Exploring Consumer Expenditure And Environmental Impacts Across European Nations : A Data-Mining Approach

Abstract: As the pressures on the environment created by humanity increase, understanding how products influence a person’s overall impact becomes more important in order to make choices about how a person chooses to consume. Recent literature shows that household consumption is responsible for 51% to 81% of a nation’s total emissions (Ivanova, Stadler, et al. 2016). This study investigates whether expenditure is a viable proxy measurement for consumer impacts in Europe. Without knowledge about the relative impacts of consumer expenditure on the environment, European citizens cannot make conscious choices regarding their expenses and how they relate to environmental impact, while policy-makers have no basis to develop tailored environmental policy mechanisms. This study combined Input-Output Analysis, Data Mining, and Regression Analysis to check if a correlation between expenditure and impact exists. The results are contextualised in consumption categories. Results from Input-Output Analysis suggest that Housing, Food, and Transport are the largest categories of expense throughout Europe. While expenses vary significantly throughout Europe, common trends emerge. Pattern Recognition and Cluster Analysis algorithms show that expenditure habits differ especially between north-west, east, south, and coastal Europe. Each of the four groups consists of between six and eleven nations. In general, lower economic development indicates higher expenditure in Housing and Food, while higher economic development indicates higher expenditure in Recreation & Culture, and Goods & Services. Coastal Europe spends more on Restaurants & Hotels, and Education. The expenditures were translated to four environmental impacts; Global Warming Potential, Land Use, Material Use, and Blue Water Consumption. Next, correlation between expenditure and environmental impact was checked using Regression Analysis. The analysis showed that out of the twelve expenditure categories, Clothing & Footwear and Furnishing & Household, showed a significant correlation between expenditure and the four impact categories. Food, Alcohol & Tobacco, and Recreation & Culture showed significance in two impact categories, and Transport showed significance in one category. In total 15 out of the 48 (31%) tested impact categories showed significance. Using the identified groups, the amount of impact categories that show correlation with expenditure grows to 44%, and up to 68%. Unfortunately, given the size of these groups, these results are not statistically significant. That said, the method shows promise, and further research with a larger scope could produce statistically significant results.