Scope 3 Financed Emissions Databases


MAISY Emissions APIs and Databases Provide Scope 3 Financed Emissions Estimates for Residential Mortgages and Commercial Real Estate Loans

API ENERGY USE AND EMISSIONS OPTION:

The MAISY nearest-neighbor machine learning process provides energy use, costs and emissions estimates for one or even millions of individual US residential or commercial utility customers or customer segments.

These applications use a variable number of household/dwelling unit or business characteristics (depending on available customer input data) in either a batch or an api application to estimate energy use and emissions and other data for any dwelling unit/commercial business in the US. These applications apply a k-nearest neighbor (KNN) analysis that matches client residential or household records to the most closely matching records in the MAISY Databases and along with regression refinements applies the matching ACTUAL MAISY customer energy use to client records. Dwelling units or businesses from MAISY master databases are identified as belonging in the user’s “neighborhood” with a weighted distance measure that includes household/dwelling unit or business characteristics. Additional statistical regression analysis of energy use and emissions from the nearest neighbors provides estimates of the energy use and emissions. The master databases includes energy use and emissions for more than 7 million households and businesses across the US.

ZIP-LEVEL ENERGY USE AND EMISSIONS DATA OPTIONS:

Option 1: Send us a list of your customer's ZIP codes and we will send you average Scope 3 Emissions estimates for mortgage-holding, owner-occupied dwelling units and commercial real estate buildings in each ZIP code. Emissions data include eight emissions categories (see below).

Option 2: Send us the number of residential and commercial real estate loan holders in each ZIP code. If available, include the number of customers within important segment categories (e.g., income, commercial building type) in each ZIP. If available, include the ratio of average outstanding loan balances to the property value at the time of loan origination for residential and commercial loan holders in each ZIP code and within each segment category (see the note on attribution factor in the methodology summary section below). We will send you average Scope 3 Emissions estimates for mortgage-holding, owner-occupied, single family/duplex dwelling units and commercial real estate buildings in each ZIP code and in each customer segment. We will send you a database of detailed GHG financed emissions for each ZIP and customer segment and for the entire financial institution along with an emissions intensity statistic that adjusts for changes in year-to-year customer population characteristics with an indexing methodology used by the US Department of Commerce.

Option 3: Send us a list of your individual mortgagors and commercial real estate customers (excluding customer data that would identify individual customers) with ZIP codes, and other loan information (e.g., income, household members, homeowner ages, etc.) that we will use to segment your customer population. If available, include the ratio of total outstanding loan balance to the property value at the time of loan origination for each customer. (see the note on attribution factor in the methodology summary section below). This option uses an artificial intelligence K nearest neighbor (KNN) machine learning process along with regression analysis and the MAISY ZIP Level Utility Customer Energy Use Databases to match each individual financial institution customer to its closest Database "neighbors" to estimate energy use and emissions. We will send you a database of detailed GHG financed emissions for each ZIP and customer segment and for the entire financial institution along with an emissions intensity statistic that adjusts for year-to-year changes in customer population characteristics with an indexing methodology used by the US Department of Commerce.


Data Items: ZIP Code Scope Financed Emissions Databases
(Data provided for each ZIP code)

VARIABLE GROUPING DATA ITEMS
LOCATION INFORMATION ZIP Code
ZIP Name
County & State
Metro Area
Emissions Data Items Calculated for Each ZIP Code
MORTGAGE EMISSIONS DATA Total Annual Emissions (CO2e) (including emissions generated by household electricity,
natural gas, fuel oil and propane use)
CO2 Emissions
CH4 Emissions
N2O Emissions
HFCs Emissions
PFCs Emissions
SF6 Emissions
NF3 Emissions
CO2e Emissions
GHG Intensity Data (see the whitepaper Scope 3 Financed Emissions Reporting: How Good Intentions Can Lead to Bad Outcomes )
COMMERCIAL REAL ESTATE LOAN EMISSIONS DATA Total Annual Emissions (CO2e) (including emissions generated by commercial firm electricity,
natural gas, fuel oil and propane use)
CO2 Emissions
CH4 Emissions
N2O Emissions
HFCs Emissions
PFCs Emissions
SF6 Emissions
NF3 Emissions
GHG Intensity Data
CO2e Emissions

Data Items: Consolidated Financed Emissions Data (Option 2 or 3 Only)
(Sum of all ZIP-level data )


