Global Electrical Sustainability

Quest: A Journal of Undergraduate Student Research Page 167 Global Electrical Sustainability Paper 12 By Sarah Budney and Alex Shepherd

Paper 12 Quest: A Journal of Undergraduate Student Research Page 168 Abstract: This project assessed the electrically sustainability of various countries using fuzzy logic mathematical techniques. To calculate these overall energy efficiency scores, expert opinion was obtained on various factors related to energy sustainability. These factors were renewable energy growth, amount of renewable resources generated, and amount of nonrenewable resources consumed. The amount of renewable resources generated and nonrenewable resources generated were also dependent on a set of sub-factors. Then, though the use of five analysis methods, Analytical Hierarchy Process (AHP) method, Guiasu Method, Yen Method, Dempster Shafer Theory, and the Set Value Statistical Method, an overall energy sustainability score was calculated for each country and the countries were then ranked according to these scores to determine the most and least energy efficient countries in today s world. Key Words: Electrical Sustainability, Fuzzy Math, Renewable Energy Introduction: With the increasing volatility of fossil fuels prices as well as the increasing insight of the many benefits seen from renewable energy, energy sustainability is starting to become of paramount importance in today s world. As countries adapt to the changing times, many countries are turning towards renewable energy to power their energy demands. Some countries seem to be quite sustainable while other countries seem to be slow to change. This project assessed which countries were the most energy sustainable by ranking them using fuzzy logic mathematical techniques. Previous researchers have used fuzzy mathematical techniques in the past estimate energy consumption as well as assess which types of energy are the most sustainable [1, 2]. However, there has not been any fuzzy mathematical research on ranking countries by how energy sustainable they are. Methods: This fuzzy mathematical model combines energy factors of countries with expert opinion from three different energy experts. The expert opinions weight the significance of each of the factors. This way, countries have to excel in the factors that are heavily weighted in order to have a better energy sustainability score. Factors were ranked on a scale of 0-10, 0 meaning there was no contribution toward energy sustainability and 10 being very important toward sustainability. Factors were picked specifically because of their impact on energy sustainability. The three main factors assessed were amount of renewable energy created annually, amount of non-renewable energy used annually, and the renewable energy growth in the past three years. In addition, the

Paper 12 Quest: A Journal of Undergraduate Student Research Page 169 amount of non-renewable and renewable energy factors each had sub-factors assessing the impacts of the various types of energy sources such as coal, nuclear, hydro, solar, wind, and other forms of energy. Table 1: Expert Opinions on Three Major Factors Risk Factors Expert Opinion Expert 1 Expert 2 Expert 3 Growth of Renewable Energy (Last 5 Years) 10 3 6 Production of Renewable Energy 10 9 6 Use of Non-Renewable Energy 10 6 4 Table 2: Expert Opinions on Renewable Energy Production Most Efficient of Renewable Resources Expert 1 Expert 2 Expert 3 Hydro 8 8 8 Geothermal 10 6 4 Solar 7 6 7 Wind 7 7 6 Biomass 7 6 5 Table 3: Expert Opinions on Non-Renewable Energy Use Use of Non-Renewable Energy Expert 1 Expert 2 Expert 3 Nuclear 3 3 6 Coal 9 7 3 Natural Gas 8 8 9 To create the ranking algorithm, five different fuzzy mathematical methods were used. These methods were the Analytical Hierarchy Method (AHP), Guiasu (G Method), Yen Method, Dempster Shafer Theory, and the Set Value Statistical Method (SVSM). These methods created 5 different equations. Once these equations were created, then energy data for each country could be inputted in these equations to obtain an overall electrical sustainability score. Energy data was obtained from the United States Energy Information Administration website (9). All data was converted to the same units of energy and then normalized according to the equation: (Xmin)/(Max-Min) where x is the participant s score for that factor minus the minimum score from all the participants for that factor divided by the difference between the maximum and minimum score for that factor. This was done to ensure that various units and orders of magnitude did not

