{
"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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31342/flight+travel+estimates?iata_airport_from=SFO&iata_airport_to=KUL&flight_class=Economy&round_trip=Y&number_of_passengers=3&add_rf=Y&include_wtt=Y' --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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31343/hotel+stay+emission+estimates?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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31344/vehicles+estimates+by+type?vehicle_type=Car-Type-Executive&distance_unit=km&distance_value=4800&fuel_type=petrol' --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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31345/vehicles+estimates+by+model?vehicle_make=lexus&vehicle_model=rx 300&distance_unit=km&distance_value=2100' --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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31346/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31347/fuel+combustion+estimates?fuel_usage=gas&fuel_name=CNG&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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31348/freight+shipping+estimates?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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31349/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31350/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31351/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31352/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31353/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31354/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31355/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/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31356/estimated+flight+time+between+airports?iata_airport_from=LAX&iata_airport_to=SIN' --header 'Authorization: Bearer YOUR_API_KEY'
Después de registrarte, a cada desarrollador se le asigna una clave de acceso a la API personal, una combinación única de letras y dígitos proporcionada para acceder a nuestro endpoint de la API. Para autenticarte con el Cloud Based CarbonSutra Carbon Emission Estimation API simplemente incluye tu token de portador en el encabezado de Autorización.
| Encabezado | Descripción |
|---|---|
Autorización
|
Requerido
Debería ser Bearer access_key. Consulta "Tu Clave de Acceso a la API" arriba cuando estés suscrito.
|
Sin compromiso a largo plazo. Mejora, reduce o cancela en cualquier momento. La Prueba Gratuita incluye hasta 50 solicitudes.
(Ahorra 2 meses pagando anualmente 🎉)
Carbon emission footprint calculation for business travel, hotel stays, vehicles, freight, shipments, fuel and electricity with supplementary APIs on airports; Used by 3,000+ developers since 2022.
Cada punto final devuelve las emisiones estimadas de gases de efecto invernadero (CO2e) en múltiples unidades (gramos, kilogramos, toneladas métricas, libras) basadas en parámetros específicos como detalles de vuelos, estancias en hoteles, tipos de vehículos y uso de electricidad. Por ejemplo, el punto final de Estimaciones de Viaje en Vuelo proporciona emisiones basadas en códigos de aeropuerto, clase de vuelo y número de pasajeros
Los campos clave en los datos de respuesta incluyen "co2e_gm" "co2e_kg" "co2e_mt" y "co2e_lb" que representan emisiones en diferentes unidades Otros campos pueden incluir detalles como "aeropuerto_de" "aeropuerto_a" "calificación_de_hotel" y "tipo_de_vehículo" según el endpoint utilizado
Los parámetros varían según el punto final pero generalmente incluyen entradas como "códigos de aeropuerto" "clase de vuelo" "número de pasajeros" "nombre del país" "tipo de combustible" y "valor de distancia" Por ejemplo el punto final de Estimaciones de Estancia en Hoteles requiere "país" "número de noches" y "calificación del hotel"
Los datos de respuesta están estructurados en un formato JSON con un objeto "data" que contiene atributos específicos relacionados con la estimación de emisiones Cada tipo de estimación tiene su propia estructura detallando los parámetros utilizados y las emisiones calculadas lo que facilita su análisis y utilización
Las fuentes de datos incluyen publicaciones y normas gubernamentales como los factores de conversión de GHG del gobierno del Reino Unido el Índice de Referencia de Sostenibilidad del Hotel Cornell y diversas agencias ambientales Esto asegura que los cálculos de emisiones se basen en información confiable y actualizada
Los casos de uso típicos incluyen la elaboración de informes de sostenibilidad corporativa el cumplimiento de las regulaciones ambientales y el análisis de las huellas de carbono para viajes alojamiento y logística Las organizaciones pueden utilizar estos datos para identificar áreas de reducción de emisiones y mejorar sus estrategias de sostenibilidad
Los usuarios pueden personalizar las solicitudes especificando parámetros relevantes para sus necesidades como seleccionar diferentes marcas/modelos de vehículos ajustar las clases de vuelo o elegir países específicos para estancias en hoteles Esta flexibilidad permite cálculos de emisiones adaptados a las actividades organizacionales únicas
Los usuarios pueden utilizar los datos devueltos para informes y análisis al integrarlos en tableros de sostenibilidad informes de cumplimiento o herramientas de toma de decisiones El formato estructurado permite la extracción fácil de métricas clave lo que permite a las organizaciones rastrear y gestionar sus emisiones de carbono de manera efectiva