Carbon emissions calculation

carbon.py

# Quick and dirty carbon emissions calculation.
#
# See https://attackllama.com/carbon/.
#
# Requires Python 3.6+.
# Non-core dependencies: requests, numpy, pandas, matplotlib, pytablewriter.
#
# Note that the input text file "flights.txt" has a "distance" column. This is
# obtained manually using online flight distance calculators, and is simply for
# statistics. If you don't want to use this data, set each row in this column
# to zero.
# 
#
# Sean Leavey
# <[email protected]>

from collections import defaultdict
import csv
import requests
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pytablewriter import RstGridTableWriter

API_KEY = "" # get from goclimateneutral.org
API_BASE = 'https://api.goclimateneutral.org'

# https://trees.org/carboncalculator/, 15.6943 kg/year, assumes 40 year life
CO2_KG_ABSORBED_PER_TREE = 15.6943 * 40


def get_carbon():
    co2total = 0
    nflights = 0
    dtotal = 0
    fdata = []
    headers = ["Year", "From", "To", "Via 1", "Via 2", "Is Return", "Distance (km)", "Footprint (CO2 kg)", "Notes"]
    with open('flights.txt') as csvfile:
        reader = csv.reader(csvfile, delimiter='\t')
        # Skip header.
        next(reader)
        for row in reader:
            (year, airport_from, airport_to, airport_via1, airport_via2, is_return,
            distance, notes) = row
            # Fix types.
            is_return = True if is_return.lower() == "yes" else False
            distance = float(distance)
            nflight = 1
            payload = {
                'segments': {},
                'cabin_class': 'economy',
                'currencies[]': 'EUR',
            }
            if airport_via1 != "":
                nflight += 1
                payload["segments[0][origin]"] = airport_from
                payload["segments[0][destination]"] = airport_via1
                if airport_via2 != "":
                    nflight += 1
                    payload["segments[1][origin]"] = airport_via1
                    payload["segments[1][destination]"] = airport_via2
                    payload["segments[2][origin]"] = airport_via2
                    payload["segments[2][destination]"] = airport_to
                else:
                    payload["segments[1][origin]"] = airport_via1
                    payload["segments[1][destination]"] = airport_to
            else:
                payload["segments[0][origin]"] = airport_from
                payload["segments[0][destination]"] = airport_to
            # Get data.
            response = requests.get(API_BASE + '/v1/flight_footprint',
                                    params=payload,
                                    auth=(API_KEY, ''))
            if response.status_code != 200:
                raise Exception(f"invalid response: {response.text}")
            data = response.json()
            scale = 2 if is_return else 1
            footprint = scale * float(data["footprint"])
            co2total += footprint
            if is_return:
                nflight *= 2
            nflights += nflight
            dtotal += distance
            # Create new data
            fdata.append(
                (year, airport_from, airport_to, airport_via1, airport_via2,
                 is_return, distance, footprint, notes)
            )

    with open('carbon.csv', 'w') as csvfile:
        writer = csv.writer(csvfile, delimiter='\t', quotechar='|',
                            quoting=csv.QUOTE_MINIMAL)
        writer.writerow(headers)
        writer.writerows(fdata)

    print(f"Number of flights: {nflights}")
    print(f"Total distance: {dtotal} km")
    print(f"Total CO2: {co2total} kg")

def load_carbon():
    return pd.read_csv('carbon.csv', sep='\t')

def to_rst_table():
    table = load_carbon()
    table = table[["Year", "From", "To", "Is Return", "Distance (km)", "Footprint (CO2 kg)"]]
    writer = RstGridTableWriter()
    writer.from_dataframe(table)
    writer.write_table()

def plot_timeline():
    data = load_carbon()
    subdata = data[["Year", "Distance (km)", "Footprint (CO2 kg)"]]
    subsum = subdata.groupby(by=["Year"]).sum().cumsum()

    distance = subsum["Distance (km)"]
    earths = distance / 40075.017
    earths.rename("Earths")
    footprint = subsum["Footprint (CO2 kg)"]
    footprint_tonnes = footprint / 1000
    footprint_tonnes.rename("Footprint (CO2 tonnes)")
    trees = footprint / CO2_KG_ABSORBED_PER_TREE

    ax1 = distance.plot()
    ax1.ticklabel_format(axis='y', style='sci', scilimits=(3,3))
    lines1, labels1 = ax1.get_legend_handles_labels()

