Cloud Based CarbonSutra Carbon Emission Estimation API

API ID 13720

Access cloud-based carbon emission estimations from anywhere with our innovative API, simplifying your sustainability journey.

API 文档

端点

请求
# 商务航班旅行的排放 根据出发和到达机场代码、航班等级和乘客人数,返回商务旅行通过航班/空中的估计温室气体排放(CO2e),单位可以是克、千克、公吨或磅 此计算用于报告针对工作目的飞行的个人的范围三排放。航班旅行的碳足迹是根据机场之间的距离和乘客的旅行等级进行计算的 operationId: calculate_emissions_from_business_flight_travel ## 注意事项 - 如果同一航班有两名乘客,但坐在不同的舱位,则建议进行两次单独的 API 调用,每人一次 - 由于国际民航组织(ICAO)未对辐射强迫(RF)乘数(考虑到高空航空运输排放对气候的更广泛影响)或距离提升因子(用于考虑延迟/盘旋和非直达航线,即不沿目的地之间的直线/大圆航线)进行应用,我们提供了一个选项让您在计算中考虑这些因素。使用“add_rf”字段来添加这个因素 - WTT(井到油箱)排放包括从燃料源(井)到提取、加工(精炼)、存储和交付阶段,直到飞机起飞前的使用点(油箱)之间的所有流程。使用“include_wtt”字段将在估算中添加与此航班相关的上游排放
Endpoint ID: 31342
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31342/flight+travel+estimates
输入参数

航班旅行估算 — 端点功能

对象 描述
请求体 必需 Json
iata_airport_from 必需 The departing airport's IATA airport code (The unique 3 digit geocode, also known as IATA location identifier, IATA station code, or location identifier)
iata_airport_to 必需 The arrival airport's IATA airport code (The unique 3 digit geocode, also known as IATA location identifier, IATA station code, or location identifier)
flight_class 必需 The class in which the passenger is travelling. Allowed: [Economy, Premium, Business, First]. It is optional. For NULL or undefined values, an Average factor is used.
round_trip 必需 The default value of "Y" means that the traveller has taken the flight from origin to destination and then back to the same origin in the same class, while a "N" means a one-way journey.
number_of_passengers 必需 Default: 1. If a NULL or <0 is entered, it is set to 1
add_rf 必需 The default value of "Y" adds the radiative forcing multiplier factor in the footprint calculation; "N" bypasses it
include_wtt 必需 The default value of "Y" includes the Well-to-Tank (WTT) factors while "N" bypasses it
cluster_name 可选 Identifier to log and aggregate these results under using the Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

iata_airport_from
iata_airport_to
flight_class
round_trip
number_of_passengers
add_rf
include_wtt
cluster_name
API 示例响应
JSON
{
    "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
}
航班旅行估算 — 代码片段

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
}'

    
请求
酒店住宿的排放 根据酒店所在国家、住宿时长、房间数量和酒店星级返回以多种单位(克、千克、吨、磅)估算的温室气体排放(CO2e) CarbonSutra计算酒店住宿碳足迹的方法基于康奈尔酒店可持续性基准指数和2026年发布的英国政府温室气体转换因素 operationId: calculate_emissions_from_hotel_stay
Endpoint ID: 31343
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31343/hotel+stay+emission+estimates
输入参数

酒店住宿排放估计 — 端点功能

对象 描述
请求体 必需 Json
country_code 必需 Two digit ISO 3166-1 alpha-2 country code
city_name 必需 City of hotel within the country
hotel_rating 必需 Expedia star classification rating of the hotel. Allowed: [2-5], Default: 4
number_of_nights 必需 Length of stay in nights. Default: 1
number_of_rooms 必需 Number of rooms booked, irrespective of count of persons per room. Default: 1
cluster_name 可选 Identifier to log and aggregate results under Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

country_code
city_name
hotel_rating
number_of_nights
number_of_rooms
cluster_name
API 示例响应
JSON
{
    "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
}
酒店住宿排放估计 — 代码片段

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
}'

    
请求
# 根据车辆类型的使用排放 返回根据车辆类型进行旅行的估计温室气体排放(CO2e),以多种单位(克,千克,公吨,磅)表示。 operationId: calculate_emissions_from_vehicle_usage_based_on_type
Endpoint ID: 31344
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31344/vehicles+estimates+by+type
输入参数

