[Use of different modern strategies to reduce the energy use and CO2 emissions in dwellings]

by

ACKNOWLEDGEMENT

I would take this opportunity to thank my research supervisor, family and friends for their support and guidance without which this research would not have been possible (Adams, 2004,, 559).

DECLARATION

I, [type your full first names and surname here], declare that the contents of this dissertation/thesis represent my own unaided work, and that the dissertation/thesis has not previously been submitted for academic examination towards any qualification. Furthermore, it represents my own opinions and not necessarily those of the University (Adams, 2004,, 559).

Signed __________________ Date _________________

TABLE OF CONTENTS

ACKNOWLEDGEMENT2

DECLARATION3

CHAPTER 3: METHODOLOGY5

Data8

CHAPTER 4: RESULTS AND ANALSIS12

Economic sectors12

Transport modes17

Residential sector19

Synthesis and aggregated change21

Sensitivity and Approach23

Regression Estimation Results25

Sensitivity of results to assumptions27

Discussion27

Results for Other Determinants31

Energy Consumption32

Greenhouse Gas Emission34

CHAPTER 5: STRATEGIES39

Passive Strategies39

Descriptive analysis40

Model setup41

Discussion46

Orientation and urban form46

CHAPTER 6: CONCLUSION50

REFERENCES59

CHAPTER 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] and [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) and (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).

Assume that CEtij=Ctij/FFtij is the carbon emissions coefficient for fuel i ...