The Quest for a Spatial ESG Framework for the Real Estate Sector ^†

ESG (Environment, Social & Governance) has arrived in European finance and the real economy. Assessments of companies and their economic activities aim to measure their impact on the environment and society, as well as the interrelated financial risks. The ESG information obtained must be disclosed to ensure market transparency for all participants, helping to direct capital flows toward sustainable activities and mitigate the financial risks associated with loans or investments in unsustainable practices. ESG can represent either risks or growth opportunities, often arising from changing market conditions. These include regulatory interventions (e.g., CO₂ taxation) or shifts in market demand for sustainable products. Additionally, the occurrence of natural disasters (e.g., floods) or the loss of economic basis (e.g., natural resources) are regarded as risk drivers. Technology-driven approaches can help address existing challenges. So far, techniques like web scraping are already being utilized for data acquisition, while machine learning (ML) and artificial intelligence (AI) algorithms (e.g., large language models) are employed for data analysis. Additionally, new tools and geodata, such as the Internet of Things (IoT), Earth observation (EO) (via satellites and aerial sensors), and other monitoring devices, as well as 3D and 2D data, are driving the development of innovative digital solutions for ESG indicators. For example, smart meters enable automatic reporting of energy consumption. These technologies offer a valuable alternative to traditional data, with the potential to enhance, validate, or even partially replace it. Geospatial ESG holds significant potential to play a key role by analysing geolocated assets and comparing them with observed or modelled data, enabling a deeper, more comprehensive understanding of a site’s impact. This technology supports automated and efficient ESG assessments, delivering accurate, objective (reproducible and transparent), and comparable ESG data at a granular level. In the case of sufficient data, the technology is scalable and globally applicable. For example, EO data can provide worldwide, significant spatial–temporal ESG insights. We explore the potential of geospatial ESG in the real estate sector at property level. We target the intersection of ESG, the real estate industry, and geospatial technology, determining how geodata and spatial analysis can be utilized to assess ESG indicators. The real estate sector is of particular interest, as it accounts for 40% of total energy consumption and 36% of CO₂ emissions in the EU, while also consuming substantial resources, leading to significant negative impacts on the climate, biodiversity and human health.