按类型的车辆估计 — 端点功能

对象 描述
请求体 必需 Json
vehicle_type 必需 Type of vehicle
distance_unit 必需 Enter "km" for kilometers and "mi" for miles. Default is "km"
distance_value 必需 Distance travelled. If an undefined value is passed, it will be set to 1.00
fuel_type 必需 Applicable for cars, these 5 values are allowed: ["Diesel", "Petrol", "PHEV", "BEV", "Unknown"]. If unsure then use "Unknown", which is the default value.
include_wtt 可选 The default value of "Y" includes the Well-to-Tank (WTT) factors while "N" bypasses it.
cluster_name 可选 Identifier to log and aggregate results under the Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

vehicle_type
distance_unit
distance_value
fuel_type
include_wtt
cluster_name
API 示例响应
JSON
{
    "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
}
按类型的车辆估计 — 代码片段

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"
}'

    
请求
# 基于车辆品牌/型号的使用排放 返回基于车辆品牌和型号的出行的估计温室气体排放(CO2e),单位包括克、千克、公吨、磅 涵盖145种品牌和5000种型号。要获取具体的品牌和型号名称,请使用补充API ## 注释 - 如果您不知道车辆的品牌和型号,我们建议使用根据车辆类型计算排放的API - 对于双燃料车辆,使用其常规燃料和替代燃料的排放平均值 - 如果同一制造商在多个年份推出同一型号,则该型号的排放值取平均值
Endpoint ID: 31345
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31345/vehicles+estimates+by+model
输入参数

车型车辆估算 — 端点功能

对象 描述
请求体 必需 Json
vehicle_make 必需 Name of Vehicle Make. Retrieve it from Supplementary API or documentation
vehicle_model 必需 Name of Vehicle Model. Retrieve it from Supplementary API or documentation
distance_unit 必需 Enter "km" for kilometers and "mi" for miles. Default is "mi".
distance_value 必需 Distance travelled. If an undefined value is passed, it will be set to 1.00
cluster_name 可选 Identifier to log and aggregate these results under using the Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

vehicle_make
vehicle_model
distance_unit
distance_value
cluster_name
API 示例响应
JSON
{
    "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
}
车型车辆估算 — 代码片段

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"
}'

    
请求
# 电力使用的排放 返回基于国家名称和电力消耗单位的电力使用估计的温室气体排放(CO2e),以多种单位(克、千克、公吨、磅)呈现 来自近90个国家在2020年、2024年和2026年的数据,汇编自多个来源 operationId:calculate_emissions_from_electricity_usage
Endpoint ID: 31346
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31346/electricity+usage+estimates
输入参数

电力使用估算 — 端点功能

对象 描述
请求体 必需 Json
country_name 必需 Name of the country for which the emission is being measured.
electricity_unit 必需 Electricity unit of consumption. Allowed values: [KWh, MWh]. Default: MWh
electricity_value 必需 Number of units of electricity consumed. If an undefined value is passed, it will be set to 1.00
cluster_name 可选 Identifier to log and aggregate results under Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

country_name
electricity_unit
electricity_value
cluster_name
API 示例响应
JSON
{
    "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
}
电力使用估算 — 代码片段

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
}'

    
请求
# 燃料消耗排放 返回基于使用情况、燃料名称及其吨位价值的估计温室气体排放(CO2e),以多个单位(克、千克、公吨、磅)表示 CarbonSutra 计算固定单位或资产中燃烧的静态燃料的排放,这些单位或资产由报告组织拥有或控制,通常作为范围 1 直接排放报告 数据来源和排放因子计算方法来自英国政府的 2026 年温室气体转换因子 operationId: calculate_emissions_from_fuel_consumption
Endpoint ID: 31347
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31347/fuel+combustion+estimates
输入参数

