Estimating Relative Efficiency of Electricity Consumption in 42 Countries during the Period of 2008–2017
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Proposal Research
- Step 1: Present the purpose of the selected topic, input, and output variables. The theme and data must be reselected if they are inappropriate. The suitable materials of electricity, as listed on Enerdata [38], Worldbank [39], and Epa [40], are collected. Then, the EC from all over the world is introduced and factors relating the production process with EC are described.
- Step 2: Show the benefits of electricity. The study overviews EC and its influences on the environment; and the undesirable model theory is used to demonstrate in feasibility of the method. Especially, previous studies that relate to EC and the undesirable model to indicate a probability theme are discussed.
- Step 3: The first stage of the analysis process must check the Pearson coefficient to ensure the data is isotonic; any value does not range from −1 to +1 it must be removed and reselected. Next, the suitable values are applied into an undesirable outputs model to compute scores. The scores are used for determining the efficiency/inefficiency of 42 countries over the years. The scores propound their ranking over each term as well. The empirical results present a stable or upward and downward interplay of countries during the period of 2008–2017 in particular. Moreover, the analysis results suggest the current status of the effect level in each year when utilizing electrical energy.
- Step 4: Manifest main points of the empirical results of efficient/inefficient countries, and ranking, in addition to recommendations on the analysis of a variable pathway of each country in every year. The suggestion points out improvements for inefficient countries.
3.2. Data Source
- Population (Input): When the population of a nation increases, the electricity usage increases because the amount of electronic equipment will be augmented as well.
- Electricity consumption (Input): The electricity is consumed by providing electrical energy for light, heating, cooling, machines, and so on.
- GDP (desirable output): The economic performance of every country is measured by market value. In the electricity sector, the volumes of EC are used by consumers for any application, i.e., they contribute to extending GDP indicators.
- CO2, CH4, N2O (undesirable outputs): Coal, oil, natural gas, and biomass are burned in combustion power plants. Nuclear power plants create heat, in addition to the heat of the Sun in solar power, turbines in hydropower plants via the energy power of water from natural waterfalls, tides, and flowing rivers create electricity, or turbines in wind power plants by the wind’s energy. These processes all generate electricity, then the generation electricity is transmitted to customers via wires. When the electrical energy is consumed, the EC process produces emissions, including CO2, CH4, and N2O.
3.3. Undesirable Outputs Model
4. Results
4.1. Data Analysis
4.2. Efficienct and Inefficient Terms
4.3. Ranking Countries
4.4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Years | Population | EC (TWh) | CO2 (Mtons) | CH4 (Mtons) | N2O (Mtons) | GDP (Million in USD) |
---|---|---|---|---|---|---|
2008 | 1,324,655,000 | 3907.229 | 2598.6199 | 0.1445 | 0.0271 | 14,718,582 |
