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Summary

Description
English: Greenhouse gas emission and absorption by Turkey by year from 1990
Date
Source Data from https://di.unfccc.int/detailed_data_by_party
Author Chidgk1
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Source code
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Python code

Source code
lang = "en"
#lang = "tr"

import numpy as np
import matplotlib.pyplot as plt
import sys
import pandas as pd
import seaborn as sns

# DATA - UPDATE EACH YEAR AND PUT NEW VERSION OF THIS AS CODE ON https://commons.wikimedia.org/wiki/File:Greenhouse_gas_emission_and_absorption_by_Turkey.svg

#Labelling all the years is a bit busy so just label some
numyears=(np.arange(32)) #ADD 1 TO THIS WHEN YOU ADD A YEAR
xyears=['1990','','','','','1995','','','','','2000','','','','','2005','','','','','2010','','','','','2015','','','','','2020','2021']

#Please add population from Turkstat "Population by Years"
#The resulting GhG per person per year graph can be checked against that in Turkstat press releases

population = (53921760, 54840590,  64729501, 55748948, 56653804, 57564204, 58486456, 59423282, 60372568, 61329676, 62287397, 64729502, 64729503, 64729504, 68010215, 68860539, 69729967, 70586256, 71517100, 72561312, 73722988, 74724269, 75627384, 76667864, 77695904, 78741053, 79814871, 80810525, 82003882, 83154997, 83614362, 84680273)

# Data from https://di.unfccc.int/detailed_data_by_party
# Select Turkey, All Years, Totals, Aggregate GHG, Mt CO2 equivalent
# Cut and paste in here: 'Total GHG emissions excluding LULUCF'
# Older figures are sometimes revised so worth recopying them all occasionally - last copied April 2022 but only 2015 onwards
# However new data is likely to be released first in a press release by Turkstat which cannot easily be copied and pasted
year =       ('1990','1991','1992','1993','1994','1995','1996','1997','1998','1999','2000','2001','2002','2003','2004','2005','2006','2007','2008','2009','2010','2011','2012','2013','2014','2015','2016','2017','2018','2019','2020','2021')

CO2 =         [151.5, 158.0, 163.9, 171.0, 167.4, 180.9, 199.5, 212.0, 212.0, 207.8, 229.8, 213.5, 221.0, 236.5, 244.5, 264.2, 281.6, 312.7, 309.3, 315.4, 314.4, 339.5, 353.7, 345.2, 361.7, 384.3, 405.3, 430.2, 422.6, 401.7, 413.4, 452.7]
CH4_CO2eq =   [ 42.5,  43.4,  43.3,  43.1,  42.8,  42.6,  43.0,  42.2,  42.4,  43.8,  43.7,  42.9,  41.0,  43.0,  43.5,  45.2,  46.6,  49.0,  49.9,  49.6,  51.4,  53.7,  57.1,  55.5,  57.5,  52.8,  55.6,  56.8,  60.3,  63.1,  64.0,  64.0]
N2O_CO2eq =   [ 25.0,  24.7,  25.3,  26.0,  23.6,  23.9,  24.5,  24.0,  25.3,  25.6,  24.8,  23.3,  23.3,  24.6,  25.4,  26.2,  28.4,  27.6,  26.2,  28.5,  29.8,  30.9,  32.1,  34.1,  34.6,  32.3,  34.4,  35.6,  35.5,  37.0,  40.5,  40.3]
Fgases_CO2eq = [ 0.6,   0.9,   0.7,   0.4,   0.7,   0.6,   0.6,   0.6,   0.6,   0.6,   0.7,   0.8,   1.0,   1.2,   1.5,   1.7,   1.9,   2.3,   2.4,   2.4,   3.6,   4.0,   4.7,   4.8,   5.3,   5.1,   5.5,   5.7,   5.6,   6.3,   6.0,   7.4]

# Probably will not be in press release. Don't forget minus signs when copying and pasting fron National Inventory Report - probably in Table ES 3 Greenhouse gas emissions by sectors
LULUCF =      [-55.7,   -56.7,  -56.9,  -56.1,  -57.6,  -57.4,  -57.6,  -61.7,  -62.7,  -64.1,  -61.6,  -64.9,  -72.6,  -74.6,  -73.7,  -74.5,  -74.8,  -74.5,  -69.5,  -73.2,  -73.6,  -77.5,  -74.8,  -76.9,  -77.9,  -97.5,  -96.0,  -99.8,  -94.4,  -84.0,  -56.9,  -47.1] 

