Advertisement

QoL Modeling Against Energy Consumption Per Capita

  • Reza NadimiEmail author
Chapter

Abstract

The previous chapter made a brief explanation about energy production and consumption, human development, QoL and its interaction with human energy needs. The capability approach was introduced as an approach to use in this study to construct QoL indicator. Moreover, a review was carried out for previous researches regarding energy and QoL, then emphasized the contribution of this study. This chapter provides a foundation for the rest of chapters through introducing mathematical principles to construct a proposed QoL indicator. The proposed QoL indicator data are modeled against total primary energy supply (TPES) data, to illustrate how QoL changes with variation of the TPES. The results are compared with similar researches in which variation of Human Development Index (HDI) and TPES have been addressed. Some scholars have addressed the relationship between the HDI and energy consumption in terms of the semi-logarithmic or hyperbolic function. One of the results from this relationship is to divide the world countries into two categories, developed and developing countries. According to this classification, pre-developing and developing countries are located into one category (developing), while the proportion of people with fundamental energy needs in the former is far higher than in the latter. The objective of this chapter is to allocate a separate class for pre-developing countries which their fundamental energy needs have not completely satisfied. The methodology section in this chapter is divided into two parts. Initially, based on the longitudinal data (112 country’s data during the period of 2005–2013), a linear QoL indicator is proposed in terms of six variables. Then an S-shape (sigmoid) curve is fitted to the QoL indicator data against TPES per capita (or Energy consumption per capita, ECpc) and electricity consumption per capita (Elcpc) data. Three types of countries, developed, developing, and pre-developing are identified based on the sigmoid function. The results of the proposed model demonstrate that the “pre-developing” category has different QoL and ECpc as compared to developing and developed classes that demands different energy policy in global energy strategy establishment. Another result of this chapter is that the entry of new technologies has influenced the QoL and Elcpc to a greater extent in developing countries than in developed countries. One-way analysis of variance is a method to shed light on the latter consequence. In the case of pre-developing countries, the pertinent analysis shows an insignificant impact. This chapter concludes the new classification of countries appropriately addresses variation of QoL against ECpc in each class.

Keywords

Energy consumption Electricity consumption Quality of life Factor analysis Technological change 

