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FINVIS

The financial geography of sustainability data: a mapping exercise of the spatial dimension of the ESG information industry

Pages 76-79 | Received 21 Aug 2023, Accepted 15 Dec 2023, Published online: 07 Apr 2024

ABSTRACT

This FinVis explores the evolving landscape of environmental, social and governance (ESG) information providers, focusing on their geographical distribution and consolidation dynamics. Utilising a novel dataset of 143 ESG firms, the article figure maps the headquarters and merger and acquisition (M&A) interactions across three time periods. The findings reveal the dominance of Europe and North America, which concentrate the ownership of over 80% of active firms. In particular, the acquisition trends depict North America's growing influence in the ESG information industry.

1. INTRODUCTION

Sustainable investments have considerably proliferated in recent years. In the absence of globally agreed upon definitions, estimates of their size vary between US$793 billion and US$35.2 trillion (Fichtner et al., Citation2023). One reason for this variation is that measurements of environmental, social and governance (ESG) investments come from private data providers.

Academic contributions have problematised the reliance on these private providers, highlighting issues with proprietary methodologies (Berg et al., Citation2022), biases in favour of larger companies (Drempetic et al., Citation2020) and possibilities for ‘greenwashing' (Condon, Citation2021; Gabor, Citation2021). These critiques dwell on the question of who is governing sustainable investments. Yet, from a financial geography perspective (cf. Hughes et al., Citation2021), a perhaps equally important question is where ESG data are produced.

2. DESCRIPTION OF THE FIGURE

The figure builds on a dataset of 143 ESG information firms (Dimmelmeier Citation2023). The upper panel maps the headquarters of firms. Circles symbolise cities with node sizes proportional to the number of headquartered firms. To avoid the overcrowding of labels in Europe and North America (US and Canada), in these regions, city names are displayed for locations with a minimum of four (world map) or two (regional maps) ESG firms. Circle colours indicate the dominant type, measured by simple majority, of firm per city. The typology separates between ‘traditional' ESG agencies that rely on specialised analysts and protocols to rate corporates, ‘mainstream' providers of financial data and services that have integrated ESG data into their product suites and ‘specialist' providers that use advanced modelling techniques and new technologies, such as artificial intelligence. These types were derived from existing categorisations (Demartini, Citation2020; Eccles et al., Citation2020).

Arrows show merger and acquisition (M&A) events with arrows originating from the headquarters of the acquired firm and arrow heads pointing to the city where the acquiring firm is headquartered. Self-links, i.e., a firm acquiring a competitor headquartered in the same city, are not displayed. Arrow colours denote three distinct periods, each of which contains observations for six years. The first period in this separation is ‘longer' due to three years without events ().

Figure 1 The Geography of ESG information firms.

Figure 1 The Geography of ESG information firms.

The lower panel aggregates geographical changes of ESG firms’ headquarters by world region. The vertical axis shows the total number of firms (143) and strata’s heights detail the number of firms per category and period. The thickness of flows between strata corresponds to the number of firms, whose ownership passed to entities headquartered in different regions. Where the height of a region is greater than the inflows from other regions, the difference can be attributed to newly founded firms.

3. INTERPRETATION

The lower panel reveals that Europe and North America dominate the ESG information industry, with 87.9% of active firms in 2000 and 81.1% in 2023 being located in either region. This aligns with historical accounts of ESG as a ‘Western' phenomenon and the distribution of about 85% of all sustainable investments in these regions (GSIA, Citation2020, p. 9). Starting in the 2010s, there is also a growing presence of Asian, especially Chinese, firms.

The dominance of European and North American providers could disadvantage the not-represented regions of South America and Africa as regional biases in ESG metrics (La Bella et al., Citation2019) might act as a bar to channel investments to these geographies.

Looking at the dominant firm types in the core regions it can be observed that Europe is chiefly home to traditional ESG providers (purple circles), while North America hosts mainstream financial data providers, particularly on the east coast (red circles). Combining these findings with the inter-regional dimension of M&A activities – North American providers took ownership of 22 initially European firms – suggests a pattern of integration of European traditional ESG providers into mainstream US financial companies. Examples of this dynamic include the acquisitions of the European ESG firms Sustainalytics (Amsterdam) and Vigeo Eiris (Paris) by the US-based financial conglomerates Morningstar (Chicago) and Moody’s (New York), respectively.

The figure illustrates that US-based mainstream financial service providers increasingly take ownership of traditional and specialist ESG information firms. Through this concentration of ownership these providers also influence what is offered as ESG information. This is noteworthy as, in some cases, companies that were explicitly established to challenge the hegemony of mainstream financial metrics are becoming re-appropriated by the main purveyors of the financial data infrastructure (Eccles et al., Citation2020).

ACKNOWLEDGEMENTS

The author is grateful to Lukas Böhm, who arranged the different plots in a combined visualisation and who provided valuable inputs to increase the interpretability of the plots. In addition, I would like to express my gratitude to Xinke Gu, who worked on the revised version of the dataset and to Guillaume Beaumier, who provided valuable comments for the revision.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by Bayerische Akademie der Wissenschaften: [Grant Number KON-22-031].

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