# Insights on Global Greenhouse Gase Trends and Climate Change Action¶

EPA1333 – Computer Engineering for Scientific Computing Final Project

### Authors¶

Group 6:

• Aashna Mittal (4739736)
• Gamze Ünlü (4744640)
• Jason R Wang (4788281)

Project was managed in git and hosted on GitHub here. An HTML version can be found here.

## Executive Summary¶

In this analysis, we analyzed the implications of United Nations Framework Convention on Climate Change (UNFCCC) on member states' Nationally Determined Contributions (NDCs).

In Section 1, we combine and clean differnet data sets to get the data in useful form for the later analysis. Later we provide an overview of the world and the current situation comparing the NDCs submitted and temperature targets.

In Section 2, we start focusing on the countries and the question "Which countries are polluting more?". We narrow down the analysis to top 10 most polluting countries in 2015 (the top three are China, the United States, and India) and visualize their 2030 projected emissions in no policy case and their submitted NDCs. We compute their target reduction of emissions. We find that submitted NDCs are able to meet the 2ºC Paris target but not the 1.5ºC.

In Section 3, we broaden our analysis with the notion of "Historical Debt" which is one of the important discussion points within Paris Agreement. We see the historical emissions of the top 10 emittors. Later we find the relation between their percent reduction target (from forecasts), GDP per capita, and cumualative emissions. With this analysis countries are grouped into four different categories in order to compare their contributions to global warming and their targets. We found that only Mexico has low Historical Debt and has a good ambition. All nine other countries had insufficient ambition and ranged in their level of Historical Debt.

In Section 4, we introduce the Green Climate Fund pledges and visualize how much each country pledged to contribute. We later compare this amount with the number they should contribute which is found within the analysis in this section, finding that the United States should contribute more than 50%, rather than only 3% (its current pledge). Following up from Section 3, a "Guilt factor" is proposed to make recommendations on how much top 10 emitters should contribute to the Green Climate Fund and how much their NDCs targets should be.

Section 5 discusses the reccomendations and conclusions of the analysis. We found that the United States holds the almost as much carbon debt as the rest of the other top 10 polluters combined (apart from China). Since its GDP per capita is 7x larger than China's, its guilt index is accordingly larger too. Compared to similarly wealthy and industrialized nations, its cumulative emissions were around 10x greater. Overall, the United States' Guilt factor was an order of magnitude higher than every other country in the world.

Under this analysis approach, the United States should be highlighted as the major actor to combat global climate change. Given the US's current stated intention to exit the Paris Agreement, its absence could be detrimental to the Paris ambitions. Luckily, American states, cities, companies, and other organizations have pledged their own NDC-equivalents too and are largely helping the United States' climate action..

Obviously, further analysis needs to be conducted to consider other factors like other indicators for technological ability, level of development, or level of carbon lock-in. The Guilt factor defined in this analysis should not be applied in any other context without understanding the limitations of its assumptions; the Guilt factor here is by no means exhaustive.

## Introduction¶

Anthropogenic climate change was first introduced into the global political arena as the United Nations Framework on Climate Change Convention (UNFCCC) in 1992. Since then, other international agreements have continued to refine mitigation action. The United Nations Sustainable Development Goal 13, 'Take urgent action to combat climate change and its impacts*', specifically targets this global issue.

At 19th Conference of the Parties (to the UNFCCC) in 2013 in Warsaw, the UNFCCC members agreed to submit "Intended Nationally Determined Contributions" (INDCs) to signal what each country's greenhouse gas emission targets would be. At the 21st Conference in 2015, the Paris Agreement formalized these intended emissions into simply "Nationally Determined Contributions" (NDCs).

Furthermore, the signatories to the Paris Agreement (which includes all UNFCCC signatories, and therefore, all UN member nations) have agreed to maintain global warming to 2ºC, but preferrably 1.5ºC, above pre-industrial levels. This Notebook intends to analyze the NDCs to estimate their potential to reach these temperature goals.

For some nations, these NDCs require a net reduction. For industrializing nations, they are simply lower than a calculated 'business-as-usual' (BAU) scenario – they are still allowed to grow their total emission footprint.

## Methodology¶

Our methodology during the analysis follows the below steps:

• Gathering data from different sources and selecting which one to use
• Cleaning the data
• Visual inferences to answer the research questions
• Defining and implementing an index ('Guilt factor') to determine future steps for top polluters
In [1]:
# Import libraries used throughout analysis

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from numpy import array
from numpy import NaN
plt.style.use('ggplot')


## 1. NDCs and Temperature Targets ¶

To determine the impact of NDCs, we need to first understand their context by answering the following questions:

1. What do global emissions look like today? (and what datasets can we rely on?)
2. If nothing changes, and the world continues doing business as usual (BAU), how will the world look like in 2030?

Then, we can examine how NDCs compare:

1. If all NDCs are met, what will the total amount of emissions be?
2. What emission amounts are required to meet temperature targets?

Note: Emissions are quantified in units of 'megatons of carbon dioxide-equivalent per year' [MtCO2e/yr] because the strength all greenhouse gases are measured relative to carbon dioxide and because the carbon cycle is a process. Global climate targets assume that natural GHG uptake will continue steadily, so reducing the rate of emissions from countries will lead to a net decrease in the concentration of GHGs in the atmosphere.

### 1.0 Import and Clean Greenhouse Gas Emission Data¶

Many organizations maintain databases of current (and historical) GHG emissions. The primary data source for most of them is from the UNFCCC's reporting window; each UNFCCC member submits annual 'GHG Inventories', which track national emissions with two-year delay.

The World Bank, the World Resources Institute, and the Potsdam Institute for Climate all have published datasets based on varying methodology. Below is an explanation of our approach to finding a valid set to perform further analysis on.

First, we examine WB data since it is most conveniently accessible and matches other easily-comparable datasets. The WB set records GHG information in ktCO2e, includes natural emissions, and has data from 1970-2012 and uses global warming potentials from the IPCC AR2 report.

In [2]:
# Import World Bank data on GHGs and use ISO country names as the index
ghgDf_WB = pd.read_csv("data/GreenhouseGasData.csv", sep=',', skipinitialspace=True, skiprows=4, index_col=1)

# Drop the indicator name and indicator code as the values are same across the whole dataframe
ghgDf_WB = ghgDf_WB.drop(["Indicator Code", "Indicator Name"], axis = 1)

# Drop all the columns that contain only null values
ghgDf_WB.dropna(axis = 1, how="all", inplace=True)

# Drop all the rows that contain only null values, starting from column 2
ghgDf_WB.dropna(axis = 0, how="all", subset = ghgDf_WB.columns[2:], inplace= True)

# Interpolate missing values and then use backfill to fill starting NA values of a row
ghgDf_WB.iloc[:,2:] = ghgDf_WB.iloc[:,2:].interpolate(axis = 1).bfill(axis=1)

# Convert all emissions data into MtCO2e
ghgDf_WB.iloc[:,1:] = ghgDf_WB.iloc[:,1:].divide(1000)

In [3]:
# View the cleaned WB GHG DataFrame


There are some aggregate regions in our data, like ARB-Arab World. Luckily, the World Bank data has a list we can use to remove these aggregate regions.

In [4]:
# Import the dataframe which contains the codes of country group aggregates except 'WLD-World', the last row.
# We want to keep the world row for now.