2,652,340 | 18.517 | 12.3153 | 0.0007 | 0.0001 | 29,549.44 | |
118,126,627.26 | 373.5982 | 248.4727 | 0.0138 | 0.0026 | 1,336,946.326 | |
263,672,064.05 | 728.7291 | 484.6632 | 0.027 | 0.0051 | 2,411,815.529 | |
2009 | 1,331,260,000 | 3724.658 | 2477.1955 | 0.1378 | 0.0258 | 14,418,739 |
2,818,939 | 18.051 | 12.0054 | 0.0007 | 0.0001 | 33,689.22 | |
119,287,116.55 | 371.3682 | 246.9896 | 0.0137 | 0.0026 | 1,268,097.807 | |
266,092,473.27 | 728.332 | 484.3991 | 0.0269 | 0.0051 | 2,386,203.268 | |
2010 | 1,337,705,000 | 3894.367 | 2598.6199 | 0.1445 | 0.0271 | 14,964,372 |
2,998,083 | 20.876 | 12.3153 | 0.0007 | 0.0001 | 39,332.77 | |
120,434,614.83 | 397.1979 | 248.4727 | 0.0138 | 0.0026 | 1,393,321.39 | |
268,462,393.21 | 786.178 | 484.6632 | 0.027 | 0.0051 | 2,506,496.099 | |
2011 | 1,344,130,000 | 4051.605 | 2694.6415 | 0.1499 | 0.0281 | 15,517,926 |
3,191,051 | 23.679 | 15.7484 | 0.0009 | 0.0002 | 45,915.19 | |
121,536,746.24 | 409.9938 | 272.6786 | 0.0152 | 0.0029 | 1,549,594.858 | |
270,791,913.9 | 827.6549 | 550.4567 | 0.0306 | 0.0057 | 2,671,950.305 |
Years | Population | EC (TWh) | CO2 (Mtons) | CH4 (Mtons) | N2O (Mtons) | GDP (Million in USD) |
---|---|---|---|---|---|---|
2012 | 1,350,695,000 | 4326.079 | 2877.188 | 0.16 | 0.03 | 16,155,255 |
3,395,556 | 25.399 | 16.8924 | 0.0009 | 0.0002 | 51,821.57 | |
122,670,658.43 | 419.9453 | 279.1316 | 0.0155 | 0.0029 | 1,583,732.445 | |
273,099,786.91 | 851.7249 | 566.4128 | 0.0315 | 0.0059 | 2,799,627.018 | |
2013 | 1,357,380,000 | 4717.568 | 3137.5601 | 0.1745 | 0.0327 | 16,691,517 |
3,598,385 | 23.689 | 15.7551 | 0.0009 | 0.0002 | 57,690.45 | |
123,810,535.81 | 432.4245 | 287.745 | 0.016 | 0.003 | 1,622,822.758 | |
275,389,972.61 | 899.751 | 598.4121 | 0.0333 | 0.0062 | 2,899,088.887 | |
2014 | 1,364,270,000 | 4938.623 | 3284.5794 | 0.183 | 0.0343 | 17,427,609 |
3,782,450 | 24.625 | 16.3776 | 0.001 | 0.0002 | 63,067.08 | |
124,952,217.6 | 441.3991 | 292.8808 | 0.0163 | 0.0031 | 1,662,353.067 | |
277,690,414.5 | 927.2612 | 616.5223 | 0.0343 | 0.0064 | 3,046,071.083 | |
2015 | 1,371,220,000 | 5103.889 | 3301.0023 | 0.1836 | 0.0344 | 18,120,714 |
3,935,794 | 25.268 | 17.848 | 0.001 | 0.0002 | 66,903.8 | |
126,084,336.38 | 448.5672 | 292.5948 | 0.0163 | 0.0031 | 1,572,917.053 | |
279,993,795.16 | 946.811 | 619.273 | 0.0344 | 0.0065 | 3,157,505.409 | |
2016 | 1,378,665,000 | 5366.78 | 3471.2873 | 0.1931 | 0.0362 | 1,862,4475 |
4,052,584 | 24.5605 | 16.2416 | 0.0009 | 0.0002 | 67,067.57 | |
127,216,593.57 | 459.7215 | 307.2145 | 0.0171 | 0.0031 | 1,596,048.935 | |
282,345,525.38 | 977.6329 | 635.8452 | 0.0354 | 0.0066 | 3,243,650.311 | |
2017 | 1,386,395,000 | 5683.42 | 3779.929 | 0.2102 | 0.0394 | 19,390,604 |
4,136,528 | 24.4774 | 16.2794 | 0.0009 | 0.0002 | 48,717.69 | |
128,333,654.5 | 471.4782 | 313.5707 | 0.0174 | 0.0033 | 1,692,506.563 | |
284,721,026.7 | 1010.7354 | 672.2199 | 0.0374 | 0.007 | 3,412,183.899 |
Factors | Cases | |||||
---|---|---|---|---|---|---|
Valid | Missing | Total | ||||
N | Percent | N | Percent | N | Percent | |
(I) Population | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
(I) Electricity consumption (TWh) | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
(O) GDP (million USD) | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
(Obad) CO2 (Mtons) | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
(Obad) CH4 (Mtons) | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
