ekonometri 2 final odevi
dışsallık ve coase Teorimi
Dışsallık, bir ekonomik faaliyetin üçüncü kişilere olan maliyet veya faydalarını ifade eder. Coase Teoremi, mülkiyet hakları belirgin ve pazarlık maliyetleri düşük olduğunda, tarafların dışsallıkları azaltmak için pazarlık yapabileceğini öne sürer.
giriş
Ekonomi teorisinde dışsallık ve Coase Teoremi, piyasa başarısızlıklarının ve bu sorunlara yönelik çözüm yollarının anlaşılmasında önemli rol oynamaktadır. Dışsallık, bir ekonomik faaliyetin üçüncü kişileri olumlu veya olumsuz etkilemesi durumudur. Örneğin, bir fabrikanın üretim sürecinde çevreye yaydığı kirlilik, yakın çevrede yaşayan insanlar üzerinde olumsuz dışsallık yaratır. Bu gibi durumlarda, piyasa mekanizmaları dışsallıkların maliyetlerini veya faydalarını tam olarak yansıtamadığında kaynak tahsisi etkinliği bozulur. Negatif Dışsallıklar Negatif dışsallıklar, bir ekonomik faaliyetin üçüncü taraflara olumsuz etkilerde bulunmasıdır. Örneğin: Çevre Kirliliği: Bir fabrika üretim yaparken hava veya su kirliliğine neden olabilir. Bu kirlilik, fabrikanın yakınında yaşayan insanların sağlığını olumsuz etkileyebilir ve doğal çevreye zarar verebilir.
Coase Teoremi ise dışsallıkların piyasa içinde nasıl çözülebileceğine dair önemli bir yaklaşım sunar. Ronald Coase tarafından ileri sürülen bu teorem, mülkiyet haklarının belirli ve transfer maliyetlerinin düşük olduğu durumlarda, taraflar arasında pazarlık yapılarak dışsallıkların içselleştirilebileceğini savunur. Bu makalede, dışsallıklar ve Coase Teoremi üzerine bir model kurarak bu teorik kavramları analiz edeceğiz ve ilgili literatürden bulgularla destekleyerek sonuçlar çıkaracağız.
LİTERATÜR
Dışsallık kavramı ve piyasa başarısızlıkları üzerine yapılan çalışmalar, ekonomik teori ve politika önerileri açısından zengin bir literatüre sahiptir. Pigou (1920), dışsallıkları içselleştirmek için vergiler ve sübvansiyonlar gibi devlet müdahalelerinin gerekliliğini savunurken, Coase (1960), dışsallık sorunlarının mülkiyet hakları ve düşük pazarlık maliyetleri ile çözülmesinin mümkün olduğunu öne sürmüştür.Coase Teoremi’nin geçerliliği ve uygulamaları üzerine yapılan çalışmalar, bu teoremin çeşitli koşullarda nasıl işleyeceğini ve sınırlarını incelemiştir.
Pigou’nun Çözümü(DEVLET MÜDAHALESİ)
Arthur Cecil Pigou, 1920’de yayınlanan “Ekonomi Prensipleri” kitabında, dışsallıkların “içselleştirilmesi” için devlet müdahalesinin gerekliliğini savunmuştur. Dışsallık yaratan eylemleri azaltmak için vergiler ve bu eylemleri teşvik etmek için sübvansiyonlar önermiştir. Örneğin, hava kirliliği yaratan bir fabrika, kirliliği azaltmak için vergiye tabi tutulabilir veya yenilenebilir enerji kullanımı gibi çevre dostu teknolojiler için sübvansiyonlar alınabilir.
Coase Teoremi(PAZARLIK ÇÖZÜMÜ)
Ronald Coase, 1960’da yayınlanan “Sosyal Maliyet Problemi” adlı makalesinde, dışsallık sorunlarının piyasa mekanizmaları yoluyla çözülebileceğini iddia etmiştir. Coase Teoremi, mülkiyet haklarının açıkça tanımlanmış ve pazarlık maliyetlerinin düşük olduğu durumlarda, tarafların dışsallığı karşılıklı olarak yararlı bir şekilde çözebileceğini öne sürer. Örneğin, bir fabrika tarafından yayılan duman çevre kirliliğine neden oluyorsa, çevre kirliliğinden etkilenenler fabrika sahibine kirliliği azaltması için para ödeyebilir veya fabrika sahibi, kirliliği azaltmak için çevre kirliliğinden etkilenenlere tazminat ödeyebilir.
Coase Teoremi’nin Sınırları
Yüksek Pazarlık Maliyetleri: Taraflar çok sayıda olduğunda veya anlaşma karmaşık olduğunda, pazarlık maliyetleri yüksek olabilir ve anlaşmaya varmayı zorlaştırabilir. Bilgi Asimetrisi: Taraflar arasında bilgi asimetrisi, pazarlık süreçlerini zorlaştırır. Örneğin, bir fabrika sahibinin kirlilik seviyesini tam olarak bilmesi mümkün olmayabilir, bu da çevre kirliliğinden etkilenenlerle anlaşmayı zorlaştırır. Mülkiyet Haklarının Belirsizliği: Coase Teoremi, mülkiyet haklarının açıkça tanımlanmış olmasını gerektirir.
Kyoto Protokolü
Kyoto :(Japonca :京都市 Kyōto-shi; “başkent başkenti” ya da “başkentlerin başkenti Kyoto japanyoyada bulun bir şehirdir, anlaşmanın imzalandığı yerdir. Kyoto Protokolü, küresel ısınma ve iklim değişikliği konusunda mücadeleyi sağlamaya yönelik uluslararası tek çerçeve. Birleşmiş Milletler İklim Değişikliği Çerçeve Sözleşmesi içinde imzalanmıştır. Bu protokolü imzalayan ülkeler, karbon dioksit ve sera etkisine neden olan diğer beş osmanım nereye gidersin gazın salımını azaltmaya veya bunu yapamıyorlarsa karbon ticareti yoluyla haklarını arttırmaya söz vermişlerdir. Protokol, ülkelerin atmosfere saldıkları karbon miktarını 1990 yılındaki düzeylere düşürmelerini gerekli kılmaktadır. 1997’de imzalanan protokol, 2005’te yürürlüğe girebilmiştir.
Sözleşmenin Bazı Maddeleri
Atmosfere salınan sera gazı miktarı %5’e çekilecek.
Endüstriden, motorlu taşıtlardan, ısıtmadan kaynaklanan sera gazı miktarını azaltmaya yönelik mevzuat yeniden düzenlenecek.
Daha az enerji ile ısınma, daha az enerji tüketen araçlarla uzun yol alma, daha az enerji tüketen teknoloji sistemlerini endüstriye yerleştirme sağlanacak, ulaşımda, çöp depolamada çevrecilik temel ilke olacak.
Atmosfere bırakılan metan ve karbon dioksit oranının düşürülmesi için alternatif enerji kaynaklarına yönlenecek.
Fosil yakıtlar yerine örneğin bio dizel yakıt kullanılacak,
Çimento, demir-çelik ve kireç fabrikaları gibi yüksek enerji tüketen işletmelerde atık işlemleri yeniden düzenlenecek.
Termik santrallerde daha az karbon çıkartan sistemler, teknolojiler devreye sokulacak.
Kyoto Protokolü Temel Prensipleri
Kyoto Protokolündeki hedeflerine uymayan herhangi bir Ek 1 ülkesi bir sonraki dönem azaltma hedeflerinin %30 daha azaltılması ile cezalandırılacaktır.
Kyoto Protokolü devletler tarafından desteklenir ve BM şemsiyesi altında küresel kurallar ile belirlenir
Kyoto Protokolü, Ek 1 ülkelerinin sera gazı salımı hedeflerine ulaşmak için başka ülkelerden salım azalması satın alabilmeleri esnekliğine imkân tanımıştır. Bu, çeşitli borsalardan (AB Salım Ticaret Sistemi gibi) veya Ek 1’de yer almayan ülkelerin salımlarını azaltan Temiz Gelişim Tekniği (TGT) projeleri ile veya diğer Ek 1 ülkelerinden satın alınabilinir.
Tüm Ek 1 ülkeleri Kyoto Protokolü içinde sera gazı salım değerlerini gözetim altında tutmak için ulusal daireler kurmuşlardır.Japonya,kanda, İtalya, Hollanda, Almanya ve daha birçok ülke devletleri karbon kredisi için bütçeden pay ayırmışlardır.
Anlaşma Sonuçu
Birleşmiş Milletler Çevre Programı basın bildirisine göre:
“Kyoto Protokolü gelişmiş ülkelerin sera gazı salımlarını 1990 yılına göre %5.2 azaltmalarını öngören bir anlaşmadır (protokolün uygulanmaması durumunda 2010 yılı salım tahminleri dikkate alınırsa bu, %29’luk bir azalmaya karşılık gelmektedir).
Modelin oluşturulması
Amacı
Tarımda GSYİH içinde bulunan CO2 emisyonları ve hava kirliliği arasındaki ilişkiye bakıcaz.
