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import streamlit as st
import pandas as pd
# Path to the data file relative to the app.py file
DATA_PATH = "data.csv"
INSTRUCTIONS_PATH = "instructions.md"
def load_data():
try:
df = pd.read_csv(DATA_PATH)
return df
except FileNotFoundError:
st.error(
f"Error: Could not find the data file at {DATA_PATH}. Please ensure the file exists."
)
return None
def load_instructions():
try:
with open(INSTRUCTIONS_PATH, "r") as f:
return f.read()
except FileNotFoundError:
return "Error: Instructions file not found."
def format_currency(value, display_unit):
if display_unit == "Millions":
formatted_value = f"${value / 1_000_000:.2f} MN"
elif display_unit == "Billions":
formatted_value = f"${value / 1_000_000_000:.2f} BN"
return formatted_value
def main():
st.title("GHG Emissions Monetization Calculator")
st.markdown(
"The purpose of this tool and demonstration is to allow users to explore How monetizing companies proposed greenhouse gas emissions might work in practice."
)
st.markdown(
"This calculator accompanies a repository shared on Github and Hugging Face which aggregates proposals for the social cost of carbon, which have been advanced at various points in time by various world bodies."
)
st.markdown(
"Detailed notes and instructions about the use of this calculator can be found in the Instructions tab."
)
st.markdown(
"This calculator was developed by Daniel Rosehill in December 2024 (danielrosehill.com)."
)
# Load the data and instructions
df = load_data()
instructions = load_instructions()
if df is None:
return # Don't proceed if data can't be loaded
# Tabs for calculator and instructions
tabs = st.tabs(["Calculator", "Instructions"])
with tabs[0]: # Calculator tab
with st.container():
left, right = st.columns(2)
with left:
st.subheader("Input Values")
scope1_emissions = st.number_input("Scope 1 Emissions", value=0.0)
scope2_emissions = st.number_input("Scope 2 Emissions", value=0.0)
scope3_emissions = st.number_input("Scope 3 Emissions", value=0.0)
unit_of_reporting = st.selectbox("Unit of Reporting", ["TCO2E", "MTCO2E"])
proposal_names = df['proposal_with_date'].tolist()
selected_proposal = st.selectbox("Social cost of carbon", proposal_names)
with right:
st.subheader("Calculated Values")
# Calculated emissions
scope1_2_emissions = scope1_emissions + scope2_emissions
all_scopes_emissions = (
scope1_emissions + scope2_emissions + scope3_emissions
)
st.markdown(
f"Scope 1 and 2 Emissions: {scope1_2_emissions:.2f} {unit_of_reporting}"
)
st.markdown(
f"All Scopes Emissions: {all_scopes_emissions:.2f} {unit_of_reporting}"
)
# Find the value in USD per ton
selected_row = df[df['proposal_with_date'] == selected_proposal].iloc[0]
multiplier = selected_row['usd_proposed_value']
st.subheader("Monetized Emissions")
display_unit = st.radio("Display units", ["Millions", "Billions"])
if unit_of_reporting == "MTCO2E":
scope1_emissions = scope1_emissions * 1_000_000
scope2_emissions = scope2_emissions * 1_000_000
scope3_emissions = scope3_emissions * 1_000_000
all_scopes_emissions = all_scopes_emissions * 1_000_000
# Monetization calculations
monetized_scope1 = scope1_emissions * multiplier
monetized_scope2 = scope2_emissions * multiplier
monetized_scope3 = scope3_emissions * multiplier
monetized_all_scopes = all_scopes_emissions * multiplier
st.markdown(f"Scope 1: {format_currency(monetized_scope1, display_unit)}")
st.markdown(f"Scope 2: {format_currency(monetized_scope2, display_unit)}")
st.markdown(f"Scope 3: {format_currency(monetized_scope3, display_unit)}")
st.markdown(
f"All Scopes: {format_currency(monetized_all_scopes, display_unit)}"
)
with tabs[1]: # Instructions tab
st.markdown(instructions)
if __name__ == "__main__":
main() |