(Obad) N2O (Mtons) | 417 | 100.00% | 0 | 0.00% | 42 | 100.00% |
Indicators | Year | Population | EC (TWh) | CO2 (Mtons) | CH4 (Mtons) | N2O (Mtons) | GDP (Million USD) |
---|---|---|---|---|---|---|---|
Population | 2008 | 1 | 0.580126 | 0.580126 | 0.580341 | 0.579862 | 0.303741 |
EC (TWh) | 0.580126 | 1 | 1 | 0.999999 | 0.999986 | 0.901139 | |
CO2 (Mtons) | 0.580126 | 1 | 1 | 0.999999 | 0.999986 | 0.901139 | |
CH4 (Mtons) | 0.580341 | 0.999999 | 0.999999 | 1 | 0.999985 | 0.901071 | |
N2O (Mtons) | 0.579862 | 0.999986 | 0.999986 | 0.999985 | 1 | 0.901355 | |
GDP (million USD) | 0.303741 | 0.901139 | 0.901139 | 0.901071 | 0.901355 | 1 | |
Population | 2009 | 1 | 0.616812 | 0.616812 | 0.616607 | 0.616654 | 0.33962 |
EC (TWh) | 0.616812 | 1 | 1 | 0.999999 | 0.999985 | 0.894023 | |
CO2 (Mtons) | 0.616812 | 1 | 1 | 0.999999 | 0.999985 | 0.894023 | |
CH4 (Mtons) | 0.616607 | 0.999999 | 0.999999 | 1 | 0.999984 | 0.894111 | |
N2O (Mtons) | 0.616654 | 0.999985 | 0.999985 | 0.999984 | 1 | 0.893678 | |
GDP (million USD) | 0.33962 | 0.894023 | 0.894023 | 0.894111 | 0.893678 | 1 | |
Population | 2010 | 1 | 0.633979 | 0.576015 | 0.576229 | 0.575756 | 0.380058 |
EC (TWh) | 0.633979 | 1 | 0.994505 | 0.994535 | 0.994407 | 0.900126 | |
CO2 (Mtons) | 0.576015 | 0.994505 | 1 | 0.999999 | 0.999986 | 0.934326 | |
CH4 (Mtons) | 0.576229 | 0.994535 | 0.999999 | 1 | 0.999985 | 0.93427 | |
N2O (Mtons) | 0.575756 | 0.994407 | 0.999986 | 0.999985 | 1 | 0.934449 | |
GDP (million USD) | 0.380058 | 0.900126 | 0.934326 | 0.93427 | 0.934449 | 1 | |
Population | 2011 | 1 | 0.664182 | 0.664182 | 0.664216 | 0.664145 | 0.419236 |
EC (TWh) | 0.664182 | 1 | 1 | 1 | 0.999989 | 0.898789 | |
CO2 (Mtons) | 0.664182 | 1 | 1 | 1 | 0.999989 | 0.898789 | |
CH4 (Mtons) | 0.664216 | 1 | 1 | 1 | 0.999989 | 0.898727 | |
N2O (Mtons) | 0.664145 | 0.999989 | 0.999989 | 0.999989 | 1 | 0.899497 | |
GDP (million USD) | 0.419236 | 0.898789 | 0.898789 | 0.898727 | 0.899497 | 1 | |
Population | 2012 | 1 | 0.680572 | 0.680718 | 0.680613 | 0.680276 | 0.440727 |
EC (TWh) | 0.680572 | 1 | 0.999997 | 0.999997 | 0.999983 | 0.902231 | |
CO2 (Mtons) | 0.680718 | 0.999997 | 1 | 1 | 0.999989 | 0.902234 | |
CH4 (Mtons) | 0.680613 | 0.999997 | 1 | 1 | 0.999988 | 0.902301 | |
N2O (Mtons) | 0.680276 | 0.999983 | 0.999989 | 0.999988 | 1 | 0.90262 | |
GDP (million USD) | 0.440727 | 0.902231 | 0.902234 | 0.902301 | 0.90262 | 1 |
Indicators | Year | Population | EC (TWh) | CO2 (Mtons) | CH4 (Mtons) | N2O (Mtons) | GDP (Million USD) |
---|---|---|---|---|---|---|---|
Population | 2013 | 1 | 0.695153 | 0.696013 | 0.695992 | 0.695301 | 0.464744 |
EC (TWh) | 0.695153 | 1 | 0.999998 | 0.999998 | 0.999988 | 0.907294 | |
CO2 (Mtons) | 0.696013 | 0.999998 | 1 | 1 | 0.999988 | 0.907197 | |
CH4 (Mtons) | 0.695992 | 0.999998 | 1 | 1 | 0.999987 | 0.907255 | |
N2O (Mtons) | 0.695301 | 0.999988 | 0.999988 | 0.999987 | 1 | 0.90767 | |
GDP (million USD) | 0.464744 | 0.907294 | 0.907197 | 0.907255 | 0.90767 | 1 | |
Population | 2014 | 1 | 0.706217 | 0.705663 | 0.705031 | 0.705695 | 0.480833 |
EC (TWh) | 0.706217 | 1 | 0.999991 | 0.999952 | 0.99998 | 0.907172 | |
CO2 (Mtons) | 0.705663 | 0.999991 | 1 | 0.999961 | 0.999989 | 0.907172 | |
CH4 (Mtons) | 0.705031 | 0.999952 | 0.999961 | 1 | 0.999947 | 0.907122 | |
N2O (Mtons) | 0.705695 | 0.99998 | 0.999989 | 0.999947 | 1 | 0.906706 | |