Veriler
EN.ATM.CO2E.KT = CO2 emisyonu (kiloton)
EN.ATM.PM25.MC.M3 = Hava Kirliliği(metreküp başına mikrogram)
IP.IDS.RSCT = endüstiryel baca
Verilerin İşlenmesi (Temizleme Ve Birleştirilmesi)
##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionveri <- WDI(
country = "all",
indicator = c("EN.ATM.CO2E.KT", "IP.IDS.RSCT", "EN.ATM.PM25.MC.M3"),
start = 2010,
end = 2019
)
head(veri)## country iso2c iso3c year EN.ATM.CO2E.KT IP.IDS.RSCT EN.ATM.PM25.MC.M3
## 1 Afghanistan AF AFG 2010 8576.15 NA 51.82179
## 2 Afghanistan AF AFG 2011 11961.89 NA 56.24723
## 3 Afghanistan AF AFG 2012 10208.13 NA 54.70023
## 4 Afghanistan AF AFG 2013 9402.05 NA 58.79166
## 5 Afghanistan AF AFG 2014 9281.34 NA 61.86533
## 6 Afghanistan AF AFG 2015 10057.59 NA 60.59701-NA ların silinmesi
## country iso2c iso3c year EN.ATM.CO2E.KT IP.IDS.RSCT
## 1 Albania AL ALB 2010 4785.400 21
## 2 Albania AL ALB 2011 5136.700 16
## 3 Albania AL ALB 2013 4795.400 35
## 4 Albania AL ALB 2014 5188.000 16
## 5 Algeria DZ DZA 2011 120784.900 699
## 6 Algeria DZ DZA 2012 134934.200 873
## 7 Algeria DZ DZA 2014 147735.200 825
## 8 Algeria DZ DZA 2017 157704.400 1181
## 9 Algeria DZ DZA 2018 164534.100 1033
## 10 Algeria DZ DZA 2019 170582.400 1358
## 11 Antigua and Barbuda AG ATG 2012 469.900 1
## 12 Argentina AR ARG 2010 167226.300 1017
## 13 Argentina AR ARG 2011 176641.600 701
## 14 Argentina AR ARG 2012 177955.300 737
## 15 Argentina AR ARG 2013 183255.700 703
## 16 Argentina AR ARG 2014 179600.700 798
## 17 Argentina AR ARG 2015 185550.000 1016
## 18 Argentina AR ARG 2016 183158.700 1115
## 19 Argentina AR ARG 2017 179267.300 972
## 20 Argentina AR ARG 2018 176894.600 932
## 21 Argentina AR ARG 2019 168162.000 1052
## 22 Armenia AM ARM 2010 4336.600 25
## 23 Armenia AM ARM 2011 4937.100 27
## 24 Armenia AM ARM 2012 5716.500 45
## 25 Armenia AM ARM 2013 5501.500 33
## 26 Armenia AM ARM 2014 5479.000 31
## 27 Armenia AM ARM 2015 5343.400 31
## 28 Armenia AM ARM 2016 5066.700 23
## 29 Armenia AM ARM 2017 5371.300 54
## 30 Armenia AM ARM 2018 5712.200 61
## 31 Armenia AM ARM 2019 6195.600 39
## 32 Australia AU AUS 2010 395993.200 2828
## 33 Australia AU AUS 2011 394436.700 2664
## 34 Australia AU AUS 2012 395690.000 2714
## 35 Australia AU AUS 2013 388427.400 2994
## 36 Australia AU AUS 2014 379267.200 2630
## 37 Australia AU AUS 2015 385782.400 2821
## 38 Australia AU AUS 2016 394803.600 2739
## 39 Australia AU AUS 2017 397149.400 2854
## 40 Australia AU AUS 2018 396059.900 3095
## 41 Australia AU AUS 2019 395199.100 3147
## 42 Austria AT AUT 2010 69965.100 681
## 43 Austria AT AUT 2011 68276.000 495
## 44 Austria AT AUT 2012 65112.000 526
## 45 Austria AT AUT 2013 65752.700 451
## 46 Austria AT AUT 2014 62054.500 491
## 47 Austria AT AUT 2015 63254.700 288
## 48 Austria AT AUT 2016 63697.100 299
## 49 Austria AT AUT 2017 65867.700 405
## 50 Austria AT AUT 2018 63131.400 304
## 51 Austria AT AUT 2019 64497.800 396
## 52 Azerbaijan AZ AZE 2010 24311.900 27
## 53 Azerbaijan AZ AZE 2011 27282.100 27
## 54 Azerbaijan AZ AZE 2012 30110.600 23
## 55 Azerbaijan AZ AZE 2013 30939.600 54
## 56 Azerbaijan AZ AZE 2015 31773.300 11
## 57 Azerbaijan AZ AZE 2016 32239.400 22
## 58 Azerbaijan AZ AZE 2017 31957.000 114
## 59 Azerbaijan AZ AZE 2018 32728.100 54
## 60 Azerbaijan AZ AZE 2019 35521.000 139
## 61 Bahamas, The BS BHS 2012 2394.300 58
## 62 Bahamas, The BS BHS 2013 2805.400 33
## 63 Bahamas, The BS BHS 2014 2518.900 23
## 64 Bahrain BH BHR 2012 27174.200 9
## 65 Bahrain BH BHR 2013 28853.000 10
## 66 Bahrain BH BHR 2014 30282.700 11
## 67 Bahrain BH BHR 2015 30492.400 3
## 68 Bahrain BH BHR 2016 30301.100 23
## 69 Bahrain BH BHR 2017 30514.900 26
## 70 Bahrain BH BHR 2018 30844.400 5
## 71 Bahrain BH BHR 2019 32966.800 7
## 72 Bangladesh BD BGD 2010 50487.600 853
## 73 Bangladesh BD BGD 2011 54309.900 1155
## 74 Bangladesh BD BGD 2012 58985.200 1114
## 75 Bangladesh BD BGD 2013 62965.700 1100
## 76 Bangladesh BD BGD 2014 66313.100 1245
## 77 Bangladesh BD BGD 2015 73156.900 1284
## 78 Bangladesh BD BGD 2016 81128.900 1359
## 79 Bangladesh BD BGD 2017 87658.000 1577
## 80 Bangladesh BD BGD 2018 95944.600 1897
## 81 Bangladesh BD BGD 2019 92645.000 1494
## 82 Barbados BB BRB 2010 1481.500 2
## 83 Barbados BB BRB 2011 1516.250 3
## 84 Barbados BB BRB 2012 1467.620 3
## 85 Barbados BB BRB 2013 1439.470 2
## 86 Barbados BB BRB 2015 1269.570 1
## 87 Barbados BB BRB 2016 1290.770 3
## 88 Barbados BB BRB 2017 1179.970 1
## 89 Belarus BY BLR 2010 61443.700 173
## 90 Belarus BY BLR 2011 58325.800 236
## 91 Belarus BY BLR 2012 59738.300 422
## 92 Belarus BY BLR 2013 59891.200 383
## 93 Belarus BY BLR 2014 59431.800 171
## 94 Belarus BY BLR 2015 54871.300 202
## 95 Belarus BY BLR 2016 55201.400 202
## 96 Belarus BY BLR 2017 56247.000 177
## 97 Belarus BY BLR 2018 59271.600 183
## 98 Belarus BY BLR 2019 57681.200 317
## 99 Belize BZ BLZ 2018 630.000 2
## 100 Belize BZ BLZ 2019 736.200 5
## 101 Bhutan BT BTN 2015 1042.100 1
## 102 Bhutan BT BTN 2019 1433.000 4
## 103 Bolivia BO BOL 2014 19478.300 26
## 104 Bolivia BO BOL 2016 21328.700 40
## 105 Bolivia BO BOL 2017 21628.200 16
## 106 Bosnia and Herzegovina BA BIH 2010 20842.500 22
## 107 Bosnia and Herzegovina BA BIH 2011 23738.200 25
## 108 Bosnia and Herzegovina BA BIH 2012 21960.200 155
## 109 Bosnia and Herzegovina BA BIH 2013 21847.800 19
## 110 Bosnia and Herzegovina BA BIH 2014 19506.800 64
## 111 Bosnia and Herzegovina BA BIH 2015 19629.000 24
## 112 Bosnia and Herzegovina BA BIH 2016 22356.600 143
## 113 Bosnia and Herzegovina BA BIH 2017 22692.700 108
## 114 Bosnia and Herzegovina BA BIH 2018 22615.800 76
## 115 Bosnia and Herzegovina BA BIH 2019 21124.300 84
## 116 Botswana BW BWA 2011 3883.337 7
## 117 Botswana BW BWA 2012 3414.335 12
## 118 Botswana BW BWA 2013 5421.539 10
## 119 Botswana BW BWA 2014 6988.912 12
## 120 Brazil BR BRA 2010 397931.100 4134
## 121 Brazil BR BRA 2011 418295.400 4364
## 122 Brazil BR BRA 2012 454232.900 3746
## 123 Brazil BR BRA 2013 486844.700 3818
## 124 Brazil BR BRA 2014 511618.000 3693
## 125 Brazil BR BRA 2015 485344.100 3289
## 126 Brazil BR BRA 2016 447077.300 3400
## 127 Brazil BR BRA 2017 455684.800 3532
## 128 Brazil BR BRA 2018 433838.800 3696
## 129 Brazil BR BRA 2019 434318.000 4226
## 130 Brunei Darussalam BN BRN 2011 7332.700 4
## 131 Brunei Darussalam BN BRN 2012 7299.500 1
## 132 Brunei Darussalam BN BRN 2014 7080.300 4
## 133 Brunei Darussalam BN BRN 2017 7290.200 1
## 134 Bulgaria BG BGR 2010 44740.600 520
## 135 Bulgaria BG BGR 2011 49637.600 615
## 136 Bulgaria BG BGR 2012 45023.100 743
## 137 Bulgaria BG BGR 2013 39660.700 831
## 138 Bulgaria BG BGR 2014 42054.400 885
## 139 Bulgaria BG BGR 2015 44556.500 620
## 140 Bulgaria BG BGR 2016 41586.700 648
## 141 Bulgaria BG BGR 2017 43885.100 631
## 142 Bulgaria BG BGR 2018 40898.200 433
## 143 Bulgaria BG BGR 2019 39159.900 718
## 144 Burkina Faso BF BFA 2010 2094.300 4
## 145 Burundi BI BDI 2017 529.100 2
## 146 Cabo Verde CV CPV 2018 594.700 31
## 147 Cambodia KH KHM 2010 5140.800 7
## 148 Cambodia KH KHM 2012 5673.900 8
## 149 Cambodia KH KHM 2013 5737.600 3
## 150 Cambodia KH KHM 2014 6947.200 37
## 151 Cambodia KH KHM 2015 8432.600 9
## 152 Cambodia KH KHM 2019 18093.200 16
## 153 Canada CA CAN 2010 537091.500 851
## 154 Canada CA CAN 2011 549369.900 790
## 155 Canada CA CAN 2012 546291.600 847
## 156 Canada CA CAN 2013 555742.400 846
## 157 Canada CA CAN 2014 561760.500 859
## 158 Canada CA CAN 2015 558747.200 797
## 159 Canada CA CAN 2016 556874.100 916
## 160 Canada CA CAN 2017 568175.900 815
## 161 Canada CA CAN 2018 579573.900 776
## 162 Canada CA CAN 2019 566001.800 734
## 163 Chile CL CHL 2010 69749.100 41
## 164 Chile CL CHL 2011 76470.500 57
## 165 Chile CL CHL 2012 78412.600 91
## 166 Chile CL CHL 2013 82998.500 91
## 167 Chile CL CHL 2014 76560.300 110
## 168 Chile CL CHL 2015 82260.400 43
## 169 Chile CL CHL 2016 86504.700 89
## 170 Chile CL CHL 2017 87136.900 82
## 171 Chile CL CHL 2018 86581.300 49
## 172 Chile CL CHL 2019 91915.400 51
## 173 China CN CHN 2010 8474922.700 409124
## 174 China CN CHN 2011 9282553.700 507538
## 175 China CN CHN 2012 9540539.700 642401
## 176 China CN CHN 2013 9979128.000 644398
## 177 China CN CHN 2014 10021043.400 548428
## 178 China CN CHN 2015 9859281.200 551481
## 179 China CN CHN 2016 9860914.000 631949
## 180 China CN CHN 2017 10089273.200 610817
## 181 China CN CHN 2018 10567262.000 689097
## 182 China CN CHN 2019 10762824.000 691771
## 183 Colombia CO COL 2010 64146.100 120
## 184 Colombia CO COL 2011 69771.100 147
## 185 Colombia CO COL 2012 70223.200 210
## 186 Colombia CO COL 2013 77725.400 318
## 187 Colombia CO COL 2014 80091.000 271
## 188 Colombia CO COL 2015 81017.200 358
## 189 Colombia CO COL 2016 83526.200 227
## 190 Colombia CO COL 2017 75674.000 259
## 191 Colombia CO COL 2018 79355.200 288
## 192 Colombia CO COL 2019 79186.700 331
## 193 Costa Rica CR CRI 2010 7105.500 10
## 194 Costa