Data-Driven Approaches to Measuring a Social License to Operate

摘要

Companies in the energy and resources sectors often conduct surveys to understand their acceptance within the community. Such surveys generate rich data, yet sometimes key insights can be missed using conventional plots of average responses for each question. Here, we investigated how multivariate statistics might be used to analyze and communicate information from a social impact assessment of an Australian coal seam gas (LNG) project. The drivers of community acceptance were complex and impacts with the greatest/least average scores were not necessarily those most correlated with acceptance. For example, while housing affordability and availability were consistently seen as negative impacts, individuals' views on employment and economic opportunities were better correlated with acceptance -- even though these were, on average, not seen as positive or negative impacts of development. Consistent with previous statistical (path analysis) assessment of the same data, a perceptual map based on r-mode analyses suggested relational factors such as trust and perceptions of good environmental regulation were the most important drivers of acceptance of the LNG industry. Community response maps created using q-mode analyses represented the diversity of opinions for multiple drivers, highlighting that “the community” is not a uniform entity. For example, although those involved in (non-LNG) industry generally reported greater levels of acceptance and trust than others in the community, there were still some individuals within this group that did not trust or accept the LNG industry. While a SLO can be complex and is likely to constantly change, our study shows multidimensional scaling may be a useful tool for communicating social survey results to engineers and managers in a way that encapsulates some of the important details of a SLO, yet still be intuitive enough to include in reporting dashboards.