Paper 12 Quest: A Journal of Undergraduate Student Research Page 170 skew the expert s weight for each factor. Additionally, to account for the fact that the use of nonrenewable energy should negatively contribute to a country s sustainability score, we used the compliment of the data for these sub-factors. This meant that as the amount of non-renewable energy increased, the lower the overall energy efficiency score would be for that country. Next, energy growth was calculated by taking the difference in energy between the last three years and dividing it by the last year output. Finally, all energy data was divided by the countries total energy output to obtain an energy percent. This was done to ensure that the countries size and totally energy production did not create a significant advantage on a countries energy sustainability. Once all the data was incorporated into the algorithms, an energy sustainability score could be calculated and then the countries could then be ranked according to this score on how energy sustainable they were. The different methods analyze the expert opinions in different ways. Thus, the energy sustainability equation changes for each method, but the same data can be used for all methods. The Analytical Hierarchy Method is the most basic of all of the methods we used for analysis. In order to derive the equation for the energy efficiency score, we first find the average of the expert opinions for each factor (find the row averages of the tables). After summing the averages, divide each average by the average total. The resulting number serves as the coefficient for each factor and add to 1 if summed. This means the top score a country can achieve is 1. This is also the case for all of the other methods [13]. Ex: Energy Sustainability Score = 0.25F1+0.5F2+0.25F3 The next method we used was the Guiasu Method. To find the equation for the Guiasu method, we first find the column total of each expert/factor table. Then we divide each element of the column by the corresponding sum. After we have the new values, we find the row average of each factor. These values now serve as the coefficients for the energy sustainability score equation (13). This method helps to speak to the consistency between experts. For example, if one expert ranked all their factors a 10 and another expert ranked all of their factors as a 5, after dividing each factor by its corresponding column total, the 10 and 5 will have the same values. The third method, the Yen method, begins by normalizing all of the expert opinions. Each factor value is divided by the largest factor value that appears in that column. This puts the expert opinions on a scale of 0-1 and 1 is always present. After this normalization, we find the row average. Then we divide each average by the sum of the averages (13). The Dempster Shafer method is somewhat of an extension of the Guiasu method. We begin with the values of the Guiasu table (the values after dividing each element in a given column by the corresponding row column). Then we calculate the row product for each factor. Each row product is divided by the row product total and the resulting number serves as the factor coefficient in the energy sustainability score equation. The procedure in the DSM is based on the following theorem. Let X be a finite set. Let m1, mn be basic probability assignments on X. Suppose m. ([{x})>0 for all x X and i=1, n. then x X m2...n ({x}) = m ({x}) m n ({x}) X X m 1 ({x}) m n ({x}) The final method we used is the Set Value Statistical Method (SVSM). This method uses a ranking process in order to determine the factor coefficients. The lowest weighted factor

Paper 12 Quest: A Journal of Undergraduate Student Research Page 171 receives a score of 1 on up to the to the highest ranked factor n, which receives a score of n. If two factors are ranked the same, then the average of that ranking and the next is found, and the resulting value is assigned to both factors. For example, if we have three factors where the first factor has a score of 0.8 and the other two have a score of 0.6, then the first factor will receive a score of 3, and the second two factors will receive a score of 1.5. After finding the different ranking of the factors, we found find the row averages and then sum the averages. The coefficients of the SVSM equation is the row average divided by the row average column. For a further discussion of the SVSM see [12 p. 112]. The Equations and Subsequent Factors: AHP G+ equation G+ = 0.2969F1 + 0.3906F2 + 0.3125F3 Guiasu G+ equation G+ = 0.2917F1 + 0.4028F2 + 0.3056F3 Yen G+ equation G+ = 0.3043F1 + 0.3913F2 + 0.3043F3 DS G+ equation G+ = 0.1875F1 + 0.5625F2 + 0.2500F3 SVSM G+ equation G+ = 0.3056F1 + 0.4167F2 + 0.2778F3 F1 - F1: Growth-of-Renewable-Energy-(Last-5-Years) F2 - F2: Production-of-Renewable-Energy F3 - F3: Use-of-Non-Renewable-Energy ************************************** F2: Production-of-Renewable-Energy ------------------------------ Mu table: E1 E2 E3 RowAvg F1 0.8000 0.8000 0.8000 0.8000 F2 1.0000 0.6000 0.4000 0.6667 F3 0.7000 0.6000 0.7000 0.6667 F4 0.7000 0.7000 0.6000 0.6667 F5 0.7000 0.6000 0.5000 0.6000 Col Sum 3.9000 3.3000 3.0000 3.4000