Rica CR CRI 2011 7430.800 18
## 195 Costa Rica CR CRI 2012 7446.700 21
## 196 Costa Rica CR CRI 2013 7778.700 25
## 197 Costa Rica CR CRI 2014 7832.300 7
## 198 Costa Rica CR CRI 2015 7537.900 10
## 199 Costa Rica CR CRI 2016 8008.900 17
## 200 Costa Rica CR CRI 2017 8173.500 5
## 201 Costa Rica CR CRI 2018 8166.600 8
## 202 Costa Rica CR CRI 2019 7956.400 11
## 203 Croatia HR HRV 2010 19457.300 733
## 204 Croatia HR HRV 2011 19027.600 622
## 205 Croatia HR HRV 2012 17403.000 732
## 206 Croatia HR HRV 2013 17010.600 585
## 207 Croatia HR HRV 2014 16236.400 508
## 208 Croatia HR HRV 2015 16602.300 407
## 209 Croatia HR HRV 2016 16876.800 572
## 210 Croatia HR HRV 2017 17407.900 375
## 211 Croatia HR HRV 2018 16433.400 367
## 212 Croatia HR HRV 2019 16523.300 316
## 213 Cuba CU CUB 2011 27586.300 8
## 214 Cuba CU CUB 2012 28690.900 5
## 215 Cuba CU CUB 2013 28869.700 5
## 216 Cuba CU CUB 2014 26677.100 8
## 217 Cuba CU CUB 2016 27176.400 7
## 218 Cuba CU CUB 2017 26241.300 6
## 219 Cuba CU CUB 2018 26674.600 11
## 220 Cuba CU CUB 2019 24418.500 21
## 221 Cyprus CY CYP 2011 7634.600 206
## 222 Cyprus CY CYP 2012 7098.800 99
## 223 Cyprus CY CYP 2013 6427.700 43
## 224 Cyprus CY CYP 2014 6811.800 40
## 225 Cyprus CY CYP 2015 6851.900 123
## 226 Cyprus CY CYP 2016 7219.200 46
## 227 Cyprus CY CYP 2017 7361.200 58
## 228 Cyprus CY CYP 2018 7198.600 86
## 229 Cyprus CY CYP 2019 7191.000 85
## 230 Czechia CZ CZE 2010 114167.600 1427
## 231 Czechia CZ CZE 2011 111220.100 1189
## 232 Czechia CZ CZE 2012 107320.200 1031
## 233 Czechia CZ CZE 2013 102910.300 1149
## 234 Czechia CZ CZE 2014 99960.600 1149
## 235 Czechia CZ CZE 2015 101167.500 928
## 236 Czechia CZ CZE 2016 102998.000 905
## 237 Czechia CZ CZE 2017 103549.900 782
## 238 Czechia CZ CZE 2018 102734.300 627
## 239 Czechia CZ CZE 2019 97712.900 837
## 240 Denmark DK DNK 2010 48124.800 245
## 241 Denmark DK DNK 2011 43098.000 209
## 242 Denmark DK DNK 2012 38216.200 175
## 243 Denmark DK DNK 2013 39960.700 126
## 244 Denmark DK DNK 2014 35820.000 140
## 245 Denmark DK DNK 2015 33704.900 166
## 246 Denmark DK DNK 2016 35340.300 144
## 247 Denmark DK DNK 2017 33184.000 151
## 248 Denmark DK DNK 2018 33133.300 177
## 249 Denmark DK DNK 2019 29696.500 113
## 250 Djibouti DJ DJI 2013 558.790 2
## 251 Dominican Republic DO DOM 2012 21681.500 32
## 252 Dominican Republic DO DOM 2013 21287.200 7
## 253 Dominican Republic DO DOM 2014 21450.700 28
## 254 Dominican Republic DO DOM 2015 23590.500 48
## 255 Dominican Republic DO DOM 2016 24593.500 50
## 256 Dominican Republic DO DOM 2017 23551.300 35
## 257 Dominican Republic DO DOM 2018 25366.000 5
## 258 Dominican Republic DO DOM 2019 25775.200 5
## 259 East Asia & Pacific Z4 EAS 2010 11863420.200 503920
## 260 East Asia & Pacific Z4 EAS 2011 12805112.100 600405
## 261 East Asia & Pacific Z4 EAS 2012 13143266.300 743004
## 262 East Asia & Pacific Z4 EAS 2013 13591102.730 751368
## 263 East Asia & Pacific Z4 EAS 2014 13640572.800 650988
## 264 East Asia & Pacific Z4 EAS 2015 13510201.200 656558
## 265 East Asia & Pacific Z4 EAS 2016 13550289.300 734679
## 266 East Asia & Pacific Z4 EAS 2017 13869889.000 710436
## 267 East Asia & Pacific Z4 EAS 2018 14437176.300 788470
## 268 East Asia & Pacific Z4 EAS 2019 14703257.400 791562
## 269 Ecuador EC ECU 2010 37254.000 52
## 270 Ecuador EC ECU 2013 39701.100 82
## 271 Ecuador EC ECU 2014 41801.200 80
## 272 Ecuador EC ECU 2015 41390.300 65
## 273 Ecuador EC ECU 2016 39791.700 71
## 274 Ecuador EC ECU 2017 38549.300 192
## 275 Ecuador EC ECU 2018 40273.200 246
## 276 Ecuador EC ECU 2019 39631.400 79
## 277 Egypt, Arab Rep. EG EGY 2010 200313.300 3366
## 278 Egypt, Arab Rep. EG EGY 2011 205767.300 1700
## 279 Egypt, Arab Rep. EG EGY 2012 215000.900 1497
## 280 Egypt, Arab Rep. EG EGY 2013 213856.400 1678
## 281 Egypt, Arab Rep. EG EGY 2014 219121.100 1829
## 282 Egypt, Arab Rep. EG EGY 2015 226283.600 1625
## 283 Egypt, Arab Rep. EG EGY 2016 235425.800 1751
## 284 Egypt, Arab Rep. EG EGY 2017 244540.500 2063
## 285 Egypt, Arab Rep. EG EGY 2018 237983.000 1668
## 286 Egypt, Arab Rep. EG EGY 2019 217908.300 1720
## 287 El Salvador SV SLV 2012 6771.600 8
## 288 El Salvador SV SLV 2013 6455.800 8
## 289 El Salvador SV SLV 2014 6592.200 47
## 290 El Salvador SV SLV 2015 6997.600 11
## 291 El Salvador SV SLV 2016 7125.100 17
## 292 El Salvador SV SLV 2017 6344.900 10
## 293 El Salvador SV SLV 2018 6844.900 19
## 294 El Salvador SV SLV 2019 7912.700 7
## 295 Estonia EE EST 2010 18500.400 71
## 296 Estonia EE EST 2011 18502.600 52
## 297 Estonia EE EST 2012 16989.000 86
## 298 Estonia EE EST 2013 18846.500 51
## 299 Estonia EE EST 2014 17594.900 74
## 300 Estonia EE EST 2015 14377.100 49
## 301 Estonia EE EST 2016 15907.200 59
## 302 Estonia EE EST 2017 16773.500 46
## 303 Estonia EE EST 2018 15716.900 62
## 304 Estonia EE EST 2019 10060.700 37
## 305 Europe & Central Asia Z7 ECS 2010 6579733.278 202406
## 306 Europe & Central Asia Z7 ECS 2011 6594980.408 215130
## 307 Europe & Central Asia Z7 ECS 2012 6524680.837 227804
## 308 Europe & Central Asia Z7 ECS 2013 6386728.465 241249
## 309 Europe & Central Asia Z7 ECS 2014 6104308.523 236371
## 310 Europe & Central Asia Z7 ECS 2015 6073951.402 200937
## 311 Europe & Central Asia Z7 ECS 2016 6099478.730 289566
## 312 Europe & Central Asia Z7 ECS 2017 6166584.297 288740
## 313 Europe & Central Asia Z7 ECS 2018 6177967.890 283927
## 314 Europe & Central Asia Z7 ECS 2019 6034379.547 272902
## 315 Finland FI FIN 2010 62526.400 249
## 316 Finland FI FIN 2011 55123.400 258
## 317 Finland FI FIN 2012 49408.100 280
## 318 Finland FI FIN 2013 50191.300 336
## 319 Finland FI FIN 2014 46161.700 307
## 320 Finland FI FIN 2015 42815.400 310
## 321 Finland FI FIN 2017 43015.400 200
## 322 Finland FI FIN 2018 44395.500 157
## 323 Finland FI FIN 2019 40987.100 166
## 324 France FR FRA 2016 313835.200 52243
## 325 France FR FRA 2017 317721.200 44127
## 326 France FR FRA 2018 306948.400 41169
## 327 France FR FRA 2019 300561.600 35732
## 328 Georgia GE GEO 2010 5322.100 186
## 329 Georgia GE GEO 2011 6521.800 206
## 330 Georgia GE GEO 2012 7193.000 165
## 331 Georgia GE GEO 2013 8024.000 110
## 332 Georgia GE GEO 2014 8654.000 87
## 333 Georgia GE GEO 2015 9399.400 175
## 334 Georgia GE GEO 2016 9813.600 120
## 335 Georgia GE GEO 2017 9907.100 472
## 336 Georgia GE GEO 2018 9725.200 204
## 337 Georgia GE GEO 2019 10549.600 186
## 338 Germany DE DEU 2010 773069.100 40396
## 339 Germany DE DEU 2011 746477.300 41848
## 340 Germany DE DEU 2012 760127.700 43323
## 341 Germany DE DEU 2013 776151.800 46757
## 342 Germany DE DEU 2014 736011.700 47189
## 343 Germany DE DEU 2015 742314.400 45458
## 344 Germany DE DEU 2016 747147.200 46216
## 345 Germany DE DEU 2017 732204.200 39121
## 346 Germany DE DEU 2018 707770.200 38815
## 347 Germany DE DEU 2019 658693.500 36617
## 348 Ghana GH GHA 2016 14545.300 453
## 349 Ghana GH GHA 2017 15135.000 735
## 350 Ghana GH GHA 2018 16774.900 796
## 351 Greece GR GRC 2010 87578.500 1526
## 352 Greece GR GRC 2012 80085.300 918
## 353 Greece GR GRC 2013 72484.200 1286
## 354 Greece GR GRC 2014 69548.700 1066
## 355 Greece GR GRC 2015 68010.200 943
## 356 Greece GR GRC 2016 66848.600 912
## 357 Greece GR GRC 2017 66795.500 829
## 358 Greece GR GRC 2018 65021.200 541
## 359 Greece GR GRC 2019 59990.300 783
## 360 Guatemala GT GTM 2010 11477.600 5
## 361 Guatemala GT GTM 2011 11774.900 35
## 362 Guatemala GT GTM 2012 12183.700 25
## 363 Guatemala GT GTM 2013 13046.300 25
## 364 Guatemala GT GTM 2014 13998.500 65
## 365 Guatemala GT GTM 2015 16455.900 20
## 366 Guatemala GT GTM 2016 17451.900 205
## 367 Guatemala GT GTM 2017 16530.100 10
## 368 Guatemala GT GTM 2018 18075.700 35
## 369 Guatemala GT GTM 2019 19017.800 5
## 370 Guinea-Bissau GW GNB 2011 243.500 1
## 371 Guinea-Bissau GW GNB 2012 246.000 1
## 372 Guinea-Bissau GW GNB 2013 247.400 9
## 373 Honduras HN HND 2011 9470.400 11
## 374 Honduras HN HND 2013 9685.000 8
## 375 Honduras HN HND 2015 10538.200 7
## 376 Honduras HN HND 2016 10394.700 15
## 377 Honduras HN HND 2017 9377.000 4
## 378 Honduras HN HND 2018 9031.300 4
## 379 Hungary HU HUN 2010 47880.500 663
## 380 Hungary HU HUN 2011 46907.300 754
## 381 Hungary HU HUN 2012 43427.200 871
## 382 Hungary HU HUN 2013 40762.600 1271
## 383 Hungary HU HUN 2014 40625.200 788
## 384 Hungary HU HUN 2015 43384.100 674
## 385 Hungary HU HUN 2016 44248.400 856
## 386 Hungary HU HUN 2017 49449.800 682
## 387 Hungary HU HUN 2018 49202.900 595
## 388 Hungary HU HUN 2019 47261.800 433
## 389 Iceland IS ISL 2010 1959.124 86
## 390 Iceland IS ISL 2011 1888.350 51
## 391 Iceland IS ISL 2012 1859.900 67
## 392 Iceland IS ISL 2013 2031.800 68
## 393 Iceland IS ISL 2014 2047.500 37
## 394 Iceland IS ISL 2015 2057.500 15
## 395 Iceland IS ISL 2016 1633.000 49
## 396 Iceland IS ISL 2017 1671.000 15
## 397 Iceland IS ISL 2019 1638.200 16
## 398 India IN IND 2010 1659983.000 4416
## 399 India