"data/CountryGroups.xls", sheet_name = "List of economies", skiprows=226, header = None)
CountryGroupCodes = CountryGroupCodes.dropna(how="all",axis=1).dropna(how='any',axis=0)
CountryGroupCodes.drop(columns=0,inplace=True) # These are redundant indices
CountryGroupCodes.columns = ["Aggregate Name", "Aggregate Code"]
CountryGroupCodes = CountryGroupCodes.iloc[:-1]

In [5]:
# Drop the rows corresponding to aggregate country codes from existing dataframe to create a new country dataframe
ghgDf_WB = ghgDf_WB.drop(CountryGroupCodes["Aggregate Code"].values)


We create a dictionary for simplicity with key as country codes and values as the country names. Since the World Bank's naming scheme matches the ISO-3166-1 standard for country names and country codes, it will be used as the reference list. Note that this still includes 'WLD'-'World'.

In [6]:
countryDictionary = dict( ghgDf_WB.reset_index().set_index('Country Code').iloc[:,0] )
list(countryDictionary.items())[:5]

Out[6]:
[('ABW', 'Aruba'),
('AFG', 'Afghanistan'),
('AGO', 'Angola'),
('ALB', 'Albania'),
('ARE', 'United Arab Emirates')]

The World Bank's data spans from 1970 to 2012, but the CAIT greenhouse gas data spans 1990 to 2014. It would be interesting to see a large a temporal range as much as possible. Note that the UNFCCC started recording emissions data from 1990.

The CAIT Excel workbook also contains another sheet with total CO2 emissions from 1850 to 2014. This may also be interesting for analysis.

In [7]:
# For both sets, use the ISO code as the index because it follows the ISO-3166 standard, unlike the country names!
# GHGs from 2013 and 2014
sep=',', sheet_name='GHG Emissions', skipinitialspace=True, skiprows=1, index_col=1)
# CO2 emissions from 1850
sep=',', sheet_name='CO2 Total Emissions', skipinitialspace=True, index_col=1).dropna()

In [8]:
# ghgDf_CAITghg.head()


There are many columns of CAIT data that we do not need. Also, since, we already have a large set of World Bank greenhouse gas data, we shall first attempt to append the CAIT greenhouse gas data onto it. To do so, we must filter, clean, and structure the 2013 and 2014 years into the same format that the World Bank uses.

In [9]:
# Filter the CAIT data and see if we can simply merge it with the World Bank's data.
ghgDf_CAITghg1314 = ghgDf_CAITghg[ (ghgDf_CAITghg['Year'] == 2013) | (ghgDf_CAITghg['Year'] == 2014) ]\
.loc[:,['Year','Total GHG Emissions Including Land-Use Change and Forestry (MtCO₂e‍)']]

# Pivot the table to be in the same format as the World Bank data, which is in a nicer format
# (since we are only looking at total emissions).
ghgDf_CAITghg1314 = ghgDf_CAITghg1314.pivot(columns='Year',
values='Total GHG Emissions Including Land-Use Change and Forestry (MtCO₂e‍)')



Before we merge the datasets, it is important to see how the two sets of data might align. Do the countries match? Are the greenhouse gases quantified in the same way?

In [10]:
# These countries are in CAIT data but not the World Bank's:
ghgDf_CAITghg.loc[ ghgDf_CAITghg1314.index[~ghgDf_CAITghg1314.index.isin(ghgDf_WB.index)], 'Country' ].unique()

Out[10]:
array(['Andorra', 'Cook Islands', 'European Union (28)', 'Liechtenstein',
'Montenegro', 'Niue', 'Nauru', 'Palau', 'Serbia', 'World'],
dtype=object)
In [11]:
# These countries are in World Bank data but not CAIT's – except World (it uses a different three-letter code):
[countryDictionary[i] for i in ghgDf_WB.index[~ghgDf_WB.index.isin(ghgDf_CAITghg1314.index)] ]

Out[11]:
['Aruba',
'American Samoa',
'Bermuda',
'Cayman Islands',
'Gibraltar',
'Guam',
'Hong Kong SAR, China',
'Macao SAR, China',
'Northern Mariana Islands',
'New Caledonia',
'Puerto Rico',
'French Polynesia',
'Turks and Caicos Islands',
'Timor-Leste',
'British Virgin Islands',
'Virgin Islands (U.S.)',
'World']

Clearly, there are some discrepancies. There is some CAIT data for smaller states that do not appear in the World Bank's data. The World Bank includes many regions that CAIT does not care for. 'World' shows up in both but uses a different three-letter code ('World' is not in ISO-3166 since it is not a country).

But, for the rest of the ISO-3166 countries, we can join the datasets.

In [12]:
# Join by matching index. Recall that we pivoted ghgDf_CAITghg1314 to be in the same format as the World Bank data.
ghgDf_merged = ghgDf_WB.join(ghgDf_CAITghg1314)

# And let's fill in missing data using interpolate.
ghgDf_merged.iloc[:,2:] = ghgDf_merged.iloc[:,2:].interpolate(axis = 1).bfill(axis=1)

# Convert all emissions data into MtCO2e
ghgDf_merged.iloc[:,1:] = ghgDf_merged.iloc[:,1:].divide(1000)



Lastly, let's add in the Potsdam Institute for Climate's (PIK) PRIMAP data, which also interpolates in years where data is missing for countries like we did.

Gütschow, Johannes; Jeffery, Louise; Gieseke, Robert; Gebel, Ronja (2018): The PRIMAP-hist national historical emissions time series (1850-2015). V. 1.2. GFZ Data Services. http://doi.org/10.5880/PIK.2018.003

In [13]:
ghgDf_PIK = pd.read_csv('data/primap-hist_v1/PRIMAP-hist_v1.2_14-Dec-2017.csv')
ghgDf_PIK = ghgDf_PIK.rename(columns = {'country': 'Country Code'}).drop(columns=['scenario'])


This data set only has country codes and not country names. Fortunately, since it uses standardized names, the World Bank maintains a matching sheet.

In [14]:
# Drop the columns of countries that are not in the WB database
ghgDf_PIK = ghgDf_PIK[ ghgDf_PIK['Country Code'].isin(countryDictionary.keys()) ]

In [15]:
# The PIK data doesn't come with names, so let's add them.
ghgDf_PIK['Country Name'] = [ countryDictionary[i] for i in ghgDf_PIK['Country Code'] ]

In [16]:
# From the user guide file included with this database, we only want:
# scenario = 'HISTORY' and category = 'CAT0' (all emissions including LULUCF).
# Luckily, the country codes are in ISO format and the format is otherwise similar
# to the World Bank's dataset. 'Country Name' is also used as an index to match WB.

ghgDf_PIK = ghgDf_PIK.set_index(ghgDf_PIK['Country Code'])\
.query("category == 'CAT0'").query("entity == 'KYOTOGHG'")\
.drop(columns=['Country Code','category','entity','unit'])

In [17]:
# Convert from GgCO2e (same as KtCO2e) to MtCO2e
ghgDf_PIK.iloc[:,:-1] = ghgDf_PIK.iloc[:,:-1].divide(1000)

In [18]:
# ghgDf_PIK.head()


### 1.1 Data Selection¶

Now, let's see how all the data compare. Let's take the simple case of world emissions from the World Bank, the CAIT databases, and from PIK.