# CALCULATION

# Could not see how to add more than 2 arrays
GHG_exc_LULUCF = np.add(CO2,CH4_CO2eq).tolist()
GHG_exc_LULUCF = np.add(GHG_exc_LULUCF,N2O_CO2eq).tolist()
GHG_exc_LULUCF = np.add(GHG_exc_LULUCF,Fgases_CO2eq).tolist()
# delete this? GHG_net =     [163.4, 169.9, 175.9, 184.1, 176.7, 190.2, 209.5, 216.9, 217.7, 213.7, 237.3, 215.7, 213.6, 231.0, 241.3, 262.5, 283.4, 317.0, 318.4, 322.7, 325.2, 350.5, 372.5, 362.5, 380.5, 375.3, 401.8, 423.9, 426.4, 422.1, ]

# Therefore add don't subtract - alternatively could just copy and paste from NIR
GHG_inc_LULUCF =     np.add(GHG_exc_LULUCF, LULUCF).tolist()
# left over from previous code - maybe can use to check GHG_exc_LULUCF_old =  [219.20,226.58,232.80,240.15,234.13,247.58,267.23,278.61,280.29,277.76,298.89,280.41,286.07,305.60,314.95,337.21,358.15,391.42,387.59,395.52,398.66,427.57,446.94,438.97,457.96,472.19,498.47,523.8,520.9]

# EMISSION AND ABSORPTION BAR GRAPH

#Dataframe 1 for the bars
df1 = pd.DataFrame({'GHG_exc_LULUCF': GHG_exc_LULUCF,  'LULUCF': LULUCF}, index=year)

#Dataframe 2 for the line
df2 = pd.DataFrame({'GHG_inc_LULUCF': GHG_inc_LULUCF}, index=year)

ax=df1.plot(kind='bar', stacked=True, color = ['grey', 'green'])

df2.plot(ax=ax)

#Avoid chopping off xlabel at bottom
plt.gcf().subplots_adjust(bottom=0.15)

#Label years
plt.xticks(ticks=numyears, labels=xyears)
#Vertical years so we can see which bar is which year 
plt.xticks(rotation=90)
#Don't need the frame
sns.despine(bottom = True, left = True)
#Don't need the dash marks
ax.tick_params(left=False, bottom=False)

if lang == "en":
 ax.set_title ("Greenhouse gases emitted in Turkey", fontsize = 'xx-large')
 ax.set_ylabel("Million metric tons of carbon dioxide equivalent")
# ax.figure.text(0.99, 0.01, 'Source: https://di.unfccc.int/detailed_data_by_party', ha='right', va='bottom', fontsize='small', color='grey')
 ax.set_xlabel('Source: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 plt.legend(['Net emissions', 'Emissions:  mostly by fossil fuels','Absorption: mostly by forests'])
elif lang == "tr":
 ax.set_title ("Türkiye’de sera gazı emisyonu", fontsize = 'xx-large')
 ax.set_ylabel("Milyon ton karbon dioksit eşdeğer")
 ax.set_xlabel('Kaynak: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 plt.legend(['net emisyonlar', 'emisyonlar: en çok fosil yakıt','emme:         en çok orman'])
else:
 print("Unknown language " + lang)
 sys.exit()

# Save graphic
if lang == "en":
 plt.savefig("Greenhouse gas emission and absorption by Turkey.svg")
elif lang == "tr":
 plt.savefig("sera_gazlar_Türkiye_emisyon_emme.svg")
else:
 print("Unknown language " + lang)
 sys.exit()  

# Show graphic
plt.show()

# GASES LINE GRAPH

color_CO2    = "black"
color_CH4    = "brown"
color_N2O    = "khaki"
color_Fgases = "pink"

plt.plot(year, CO2,          color=color_CO2) 
plt.plot(year, CH4_CO2eq,    color=color_CH4)
plt.plot(year, N2O_CO2eq,    color=color_N2O)
plt.plot(year, Fgases_CO2eq, color=color_Fgases)

#Avoid chopping off xlabel at bottom
plt.gcf().subplots_adjust(bottom=0.15)

#Label years
plt.xticks(ticks=numyears, labels=xyears)
#Vertical years as otherwise may overlap 
plt.xticks(rotation=90)
#Don't need the left part of frame
sns.despine(left = True)
#Don't need the dash marks
plt.tick_params(left=False, bottom=False)
#Start at zero to show how small f-gas is
plt.ylim(0)

#Chemical formula to label lines are same all languages
label_CO2    = "CO₂"
label_CH4    = "CH₄"
label_N2O    = "N₂O"

if lang == "en":
 plt.title ("Greenhouse gases emitted in Turkey",fontsize = 'x-large')
 plt.ylabel("Million metric tons of carbon dioxide equivalent")
 plt.xlabel('Source: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 label_Fgases = "F-gases"
elif lang == "tr":
 plt.title ("Türkiye'de atmosfere giden karbondioksit eşdeğer",fontsize = 'x-large')
 plt.ylabel("Megatonne")
 plt.xlabel('Kaynak: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 label_Fgases = "F-gazlar"
else:
 print("Unknown language: " + lang)
 sys.exit()