IN IND 2011 1756744.000 5156
## 400 India IN IND 2012 1909442.000 5100
## 401 India IN IND 2013 1972429.400 5182
## 402 India IN IND 2014 2147107.000 6168
## 403 India IN IND 2015 2158023.200 6829
## 404 India IN IND 2016 2195248.500 6753
## 405 India IN IND 2017 2308804.400 7534
## 406 India IN IND 2018 2458175.900 8928
## 407 India IN IND 2019 2423951.400 9381
## 408 Indonesia ID IDN 2013 448400.200 2771
## 409 Indonesia ID IDN 2014 484640.100 2534
## 410 Indonesia ID IDN 2015 489052.800 2651
## 411 Indonesia ID IDN 2016 483978.700 2581
## 412 Indonesia ID IDN 2017 515395.700 2319
## 413 Indonesia ID IDN 2018 568007.600 2432
## 414 Indonesia ID IDN 2019 605290.600 1798
## 415 Iran, Islamic Rep. IR IRN 2010 541171.100 3699
## 416 Iran, Islamic Rep. IR IRN 2011 552050.900 4089
## 417 Iran, Islamic Rep. IR IRN 2012 559122.100 3528
## 418 Iran, Islamic Rep. IR IRN 2013 583703.100 4632
## 419 Iran, Islamic Rep. IR IRN 2014 605356.100 8772
## 420 Iran, Islamic Rep. IR IRN 2016 607216.300 15811
## 421 Iran, Islamic Rep. IR IRN 2017 626427.900 17818
## 422 Iran, Islamic Rep. IR IRN 2018 637433.700 14610
## 423 Iran, Islamic Rep. IR IRN 2019 625251.500 17489
## 424 Iraq IQ IRQ 2016 143281.100 52
## 425 Iraq IQ IRQ 2018 168154.300 81
## 426 Ireland IE IRL 2010 40347.700 110
## 427 Ireland IE IRL 2015 37087.200 115
## 428 Ireland IE IRL 2016 39009.800 122
## 429 Ireland IE IRL 2017 37729.100 125
## 430 Ireland IE IRL 2018 37473.600 88
## 431 Ireland IE IRL 2019 35812.200 105
## 432 Israel IL ISR 2010 70520.300 1200
## 433 Israel IL ISR 2011 69822.800 1030
## 434 Israel IL ISR 2012 76063.200 1011
## 435 Israel IL ISR 2013 67002.500 860
## 436 Israel IL ISR 2014 64717.800 921
## 437 Israel IL ISR 2015 66314.700 1049
## 438 Israel IL ISR 2016 65233.000 1181
## 439 Israel IL ISR 2017 65906.300 1075
## 440 Israel IL ISR 2018 61424.500 981
## 441 Israel IL ISR 2019 62796.300 819
## 442 Italy IT ITA 2010 405272.300 31015
## 443 Italy IT ITA 2011 396687.400 28307
## 444 Italy IT ITA 2012 376747.200 29923
## 445 Italy IT ITA 2013 346458.300 30652
## 446 Italy IT ITA 2014 327498.000 30396
## 447 Italy IT ITA 2016 333344.800 26699
## 448 Italy IT ITA 2017 329193.300 28892
## 449 Italy IT ITA 2018 324884.200 34812
## 450 Italy IT ITA 2019 317223.000 30302
## 451 Jamaica JM JAM 2010 7479.800 40
## 452 Jamaica JM JAM 2011 7529.400 41
## 453 Jamaica JM JAM 2012 6959.000 94
## 454 Jamaica JM JAM 2013 7402.300 160
## 455 Jamaica JM JAM 2014 7191.300 72
## 456 Jamaica JM JAM 2015 7088.700 65
## 457 Jamaica JM JAM 2016 7549.500 178
## 458 Jamaica JM JAM 2017 7225.000 128
## 459 Jamaica JM JAM 2018 8589.000 114
## 460 Jamaica JM JAM 2019 8394.300 204
## 461 Japan JP JPN 2010 1157241.800 28083
## 462 Japan JP JPN 2011 1213775.600 26658
## 463 Japan JP JPN 2012 1254319.400 27933
## 464 Japan JP JPN 2013 1267376.200 26407
## 465 Japan JP JPN 2014 1217306.600 24868
## 466 Japan JP JPN 2015 1178349.100 24829
## 467 Japan JP JPN 2016 1164869.400 24552
## 468 Japan JP JPN 2017 1150835.000 24435
## 469 Japan JP JPN 2018 1111115.300 23460
## 470 Japan JP JPN 2019 1073645.300 22920
## 471 Jordan JO JOR 2010 20196.800 42
## 472 Jordan JO JOR 2011 20756.300 9
## 473 Jordan JO JOR 2012 24123.300 38
## 474 Jordan JO JOR 2013 23803.700 30
## 475 Jordan JO JOR 2014 25553.100 17
## 476 Jordan JO JOR 2015 25309.100 55
## 477 Jordan JO JOR 2016 24730.000 53
## 478 Jordan JO JOR 2017 26021.800 71
## 479 Jordan JO JOR 2018 24710.000 26
## 480 Jordan JO JOR 2019 23147.900 71
## 481 Kazakhstan KZ KAZ 2010 229702.200 149
## 482 Kazakhstan KZ KAZ 2011 245455.300 136
## 483 Kazakhstan KZ KAZ 2012 244599.000 119
## 484 Kazakhstan KZ KAZ 2013 260015.400 135
## 485 Kazakhstan KZ KAZ 2014 209261.300 107
## 486 Kazakhstan KZ KAZ 2015 191059.600 94
## 487 Kazakhstan KZ KAZ 2016 202475.700 89
## 488 Kazakhstan KZ KAZ 2017 214897.700 105
## 489 Kazakhstan KZ KAZ 2018 216896.700 83
## 490 Kenya KE KEN 2010 13424.400 69
## 491 Kenya KE KEN 2011 13963.900 86
## 492 Kenya KE KEN 2012 12981.300 93
## 493 Kenya KE KEN 2013 14387.700 78
## 494 Kenya KE KEN 2014 16700.000 78
## 495 Kenya KE KEN 2015 18090.300 73
## 496 Kenya KE KEN 2016 19310.100 89
## 497 Kenya KE KEN 2017 20097.200 141
## 498 Kenya KE KEN 2018 19253.900 170
## 499 Kenya KE KEN 2019 19481.900 154
## 500 Kiribati KI KIR 2013 63.800 10
## 501 Korea, Rep. KR KOR 2010 575215.700 55369
## 502 Korea, Rep. KR KOR 2011 598480.800 54300
## 503 Korea, Rep. KR KOR 2012 600316.300 60867
## 504 Korea, Rep. KR KOR 2013 599601.400 65485
## 505 Korea, Rep. KR KOR 2014 588088.600 63083
## 506 Korea, Rep. KR KOR 2015 607827.100 65895
## 507 Korea, Rep. KR KOR 2016 615443.600 62631
## 508 Korea, Rep. KR KOR 2017 626178.400 60402
## 509 Korea, Rep. KR KOR 2018 630186.600 60077
## 510 Korea, Rep. KR KOR 2019 612133.700 61364
## 511 Kuwait KW KWT 2016 91376.200 26
## 512 Kuwait KW KWT 2017 90378.300 97
## 513 Kuwait KW KWT 2018 92652.100 197
## 514 Kyrgyz Republic KG KGZ 2010 6394.400 13
## 515 Kyrgyz Republic KG KGZ 2011 7690.400 17
## 516 Kyrgyz Republic KG KGZ 2012 10146.000 50
## 517 Kyrgyz Republic KG KGZ 2013 9455.000 14
## 518 Kyrgyz Republic KG KGZ 2014 9848.500 48
## 519 Kyrgyz Republic KG KGZ 2015 10266.600 21
## 520 Kyrgyz Republic KG KGZ 2016 9675.600 17
## 521 Kyrgyz Republic KG KGZ 2017 9439.000 11
## 522 Latin America & Caribbean ZJ LCN 2010 1570608.870 7384
## 523 Latin America & Caribbean ZJ LCN 2011 1635034.920 7426
## 524 Latin America & Caribbean ZJ LCN 2012 1707709.790 7134
## 525 Latin America & Caribbean ZJ LCN 2013 1752196.840 7313
## 526 Latin America & Caribbean ZJ LCN 2014 1760980.099 7318
## 527 Latin America & Caribbean ZJ LCN 2015 1750693.940 6838
## 528 Latin America & Caribbean ZJ LCN 2016 1710227.796 7266
## 529 Latin America & Caribbean ZJ LCN 2017 1685948.340 7329
## 530 Latin America & Caribbean ZJ LCN 2018 1633570.340 7511
## 531 Latin America & Caribbean ZJ LCN 2019 1620695.030 7947
## 532 Latvia LV LVA 2010 8518.100 133
## 533 Latvia LV LVA 2011 7897.300 117
## 534 Latvia LV LVA 2012 7570.700 104
## 535 Latvia LV LVA 2013 7452.400 113
## 536 Latvia LV LVA 2014 7282.300 79
## 537 Latvia LV LVA 2015 7315.900 102
## 538 Latvia LV LVA 2016 7145.300 176
## 539 Latvia LV LVA 2017 7116.700 141
## 540 Latvia LV LVA 2018 7785.200 154
## 541 Latvia LV LVA 2019 7569.100 124
## 542 Lithuania LT LTU 2010 12603.000 29
## 543 Lithuania LT LTU 2011 11845.600 61
## 544 Lithuania LT LTU 2012 11925.800 66
## 545 Lithuania LT LTU 2013 11331.100 86
## 546 Lithuania LT LTU 2014 10862.900 62
## 547 Lithuania LT LTU 2015 11067.400 89
## 548 Lithuania LT LTU 2016 11203.100 55
## 549 Lithuania LT LTU 2017 11222.200 205
## 550 Lithuania LT LTU 2018 11652.500 160
## 551 Lithuania LT LTU 2019 11735.800 177
## 552 Madagascar MG MDG 2010 1869.800 279
## 553 Madagascar MG MDG 2011 2193.890 307
## 554 Madagascar MG MDG 2012 2741.740 297
## 555 Madagascar MG MDG 2013 2930.230 165
## 556 Madagascar MG MDG 2014 3010.620 203
## 557 Madagascar MG MDG 2015 3284.620 205
## 558 Madagascar MG MDG 2016 3181.720 166
## 559 Madagascar MG MDG 2017 3481.000 163
## 560 Madagascar MG MDG 2018 3304.870 293
## 561 Madagascar MG MDG 2019 3927.700 314
## 562 Malaysia MY MYS 2010 199867.000 737
## 563 Malaysia MY MYS 2011 202401.300 743
## 564 Malaysia MY MYS 2012 205415.800 857
## 565 Malaysia MY MYS 2013 223086.500 679
## 566 Malaysia MY MYS 2014 236264.600 827
## 567 Malaysia MY MYS 2015 236229.400 627
## 568 Malaysia MY MYS 2016 231669.400 701
## 569 Malaysia MY MYS 2017 225104.100 517
## 570 Malaysia MY MYS 2018 241451.400 528
## 571 Malaysia MY MYS 2019 244882.000 574
## 572 Malta MT MLT 2010 2586.600 4
## 573 Malta MT MLT 2011 2573.100 7
## 574 Malta MT MLT 2012 2715.600 13
## 575 Malta MT MLT 2013 2371.500 5
## 576 Malta MT MLT 2014 2356.300 10
## 577 Malta MT MLT 2019 1658.600 11
## 578 Mauritania MR MRT 2011 2182.900 62
## 579 Mauritania MR MRT 2012 2355.400 19
## 580 Mauritania MR MRT 2013 2101.200 10
## 581 Mauritania MR MRT 2018 3716.100 75
## 582 Mauritania MR MRT 2019 3824.800 115
## 583 Mauritius MU MUS 2011 3639.000 62
## 584 Mauritius MU MUS 2012 3725.000 19
## 585 Mauritius MU MUS 2013 3818.000 10
## 586 Mauritius MU MUS 2018 4133.900 75
## 587 Mauritius MU MUS 2019 4173.700 115
## 588 Mexico MX MEX 2010 462869.500 1691
## 589 Mexico MX MEX 2011 478403.500 1909
## 590 Mexico MX MEX 2012 486454.200 1954
## 591 Mexico MX MEX 2013 475737.500 1749
## 592 Mexico MX MEX 2014 462240.100 1774
## 593 Mexico MX MEX 2015 471632.600 1729
## 594 Mexico MX MEX 2016 476394.600 1651
## 595 Mexico MX MEX 2017 474498.500 1635
## 596 Mexico MX MEX 2018 444898.400 1627
## 597 Mexico MX MEX 2019 451828.800 1348
## 598 Middle East & North Africa ZQ MEA 2010 2128654.500 12466
## 599 Middle East & North Africa ZQ MEA 2011 2172950.600 11481
## 600 Middle East & North Africa ZQ MEA 2012 2293100.100 9814
## 601 Middle East & North Africa ZQ MEA 2013 2341101.890 10664
## 602 Middle East & North