VARIABLE GROUPING DATA ITEMS
  Total Number of Morrtgages
Total Number of Commercial Real Estate Loans
Total Financial Firm Emissions Data
MORTGAGE EMISSIONS DATA Total Annual Emissions (CO2e) (including emissions generated by household electricity,
natural gas, fuel oil and propane use)
CO2 Emissions
CH4 Emissions
N2O Emissions
HFCs Emissions
PFCs Emissions
SF6 Emissions
NF3 Emissions
GHG Intensity Data (see the whitepaper Scope 3 Financed Emissions Reporting: How Good Intentions Can Lead to Bad Outcomes )
COMMERCIAL REAL ESTATE LOAN EMISSIONS DATA Total Annual Emissions (CO2e) (including emissions generated by commercial firm electricity,
natural gas, fuel oil and propane use)
CO2 Emissions
CH4 Emissions
N2O Emissions
HFCs Emissions
PFCs Emissions
SF6 Emissions
NF3 Emissions
GHG Intensity Data


Methodology Summary

Option 1: This option applies MAISY ZIP Level Utility Customer Energy Use Database detail on electricity, natural gas, fuel oil and propane in each ZIP code area to calculate detailed Scope 3 Financed Emissions data for that ZIP code for the average mortgagor and comercial real estate customer. This ZIP level data is provided for each ZIP code area submitted by the financial institution.

Option 2: MAISY Scope 3 mortgage emissions estimates are developed by applying the MAISY ZIP Code Utility Customer Energy Use Database by multiplying (1) the number of residential mortgages in each ZIP code by (2) average mortgage-holding single family/duplex electricity kWh, fuel oil, natural gas and propane use (by customer segment if available), and by (3) fuel-specific EPA emissions factors for that ZIP code. The same process is applied for commercial real estate loan customers. A Consolidated Financed Emissions Data provides the sum of all ZIP-Level emissions. An emission intensity is also provided for the entire financial institution that applies a methodology drawn from economic price index theory which adjusts for changes in customer geographic, household and commercial firm characteristics and weather to minimize reported emissions biases and an applies-to-apples year-to-year comparison of total CO2e emissions.

Option 3: Option 3 applies information from your individual mortgagors and commercial real estate customers (excluding customer data that would identify individual customers) with ZIP codes, and other loan information (e.g., income, household members, homeowner ages, etc.) that we will use to segment your customer population for more accurate emissions calculations. If available, include the ratio of total outstanding loan balance to the property value at the time of loan origination for each customer. (see the note on attribution factor below).

Option 3 uses an artificial intelligence K nearest neighbor (KNN) machine learning process along with regressions analysis and the MAISY ZIP Level Utility Customer Energy Use Databases to match each individual financial institution customer to it's closest Database "neighbors" to estimate energy use and emissions. This option provides the most accurate and unbiased ZIP level, enterprise and emissions intensity calculations available.

Attribution Factor:

ZIP code-detail is crucial for accurate accounting of financed emissions as there is significant variation in ZIP-average energy use and resulting emissions. For financial institutions that have mortgage investments across even sub-state regions, variations in emissions factors resulting from variations in electric generation fuels use, can also be significant.

The "gold-standard" PCAF scope 3 financed emissions methodology recognized by the SEC as an acceptable emissions calculations methodology requires multiplying these emissions totals by an attribution factor, which is equal to the ratio of the outstanding mortgage balance at the time of GHG accounting to the property value at loan origination. If the financial client provides this data for individual mortgagors (option 3) or for the ZIP code and/or for customer segments in each ZIP code (option 2 or 3), these attribution factors are applied in our analysis to provide emissions intensities that are used for year-to-year evaluations. We can also report emissions data without adjustment for attribution factors that financial firms can apply inhouse, if desired.

By way of background, the proposed rule requires emissions reporting in terms of GHG intensity to provide “context to a registrant’s emission in relation to its business scale.” The rule emphasizes the importance of being able to compare the value of the GHG intensity over time to assess the extent to which a financial institution is meeting its goals. The problem with this approach for financed emissions is that (1) year-to-year weather variations, (2) geographic variations in emissions associated with new mortgage originations (as much as a factor of 4 or more) and (3) variations in new mortgage originations floor space (i.e., size of dwelling units) and other customer characteristics can create a picture of increasing SCOPE 3 GHG emissions intensity even though a GHG intensity adjusted for these factors would show a reduction in emissions. See a recent whitepaper titled Scope 3 Financed Emissions Reporting: How Good Intentions Can Lead to Bad Outcomes which describes this issue in more detail and provides several procedures compatible with the proposed rule and based on long-standing economic index theories and practices.

The paper offers a new price-index based methodology drawn from economic price index theory that adjusts for year-to-year changes in customer geographic and household characteristics to minimize biases in reported emissions statistics. This methodology is used by the US Department of Commerce in price index calculations and is a well-accepted approach to account for the kind of computational complexity inherent in the emissions intensity statistic.

Finally, MAISY Scope 3 Database results will be modified to be consistent with final SEC rules that are expected to be announced as early as May, 2023.

Other MAISY Financed Emissions Accounting Data and Services Topics