    ax2 = ax1.twinx()
    earths.plot(ax=ax2, alpha=0)
    ax2.spines['left'].set_position(('axes', -0.22))
    ax2.spines["left"].set_visible(True)
    ax2.yaxis.set_label_position('left')
    ax2.yaxis.set_ticks_position('left')

    ax3 = ax1.twinx()
    footprint_tonnes.plot(ax=ax3, color="orange")
    ax3.spines['right'].set_position(('axes', 1.0))
    lines2, labels2 = ax3.get_legend_handles_labels()

    ax4 = ax1.twinx()
    trees.plot(ax=ax4, alpha=0)
    ax4.spines['right'].set_position(('axes', 1.2))

    ax1.grid(True)
    fig = plt.gcf()
    ax1.legend(lines1+lines2, ["Distance", "Footprint"])

    ax1.set_ylabel('Distance (km)')
    ax2.set_ylabel('Earth circumnavigations')
    ax3.set_ylabel('Footprint (tonnes CO2)')
    ax4.set_ylabel('Required trees')

    ax1.set_title("Lifetime flight footprint")

    #fig.autofmt_xdate()
    fig.tight_layout()
    fig.savefig("carbon.png")
    plt.show()

flights.txt

Year	From	To	Via 1	Via 2	Return?	Distance (km)	Notes
1991	GLA	YYZ			Yes	10600	
1993	GLA	YYZ			Yes	10600	
1995	GLA	YYZ			Yes	10600	
1998	GLA	BJV			Yes	6300	
1998	GLA	YYZ			Yes	10600	
1998	YYZ	YFC			Yes	2100	
1999	GLA	JSI			Yes	5500	
2000	GLA	LHR			Yes	1100	
2000	GLA	YYZ			Yes	10600	
2001	GLA	BCN			Yes	3400	
2002	GLA	GVA			Yes	2600	
2003	GLA	YYZ			Yes	10600	
2006	MAN	BVA			Yes	1800	
2006	EDI	FNC			Yes	5500	
2007	GLA	YYZ			Yes	10600	
2009	GLA	FNC			Yes	5500	
2010	GLA	YYC			Yes	13000	
2010	EDI	MXP	LHR		No	1500	
2010	AMS	EDI			No	700	
2011	GLA	MAD			Yes	3400	
2011	EDI	MUC			Yes	2700	
2012	GLA	LHR			Yes	1100	
2012	HAJ	GLA	LHR		Yes	2500	
2013	GLA	FNC	LHR		Yes	6100	
2013	GLA	WAW			Yes	3300	
2013	EDI	MUC			Yes	2700	
2013	GLA	HAJ	LHR		No	1250	
2013	HAJ	GLA	LHR		Yes	2500	
2014	HAJ	MUC			Yes	1000	
2014	HAJ	GLA	LHR		No	1250	
2014	GLA	SFO	BGR		No	9000	
2014	LAX	MAN	PHL		No	9400	
2014	GLA	HAJ	LHR		Yes	2500	
2014	GLA	LYS	AMS		Yes	2900	
2015	GLA	HAJ	LHR		Yes	2500	
2015	GLA	ANC	LHR	SEA	Yes	21200	
2015	EDI	BRE			No	800	
2015	FRA	GLA			No	1100	
2015	GLA	HAJ	LHR		Yes	2500	
2016	GLA	BLQ			No	1700	
2016	FLR	GLA			No	1700	
2016	EDI	FNC	LIS		No	2900	
2016	FNC	EDI			No	2800	
2016	GLA	LAX	LHR		Yes	18600	
2016	GLA	PMI			Yes	3800	
2016	GLA	IAD	LHR		No	6500	
2016	JFK	GLA	LHR		No	6100	
2017	GLA	XRY	MAD		Yes	4400	
2017	GLA	FRA			No	1100	
2017	TXL	GLA			No	1200	
2017	GLA	HAJ	LHR		Yes	2500	
2017	GLA	HAJ	LHR		No	1250	
2017	HAJ	GLA	LHR		Yes	2500	
2017	HAJ	GLA	LHR		Yes	2500	
2018	HAJ	LHR			Yes	1400	
2018	HAJ	FNC			Yes	6100	
2018	HAJ	GLA	LHR		Yes	2500	
2018	HAJ	PMI			Yes	3100	
2018	HAJ	GLA	LHR		Yes	2500	
2019	HAJ	GLA	LHR		Yes	2500	
2019	HAJ	FCO	FRA		No	1200	
2019	FCO	HAJ	MUC		No	1200	
2019	HAJ	GLA	LHR		Yes	2500	
2019	HAJ	LHR			Yes	1400