Africa ZQ MEA 2014 2419002.470 16603
## 603 Middle East & North Africa ZQ MEA 2015 2453243.850 7248
## 604 Middle East & North Africa ZQ MEA 2016 2476429.970 23594
## 605 Middle East & North Africa ZQ MEA 2017 2511165.400 27168
## 606 Middle East & North Africa ZQ MEA 2018 2500417.400 23224
## 607 Middle East & North Africa ZQ MEA 2019 2508614.570 25934
## 608 Moldova MD MDA 2010 8295.100 518
## 609 Moldova MD MDA 2011 8344.500 930
## 610 Moldova MD MDA 2012 8137.300 1293
## 611 Moldova MD MDA 2013 7181.500 1569
## 612 Moldova MD MDA 2014 7712.200 309
## 613 Moldova MD MDA 2015 8020.600 1207
## 614 Moldova MD MDA 2016 8143.000 351
## 615 Moldova MD MDA 2017 8064.000 290
## 616 Moldova MD MDA 2018 8555.900 433
## 617 Moldova MD MDA 2019 8921.600 451
## 618 Mongolia MN MNG 2010 14311.100 304
## 619 Mongolia MN MNG 2011 15729.400 182
## 620 Mongolia MN MNG 2013 18384.130 122
## 621 Mongolia MN MNG 2014 18122.000 257
## 622 Mongolia MN MNG 2016 18172.800 311
## 623 Mongolia MN MNG 2017 19575.500 711
## 624 Mongolia MN MNG 2018 21545.700 739
## 625 Mongolia MN MNG 2019 23146.900 855
## 626 Montenegro ME MNE 2010 2584.200 12
## 627 Montenegro ME MNE 2011 2535.900 14
## 628 Montenegro ME MNE 2012 2331.600 9
## 629 Montenegro ME MNE 2013 2267.300 9
## 630 Montenegro ME MNE 2014 2218.400 14
## 631 Montenegro ME MNE 2015 2359.100 8
## 632 Montenegro ME MNE 2016 2150.700 8
## 633 Montenegro ME MNE 2018 2500.300 1
## 634 Montenegro ME MNE 2019 2601.100 1
## 635 Morocco MA MAR 2010 51749.500 4077
## 636 Morocco MA MAR 2011 55923.500 3454
## 637 Morocco MA MAR 2012 58076.000 2622
## 638 Morocco MA MAR 2013 57595.500 2996
## 639 Morocco MA MAR 2014 58691.700 3695
## 640 Morocco MA MAR 2015 60362.500 3727
## 641 Morocco MA MAR 2016 60289.900 4056
## 642 Morocco MA MAR 2017 63014.900 3714
## 643 Morocco MA MAR 2018 64286.100 3879
## 644 Morocco MA MAR 2019 70986.300 3620
## 645 Mozambique MZ MOZ 2012 3640.800 10
## 646 Mozambique MZ MOZ 2013 4150.500 28
## 647 Mozambique MZ MOZ 2014 4823.300 22
## 648 Mozambique MZ MOZ 2015 5478.300 30
## 649 Mozambique MZ MOZ 2016 7243.800 29
## 650 Mozambique MZ MOZ 2017 7162.300 30
## 651 Mozambique MZ MOZ 2018 6875.100 5
## 652 Mozambique MZ MOZ 2019 7530.200 40
## 653 Namibia NA NAM 2017 4224.000 4
## 654 Namibia NA NAM 2018 4279.300 55
## 655 Namibia NA NAM 2019 4316.300 79
## 656 Nepal NP NPL 2011 5199.200 21
## 657 Nepal NP NPL 2012 5997.800 3
## 658 Nepal NP NPL 2013 6087.800 21
## 659 Nepal NP NPL 2015 7186.200 16
## 660 Nepal NP NPL 2016 10735.700 11
## 661 Nepal NP NPL 2017 13265.300 15
## 662 New Zealand NZ NZL 2011 30290.600 1021
## 663 New Zealand NZ NZL 2012 32107.400 1219
## 664 New Zealand NZ NZL 2013 31885.500 956
## 665 New Zealand NZ NZL 2014 31970.700 400
## 666 New Zealand NZ NZL 2015 32281.200 345
## 667 New Zealand NZ NZL 2016 31185.700 358
## 668 New Zealand NZ NZL 2017 32927.400 343
## 669 New Zealand NZ NZL 2018 32409.000 463
## 670 New Zealand NZ NZL 2019 34008.200 321
## 671 Nicaragua NI NIC 2013 4473.300 1
## 672 Nigeria NG NGA 2011 94996.500 800
## 673 Nigeria NG NGA 2012 95335.300 638
## 674 Nigeria NG NGA 2013 108116.800 829
## 675 Nigeria NG NGA 2016 110817.500 640
## 676 Nigeria NG NGA 2017 108481.200 910
## 677 Nigeria NG NGA 2018 113633.100 1889
## 678 Nigeria NG NGA 2019 119544.100 1913
## 679 North Macedonia MK MKD 2010 8329.600 127
## 680 North Macedonia MK MKD 2011 9157.100 87
## 681 North Macedonia MK MKD 2012 8798.900 67
## 682 North Macedonia MK MKD 2013 7862.200 104
## 683 North Macedonia MK MKD 2015 7142.100 48
## 684 North Macedonia MK MKD 2016 6962.700 81
## 685 North Macedonia MK MKD 2017 7436.200 99
## 686 North Macedonia MK MKD 2018 6950.600 63
## 687 North Macedonia MK MKD 2019 7956.100 73
## 688 Norway NO NOR 2014 39643.000 701
## 689 Norway NO NOR 2015 40288.800 623
## 690 Norway NO NOR 2016 39578.400 574
## 691 Norway NO NOR 2017 39174.900 572
## 692 Norway NO NOR 2018 38571.500 555
## 693 Norway NO NOR 2019 37661.600 464
## 694 Oman OM OMN 2015 70131.700 19
## 695 Oman OM OMN 2016 72197.600 15
## 696 Oman OM OMN 2017 71633.200 25
## 697 Oman OM OMN 2018 75365.000 2
## 698 Oman OM OMN 2019 75744.800 8
## 699 Pakistan PK PAK 2010 140378.600 450
## 700 Pakistan PK PAK 2011 141690.000 746
## 701 Pakistan PK PAK 2012 143819.100 395
## 702 Pakistan PK PAK 2013 145993.700 331
## 703 Pakistan PK PAK 2014 154235.200 475
## 704 Pakistan PK PAK 2015 164152.300 364
## 705 Pakistan PK PAK 2016 181113.300 435
## 706 Pakistan PK PAK 2017 198738.800 387
## 707 Pakistan PK PAK 2018 186865.600 453
## 708 Pakistan PK PAK 2019 184096.300 453
## 709 Panama PA PAN 2012 10461.600 4
## 710 Panama PA PAN 2013 10252.300 4
## 711 Panama PA PAN 2014 10758.100 13
## 712 Panama PA PAN 2015 10609.900 19
## 713 Panama PA PAN 2016 10652.400 9
## 714 Panama PA PAN 2017 10136.400 3
## 715 Papua New Guinea PG PNG 2013 5224.500 1
## 716 Papua New Guinea PG PNG 2015 6369.700 3
## 717 Papua New Guinea PG PNG 2018 7114.000 15
## 718 Paraguay PY PRY 2010 5043.300 121
## 719 Paraguay PY PRY 2018 8360.100 159
## 720 Paraguay PY PRY 2019 8101.600 18
## 721 Peru PE PER 2010 44998.500 124
## 722 Peru PE PER 2011 48427.400 86
## 723 Peru PE PER 2012 48120.600 101
## 724 Peru PE PER 2013 49751.900 133
## 725 Peru PE PER 2014 53167.800 104
## 726 Peru PE PER 2015 54553.500 131
## 727 Peru PE PER 2016 56860.300 102
## 728 Peru PE PER 2017 54274.300 92
## 729 Peru PE PER 2018 54589.900 134
## 730 Peru PE PER 2019 56972.200 133
## 731 Philippines PH PHL 2010 81917.800 435
## 732 Philippines PH PHL 2011 82544.500 533
## 733 Philippines PH PHL 2012 86165.000 786
## 734 Philippines PH PHL 2013 95504.100 887
## 735 Philippines PH PHL 2014 101822.500 829
## 736 Philippines PH PHL 2015 110990.900 539
## 737 Philippines PH PHL 2016 120895.100 1043
## 738 Philippines PH PHL 2017 133456.700 751
## 739 Philippines PH PHL 2018 139119.100 929
## 740 Philippines PH PHL 2019 145871.300 1110
## 741 Poland PL POL 2019 294948.300 1053
## 742 Portugal PT PRT 2010 50937.300 1565
## 743 Portugal PT PRT 2011 49867.900 1598
## 744 Portugal PT PRT 2012 48220.300 1944
## 745 Portugal PT PRT 2013 46555.200 1855
## 746 Portugal PT PRT 2014 45931.100 2410
## 747 Portugal PT PRT 2015 49851.900 1862
## 748 Portugal PT PRT 2016 48695.200 2096
## 749 Portugal PT PRT 2017 53272.700 1450
## 750 Portugal PT PRT 2018 49461.200 1288
## 751 Portugal PT PRT 2019 44564.700 1920
## 752 Romania RO ROU 2010 77601.900 1302
## 753 Romania RO ROU 2011 84049.500 1030
## 754 Romania RO ROU 2012 81843.800 1046
## 755 Romania RO ROU 2013 72090.600 1535
## 756 Romania RO ROU 2014 71539.000 1012
## 757 Romania RO ROU 2015 73314.400 830
## 758 Romania RO ROU 2016 71585.200 624
## 759 Romania RO ROU 2017 74208.100 666
## 760 Romania RO ROU 2018 75190.200 438
## 761 Romania RO ROU 2019 73942.800 542
## 762 Russian Federation RU RUS 2010 1617827.500 2880
## 763 Russian Federation RU RUS 2011 1699083.200 2887
## 764 Russian Federation RU RUS 2012 1675755.900 3638
## 765 Russian Federation RU RUS 2013 1632679.700 2650
## 766 Russian Federation RU RUS 2014 1611960.700 3183
## 767 Russian Federation RU RUS 2015 1592559.400 2616
## 768 Russian Federation RU RUS 2016 1571517.300 2912
## 769 Russian Federation RU RUS 2017 1594550.300 3789
## 770 Russian Federation RU RUS 2018 1661000.000 3823
## 771 Russian Federation RU RUS 2019 1703588.700 4412
## 772 Rwanda RW RWA 2012 814.130 20
## 773 Rwanda RW RWA 2014 923.920 2
## 774 Rwanda RW RWA 2015 1080.440 5
## 775 Rwanda RW RWA 2018 1403.400 5
## 776 Rwanda RW RWA 2019 1451.100 4
## 777 Samoa WS WSM 2012 198.100 3
## 778 Samoa WS WSM 2014 207.300 15
## 779 Samoa WS WSM 2016 246.700 2
## 780 Samoa WS WSM 2018 248.700 44
## 781 Samoa WS WSM 2019 278.700 40
## 782 Saudi Arabia SA SAU 2011 463763.700 246
## 783 Saudi Arabia SA SAU 2013 503213.700 168
## 784 Saudi Arabia SA SAU 2014 540520.200 234
## 785 Saudi Arabia SA SAU 2015 565190.100 321
## 786 Saudi Arabia SA SAU 2016 561229.700 386
## 787 Saudi Arabia SA SAU 2017 549749.900 461
## 788 Saudi Arabia SA SAU 2018 527565.700 345
## 789 Saudi Arabia SA SAU 2019 526770.300 348
## 790 Serbia RS SRB 2010 47103.900 110
## 791 Serbia RS SRB 2011 51230.200 108
## 792 Serbia RS SRB 2012 45790.200 94
## 793 Serbia RS SRB 2013 46472.500 156
## 794 Serbia RS SRB 2014 38980.700 150
## 795 Serbia RS SRB 2015 45410.100 122
## 796 Serbia RS SRB 2016 46635.000 190
## 797 Serbia RS SRB 2017 47342.600 197
## 798 Serbia RS SRB 2018 46192.400 196
## 799 Serbia RS SRB 2019 46030.100 134
## 800 Sierra Leone SL SLE 2018 1043.700 5
## 801 Singapore SG SGP 2010 42413.600 551
## 802 Singapore SG SGP 2011 44766.900 665
## 803 Singapore SG SGP 2012 43692.300 604
## 804 Singapore SG SGP 2013 43912.300 724
## 805 Singapore SG SGP 2014 44398.500 830
## 806 Singapore SG SGP 2015 45431.900 780
## 807 Singapore SG SGP 2016 44969.800 644
## 808 Singapore SG SGP 2017 47324.200 592
## 809 Singapore SG SGP 2018 45212.100 342
## 810 