{
"data": {
"type": "estimate-travel-flight",
"iata_airport_from": "SFO",
"airport_from": "San Francisco International Airport",
"iata_airport_to": "KUL",
"airport_to": "Kuala Lumpur International Airport",
"flight_class": "Economy",
"round_trip": "Y",
"add_rf": "Y",
"include_wtt": "Y",
"number_of_passengers": 3,
"co2e_gm": 10284950,
"co2e_kg": 10284.95,
"co2e_mt": 10.28,
"co2e_lb": 22674.41,
"explanation": "This emission profile is computed using the UK Government's 2026 conversion factors for greenhouse gas (GHG) reporting. The calculation is for 3 passengers travelling in Economy class between San Francisco International Airport(SFO) and Kuala Lumpur International Airport(KUL). The distance between these airports is estimated at 13,635 km, utilizing the Haversine equation for Great-Circle Distance (GCD). Since it is a round trip, the distance is doubled to 27,269 km. Radiative forcing uplift is included and indirect/WTT factors are added, as per inputs. Based on these operational parameters, the total footprint evaluates to 10,284,950 grams of CO2e. Standardized metric conversions render this as 10284.95 kg (rounded to two decimal places) or 10.28 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 22674.41 lbs."
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31396/flight+travel+estimates?number_of_passengers=3&include_wtt=Y&add_rf=Y&include_wtt=Y&number_of_passengers=3&include_wtt=Y&add_rf=Y&add_rf=Y&round_trip=Y&flight_class=Economy&number_of_passengers=3&round_trip=Y&iata_airport_to=KUL&round_trip=Y&flight_class=Economy&iata_airport_from=SFO&flight_class=Economy&iata_airport_to=KUL&iata_airport_to=KUL' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name":"KrugerBrent-ConferenceLAX_Jun26",
"iata_airport_from":"DXB",
"iata_airport_to":"LAX",
"flight_class":"Business",
"round_trip":"Y",
"add_rf":"Y",
"include_wtt":"Y",
"number_of_passengers":1
}'
{
"data": {
"type": "estimate-hotel-stay",
"country": "Japan",
"city_name": "Kyoto",
"hotel_rating": 4,
"number_of_nights": 5,
"number_of_rooms": 1,
"co2e_gm": 208850,
"co2e_kg": 208.85,
"co2e_mt": 0.21,
"co2e_lb": 460.43,
"explanation": "CarbonSutra uses the Cornell Hotel Sustainability Benchmarking Index 2026 (CHSB2026) for this calculation, specifically leveraging the mean value from the CHSB validity test for Measure 1 - HCMI Room Night Emission (Carbon Category). Because specific emission factors for Kyoto are unlisted in CHSB2026, fallback data for Japan has been applied to this calculation of one room and 5 nights. This methodology relies on the Expedia Star Rating classification system. Where regional data gaps exist, proxy assignments are made according to the transparent governance rules published on carbonsutra.com. For the hotel rating of four stars, an emission factor of 41.77 kgCO2e per room night is used. This footprint evaluates to 208,850 grams of CO2e. Standardized unit conversions render this as 208.85 kg (rounded to two decimal places) or 0.21 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 460.43 lbs."
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31397/hotel+stay+emission+estimates?number_of_rooms=1&country_code=JP&city_name=Kyoto&hotel_rating=4&number_of_nights=5&number_of_rooms=1' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name": "KrugerBrent-Jun26",
"country_code": "GB",
"city_name": "Manchester",
"hotel_rating": "5",
"number_of_nights": 4,
"number_of_rooms": 1
}'
{
"data": {
"type": "estimate-vehicle-usage",
"vehicle_type": "Car-Type-Executive",
"fuel_type": "Petrol",
"distance_value": 4800,
"distance_unit": "km",
"include_wtt": "Y",
"co2e_gm": 1232352,
"co2e_kg": 1232.35,
"co2e_mt": 1.23,
"co2e_lb": 2716.86,
"explanation": "CarbonSutra computed this emission profile using the UK Government's 2026 conversion factors for greenhouse gas (GHG) reporting. The car is an Executive type, running on petrol. For a transit distance of 4,800 kilometers and with indirect/WTT factors added, the total footprint evaluates to 1,232,352 grams of CO2e. Standardized metric conversions render this as 1232.35 kg (rounded to two decimal places) or 1.23 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 2716.86 lbs."