燃料燃烧估算 — 端点功能

对象 描述
请求体 必需 Json
fuel_usage 必需 Fuel usage or activity. Allowed: ["Gas", "Liquid", "Solid"]
fuel_name 必需 The fuel name is based on value of fuel usage field. Only the following values are allowed: - For fuel_usage = ""gas"", the allowed values for fuel_name are: [Butane, CNG, LNG, LPG, Natural gas, Natural gas (100% mineral blend), Other petroleum gas, Propane]- For fuel_usage = ""liquid"", the allowed values for fuel_name are: [Aviation spirit, Aviation turbine fuel, Burning oil, Diesel (100% mineral diesel), Diesel (average biofuel blend), Fuel oil, Gas oil, Lubricants, Marine fuel oil, Marine gas oil, Naphtha, Petrol (100% mineral petrol), Petrol (average biofuel blend), Processed fuel oils - distillate oil, Processed fuel oils - residual oil, Refinery miscellaneous, Waste oils]- For fuel_usage = ""solid"", the allowed values for fuel_name are: [Coal (domestic), Coal (electricity generation - home produced coal only), Coal (electricity generation), Coal (industrial), Coking coal, Petroleum coke]
fuel_value 必需 The amount of fuel that is consumed in number of tonnes. Default is 1.00
cluster_name 可选 Identifier to log and aggregate results under Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

fuel_usage
fuel_name
fuel_value
cluster_name
API 示例响应
JSON
{
    "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
}
燃料燃烧估算 — 代码片段

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
}'

    
请求
# 货运的排放 返回通过公路、铁路、航空和海运(分为短途海运和深海运)的货物运输的估计温室气体排放(CO2e),单位包括克、千克、吨和磅。 还有两种额外的多式联运计算可供选择:1)公路与铁路 2)公路与短途海运。在这两种情况下,公路距离被假定为整个行程的10%。 operationId: calculate_emissions_from_shipment_of_freight
Endpoint ID: 31348
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31348/freight+shipping+estimates
输入参数

货运运输估算 — 端点功能

对象 描述
请求体 必需 Json
transport_mode 必需 The mode of shipment of freight. Deep sea (value: DeepSea) shipping refers to the maritime transport of goods on intercontinental routes, crossing oceans as opposed to short sea (value: ShortSea) shipping over relatively short distances.​ For Intermodal shipping of 1) Road with Rail and 2) Road with Short Sea, the road distance is assumed to be at 10% of the entire journey.
freight_weight 必需 Weight of freight in kilograms. If a NULL or <=0 is entered, it is set to 1.00
distance_value 必需 Distance of journey in kilometers (KM). If a NULL or <=0 is entered, it is set to 1.00
cluster_name 可选 Identifier to log and aggregate results under the Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

transport_mode
freight_weight
distance_value
cluster_name
API 示例响应
JSON
{
    "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
}
货运运输估算 — 代码片段

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
}'

    
请求
# 电子商务货件的排放 返回估计的温室气体排放(CO2e),以多种单位(克、千克、公吨、磅)表示电子商务货件的排放 这是一个先进的算法,用于估算包裹从发货地点到收集点的旅程排放。如果两个邮政编码之间的距离超过阈值,我们会找到发货地点和目的地最近的机场。最后的旅程距离是发货地点邮政编码与最近机场之间的陆地距离、最近机场之间的航空旅行和目的地机场与收集点邮政编码之间的陆地旅行的总和。最终的计算遵循ECTA/Cefic和BEIS/DEFRA推荐的转换因子,并基于覆盖95个国家和9000个全球机场的超过150万个邮政编码的数据库进行工作 operationId: calculate_emissions_from_ecommerce_shipment
Endpoint ID: 31349
POST https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31349/ecommerce+shipment+estimates
输入参数

电子商务运输估算 — 端点功能

对象 描述
请求体 必需 Json
origin_country_code 必需 Two letter ISO country code of the origin, ie from where the package is being sent
origin_postal_code 必需 The postal code within the origin country from where the package is being shipped
destination_country_code 必需 Two letter ISO country code of the destination, ie to where the package is being sent
destination_postal_code 必需 The postal code within the destination country to where the package is being shipped
package_weight 必需 Weight of package in kilograms. If a NULL or <=0 is entered, it is set to 1.00
add_rf 可选 The default value of "Y" adds the radiative forcing multiplier factor in the footprint calculation; "N" bypasses it
include_wtt 可选 The default value of "Y" includes the Well-to-Tank (WTT) factors while "N" bypasses it
cluster_name 可选 Identifier to log and aggregate results under the Cluster Data API