In [19]:
from ipywidgets import interact

@interact( lowerYear=(1850,2014), upperYear=(1851,2015) )
def h( lowerYear=1970, upperYear=2015 ):
plt.figure(figsize=(10, 5), dpi=80)
CAIT_world = ghgDf_CAITghg\
.loc['WORLD',['Year','Total GHG Emissions Including Land-Use Change and Forestry (MtCO₂e‍)']]
plt.plot(CAIT_world['Year'].values,CAIT_world.iloc[:,1])

WB_world = ghgDf_WB.loc[['WLD']].melt(id_vars='Country Name',var_name='Year')
plt.plot(WB_world.iloc[:,1].astype(int).values,WB_world.iloc[:,2])

CAIT_world_co2 = ghgDf_CAITco2.loc['WORLD']
plt.plot(CAIT_world_co2['Year'].values,CAIT_world_co2.iloc[:,-1])

PIK_world = ghgDf_PIK.loc[:,:'2015'].sum()
plt.plot( PIK_world.index.astype(int) ,PIK_world.values)

plt.xlim([lowerYear,upperYear])
plt.xlabel('Year')
plt.ylabel('Emissions [GtCO2e]')
plt.title('Emissions [GtCO2e/yr] from All GHG Data Sources')
plt.legend(['CAIT - All GHGs','World Bank - All GHGs','CAIT – CO2','PIK - All GHGs'])
plt.show()


Unfortunately, there are discrepancies in the data that overlaps between these countries in all of these datasets! Note the huge change between 2012 and 2013 data between the World Bank and CAIT data. Therefore, merging them really isn't a good idea. ghgDf_merged should be considered deprecated.

Upon further investigation, these discrepancies are rooted in the different methodologies. Looking deeper in both datasets' sources, they both use CO2 emissions from the International Energy Agency (IEA) but other sources separately too.

• World Bank: Uses IEA and their own independent research (World Bank Methodology)
• CAIT: Uses the "IEA source for CO₂ emissions from fossil fuel combustion from 1971 to 2011, and draws the remaining CO₂ and non-CO₂ emissions data from a variety of other sources including CDIAC, U.S. EPA, and FAO." (CAIT Methodology)
• PIK: Consolidates many published datasets similar to the above (see section 3.1 of Nabel et al.). (PIK Methodology | Nabel et al. (2011))

Typical discrepancies relate to:

• Accounting for biomass emissions (some forms of biomass is treated as 'biogenic' and counted as zero)
• Natural fires and other land-based occurences, which are incredibly difficult to count.

Final Choice of Data:

Therefore, going forward, we are going to use only the PIK GHG data from 1850 to 2014. It is the most comprehensive, within the range of the others (through visual inspection), and is equally valid as the others in that it is used by authorities and decision-makers around the world.

### 1.2 BAU Forecasts¶

The Climate Watch dataset, which is related to the CAIT data, at https://climatewatchdata.org (maintained by the World Resource Institute and supported by other organizations) includes the Global Change Assessment Model (GCAM), which includes a 'no policy' scenario for global emissions. This scenario is useful to make comparison with 2030 NDC targets of the countries.

In [20]:
# Use index_col=2, the region, as the index. Drop the Model column, since it is the same across the whole Df.
sheet_name = "GCAM_Timeseries data",index_col=2)\
.drop(['Model'],axis=1)


The GCAM data unfortunately does not come with ISO codes. For sake of consistency, we should match these names up with corresponding ISO codes. Since this data is from CAIT, hopefully we can match names and ISO codes with ghgDf_CAITghg.

In [21]:
ghgForecast_GCAM[ghgForecast_GCAM.index.isin(ghgDf_CAITghg['Country'])].index.unique()

Out[21]:
Index(['Argentina', 'Brazil', 'Canada', 'China', 'Colombia', 'India',
'Indonesia', 'Japan', 'Mexico', 'Pakistan', 'South Africa', 'World'],
dtype='object', name='Region')

And for completeness, check in with the World Bank country names:

In [22]:
GCAMinWB = ghgForecast_GCAM[ghgForecast_GCAM.index.isin(countryDictionary.values())].index.unique()
GCAMinWB

Out[22]:
Index(['Argentina', 'Brazil', 'Canada', 'China', 'Colombia', 'India',
'Indonesia', 'Japan', 'Mexico', 'Pakistan', 'South Africa',
'United States', 'World'],
dtype='object', name='Region')

The country names we have from the World Bank data matches one more country with the GCAM dataset – the United States, so we should use it instead for matching data. However, countryDictionary provided a dictionary with ISO codes as keys and GCAM only has country names.

In [23]:
# Invert our earlier dictionary of country names and codes
countryDictionaryInv = {v: k for k, v in countryDictionary.items()}

In [24]:
# Create a new column with country codes
ghgForecast_GCAM.loc[ GCAMinWB, 'Country Code'] = \
[ countryDictionaryInv[i] for i in ghgForecast_GCAM.loc[ GCAMinWB ].index ]

# Drop everything else and use the new country code column as the index like the other countries
ghgForecast_GCAM.dropna(axis='rows',subset=['Country Code'],inplace=True)
ghgForecast_GCAM = ghgForecast_GCAM.set_index('Country Code')

In [25]:
#ghgForecast_GCAM.head()


Filter the dataset for just the information we're looking for: the 'No policy' scenario and for total GHG emissions. This dataset categorizes GHG emissions in four sections, so we should use the startswith string method to do some fancy slicing. Note that all the emissions are in [MtCO2e/yr] format already.

In [26]:
ghgForecast_GCAM_BAU_all = \
ghgForecast_GCAM[ (ghgForecast_GCAM['Scenario'] == 'No policy') &
(ghgForecast_GCAM['ESP Indicator Name'].str.startswith('Emissions|GHG')) ] \
.drop(columns=['Scenario','ESP Indicator Name','Unit of Entry'])


In [27]:
# Merge the emissions from each type of greenhouse gas to find a total amount.
ghgForecast_GCAM_BAU_all = ghgForecast_GCAM_BAU_all.reset_index() # We have duplicate indices.

ghgForecast_GCAM_BAU = {}

for i in ghgForecast_GCAM_BAU_all['Country Code'].unique():
ghgForecast_GCAM_BAU[i] = ghgForecast_GCAM_BAU_all[ghgForecast_GCAM_BAU_all['Country Code']==i].sum()['2005':]

ghgForecast_GCAM_BAU = pd.DataFrame(ghgForecast_GCAM_BAU).T

In [28]:
# ghgForecast_GCAM_BAU.head()

In [29]:
print('The total projected GHG emissions for the no policy scenario in 2030 is: {:.2f} MtCO2e/yr.'.format(
ghgForecast_GCAM_BAU.loc['WLD','2030'] ) )

The total projected GHG emissions for the no policy scenario in 2030 is: 62542.00 MtCO2e/yr.


### 1.3 NDCs in 2030¶

The same CAIT/ClimateWatch source contains NDCs in the format:

ISO Country Code, Country Name, Goal Year, Value (in MtCO2e/yr), if goal is a range, and the type of goal.
In [30]:
NDCsDf_raw = pd.read_csv('data/wri/CW_NDC_quantification_April30.csv')
NDCsDf = NDCsDf_raw.dropna(axis=0).drop(328) #328 is a duplicate mis-entry, as determined through inspection

# Check data input


Some countries' NDCs are given as a range. For simplicity, this analysis will only examine the mean of that range.