GDP (million USD) | 0.480833 | 0.907172 | 0.907172 | 0.907122 | 0.906706 | 1 | |
Population | 2015 | 1 | 0.714941 | 0.708444 | 0.708326 | 0.70894 | 0.491955 |
EC (TWh) | 0.714941 | 1 | 0.999494 | 0.999493 | 0.999839 | 0.908562 | |
CO2 (Mtons) | 0.708444 | 0.999494 | 1 | 1 | 0.999616 | 0.913374 | |
CH4 (Mtons) | 0.708326 | 0.999493 | 1 | 1 | 0.999616 | 0.91337 | |
N2O (Mtons) | 0.70894 | 0.999839 | 0.999616 | 0.999616 | 1 | 0.914613 | |
GDP (million USD) | 0.491955 | 0.908562 | 0.913374 | 0.91337 | 0.914613 | 1 | |
Population | 2016 | 1 | 0.724975 | 0.718889 | 0.71891 | 0.720908 | 0.490153 |
EC (TWh) | 0.724975 | 1 | 0.998089 | 0.9981 | 0.999912 | 0.8947 | |
CO2 (Mtons) | 0.718889 | 0.998089 | 1 | 1 | 0.998155 | 0.897223 | |
CH4 (Mtons) | 0.71891 | 0.9981 | 1 | 1 | 0.998165 | 0.897175 | |
N2O (Mtons) | 0.720908 | 0.999912 | 0.998155 | 0.998165 | 1 | 0.898646 | |
GDP (million USD) | 0.490153 | 0.8947 | 0.897223 | 0.897175 | 0.898646 | 1 | |
Population | 2017 | 1 | 0.73631 | 0.73631 | 0.736469 | 0.73605 | 0.508286 |
EC (TWh) | 0.73631 | 1 | 1 | 1 | 0.999992 | 0.891115 | |
CO2 (Mtons) | 0.73631 | 1 | 1 | 1 | 0.999992 | 0.891115 | |
CH4 (Mtons) | 0.736469 | 1 | 1 | 1 | 0.999992 | 0.89107 | |
N2O (Mtons) | 0.73605 | 0.999992 | 0.999992 | 0.999992 | 1 | 0.891228 | |
GDP (million USD) | 0.508286 | 0.891115 | 0.891115 | 0.89107 | 0.891228 | 1 |
Appendix B
References
- Richard, F.H.; Jonathan, G.K. Electricity Consumption and Economic Growth: A New Relationship with Significant Consequences. Electr. J. 2015, 28, 72–84. [Google Scholar] [CrossRef]
- Tewathia, N. Determinant of the Household Electricity Consumption: A Case Study of Delhi. Int. J. Energy Econ. Policy 2014, 4, 337–348. [Google Scholar]
- Hameed, L.; Khan, A.A. Population Growth and Increase in Domestic Electricity Consumption in Pakistan: A Case Study of Bahawalpur City. J. Soc. Sci. 2016, 2, 27–33. [Google Scholar]
- Lu, W.-C. Electricity Consumption and Economic Growth: Evidence from 17 Taiwanese industries. Sustainability 2016, 9, 50. [Google Scholar] [CrossRef]
- Enu, P.; Havi, E.D.K. Influence of Electricity Consumption on Economic Growth in Ghana. Int. J. Econ. Commer. Manag. 2014, II, 1–20. [Google Scholar]
- Altinay, G.; Karagol, E. Electricity Consumption and Economic Growth: Evidence from Turkey. Energy Econ. 2005, 27, 849–856. [Google Scholar] [CrossRef]
- Ologun, O.O.; Wara, S.T. Carbon Footprint Evaaluation and Reduction as a Climate Change Mitigation Tool—Case Study of Federal University of Agriculture Abeokuta, Ogun, State, Nigeria. Int. J. Renew. Energy Res. 2014, 4, 176–181. [Google Scholar]
- To, W.M.; Peter, K.C.L. GHG emissions from electricity consumption: A case study of Hong Kong from 2002 to 2015 and trends to 2030. J. Clean. Prod. 2017, 165, 589–598. [Google Scholar] [CrossRef]
- Hooi, H.L.; Russell, S. CO2 emissions, electricity consumption and output in ASEAN. Appl. Energy 2010, 87, 1858–1864. [Google Scholar] [CrossRef]
- Mohammed Redha Qader. Electricity Consumption and GHG Emissions in GCC Countries. Energies 2009, 2, 1201–1213. [Google Scholar] [CrossRef] [Green Version]
- Albiman, M.M.; Suleiman, N.N.; Baka, M.O. The Relationship between Energy Consumption, CO2 Emissions and Economic Growth in Tanzania. Int. J. Energy Sect. Manag. 2015, 9, 361–375. [Google Scholar] [CrossRef]
- Battery and Energy Technologies. Available online: https://www.mpoweruk.com (accessed on 10 August 2018).