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31398/vehicles+estimates+by+type?distance_value=4800&fuel_type=petrol&vehicle_type=Car-Type-Executive&distance_unit=km' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name":"KrugerBrent-Exhibition-2025",
"vehicle_type":"Car-Type-Supermini",
"fuel_type":"petrol",
"distance_value":8800,
"distance_unit":"km",
"include_wtt":"N"
}'
{
"data": {
"type": "estimate-vehicle",
"distance_unit": "km",
"distance_value": 2100,
"vehicle_make": "Lexus",
"vehicle_model": "RX 300",
"co2e_gm": 630686,
"co2e_kg": 630.69,
"co2e_mt": 0.63,
"co2e_lb": 1390.43
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31399/vehicles+estimates+by+model?vehicle_model=rx 300&distance_unit=km&distance_value=2100&vehicle_make=lexus' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name":"KB-Oct-2025",
"distance_unit":"km",
"distance_value":9500,
"vehicle_make":"Lexus",
"vehicle_model":"RX 300"
}'
{
"data": {
"type": "estimate-electricity",
"electricity_unit": "MWh",
"electricity_value": 4000,
"country_name": "France",
"co2e_gm": 172000000,
"co2e_kg": 172000,
"co2e_mt": 172,
"co2e_lb": 379194.64
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31400/electricity+usage+estimates?country_name=france&electricity_unit=MWH&electricity_value=4000' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"country_name":"Germany",
"electricity_unit":"mwh",
"electricity_value":1100
}'
{
"data": {
"type": "estimate-fuel",
"fuel_usage": "gas",
"fuel_name": "CNG",
"fuel_unit": "tonnes",
"fuel_value": "4500",
"co2e_gm": 11284759845,
"co2e_kg": 11284759.85,
"co2e_mt": 11284.76,
"co2e_lb": 24878607.26
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31401/fuel+combustion+estimates?fuel_usage=gas&fuel_usage=gas&fuel_name=CNG&fuel_name=CNG&fuel_value=4500&fuel_value=4500' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name":"KrugerBrent-Plant3A-May26",
"fuel_usage":"liquid",
"fuel_name":"Gas oil",
"fuel_value":7800
}'
{
"data": {
"type": "estimate-freight",
"distance_value": 2000,
"transport_mode": "rail",
"freight_weight": 2235,
"co2e_gm": 98340,
"co2e_kg": 98.34,
"co2e_mt": 0.1,
"co2e_lb": 216.8
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31402/freight+shipping+estimates?freight_weight=2235&distance_value=2000&transport_mode=rail&freight_weight=2235&distance_value=2000' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name":"KrugerBrent-PO72-Feb26",
"transport_mode":"Rail",
"distance_value":1500,
"freight_weight":19000
}'
{
"data": {
"type": "estimate-ecommerce",
"origin_country_code": "SG",
"origin_postal_code": "436915",
"destination_country_code": "IN",
"destination_postal_code": "248001",
"package_weight": 3.5,
"add_rf": "Y",
"include_wtt": "Y",
"co2e_gm": 15,
"co2e_kg": 0.02,
"co2e_mt": 0,
"co2e_lb": 0.04
},
"success": true,
"status": 200
}
curl --location --request POST 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31403/ecommerce+shipment+estimates?origin_country_code=SG&origin_postal_code=436915&destination_country_code=IN&destination_postal_code=248001&package_weight=3.5&add_rf=Y&include_wtt=Y' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{
