Abstract

ESG (Environment, Social, Governance) has become a key element in the European financial and real economy landscape, particularly since the introduction of the EU’s Sustainable Finance Action Plan (2018). It assesses economic activities based on sustainable criteria and serves to examine their impact on the environment and society, as well as the interrelated financial risks. However, the implementation of ESG frameworks faces significant challenges—such as the selection of appropriate sustainability criteria, a lack of standardization, and limited data availability and quality—which increase the risk of greenwashing. Additionally, there is a growing need for ESG assessments to be efficient, objective, and ideally automated. Geospatial technologies can partially address these challenges by overcoming current data limitations as well as supporting the automation and objectivity of ESG assessments.

This conceptual study explores the potential of ‘Geospatial ESG’ through the lens of the real estate sector—a sector responsible for 40% of energy consumption, 36% of CO₂ emissions, and significant resource use within the EU. The analysis centres on the property as the core economic unit of the sector’s activities. A central challenge lies in effectively aligning geospatial technologies with ESG requirements and sector-specific standards. The aim of the study is to develop a spatial ESG framework for the (residential) real estate sector, focusing on the following research question: ‘Which ESG indicators of a property can be assessed using spatial data and methods?’

The study systematically identifies and derives quantitative, highly granular ESG indicators at the asset-level from existing ESG ratings, sustainable building frameworks, the EU taxonomy, and RICS ESG data list. Properties and their locations are particularly well-suited for spatial-temporal analysis, as they represent fixed, three-dimensional entities embedded within their environmental and socio-economic context. Due to scope limitations, this research focuses solely on the sustainable impact of residential properties. Emphasis is placed on indicators which can be assessed by 2D/3D geodata and earth observation data. The geospatial potential of the derived indicators are outlined in a generic manner. Specific thresholds or criteria have not been defined.

The indicators reflect assessments that combine ‘asset’ data with ‘observational’ data, offering comprehensive insights of both the building and its site-specific impact across environmental (‘E’) and socio-environmental (‘S’) dimensions. The greatest geospatial potential was identified in the sustainability categories of ‘Physical Risk’, ‘Mobility and Infrastructure’, and ‘Sustainable Site’. In contrast, the geospatial applicability for categories such as ‘Energy & GHG’, ‘Water’, ‘Resources & Circular Economy’, and ‘Health, Well-Being & User Comfort’ remains relatively limited. Methodologically, the framework leverages the full spectrum of geospatial analysis—from simple overlay techniques to advanced methods such as flood modelling, microclimate simulations, and network analysis, all the way to ‘GeoAI’ applications like automated building feature extraction from street-level imagery.

The resulting framework provides a comprehensive overview of asset-level geospatial ESG indicators (49 in total) related to direct sustainability impacts. To the best of my knowledge, it represents the first spatial ESG framework specifically developed for the real estate sector.