{
"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'
Após se cadastrar, cada desenvolvedor recebe uma chave de acesso à API pessoal, uma combinação única de letras e dígitos para acessar nosso endpoint de API. Para autenticar com a Cloud Based CarbonSutra Carbon Emission Estimation API basta incluir seu token Bearer no cabeçalho Authorization.
| Cabeçalho | Descrição |
|---|---|
Authorization
|
Obrigatório
Deve ser Bearer access_key. Veja "Sua chave de acesso à API" acima quando você estiver inscrito.
|
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(Economize 2 meses com cobrança anual 🎉)
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 endpoint retorna as emissões estimadas de gases de efeito estufa (CO2e) em várias unidades (gramas, quilogramas, toneladas métricas, libras) com base em parâmetros específicos como detalhes de voo, estadias em hotéis, tipos de veículos e uso de eletricidade Por exemplo o endpoint de Estimativas de Viagem de Voo fornece emissões com base em códigos de aeroporto, classe de voo e número de passageiros
Os campos principais nos dados de resposta incluem "co2e_gm," "co2e_kg," "co2e_mt," e "co2e_lb," que representam emissões em diferentes unidades. Outros campos podem incluir detalhes como "airport_from," "airport_to," "hotel_rating," e "vehicle_type," dependendo do endpoint utilizado
Os parâmetros variam de acordo com o endpoint, mas geralmente incluem entradas como "códigos de aeroporto", "classe de voo", "número de passageiros", "nome do país", "tipo de combustível" e "valor da distância". Por exemplo, o endpoint de Estimativas de Estadia em Hotel requer "país", "número de noites" e "classificação do hotel"
Os dados da resposta estão estruturados em um formato JSON com um objeto "dados" contendo atributos específicos relacionados à estimativa de emissão Cada tipo de estimativa tem sua própria estrutura detalhando os parâmetros usados e as emissões calculadas facilitando a análise e utilização
As fontes de dados incluem publicações e normas do governo, como os fatores de conversão de GEE do Governo do Reino Unido, o Índice de Benchmark de Sustentabilidade do Hotel Cornell e várias agências ambientais. Isso garante que os cálculos de emissões sejam baseados em informações confiáveis e atualizadas
Os casos de uso típicos incluem relatórios de sustentabilidade corporativa, conformidade com regulamentos ambientais e análise de pegadas de carbono para viagens, alojamento e logística As organizações podem usar esses dados para identificar áreas para redução de emissões e melhorar suas estratégias de sustentabilidade
Os usuários podem personalizar as solicitações especificando parâmetros relevantes para suas necessidades, como selecionar diferentes marcas/modelos de veículos, ajustar classes de voo ou escolher países específicos para estadias em hotéis. Essa flexibilidade permite cálculos de emissões personalizados com base em atividades organizacionais únicas
Os usuários podem utilizar os dados retornados para relatórios e análise ao integrá-los em painéis de sustentabilidade, relatórios de conformidade ou ferramentas de tomada de decisão O formato estruturado permite a extração fácil de métricas-chave, permitindo que as organizações acompanhem e gerenciem suas emissões de carbono de forma eficaz