"cluster_name": "KrugerBrent_Marketing_Oct25",
"origin_country_code": "IN",
"origin_postal_code": "604407",
"destination_country_code": "SG",
"destination_postal_code": "436915",
"package_weight":24.40,
"add_rf": "Y",
"include_wtt": "Y"
}'
{
"data": {
"airport_from": "San Francisco International Airport",
"closest_airport_code": "SQL",
"closest_airport_name": "San Carlos Airport",
"distance": "16.13"
},
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31404/airport+to+nearest+airport?iata_airport_code=SFO&same_country=Y' --header 'Authorization: Bearer YOUR_API_KEY'
{
"data": [
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dehradun City Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Kanwali Road Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Patel Nagar (Dehradun) Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "CDA(A.F) Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Hathi Barkala Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Neshvilla Road Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Sachivalaya Parisar Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dalanwala Dehradun Uttarakhand",
"distance": "18.14",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Arahat Bazar Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dehradun IBC Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Rispana Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Nivh Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Karanpur Dehradun Uttarakhand",
"distance": "18.82",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Govindgarh (Dehradun) Dehradun Uttarakhand",
"distance": "21.5",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dehradun G.P.O. Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Kanwali Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dilaram Bazar Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Araghar Dehradun Uttarakhand",
"distance": "17.58",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Dehradun Kty Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Sayedwala Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Cannaught Place Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Mothrowala Dehradun Uttarakhand",
"distance": "19.69",
"unit": "km"
},
{
"airport_name": "Dehradun Jolly Grant Airport",
"iata_code": "DED",
"postal_code": "248001",
"address": "Ballupur Dehradun Uttarakhand",
"distance": "22.64",
"unit": "km"
}
],
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31405/postal+code+to+nearest+airport?country_code=IN&postal_code=248001' --header 'Authorization: Bearer YOUR_API_KEY'
{
"data": {
"airport_from": "Ninoy Aquino International Airport",
"airport_to": "Tokyo Haneda International Airport",
"distance": 2996.08
},
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31406/distance+between+two+airports?iata_airport_from=MNL&iata_airport_to=HND' --header 'Authorization: Bearer YOUR_API_KEY'
{
"data": [
{
"iata_code": "DEL",
"airport_name": "Indira Gandhi International Airport"
}
],
