Financial Impact Metrics - Location Level Scoring
Calculate financial impact scores for facilities
Overview
Climate on Demand financial impact modeling enables organizations to evaluate the potential financial impact of catastrophe events on their facilities. Facilities may be modeled individually (one or more at a time) or as part of a portfolio (a collection of facilities). This endpoint returns financial impact metrics at the facility level for all the facilities specified in a request.
Financial modeling is supported across seven risk categories. including floods, heat stress, hurricanes & typhoons, sea level rise, water stress, wildfires, and earthquakes. Climate on Demand models project changes in the future severity or frequency of catastrophe events for each risk category due to climate change.
For each risk category, this Climate on Demand enables you to calculate financial impact metrics that measure the projected cost of damages to a facility or group of facilities. These projections are based on the stated value of the facilities analyzed, the time period analyzed, the assumptions made about future climate change, and other configurable factors.
Climate on Demand Pro Licensing
This tutorial describes a financial impact modeling process that leverages operations that are are only available to tenants who have licensed Climate on Demand Pro.
In this tutorial, we will review the process for impact scoring a single facility. We will review key request parameters to understand how these parameters determine the calculated impact scores. Finally, we will review the impact scores returned. This process is described using sample code in cURL and Python.
Metrics include Average Annual Damages (AAD), Annualized Damage Rates (ADR), Standard Deviations in ADR (damage volatility), and Impact Scores that contextualize estimated damages globally.
Step 1: Calculate financial impact
The Calculate financial impact by facility operation calculates the financial impact of climate change on one or more facilities. In this example, we will calculate impact scores for a single facility.
The request body defines an object with three required parameters: facilities, an array of objects that defines the assets analyzed, th (the time horizon analyzed), and m, which specifies the version of the Financial Impact Methodology used to calculate the impact scores.
curl --request POST \
--url https://{host}/AppsServices/api/v1/score-facilities-impact/jobs \
--header 'accept: application/json' \
--header 'content-type: application/json' \
--data '
{
"m": "P.2023.1",
"th": "2050",
"rcp": "RCP8.5",
"facilities": [
{
"id": "1",
"name": "newark office",
"activity": "office",
"street1": "7575 gateway blvd., suite 300",
"city": "newark",
"state": "ca",
"postal_code": "94560",
"country": "united states",
"latitude": 37.5412000000,
"longitude": -122.060610000
}
]
}
'
def scoreFacilitiesImpact(token, request_json, host):
endpoint = host + "/cod/AppsServices/api/v1/score-facilities-impact/jobs"
r = requests.post(url=endpoint, json=request_json, headers={'Authorization':'Bearer ' + token})
print(f'response code: {r.status_code}') if r.status_code == requests.codes.ok:
result = r.json()
return result
else:
return None
def getJobResult(token, host, job_id):
endpoint = host + "/cod/AppsServices/api/jobs/" + job_id
r = requests.get(url=endpoint, headers={'Authorization': 'Bearer ' + token})
print(f'response code: {r.status_code}')
if r.status_code == requests.codes.ok:
result = r.json()
return result
else:
return None
data = {
"m": "P.2023.1",
"th": "2050",
"rcp": "RCP8.5",
"facilities": [{
"id": "1",
"name": "Newark Office",
"activity": "Office",
"exposure_value": 1000000,
"street1": "7575 gateway blvd., suite 300",
"city": "newark",
"state": "CA",
"postal_code": "94560",
"country": "USA",
"latitude": 37.5412000000,
"longitude": -122.060610000
}]
}
response_job = scoreFacilitiesImpact(access_token, data, my_host)
my_job = response_job.get('job_id')
time.sleep(30)
result = getJobResult(access_token, my_host, str(my_job))
For each facility, you must specify the ID number of the facility (id) and how that facility is used (activity), e.g. Agriculture, Data Center, Residential.
If the latitude and longitude of the facility are not provided, Climate on Demand Pro attempts to automatically geocode the address.
If successful, initiates a workflow job and returns a 200 response includes the job ID and job status (IN PROGRESS, FINISHED, FAILED):
{
"job_id": 1676
}
Once the job is FINISHED, you can retrieve financial impact scores for the specified facilities.