In [31]:
rangedIndices = NDCsDf[NDCsDf['Range'] == 'Yes'].index # Finds submissions with ranges

# Note that each range is a pair, so we should skip every other index
for i in range(0,len(rangedIndices)-1,2):
NDCsDf.loc[rangedIndices[i],'Value'] = (
(NDCsDf['Value'][rangedIndices[i]] + NDCsDf['Value'][rangedIndices[i+1]])/2
)

# Drop the column 'Range', since it is not really needed anymore,
# and drop the EU-28 (since they have been disaggregated by country already).
NDCsDf = NDCsDf.drop(labels=rangedIndices[1::2], axis=0).drop(labels='Range', axis=1)
NDCsDf = NDCsDf.drop(index=NDCsDf.loc[NDCsDf['ISO'] == 'EU28'].index.values, axis=0)


From here on, the situation in 2030 will be the primary focus. Where countries have not submitted data for 2030, the furthest value is used. Furthermore, the best case where the higher goal between choices (e.g. uncondintional is chosen if both unconditional and conditional exist) is taken.

Note that the EU, which is collectively a globally large emitter, has only submitted NDCs for 2020.

In [32]:
NDC_byCountry = []

for i in NDCsDf['ISO'].unique():
NDC_byCountry.append(NDCsDf[NDCsDf['ISO']==i]['Value'].min() )

In [33]:
NDCs_clean = pd.DataFrame({'Country': NDCsDf['ISO'].unique(),'Goal':    NDC_byCountry})
NDCs_clean = NDCs_clean.set_index('Country')

In [34]:
print('If this best case, where all NDCs are met, then the 2030 emissions will be {:.2f} MtCO2e/yr.' \
.format( NDCs_clean.values.sum() ))

If this best case, where all NDCs are met, then the 2030 emissions will be 42330.46 MtCO2e/yr.


### 1.4 Comparison of NDCs with Required Temperature Targets¶

Before we can directly compare NDCs to global emissions and targets, we have to filter some data. Not every country has submitted NDCs – as of 2018-10-23, only 177 of 195 UNFCCC members.

For those have not yet submitted NDCs, they will be given the benefit of the doubt; the global pathways projections should also filter out the countries that have not yet submitted NDCs by using a 'conversion factor' to neglect these countries.

In [35]:
countriesWithNDCs = NDCsDf['ISO'].unique()

# To make sure the consistency between PIK and CAIT data sources we come up with a conversion factor:
# Divide the 'World' values used in the GCAM projection by filtered actual GHG emissions in 2005 and 2010
convFactor = ghgForecast_GCAM_BAU.loc['WLD',['2005','2010']] \
/ ghgDf_PIK.reindex(countriesWithNDCs).loc[:,['2005','2010']].sum().values

# Take the average conversion factor between 2005 and 2010
convFactor = convFactor.mean()
convFactor

Out[35]:
1.1274161667432092

The Intergovernmental Panel on Climate Change (IPCC) recently released a report about emissions pathways required to reach 1.5ºC of warming. They noted that "all but one" model require emissions reduce to at most 35 GtCO2e/yr by 2030. Most pathways require at most 50 GtCO2e/yr in 2030 for 2.0ºC of warming.

IPCC. (2018). IPCC special report on the impacts of global warming of 1.5 °C - Summary for policy makers. Retrieved from http://www.ipcc.ch/report/sr15/

In [36]:
## Visualize the analysis up until now.

# Set the size of the figure
plt.figure(figsize=(8,4))

# Plot all the data points for the years 2015 and 2030
plt.plot(2015,ghgDf_PIK['2015'].sum()/convFactor,'o',
2030, ghgForecast_GCAM_BAU.loc['WLD','2030']/convFactor,'o',
2030,50000,'o',
2030,NDCs_clean.values.sum(),'o',
2030,35000,'o')

# Annotate the plotted points to visualize meaningful labels for the scenarios
plt.annotate("Business as Usual", xy=(2030, ghgForecast_GCAM_BAU.loc['WLD','2030']/convFactor), xycoords = 'data',\
xytext=(10,0), textcoords='offset points')
plt.annotate("2 deg. Target", xy = (2030, 50000), xycoords = 'data', xytext=(10,0), textcoords='offset points')
plt.annotate("NDCs", xy=(2030, NDCs_clean.values.sum()), xycoords='data', xytext=(10,0), textcoords='offset points')
plt.annotate("1.5 deg. Target", xy = (2030, 35000), xycoords = 'data', xytext=(10,0), textcoords='offset points')

# Initialize the x and y values to be used for plotting scenario lines
x1 = [2015,2030]
y1 = [ghgDf_PIK['2015'].sum()/convFactor, ghgForecast_GCAM_BAU.loc['WLD','2030']/convFactor]
y2 = [ghgDf_PIK['2015'].sum()/convFactor, 50000]
y3 = [ghgDf_PIK['2015'].sum()/convFactor, NDCs_clean.values.sum()]
y4 = [ghgDf_PIK['2015'].sum()/convFactor, 35000]

# Calculate coefficients for the fitting 1 degree polynomial
CoeffLineNoPolicy = np.polyfit(x1,y1,1)
CoeffLineLowTarget = np.polyfit(x1,y2,1)
CoeffLineNDCs = np.polyfit(x1,y3,1)
CoeffLineHighTarget = np.polyfit(x1,y4,1)

# Pass the coefficents of the polynomial to get the linear equation
LineNoPolicy = np.poly1d(CoeffLineNoPolicy)
LineLowTarget = np.poly1d(CoeffLineLowTarget)
LineNDCs = np.poly1d(CoeffLineNDCs)
LineHighTarget = np.poly1d(CoeffLineHighTarget)

# Plot the lines
plt.plot(x1, LineNoPolicy(x1), "-o", color = "black")
plt.plot(x1, LineLowTarget(x1), "-o", color = "grey")
plt.plot(x1, LineNDCs(x1), "-o", color = "orange")
plt.plot(x1, LineHighTarget(x1), "-o", color = "green")

# Fill color between lines
plt.fill_between(x1, LineNoPolicy(x1), LineLowTarget(x1), color = "grey")
plt.fill_between(x1, LineLowTarget(x1), LineNDCs(x1), color = "orange")
plt.fill_between(x1, LineNDCs(x1), LineHighTarget(x1), color = "green")

# Set attributes of the plot
plt.xticks(range(2015, 2036, 5))
plt.xlabel('Year')
plt.ylabel('GHG Emissions per Year [MtCO2e/yr]')
plt.title('Comparison of GHG Emissions per Year, NDCs,\n No-policy GCAM Pathway, and 1.5ºC Requirement')

# Show the plot
plt.show()


This graph shows different pathways starting from year 2015. If the world continiues without taking any measures for the climate change (Business-as-Usual scenario) emissions continiue to increase and end up in a way more higher position than the 2 degrees target.

As seen above, if all of the countries keep their promises and satisfy their NDCs, we observe that emissions will be below the 2 degrees target. However, this can be considered as an optimistic scenario since there are already some countries like the US and Brazil who plan to withdraw from the agreement and there are some assumptions for the NDC data used here, as explained above.

