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Potential Energy Saving in Energy System Via DEA Technique by Relying on QoL

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Relationship Between Quality of Life and Energy Usage

Abstract

Previous chapter analyzed potential final energy consumption (FEC) saving while either improving or maintaining QoL. In other words, demand side analysis was the target of previous chapter without considering any economic limitation. The current chapter looks for potential improvement of QoL or at least keeping QoL by means of FEC and TPES together. Furthermore, energy supply constraint is added to ensure global energy supply and economic growth, which is the target of the sustainable energy strategy by IEA (Chap. 1, Sect. 1.4). Thus, this chapter focuses on both demand and supply sides to investigate potential amount of energy reduction while either keeping or improving QoL. Previous chapter utilized the concept of efficient area to present three energy policies corresponding with three types of country classifications; developed, developing, and pre-developing. This chapter employes the data envelopment analysis (DEA) technique to measure the efficiency of each country [decision making units (DMUs)]. The energy supply constraint can be defined through an appropriate type of DEA model orientation in the supply side analysis. Moreover, considering the output-oriented DEA model in the demand side of energy ensures the economic growth related to the energy consumption. However, various energy related studies have used the DEA technique to measure the efficiency of DMUs. While, heterogeneous DMUs and either inappropriate input or output-oriented DEA model lead to unreasonable results. The K-Means clustering algorithm is applied for energy data to classify homogenous countries. The input oriented DEA model is performed for power stations (PS) under renewables. Whereas, the output-oriented model is used to examine the efficiency of PS under non-renewables and refineiries as well as demand analysis. The energy related quality of life (QoL) is the output variable of the demand efficiency analysis. The overall energy efficiency is calculated by multiplying the efficiency of supply side into the demand side. The results of this chapter specify that the highest potential energy saving (PES) source in the supply side belongs to the non-renewables in power stations, followed by refineries, and finally deployment of renewables. Demand side analysis identifies that the highest PES belongs to countries with high population, and high-income economy. In conclusion, the results of overall energy efficiency based on QoL, suggests an allowance for the use of fossil fuels in countries with weak economic and low population. The allowance is proposed to support energy poverty, health improvement, and promotion of education.

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Notes

  1. 1.

    Certain commodities and services are required to satisfy the human wants. The ultimate desired outcome is defined to be the satisfaction of human wants [4], which can be replaced by “well-being”, “happiness”, or “QoL”.

  2. 2.

    Ecological sacrifice contains the man-made resources (tools, machines, commercial vehicles), and the undesired consequences, which sacrifice the surrounding natural ecology.

  3. 3.

    Commodities include goods (tangible commodities, materials) and services (intangible, or some desired actions). By excluding services, commodities here imply to goods.

  4. 4.

    Eco-efficiency focuses on creating more goods and services, while using fewer resources and creating less waste and pollution (Increase output per unit of input) [18].

  5. 5.

    De-commoditization emphasizes on decreasing the influence of tangible commodities (material) in human needs satisfaction [13]. Typically, moving toward sustainable production systems is called de-commoditization [63] (inclination toward RE sources in energy production system).

  6. 6.

    Eco-sufficiency focuses on reduction of the natural energy resource consumption such as oil, coal, gas, and uranium in the energy production system to generate less waste and pollution (Input reduction) [13].

  7. 7.

    In previous work, authors conducted correlation coefficient analysis between the QoL index and other indices such as HDI [40], and quality index proposed by Pasten and Santamarina [30]. There were similarity in the three methods for country ranking. The highest correlation was between the HDI and the QoL indicator.

  8. 8.

    This study examines data from 112 countries (due to the sparse amount of data pertaining to a country for six variables) over nine years, from 2005 until 2013.

  9. 9.

    “Other transformation” energy is also supposed as part of end-use energy, which obtains without conversion in the energy balance flows (Sankey Diagram).

  10. 10.

    Equation (4.5) is used to calculate the “before” QoL, while Eq. (4.6) is applied to estimate the “after” QoL.

  11. 11.

    Power plant, combined heat and power plant (CHP), and heat plant.

  12. 12.

    FECpc/GDP per capita, PPP.

  13. 13.

    Solar PV, wind, and hydroelectricity systems.

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Authors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Reza Nadimi

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  1. Reza Nadimi
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Correspondence to Reza Nadimi .

Appendices

Appendix 1

See Table 4.6.

Table 4.6 Name of countries in terms of G_1 to G_9
Full size table

Appendix 2

See Table 4.7.

Table 4.7 QoL data (year 2013 without normalization) corresponding with the name of countries in Appendix 2 (same order)
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Appendix 3

See Table 4.8.

Table 4.8 Factor analysis results (A), and the value of parameters for the sigmoid function (B)
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Appendix 4: Efficiency Change Over Nine Years for Refinery and QoL (G_9 Countries)

See Fig. 4.16.

Fig. 4.16
figure 16

Efficiency change over nine years for G_9 countries [demand analysis (QoL)]

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Appendix 5: Efficiency Values for Both CCR and BCC Models of the DEA

See Tables 4.9, 4.10 and 4.11.

Table 4.9 Efficiency scores for non-renewable and renewable energy data (G_9 category, year 2013)
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Table 4.10 Efficiency scores for refinery data (G_9 category, year 2013)
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Table 4.11 Supply, demand, and overall efficiency data (G_9 category, year 2013)
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Nadimi, R. (2019). Potential Energy Saving in Energy System Via DEA Technique by Relying on QoL. In: Relationship Between Quality of Life and Energy Usage. Springer, Singapore. https://doi.org/10.1007/978-981-13-7840-9_4

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