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Item type:Publication, Are Peruvians moving toward healthier diets with lower environmental burden? Household consumption trends for the period 2008–2021(John Wiley and Sons Inc, 2024-10-01)Peru is one of the most diverse countries in the world in terms of food production, but also suffers a wide range of food security challenges, including malnutrition, the impact of natural hazards, and rising food prices. People living in poverty conditions are the main victims of these problems, which trigger undernutrition, obesity, and diet-related non-transmittable diseases. Despite these challenges, Peru lacks historical food intake data. Therefore, in the current study, we assess the diet quality evolution in the period 2008–2021 based on apparent household purchases extracted from the National Household Survey. The results reveal significant variations in the consumption of certain food items and groups, and the consequences of these changes are discussed in environmental and human health terms. The consumption of lower environmental impact animal protein, such as chicken, eggs, and marine fish, has increased by 37%, 69%, and 29%, respectively; whereas the consumption of high environmental impact foods, such as beef and other red meat, has decreased. Moreover, consumption of less processed carbohydrate sources (e.g., legumes, fruits, and vegetables) has risen, while refined sugar and sugar-sweetened beverages have decreased significantly (almost 45%). Regional differences were also visible; hence, cities on the Northern coast and the Amazon basin had similar consumption habits, whereas Central/Southern coastal and Andean cities had closer consumption patterns. On average, this improvement was reflected in the increase in calories (9.9%) and macronutrient intake (up to 15%), but at the socioeconomic level, food inequality persists, with consumption of many food groups below minimum thresholds in lower socioeconomic strata. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Machine Learning Approach to Understand How Accessibility Influences Alluvial Gold Mining Expansion in the Peruvian Amazon(Elsevier Ltd, 2023-06-01)Alluvial small-scale gold mining (ASGM) mining in the Amazon is expanding fiercely, generating severe environmental degradation, which includes the fast disappearance of primary forests in a highly biodiverse area of the world. Different factors motivate the growth of mining in the areas and understanding this expansion is important to safeguard protected areas or implement strategies to mitigate the related social and environmental impacts. Thus, the goal of this study is to apply machine learning techniques to explore gold mining expansion in Madre de Dios, in the Peruvian Amazon, and to identify possible future hotspots of these activities. Using an unsupervised learning algorithm and a random forest classification model, past expansion trends were analyzed and an explicit geo-spatial model was built. Results demonstrate that proximity to infrastructure is not always indicative of high mining probability. In fact, when analyzing the spatial distribution of model accuracy, it is observed that model performance decreases in clusters where accessibility and mining activity showed opposite trends. In contrast, the models yield accuracies greater than 0.9 when accessibility-related variables stand out as the most important. The model, which is flexible and reproducible, demonstrates to be useful to enhance decision making when implementing geo-spatial policies to address the problem of ASGM expansion in the Amazon.