Spatial finance:practical and theoretical contributions to financial analysis

We introduce and define a new concept, ‘Spatial Finance’, as the integration of geospatial data and analysis into financial theory and practice, and describe how developments in earth observation, particularly as the result of new satellite constellations, combined with new artificial intelligence methods and cloud computing, create a plethora of potential applications for Spatial Finance. We argue that Spatial Finance will become a core future competency for financial analysis, and this will have significant implications for information markets, risk modelling and management, valuation modelling, and the identification of investment opportunities. The paper reviews the characteristics of geospatial data and related technology developments, some current and future applications of Spatial Finance, and its potential impact on financial theory and practice

Influence of vacancy diffusional anisotropy: Understanding the growth of zirconium alloys under irradiation and their microstructure evolution

International audienceIn this work, we propose a series of Object Kinetic Monte Carlo simulations complemented by an analytical model that allows rationalizing a certain number of experimental facts related to the growth of high purity, recrystallized zirconium alloys under irradiation. Our vision of the phenomenon rests essentially on vacancy diffusion anisotropy (with faster diffusion in the basal planes than perpendicular to them) that is necessary to lead to the formation of layers of prismatic interstitial dislocation loops parallel to the basal plane. The acceleration of the deformation under irradiation and this localization of the damage are strongly connected. An analytical model developed using the concepts of difference of anisotropic diffusion between vacancies and interstitials makes it possible to account for the observed phenomena

Detection and characterisation of pollutant assets with AI and EO to prioritise green investments: the geoasset framework

Detailed and complete data on physical assets are required in order to adequately assess environment-related risk and impact exposure and the diffusion of these risks and impacts through the financial system. Investors need to know where the physical assets (e.g., power plant, factory, farm) are located of companies in their portfolios, and what their polluting characteristics are. This is essential to manage these environment-related risks and to channel investments to more sustainable alternatives. At present, data on physical assets is typically incomplete, inaccurate, or not released in a timely manner. As a result, key stakeholders including asset owners, asset managers, regulators and policymakers are frequently forced to make crucial decisions with incomplete information. Accurate and comprehensive global asset-level databases are a prerequisite for meaningful innovation in green and digital finance. They provide the link between the financial system and the “real economy” and allows the wealth of EO datasets and insights that we have available to be made actionable for sustainable finance decision making. We created a framework to derive a global database of pollutant plants, such as cement, iron, and steel, which represent about 15% of the global CO2 emissions. Our solution makes use of state-of-the-art deep learning architectures coupled with Earth observation data

Global database of cement production assets and upstream suppliers.

Acknowledgements: This work was supported by the Children’s Investment Fund Foundation (CIFF).Funder: Children's Investment Fund Foundation (CIFF); doi: https://doi.org/10.13039/100010409Funder: Children's Investment Fund Foundation (CIFF)Cement producers and their investors are navigating evolving risks and opportunities as the sector's climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company's greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics

Global database of cement production assets and upstream suppliers

Acknowledgements: This work was supported by the Children’s Investment Fund Foundation (CIFF).Funder: Children's Investment Fund Foundation (CIFF); doi: https://doi.org/10.13039/100010409Funder: Children's Investment Fund Foundation (CIFF)Cement producers and their investors are navigating evolving risks and opportunities as the sector’s climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company’s greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics

Global database of cement production assets and upstream suppliers

Abstract Cement producers and their investors are navigating evolving risks and opportunities as the sector’s climate and sustainability implications become more prominent. While many companies now disclose greenhouse gas emissions, the majority from carbon-intensive industries appear to delegate emissions to less efficient suppliers. Recognizing this, we underscore the necessity for a globally consolidated asset-level dataset, which acknowledges production inputs provenance. Our approach not only consolidates data from established sources like development banks and governments but innovatively integrates the age of plants and the sourcing patterns of raw materials as two foundational variables of the asset-level data. These variables are instrumental in modeling cement production utilization rates, which in turn, critically influence a company’s greenhouse emissions. Our method successfully combines geospatial computer vision and Large Language Modelling techniques to ensure a comprehensive and holistic understanding of global cement production dynamics