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31407/airports+keyword+search?keyword=delhi' --header 'Authorization: Bearer YOUR_API_KEY'
{
"data": [
{
"make": "Acura",
"number_of_models": 51
},
{
"make": "Alfa Romeo",
"number_of_models": 13
},
{
"make": "AM General",
"number_of_models": 4
},
{
"make": "American Motors Corporation",
"number_of_models": 4
},
{
"make": "ASC Incorporated",
"number_of_models": 1
},
{
"make": "Aston Martin",
"number_of_models": 42
},
{
"make": "Audi",
"number_of_models": 170
},
{
"make": "Aurora Cars Ltd",
"number_of_models": 1
},
{
"make": "Autokraft Limited",
"number_of_models": 2
},
{
"make": "Avanti Motor Corporation",
"number_of_models": 1
},
{
"make": "Azure Dynamics",
"number_of_models": 1
},
{
"make": "Bentley",
"number_of_models": 30
},
{
"make": "Bertone",
"number_of_models": 1
},
{
"make": "Bill Dovell Motor Car Company",
"number_of_models": 2
},
{
"make": "Bitter Gmbh and Co. Kg",
"number_of_models": 2
},
{
"make": "BMW",
"number_of_models": 434
},
{
"make": "BMW Alpina",
"number_of_models": 2
},
{
"make": "Bugatti",
"number_of_models": 5
},
{
"make": "Buick",
"number_of_models": 50
},
{
"make": "BYD",
"number_of_models": 1
},
{
"make": "Cadillac",
"number_of_models": 84
},
{
"make": "CCC Engineering",
"number_of_models": 1
},
{
"make": "Chevrolet",
"number_of_models": 277
},
{
"make": "Chrysler",
"number_of_models": 64
},
{
"make": "CODA Automotive",
"number_of_models": 1
},
{
"make": "Consulier Industries Inc",
"number_of_models": 1
},
{
"make": "CX Automotive",
"number_of_models": 7
},
{
"make": "Dabryan Coach Builders Inc",
"number_of_models": 1
},
{
"make": "Dacia",
"number_of_models": 3
},
{
"make": "Daewoo",
"number_of_models": 8
},
{
"make": "Daihatsu",
"number_of_models": 3
},
{
"make": "Dodge",
"number_of_models": 124
},
{
"make": "E. P. Dutton, Inc.",
"number_of_models": 1
},
{
"make": "Eagle",
"number_of_models": 10
},
{
"make": "Environmental Rsch and Devp Corp",
"number_of_models": 1
},
{
"make": "Evans Automobiles",
"number_of_models": 2
},
{
"make": "Excalibur Autos",
"number_of_models": 1
},
{
"make": "Federal Coach",
"number_of_models": 14
},
{
"make": "Ferrari",
"number_of_models": 82
},
{
"make": "Fiat",
"number_of_models": 10
},
{
"make": "Fisker",
"number_of_models": 2
},
{
"make": "Ford",
"number_of_models": 266
},
{
"make": "General Motors",
"number_of_models": 1
},
{
"make": "Genesis",
"number_of_models": 17
},
{
"make": "Geo",
"number_of_models": 15
},
{
"make": "GMC",
"number_of_models": 173
},
{
"make": "Goldacre",
"number_of_models": 1
},
{
"make": "Grumman Allied Industries",
"number_of_models": 1
},
{
"make": "Grumman Olson",
"number_of_models": 1
},
{
"make": "Honda",
"number_of_models": 74
},
{
"make": "Hummer",
"number_of_models": 2
},
{
"make": "Hyundai",
"number_of_models": 112
},
{
"make": "Import Foreign Auto Sales Inc",
"number_of_models": 1
},
{
"make": "Import Trade Services",
"number_of_models": 11
},
{
"make": "INEOS Automotive",
"number_of_models": 2
},
{
"make": "Infiniti",
"number_of_models": 87
},
{
"make": "Isis Imports Ltd",
"number_of_models": 1
},
{
"make": "Isuzu",
"number_of_models": 36
},
{