Even if the all countries satisfy their NDCs, the 1.5 degree target will not be met. This means most of the countries still need to do more than they already promised. In the next section we analyze the level of ambition from countries in their NDCs and discuss which countries might need make more ambitious goals.

## 2. Which countries are polluting more? ¶

### 2.1 Top 10 greenhouse gas emitters¶

In [37]:
# Sorted bar chart for 2015 greenhouse gas emissions.
ghgDf_PIK.drop

GHGTop10 = ghgDf_PIK.sort_values(by = "2015", ascending = False).iloc[:10,:]
GHGTop10["2015"].plot(kind="bar")

plt.ylabel("GreenHouse Emission [MtCO2e]")
plt.title('Top 10 Emitters in 2015')
plt.show()


In this simple graph for 2015, we see the top 10 most polluting countries in the world. We observe that China, USA and India are the most polluting countries followed by Russia, Indonesia, Brazil, Japan, Iran, Germany and Canada. We can see China has a huge difference with the rest of the world.

### 2.2 Top 10 Polluters by pledged NDCs¶

In [38]:
# See who the top 10 polluters are in 2030 when all the NDCs are met.

NDCs_clean.sort_values(by = "Goal", ascending=False).iloc[:10].plot(kind="bar")

plt.ylabel("NDC [MtCO2e/yr]")
plt.title("Top 10 Emitters if Pledged\nNationally Determined Contributions Are Met in 2030")
plt.show()


Note that: IND-India, IDN-Indonesia and MYS–Malaysia.

This graph shows the greenhouse gas emissions in 2030 in case countries meet their currently pledged NDCs. We observe that in this case, the major polluters don't change and the top 3 stays same: China, USA and India. We observe that Pakistan and Malaysia are added to the list. Even though they are not among top 10 polluters in the first graph, with their pledged NDC targets they are among the top 10 emitters in 2030. This might be due to better reduction performance of the other countries or less reduction amount of Pakistan and Malaysia compared to others.

### 2.3 2030 Projected Emissions vs. Emissions with NDCs Achieved¶

It would be interesting to see how projected emissions match with NDCs and to then see who is reducing the most.

Unfortunately, the projections from GCAM do not include every country – even ones that are in the top current 10 emitters or in the top 10 emitters under the NDC-achieved scenario!

However, we can try to approximate the GCAM projections for each country by making a few assumptions.

1. The 'World' boundary in both the GCAM projection and in the PIK data are the same.
2. A scaled down 'World' (let us call it World*) value will only eliminate countries who have not submitted their NDCs.
3. There are no countries that have a projection but have not submitted their NDCs (we will verify this below).
4. We can subtract the current and projected emissions of countries who have projections from the remaining World* and the 2015 PIK data to have only countries who have submitted NDCs and do not have a projection.
5. The proportional contribution of these countries in 2015 will be the same in 2030 (i.e. we will not account for differences in growth rate).
In [39]:
# Check assumption 3 listed above
GCAMnotInNDC = ghgForecast_GCAM.index.unique()\
[~ghgForecast_GCAM.index.unique().isin(NDCsDf['ISO'].unique())]
[countryDictionary[i] for i in GCAMnotInNDC]

Out[39]:
['World']

World' is a region and not a country. Thus, we can be assured that we can apply 4. appropriately from the scaled-down World*.

In [40]:
# We actually want to know the opposite list of countries – those with projections and NDCs.
GCAMinNDC = ghgForecast_GCAM.index.unique()\
[ghgForecast_GCAM.index.unique().isin(NDCsDf['ISO'].unique())]

# 4. Find countries with NDCs, drop those with projections (or no GHG data)
#    and their proportional contributions to 2015 global emissions
ghg_NDCnoGCAM = ghgDf_PIK.reindex(NDCsDf['ISO'].unique())\
.drop(GCAMinNDC).dropna().loc[:,['2015','Country Name']]
ghg_NDCnoGCAM['2015 Proportion'] = ghg_NDCnoGCAM['2015']/ghg_NDCnoGCAM['2015'].sum()

In [41]:
# 5. Distribute these proportions to 2030 projections (minus countries with projections)
ghg_NDCnoGCAM['2030'] = ghg_NDCnoGCAM['2015 Proportion'] * \
( ghgForecast_GCAM_BAU.loc['WLD','2030'] / convFactor \
- ghgForecast_GCAM_BAU.reindex(GCAMinNDC)['2030'].sum() )

In [42]:
# Combine GCAM forecasts and our own extrapolated forecasts into a DataFrame
ghgForecast_BAU = pd.concat([ghgForecast_GCAM_BAU,ghg_NDCnoGCAM.reindex(columns=['2030'])],sort=True)

In [43]:
ghgForecast_BAU.head()

Out[43]:
2005 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
ARG 294.678 708.487 427.928 519.111 602.419 661.391 711.014 779.129 862.189 940.489 1063.23
BRA 1565.78 1957.24 1458.74 1860.84 2231.86 2319.03 2379.19 2565.83 2877.71 3124.74 3407.9
CAN 631.481 955.523 618.312 753.145 782.063 818.413 855.111 880.473 896.781 998.638 1073.52
CHN 9061.54 10756.2 13027.8 15607.4 17643.3 18984.4 19991.6 20734.1 20822.9 20607.8 19600.2
COL 91.9607 118.837 237.045 309.074 425.59 476.541 506.414 592.094 704.413 829.162 961.265

Now we can plot the 2030 projections vs. the NDCs of the top 10 polluting countries.

In [44]:
# Here, we used the mpatches methods to construct bars side-by-side
import matplotlib.patches as mpatches

ghgTop20 = ghgDf_PIK.sort_values(by = "2015", ascending = False).iloc[:20,:].index

# NDCs of the top 10 current countries, sorted by the top 2015 emitters.
NDCsTop10 = NDCs_clean.reindex(ghgTop20).dropna()[['Goal']][:10].astype(float)

# Filter the forecasts to the same top 10 current emitters.
topForecast = ghgForecast_BAU.reindex(ghgTop20).dropna(subset=['2030'])[['2030']][:10].astype(float)

# Values to use in the graph. Missing countries are dropped from NDC list.
y1 = topForecast["2030"].values
y2 = NDCsTop10["Goal"].values
x = np.arange(len(y1))

# Plot bar-chart.
plt.figure(figsize=(10,6))
bar_width = 0.35
plt.bar(x,y1,width=bar_width,color="green")
plt.bar(x+bar_width,y2,width=bar_width,color="purple")

plt.xticks(x+bar_width/2,NDCsTop10.index)
plt.ylabel('Annual GHG Emissions [MtCO2e/yr]')
plt.title("2030 Projections vs. NDCs")

# Patches are used to plot the NDCs and 2030 projections side by side.

green_patch=mpatches.Patch(color="green",label="2030 Projetion")
purple_patch=mpatches.Patch(color="purple",label="2030 NDC")
plt.legend(handles=[green_patch,purple_patch])

#The % amount that countries need to reduce to achieve their targets:
NDCTop10 = NDCsTop10.join(topForecast)

NDCTop10["% reduction"] = NDCTop10[["Goal","2030"]]\
.apply(lambda x: round(((x["2030"] - x["Goal"]) * 100) / x["2030"]), axis = 1)

NDCreductionlabels = NDCTop10["% reduction"].astype(str).values + " %"

for i in range(len(y1)):
plt.text(x=x[i],y=y1[i],\
s = NDCreductionlabels[i])

plt.show()


This graph shows for 2030 the differences between Bussines-as-Usual case and the NDCs for the countries, including as an absolute percent change (they are all negative). We observe that USA has to do more compared to China, the top current annual polluter. Mexico has the largest relative change at 61% followed by Brazil at 36%. Below, we will compare the percentage reductions with the historical debts (cumulative emissions since 1850) of the countries to see the relation between their targets and historical emissions – and weigh how ambitious their targets are.