Bibliography

  1. 1.
    GEA, Global Energy Assessments (International Institute for Applied Systems Analysis, 2012)Google Scholar
  2. 2.
    S. Bin, H. Dowlatabadi, Consumer lifestyle approach to US energy use and the related CO2 emissions. Energy Policy 33, 197–208 (2005)CrossRefGoogle Scholar
  3. 3.
    S. Pachauri, D. Spreng, Direct and indirect energy requirements of households in India. Energy Policy 30, 511–523 (2002)CrossRefGoogle Scholar
  4. 4.
    A. Reinders, K. Vringer, K. Blok, The direct and indirect energy requirement of households in the European Union. Energy Policy 31, 139–153 (2003)CrossRefGoogle Scholar
  5. 5.
    Q. Ding, W. Cai, C. Wang, M. Sanwal, The relationships between household consumption activities and energy consumption in china—an input-output analysis from the lifestyle perspective. Appl. Energy 207, 520–532 (2017)CrossRefGoogle Scholar
  6. 6.
    S.D. Lauretis, F. Ghersi, J.-M. Cayla, Energy consumption and activity patterns: an analysis extended to total time and energy use for French households. Appl. Energy 206, 634–648 (2017)CrossRefGoogle Scholar
  7. 7.
    L. Liu, T. Chen, Y. Yin, Energy consumption and quality of life: energy efficiency index. Energy Procedia 88, 224–229 (2016)CrossRefGoogle Scholar
  8. 8.
    J.G. Lambert, C.A. Hall, S. Balogh, A. Gupta, M. Arnold, Energy, EROI and quality of life. Energy Policy 64, 153–167 (2014)CrossRefGoogle Scholar
  9. 9.
    C. Pasten, J.C. Santamarina, Energy and quality of life. Energy Policy 49, 468–476 (2012)CrossRefGoogle Scholar
  10. 10.
    A. Mazur, Does increasing energy or electricity consumption improve quality of life in industrial nations? Energy Policy 39, 2568–2572 (2011)CrossRefGoogle Scholar
  11. 11.
    M.S. Alam, B.K.B.K. Bala, A.M.Z. Huq, M.A. Matin, A model for the quality of life as a function of electrical energy consumption. Energy 16, 739–745 (1991)CrossRefGoogle Scholar
  12. 12.
    U. Al-mulali, Exploring the bi-directional long run relationship between energy consumption and life quality. Renew. Sustain. Energy Rev. 54, 824–837 (2016)CrossRefGoogle Scholar
  13. 13.
    R. Nadimi, K. Tokimatsu, Modeling of quality of life in terms of energy and electricity consumption. Appl. Energy 212, 1282–1294 (2017)CrossRefGoogle Scholar
  14. 14.
    J.K. Steinberger, J.T. Roberts, From constraint to sufficiency: the decoupling of energy and carbon from human needs, 1975–2005. Ecol. Econ. 70, 425–433 (2010)CrossRefGoogle Scholar
  15. 15.
    A.D. Pasternak, Global Energy Futures and Human Development: A Framework for Analysis (U.S. Department of Energy 2000)Google Scholar
  16. 16.
    T. Sanchez, A. Scott, Energy poverty: the hidden energy crisis (2010). [Online]. Available www.practicalaction.org. Accessed Oct 2017
  17. 17.
    S. Pachauri, Reaching an international consensus on defining modern energy access. Curr. Opin. Environ. Sustain. 240(3), 235 (2011)CrossRefGoogle Scholar
  18. 18.
    T. Sanchez, The Hidden Energy Crisis: How Policies are Failing the World’s Poor (Practical Action Publication, 2010)Google Scholar
  19. 19.
    UNDP, Human Development Report (UNDP, New York, 1990–2015)Google Scholar
  20. 20.
    E. S. M. A. P. (ESMAP), Beyond Connections Energy Access Redefined (World Bank, Washington DC 20433, 2015)Google Scholar
  21. 21.
    B.S. Everitt, G. Dunn, Applied Multivariate Data Analysis (Edward Arnold, London, 1991)Google Scholar
  22. 22.
    I.H. Witten, E. Frame, Data Mining Practical Machine Learning Tools and Techniques (Morgan Kaufmann, 2005)Google Scholar
  23. 23.
    E. Guadagnoli, W.F. Velicer, Relation of Sample Size to the Stability of Component Patterns, vol. 103, no. 2 (American Psychological Association, Inc., 1988), pp. 265–275Google Scholar
  24. 24.
    V. Govindarajan, P.K. Kopalle, Disruptiveness of innovations: measurement and an assessment of reliability and validity. Strateg. Manag. J. 27, 189–199 (2006)CrossRefGoogle Scholar
  25. 25.
    F. Jolai, R. Nadimi, Joint use of factor analysis (FA) and data envelopment analysis (DEA) for ranking of data envelopment analysis. Int. J. Math. Phys. Eng. Sci. 218–222 (2008)Google Scholar
  26. 26.
    D.E. Farrar, R.R. Glauber, Multicollinearity in regression analysis: the problem revisited. Rev. Econ. Stat., JSTOR (1967)Google Scholar
  27. 27.
    R. Nadimi, H.G. Shakouri, Factor analysis (FA) as ranking and an efficient data reducing approach for decision making units: SAFA Rolling & Pipe Mills Company case study. Appl. Math. Sci. 5, 3917–3927 (2011)Google Scholar
  28. 28.
    A.D. Sagar, Alleviating energy poverty for the world’s poor. Energy Policy 33, 1367–1372 (2005)CrossRefGoogle Scholar
  29. 29.
    A.B. Lovins, Energy efficiency, taxonomic overview. Encycl. Energy 6, 383–401 (2004)CrossRefGoogle Scholar
  30. 30.
    K.R. Smith, H. Frumkin, K. Balakrishnan, C.D. Butler, Z.A. Chafe, I. Fairlie, P. Kinney, T. Kjellstrom, D.L. Mauzerall, T.E. McKone, A.J. McMichael, M. Schneider, Energy and human health. Annu. Rev. Public Health 34, 159–188 (2013)CrossRefGoogle Scholar
  31. 31.
    R. Johnson, D. Wichern, Applied Multivariate Statistical Methods, 3rd edn. (Prentice Hall, 1992)Google Scholar
