[2405.20191] Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Statistics > Applications arXiv:2405.20191 (stat) [Submitted on 30 May 2024] Title: Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe Authors: Caterina Morelli , Simone Boccaletti , Paolo Maranzano , Philipp Otto View a PDF of the paper titled Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe, by Caterina Morelli and 3 other authors View PDF HTML (experimental) Abstract: The assessment of corporate sustainability performance is extremely relevant in facilitating the transition to a green and low-carbon intensity economy. However, companies located in different areas may be subject to different sustainability and environmental risks and policies. Henceforth, the main objective of this paper is to investigate the spatial and temporal pattern of the sustainability evaluations of European firms. We leverage on a large dataset containing information about companies' sustainability performances, measured by MSCI ESG ratings, and geographical coordinates of firms in Western Europe between 2013 and 2023. By means of a modified version of the Chavent et al. (2018) hierarchical algorithm, we conduct a spatial clustering analysis, combining sustainability and spatial information, and a spatiotemporal clustering analysis, which combines the time dynamics of multiple sustainability features and spatial dissimilarities, to detect groups of firms with homogeneous sustainability performance. We are able to build cross-national and cross-industry clusters with remarkable differences in terms of sustainability scores. Among other results, in the spatio-temporal analysis, we observe a high degree of geographical overlap among clusters, indicating that the temporal dynamics in sustainability assessment are relevant within a multidimensional approach. Our findings help to capture the diversity of ESG ratings across Western Europe and may assist practitioners and policymakers in evaluating companies facing different sustainability-linked risks in different areas. Subjects: Applications (stat.AP) ; Econometrics (econ.EM); Computation (stat.CO) Cite as: arXiv:2405.20191 [stat.AP] (or arXiv:2405.20191v1 [stat.AP] for this version) https://doi.org/10.48550/arXiv.2405.20191 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.1002/env.2893 Focus to learn more DOI(s) linking to related resources Submission history From: Philipp Otto [ view email ] [v1] Thu, 30 May 2024 15:56:06 UTC (768 KB) Full-text links: Access Paper: View a PDF of the paper titled Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe, by Caterina Morelli and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: stat.AP < prev | next > new | recent | 2024-05 Change to browse by: econ econ.EM stat stat.CO References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from