Singapore SG SGP 2019 45163.200 392
## 811 Slovak Republic SK SVK 2010 35432.200 544
## 812 Slovak Republic SK SVK 2011 34116.900 362
## 813 Slovak Republic SK SVK 2012 32347.100 468
## 814 Slovak Republic SK SVK 2013 32920.400 366
## 815 Slovak Republic SK SVK 2014 30438.100 340
## 816 Slovak Republic SK SVK 2015 30753.700 201
## 817 Slovak Republic SK SVK 2016 31497.800 258
## 818 Slovak Republic SK SVK 2017 33576.500 329
## 819 Slovak Republic SK SVK 2018 33005.100 272
## 820 Slovak Republic SK SVK 2019 31032.500 180
## 821 Slovenia SI SVN 2010 15777.200 133
## 822 Slovenia SI SVN 2011 15700.800 155
## 823 Slovenia SI SVN 2018 14072.900 40
## 824 South Africa ZA ZAF 2011 409480.300 853
## 825 South Africa ZA ZAF 2012 427001.700 1014
## 826 South Africa ZA ZAF 2013 437261.700 950
## 827 South Africa ZA ZAF 2014 448298.100 772
## 828 South Africa ZA ZAF 2015 425063.100 723
## 829 South Africa ZA ZAF 2016 425682.900 1087
## 830 South Africa ZA ZAF 2017 435214.500 1012
## 831 South Africa ZA ZAF 2018 439644.600 977
## 832 South Africa ZA ZAF 2019 446626.000 976
## 833 South Asia 8S SAS 2010 1878594.230 5952
## 834 South Asia 8S SAS 2011 1987072.190 7465
## 835 South Asia 8S SAS 2012 2147871.130 6977
## 836 South Asia 8S SAS 2013 2213363.550 6894
## 837 South Asia 8S SAS 2014 2403894.740 7888
## 838 South Asia 8S SAS 2015 2434198.490 8884
## 839 South Asia 8S SAS 2016 2503383.230 8795
## 840 South Asia 8S SAS 2017 2644485.380 9800
## 841 South Asia 8S SAS 2018 2792018.580 11506
## 842 South Asia 8S SAS 2019 2752652.430 11812
## 843 Spain ES ESP 2010 274140.600 14716
## 844 Spain ES ESP 2011 275470.100 18540
## 845 Spain ES ESP 2012 270280.400 17389
## 846 Spain ES ESP 2013 243794.900 18015
## 847 Spain ES ESP 2014 241979.900 17841
## 848 Spain ES ESP 2015 257251.200 17263
## 849 Spain ES ESP 2016 248252.100 17551
## 850 Spain ES ESP 2017 264723.700 21845
## 851 Spain ES ESP 2018 258373.200 18219
## 852 Spain ES ESP 2019 241886.500 15728
## 853 Sri Lanka LK LKA 2010 13071.800 233
## 854 Sri Lanka LK LKA 2011 15410.200 387
## 855 Sri Lanka LK LKA 2012 17440.400 365
## 856 Sri Lanka LK LKA 2013 14448.400 260
## 857 Sri Lanka LK LKA 2015 19240.900 390
## 858 Sri Lanka LK LKA 2016 23167.600 237
## 859 Sri Lanka LK LKA 2017 23140.500 287
## 860 Sri Lanka LK LKA 2018 21690.300 228
## 861 Sri Lanka LK LKA 2019 23427.900 480
## 862 St. Lucia LC LCA 2013 506.400 1
## 863 Sub-Saharan Africa ZG SSF 2010 692188.097 359
## 864 Sub-Saharan Africa ZG SSF 2011 690836.384 2306
## 865 Sub-Saharan Africa ZG SSF 2012 717126.535 2442
## 866 Sub-Saharan Africa ZG SSF 2013 755034.179 2509
## 867 Sub-Saharan Africa ZG SSF 2014 788024.602 2058
## 868 Sub-Saharan Africa ZG SSF 2015 770629.632 1607
## 869 Sub-Saharan Africa ZG SSF 2016 781618.462 3236
## 870 Sub-Saharan Africa ZG SSF 2017 791456.382 3892
## 871 Sub-Saharan Africa ZG SSF 2018 809339.848 4810
## 872 Sub-Saharan Africa ZG SSF 2019 833713.850 4262
## 873 Sudan SD SDN 2012 15806.300 88
## 874 Sudan SD SDN 2013 15853.200 115
## 875 Sudan SD SDN 2014 16658.500 545
## 876 Sudan SD SDN 2015 19259.200 230
## 877 Sudan SD SDN 2016 21503.100 348
## 878 Sudan SD SDN 2017 21579.500 488
## 879 Sudan SD SDN 2018 21670.300 114
## 880 Sudan SD SDN 2019 22131.500 218
## 881 Sweden SE SWE 2010 47985.500 734
## 882 Sweden SE SWE 2011 44452.000 583
## 883 Sweden SE SWE 2012 41996.600 735
## 884 Sweden SE SWE 2013 40388.100 694
## 885 Sweden SE SWE 2014 38993.000 549
## 886 Sweden SE SWE 2015 39120.100 821
## 887 Sweden SE SWE 2016 38691.200 689
## 888 Sweden SE SWE 2017 38168.200 551
## 889 Sweden SE SWE 2018 35915.900 457
## 890 Sweden SE SWE 2019 34964.600 529
## 891 Switzerland CH CHE 2010 45207.800 4177
## 892 Switzerland CH CHE 2011 41192.300 4408
## 893 Switzerland CH CHE 2012 42516.600 3991
## 894 Switzerland CH CHE 2013 43531.200 4095
## 895 Switzerland CH CHE 2014 39793.300 4244
## 896 Switzerland CH CHE 2015 39090.800 4800
## 897 Switzerland CH CHE 2016 39666.500 3817
## 898 Switzerland CH CHE 2017 38699.000 4301
## 899 Switzerland CH CHE 2018 37481.300 3458
## 900 Switzerland CH CHE 2019 37376.300 3334
## 901 Syrian Arab Republic SY SYR 2015 25260.700 251
## 902 Syrian Arab Republic SY SYR 2017 25794.400 450
## 903 Syrian Arab Republic SY SYR 2018 28303.100 359
## 904 Syrian Arab Republic SY SYR 2019 26840.400 148
## 905 Tajikistan TJ TJK 2013 3195.100 1
## 906 Thailand TH THA 2010 240768.300 3276
## 907 Thailand TH THA 2011 238968.500 2905
## 908 Thailand TH THA 2012 256286.000 2432
## 909 Thailand TH THA 2013 264681.000 2774
## 910 Thailand TH THA 2014 261377.000 3026
## 911 Thailand TH THA 2015 268853.300 3383
## 912 Thailand TH THA 2016 267916.300 3759
## 913 Thailand TH THA 2017 267137.100 3698
## 914 Thailand TH THA 2018 264483.600 4044
## 915 Thailand TH THA 2019 274466.700 3541
## 916 Trinidad and Tobago TT TTO 2012 21497.200 29
## 917 Trinidad and Tobago TT TTO 2013 22186.300 119
## 918 Trinidad and Tobago TT TTO 2014 22014.200 179
## 919 Trinidad and Tobago TT TTO 2015 21310.600 18
## 920 Trinidad and Tobago TT TTO 2016 18291.600 70
## 921 Trinidad and Tobago TT TTO 2017 18224.700 294
## 922 Trinidad and Tobago TT TTO 2018 17763.700 160
## 923 Trinidad and Tobago TT TTO 2019 17195.300 20
## 924 Tunisia TN TUN 2011 26717.400 179
## 925 Tunisia TN TUN 2012 29106.200 178
## 926 Tunisia TN TUN 2013 28601.900 189
## 927 Tunisia TN TUN 2014 30976.000 164
## 928 Tunisia TN TUN 2015 31627.400 129
## 929 Tunisia TN TUN 2016 30317.800 159
## 930 Tunisia TN TUN 2017 30742.600 150
## 931 Tunisia TN TUN 2018 31023.100 164
## 932 Tunisia TN TUN 2019 31045.000 222
## 933 Turkiye TR TUR 2010 297814.000 29570
## 934 Turkiye TR TUR 2011 318641.200 35491
## 935 Turkiye TR TUR 2012 329797.800 39921
## 936 Turkiye TR TUR 2013 319088.600 43631
## 937 Turkiye TR TUR 2014 341671.500 41249
## 938 Turkiye TR TUR 2015 353413.800 38709
## 939 Turkiye TR TUR 2016 376399.000 39475
## 940 Turkiye TR TUR 2017 418098.200 39255
## 941 Turkiye TR TUR 2018 414111.900 35462
## 942 Turkiye TR TUR 2019 398772.900 39239
## 943 Uganda UG UGA 2017 5172.000 40
## 944 Uganda UG UGA 2018 5866.600 65
## 945 Uganda UG UGA 2019 5943.000 154
## 946 Ukraine UA UKR 2010 268924.600 2949
## 947 Ukraine UA UKR 2011 283342.400 3445
## 948 Ukraine UA UKR 2012 277110.200 3480
## 949 Ukraine UA UKR 2013 270268.700 8087
## 950 Ukraine UA UKR 2014 237728.700 4962
## 951 Ukraine UA UKR 2015 191067.800 4294
## 952 Ukraine UA UKR 2016 201655.400 5382
## 953 Ukraine UA UKR 2017 174938.300 4954
## 954 Ukraine UA UKR 2018 185623.800 5261
## 955 Ukraine UA UKR 2019 174599.900 4660
## 956 United Arab Emirates AE ARE 2010 162788.900 27
## 957 United Arab Emirates AE ARE 2011 166631.500 55
## 958 United Arab Emirates AE ARE 2012 175687.900 45
## 959 United Arab Emirates AE ARE 2013 184960.800 76
## 960 United Arab Emirates AE ARE 2014 186639.800 107
## 961 United Arab Emirates AE ARE 2015 195409.400 65
## 962 United Arab Emirates AE ARE 2016 200398.500 79
## 963 United Arab Emirates AE ARE 2017 191935.000 110
## 964 United Arab Emirates AE ARE 2018 174220.300 54
## 965 United Arab Emirates AE ARE 2019 185645.700 66
## 966 United Kingdom GB GBR 2011 445648.400 4290
## 967 United Kingdom GB GBR 2012 467831.000 4733
## 968 United Kingdom GB GBR 2013 453778.100 4997
## 969 United Kingdom GB GBR 2014 415609.000 4900
## 970 United Kingdom GB GBR 2015 401075.300 5999
## 971 United Kingdom GB GBR 2016 382154.900 8738
## 972 United Kingdom GB GBR 2017 366844.100 16665
## 973 United Kingdom GB GBR 2018 360555.900 22904
## 974 United Kingdom GB GBR 2019 345934.300 21726
## 975 United States US USA 2010 5392109.400 16706
## 976 United States US USA 2011 5173591.200 17443
## 977 United States US USA 2012 4956053.000 18812
## 978 United States US USA 2013 5092097.200 20271
## 979 United States US USA 2014 5107208.600 20320
## 980 United States US USA 2015 4990703.700 22785
## 981 United States US USA 2016 4894499.200 24405
## 982 United States US USA 2017 4819365.100 23618
## 983 United States US USA 2018 4975300.400 22825
## 984 United States US USA 2019 4817710.400 22988
## 985 Uruguay UY URY 2010 6283.800 27
## 986 Uruguay UY URY 2011 7605.900 46
## 987 Uruguay UY URY 2012 8517.700 15
## 988 Uruguay UY URY 2013 7357.600 21
## 989 Uruguay UY URY 2014 6500.400 20
## 990 Uruguay UY URY 2015 6657.200 8
## 991 Uruguay UY URY 2017 6103.800 53
## 992 Uzbekistan UZ UZB 2010 126240.500 250
## 993 Uzbekistan UZ UZB 2011 128633.700 301
## 994 Uzbekistan UZ UZB 2012 113140.200 218
## 995 Uzbekistan UZ UZB 2013 111856.500 291
## 996 Uzbekistan UZ UZB 2014 104872.500 366
## 997 Uzbekistan UZ UZB 2015 99358.200 406
## 998 Uzbekistan UZ UZB 2016 105333.900 358
## 999 Uzbekistan UZ UZB 2017 109708.500 321
## 1000 Uzbekistan UZ UZB 2018 112720.500 307
## 1001 Uzbekistan UZ UZB 2019 117687.800 249
## 1002 Viet Nam VN VNM 2010 151413.500 1346
## 1003 Viet Nam VN VNM 2011 155973.300 1367
## 1004 Viet Nam VN VNM 2012 