conference_footprint

compute the CO2 footprint of an academic conference
git clone https://a3nm.net/git/conference_footprint/
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compute_trips.py (2706B)

      1 #!/usr/bin/env python3
      2 import csv
      3 import sys
      4 import math
      5 from geopy.geocoders import Nominatim
      6 from functools import cache
      7 
      8 # Define carbon emissions data for each mode of transportation (in gCO2e per passenger-kilometer)
      9 emissions_data = {
     10     "train": 41,  
     11     "plane": 160, 
     12     "bike": 0,    
     13     "bus": 105,    
     14     "car": 192,
     15     "car_shared": 0
     16 }
     17 
     18 if len(sys.argv) != 4:
     19     print("Usage: python script.py <input_csv_file> <country> <city>")
     20     sys.exit(1)
     21 
     22 geolocator = Nominatim(user_agent='Highlights Conference 2023')
     23 
     24 @cache
     25 def get_lat_lon(country, city):
     26     location = geolocator.geocode(country + ", " + city)
     27     if location is None:
     28         print(f"got None response for {country} and {city}")
     29     return location.latitude, location.longitude
     30 
     31 target_lat, target_lon = get_lat_lon(sys.argv[2], sys.argv[3])
     32 
     33 def haversine_distance(lat1, lon1, lat2, lon2):
     34     # Convert latitude and longitude from degrees to radians
     35     lat1 = math.radians(lat1)
     36     lon1 = math.radians(lon1)
     37     lat2 = math.radians(lat2)
     38     lon2 = math.radians(lon2)
     39 
     40     # Radius of the Earth in kilometers (mean value)
     41     earth_radius = 6371.0  # km
     42 
     43     # Haversine formula
     44     dlon = lon2 - lon1
     45     dlat = lat2 - lat1
     46     a = math.sin(dlat / 2)**2 + math.cos(lat1) * math.cos(lat2) * math.sin(dlon / 2)**2
     47     c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
     48     distance = earth_radius * c
     49 
     50     return distance
     51 
     52 def estimate_distance(country, city):
     53     lat, lon = get_lat_lon(country, city)
     54     return haversine_distance(lat, lon, target_lat, target_lon)
     55 
     56 
     57 total_carbon_footprint = 0
     58 total_carbon_footprint2 = 0
     59 # Available fields are
     60 # timestamp,firstname,lastname,email,affiliation,no_zulip,level,presenting,extra,physical,hcrw,country,city,mode
     61 with open(sys.argv[1], 'r') as csv_file:
     62     reader = csv.DictReader(csv_file)
     63     for row in reader:
     64         if row['physical'] == "true" and emissions_data.get(row['mode']) is not None:
     65             # Calculate the carbon footprint for the round trip
     66             mode = None
     67             match row['mode']:
     68                 case "car":
     69                     mode = "car"
     70                 case "train":
     71                     mode = "train"
     72                 case "bus":
     73                     mode = "bus"
     74                 case "plane":
     75                     mode = "plane"
     76                 case _:
     77                     mode = "none"
     78                     
     79             lat, lon = get_lat_lon(row['country'], row['city'])
     80             distance = haversine_distance(lat, lon, target_lat, target_lon)
     81             if distance != 0 and mode != "none":
     82                 print(f"{mode}, {lat}, {lon}, {distance*1000}")
     83                 print(f"{mode}, {lat}, {lon}, {distance*1000}")
     84 
     85