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srs_ghg_us_taxiii by Spatial Risk Systems

Dataset Name: srs_ghg_us_taxiii

Group: altdata
Vendor: Spatial Risk Systems
Data Starts at: 2010-01-01 00:00:00
Symbol Set: US Equities
Asset Class: Equity, ADRs, ETFs,Fixed Income,Options,FX,Futures,Crypto,Commodity,Options on Futures
Data Update Frequency: week

Green House Gas

Scope I Green House Gas (GHG) Emissions - 10-Year History (Facility, Ultimate Parent, Sector, etc..)

  • srs_ghg_us_county
  • srs_ghg_us_fac
  • srs_ghg_us_state
  • srs_ghg_us_taxii - Taxonomy Level 2
  • srs_ghg_us_taxiii - Taxonomy Level 3
  • srs_ghg_us_ult_parent

GHG (Green House Gas) datasets are generated by leveraging Facility Level GHG Emissions and aggregating up.

There are 6,500 scope I emitters (Facilities that directly emit GHG emissions( that roll up to 1900 Ultimate Parents)

Investable Universe:

US Municipal Finance ESG/Sustainable Investing Corporate Fixed Income/Equities Insurance/Reinsuracne Real Estate/Property Development

Dataset Asset Classes:

Equities - Stocks, ADRs, ETFs etc;Fixed Income

Data Update Frequency:


Date Range

Sample datasets are for one day 2022/10/04

Full Datasets go back to 2010/01/01

About Spatial Risk Systems

SRS is an innovative data and analytics company focused on building a playing field level data base enabling institutions to accurately assess risks and opportunities at the underlying asset locations of their investment, transaction, and operating activities.

Founded by data science leaders from the financial sector, SRS quantifies risk by unifying, standardizing, and analyzing empirical data sources, helping investors to better understand ESG and sustainable investing outcomes, from a facility to a large-scale geographic perspective.

Spatial Risk Systems created and operates a massive cloud-based data management infrastructure hosting hundreds of complex data sets with billions of interconnected data records.

The technical architecture, operations and information governance policies of the SRS data management platform have been established by the team of seasoned industry professionals with combined 100 years of experience designing and operating commercial data products.

Particular attention has been given to ensure content integrity by applying the stringent system of quality controls on each of the steps of data collection, ingest and transformation. The SRS team has implemented sound operational resilience, data access and security practices to ensure integrity and effective performance.

Spatial Risk Systems (SRS) has engineered a massive cloud-based data network connecting and standardizing fact-based spatial-level data sources into a single publishing solution.

Spatial Risk Scores measures and quantifies hundreds of location-specific factors that can have a long-term effect on asset value, environmental impact, operational effectiveness, and social sustainability.

10 Different Spatial Layers:

  • Census Tract
  • Postal Code
  • City
  • School District
  • County
  • Congressional District
  • State
  • Core-Based Statistical Areas (CBSA)
  • Corporate
  • Municipal Revenue Authorities

Five Major Data Dimensions:

1) Climate

  • 18 Natural Disaster Risks
  • Expected Annual Losses (EAL)
  • 1.5 million+ Weather Events and Impacts

2) Environmental

  • Scope I Carbon Emissions
  • Toxic Releases
  • Air and Water Quality Measures

3) Socio-Economic

  • Community Vulnerability-Related Factors
  • Community Resiliency-Related Factors

4) Carbon Emissions/Accounting

  • Scope I
  • Scope II
  • Social Costs of Carbon

5) Facility Location, Function, and Ownership

Scope 1 - direct emissions

Scope 1 (direct) emissions are an immediate product of entity activities such as energy generated by burning fossil or other organic fuels, fuels used for transportation, cement production, etc.

Scope 2 - indirect emissions

Scope 2 emissions (indirect) result from energy consumption, heating and cooling, food preparation, and other needs.

Data Contained in this Dataset

Column Type Description
_seq uint Internal sequence number used to keep data rows in order
timestamp string Timestamp of the Data (underlying field is nc_publish_date_actual)
muts uint64 Microseconds Unix Timestamp. An integer representation of a timestamp with microsecond precision that can be compared directly to other timestamps. (underlying field is nc_publish_date_actual)
symbol string Trading Symbol or Ticker
Taxonomy Level III string Taxonomy Level III
Facility Count int64 Facility Count
Average Scope I + Scope II Cost USD per 1000 People double Average Scope I + Scope II Cost USD per 1000 People
Average Climate EAL USD per 1000 People double Average Climate EAL USD per 1000 People
2011 GHG Metric Tons double 2011 GHG Metric Tons
2012 GHG Metric Tons double 2012 GHG Metric Tons
2013 GHG Metric Tons double 2013 GHG Metric Tons
2014 GHG Metric Tons double 2014 GHG Metric Tons
2015 GHG Metric Tons double 2015 GHG Metric Tons
2016 GHG Metric Tons double 2016 GHG Metric Tons
2017 GHG Metric Tons double 2017 GHG Metric Tons
2018 GHG Metric Tons double 2018 GHG Metric Tons
2019 GHG Metric Tons double 2019 GHG Metric Tons
2020 GHG Metric Tons double 2020 GHG Metric Tons

Important Dataset Notes

This dataset is not available for direct online purchase. Please contact sales directly at The data is available through our normal sales department who can provide you with current pricing and a quote for accessing this valuable dataset. This may be due to a number of reasons such as dataset intended use, size of the company (or investment fund) using the dataset, or for simple legal requirements that CloudQuant needs to ensure are in place prior to licensing the dataset to you.