- Seyed Esmaeili, F.; Rostamy-Malkhalifed, M. Data Envelopment Analysis with Fixed Inputs, Undesirable Outputs and Negative Data. J. Data Envel. Anal. Decis. Sci. 2017, 2017, dea–00140. [Google Scholar] [CrossRef]
- Lawrence, M.S.; Joe, Z. Modeling Undesirable Factors in Efficiency Evaluation. Eur. J. Oper. Res. 2002, 142, 16–20. [Google Scholar] [CrossRef]
- Chung, Y.H.; Färe, R.; Grosskopf, S. Productivity and Undesirable Outputs: A Directional Distance Function Approach. J. Environ. Manag. 1997, 51, 229–240. [Google Scholar] [CrossRef]
- Chen, W.-J.; He, G. Electricity Consumption and its Impact Factors: Based on the Nonparametric Model. Syst. Eng. Theory Pract. 2009, 29, 92–97. [Google Scholar] [CrossRef]
- Gajowniczek, K.; Ząbkowski, T. Short term electricity forecasting based on user behavior using individual smart meter data. Intell. Fuzzy Syst. 2015, 30, 223–234. [Google Scholar] [CrossRef]
- Gajowniczek, K.; Ząbkowski, T. Two-stage electricity demand modelling using machine learning algorithms. Energies 2017, 10, 1547. [Google Scholar] [CrossRef]
- Singh, S.; Yassine, A. Big data mining of energy time series for behavioral analytics and energy consumption forecasting. Energies 2018, 11, 452. [Google Scholar] [CrossRef]
- Paul, E.H.; Tom, S.C.; Robert, G.H. Life cycle greenhouse gas emissions from electricity generation: A comparative analysis of Australian energy source. Energies 2012, 5, 872–897. [Google Scholar] [CrossRef]
- Hanqin, T.; Guangsheng, C.; Chaoqun, L. Global Methane and Nitrous Oxide Emissions from Terrestrial Ecosystems due to Multiple Environmental Changes. Ecosyst. Health Sustain. 2015, 1, 1–20. [Google Scholar] [CrossRef]
- Jiang, L.; Ou, X.; Ma, L.; Li, Z.; Ni, W. Life-cycle GHG Emission Factors of Final Energy in China. Energy Procedia 2013, 37, 2848–2855. [Google Scholar] [CrossRef]
- Timo, A.S.; Olli, V.; Laura, S.; Matti, K. Greenhouse Gas Emissions of Hydropower in the Mekong River Basin. Environ. Res. Lett. 2018, 13, 034030. [Google Scholar] [CrossRef]
- Yasutoshi, S.; Satoshi, D.; Kanako, T. The CO2 Emission Factor of Water in Japan. Water 2012, 4, 759–769. [Google Scholar] [CrossRef]
- Xiaochun, Z.; Nathan, P.M.; Ken, C. Key Factors for Assessing Climate Benefits of Natural Gas Versus Coal Electricity Generation. Environ. Res. Lett. 2014, 9, 114022. [Google Scholar] [CrossRef]
- Chang-Sang, C.; Jae-Hwan, S.; Ki-Kyo, L.; Tae-Mi, Y. Development of Methane and Nitrous Oxide Emission Factors for the Biomass Fired Circulating Fluidized Bed Combustion Power Plant. Sci. World J. 2012. [Google Scholar] [CrossRef]
- Yuxuan, W.; Tianye, S. Life Cycle Assessment of CO2 Emissions from Wind Power Plants: Methodology and Case Studies. Renew. Energy 2012, 43, 30–36. [Google Scholar] [CrossRef]
- Ma, C.M.; Ge, Q.S. Method for Calculating CO2 Emissions from the Power Sector at the Provincial Level in China. Adv. Clim. Chang. Res. 2014, 5, 92–99. [Google Scholar] [CrossRef]
- Greg, S.; Ines, A.; Constantine, S. Assessing the Evolution of Power Sector Carbon Intensity in the United States. Environ. Res. Lett. 2018, 13. [Google Scholar] [CrossRef]
- Tone, K. Dealing with Undesirable Outputs in DEA: A Slacks-Based Measure (SBM) Approach; National Graduate Institute for Policy Studies: Tokyo, Japan, 2003. [Google Scholar]
- Amiteimoori, A.; Toloie-Eshlaghi, A.; Homayoonfar, M. Efficiency Measurement in Two-Stage Network Structures Considering Undesirable Outputs. Int. J. Ind. Math. 2014, 6, 65–72. [Google Scholar]
- Scheel, H. Undesirable Outputs in Efficiency Valuations. Eur. J. Oper. Res. 2001, 132, 400–410. [Google Scholar] [CrossRef]
- Gongbing, B.; Liang, L.; Jie, W. Radial and non-radial DEA Models Undesirable Outputs: An Application to OECD Countries. Int. J. Sustain. Soc. 2010, 2. [Google Scholar] [CrossRef]
- Tone, K.; Tsutsui, M. Applying an Efficiency Measure of Desirable and Undesirable Outputs in DEA to U.S. Electric Utilities. J. Cent. Cathedra 2001, 4, 236–249. [Google Scholar] [CrossRef]
- Wen-Hsien, T.; Hsin-Li, L.; Chid-Hao, Y.; Chung-Chen, H. Input-Output Analysis for Sustainability by Using DEA Method: A Comparison Study between European and Asian Countries. Sustainability 2016, 8, 1230. [Google Scholar] [CrossRef]
- You, S.; Yan, H. A New Approach in Modelling Undesirable Output in DEA Model. J. Oper. Res. Soc. 2001, 62, 2146–2156. [Google Scholar] [CrossRef]
- Flavia, D.C.C.; Daisy, A.D.N.R.; Roberta, T.R. Energy Efficiency Analysis of BRICS Countries: A Study Using Data Envelopment Analysis. Gest. Prod. 2016, 23, 192–203. [Google Scholar] [CrossRef]
- Electricity Consumption. Available online: https://yearbook.enerdata.net (accessed on 8 July 2018).