Step 2: Get impact scoring results
The Get results by job operation enables you to retrieve hazard scores or financial impact scores calculated by a Climate on Demand modeling job. In this step, we specify the jobIdthat the Climate on Demand API returned Step 1.
curl --request GET \
--url https://[host]/AppsServices/api/jobs/jobId \
--header 'Authorization: XXXXXXXXXX' \
--header 'accept: application/json'
def scoreFacilitiesImpact(token, request_json, host):
endpoint = host + "/cod/AppsServices/api/v1/score-facilities-impact/jobs"
r = requests.post(url=endpoint, json=request_json, headers={'Authorization':'Bearer ' + token})
print(f'response code: {r.status_code}') if r.status_code == requests.codes.ok:
result = r.json()
return result
else:
return None
def getJobResult(token, host, job_id):
endpoint = host + "/cod/AppsServices/api/jobs/" + job_id
r = requests.get(url=endpoint, headers={'Authorization': 'Bearer ' + token})
print(f'response code: {r.status_code}')
if r.status_code == requests.codes.ok:
result = r.json()
return result
else:
return None
data = {
"m": "P.2023.1",
"th": "2050",
"rcp": "RCP8.5",
"facilities": [{
"id": "1",
"name": "Newark Office",
"activity": "Office",
"exposure_value": 1000000,
"street1": "2000 Hearst Avenue",
"city": "Newark",
"state": "CA",
"postal_code": "94560",
"country": "USA",
"latitude": 37.5412000000,
"longitude": -122.060610000
}]
}
response_job = scoreFacilitiesImpact(access_token, data, my_host)
my_job = response_job.get('job_id')
time.sleep(30)
result = getJobResult(access_token, my_host, str(my_job))
If the specified job is finished, the Climate on Demand API returns a response that includes the impact scores. The financial impact modeling job that we initiated in Step 1 calculates hazard scores at our facility for each risk category.
{
"job_id": 6189,
"user_id": 639,
"status": "FINISHED",
"percent_complete": 100,
"output": [
{
"id": "1",
"m": "P.2023.1",
"latitude": 37.5412000000,
"longitude": -122.060610000,
"geo_status": "User",
"exposure_value": 1000000,
"line_of_business": "COM",
"rcp": "RCP8.5",
"TH": "2050",
"risk_categories": {
"all categories": {
"adr_std_dev": 0.038314,
"annualized_damage_rate": 0.0032593,
"average_annual_damage": 3259.306,
"impact_score": 97
},
"Floods": {
"adr_std_dev": 0.0
"annualized_damage_rate": 0.0,
"average_annual_damage": 0.0,
"impact_score": 0
},
"Heat Stress": {
"adr_std_dev": 0.0000735,
"annualized_damage_rate": 0.0000696,
"average_annual_damage": 69.586,
"impact_score": 51
},
"Hurricanes & Typhoons": {
"adr_std_dev": 0.0,
"annualized_damage_rate": 0.0,
"average_annual_damage": 0.0,
"impact_score": 0
},
"Sea Level Rise": {
"adr_std_dev": 0.0,
"annualized_damage_rate": 0.0,
"average_annual_damage": 0.0,
"impact_score": 0
},
"Water Stress": {
"adr_std_dev": 0.0,
"annualized_damage_rate": 0.0,
"average_annual_damage": 0.0,
"impact_score": 0
},
"Wildfires": {
"adr_std_dev": 0.0021415,
"annualized_damage_rate": 0.000056,
"average_annual_damage": 55.98,
"impact_score": 77
},
"Earthquakes": {
"adr_std_dev": 0.038254,
"annualized_damage_rate": 0.0031337,
"average_annual_damage": 3133.74,
"impact_score": 100
}
}
}
]
}
The response includes the annualized damage rate (ADR), ADR standard deviation, average annual damage, and a financial score for each risk category. Financial impact scores vary depending on the projected frequency and severity of future catastrophe events due to climate change.
| Attribute | Description |
|---|---|
annualized_damage_rate | The annualized damage rate (ADR) projects the expected damage per year, as a ratio of the total value. ADR enables comparisons between facilities and portfolios on a normalized basis, as it does not depend on the value of the facilities, but still distinguishes between locations based on hazard and vulnerability. |
adr_std_dev | The ADR standard deviation is a measure of volatility in the annualized damage rate (ADR) each year and varies by peril and by location. A facility or a portfolio with a higher standard deviation is riskier than one with a low standard deviation, even if on average the expected damage (the ADR) is the same, with the potential for bigger extreme losses and large volatility year-to-year. |
average_annual_damage | The AAD is the expected damage in dollars, or other currency, per year, on average, over time and depends on the value of the facilities as well as the hazard and the vulnerability. It is the product of the ADR * value. This metric is provided only when Climate on Demand Pro users provide an exposure value. |
impact_score | The risk at any location or for a portfolio is defined as the combination of the ADR and standard deviation at each location or for a whole portfolio, accounting for both the expected damage and the volatility. Climate on Demand Pro provides an impact score on a scale from 0–100 to contextualize the risk metrics, enabling quick comparisons between facilities and portfolios, and banding of locations into different risk zones. |
For details on Climate On Demand impact scoring, see Real Assets Physical Climate Risk Methodology: Climate on Demand Methodology Version 2023.3 in the Moody's RMS Support Center.
Climate on Demand also supports the financial impact modeling of entire portfolios of facilities. For step-by-step instructions, see Porfolio Financial Impact Scoring.
Updated 8 months ago