#Position labels manually to make them quicker to read than a legend in a corner - might need slight adjustment as years added
plt.text(32,445,label_CO2,    fontsize = 'large')
plt.text(32, 60,label_CH4,    fontsize = 'large')
plt.text(32, 30,label_N2O,    fontsize = 'large')
plt.text(32,  5,label_Fgases, fontsize = 'large')

# Save graphic
if lang == "en":
 plt.savefig("ghg_gases_by_year_Turkey.svg")
elif lang == "tr":
 plt.savefig("sera_gazlar_Türkiye.svg")
else:
 print("Unknown language " + lang)
 sys.exit()  

# Show graphic
plt.show()

# PER PERSON LINE GRAPH

pop_millions = np.divide(population,1000000)
GHG_inc_LULUCF_per_person = np.divide(GHG_inc_LULUCF,pop_millions)
GHG_exc_LULUCF_per_person = np.divide(GHG_exc_LULUCF,pop_millions)

plt.plot(year, GHG_inc_LULUCF_per_person)
plt.plot(year, GHG_exc_LULUCF_per_person)

#Label years
plt.xticks(ticks=numyears, labels=xyears)
#Vertical years so we can see which year is which 
plt.xticks(rotation=90)
#Don't need the frame
sns.despine(bottom = True, left = True)
#Don't need the dash marks
plt.tick_params(left=False, bottom=False)
#Start at zero to show that eventually country should go to net zero
plt.ylim(0)

if lang == "en":
 plt.title ("Greenhouse gases emitted per person in Turkey",fontsize = 'x-large')
 plt.ylabel("Tonnes")
 plt.xlabel('Source: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 label_inc_LULUCF   = "Inc. LULUCF"
 label_exc_LULUCF = "Exc. LULUCF"
elif lang == "tr":
 plt.title ("Türkiye'de atmosfere giden karbondioksit kişi başı",fontsize = 'x-large')
 plt.ylabel("Tonne")
 plt.xlabel('Kaynak: https://di.unfccc.int/detailed_data_by_party', fontsize = 'small', color = 'grey')
 label_inc_LULUCF   = "net"
 label_exc_LULUCF = "brut"
else:
 print("Unknown language: " + lang)
 sys.exit()

#Position labels manually to make them quicker to read than a legend in a corner - might need slight adjustment as years added
plt.text(32, 6.1 ,label_inc_LULUCF, fontsize = 'large')
plt.text(32, 6.5 ,label_exc_LULUCF, fontsize = 'large')

#Avoid chopping off xlabel at bottom
plt.tight_layout()

# Save graphic
if lang == "en":
 plt.savefig("ghg_per_person_Turkey.svg")
elif lang == "tr":
 plt.savefig("sera_gazlar_Türkiye_kişi_başı.svg")
else:
 print("Unknown language " + lang)
 sys.exit()  

# Show graphic
plt.show()

Licensing

I, the copyright holder of this work, hereby publish it under the following license:
w:en:Creative Commons
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You are free:
  • to share – to copy, distribute and transmit the work
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Under the following conditions:
  • attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
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Date/TimeThumbnailDimensionsUserComment
current09:41, 15 April 2023Thumbnail for version as of 09:41, 15 April 2023576 × 432 (56 KB)Chidgk1added 2021
08:42, 16 April 2022Thumbnail for version as of 08:42, 16 April 2022576 × 432 (55 KB)Chidgk1added 2020
08:29, 14 April 2021Thumbnail for version as of 08:29, 14 April 2021576 × 432 (55 KB)Chidgk1added 2019
08:50, 19 March 2021Thumbnail for version as of 08:50, 19 March 2021576 × 432 (57 KB)Chidgk1make easier to read by bigger heading and fewer year labels
12:19, 31 January 2021Thumbnail for version as of 12:19, 31 January 2021576 × 432 (66 KB)Chidgk1added more years
06:03, 16 May 2020Thumbnail for version as of 06:03, 16 May 2020576 × 432 (32 KB)Chidgk1increased font size to 14 to match other diags in ghg article
08:26, 15 May 2020Thumbnail for version as of 08:26, 15 May 2020576 × 432 (32 KB)Chidgk1Increased font size slightly as text was hard to read
14:26, 22 April 2020Thumbnail for version as of 14:26, 22 April 2020576 × 432 (34 KB)Chidgk1Uploaded own work with UploadWizard

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