- Population, GDP. Available online: https://data.worldbank.org/indicator (accessed on 8 July 2018).
- CO2, CH4, N2O. Available online: https://www.epa.gov/sites/production/files/2015-08/sgec_tool_v3_2.xls (accessed on 8 July 2018).
- Tone, K. A slack-based measure of super-efficiency in data envelopment analysis. Eur. J. Oper. Res. 2002, 143, 34–41. [Google Scholar] [CrossRef]
- Tone, K. A slacks-based measure of efficiency in data envelopment analysis. Eur. J. Oper. Res. 2001, 130, 498–509. [Google Scholar] [CrossRef] [Green Version]
- Charnes, A.; Cooper, W.W. Programming with linear fractional functionals. Nav. Res. Logist. Q. 1962, 9, 181–186. [Google Scholar] [CrossRef]
- Pilli-Sihvola, K.; Aatola, P.; Ollikainen, M.; Tuomenvirta, H. Climate change and electricity consumption—Witnessing increasing or decreasing use and costs? Energy Policy 2010, 38, 2409–2419. [Google Scholar] [CrossRef]
No | Country | No | Country | No | Country |
---|---|---|---|---|---|
1 | Belgium | 15 | Kazakhstan | 29 | Japan |
2 | Czech Republic | 16 | Russia | 30 | Malaysia |
3 | France | 17 | Ukraine | 31 | South Korea |
4 | Germany | 18 | Uzbekistan | 32 | Thailand |
5 | Italy | 19 | Canada | 33 | Australia |
6 | Netherlands | 20 | United States | 34 | New Zealand |
7 | Poland | 21 | Argentina | 35 | Algeria |
8 | Portugal | 22 | Brazil | 36 | Egypt |
9 | Romania | 23 | Chile | 37 | Nigeria |
10 | Spain | 24 | Colombia | 38 | South Africa |
11 | Sweden | 25 | Mexico | 39 | Iran |
12 | United Kingdom | 26 | China | 40 | Kuwait |
13 | Norway | 27 | India | 41 | Saudi Arabia |
14 | Turkey | 28 | Indonesia | 42 | United Arab Emirates |
Country | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|
Belgium | 0.8017 | 0.8902 | 0.8611 | 0.8847 | 0.8534 | 0.856 | 0.8389 | 0.8116 | 0.8396 | 0.8344 |
Czech Republic | 0.5408 | 0.6169 | 0.6054 | 0.6107 | 0.5418 | 0.5202 | 0.4939 | 0.5161 | 0.5019 | 0.5129 |
France | 0.7824 | 0.8875 | 0.8604 | 0.8677 | 0.7476 | 0.7681 | 0.6953 | 0.5982 | 0.6601 | 0.7055 |
Germany | 1 | 1 | 1 | 1 | 0.9062 | 1 | 1 | 0.776 | 0.9115 | 0.9861 |
Italy | 0.8414 | 1 | 0.9036 | 0.8661 | 0.7512 | 0.7741 | 0.6914 | 0.5955 | 0.6733 | 0.6911 |
Netherlands | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.9601 | 1 | 1 |
Poland | 0.3474 | 0.338 | 0.3516 | 0.3518 | 0.3402 | 0.3395 | 0.3338 | 0.321 | 0.3163 | 0.3242 |
Portugal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.9999 |
Romania | 0.6265 | 0.6841 | 0.569 | 0.558 | 0.4867 | 0.5193 | 0.4855 | 0.514 | 0.4696 | 0.5932 |
Spain | 0.6551 | 0.7297 | 0.6761 | 0.6463 | 0.546 | 0.5723 | 0.5387 | 0.4956 | 0.5447 | 0.5649 |
Sweden | 0.6287 | 0.6061 | 0.6952 | 0.7498 | 0.7034 | 0.7421 | 0.7481 | 0.7491 | 0.8222 | 0.7904 |
United Kingdom | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Norway | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Turkey | 0.3291 | 0.3074 | 0.3542 | 0.3248 | 0.3417 | 0.3569 | 0.3143 | 0.301 | 0.3051 | 0.2696 |
