Abstract
Carbon footprints show the greenhouse gas emissions associated with consumption of goods and services. Many countries now calculate carbon footprints at the national level, published as part of national environmental accounts. However, carbon footprint estimates at the local or regional levels are less common. Municipalities call for consistent and comparable carbon footprint data to support their low carbon transitions.
This study presents estimates of households' carbon footprints for all Swedish municipalities, down to the postcode level. It uses a downscaling approach to disaggregate the Swedish national carbon footprint from the national level to Swedish postcode level, using the best available local data on expenditure, greenhouse gas emissions or other data such as energy use, at the smallest geographical scale. The variation in carbon footprints at municipal and postcode levels is analysed and key contributing consumption categories identified. The study then discusses how these data can and have been used to support policymaking.
The results show considerable variations in average carbon footprints at the postcode level in Sweden, between about 3.7 and 17.8 t CO2eq per capita. Footprints of items such as flights and personal vehicles have high variation, whereas the consumption categories of food, electricity, and clothing are more similar. A strong positive correlation exists between incomes and the total carbon footprint. Carbon footprints from personal vehicles have a negative correlation to population density, while the footprint of clothing, restaurants and flights have a positive correlation to population density.
In terms of policy, several municipalities in Sweden were found to have carbon footprint reduction goals and related policies. A number of municipalities also demonstrated the application of preliminary data from the study for raising public awareness, policymaking, and monitoring progress towards reduction targets. The recommendations for future development and use of these data for policy and decision-making include: (1) ensuring data access, learning and stakeholder engagement with the data; (2) using the data to develop policy visions and goals and associated reduction targets and; (3) designing policy interventions that will reduce carbon footprints, taking into account factors such as variation at the local scale and between consumption categories, geography, socio-economic circumstances, inequalities, and systemic change.