Breaking the ESG rating divergence: An open geospatial framework for environmental scores

Area of Science:

• Environmental Science
• Finance
• Geospatial Analysis

Background:

• Financial institutions increasingly rely on Environmental, Social, and Governance (ESG) performance data for decision-making.
• Current ESG ratings face challenges due to data inconsistencies and lack of standardized methodologies.
• Geospatial data offers potential for improved accuracy, consistency, and asset-level assessment in ESG evaluations.

Purpose of the Study:

• To explore the integration of geospatial data into ESG ratings for a more robust and standardized assessment.
• To propose a geospatial environmental scoring framework for evaluating company ESG performance.
• To address the disparities in current ESG rating methodologies.

Main Methods:

• Developing a geospatial environmental scoring framework categorizing impacts into localized and delocalized effects.
• Utilizing open-source geospatial data for broad geographic coverage and asset-level analysis.
• Defining sub-scores for land use, biodiversity, soils, hydrology, atmospheric emissions, and global climate impacts.

Main Results:

• The proposed framework allows for the incorporation of geospatial data into ESG analysis, improving rating accuracy and consistency.
• The framework categorizes environmental impacts, enabling a more granular assessment of ESG risks and opportunities.
• A test case demonstrated the feasibility of generating E-scores using geospatial data.

Conclusions:

• Geospatial data and analysis offer a promising solution to enhance the reliability and standardization of ESG ratings.
• The developed framework provides a scalable and generalizable approach for incorporating spatial intelligence into ESG assessments.
• This methodology supports more informed and precise decision-making for financial institutions regarding ESG performance.