## 3. Historical Responsibility for Climate Change ¶

### Is it fair to put the same burden of greenhouse reduction on developing countries considering the historical emissions produced by developed countries?¶

Developing countries and international advocacy organization have argued that owing to their historical emissions, the developed countries owe a "climate debt" to poor countries (Pickering & Barry, 2012). The developed countries have enjoyed the fruit of industrial development way before the developing countries, and have used more than their fair share of Earth's ability to absorb greenhouse gases. Now, the call for reducing global emissions to combat climate change constrains the development of developing countries. Therefore, the developed countries should repay the climate debt by rapdily reducing their emissions and providing financial support to developing countries to upgrade their technologies (Pickering & Barry, 2012).

The UNFCCC acknowledges this point of contention through the principle of Common but Differentiated Responsibilities and Respective Capabilities (CBDR–RC) stating that the countries should "protect the climate system for the benefit of present and future generations of humankind, on the basis of equity and in accordance with their common but differentiated responsibilities and respective capabilities" thereby urging the developed countries to take the lead on climate action (UNFCCC, 1992). The Paris Agreement also reaffirmed this obligation of developed countries.

However, the developed countries have argued to revise the crude 1992 definition of developing countries that sees 6 out of the 10 richest nations of the world as 'developing'. They have stressed that countries who are in a position to contribute financially should do so.

This section analyses the cumulative historical emissions of the top polluters of the world and throws light on the their NDC reductions in relation to the cummulative emissions and GDP per capita. Further, the countries' contribution to the Green Climate Fund is analyzed to understand if the historical polluters are doing their bit to support climate mitigation in the developing countries.

References:

UNFCCC. (1992). United Nations Framework Convention on Climate Change. Retrieved from http://unfccc.int/files/essential_background/convention/background/application/pdf/convention_text_with_annexes_english_for_posting.pdf

Pickering, J., & Barry, C. (2012). On the concept of climate debt: Its moral and political value. Critical Review of International Social and Political Philosophy, 15(5), 667–685. https://doi.org/10.1080/13698230.2012.727311

The Telegraph. 2018. What is the Paris Agreement on climate change? Everything you need to know. https://www.telegraph.co.uk/business/0/paris-agreement-climate-change-everything-need-know/

### 3.1 Time series for greenhouse gases of major polluters (from 1990 to 2015)¶

In [45]:
# First lets observe the time series for ghg emissions of the top 10 most polluting countries.

plt.figure(figsize=(12, 8), dpi=80)

for i in range(0,10):
row = GHGTop10.iloc[i,140:-1]
plt.plot(row.index.astype(int),row)

plt.MaxNLocator(5)
plt.xlabel("Years")
plt.ylabel("GHG Emissions [MtCO2e]")
plt.title("GHG Emissions of Top 10 2014 Polluters between 1990-2015")
plt.xticks(range(1990,2015,2))
plt.xlim([1990,2015])
plt.legend(loc=((1.05,0.3)))
plt.legend( [countryDictionary[i] for i in GHGTop10.index] )
plt.show()


In this graph, we observe the time series emissions for GHG emissions between 1990 to 2015 for the top polluters in the year 2015. We observe that even though the United States has been a historically large polluter, its emissions have stabilized over time. Until the beginning of 2000s, US is the largest polluter; after 2000, China's emissions sky-rocketed and overtook the US's in 2004, making it the top polluter as of 2015.

This change in the order intensifies the debate on whether historical emissions or present emissions should be taken into account for deciding the responsibility of countries towards combating climate change. Therefore, we now analyze the historial carbon debt of the countries.

### 3.2 Carbon Debt¶

Which countries are the major emitters of greenhouse gases considering the emissions starting from the year 1850? 1850 is the year that emissions data can be traced back to with acceptable certainty.

In [46]:
# Add a new column to the PIK dataset called cummulative emissions for each country which is
# equal to the sum of its emissions from 1850 to 2015.
ghgDf_PIK["Cummulative Emissions"] = ghgDf_PIK.loc[:,"1850":"2015"].sum(axis = 1)

In [47]:
# Join the cumulative emissions to the dataset containing NDCs data of the top 10 2030 emitters
# according to sorted NDC list.
CountriesHistDebt = NDCTop10.join(ghgDf_PIK[["Cummulative Emissions"]])

# Plot the cumulative emissions of the top 10 present emitters in the form of a bar chart (show GtCO2e)
plt.figure(figsize=(10,4))
CountriesHistDebt["Cummulative Emissions"].divide(1000).sort_values(ascending=False)\
.plot(kind="bar",title = "Historical CO2 Emissions up to 2015 \nof 2030's Projected Top Polluters")
plt.ylabel("Cumulative GHG Emissions [GtCO2e]")
plt.show()


The above plot shows that even thoguh China is the top polluter, USA has been the largest historical emitter of greenhouse gases (followed by China and Russia). Thus, there can be a moral argument that United States should provide the leadership for supporting climate change domestically and abroad in other countries, especially industrializing ones. This graph can be compared with graph of the present emissions.

### 3.3 Relation between historical debt, % reduction in greenhouse gases promised and GDP per capita¶

In this section, we bring in another aspect/indicator that throws light on the relative economic positions of these countries: their GDP per capita.

How does the historical debt relate to countries' present GDP per capita (2017 values) and the % reduction in greenhouse gases promised for the year 2030?

In [48]:
# Import the World Bank csv containing the gdp per capita values for all countries
GDPperCapitaWB = pd.read_csv("data/world bank/GDP_per_capita.csv", skiprows=4, index_col = "Country Code", header = 0,\
skipinitialspace = True)

# Drop entries where row values are all null
GDPperCapitaWB.dropna(how="all", axis=0, inplace=True)

# Drop entries where column values are all null
GDPperCapitaWB.dropna(how="all", axis=1, inplace=True)
GDPperCapitaWB.drop(columns = ["Indicator Name","Indicator Code"], inplace = True)
GDPperCapitaWB.iloc[:,1:] = GDPperCapitaWB.iloc[:,1:].interpolate(axis = 1)

# Extract only the latest data i.e. the 2017 column
GDPperCapita2017 = GDPperCapitaWB[["2017"]]

Out[48]:
2017
Country Code
ABW 25324.720362
AFG 585.850064
AGO 4170.312280
ALB 4537.862492
AND 39146.548836
In [49]:
#Merge the GDP dataset to the Historical Emissions dataset by their index
HistoricalDebtMergedDf = CountriesHistDebt.join(GDPperCapita2017)
HistoricalDebtMergedDf.rename(columns = {"2017":"GDP per cap", "2030":"2030 NDC emissions"}, inplace=True)

