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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 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.

Suggested Citation

  • Caterina Morelli & Simone Boccaletti & Paolo Maranzano & Philipp Otto, 2024. "Multidimensional spatiotemporal clustering -- An application to environmental sustainability scores in Europe," Papers 2405.20191, arXiv.org.
    • Handle: RePEc:arx:papers:2405.20191
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    References listed on IDEAS

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    1. He, Yu & Zhao, Xiaoling & Zheng, Huan, 2023. "How does the environmental protection tax law affect firm ESG? Evidence from the Chinese stock markets," Energy Economics, Elsevier, vol. 127(PA).
    2. Mattera, Raffaele & Franses, Philip Hans, 2023. "Are African business cycles synchronized? Evidence from spatio-temporal modeling," Economic Modelling, Elsevier, vol. 128(C).
    3. Marie Chavent & Vanessa Kuentz-Simonet & Amaury Labenne & Jérôme Saracco, 2018. "ClustGeo: an R package for hierarchical clustering with spatial constraints," Computational Statistics, Springer, vol. 33(4), pages 1799-1822, December.
    4. Yalin Mo & Yuchen Che & Wenqiao Ning, 2023. "Digital Finance Promotes Corporate ESG Performance: Evidence from China," Sustainability, MDPI, vol. 15(14), pages 1-23, July.
    5. Niranjan Chipalkatti & Quan Vu Le & Meenakshi Rishi, 2021. "Sustainability and Society: Do Environmental, Social, and Governance Factors Matter for Foreign Direct Investment?," Energies, MDPI, vol. 14(19), pages 1-18, September.
    6. Abdulaziz Abdulmohsen Alfalih, 2023. "ESG disclosure practices and financial performance: a general and sector analysis of SP-500 non-financial companies and the moderating effect of economic conditions," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 13(4), pages 1506-1533, October.
    7. Montero, Pablo & Vilar, José A., 2014. "TSclust: An R Package for Time Series Clustering," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i01).
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