Paper 12 Quest: A Journal of Undergraduate Student Research Page 172 AHP G+ equation G+ = 0.2353F1 + 0.1961F2 + 0.1961F3 + 0.1961F4 + 0.1765F5 Guiasu G+ equation G+ = 0.2381F1 + 0.1905F2 + 0.1982F3 + 0.1972F4 + 0.1760F5 Yen G+ equation G+ = 0.2378F1 + 0.1911F2 + 0.1975F3 + 0.1975F4 + 0.1762F5 DS G+ equation G+ = 0.3303F1 + 0.1548F2 + 0.1897F3 + 0.1897F4 + 0.1355F5 SVSM G+ equation G+ = 0.3111F1 + 0.1778F2 + 0.1778F3 + 0.2000F4 + 0.1333F5 F1 - F21: Hydro F2 - F22: Geothermal F3 - F23: Solar F4 - F24: Wind F5 - F25: Biomass ************************************** F3: Use-of-Non-Renewable-Energy ------------------------------ Mu table: E1 E2 E3 RowAvg F1 0.3000 0.3000 0.6000 0.4000 F2 0.9000 0.7000 0.3000 0.6333 F3 0.8000 0.8000 0.9000 0.8333 Col Sum 2.0000 1.8000 1.8000 1.8667 AHP G+ equation G+ = 0.2143F1 + 0.3393F2 + 0.4464F3 Guiasu G+ equation G+ = 0.2167F1 + 0.3352F2 + 0.4481F3 Yen G+ equation G+ = 0.2124F1 + 0.3412F2 + 0.4464F3

Paper 12 Quest: A Journal of Undergraduate Student Research Page 173 DS G+ equation G+ = 0.0659F1 + 0.2308F2 + 0.7033F3 SVSM G+ equation G+ = 0.2222F1 + 0.3333F2 + 0.4444F3 F1 - F31: Nuclear F2 - F32: Coal F3 - F33: Natural-Gas ************************************** --------------------Final Equations---------------- AHP final F+ equation: AHPF+ = 0.2969 F1 + 0.0919 F2 + 0.0766 F3 + 0.0766 F4 + 0.0766 F5 + 0.0689 F6 + 0.0670 F7 + 0.1060 F8 + 0.1395 F9 ------- Guiasu final F+ equation: Guiasu F+ = 0.2917 F1 + 0.0959 F2 + 0.0767 F3 + 0.0798 F4 + 0.0794 F5 + 0.0709 F6 + 0.0662 F7 + 0.1024 F8 + 0.1369 F9 ------- Yen final F+ equation: Yen F+ = 0.3043 F1 + 0.0930 F2 + 0.0748 F3 + 0.0773 F4 + 0.0773 F5 + 0.0690 F6 + 0.0647 F7 + 0.1038 F8 + 0.1358 F9 ------- DS final F+ equation: DS F+ = 0.1875 F1 + 0.1858 F2 + 0.0871 F3 + 0.1067 F4 + 0.1067 F5 + 0.0762 F6 + 0.0165 F7 + 0.0577 F8 + 0.1758 F9 ------- SVSM final F+ equation: SVSM F+ = 0.3056 F1 + 0.1296 F2 + 0.0741 F3 + 0.0741 F4 + 0.0833 F5 + 0.0556 F6 + 0.0617 F7 + 0.0926 F8 + 0.1235 F9 ------- Factors descriptions in the final equation.

Paper 12 Quest: A Journal of Undergraduate Student Research Page 174 F1 - F1: Growth-of-Renewable-Energy-(Last-5-Years) F2 - F21: Hydro F3 - F22: Geothermal F4 - F23: Solar F5 - F24: Wind F6 - F25: Biomass F7 - F31: Nuclear F8 - F32: Coal F9 - F33: Natural-Gas The Results: Country AHP ESS Guiasu ESS Yen ESS DS ESS SVSM ESS ==================== = ========= = ============= = Afghanistan 0.458 0.4493 0.4597 0.2943 0.4462 Albania 0.476 0.468 0.4769 0.3626 0.4626 Algeria 0.4442 0.4351 0.4465 0.2457 0.4335 Angola 0.4454 0.4362 0.4477 0.2462 0.4346 Argentina 0.446 0.4369 0.4483 0.2502 0.435 Armenia 0.445 0.4358 0.4475 0.2589 0.434 Australia 0.4642 0.4555 0.466 0.2968 0.4532 Austria 0.5326 0.5243 0.534 0.3872 0.5295 Azerbaijan 0.4453 0.4361 0.4476 0.2461 0.4346 Bahrain 0.4441 0.435 0.4465 0.2454 0.4334 Bangladesh 0.4451 0.4361 0.4475 0.248 0.4343 Barbados 0.4441 0.435 0.4465 0.2454 0.4334 Belarus 0.4444 0.4353 0.4468 0.2459 0.4337 Belgium 0.4544 0.4454 0.4569 0.2893 0.4419 Bhutan 0.5033 0.4948 0.5049 0.3798 0.4907 Bolivia 0.4455 0.4364 0.4478 0.2469 0.4346 Bosnia and Herzegovina. 0.4747 0.4665 0.4757 0.3531 0.4616 Botswana 0.4776 0.4695 0.4785 0.3635 0.4642 Brazil 0.5913 0.5836 0.5923 0.4856 0.5967 Brunei 0.4441 0.435 0.4465 0.2454 0.4334 Bulgaria 0.4652 0.4567 0.4668 0.3387 0.4523 Burma (Myanmar) 0.4471 0.438 0.4493 0.2548 0.4361 Cambodia 0.2854 0.2834 0.2793 0.2781 0.2564 Cameroon 0.3186 0.3119 0.3175 0.1708 0.3028 Canada 0.5168 0.5084 0.5185 0.3751 0.5142 Chile 0.5174 0.5091 0.5188 0.3714 0.5124 China 0.4949 0.4868 0.4959 0.3747 0.4851 Colombia 0.4476 0.4386 0.4498 0.2576 0.4365 Congo (Brazzaville) 0.4401 0.431 0.4423 0.2429 0.4292

