Socio-ecological distribution conflicts in the mining sector in Guatemala (2005–2013): Deep rooted injustice and weak environmental governance. Issue 3 (July 2018) - Aguilar-González, Bernardo; Navas, Grettel; Brun, Carole; Aguilar-Umaña, Andrea; Cerdán, Paloma
Highlights: Mining conflicts (EDC) in Guatemala affect mostly rural and indigenous populations. They are embedded in structural conditions of injustice, power concentration and weak governance. They evolve in swarms at a national scale. CSR initiatives are insufficient to address the underlying causes of EDCs. EDCs may stimulate creative forces to seek sustainability and environmental justice in Guatemala. Abstract: This study characterizes ecological distribution conflicts (EDC) related to the mining industry and derives a series of political implications for Guatemala. The characterization includes a placement in the context of Central America, regional location, intensity of the EDC and the trends in social and environmental consequences, with special emphasis on the groups of social actors affected and the degree to which the institutional framework does not provide effective means of participatory environmental governance. The time period covers 2005 to 2013. In order to understand trends in actor behavior and diverse moments of high intensity we introduce the use of action and response timelines as a methodology for EDC analysis. We propose the notions of embedded conflicts to describe their relation with the structural social conditions prevailing in the country and swarms of conflicts to describe their escalation through time. We conclude that conflictivity is inherent to the unsustainable characteristics of metallic mining and is aggravated by Guatemalás history of social inequality and power concentration. The attempts to reduce "conflictivity" through CSR have been insufficient in addressing these structural conditions. EDCs may have helped create a positive environment for creative forces to seek sustainability and justice in Guatemalás development model.