Drivers of the environmental impact of consumption: evaluating the role of urbanization in reducing the Swiss carbon footprint

Greenhouse gas emission inventories form the basis of evidence-based climate change planning across the local, regional, national and international levels. However, the method employed in producing these inventories can greatly influence the final accounts, subsequently having an impact on the mitigation options and priorities. This study employs an environmentally extended input-output analysis approach to estimate the environmental impact of Swiss household consumption using data from household budget surveys. These estimates are then compared between urban and rural areas to evaluate the role of urbanization in influencing the overall carbon footprint (CF) of consumption. Across Switzerland, the most important consumption categories are nutrition and housing (including energy). Together, these two sectors account for approximately 50% of the Swiss total CF. Urban areas in Switzerland consistently record a larger per capita CF than rural areas. Between the two levels of urbanity, there is no notable difference between the structure of household consumption in terms of the share of each consumption category. Urban households have a larger overall CF simply due to their larger consumption volume; urban residents generally have a higher per capita income, and hence a larger per capita expenditure than their rural counterparts. Multilinear regression reveals that income is the most important driver of the environmental impact of consumption in Switzerland. The level of urbanity, on the other hand, has negligible impact on the Swiss CF as it is largely outweighed by the socioeconomic drivers that more directly influence consumption behaviour. Finally, the importance of adequately including imports in the accounting model is recognized if a more accurate and representative estimate of environmental impact is to be obtained. To this end, process-based life cycle assessment can supplement the input-output model. Alternatively, a multi- regional input-output analysis approach can be considered.