Paper 12 Quest: A Journal of Undergraduate Student Research Page 175 Congo (Kinshasa) 0.4736 0.4654 0.4746 0.3501 0.4605 Costa Rica 0.6743 0.6639 0.6718 0.5324 0.647 Cote div.(ivorycoast) 0.4449 0.4358 0.4472 0.2468 0.4339 Croatia 0.4489 0.4399 0.4511 0.258 0.4379 Cuba 0.4553 0.4462 0.4575 0.2592 0.4431 Cyprus 0.7014 0.696 0.7018 0.6034 0.6816 Czech Republic 0.4681 0.4596 0.4699 0.3306 0.4555 Denmark 0.5049 0.4962 0.5064 0.336 0.4901 Dominican Republic 0.4506 0.4416 0.4527 0.2635 0.4394 Ecuador 0.4309 0.4224 0.4321 0.2485 0.4162 Egypt 0.4449 0.4358 0.4472 0.2468 0.4341 Equatorial Guinea 0.4441 0.435 0.4465 0.2454 0.4334 Estonia 0.4897 0.4805 0.4918 0.2965 0.4756 Eurasia 0.4472 0.4382 0.4495 0.2627 0.4361 Finland 0.5149 0.5063 0.5167 0.3932 0.5049 France 0.4595 0.4503 0.4623 0.3408 0.4487 Gabon 0.4412 0.4321 0.4434 0.2446 0.43 Georgia 0.4479 0.4387 0.4503 0.2488 0.4372 Germany 0.4803 0.4719 0.4822 0.3292 0.4673 Ghana 0.3411 0.3337 0.3408 0.1803 0.3273 Greece 0.4802 0.4719 0.4814 0.3465 0.4682 Guatemala 0.6321 0.6233 0.6312 0.491 0.5953 Honduras 0.5262 0.5179 0.5265 0.4034 0.5052 Hong Kong 0.4597 0.451 0.4613 0.3003 0.4477 Hungary 0.4523 0.4433 0.4545 0.2832 0.4404 Iceland 0.6993 0.6915 0.6985 0.6292 0.7072 India 0.4845 0.4762 0.4857 0.3512 0.474 Indonesia 0.4575 0.4486 0.4593 0.2813 0.4457 Iran 0.4444 0.4353 0.4467 0.2459 0.4337 Iraq 0.456 0.4467 0.4586 0.2529 0.4456 Ireland 0.471 0.4622 0.473 0.2875 0.46 Israel 0.4631 0.4546 0.4647 0.3069 0.4509 Italy 0.4706 0.4618 0.4726 0.2872 0.4604 Jamaica 0.6162 0.6082 0.6161 0.4925 0.5944 Japan 0.4637 0.4549 0.4659 0.3058 0.4528 Jordan 0.4445 0.4353 0.4468 0.2458 0.4337 Kazakhstan 0.4642 0.4557 0.4657 0.3164 0.4519 Kenya 0.6699 0.6592 0.6671 0.5238 0.6389 Korea, North 0.4776 0.4695 0.4785 0.3635 0.4642 Korea, South 0.4533 0.4443 0.4554 0.3034 0.4412 Kuwait 0.4441 0.435 0.4465 0.2454 0.4334 Kyrgyzstan 0.4569 0.4482 0.4586 0.2916 0.4451 Laos 0.4773 0.4692 0.4782 0.3634 0.464