"make": "J.K. Motors",
"number_of_models": 21
},
{
"make": "Jaguar",
"number_of_models": 105
},
{
"make": "JBA Motorcars, Inc.",
"number_of_models": 1
},
{
"make": "Jeep",
"number_of_models": 78
},
{
"make": "Kandi",
"number_of_models": 1
},
{
"make": "Karma",
"number_of_models": 4
},
{
"make": "Kenyon Corporation Of America",
"number_of_models": 4
},
{
"make": "Kia",
"number_of_models": 81
},
{
"make": "Koenigsegg",
"number_of_models": 2
},
{
"make": "Laforza Automobile Inc",
"number_of_models": 1
},
{
"make": "Lambda Control Systems",
"number_of_models": 1
},
{
"make": "Lamborghini",
"number_of_models": 39
},
{
"make": "Land Rover",
"number_of_models": 76
},
{
"make": "Lexus",
"number_of_models": 112
},
{
"make": "Lincoln",
"number_of_models": 46
},
{
"make": "London Coach Co Inc",
"number_of_models": 1
},
{
"make": "London Taxi",
"number_of_models": 1
},
{
"make": "Lordstown",
"number_of_models": 1
},
{
"make": "Lotus",
"number_of_models": 10
},
{
"make": "Lucid",
"number_of_models": 20
},
{
"make": "Mahindra",
"number_of_models": 1
},
{
"make": "Maserati",
"number_of_models": 59
},
{
"make": "Maybach",
"number_of_models": 6
},
{
"make": "Mazda",
"number_of_models": 83
},
{
"make": "Mcevoy Motors",
"number_of_models": 4
},
{
"make": "McLaren Automotive",
"number_of_models": 27
},
{
"make": "Mercedes-Benz",
"number_of_models": 435
},
{
"make": "Mercury",
"number_of_models": 51
},
{
"make": "Merkur",
"number_of_models": 2
},
{
"make": "MINI",
"number_of_models": 51
},
{
"make": "Mitsubishi",
"number_of_models": 56
},
{
"make": "Mobility Ventures LLC",
"number_of_models": 2
},
{
"make": "Morgan",
"number_of_models": 1
},
{
"make": "Nissan",
"number_of_models": 140
},
{
"make": "Oldsmobile",
"number_of_models": 35
},
{
"make": "Pagani",
"number_of_models": 3
},
{
"make": "Panos",
"number_of_models": 1
},
{
"make": "Panoz Auto-Development",
"number_of_models": 1
},
{
"make": "Panther Car Company Limited",
"number_of_models": 1
},
{
"make": "PAS Inc - GMC",
"number_of_models": 2
},
{
"make": "PAS, Inc",
"number_of_models": 2
},
{
"make": "Peugeot",
"number_of_models": 8
},
{
"make": "Pininfarina",
"number_of_models": 1
},
{
"make": "Plymouth",
"number_of_models": 23
},
{
"make": "Polestar",
"number_of_models": 11
},
{
"make": "Pontiac",
"number_of_models": 60
}
],
"_note": "Response truncated for documentation purposes"
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31408/vehicle+makes' --header 'Authorization: Bearer YOUR_API_KEY'
{
"data": [
{
"model": "CT 200h"
},
{
"model": "ES 250"
},
{
"model": "ES 250 AWD"
},
{
"model": "ES 300"
},
{
"model": "ES 300h"
},
{
"model": "ES 330"
},
{
"model": "ES 350"
},
{
"model": "ES 350 F Sport"
},
{
"model": "GS 200t"
},
{
"model": "GS 200t F Sport"
},
{
"model": "GS 300"
},
{
"model": "GS 300 4WD"
},
{
"model": "GS 300 F Sport"
},
{
"model": "GS 300/GS 400"
},
{
"model": "GS 300/GS 430"
},
{
"model": "GS 350"
},
{
"model": "GS 350 AWD"
},
{
"model": "GS 350 F Sport"
},
{
"model": "GS 430"
},
{
"model": "GS 450h"
},
{
"model": "GS 460"
},
{
"model": "GS F"
},
{
"model": "GS300"
},
{
"model": "GX 460"
},
{
