energy consumption per household unit. Cffse is a structural effect that represents the ratio of each fuel type in total fossil fuels. This effect measures the substitution of fossil fuels within each sector but not in electricity as this is a demand side analysis. Crepe shows the penetration of renewable energy into total final consumption under demand side control in each sector and not that in power generation. Cesmeasures the change in the structure of the economy, and Cts measures change in the structure of transport modes. The scale effects Cet, Ctt and Chn measure the changes in CO2emissions due to the changes in total economic output of the economic sectors, total transport work performed and total number of households respectively. Ctot indicates the aggregated change of all effects over time in each sector.
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