Paper 12 Quest: A Journal of Undergraduate Student Research Page 176 Latvia 0.4486 0.4395 0.4508 0.2535 0.4375 Lebanon 0.4777 0.4696 0.4786 0.3636 0.4644 Libya 0.4441 0.435 0.4465 0.2454 0.4334 Lithuania 0.4436 0.4343 0.4462 0.2794 0.4319 Luxembourg 0.4521 0.4431 0.4543 0.2571 0.4408 Macedonia 0.475 0.4669 0.476 0.3544 0.4619 Madagascar 0.4765 0.4684 0.4773 0.3629 0.4631 Malawi 0.4788 0.4707 0.4797 0.3643 0.4655 Malaysia 0.4493 0.4403 0.4514 0.2619 0.4381 Mauritius 0.5366 0.5283 0.5369 0.4122 0.5138 Mexico 0.4583 0.4493 0.4604 0.2726 0.4485 Moldova 0.4444 0.4353 0.4467 0.2464 0.4336 Mongolia 0.4776 0.4696 0.4785 0.3636 0.4643 Montenegro 0.4777 0.4697 0.4786 0.3636 0.4644 Morocco 0.4693 0.4607 0.4709 0.3119 0.4572 Mozambique 0.4663 0.4568 0.4692 0.2603 0.4562 Namibia 0.4789 0.4708 0.4798 0.3644 0.4656 Nepal 0.4777 0.4696 0.4786 0.3636 0.4644 Netherlands 0.4524 0.4434 0.4546 0.2605 0.4408 New Caledonia 0.4896 0.4816 0.4904 0.3752 0.4761 New Zealand 0.5417 0.5332 0.5429 0.3971 0.5382 Nicaragua 0.6712 0.6612 0.6694 0.5283 0.6423 Niger 0.4776 0.4695 0.4785 0.3635 0.4642 Nigeria 0.4496 0.4404 0.4521 0.2489 0.439 Norway 0.6394 0.6324 0.64 0.5649 0.6541 Oman 0.4441 0.435 0.4465 0.2454 0.4334 Pakistan 0.4452 0.4361 0.4475 0.2495 0.4344 Panama 0.5242 0.5163 0.524 0.4056 0.4998 Papua New Guinea 0.52 0.51 0.5208 0.3324 0.5041 Paraguay 0.4782 0.4701 0.4791 0.3639 0.4649 Peru 0.4468 0.4377 0.4491 0.2495 0.4358 Philippines 0.4938 0.4847 0.4946 0.3395 0.4791 Poland 0.4702 0.4617 0.4716 0.3229 0.4574 Portugal 0.5164 0.5079 0.518 0.3523 0.5056 Puerto Rico 0.4501 0.4412 0.4522 0.2665 0.4389 Qatar 0.4441 0.435 0.4465 0.2454 0.4334 Romania 0.4562 0.4474 0.4582 0.2939 0.4444 Russia 0.4547 0.4457 0.4569 0.273 0.4446 Saudi Arabia 0.4441 0.435 0.4465 0.2454 0.4334 Senegal 0.486 0.4779 0.4871 0.3535 0.4713 Serbia 0.4698 0.4614 0.471 0.3358 0.457 Singapore 0.4472 0.4381 0.4495 0.249 0.436 Slovakia 0.4596 0.4506 0.4619 0.3097 0.4489