  32. 32.
    D.C. Montgomery, Design and Analysis of Experiments, 5th edn. (Wiley, New York, 2001)Google Scholar
  33. 33.
    B. Barcaccia, Quality of life: everyone wants it, but what is it? (2013). [Online]. Available https://www.forbes.com/
  34. 34.
    J.E. Stiglitz, A. Sen, J.-P. Fitoussi, Commission on the Measurement of Economic Performance and Social Progress (2009)Google Scholar
  35. 35.
    OECD, OECD Better Life Index (2017)Google Scholar
  36. 36.
    J.D. Edgerton, L.W. Roberts, S. von Below, Education and quality of life, in Handbook of Social Indicators and Quality of Life Research (Springer Netherlands, 2011), pp. 265–296Google Scholar
  37. 37.
    M. Harsdoff, J. Peters, On-Grid Rural Electrification in Benin (RWI Materialien, 2010)Google Scholar
  38. 38.
    UN Women, Facts and figures on gender and climate change (2011). [Online]. Available www.unifem.org
  39. 39.
    SDSN, Indicators and a Monitoring Framework for the Sustainable Development Goals (Sustainable Development Solutions Network (SDSN), 2015)Google Scholar
  40. 40.
    BPN, The facts about the global drinking water crisis (2010). [Online]. Available blueplanetnetwork.org/water/facts
  41. 41.
    UNESCO, Water for a Sustainable World (United Nations Educational, Scientific and Cultural, France, 2015)Google Scholar
  42. 42.
    World Bank Open Data (2016). [Online]. Available data.worldbank.org
  43. 43.
    J.-W. Lee, R.J. Barro, Educational attainment dataset (2016). [Online]. Available http://www.barrolee.com/
  44. 44.
    Human Development Reports, United Nations Development Program (2016). [Online]Google Scholar
  45. 45.
    UNDP, Human Development Reports. United Nations Development Program (2017). [Online]. Available http://hdr.undp.org/en/indicators/
  46. 46.
    S. Hamed, R. Nadimi, Outlier detection in fuzzy linear regression with crisp input–output by linguistic variable view. Appl. Soft Comput. 13, 734–742 (2013)CrossRefGoogle Scholar
  47. 47.
    D.M. Martínez, B.W. Ebenhack, Understanding the role of energy consumption in human development through the use of saturation phenomena. Energy Policy 36(4), 1430–1435 (2008)CrossRefGoogle Scholar
  48. 48.
    N.T. Aden, N. Zheng, D.G. Fridley, How Can China Lighten Up? Urbanization, Industrialization and Energy Demand Scenarios (Lawrence Berkeley National Laboratory, 2010)Google Scholar
  49. 49.
    D. McCollum, L.G. Echeverri, K. Riahi, S. Parkinson, SDG 7 Ensure Access to Affordable, Reliable, Sustainable and Modern Energy for All (International Council for Science, Paris, 2017)Google Scholar
  50. 50.
    IEA, International Energy Agency (2016). [Online]. Available http://www.iea.org/statistics
  51. 51.
    E.A. Rosa, R. York, T. Dietz, Tracking the anthropogenic drivers of ecological impacts. J. Human Environ. 33(8), 509–512 (2004)CrossRefGoogle Scholar
  52. 52.
    J. Zinck Thellufsen, H. Lund, Roles of local and national energy systems in the integration of renewable energy. Applied Energy 183, 419–429 (2016)CrossRefGoogle Scholar
  53. 53.
    E.J.O. Promes, T. Woudstra, L. Schoenmakers, V. Oldenbroek, A. Thallam Thattai, P. Aravind, Thermodynamic evaluation and experimental validation of 253 MW Integrated Coal Gasification Combined Cycle power plant in Buggenum, Netherlands. Applied Energy 155, 181–194 (2015)CrossRefGoogle Scholar
  54. 54.
    R. Hoya, C. Fushimi, Thermal efficiency of advanced integrated coal gasification combined cycle power generation systems with low-temperature gasifier, gas cleaning and CO2 capturing units. Fuel Process. Technol. 164, 80–91 (2017)CrossRefGoogle Scholar
  55. 55.
  56. 56.
    Eltis, Energy-consumption label for passenger cars in Switzerland (2014). [Online]. Available http://www.eltis.org/discover/case-studies/energy-consumption-label-passenger-cars-switzerland. Accessed Nov 2017
  57. 57.
    P.-M. Boulanger, Three strategies for sustainable consumption. S.A.P.I.EN.S 3(2) (2010)Google Scholar
  58. 58.
    H. Chena, J.-N. Kanga, H. Liao, B.-J. Tang, Y.-M. Wei, Costs and potentials of energy conservation in China’s coal-fired power industry: a bottom-up approach considering price uncertainties. Energy Policy 104, 23–32 (2017)CrossRefGoogle Scholar
  59. 59.
    C.A. Miller, C. Altamirano-Allende, N. Johnson, M. Agyemang, The social value of mid-scale energy in Africa: redefining value and redesigning energy to reduce poverty. Energy Res. Soc. Sci. 5, 67–69 (2015)CrossRefGoogle Scholar
  60. 60.
    A. Chaurey, T.C. Kandpal, Assessment and evaluation of PV based decentralized rural electrification: an overview. Renew. Sustain. Energy Rev. 14(8), 2266–2278 (2010)CrossRefGoogle Scholar
  61. 61.
    JICA, Projects classified as Category A, B, or FI_African Countries. Japan International Cooperation Agency (2017). [Online]. Available https://www.jica.go.jp/
  62. 62.
    e. pdf_giz, Productive Use of Energy—PRODUSE A Manual for Electrification Practitioners. Eschborn (2011)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Tokyo Institute of TechnologyTokyoJapan

Personalised recommendations