On behalf of USAID/Peru, the Institute for Development Impact (I4DI) undertook a mixed-methods study to examine the conditions under which private industries become voluntary adopters of best management practices (BMPs) in hydropower, oil and gas, large-scale industrial mining, beverages, and road projects. With a particular focus on private industries working in Amazonia, Latin America, and/or areas pertinent to tropical forests, this study examined BMPs applied across various aspects of project development and implementation, including environmental assessments, siting, design, operation, and closure. The findings from this study informed the development of a model that predicts the conditions under which private industries and investors become voluntary adopters.
USAID defines best management practices as those that minimize the negative environmental, social, and/or economic impacts that stem from typical practices (USAID, 2016). BMPs may be used during environmental impact assessments, siting, project design and operation, and project closure, and are intended to reduce the deleterious impacts produced by typical practices. BMPs, either adopted and enforced by financing entities and/or voluntarily adopted by the self-financing private firms that implement high impact development projects, can result in a variety of benefits for the private sector, including risk reduction, improved operational efficiency, and reduced governmental regulatory and enforcement burdens. Although BMPs can be adopted as the result of legal requirements by host governments, industries can also voluntarily adopt BMPs. Voluntary adoption of BMPs can often yield much faster returns than typical regulatory processes.
This study contributed to the design of USAID’s Amazon regional environmental strategy, which focused on reducing the negative impacts from large-scale infrastructure projects, extractive activities, and climate change on Amazonian forests, waters, and indigenous peoples (USAID 2016b). As such, I4DI presented a series of situational and contextual factors that benefit the adoption of a voluntary practice by private actors, and the ways in which these BMPs generate positive impacts to the local communities of the influence areas and positive gains for the environment. I4DI also critically examined some situations in which impacts were not as positive as expected. We further presented a predictive model for BMP voluntary adoption, which could be tested and validated in related efforts by USAID in the Amazon region.
Since the focus of the study was the voluntary nature of adopting BMPs, it is important to note the targeted nature of our selection of case studies and informants, as opposed to seeking a representative sample of respondents across issues and examples of BMPs in general. Rather, we considered the main drivers for the voluntary adoption of BMPs in a series of selected case studies and compared them vis-à-vis a preliminary model designed by I4DI before the commencement of the study. We thus hoped to increase understanding of the motivating factors that encourage a company or a project to act above the minimal requirements and voluntarily adopt additional or innovative BMPs. By the same token, this study was not intended to assess corporate behavior, but rather is more concerned with the life cycle of the selected BMPs, irrespective of the overall performance of the implementing organizations.
The study was implemented from January to July of 2017 and consisted of two phases. First, I4DI conducted a systematic literature review to identify potential BMPs and case studies, and refined an initial methodology. With USAID’s approval of the first phase products, I4DI then selected a number of BMP case studies for which we gathered and analyzed primary and secondary data across a sample of voluntary adopters in the private sector. After analyzing our interviews and survey results, I4DI refined the predictive model identifying the motivating drivers for voluntary adoption of BMPs.