updated

README.md

@@ -1,5 +1,7 @@
 #  Monetised GHG Emissions Calculator
 
+![alt text](charts/series-2/3.png)
+
 This repository supports a Streamlit interface which will be made available through Hugging Face Spaces to provide a simple interface for converting from companies' greenhouse gas emissions disclosures, released in quantitative terms, and for converting those into their monetized rates according to various social cost of carbon proposals .
 
 This is a second version of an earlier calculator I put together. The reason that this is a new repository is because I accidentally overwrote the data in the first calculator, and since then I've decided to take a slightly different direction with this calculator by including a few additional social cost of carbon (SCC) numbers. Rather than attempt to salvage my first calculator, I figured that it was easier to start this project from scratch bundling together a few changes.  

chart-generators/1.py

@@ -2,7 +2,7 @@ import pandas as pd
 import io
 import plotly.graph_objects as go
 
-# Load the data from the provided CSV string
+
 csv_data = """proposal_with_date,organization_name,organization_description,date,country,iso3,iso2,hdi_value,hdi_category,details,original_proposed_value,original_currency_name,original_currency_iso,average_value,usd_proposed_value,use_proposed_value_mtco2e,usd_conversion_date,value_units,environmental_units,methodologies_used,calculation_scope,is_range,
 International Foundation for Valuing Impacts (Apr 2024),International Foundation for Valuing Impacts,A non-profit organization working to standardize impact valuation.,2024-04-01,United States,USA,US,0.92,Very High,Proposed a standardized approach for companies to report GHG emissions monetarily.,$236,US dollars,USD,236,236,236000000,2024-11-20,dollars per ton CO2e,tons of carbon dioxide equivalents,Standardized impact valuation,Social Cost of Carbon,0,
 EPA (Final Report) (Dec 2023),Environmental Protection Agency,The primary federal agency responsible for environmental protection.,2023-12-01,United States,USA,US,0.92,Very High,Updated SCC values using advanced methodologies and lower discount rates.,$120-$340,US dollars,USD,230,230,230000000,2024-11-20,dollars per ton CO2e,tons of carbon dioxide equivalents,Advanced methodologies,Social Cost of Carbon,1,

chart-generators/2.py

@@ -4,7 +4,7 @@ import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from datetime import datetime
 
-# Load the data from the provided CSV string
+
 csv_data = """proposal_with_date,organization_name,organization_description,date,country,iso3,iso2,hdi_value,hdi_category,details,original_proposed_value,original_currency_name,original_currency_iso,average_value,usd_proposed_value,use_proposed_value_mtco2e,usd_conversion_date,value_units,environmental_units,methodologies_used,calculation_scope,is_range,
 International Foundation for Valuing Impacts (Apr 2024),International Foundation for Valuing Impacts,A non-profit organization working to standardize impact valuation.,2024-04-01,United States,USA,US,0.92,Very High,Proposed a standardized approach for companies to report GHG emissions monetarily.,$236,US dollars,USD,236,236,236000000,2024-11-20,dollars per ton CO2e,tons of carbon dioxide equivalents,Standardized impact valuation,Social Cost of Carbon,0,
 EPA (Final Report) (Dec 2023),Environmental Protection Agency,The primary federal agency responsible for environmental protection.,2023-12-01,United States,USA,US,0.92,Very High,Updated SCC values using advanced methodologies and lower discount rates.,$120-$340,US dollars,USD,230,230,230000000,2024-11-20,dollars per ton CO2e,tons of carbon dioxide equivalents,Advanced methodologies,Social Cost of Carbon,1,

chart-generators/3.py

@@ -0,0 +1,94 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import matplotlib.dates as mdates
+
+# Load the data from the CSV file
+file_path = '/home/daniel/Git/Emissions-Monetisation-Calculator/proposals/versions/latest/scc-proposals.csv'
+df = pd.read_csv(file_path)
+
+# Convert 'date' to datetime objects
+df['date'] = pd.to_datetime(df['date'], errors='coerce')
+
+# Convert 'usd_proposed_value' to numeric, handling errors
+df['usd_proposed_value'] = pd.to_numeric(df['usd_proposed_value'], errors='coerce')
+
+
+# Filter out invalid rows.
+df_filtered = df.dropna(subset=['date', 'usd_proposed_value']).copy()
+
+# Sort by date
+df_filtered = df_filtered.sort_values('date')
+
+
+# Create a dictionary for shortened organization names
+org_name_map = {
+    "International Foundation for Valuing Impacts": "IFVI",
+    "Environmental Protection Agency": "EPA",
+     "University of California, Davis": "UC Davis",
+    "International Monetary Fund": "IMF",
+    "New York State Agencies": "NY State",
+    "Biden Administration Interagency Working Group": "Biden Admin",
+    "Trump Administration": "Trump Admin",
+    "Climate Leadership Council": "CLC",
+    "Obama Administration Interagency Working Group": "Obama Admin",
+    "UK Government Economic Service": "UK Gov",
+    "Stern Review": "Stern",
+    "Government of Canada": "Canada Gov",
+    "Yale University": "Yale",
+    "Resources for the Future": "RFF",
+     "University College London": "UCL",
+     "Cambridge University": "Cambridge"
+}
+
+# Apply shortened names to the DataFrame
+df_filtered['short_org'] = df_filtered['organization_name'].map(org_name_map)
+
+
+# Create the line plot
+plt.figure(figsize=(14, 8))
+
+# Plot with the short names.
+sns.lineplot(x='date',
+            y='usd_proposed_value',
+            hue='short_org',
+            data=df_filtered,
+            marker="o",  # Add markers
+            markersize=8, # Enlarge markers
+            linewidth=1
+           )
+
+plt.xlabel('Date')
+plt.ylabel('Proposed Social Cost of Carbon (USD/ton)')
+plt.title('Social Cost of Carbon Proposals Over Time by Organization')
+plt.legend(title='Organization', loc='upper left', bbox_to_anchor=(1, 1))
+
+# Format x-axis date display
+date_fmt = mdates.DateFormatter('%Y-%b')
+plt.gca().xaxis.set_major_formatter(date_fmt)
+plt.gca().xaxis.set_major_locator(mdates.YearLocator())
+plt.xticks(rotation=45, ha='right')
+
+plt.tight_layout(rect=[0, 0, .9, 1])
+
+# Annotate only the latest point for each organization with larger text.
+for org in df_filtered['organization_name'].unique():
+    org_df = df_filtered[df_filtered['organization_name'] == org]
+    short_org = df_filtered['short_org'].loc[df_filtered['organization_name'] == org].iloc[0]
+    
+    # Get the latest point
+    latest_point = org_df.iloc[-1]
+    
+    # Annotate the latest point
+    plt.annotate(f"{short_org}",
+                 xy=(latest_point['date'], latest_point['usd_proposed_value']),
+                 xytext=(10, 0),  # Adjust text position as needed
+                 textcoords='offset points',
+                 arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0.2'),
+                 fontsize=10 # Increase the fontsize of the label
+                 )
+
+
+
+plt.grid(True, axis='y', linestyle='--', alpha=0.7)
+plt.show()