Carbon footprint of dietary patterns in Peruvian cities

Cities are highly reliant on importing food from rural areas. In fact, consumption of food accounts for an important proportion of GHG emissions linked to urban environments. In this context, dietary patterns are defined as... [ view full abstract ]

Cities are highly reliant on importing food from rural areas. In fact, consumption of food accounts for an important proportion of GHG emissions linked to urban environments. In this context, dietary patterns are defined as the combination of different food and beverage products that are present in a particular diet, as well as the amounts that are usually consumed. A series of research studies available in the literature have delved into the nature of these dietary patterns, showing that important GHG reductions can be achieved if certain foods are consumed rather than other, more GHG intensive products. For instance, vegetarian and low-meat diets appear to be less carbon intensive than diets based on red meat diets.

Food purchase represents an important amount of the average annual Peruvian household expenditures, ranging from 35% to 55% depending on the city. Therefore, food consumption is identified as a major steering factor in order to reduce GHG emissions in Peruvian cities. However, the Peruvian government lacks a specific strategy to mitigate emissions in this sector, despite the recent ratification of the Treaty of Paris. Therefore, the main objective of this study is to analyze and compare the environmental impacts of dietary patterns in 25 Peruvian cities, including the national capital, Lima.

Carbon footprinting was used as the methodological framework to obtain the overall impacts of the components in the observed dietary patterns. Primary data linked to the composition of urban diets were collected from the ENAPREF report published by the Peruvian National Institute for Statistics. Life cycle inventories for the different products that are part of the Peruvian diet were obtained from a set of previous scientific articles and reports regarding food production. Data gaps, background data and imported food products were fulfilled by using the ecoinvent v3.2 database, but with primary information of the national electricity mix and local transport conditions. Results were computed using the IPCC 2013 assessment method to estimate GHG emissions. The software that was used was SimaPro v8.02.

Results presented in the study show a high variation in GHG emissions per capita between cities. For instance, the city of Trujillo showed one of the lowest values due to the low consumption of animal protein, whereas Puerto Maldonado showed a very high footprint due to the high proportion of beef and chicken in the diet. Despite these differences in GHG emissions between cities, no significant differences were observed between the cities located in the three Peruvian natural regions (i.e., coast, Andes and Amazon basin). In contrast, there appears to be a strong direct correlation between higher GHG emissions and lower poverty levels.

This study represents a baseline that indicates an estimate of the per capita GHG emissions that are generated in the main Peruvian cities due to households food consumption. The authors expect this study to be an input for government agencies and institutions that includes the environmental aspect for the implementation of policies regarding poverty, nutrition and health all over the country.

Keywords: Carbon footprint; diets; Peru; urban metabolism.