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Theoretical and methodological contributions for an Early Warning System of socio-environmental conflicts. The case of the Mirador Project, Ecuador

Abstract:

The article describes the theoretical and methodological design of an innovative Socio-environmental Conflicts Monitoring Model with an Early Warning System (SCMM+EWS) in El Pangui, Ecuador. The design of this model is a response to the growing socio-environmental tensions and disputes in the canton, influenced by the advent of large-scale mining. This mega-project, named “el Mirador”, is carried out by the Chinese consortium Ecua Corrientes S.A. (ECSA). It launches a transformation process that inevitably affects the use and exploitation of natural resources (land and water) and generates serious social and environmental conflicts, which have led to a strong resistance movement in the area. However, residents in favor of mining still expect the project to bring progress and economic development to the canton, through new sources of employment and investment by the national government. This generates a high rate of division and social conflict in relation to large-scale mining, which has resulted in several episodes of violence in recent years. Under these conditions of tension and violence, a special concern arises from academia and civil society organizations to contribute to non-violent management of these conflicts. From the Observatorio de Conflictos Socioambientales of the Technical University of Loja, and with support from the German Technical Cooperation (GIZ), we decided to launch an intervention combining academic vision with fieldwork, in close collaboration with institutional actors and civil society at the local level. The SCMM + EWS has been designed within the theoretical framework of the Peace and Conflicts Studies. Model implementation is a participatory process for systematic collection of quantitative and qualitative data, its multiparcial analysis and targeted dissemination. Its methodological proposal relies on the analysis of statistical data and perceptions of the local population, by monitoring a series of key social and environmental variables. The monitoring is carried out using two main methods: statistical data collection reports and official reports; and research of perception and satisfaction of the local population on the variables through surveys. In order to get a more reliable diagnosis of the perception of the population, qualitative research is carried out in parallel through in-depth interviews with key actors. With the information obtained, the need for issuance of an Early Warning report to the competent authorities (at local, provincial and state-level) can be defined. In the period analyzed, the results showed a high degree of dissatisfaction with the mining company, which generated several conflicts. These results were obtained matching information from both qualitative (interviews with local inhabitants) and quantitative sources (perception indicators), and finally contributed to the issuance of the two Early Warning reports. The first Early Warning report was issued on March 27, 2015 and was based on the information gathered about the event of socialization of the Environmental Impact Assessment (EIA) of the Mirador project, which warned of a possible violent anti-mining protest. The second report was issued on September 30, 2015, on the occasion of evictions in the Parish of Tundayme of families who had not sold their land to the company ECSA. In this case, the issuing of the report didn't work out effectively. Taking all this into account, preliminary results point out the validity of the model as a suitable methodological tool for monitoring socio-environmental conflicts. Our findings highlight the pertinence of the methodological combination of qualitative and quantitative sources in order to identify potentially violent conflicts. We also have to point out that certain limitations are presented in the Model. For the purpose of identifying conflicts at an advanced stage, the information obtained through interviews have proved more effective than analysis derived from data provided by the indicators. Regarding the process to provide early warnings, we have also found some difficulties in order to carrying it out effectively. This issue is also being reviewed in the Model to propose adjustments and refine the methodology. In conclusion, we highlight that the combination of quantitative indicators of perception with qualitative information from interviews, provides relevant information to identify potential violent escalation of conflicts. We believe that the model can help to ensure more effective decision making at the local level on the socio-environmental conflicts, promoting collaboration among local institutions, academia and civil society.