剩余免费测试请求:3 / 3。


输入参数

origin_country_code
origin_postal_code
destination_country_code
destination_postal_code
package_weight
add_rf
include_wtt
cluster_name
API 示例响应
JSON
{
    "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
}
电子商务运输估算 — 代码片段

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"
}'

    
请求
# 从机场到最近机场 返回给定机场代码最近机场的距离(以公里为单位),以及名称和IATA代码,使用Haversine函数 operationId: find_nearest_airport_from_another_airport
Endpoint ID: 31350
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31350/airport+to+nearest+airport
输入参数

机场到最近的机场 — 端点功能

对象 描述
iata_airport_code 必需 An IATA airport code is unique 3 digit geocode (also known as IATA location identifier, IATA station code, or location identifier)
same_country 必需 A value of "Y" will result in closest airport with the same country of IATA Code, else the country borders will be ignored and the nearest airport will be found.

剩余免费测试请求:3 / 3。


输入参数

iata_airport_code
same_country
API 示例响应
JSON
{
    "data": {
        "airport_from": "San Francisco International Airport",
        "closest_airport_code": "SQL",
        "closest_airport_name": "San Carlos Airport",
        "distance": "16.13"
    },
    "success": true,
    "status": 200
}
机场到最近的机场 — 代码片段

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' 


    
请求
邮政编码到最近机场 返回邮政编码与其最近机场之间的距离,单位为公里,使用哈弗辛公式。 这是一个高级算法,输入邮政编码和国家代码,返回最近机场的纬度/经度值。邮政编码及其机场坐标之间的距离使用哈弗辛公式测量,返回值以公里为单位。数据中存在多个地址的实例,这些地址与同一邮政编码关联但具有不同的纬度/经度值。在这种情况下,将返回所有地址和它们与最近机场之间距离的数组。150万个邮政编码数据的来源取自https://public.opendatasoft.com/explore/dataset/geonames-postal-code/。对于机场,使用公开可用的IATA代码和机场名称数据库。 操作ID:find_nearest_airport_from_postal_code
Endpoint ID: 31351
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31351/postal+code+to+nearest+airport
输入参数

邮政编码到最近机场 — 端点功能

对象 描述
country_code 必需 Two digit ISO 3166-1 alpha-2 country code.
postal_code 必需 A valid postal code of a location

剩余免费测试请求:3 / 3。


输入参数

country_code
postal_code
API 示例响应
JSON
{
    "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
}
邮政编码到最近机场 — 代码片段

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' 


    
请求
# 两个机场之间的距离 返回两个IATA机场代码之间的距离,单位为公里,使用Haversine函数 operationId: find_distance_between_two_airports
Endpoint ID: 31352
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31352/distance+between+two+airports
输入参数

两个机场之间的距离 — 端点功能

对象 描述
iata_airport_from 必需 The first (or from) IATA airport code is unique 3 digit geocode (also known as IATA location identifier, IATA station code or location identifier)
iata_airport_to 必需 The second (or to) IATA airport code is unique 3 digit geocode (also known as IATA location identifier, IATA station code or location identifier)

剩余免费测试请求:3 / 3。


输入参数

iata_airport_from
iata_airport_to
API 示例响应
JSON
{
    "data": {
        "airport_from": "Ninoy Aquino International Airport",
        "airport_to": "Tokyo Haneda International Airport",
        "distance": 2996.08
    },
    "success": true,
    "status": 200
}
两个机场之间的距离 — 代码片段

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' 


    
请求
# 机场关键词搜索 返回与关键词匹配的机场名称和机场IATA代码列表。算法会搜索机场名称、IATA代码、州名称以及其他来源的信息 operationId:基于关键词搜索机场
Endpoint ID: 31353
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31353/airports+keyword+search
输入参数

机场关键词搜索 — 端点功能

对象 描述
keyword 必需 A keyword to search for the airport records

剩余免费测试请求:3 / 3。


输入参数

keyword
API 示例响应
JSON
{
    "data": [
        {
            "iata_code": "DEL",
            "airport_name": "Indira Gandhi International Airport"
        }
    ],
    "success": true,
    "status": 200
}
机场关键词搜索 — 代码片段

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' 


