Business responsibility and government complicity in environmental conflicts: a quali-quantitative analysis of global patterns

ABSTRACT

Our paper aims at widening political ecology research on business’ responsibility and public governance conditions in cases of violation of Environmental-related Human Rights (ERs), by adopting a quantitative perspective. We focused on a subset of socio-environmental conflicts (SECs) that directly connect with the violations of ERs. We propose a quali-quantitative analysis to explore the country distribution of the incidence of violations of ERs leading to SECs (VERCs) worldwide; the extent to which these can be associated with the involvement of business companies; and the general governance conditions spatially associated with the emergence of VERCs, regardless of the involvement of businesses. Through statistical regression analysis, we showed that the evidence of business’ implication in VERCs and of the lack of adequate public governance conditions suggest the need to complexify our perspective with a more nuanced understanding of the patterns of liability and complicity between diverse actors involved in SECs.

KEYWORDS:

• Environmental conflicts
• human rights violations
• environmental rights
• business liability
• Environmental Justice Atlas
• LISA
• global patterns mapping

Disclosure statement

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

Notes

1 As for many of the reported cases, there has not been (or not yet) a court’s pronouncement confirmed the actual violation, all of the instances in which violations are mentioned in this paper (unless differently specified) have to be considered as alleged.

2 In the EJAtlas database, the category of businesses includes both public and private companies, thus the terms “companies” or “businesses” are used here to refer to a generic reference to enterprises.

3 A few exceptions have been detected. In Kenya (N = 8), 50% of VERCs are related to one or more businesses, whereas it is only 40% in Slovakia (N = 5), 33% in Montenegro (N = 3) and even circa 20% in Bulgaria (N = 10). A country-based analysis would be necessary to understand the nature of these outliers.

4 Weights for the spatial model were parameterized according to the Queen 1 rule in GEODA.