"model": "GX 470"
},
{
"model": "GX 550"
},
{
"model": "HS 250h"
},
{
"model": "IS 200t"
},
{
"model": "IS 250"
},
{
"model": "IS 250 AWD"
},
{
"model": "IS 250 C"
},
{
"model": "IS 250/IS 250C"
},
{
"model": "IS 300"
},
{
"model": "IS 300 AWD"
},
{
"model": "IS 350"
},
{
"model": "IS 350 AWD"
},
{
"model": "IS 350 C"
},
{
"model": "IS 350/IS 350C"
},
{
"model": "IS 500"
},
{
"model": "IS F"
},
{
"model": "LC 500"
},
{
"model": "LC 500 Convertible"
},
{
"model": "LC 500h"
},
{
"model": "LFA"
},
{
"model": "LS 400"
},
{
"model": "LS 430"
},
{
"model": "LS 460"
},
{
"model": "LS 460 AWD"
},
{
"model": "LS 460 L"
},
{
"model": "LS 460 L AWD"
},
{
"model": "LS 500"
},
{
"model": "LS 500 AWD"
},
{
"model": "LS 500h"
},
{
"model": "LS 500h AWD"
},
{
"model": "LS 600h L"
},
{
"model": "LX 450"
},
{
"model": "LX 470"
},
{
"model": "LX 570"
},
{
"model": "LX 600"
},
{
"model": "NX 200t"
},
{
"model": "NX 200t AWD"
},
{
"model": "NX 200t AWD F Sport"
},
{
"model": "NX 250"
},
{
"model": "NX 250 AWD"
},
{
"model": "NX 300"
},
{
"model": "NX 300 AWD"
},
{
"model": "NX 300 AWD F Sport"
},
{
"model": "NX 300h"
},
{
"model": "NX 300h AWD"
},
{
"model": "NX 350 AWD"
},
{
"model": "NX 350 AWD F Sport"
},
{
"model": "NX 350h AWD"
},
{
"model": "NX 450h Plus AWD"
},
{
"model": "RC 200t"
},
{
"model": "RC 300"
},
{
"model": "RC 300 AWD"
},
{
"model": "RC 350"
},
{
"model": "RC 350 AWD"
},
{
"model": "RC F"
},
{
"model": "RX 300"
},
{
"model": "RX 300 4WD"
},
{
"model": "RX 330 2WD"
},
{
"model": "RX 330 4WD"
},
{
"model": "RX 350"
},
{
"model": "RX 350 2WD"
},
{
"model": "RX 350 4WD"
},
{
"model": "RX 350 AWD"
},
{
"model": "RX 350 L"
},
{
"model": "RX 350 L AWD"
},
{
"model": "RX 350h AWD"
},
{
"model": "RX 400h 2WD"
},
{
"model": "RX 400h 4WD"
},
{
"model": "RX 450h"
},
{
"model": "RX 450h AWD"
},
{
"model": "RX 450h L AWD"
},
{
"model": "RX 500h AWD"
},
{
"model": "RZ 300e (18 inch wheels)"
},
{
"model": "RZ 300e (20 inch wheels)"
},
{
"model": "RZ 450e AWD (18 inch wheels)"
},
{
"model": "RZ 450e AWD (20 inch Wheels)"
},
{
"model": "SC"
},
{
"model": "SC 300/SC 400"
},
{
"model": "SC 300/SC 430"
},
{
"model": "SC 430"
},
{
"model": "TX 350"
},
{
"model": "TX 350 AWD"
},
{
"model": "TX 500h AWD"
},
{
"model": "UX 200"
},
{
"model": "UX 250h"
},
{
"model": "UX 250h AWD"
},
{
"model": "UX 300h"
},
{
"model": "UX 300h AWD"
}
],
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31409/vehicle+models&vehicle_make=Required' --header 'Authorization: Bearer YOUR_API_KEY'
--data-raw '{}'
{
"data": {
"airport_from": "Los Angeles International Airport",
"airport_to": "Singapore Changi Airport",
"estimated_formatted": "17 hours 27 minutes",
"hours": 17,
"minutes": 27,
"totalMinutes": 1047
},
"success": true,
"status": 200
}
curl --location --request GET 'https://zylalabs.com/api/13723/carbon+emission+calculation+api/31410/estimated+flight+time+between+airports?iata_airport_from=LAX&iata_airport_to=SIN&iata_airport_to=SIN' --header 'Authorization: Bearer YOUR_API_KEY'
