This book addresses several index decomposition analysis methods to assess progress made by EU countries in the last decade in relation to energy and climate change concerns. Several applications of these techniques are carried out in order to decompose changes in both energy and environmental aggregates. In addition to this, a new methodology based on classical spline approximations is introduced, which provides useful mathematical and statistical properties. Once a suitable set of determinant factors has been identified, these decomposition methods allow the researcher to quantify the respective contributions of these factors. A proper interpretation of findings enables the design of strategies and a number of energy and environmental policies to control the variables of interest. This book also analyses the impact of several factors that allow control of these variables; among them, assessment of the specific contribution of improved energy efficiency is particularly relevant. A number of divisia-index-based techniques for decomposing changes in a generic indicator are now available, and these range from classical techniques based on Laspeyres and Paasche weights to more refined approaches relying on logarithmic mean weighting schemes. This book is intended for undergraduates and graduates of energy economics and environmental sciences, environmental policy advisors, and industrial engineers.
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1 Literature Review and Methodology |
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1 | (16) |
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1 | (1) |
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2 | (2) |
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1.3 Divisia-Index-Based Methodology |
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4 | (13) |
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1.3.1 Structural Decomposition Versus Index-Based Decomposition Techniques |
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4 | (1) |
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1.3.2 Decomposition of the Change in an Indicator |
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4 | (1) |
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1.3.3 Types of Decomposition. Additive and Multiplicative Models |
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5 | (1) |
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1.3.4 Decomposition Techniques Based on Divisia Indices |
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5 | (2) |
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1.3.5 Parametric Divisia Methods: Some Classical Methods |
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7 | (3) |
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1.3.6 Nonparametric Divisia Method. The LMDI Approach |
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10 | (1) |
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1.3.7 Decomposition at Several Disaggregation Levels of Information |
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11 | (1) |
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1.3.8 Time Series Decomposition |
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12 | (2) |
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14 | (3) |
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2 Mathematical and Statistical Properties of Decomposition Techniques. The Splines Method |
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17 | (18) |
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17 | (1) |
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2.2 Path Reconstruction Through Interpolation. The Splines Method |
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18 | (2) |
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2.3 Mathematical Properties. Convergence |
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20 | (4) |
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2.3.1 Additive Decomposition |
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21 | (2) |
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2.3.2 Multiplicative Decomposition |
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23 | (1) |
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24 | (2) |
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26 | (9) |
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Appendix I Mathematical Proofs of
Chapter 2 |
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26 | (8) |
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34 | (1) |
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3 Multiplicative Decomposition of the Change in Aggregate Energy Intensity in the European Countries During the 1995--2010 Period |
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35 | (18) |
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35 | (1) |
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35 | (4) |
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3.2.1 Energy Intensity Approach. The Multiplicative Case |
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35 | (3) |
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3.2.2 Time Series Decomposition |
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38 | (1) |
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3.3 Analysis of the Change in Aggregate Energy Intensity in the European Union |
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39 | (12) |
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39 | (1) |
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3.3.2 Contributions of the Determinant Factors |
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40 | (11) |
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51 | (2) |
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51 | (2) |
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4 Additive Decomposition of Changes in Greenhouse Gas Emissions in the European Union in the 1990s |
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53 | (20) |
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53 | (1) |
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54 | (7) |
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55 | (1) |
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4.2.2 The Path-Based Method |
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56 | (4) |
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60 | (1) |
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61 | (1) |
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4.3 Analysis of Changes in Greenhouse Gas Emissions in the EU15 in the 1990s |
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61 | (9) |
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4.3.1 Contributions of Changes in the Factors |
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62 | (2) |
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4.3.2 Comparison with Annual Time Series Decomposition Results |
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64 | (5) |
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4.3.3 A Cross-Country Analysis |
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69 | (1) |
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70 | (3) |
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72 | (1) |
| Concluding Remarks |
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73 | (4) |
| Extra Contents and Instructions |
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77 | (2) |
| Specific Instructions for Using the MATLAB Codes |
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79 | |
Dr. Paula Fernández González is an associate professor of statistics and econometrics at the department of applied economics, University of Oviedo. She has worked for fourteen years in the fields of Energy and Environmental sciences, focusing on statistical methods, economic modelling and econometrics and paying particular attention to decomposition techniques like shift-share analysis and regression and index-based decomposition methods. She has published papers in international journals such as Energy, Energy Economics, Environmental Science and Policy and Regional Economic Studies. She is also a regular reviewer in the journals Energy, International Journal of Global Environmental Issues and Estudios de Economía Aplicada. At present, she is editor of the Journal of Economics Studies and Research and participates in several research projects of relevance.
Dr. Manuel Landajo is an associate professor of statistics and econometrics at the department of applied economics, University of Oviedo. His research interests include nonparametric statistics, time series analysis and artificial intelligence, with applications to business, economics and environmental fields. He has published papers in a number of journals, including Journal of The Royal Statistical Society-Part C, Journal of Time Series Analysis, European Journal of Operational Research, IEEE Transactions on Fuzzy Systems, Expert Systems with Applications, Knowledge-Based Systems, Empirical economics, and Journal of Forecasting. He regularly serves as project evaluator for public institutions and is also a reviewer for several leading scientific journals.
Dr. María José Presno is an associate professor of statistics and Econometrics at the department of applied economics, University of Oviedo. Her research interests cover time series econometrics, nonparametric methods and index-based decomposition techniques, with applications to economics and environmental economy. She has publishedabout these topics in Economic Letters, Journal of Time Series Analysis, Applied Economic Letters, Annals of the Institute of Statistical Mathematics, Energy and Resource and Energy Economics, among other journals.