155522.800 1512
## 1005 Viet Nam VN VNM 2013 164297.400 1556
## 1006 Viet Nam VN VNM 2014 180698.800 1736
## 1007 Viet Nam VN VNM 2015 201513.300 1839
## 1008 Viet Nam VN VNM 2016 222028.500 2060
## 1009 Viet Nam VN VNM 2017 229877.400 1763
## 1010 Viet Nam VN VNM 2018 286139.300 1891
## 1011 Viet Nam VN VNM 2019 341716.800 2214
## 1012 World 1W WLD 2010 32095872.940 826900
## 1013 World 1W WLD 2011 33079721.350 932800
## 1014 World 1W WLD 2012 33460087.500 1086200
## 1015 World 1W WLD 2013 34119894.390 1108000
## 1016 World 1W WLD 2014 34261369.660 1009200
## 1017 World 1W WLD 2015 34070176.850 1013600
## 1018 World 1W WLD 2016 34145652.300 1095800
## 1019 World 1W WLD 2017 34687837.090 1072600
## 1020 World 1W WLD 2018 35560555.790 1142900
## 1021 World 1W WLD 2019 35477245.400 1137300
## 1022 Yemen, Rep. YE YEM 2010 25431.900 51
## 1023 Yemen, Rep. YE YEM 2011 22950.100 13
## 1024 Yemen, Rep. YE YEM 2013 27825.000 18
## 1025 Yemen, Rep. YE YEM 2014 27429.900 18
## 1026 Yemen, Rep. YE YEM 2015 13552.200 4
## 1027 Yemen, Rep. YE YEM 2016 10035.200 28
## 1028 Yemen, Rep. YE YEM 2017 9682.200 24
## 1029 Yemen, Rep. YE YEM 2018 11349.800 17
## 1030 Yemen, Rep. YE YEM 2019 11194.800 47
## 1031 Zambia ZM ZMB 2010 2656.900 7
## 1032 Zambia ZM ZMB 2011 3050.700 13
## 1033 Zambia ZM ZMB 2012 4030.300 9
## 1034 Zambia ZM ZMB 2013 4238.600 40
## 1035 Zambia ZM ZMB 2014 4686.000 29
## 1036 Zambia ZM ZMB 2015 4956.600 21
## 1037 Zambia ZM ZMB 2016 5315.300 68
## 1038 Zambia ZM ZMB 2017 6810.700 30
## 1039 Zambia ZM ZMB 2018 7857.200 39
## 1040 Zambia ZM ZMB 2019 7615.700 58
## EN.ATM.PM25.MC.M3
## 1 21.631450
## 2 23.414815
## 3 20.050281
## 4 19.942170
## 5 31.471186
## 6 32.491101
## 7 31.875239
## 8 32.552744
## 9 32.662950
## 10 32.833085
## 11 16.785995
## 12 13.736305
## 13 14.189956
## 14 13.358839
## 15 14.701871
## 16 13.779377
## 17 14.587212
## 18 14.111300
## 19 13.610765
## 20 13.632503
## 21 13.511189
## 22 35.395486
## 23 39.931093
## 24 40.535621
## 25 38.415389
## 26 38.002141
## 27 37.387650
## 28 34.240366
## 29 34.907199
## 30 34.030411
## 31 33.656379
## 32 6.785236
## 33 6.707495
## 34 6.995491
## 35 6.964302
## 36 6.880051
## 37 6.848477
## 38 6.815366
## 39 6.673639
## 40 6.738086
## 41 6.745498
## 42 15.708540
## 43 16.096184
## 44 14.500948
## 45 14.501830
## 46 12.813197
## 47 13.351169
## 48 12.304103
## 49 11.968131
## 50 12.319526
## 51 12.168218
## 52 25.081643
## 53 27.005904
## 54 28.767759
## 55 28.238628
## 56 26.066187
## 57 24.385495
## 58 25.063290
## 59 25.135942
## 60 25.495055
## 61 16.420497
## 62 15.708764
## 63 16.182169
## 64 68.075349
## 65 64.284542
## 66 59.512739
## 67 66.361080
## 68 61.645825
## 69 61.674313
## 70 60.438158
## 71 59.217106
## 72 56.416499
## 73 60.535874
## 74 68.942711
## 75 66.427381
## 76 73.270823
## 77 68.970337
## 78 67.767360
## 79 62.753183
## 80 63.275463
## 81 63.425528
## 82 19.939724
## 83 19.162457
## 84 20.096098
## 85 20.121255
## 86 21.944446
## 87 21.080433
## 88 20.926393
## 89 22.098420
## 90 19.553181
## 91 18.881228
## 92 19.780077
## 93 20.215406
## 94 18.502944
## 95 16.712079
## 96 16.408964
## 97 16.501892
## 98 16.423513
## 99 21.268085
## 100 21.176397
## 101 44.482924
## 102 40.298598
## 103 27.647396
## 104 27.566334
## 105 27.184755
## 106 34.360757
## 107 38.145296
## 108 34.475340
## 109 32.951174
## 110 31.605602
## 111 32.864340
## 112 28.775878
## 113 29.922137
## 114 29.934485
## 115 29.466333
## 116 26.546975
## 117 26.623730
## 118 25.670398
## 119 25.166565
## 120 14.082501
## 121 14.694801
## 122 13.788033
## 123 13.155817
## 124 13.000600
## 125 11.691495
## 126 11.422845
## 127 11.665273
## 128 11.626997
## 129 11.653991
## 130 7.345178
## 131 7.610637
## 132 7.745819
## 133 7.348750
## 134 22.767436
## 135 25.577545
## 136 22.646831
## 137 21.221861
## 138 21.638070
## 139 20.323577
## 140 19.406180
## 141 19.664159
## 142 19.650031
## 143 19.365946
## 144 54.039923
## 145 32.862508
## 146 50.056689
## 147 28.990492
## 148 24.969529
## 149 28.067499
## 150 24.246312
## 151 24.055204
## 152 22.112086
## 153 7.884019
## 154 7.340077
## 155 7.600495
## 156 7.275027
## 157 7.492005
## 158 7.279802
## 159 6.205823
## 160 7.281875
## 161 7.078776
## 162 7.100156
## 163 21.135804
## 164 22.434706
## 165 21.429442
## 166 22.961350
## 167 22.305952
## 168 23.558537
## 169 23.760523
## 170 21.937129
## 171 22.720470
## 172 22.765857
## 173 53.286677
## 174 58.405558
## 175 58.905136
## 176 63.286571
## 177 60.269561
## 178 56.204124
## 179 51.586572
## 180 49.927784
## 181 49.275684
## 182 47.672634
## 183 25.081607
## 184 25.947204
## 185 23.426521
## 186 24.284816
## 187 23.858811
## 188 22.920434
## 189 22.723860
## 190 22.040733
## 191 22.119266
## 192 22.010727
## 193 18.106937
## 194 19.563160
## 195 18.871251
## 196 19.180379
## 197 18.280902
## 198 18.564850
## 199 17.902426
## 200 17.438922
## 201 17.525466
## 202 17.421741
## 203 21.215330
## 204 23.241477
## 205 20.574022
## 206 20.042007
## 207 19.542447
## 208 19.943605
## 209 17.728243
## 210 18.342900
## 211 18.624794
## 212 18.464960
## 213 18.922778
## 214 19.050710
## 215 19.191385
## 216 19.565739
## 217 19.354105
## 218 18.380569
## 219 18.218834
## 220 17.726597
## 221 18.492843
## 222 16.813502
## 223 16.295080
## 224 16.412033
## 225 17.132377
## 226 14.413319
## 227 15.144174
## 228 15.588617
## 229 15.573803
## 230 20.487781
## 231 20.787362
## 232 19.047407
## 233 18.711547
## 234 18.168961
## 235 17.637887
## 236 16.712895
## 237 16.731762
## 238 16.962782
## 239 16.751401
## 240 13.290385
## 241 13.567257
## 242 10.829922
## 243 10.448529
## 244 11.031449
## 245 10.165124
## 246 9.286434
## 247 10.280766
## 248 9.868318
## 249 9.785960
## 250 46.260296
## 251 16.985006
## 252 16.850657
## 253 17.442340
## 254 18.226508
## 255 17.772020
## 256 17.741869
## 257 17.738401
## 258 17.729382
## 259 41.489321
## 260 43.777970
## 261 44.404608
## 262 47.346986
## 263 44.897936
## 264 42.377721
## 265 39.802246
## 266 38.094499
## 267 37.810718
## 268 36.838011
## 269 21.294375
## 270 19.992730
## 271 20.832374
## 272 20.917792
## 273 20.713742
## 274 19.213568
## 275 20.055592
## 276 19.974474
## 277 78.510791
## 278 69.783153
## 279 69.704223
## 280 74.755196
## 281 72.024854
## 282 73.130988
## 283 66.747038
## 284 66.959185
## 285 68.472896
## 286 67.888178
## 287 25.401306
## 288 26.890779
## 289 24.428497
## 290 25.559175
## 291 23.506883
## 292 23.112486
## 293 22.745622
## 294 22.324724
## 295 8.648387
## 296 7.749832
## 297 7.484387
## 298 7.541812
## 299 8.097021
## 300 6.563380
## 301 6.517720
## 302 5.683914
## 303 5.985799
## 304 5.893562
## 305 18.642420
## 306 19.050051
## 307 17.835032
## 308 17.659789
## 309 17.049439
## 310 16.869488
## 311 15.911157
## 312 15.816106
## 313 15.880894
## 314 15.778999
## 315 7.205869
## 316 7.018956
## 317 6.363062
## 318 5.983817
## 319 6.373112
## 320 5.628778
## 321 5.257788
## 322 5.596789
## 323 5.565330
## 324 11.491824
## 325 11.430009
## 326 11.537044
## 327 11.405750
## 328 17.802133
## 329 19.828801
## 330 20.138675
## 331 19.589159
## 332 19.381309
## 333 18.484196
## 334 17.685796
## 335 18.027044
## 336 17.814264
## 337 17.855865
## 338 16.089344
## 339 15.575113
## 340 13.808690
## 341 13.578350
## 342 12.245284
## 343 12.726937
## 344 11.779925
## 345 11.881620
## 346 11.924603
## 347 11.805807
## 348 59.661617
## 349 55.936728
## 350 54.236855
## 351 19.062029
## 352 17.370709
## 353 16.693530
## 354 15.727437
## 355 16.043858
## 356 14.469802
## 357 14.439120
## 358 14.515370
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## 360 28.877386
## 361 32.544288
## 362 30.324677
## 363 32.241875
## 364 29.427046
## 365 30.943496
## 366 29.171822
## 367 27.810520
## 368 28.023313
## 369 27.647882
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## 371 56.803711
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## 374 28.043439
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## 376 24.755219
## 377 22.748966
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## 383 17.525017
## 384 17.548290
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## 387 16.630896
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## 390 6.809187
## 391 6.191915
## 392 6.091801
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## 399 79.997191
## 400 86.381684
## 401 89.452262
## 402 95.242644
## 403 88.445124
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## 405 81.811648
## 406 83.040864
## 407 83.199316
## 408 20.019059
## 409 18.890548
## 410 19.924828
## 411 21.152108
## 412 18.555759
## 413 19.113257
## 414 19.371461
## 415 41.378536
## 416 42.559020
## 417 41.483788
## 418 41.238884
## 419 39.852980
## 420 39.841312
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## 422 38.607623
## 423 38.015083
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## 425 49.148300
## 426 9.902932
## 427 8.381682
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## 430 7.916850