Out[49]:
Goal 2030 NDC emissions % reduction Cummulative Emissions GDP per cap
Country Code
CHN 10456.53 15607.407558 33.0 341004.0 8826.994096
USA 4805.10 7438.822216 35.0 576609.0 59531.661964
IND 4298.31 6073.252796 29.0 135363.0 1939.612984
RUS 2849.25 3020.476772 6.0 198066.0 10743.096592
IDN 1740.50 1908.847029 9.0 74339.0 3846.864323
In [50]:
# Set the figure size for plot
plt.figure(figsize=(10,6))

# Create a scatter plot of cummulative emissions vs. % reduction in NDCs with the size of the plot equivalent to the country's
# GDP per capita.
p = plt.scatter(x=HistoricalDebtMergedDf["Cummulative Emissions"]/1000,\
y=HistoricalDebtMergedDf["% reduction"],\
s=HistoricalDebtMergedDf["GDP per cap"]/100, color = '#069af3')

# Set the attributes for the plot
# Since the data is skewed towards large values, we convert the x axis into a logarithmic scale
plt.xscale("log")
plt.xlabel("Cummulative Emissions 1850-2015 [GtCO2e] (log)")
plt.ylabel("% Lower than GCAM/Our\nNo-Policy Scenario Forecast in 2030")
plt.grid(True)
plt.xticks([10,100,1000], ["10","100", "1000"])
plt.yticks([i*10 for i in range(11)])
plt.title("Country Classification by 'Guilt' and Effort")

# Place the country codes as a text next to the scatter points
for i in range(len(list(HistoricalDebtMergedDf.index))):
plt.text(x=HistoricalDebtMergedDf.iloc[i,3]/1000,y=HistoricalDebtMergedDf.iloc[i,2],\
s = list(HistoricalDebtMergedDf.index)[i])

## Format the plot to categorize the nations and show a relative difference in their level of ambition.

# Divide the grid horizontally
plt.axhline(y=50, color='#b9a281')
# Divide the grid vertically
plt.axvline(x=100, color='#b9a281')

# Assign a text to each quadrant in the plot
plt.text(x=150, y=80, s="Much guilt, much effort!", color = 'Orange', size = 12)
plt.text(x=15, y=80, s="Less guilt, much effort!", color = 'Green', size = 12)
plt.text(x=15, y=15, s="Less guilt, less effort!", color = '#601ef9', size = 12)
plt.text(x=150, y=15, s="Much guilt, less effort!", color = 'Red', size = 12)
plt.legend([p],['Relative Size of\n GDP per capita'])

# Show the plot
plt.show()


The above plot shows that even though both China and USA have high historical emissions, USA's GDP per capita is way higher than China which further bolsters the argument that USA has both the capacity and the responsibility to take the lead on climate change. We see other countries who are guilty but do not have high GDP as USA. These countries might need financial support. On the other hand, we see countries like Mexico reducing their emissisons by almost 60% by 2030 even though their historical emissions are realtively lower compared to the top 10 present emitters. We see some countries who have less historcial emissions are also putting less effort such as Canada, Japan and Indonesia. There are no contries who put very much effort for being guilty!

## 4. Recommendations: Required Financial Contributions and Emission Reductions ¶

What should be the NDCs and financial contributions of the top 10 countries according to their historical emissions and GDP per capita?

### 4.1 Green Climate Fund Pledges¶

The Green Climate Fund (GCF) is an international fund set up through the UNFCCC to help developing nations build projects that align with global emissions reduction goals. Projects include those for renewable elecricity or other technologies for reducing emissions.

The advanced economies have agreed to aggregate USD 100 billion per year by the year 2020 in order to support the mitigation and adaptation initiatives in developing countries.

All pledges made by countries are listed online in an interactive table but does not provide the data cleanly.

In [51]:
import requests
from bs4 import BeautifulSoup

page_name = 'https://www.greenclimate.fund/how-we-work/resource-mobilization'
page = requests.get(page_name)

soup = BeautifulSoup(page.text, 'html.parser')

contrib = soup.find(class_='res-table')
contribItems = contrib.find_all('tbody')[0].find_all('tr')

country, announced = [],[]

for i in range(len(contribItems)):
scrapedInfo = contribItems[i].find_all('td')
country.append(scrapedInfo[0].contents[0])
announced.append(float(
scrapedInfo[1].contents[0].replace('$','').strip('M').replace(',','').replace('<','').strip() ) * 1000000)  In [52]: gcfBS = pd.DataFrame({'Pledges':announced},index=country)  In [53]: for i in gcfBS.index: if i in countryDictionaryInv: gcfBS.loc[i,'Country Code'] = [countryDictionaryInv[i]] else: # escape from keys that don't exist next gcfBS.index.name = 'Country'  In [54]: # gcfBS.reset_index().set_index('Country Code')  In [55]: # From visual inspection, the column 'Year' and index 'World' is not necessary gcfDf = pd.read_csv('data/green-climate-gcf-fund-pledges.csv',index_col=0).drop(columns='Year').drop('World') # Rename the columns to be more readable and index name to be 'Country' instead of 'Entity' gcfDf = gcfDf.rename(columns={'Code':'Country Code', 'Signed pledges (GCF) (US$ per year)':'Pledges'})
gcfDf.index.name = 'Country'

# Check the data

Out[55]:
Country Code Pledges
Country
Australia AUS 187000000.0
Austria AUT 34800000.0
Belgium BEL 66900000.0
Bulgaria BGR 100000.0
In [56]:
plt.figure(figsize=(10,6))
gcfBS['Pledges'].divide(1e9).sort_values(ascending=False).plot.bar(by='Pledges')
plt.ylabel('Signed Pledge in billions $US/year') plt.title('Signed Pledge to GCF by Country') plt.show()  In this graph we observe the pledges of countries to the Green Climate Fund. US is the country who pledged to contribute most, followed by Japan and United Kingdom. We observe that the most of the top polluting countries do not pledge high amounts of contribution to the fund. This is because of their low GDP per capita values. Thus how much countries should contribute also depends on their wealth together with their historical emissions. Next we try to find out the required contribution of the countries depending on these two factors. ### 4.2 Calculating the guilt factor¶ In [57]: # Add the 2017 GDP per capita values to the ghg emissions dataset ghgDf_PIK = ghgDf_PIK.join(GDPperCapita2017) # ghgDf_PIK.head()  In [58]: # Rename the column "2017" to "GDP per cap" ghgDf_PIK.rename(columns = {"2017":"GDP per cap"}, inplace = True) # Add a new column to the ghg dataset which contains the product of the columns cummulative emissions and GDP per cap ghgDf_PIK["GDP Emission Product"] = ghgDf_PIK["Cummulative Emissions"] * ghgDf_PIK["GDP per cap"] # ghgDf_PIK.head()  In [59]: # Sum the values of the emission product column to get the denominator of the guilt factor formula # Remember, we aren't looking at the whole world and only looking at countries that have submitted the NDCs, # hence the denominator is scaled down by the conversion factor GuiltFactorDen = ghgDf_PIK["GDP Emission Product"].sum() / convFactor # Calculate the guilt factor for the top 10 current emitters by multiplying their cummulative emissions # with GDP per capita and dividing by the denominator calculated above. HistoricalDebtMergedDf["Guilt Factor"] = \ (HistoricalDebtMergedDf["Cummulative Emissions"] * HistoricalDebtMergedDf["GDP per cap"]) / GuiltFactorDen  In [60]: # Plot the guilt factor plt.figure(figsize=(10,6)) HistoricalDebtMergedDf["Guilt Factor"].sort_values(ascending = False).plot(kind='bar') plt.title("Guilt Factor of 2030's Expected Top Emitters") plt.ylabel('Guilt Factor') plt.show()  The above plot shows that USA and Germany have the highest guilt factors owing to their high historical emissions and high GDP per capita. Thus, it can be expected from these countries to contribute to the Green Climate Fund in proportion to their guilt factor. At COP21, where Paris Agreement was signed, countries agreed to raising$100 billion USD per year for the Green Climate Fund.