Paper 12 Quest: A Journal of Undergraduate Student Research Page 177 Slovenia 0.4895 0.481 0.4914 0.3739 0.4813 South Africa 0.4728 0.4645 0.4739 0.3512 0.4596 Spain 0.501 0.4929 0.5032 0.3483 0.4893 Sri Lanka 0.5617 0.5566 0.563 0.4753 0.5449 Swaziland 0.4778 0.4697 0.4787 0.3637 0.4645 Sweden 0.5814 0.5733 0.583 0.5181 0.5819 Switzerland 0.5426 0.5343 0.5445 0.4438 0.5425 Syria 0.4426 0.4335 0.4449 0.2445 0.4318 Taiwan 0.4585 0.4497 0.4604 0.31 0.4459 Tajikistan 0.4485 0.4395 0.4505 0.2641 0.4372 Tanzania 0.4438 0.4347 0.4461 0.2472 0.433 Thailand 0.4497 0.4407 0.4518 0.262 0.4384 Trinidad and Tobago 0.4441 0.435 0.4465 0.2454 0.4334 Tunisia 0.4454 0.4363 0.4478 0.247 0.4347 Turkey 0.476 0.4674 0.4777 0.3143 0.4671 Turkmenistan 0.4441 0.435 0.4465 0.2454 0.4334 Ukraine 0.4482 0.4392 0.4505 0.2817 0.4366 United Arab Emirates 0.4442 0.435 0.4465 0.2455 0.4334 United Kingdom 0.4561 0.447 0.4582 0.2773 0.4444 United States 0.4604 0.4515 0.4625 0.2938 0.4495 Uruguay 0.6085 0.5995 0.609 0.4373 0.578 Uzbekistan 0.4445 0.4354 0.4468 0.2467 0.4337 Venezuela 0.4406 0.4315 0.4428 0.2433 0.4297 Vietnam 0.4556 0.4468 0.4574 0.2855 0.4439 Yemen 0.4441 0.435 0.4465 0.2454 0.4334 Zambia 0.4812 0.4731 0.4822 0.3658 0.468 Zimbabwe 0.4776 0.4695 0.4785 0.3635 0.4642 Country AHP Rank G Rank Yen Rank DS Rank SVSM Rank Cyprus 1 1 1 2 2 Iceland 2 2 2 1 1 Costa Rica 3 3 3 4 4 Nicaragua 4 4 4 5 5 Kenya 5 5 5 6 6 Norway 6 6 6 3 3 Guatemala 7 7 7 9 8 Jamaica 8 8 8 8 9 Uruguay 9 9 9 13 11

Paper 12 Quest: A Journal of Undergraduate Student Research Page 178 Brazil 10 10 10 10 7 Sweden 11 11 11 7 10 Sri Lanka 12 12 12 11 12 Switzerland 13 13 13 12 13 New Zealand 14 14 14 17 14 Mauritius 15 15 15 14 17 Austria 16 16 16 19 15 Honduras 17 17 17 16 20 Panama 18 18 18 15 23 Papua New Guinea 19 19 19 55 22 Chile 20 20 20 25 18 Canada 21 21 21 22 16 Portugal 22 22 22 44 19 Finland 23 23 23 18 21 Denmark 24 24 24 53 25 Bhutan 25 25 25 20 24 Spain 26 26 26 48 26 China 27 27 27 23 27 Philippines 28 28 28 51 29 Estonia 29 31 29 69 31 New Caledonia 30 29 31 21 30 Slovenia 31 30 30 24 28 Senegal 32 32 32 42 33 India 33 33 33 45 32 Zambia 34 34 34 26 35 Germany 35 36 35 57 36 Greece 36 35 36 49 34 Namibia 37 37 37 27 38 Malawi 38 38 38 28 39 Paraguay 39 39 39 29 40 Swaziland 40 40 40 30 41 Montenegro 41 41 41 31 42 Lebanon 42 42 42 32 43 Nepal 43 43 43 33 44 Mongolia 44 44 44 34 45 Korea, North 45 45 45 35 46 Zimbabwe 46 46 46 36 47 Botswana 47 47 47 37 48 Niger 47 47 47 37 48 Laos 48 48 48 38 49 Madagascar 49 49 50 39 50 Albania 50 50 51 40 51 Turkey 51 51 49 60 37

Paper 12 Quest: A Journal of Undergraduate Student Research Page 179 Macedonia 52 52 52 41 52 Bosnia and Herzegovina. 53 53 53 43 53 Congo (Kinshasa) 54 54 54 47 54 South Africa 55 55 55 46 57 Ireland 56 56 56 75 56 Italy 57 57 57 76 55 Poland 58 58 58 58 58 Serbia 59 59 59 54 60 Morocco 60 60 60 61 59 Czech Republic 61 61 61 56 62 Mozambique 62 62 62 93 61 Bulgaria 63 63 63 52 65 Kazakhstan 64 64 66 59 66 Australia 65 65 64 68 63 Japan 66 66 65 65 64 Israel 67 67 67 64 67 United States 68 68 68 72 68 Hong Kong 69 69 71 67 72 Slovakia 70 70 70 63 69 France 71 71 69 50 70 Taiwan 72 72 73 62 74 Mexico 73 73 72 85 71 Afghanistan 74 74 74 70 73 Indonesia 75 75 75 80 75 Kyrgyzstan 76 76 76 73 77 Romania 77 77 79 71 79 United Kingdom 78 78 78 83 80 Iraq 79 80 77 101 76 Vietnam 80 79 81 77 81 Cuba 81 81 80 94 82 Russia 82 82 83 84 78 Belgium 83 83 82 74 83 Korea, South 84 84 84 66 84 Netherlands 85 85 85 92 85 Hungary 86 86 86 78 87 Luxembourg 87 87 87 98 86 Dominican Republic 88 88 88 88 88 Puerto Rico 89 89 89 86 90 Thailand 90 90 91 90 91 Nigeria 91 91 90 106 89 Malaysia 92 92 92 91 92 Croatia 93 93 93 96 93 Latvia 94 95 94 100 94

