Quantifying long-range correlations in complex networks beyond nearest neighbors

We propose a fluctuation analysis to quantify spatial correlations in complex networks. The approach considers the sequences of degrees along shortest paths in the networks and quantifies the fluctuations in analogy to time series. In this work, the Barabasi-Albert (BA) model, the Cayley tree at the percolation transition, a fractal network model, and examples of real-world networks are studied. While the fluctuation functions for the BA model show exponential decay, in the case of the Cayley tree and the fractal network model the fluctuation functions display a power-law behavior. The fractal network model comprises long-range anti-correlations. The results suggest that the fluctuation exponent provides complementary information to the fractal dimension

Interplay between Diets, Health, and Climate Change

The world is facing a triple burden of undernourishment, obesity, and environmental impacts from agriculture while nourishing its population. This burden makes sustainable nourishment of the growing population a global challenge. Addressing this challenge requires an understanding of the interplay between diets, health, and associated environmental impacts (e.g., climate change). For this, we identify 11 typical diets that represent dietary habits worldwide for the last five decades. Plant-source foods provide most of all three macronutrients (carbohydrates, protein, and fat) in developing countries. In contrast, animal-source foods provide a majority of protein and fat in developed ones. The identified diets deviate from the recommended healthy diet with either too much (e.g., red meat) or too little (e.g., fruits and vegetables) food and nutrition supply. The total calorie supplies are lower than required for two diets. Sugar consumption is higher than recommended for five diets. Three and five diets consist of larger-than-recommended carbohydrate and fat shares, respectively. Four diets with a large share of animal-source foods exceed the recommended value of red meat. Only two diets consist of at least 400 gm/cap/day of fruits and vegetables while accounting for food waste. Prevalence of undernourishment and underweight dominates in the diets with lower calories. In comparison, a higher prevalence of obesity is observed for diets with higher calories with high shares of sugar, fat, and animal-source foods. However, embodied emissions in the diets do not show a clear relation with calorie supplies and compositions. Two high-calorie diets embody more than 1.5 t CO 2 eq/cap/yr, and two low-calorie diets embody around 1 t CO 2 eq/cap/yr. Our analysis highlights that sustainable and healthy diets can serve the purposes of both nourishing the population and, at the same time, reducing the environmental impacts of agriculture

Variations in sustainable development goal interactions: Population, regional, and income disaggregation

To fulfill the 2030 Agenda, the complexity of sustainable development goal (SDG) interactions needs to be disentangled. However, this understanding is currently limited. We conduct a cross-sectional correlational analysis for 2016 to understand SDG interactions under the entire development spectrum. We apply several correlation methods to classify the interaction as synergy or trade-off and characterize them according to their monotony and linearity. Simultaneously, we analyze SDG interactions considering population, location, income, and regional groups. Our findings highlight that synergies always outweigh trade-offs and linear outweigh non-linear interactions. SDG 1, 5, and 6 are associated with linear synergies, SDG 3, and 7 with non-linear synergies. SDG interactions vary according to a country's income and region along with the gender, age, and location of its population. In summary, to achieve the 2030 Agenda the detected interactions and inequalities across countries need be tracked and leveraged to “leave no one behind.

Effects of changing population or density on urban carbon dioxide emissions

The question of whether urbanization contributes to increasing carbon dioxide emissions has been mainly investigated via scaling relationships with population or population density. However, these approaches overlook the correlations between population and area, and ignore possible interactions between these quantities. Here, we propose a generalized framework that simultaneously considers the effects of population and area along with possible interactions between these urban metrics. Our results significantly improve the description of emissions and reveal the coupled role between population and density on emissions. These models show that variations in emissions associated with proportionate changes in population or density may not only depend on the magnitude of these changes but also on the initial values of these quantities. For US areas, the larger the city, the higher is the impact of changing its population or density on its emissions; but population changes always have a greater effect on emissions than population density.Comment: 13 two-column pages, 2 figures, supplementary information; accepted for publication in Nature Communication