    
请求
## 所有汽车品牌的列表 返回所有汽车制造商及其车型数量的列表,可用于获取车型列表并估算足迹 该API主要供应用程序开发人员使用
Endpoint ID: 31354
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31354/vehicle+makes
输入参数

剩余免费测试请求:3 / 3。

此端点不需要任何输入参数。

API 示例响应
JSON
{
    "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"
}
车辆制造商 — 代码片段

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' 


    
请求
## 特定汽车制造商所有车型的列表
Endpoint ID: 31355
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31355/vehicle+models
输入参数

车辆型号 — 端点功能

对象 描述
请求体 必需 Json
vehicle_make 必需

剩余免费测试请求:3 / 3。


输入参数

API 示例响应
JSON
{
    "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
}
车辆型号 — 代码片段

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 '{}'

    
请求
# 机场之间的预计飞行时间 返回两个机场之间通过飞行的预计旅行时间 operationId: estimate_flight_time_between_airports
Endpoint ID: 31356
GET https://zylalabs.com/api/13720/cloud+based+carbonsutra+carbon+emission+estimation+api/31356/estimated+flight+time+between+airports
输入参数

机场之间的预计飞行时间 — 端点功能

对象 描述
iata_airport_from 必需 The departing (or from) IATA airport code is unique 3 digit geocode (also known as IATA location identifier or IATA station code)
iata_airport_to 必需 The landing (or to) IATA airport code is unique 3 digit geocode (also known as IATA location identifier or IATA station code)

剩余免费测试请求:3 / 3。


输入参数

iata_airport_from
iata_airport_to
API 示例响应
JSON
{
    "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
}
机场之间的预计飞行时间 — 代码片段

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' 


    

API 访问密钥和身份验证

注册后,每个开发者都会被分配一个个人 API 访问密钥,这是一个唯一的字母和数字组合,用于访问我们的 API 端点。要使用 Cloud Based CarbonSutra Carbon Emission Estimation API 进行身份验证,只需在 Authorization 标头中包含您的 bearer token。

标头
标头 描述
授权 必需 应为 Bearer access_key. 订阅后,请查看上方的"您的 API 访问密钥"。

无长期承诺。随时升级、降级或取消。 免费试用包括最多 50 个请求。

(年度计费可节省 2 个月 🎉)

🚀 企业版套餐

起价
$ 10,000/年


  • 自定义数量
  • 自定义速率限制
  • 专业客户支持
  • 实时 API 监控

概览

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.

Cloud Based CarbonSutra Carbon Emission Estimation API FAQs

每个端点根据特定参数如航班详情、酒店住宿、车辆类型和电力使用返回多种单位(克、千克、公吨、磅)的估计温室气体排放(CO2e)例如航班旅行估算端点基于机场代码、航班等级和乘客人数提供排放量

响应数据中的关键字段包括“co2e_gm”、“co2e_kg”、“co2e_mt”和“co2e_lb”,代表不同单位的排放量。其他字段可能包括细节,如“airport_from”、“airport_to”、“hotel_rating”和“vehicle_type”,具体取决于使用的端点

参数因端点而异,但通常包括诸如“机场代码”、“航班等级”、“乘客人数”、“国家名称”、“燃料类型”和“距离值”的输入。例如,酒店住宿估算端点要求“国家”、“住宿天数”和“酒店评分”

响应数据呈现为JSON格式,其中“data”对象包含与排放估算相关的特定属性 每种估算类型都有其独特的结构,详细说明了所使用的参数和计算出的排放量,便于解析和利用

数据来源包括政府出版物和标准,例如英国政府的温室气体转换因子、康奈尔酒店可持续性基准指数,以及各种环保机构。这确保了排放计算基于可靠和最新的信息

典型的用例包括企业可持续发展报告、遵守环境法规以及分析旅行、住宿和物流的碳足迹。组织可以利用这些数据识别减排领域并改善其可持续发展策略

用户可以通过指定与其需求相关的参数来自定义请求,例如选择不同的车辆品牌/型号、调整航班等级或选择特定国家的酒店住宿。这种灵活性允许根据独特的组织活动进行量身定制的排放计算

用户可以通过将返回的数据集成到可持续性仪表板、合规报告或决策工具中,利用这些数据进行报告和分析。结构化格式便于提取关键指标,使组织能够有效跟踪和管理其碳排放

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