#### 4.2.1 How much should be each country contribute towards Green Climate Fund?¶

In [61]:
# Initialize the yearly Green Climate Fund (GCF) Target
ClimateFundTargetYearly = 10 ** 11

# Scale down the GCF target to what the top 10 emitters should contribute based on their own cummulative emissions
# relative to the world
ScaledDownTarget = round(ClimateFundTargetYearly * (HistoricalDebtMergedDf["Cummulative Emissions"].sum()\
/ghgDf_PIK["Cummulative Emissions"].sum()) / 10 ** 9, 2)

print ("In proportion to their cummulative emissions, the top 10 emitters should collectively pay " + \
ScaledDownTarget.astype(str) + " billion USD to the Green Climate Fund yearly")

In proportion to their cummulative emissions, the top 10 emitters should collectively pay 61.42 billion USD to the Green Climate Fund yearly

In [62]:
# Add a new column to the HistoricalDebtMergedDf dataset that calculates the contribution of each top emitter as.
# Scaled Down Target * Guilt Factor
HistoricalDebtMergedDf["GCF contribution"] = HistoricalDebtMergedDf["Guilt Factor"] * ScaledDownTarget

# Plot the contribution of each country as a pie chart
HistoricalDebtMergedDf["GCF contribution"].sort_values(ascending = False).plot\
(kind='pie', title="Proposed Proportional Country Share in Green Climate Fund",\
figsize=(10,10), autopct='%.2f%%',pctdistance=1.1, labeldistance=1.2).set_aspect('equal')


#### 4.2.2 What should be the NDCs of the top 10 emitters?¶

Assuming that the total reduction in greenhouse gases should be done by the top 10 emitters, we finally comment on what NDCs should the top 10 emitters pledge corresponding to their guilt factor that would enable the world to stay within the temperature target of 1.5 degrees by 2030.

In [63]:
# Recall that the 1.5 degree target is 35000 MtCO2e/year in 2030
ghg2030RedRequired = NDCs_clean.values.sum() - 35000

# Add obligation as a column to the merged dataset
HistoricalDebtMergedDf["Emission Obligation"] = HistoricalDebtMergedDf["Guilt Factor"] * ghg2030RedRequired

Out[63]:
Goal 2030 NDC emissions % reduction Cummulative Emissions GDP per cap Guilt Factor GCF contribution Emission Obligation
Country Code
CHN 10456.53 15607.407558 33.0 341004.0 8826.994096 0.047845 2.938617 350.723005
USA 4805.10 7438.822216 35.0 576609.0 59531.661964 0.545620 33.511977 3999.644284
IND 4298.31 6073.252796 29.0 135363.0 1939.612984 0.004173 0.256322 30.591939
RUS 2849.25 3020.476772 6.0 198066.0 10743.096592 0.033822 2.077352 247.931299
IDN 1740.50 1908.847029 9.0 74339.0 3846.864323 0.004546 0.279186 33.320808
In [64]:
# This plot expands upon one created earlier in section 2.3
plt.figure(figsize=(10,6))
y3 = y2 - HistoricalDebtMergedDf["Emission Obligation"]

# Plot bar-chart.
bar_width = 0.2
plt.bar(x,y1,width=bar_width,color="green")
plt.bar(x+bar_width,y2,width=bar_width,color="purple")
plt.bar(x+bar_width*2,y3,width=bar_width,color="orange")

plt.xticks(x+bar_width/3,NDCsTop10.index)
plt.title("2030 Projections, Submitted NDCs,\n\
and Guilt-Factor Based Emission Obligation for Top 10 Projected Emitters")
plt.xlabel('Country [ISO Code]')
plt.ylabel('Annual GHG Emissions [MtCO2e/yr]')

# Patches are used to plot the NDCs and 2030 projections side by side.

green_patch=mpatches.Patch(color="green",label="2030 Projetion")
purple_patch=mpatches.Patch(color="purple",label="2030 NDC")
orange_patch=mpatches.Patch(color="orange",label="Emission Obligation")
plt.legend(handles=[green_patch,purple_patch,orange_patch])

plt.show()


When we insert the Obligations column to our our previous analysis, we can compare the differences between 2030 No Policy, 2030 NDCs and 2030 Obligations which is calculated within our analysis.

### 5. Conclusions ¶

Within this notebook, we provided an overview of the major polluters responsible from climate change and analyzed the contributions of differnet countries to mitigate the climate change. We started with a holistic view on the world and the current situation related to global warming. Even in a very optimistic scenario, Nationally Determined Contributions are not enough to meet the 1.5 degrees target and countries have to do more both financially and technologically to reduce the emissions. We later narrow down our analysis to top 10 countries, what they promised and what should their contribution be. We used two criteria to calculate the required contributions: GDP per capita and historical emissions. Historical debt is still a contraversial issue within the context of climate change. We observed that most countries do not set targets in proportion with their historcial debt.

As a result of our final analysis (Section 4), we come up with the required contributions of the top 10 polluting countries both in terms of reductions and finances. Even though countries with high emissions try to be ambitious and set high targets, finances is very important in the whole process of emission reductions. This means countries with high GDP per capita and their contributions is the key for many countries to reach their targets. As the third largest emitter of the world (after China and US), India (1.2 billion population) is example of such a country. Being one of the most important actors as a developing country, India claimed that the binding commitments should be set according to past CO2 emissions of countries and rich countries must pay back their historical debt.Minster Narendra Modi states that with their restricted financial sources, their concerns are mostly over poverty and enhancing living conditions. So although India has promising renewable energy capacity it doesn’t paint a promising picture in terms of achieving its goals if there is no finnacial support. This is why they have a really low guilt factor even though their contribution to emissions is really high.

Our analysis proves US is one of the key actors again both reducing emission targets and financial contributions. They are the second emittor after China with a higher historical debt than China. This puts more burden on US to put much more effort. Very contrary to these results, president of US is quoted as follows:

"The bottom line is that the Paris Accord is very unfair at the highest level to the United States. India will be allowed to double its coal production by 2020. India can double their coal production. We're supposed to get rid of ours." says Trump after stopping all contributions of the Green Climate Fund. Under Obama, the US had been intending to contribute more than \$3 billion to the fund.

Twenty years after the Kyoto Protocol, fossil fuels are still humanity's primary energy source which may be a sign of ineffectiveness of the protocol although it was remarkable as a first step. Next step is taken with Paris Agreement. There are still many issues to be solved related to NDCs, responsibilities or binding commitments within this complex network with many actors of different interests.

Sources:

Clémençon, Raymond. “The Two Sides of the Paris Climate Agreement.” The Journal of Environment & Development, vol. 25, no. 1, Oct. 2016, pp. 3–24.

Quotation retreived from : https://edition.cnn.com/2017/06/02/asia/india-paris-agreement-trump