साइन अप करने के बाद, प्रत्येक डेवलपर को एक पर्सनल API एक्सेस की असाइन की जाती है, जो अक्षरों और अंकों का एक यूनिक संयोजन होता है, जिसका उपयोग हमारे API एंडपॉइंट तक पहुंचने के लिए किया जाता है। प्रमाणीकरण के लिए कार्बन उत्सर्जन गणना API के साथ बस अपने बेयरर टोकन को Authorization हेडर में शामिल करें।
| हेडर | विवरण |
|---|---|
Authorization
|
आवश्यक
होना चाहिए Bearer access_key. जब आप सब्सक्राइब हों तो ऊपर "Your API Access Key" देखें।
|
कोई लंबी अवधि की प्रतिबद्धता नहीं। कभी भी अपग्रेड, डाउनग्रेड या कैंसल करें। फ्री ट्रायल में 50 रिक्वेस्ट तक शामिल हैं।
(वार्षिक बिलिंग के साथ 2 महीने बचाएँ 🎉)
व्यापार यात्रा के लिए कार्बन फुटप्रिंट का अनुमान, वैश्विक होटल आवास, वाहनों का उपयोग, माल ढुलाई, ई-कॉमर्स, शिपमेंट, ईंधन और बिजली के साथ हवाई अड्डों और क्लस्टरिंग परिणामों पर अतिरिक्त एपीआई।
प्रत्येक एंडपॉइंट विशिष्ट पैरामीटर जैसे उड़ान विवरण, होटल ठहराव, वाहन प्रकार, और बिजली उपयोग के आधार पर कई इकाइयों (ग्राम, किलोग्राम, मीट्रिक टन, पाउंड) में अनुमानित ग्रीनहाउस गैस उत्सर्जन (CO2e) लौटाता है उदाहरण के लिए उड़ान यात्रा अनुमान एंडपॉइंट हवाईअड्डे के कोड, उड़ान वर्ग, और यात्रियों की संख्या के आधार पर उत्सर्जन प्रदान करता है
प्रतिक्रिया डेटा में प्रमुख क्षेत्र "co2e_gm," "co2e_kg," "co2e_mt," और "co2e_lb" शामिल हैं जो विभिन्न इकाइयों में उत्सर्जन का प्रतिनिधित्व करते हैं अन्य क्षेत्रों में "airport_from," "airport_to," "hotel_rating," और "vehicle_type" जैसे विवरण शामिल हो सकते हैं जो उपयोग किए गए अंतिम बिंदु पर निर्भर करते हैं
पैरामीटर प्रत्येक एंडपॉइंट के अनुसार भिन्न होते हैं लेकिन आमतौर पर इसमें "एयरपोर्ट कोड," "उड़ान कक्षा," "यात्रियों की संख्या," "देश का नाम," "ईंधन का प्रकार," और "दूरी के मान" जैसे इनपुट शामिल होते हैं उदाहरण के लिए होटल स्टे एस्टीमेट्स एंडपॉइंट को "देश," "रातों की संख्या," और "होटल रेटिंग" की आवश्यकता होती है
प्रतिक्रिया डेटा एक JSON प्रारूप में संरचित है जिसमें एक "डेटा" ऑब्जेक्ट होता है जिसमें उत्सर्जन अनुमान से संबंधित विशिष्ट विशेषताएँ होती हैं प्रत्येक अनुमान प्रकार की अपनी संरचना होती है जो प्रयुक्त पैरामीटर और गणना किए गए उत्सर्जन का विवरण देती है जिससे इसे पार्स और उपयोग करना आसान हो जाता है
डेटा स्रोतों में सरकारी प्रकाशन और मानक शामिल हैं जैसे कि यूके सरकार के जीएचजी रूपांतरण कारक, कॉर्नेल होटल स्थिरता बेंचमार्क इंडेक्स और विभिन्न पर्यावरण एजेंसियां यह सुनिश्चित करता है कि उत्सर्जन की गणनाएं विश्वसनीय और अद्यतन जानकारी पर आधारित हैं
टिपिकल उपयोग के मामले में कॉर्पोरेट स्थिरता रिपोर्टिंग पर्यावरणीय नियमों के अनुपालन और यात्रा आवास और लॉजिस्टिक्स के लिए कार्बन पदचिह्नों की विश्लेषण शामिल हैं संगठन इस डेटा का उपयोग उत्सर्जन में कमी के लिए क्षेत्रों की पहचान करने और अपनी स्थिरता रणनीतियों में सुधार करने के लिए कर सकते हैं
उपयोगकर्ता अपनी आवश्यकताओं से संबंधित पैरामीटर निर्दिष्ट करके अनुरोधों को अनुकूलित कर सकते हैं जैसे कि विभिन्न वाहन ब्रांड/मॉडल का चयन करना उड़ान वर्गों को समायोजित करना या होटल स्टे के लिए विशिष्ट देशों का चयन करना यह लचीलापन अद्वितीय संगठनात्मक गतिविधियों के आधार पर अनुकूलित उत्सर्जन गणनाओं की अनुमति देता है
उपयोगकर्ता रिपोर्टिंग और विश्लेषण के लिए लौटाए गए डेटा का उपयोग कर सकते हैं इसे स्थिरता डैशबोर्ड प्रावधान रिपोर्ट या निर्णय लेने वाले उपकरणों में एकीकृत करके संरचित प्रारूप प्रमुख मेट्रिक्स को आसानी से निकालने की अनुमति देता है जिससे संगठनों को उनकी कार्बन उत्सर्जन को प्रभावी ढंग से ट्रैक और प्रबंधित करने में मदद मिलती है