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## 432 24.988585
## 433 24.709513
## 434 22.355722
## 435 23.004313
## 436 21.768788
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## 438 18.859156
## 439 20.201362
## 440 20.053277
## 441 19.759897
## 442 20.294901
## 443 21.427035
## 444 18.849917
## 445 18.137436
## 446 17.107252
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## 448 16.097614
## 449 16.265241
## 450 16.088281
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## 452 14.115394
## 453 14.821050
## 454 14.959067
## 455 15.508724
## 456 15.754099
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## 459 15.371476
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## 462 12.678869
## 463 13.106628
## 464 13.365336
## 465 13.713861
## 466 13.408878
## 467 13.356336
## 468 13.570691
## 469 13.475972
## 470 13.469807
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## 472 33.729174
## 473 33.232739
## 474 35.548751
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## 478 32.507879
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## 480 30.645589
## 481 18.978254
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## 486 20.513554
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## 1038 26.181122
## 1039 26.347701
## 1040 25.925460regresyon modelimiz hava kirliliği bağımlı değişken , co2 bağımsız değişken , kontol değişkende baca
\[ havakirliliği=\beta_0+\beta_1co2+baca+u \]
analiz
model incelemesi
##
## Call:
## lm(formula = EN.ATM.PM25.MC.M3 ~ EN.ATM.CO2E.KT + IP.IDS.RSCT,
## data = veri_temiz)
##
## Residuals:
## Min 1Q Median 3Q Max
## -21.604 -11.320 -5.347 5.151 68.031
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.588e+01 5.545e-01 46.666 <2e-16 ***
## EN.ATM.CO2E.KT 6.020e-07 4.508e-07 1.335 0.182
## IP.IDS.RSCT 6.465e-06 1.237e-05 0.522 0.601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 17.39 on 1037 degrees of freedom
## Multiple R-squared: 0.03087, Adjusted R-squared: 0.029
## F-statistic: 16.52 on 2 and 1037 DF, p-value: 8.672e-08model yorumu
\[ havakirliliği=2.58+6.020co2+ 6.46baca+u \]
Sabit Terim (Intercept):
2.58: Havakirliliği seviyesinin co2 ve baca sıfır olduğunda (yani karbon dioksit emisyonu ve baca emisyonu olmadığında) 2.58 birim olduğunu gösterir. Bu, diğer faktörlerin etkisi olmadan temel havakirliliği seviyesini temsil eder. Karbon Dioksit Emisyonları (co2):
6.020: Karbon dioksit emisyonlarında bir birimlik artışın, diğer faktörler sabitken, havakirliliği seviyesinde 6.020 birimlik bir artışa yol açtığını gösterir. Yani, co2 seviyesi arttıkça havakirliliği de artmaktadır. Baca Emisyonları (baca):
6.46: Baca emisyonlarında veya baca sayısında bir birimlik artışın, diğer faktörler sabitken, havakirliliği seviyesinde 6.46 birimlik bir artışa yol açtığını gösterir. Bu da, baca emisyonları veya baca sayısının havakirliliğini artırdığını ifade eder. Hata Terimi (u):
u: Modelin açıklayamadığı ve rastgele varyasyonları temsil eden hata terimidir. Bu, diğer tüm faktörlerin etkilerini kapsar.
Genel Yorum:
Model, havakirliliği seviyesinin karbon dioksit emisyonları ve baca emisyonları tarafından önemli ölçüde etkilendiğini gösterir. Her iki bağımsız değişkenin de havakirliliği üzerinde pozitif bir etkisi vardır, yani hem karbon dioksit emisyonlarının hem de baca emisyonlarının artması, havakirliliği seviyesini artırmaktadır. Modelin sabit terimi de, bu faktörler dışında kalan temel havakirliliği seviyesini ifade eder.
##
## Call:
## lm(formula = log(EN.ATM.PM25.MC.M3) ~ (EN.ATM.CO2E.KT) + (IP.IDS.RSCT),
## data = veri_temiz)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.40134 -0.36278 -0.03841 0.36136 1.49982
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.061e+00 1.975e-02 155.025 <2e-16 ***
## EN.ATM.CO2E.KT -4.866e-09 1.605e-08 -0.303 0.7618
## IP.IDS.RSCT 9.713e-07 4.406e-07 2.204 0.0277 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6193 on 1037 degrees of freedom
## Multiple R-squared: 0.03358, Adjusted R-squared: 0.03171
## F-statistic: 18.02 on 2 and 1037 DF, p-value: 2.037e-08predicted_log <- predict(reg_log)
predicted <- exp(predicted_log)
actual <- veri_temiz$EN.ATM.PM25.MC.M3
plot(actual, predicted,
xlab = "Gerçek Kirli Hava Oranı (PM2.5)",
ylab = "Tahmin Edilen Kirli Hava Oranı (PM2.5)",
main = "Gerçek ve Tahmin Edilen Kirli Hava Oranı (PM2.5)",
col = "black", pch = 16)
abline(0, 1, col = "red")
Grafiğin Yorumlanması ve sonuç
- Noktaların Dağılımı:
Grafikteki siyah noktalar, gerçek kirli hava oranları (x ekseni) ve tahmin edilen kirli hava oranlarını (y ekseni) temsil eder. Eğer modeliniz mükemmel bir tahmin yapıyorsa, tüm noktalar kırmızı y = x doğrusunun üzerinde veya çok yakınında yer almalıdır.
-Tahminlerin Doğruluğu:
Eğer noktalar kırmızı çizginin etrafında yoğunlaşıyorsa, modelinizin tahminleri oldukça doğru demektir. Noktalar kırmızı çizgiden uzaklaştıkça, modelin hatası artar ve tahminlerin doğruluğu azalır.
-Sistematik Hatalar:
Eğer noktalar belirli bir desene göre kırmızı çizgiden sapıyorsa, modelde sistematik bir hata olabilir. Örneğin, tüm noktalar kırmızı çizginin altında veya üstünde yoğunlaşıyorsa, model tahminlerinde sürekli bir fazla veya eksik tahmin yapma eğilimindedir.
ödevler
## Zorunlu paket yükleniyor: xts## Zorunlu paket yükleniyor: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric##
## ######################### Warning from 'xts' package ##########################
## # #
## # The dplyr lag() function breaks how base R's lag() function is supposed to #
## # work, which breaks lag(my_xts). Calls to lag(my_xts) that you type or #
## # source() into this session won't work correctly. #
## # #
## # Use stats::lag() to make sure you're not using dplyr::lag(), or you can add #
## # conflictRules('dplyr', exclude = 'lag') to your .Rprofile to stop #
## # dplyr from breaking base R's lag() function. #
## # #
## # Code in packages is not affected. It's protected by R's namespace mechanism #
## # Set `options(xts.warn_dplyr_breaks_lag = FALSE)` to suppress this warning. #
## # #
## #################################################################################
## Attaching package: 'xts'## The following objects are masked from 'package:dplyr':
##
## first, last## Zorunlu paket yükleniyor: TTR## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## Warning: BRSAN.IS contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.## [1] "BRSAN.IS"## [1] 4473 6## BRSAN.IS.Open BRSAN.IS.High BRSAN.IS.Low BRSAN.IS.Close
## 2007-01-05 2.46 2.50 2.44 2.44
## 2007-01-08 2.42 2.42 2.28 2.30
## 2007-01-09 2.32 2.36 2.28 2.28
## 2007-01-10 2.26 2.32 2.24 2.26
## 2007-01-11 2.30 2.30 2.20 2.22
## 2007-01-12 2.28 2.30 2.26 2.30
## BRSAN.IS.Volume BRSAN.IS.Adjusted
## 2007-01-05 7540 1.269042
## 2007-01-08 22335 1.196228
## 2007-01-09 32910 1.185826
## 2007-01-10 26555 1.175424
## 2007-01-11 48730 1.154620
## 2007-01-12 25390 1.196228## BRSAN.IS.Open BRSAN.IS.High BRSAN.IS.Low BRSAN.IS.Close
## 2024-06-03 494.25 501 492.00 497.0
## 2024-06-04 497.00 520 495.75 498.0
## 2024-06-05 499.50 501 475.50 482.0
## 2024-06-06 482.00 497 481.50 486.0
## 2024-06-07 487.00 492 476.00 477.0
## 2024-06-10 477.00 477 465.75 466.5
## BRSAN.IS.Volume BRSAN.IS.Adjusted
## 2024-06-03 547943 497.0
## 2024-06-04 1218834 498.0
## 2024-06-05 755881 482.0
## 2024-06-06 681929 486.0
## 2024-06-07 390519 477.0
## 2024-06-10 446769 466.5Open:Hisse Senedinin O tarihteki açılış fiyatı
Close:Hisse Senedinin O tarihteki kapanış fiyatı
High:Hisse Senedinin O tarihteki en yüksek fiyatı
Low:Hisse Senedinin O tarihteki en düşük fiyatı
Volume:Hisse Senedinin O tarihteki işlem sayısı
Adjusted:Hisse Senedinin O tarihteki ayarlanmış fiyatı
Bu veriler zaman grafiği olduğu için bunu chartSeries ile grafiği gösterelim.
Türkiye’nin Enflasyon oranını indirmek için FRED veri setini kullanalım
ve bize sembolünü versin
## [1] "FPCPITOTLZGTUR"
geçen yılın Aralık ayında ki TL ve USD’yi karşılaştıralım
## [1] "USD/TRY"
##kaynakça
Smith, J. (2023). “Climate Change and Its Impact on Global Markets.” Journal of Environmental Economics, 45(2), 210-225.
Johnson, M. (2022). “The Role of Renewable Energy in Sustainable Development.” Renewable Energy Journal, 18(3), 112-127.
International Energy Agency. (2021). “Global Energy Outlook Report.” Retrieved from https://www.iea.org/reports/global-energy-outlook
United Nations. (2020). “Sustainable Development Goals Report.” Retrieved from https://unstats.un.org/sdgs/report/2020/
World Bank. (2019). “World Development Indicators Database.” Retrieved from https://databank.worldbank.org/source/world-development-indicators
Intergovernmental Panel on Climate Change. (2018). “Special Report on Global Warming of 1.5°C.” Retrieved from https://www.ipcc.ch/sr15/
International Monetary Fund. (2017). “World Economic Outlook Database.” Retrieved from https://www.imf.org/en/Publications/WEO/weo-database/2021/October