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.

翻译：企业可持续性绩效评估对于推动向绿色低碳经济转型具有极其重要的意义。然而，不同地区的企业可能面临不同的可持续性与环境风险及政策约束。因此，本文的主要目标是探究欧洲企业可持续性评估的时空分布模式。我们利用一个大型数据集，其中包含2013年至2023年间西欧企业的可持续性绩效信息（以MSCI ESG评级衡量）及其地理坐标。通过采用Chavent等人（2018）层次聚类算法的改进版本，我们开展了空间聚类分析（结合可持续性信息与空间信息）和时空聚类分析（整合多维度可持续性特征的时间动态与空间异质性），以识别具有相似可持续性表现的企业群组。我们成功构建了跨国界、跨行业的聚类，其可持续性评分呈现显著差异。在时空分析中，我们尤其观察到不同聚类间存在高度地理重叠，这表明可持续性评估的时间动态在多维分析框架中具有重要价值。本研究结果有助于把握西欧地区ESG评级的多样性，可为从业者和政策制定者评估不同区域企业面临的可持续性相关风险提供参考。