IDEAS home Printed from https://ideas.repec.org/a/wly/envmet/v36y2025i2ne2893.html
   My bibliography  Save this article

Multidimensional Spatiotemporal Clustering – An Application to Environmental Sustainability Scores in Europe

Author

Listed:
  • Caterina Morelli
  • Simone Boccaletti
  • Paolo Maranzano
  • Philipp Otto

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 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.

Suggested Citation

  • Caterina Morelli & Simone Boccaletti & Paolo Maranzano & Philipp Otto, 2025. "Multidimensional Spatiotemporal Clustering – An Application to Environmental Sustainability Scores in Europe," Environmetrics, John Wiley & Sons, Ltd., vol. 36(2), March.
  • Handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e2893
    DOI: 10.1002/env.2893
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/env.2893
    Download Restriction: no

    File URL: https://libkey.io/10.1002/env.2893?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:envmet:v:36:y:2025:i:2:n:e2893. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1180-4009/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.