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Abstract
Although not traditionally regarded as a central theme in Industrial Ecology, food production and consumption are directly related to health and well-being, on the one hand, and with resource use and environmental impacts on the other. Increasing affluence is said to be fuelling an ongoing ‘nutrition transition’, a term coined in the 1990s by the renowned public health academic Barry Popkin, used to describe more westernized, higher calorie and more meat-intensive diets, increasingly consumed around the world. Some aspects of this transition are already evident through rising rates of diet-related disease, but full quantification and identification of factor interdependencies (particularly those between environmental and other variables) is still incomplete. There is therefore a need to unpack and rigorously test proposed conceptual models, with the aim of building an unbiased model capable of explaining patterns and monitoring the ways they have changed through time and are likely to continue evolving in the future.
The poster focuses on a small part of a holistic attempt to test a dominant conceptual model describing the phenomenon known as the nutrition transition. Data mining using Bayesian Belief Networks (BBNs) provides an ideal method to plot and test prior beliefs based on a large multivariate dataset comprised of indicators of socioeconomic development, dietary composition, environmental footprint indicators, and health risk factors at a national level covering a thirty-two year period (1980-2011). The results focus on the probabilistic relationships between affluence-driven dietary change and food-related MRIO-derived environmental footprints such as water use, land use (cropland and pasture), biomass, nitrogen and phosphorus have changed in recent decades. There is also an emphasis on how affluence compares to other variables such as geography, time or diverse socioeconomic factors in explaining the variance in dietary patterns and their associated environmental impacts.