Paper 12 Quest: A Journal of Undergraduate Student Research Page 180 Tajikistan 95 94 95 87 95 Ukraine 96 96 96 79 97 Georgia 97 97 97 107 96 Colombia 98 98 98 97 98 Eurasia 99 99 99 89 100 Singapore 100 100 100 105 101 Burma (Myanmar) 101 101 101 99 99 Peru 102 102 102 103 102 Argentina 103 103 103 102 103 Bolivia 104 104 104 112 106 Tunisia 105 105 105 111 104 Angola 106 106 106 117 105 Azerbaijan 107 107 107 118 107 Pakistan 108 108 109 104 108 Bangladesh 109 109 110 109 109 Armenia 110 112 108 95 111 Cote div.(ivorycoast) 111 110 111 114 112 Egypt 112 111 112 113 110 Uzbekistan 113 113 113 115 113 Jordan 114 114 114 121 114 Belarus 115 115 115 120 115 Moldova 116 116 117 116 117 Iran 117 117 116 119 116 Algeria 118 118 118 122 118 United Arab Emirates 119 119 119 123 119 Trinidad and Tobago 120 120 120 124 120 Bahrain 121 121 121 125 121 Turkmenistan 122 122 122 126 122 Barbados 123 123 123 127 123 Kuwait 123 123 123 127 123 Oman 123 123 123 127 123 Qatar 123 123 123 127 123 Saudi Arabia 123 123 123 127 123 Yemen 123 123 123 127 123 Libya 123 123 123 127 123 Brunei 123 123 123 127 123 Equatorial Guinea 124 124 124 128 124 Tanzania 125 125 126 110 125 Lithuania 126 126 125 81 126 Syria 127 127 127 130 127 Gabon 128 128 128 129 128 Venezuela 129 129 129 131 129 Congo (Brazzaville) 130 130 130 132 130

Paper 12 Quest: A Journal of Undergraduate Student Research Page 181 Ecuador 131 131 131 108 131 Ghana 132 132 132 133 132 Cameroon 133 133 133 134 133 Cambodia 134 134 134 82 134 ===================== =========== ======= Discussion: The fuzzy mathematical energy sustainability scores do not have a direct interpretation. Therefore, we do not exactly know what a score of 0.51668 for Canada means. However, the scores do allow us to compare the sustainability between various countries and give an overall ranking by country for how energy efficient a country is. These rankings show some very interesting results. Cyprus ranked number one as the most energy sustainable country. Although Cyprus may not be the first country many think about when they think of the top countries for energy sustainability, Cyprus may be more efficient than suspected. Recently, Cyprus won the World Renewable Energy Congress Trophy due to its recent achievements in their increased growth for renewable energy sources. Part of this recent growth may be due to its high energy fossil fuel transportation costs. Since Cyprus is an island, it has isolated from many other parts of the world, and thus makes it more difficult to transport fossil fuels to the island, resulting in higher energy transportation costs. This could, in turn, shift energy demand away from fossil fuels in Cyprus, thereby avoiding high transportation costs, and thus helping to shift demand toward more renewable energy sources. Also, the fact that we controlled for country size could have also helped to push Cyprus up to the top of the list for the most energy sustainable country. Lastly, their recent investment in solar power may also have greatly helped increase their energy sustainability score [3, 4]. It is not surprising that Iceland came in number two as the second most energy sustainable country. With its huge supply of geothermal energy production along with its impressive hydroelectric power, Iceland is definitely a very sustainable country. Also it is not surprising that Costa Rica and Nicaragua came in third and fourth on this list. They have lead the way in their region for energy efficiency and have obtained a good reputation over the last decade as very impressive examples of renewable energy efficiency [5]. Lastly, it is interesting to see that Kenya, an African country ranked in the top 5 countries. However, it is well known that Kenya has lead the way in renewable energy production in Africa and in addition, it has made huge investments recently in photovoltaic solar power production. All of this could help to explain why it was able to score in the top 5 most sustainable countries in the world [6]. The United States seemed to rank in the middle, which makes sense because although they create a lot of renewable energy, they are also the number one country for non-renewable energy use [8]. Looking at the bottom of the list also shows some interesting results. Most of the countries are either Middle Eastern countries like Saudi Arabia or Kuwait or places in Latin America like Venezuela and Ecuador. This seems to make a lot of sense as many of these countries have large reserves of fossil fuels and thus a greater ability to use fossil fuels for energy demand. These large supplies of fossil fuels, in turn, reduce demand for renewable energy growth since cheaper fossil fuel energy will drive demand away from investing in the more expensive alternative, renewable energy production. Therefore, it is no surprise that these countries with large fossil fuel supplies rank near the bottom of the list as they use large amounts of fossil fuels with relatively low renewable energy growth [7, 10].