Kazakhstan | 0.3377 | 0.3816 | 0.3819 | 0.3657 | 0.3616 | 0.3893 | 0.3342 | 0.3383 | 0.2797 | 0.2683 |
Russia | 0.1705 | 0.1386 | 0.1823 | 0.235 | 0.2442 | 0.2494 | 0.2008 | 0.1417 | 0.14 | 0.1706 |
Ukraine | 0.1298 | 0.1378 | 0.1293 | 0.1278 | 0.1296 | 0.13 | 0.1211 | 0.1162 | 0.1142 | 0.1204 |
Uzbekistan | 0.1632 | 0.2047 | 0.2181 | 0.2153 | 0.2231 | 0.2217 | 0.2197 | 0.2585 | 0.242 | 0.1755 |
Canada | 0.4752 | 0.475 | 0.6068 | 0.588 | 0.5354 | 0.5375 | 0.5343 | 0.4995 | 0.5223 | 0.5243 |
United States | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Argentina | 0.2914 | 0.3075 | 0.3575 | 0.3892 | 0.3976 | 0.3818 | 0.3284 | 0.4023 | 0.3427 | 0.4249 |
Brazil | 0.2634 | 0.3039 | 0.4215 | 0.4334 | 0.3803 | 0.3565 | 0.2991 | 0.2273 | 0.2448 | 0.2791 |
Chile | 0.4335 | 0.5036 | 0.5549 | 0.5356 | 0.5146 | 0.4716 | 0.4202 | 0.4461 | 0.429 | 0.4385 |
Colombia | 0.7522 | 0.7069 | 0.9692 | 1 | 1 | 1 | 0.7257 | 0.5765 | 0.517 | 0.5291 |
Mexico | 0.3388 | 0.2847 | 0.3641 | 0.3472 | 0.3424 | 0.3637 | 0.3281 | 1 | 0.2868 | 0.3019 |
China | 0.1257 | 0.1438 | 0.1609 | 0.1758 | 0.1946 | 0.2228 | 0.2274 | 0.2298 | 0.2181 | 0.2168 |
India | 0.0903 | 0.1057 | 0.1317 | 0.1167 | 0.1113 | 0.1032 | 0.0932 | 0.0944 | 0.1046 | 0.1132 |
Indonesia | 0.2483 | 0.2593 | 0.3491 | 0.3387 | 0.328 | 0.2885 | 0.246 | 0.2482 | 0.2663 | 0.2718 |
Japan | 0.8572 | 1 | 1 | 1 | 1 | 0.8379 | 0.7002 | 0.6128 | 0.7925 | 0.6942 |
Malaysia | 0.2642 | 0.2551 | 0.2819 | 0.2769 | 0.2691 | 0.25 | 0.2404 | 0.2407 | 0.2218 | 0.217 |
South Korea | 0.2453 | 0.2345 | 0.308 | 0.2986 | 0.277 | 0.2896 | 0.3287 | 0.3489 | 0.3744 | 0.3975 |
Thailand | 0.1782 | 0.1946 | 0.2041 | 0.2005 | 0.1966 | 0.1956 | 0.1785 | 0.1881 | 0.188 | 0.1927 |
Australia | 0.6228 | 0.5712 | 1 | 1 | 1 | 1 | 1 | 1 | 0.8924 | 1 |
New Zealand | 0.7689 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Algeria | 1 | 1 | 1 | 0.7032 | 0.5665 | 0.4903 | 0.381 | 0.3433 | 0.3091 | 0.289 |
Egypt | 0.1503 | 0.1708 | 0.1745 | 0.1497 | 0.1612 | 0.1602 | 0.1519 | 0.1705 | 0.1639 | 0.1156 |
Nigeria | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
South Africa | 0.1249 | 0.1489 | 0.171 | 0.1704 | 0.1684 | 0.1515 | 0.1412 | 0.145 | 0.1369 | 0.1475 |
Iran | 0.1824 | 0.1943 | 0.2128 | 0.2248 | 0.2245 | 0.169 | 0.143 | 0.1465 | 0.1519 | 0.1421 |
Kuwait | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Saudi Arabia | 0.2933 | 0.261 | 0.3057 | 0.3363 | 0.3482 | 0.3261 | 0.3033 | 0.2857 | 0.2778 | 0.2721 |
United Arab Emirates | 0.6857 | 0.6151 | 0.6582 | 0.6598 | 0.6818 | 0.6636 | 0.6117 | 0.6075 | 0.5588 | 0.5568 |
Country | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 |
---|---|---|---|---|---|---|---|---|---|---|
Belgium | 12 | 13 | 15 | 13 | 13 | 12 | 11 | 11 | 11 | 11 |
Czech Republic | 21 | 18 | 21 | 20 | 20 | 20 | 20 | 19 | 20 | 21 |
France | 13 | 14 | 16 | 14 | 15 | 15 | 15 | 16 | 15 | 13 |
Germany | 1 | 1 | 1 | 1 | 12 | 1 | 1 | 12 | 9 | 10 |
Italy | 11 | 1 | 14 | 15 | 14 | 14 | 16 | 17 | 14 | 15 |
