Canadian Forest Service Publications
Social Impact Assessment Methods for Predicting Cumulative Effects involving Extractive Industries and Indigenous People. 2020. Pimentel Da Silva, G.D.; Parkins, J.R.; Nadeau, S. University of Alberta. 49 p.
Year: 2020
Issued by: Laurentian Forestry Centre
Catalog ID: 40170
Language: English
Availability: PDF (request by e-mail)
Abstract
Many resource projects are located in regions inhabited by Indigenous people, whose livelihoods, culture, and spirituality are deeply affected by these projects. Researchers and consultants have developed numerous qualitative and quantitative Social Impact Assessment (SIA) methods to predict or verify cumulative social outcomes of those projects as they relate to the interests and concerns of Indigenous people. Yet there remains a lack of consensus on the best practices for SIA in this context. Given how wide-ranging these methods are, a review of the literature to identify, synthesize, and summarize SIA methods in this context is urgently needed. The variety of approaches identified in the literature reflects the worldviews of Indigenous and non-Indigenous people who design and implement these methods, as well as the growing urgency to reconcile resource development with Indigenous people and their traditional lands. With these issues in mind, this report provides a systematic review of methods addressing cumulative social effects related to natural resources extraction and Indigenous groups. First, we highlight theoretical frameworks and identify areas of potential impact that need to be addressed and cumulative effects that arise within the frameworks. Some frameworks have roots embedded in Indigenous rights and justice theory, while other frameworks focus on the economic cycles of extractive industries. Secondly, we present participatory geographic information system (SIAGIS) methods as a powerful tool for connecting physical science and social science elements of assessment. Thirdly, we provide a section presenting community engagement methods to select indicators and construct narratives for identifying historical cumulative effects. Finally, we explore modelling approaches to SIA and how they relate to regional planning.