Paper 12 Quest: A Journal of Undergraduate Student Research Page 182 Also looking at the list shows that there are a few African countries that are near the bottom of the list like Ghana and Cameroon. Although these countries do not have a large supply of fossil fuels like other countries near the bottom of the list, these African countries seem to lack renewable energy production and growth. This is probably due to lack of investment capital for these renewable energy projects. However, there is hope that many of these countries can leapfrog forward in energy development by skipping the traditional fossil fuel route and go directly toward more efficient and sustainable energy transitions in future energy development [11]. Future research will be needed to analyze if capital investments and government spending may be directly correlated to energy sustainability. Also, future research could also include more factors related to energy sustainability to gain a broader analysis of a country s energy sustainability. Lastly, obtaining more expert analysis would be beneficial in future studies to gain a better understanding of how each factor affects energy sustainability. Conclusion: This project used fuzzy mathematical models to rank countries according to how sustainable each country is in terms of the amount and growth of renewable energy produced by each country as well as the amount of non-renewable energy each country used. It was seen that Cyprus, Iceland, and Costa Rica may be some of the most energy efficient countries in the world while some countries in the Middle East and Latin America may be some of the least energy efficient due to large supplies of fossil fuels. Future directive will help to improve the algorithm for a greater analysis of energy sustainability. We remain optimistic that this method can be used to improve energy sustainability in the future, but for the moment we provide the first assessment of ranking energy sustainability by country using fuzzy mathematical techniques.

Paper 12 Quest: A Journal of Undergraduate Student Research Page 183 References: 1. Jabera, J.O., R. Mamlook, and W. Awad. "Evaluation of energy conservation programs in the residential sector using fuzzy logic methodology." Energy Policy, 33: 1329-1338. Print. 2. Azadeh, Ali, Morteza Saberi, and Anahita Gitiforouz. "An integrated simulation-based fuzzy regression-time series algorithm for electricity consumption estimation with nonstationary data." Journal of the Chinese Institute of Engineers 34: 1047-1066. Print. 3. Kalogirou, Soteris. "Solar water heating in Cyprus: current status of technology and problems." Renewable Energy 10: 107 112. Print. 4. Michaelides, J.M.. "Exploitation of solar energy in Cyprus." Renewable Energy 1: 629-637. Print. 5. Van den Akker, Johannes. "Energy Efficiency in El Salvador, Nicaragua, Costa Rica, Panama." United Nations Development Programme 1: 1-66. Print. 6. Karekezi, Stephen, and Waeni Kithyoma,. "Renewable Energy in Africa: Prospects and Limits." United Nations Sustainable Development. N.p., 2 June 2003. Web. 22 Apr. 2014. 7. "Supply and Demand." The National Acadamies. N.p., 1 Jan. 2008. Web. 22 Apr. 2014. <http://www.nap.edu/reports/energy/supply.html>. 8. Turner, John. "A Realizable Renewable Energy Future." Advancing Science, Serving Humanity 285 (): 687-689. Print. 9. "U.S. Energy Information Administration - EIA - Independent Statistics and Analysis."Countries. Energy Information Administration, 1 Mar. 2013. Web. 30 Apr. 2014. <http://www.eia.gov/countries/http://www.eia.gov/countries/>. 10. Berndt, Ernst, and David Wood. "Technology, Prices, and the Derived Demand for Energy." The Review of Economics and Statistics 57: 259-268. Print. 11. Murphy, James. "Making the energy transition in rural east Africa: Is leapfrogging an alternative?." Technological Forecasting and Social Change 68: 173-193. Science Direct. Web. 29 Apr. 2014. 12. Hong Xing, Li and Vincent C. Yen, Fuzzy Sets and Fuzzy Decision Making, CRC Press, 1995. 13. Mordeson, J.N., M.J. Wierman, T.D. Clark, A. Pham, and M.A. Redmond, Linear Models In Mathematics of Uncertainty, Studies in Computational Intelligence 463, Springer 2013.

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