Netherlands | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 10 | 1 | 1 |
Poland | 24 | 25 | 29 | 27 | 30 | 29 | 25 | 28 | 25 | 25 |
Portugal | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 |
Romania | 19 | 17 | 22 | 22 | 23 | 21 | 21 | 20 | 21 | 16 |
Spain | 17 | 15 | 18 | 19 | 19 | 18 | 18 | 22 | 17 | 17 |
Sweden | 18 | 20 | 17 | 16 | 16 | 16 | 12 | 13 | 12 | 12 |
United Kingdom | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Norway | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Turkey | 27 | 27 | 28 | 31 | 29 | 27 | 29 | 29 | 27 | 31 |
Kazakhstan | 26 | 24 | 25 | 26 | 26 | 24 | 24 | 27 | 29 | 32 |
Russia | 36 | 40 | 37 | 34 | 34 | 34 | 36 | 40 | 39 | 37 |
Ukraine | 39 | 41 | 42 | 41 | 41 | 41 | 41 | 41 | 41 | 40 |
Uzbekistan | 37 | 34 | 34 | 36 | 36 | 36 | 35 | 31 | 33 | 36 |
Canada | 22 | 23 | 20 | 21 | 21 | 19 | 19 | 21 | 18 | 20 |
United States | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Argentina | 29 | 26 | 27 | 25 | 24 | 25 | 27 | 24 | 24 | 23 |
Brazil | 31 | 28 | 24 | 24 | 25 | 28 | 31 | 35 | 32 | 28 |
Chile | 23 | 22 | 23 | 23 | 22 | 23 | 22 | 23 | 22 | 22 |
Colombia | 15 | 16 | 13 | 1 | 1 | 1 | 13 | 18 | 19 | 19 |
Mexico | 25 | 29 | 26 | 28 | 28 | 26 | 28 | 1 | 28 | 26 |
China | 40 | 39 | 40 | 38 | 38 | 35 | 34 | 34 | 35 | 34 |
India | 42 | 42 | 41 | 42 | 42 | 42 | 42 | 42 | 42 | 42 |
Indonesia | 32 | 31 | 30 | 29 | 31 | 32 | 32 | 32 | 31 | 30 |
Japan | 10 | 1 | 1 | 1 | 1 | 13 | 14 | 14 | 13 | 14 |
Malaysia | 30 | 32 | 33 | 33 | 33 | 33 | 33 | 33 | 34 | 33 |
South Korea | 33 | 33 | 31 | 32 | 32 | 31 | 26 | 25 | 23 | 24 |
Thailand | 35 | 35 | 36 | 37 | 37 | 37 | 37 | 36 | 36 | 35 |
Australia | 20 | 21 | 1 | 1 | 1 | 1 | 1 | 1 | 10 | 1 |
New Zealand | 14 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Algeria | 1 | 1 | 1 | 17 | 18 | 22 | 23 | 26 | 26 | 27 |
Egypt | 38 | 37 | 38 | 40 | 40 | 39 | 38 | 37 | 37 | 41 |
Nigeria | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
South Africa | 41 | 38 | 39 | 39 | 39 | 40 | 40 | 39 | 40 | 38 |
Iran | 34 | 36 | 35 | 35 | 35 | 38 | 39 | 38 | 38 | 39 |
Kuwait | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
Saudi Arabia | 28 | 30 | 32 | 30 | 27 | 30 | 30 | 30 | 30 | 29 |
United Arab Emirates | 16 | 19 | 19 | 18 | 17 | 17 | 17 | 15 | 16 | 18 |
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Wang, C.-N.; Luu, Q.-C.; Nguyen, T.-K.-L. Estimating Relative Efficiency of Electricity Consumption in 42 Countries during the Period of 2008–2017. Energies 2018, 11, 3037. https://doi.org/10.3390/en11113037
Wang C-N, Luu Q-C, Nguyen T-K-L. Estimating Relative Efficiency of Electricity Consumption in 42 Countries during the Period of 2008–2017. Energies. 2018; 11(11):3037. https://doi.org/10.3390/en11113037
Chicago/Turabian StyleWang, Chia-Nan, Quoc-Chien Luu, and Thi-Kim-Lien Nguyen. 2018. "Estimating Relative Efficiency of Electricity Consumption in 42 Countries during the Period of 2008–2017" Energies 11, no. 11: 3037. https://doi.org/10.3390/en11113037