Forecasting national CO2 emissions worldwide.

Urgent climate action, especially carbon emissions reduction, is required to achieve sustainable goals. Therefore, understanding the drivers of and predicting [Formula: see text] emissions is a compelling matter. We present two global modeling frameworks-a multivariate regression and a Random Forest Regressor (RFR)-to hindcast (until 2021) and forecast (up to 2035) [Formula: see text] emissions across 117 countries as driven by 12 socioeconomic indicators regarding carbon emissions, economic well-being, green and complexity economics, energy use and consumption. Our results identify key driving features to explain emissions pathways, where beyond-GDP indicators rooted in the Economic Complexity field emerge. Considering current countries' development status, divergent emission dynamics appear. According to the RFR, a -6.2% reduction is predicted for developed economies by 2035 and a +19% increase for developing ones (referring to 2020), thus stressing the need to promote green growth and sustainable development in low-capacity contexts.

An R&D perspective on international trade and sustainable development.

Research and Development (R&D) is the common denominator of innovation and technological progress, supporting sustainable development and economic growth. In light of the availability of new datasets and innovative indicators, in this work, we introduce a novel perspective to analyse the international trade of goods through the lenses of the nexus R&D-industrial activities of countries. We propose two new indices, RDE and RDI, summarizing the R&D content of countries' export and import baskets-respectively-and investigate their evolution in time, during the period 1995-2017, and space. We demonstrate the potential of these indices to shed new light on the evolution of R&D choices and trade, innovation, and development. In fact, compared to standard measures of countries' development and economic growth (e.g., the Human Development Index among the others tested), these indices provide complementary information. In particular, tracing the trajectories of countries along the RDE-HDI plane, different dynamics appear for countries with increased HDI, which we speculate can be reasoned with countries' availability of natural resources. Eventually, we identify two insightful applications of the indices to investigate further countries' environmental performances as related to their role in international trade.

Measuring node centrality when local and global measures overlap.

Centrality metrics aim to identify the most relevant nodes in a network. In the literature, a broad set of metrics exists, measuring either local or global centrality characteristics. Nevertheless, when networks exhibit a high spectral gap, the usual global centrality measures typically do not add significant information with respect to the degree, i.e., the simplest local metric. To extract different information from this class of networks, we propose the use of the Generalized Economic Complexity index (GENEPY). Despite its original definition within the economic field, the GENEPY can be easily applied and interpreted on a wide range of networks, characterized by high spectral gap, including monopartite and bipartite network systems. Tests on synthetic and real-world networks show that the GENEPY can shed light about the node centrality, carrying information generally poorly correlated with the node number of direct connections (node degree).

A network approach to rank countries chasing sustainable development.

In 2015, the United Nations established the Agenda 2030 for sustainable development, addressing the major challenges the world faces and introducing the 17 Sustainable Development Goals (SDGs). How are countries performing in their challenge toward sustainable development? We address this question by treating countries and Goals as a complex bipartite network. While network science has been used to unveil the interconnections among the Goals, it has been poorly exploited to rank countries for their achievements. In this work, we show that the network representation of the countries-SDGs relations as a bipartite system allows one to recover aggregate scores of countries' capacity to cope with SDGs as the solutions of a network's centrality exercise. While the Goals are all equally important by definition, interesting differences self-emerge when non-standard centrality metrics, borrowed from economic complexity, are adopted. Innovation and Climate Action stand as contrasting Goals to be accomplished, with countries facing the well-known trade-offs between economic and environmental issues even in addressing the Agenda. In conclusion, the complexity of countries' paths toward sustainable development cannot be fully understood by resorting to a single, multipurpose ranking indicator, while multi-variable analyses shed new light on the present and future of sustainable development.

Reconciling contrasting views on economic complexity.

Summarising the complexity of a country's economy in a single number is the holy grail for scholars engaging in data-based economics. In a field where the Gross Domestic Product remains the preferred indicator for many, economic complexity measures, aiming at uncovering the productive knowledge of countries, have been stirring the pot in the past few years. The commonly used methodologies to measure economic complexity produce contrasting results, undermining their acceptance and applications. Here we show that these methodologies - apparently conflicting on fundamental aspects - can be reconciled by adopting a neat mathematical perspective based on linear-algebra tools within a bipartite-networks framework. The obtained results shed new light on the potential of economic complexity to trace and forecast countries' innovation potential and to interpret the temporal dynamics of economic growth, possibly paving the way to a micro-foundation of the field.

Rainfall as a driver of epidemic cholera: Comparative model assessments of the effect of intra-seasonal precipitation events.

The correlation between cholera epidemics and climatic drivers, in particular seasonal tropical rainfall, has been studied in a variety of contexts owing to its documented relevance. Several mechanistic models of cholera transmission have included rainfall as a driver by focusing on two possible transmission pathways: either by increasing exposure to contaminated water (e.g. due to worsening sanitary conditions during water excess), or water contamination by freshly excreted bacteria (e.g. due to washout of open-air defecation sites or overflows). Our study assesses the explanatory power of these different modeling structures by formal model comparison using deterministic and stochastic models of the type susceptible-infected-recovered-bacteria (SIRB). The incorporation of rainfall effects is generalized using a nonlinear function that can increase or decrease the relative importance of the large precipitation events. Our modelling framework is tested against the daily epidemiological data collected during the 2015 cholera outbreak within the urban context of Juba, South Sudan. This epidemic is characterized by a particular intra-seasonal double peak on the incidence in apparent relation with particularly strong rainfall events. Our results show that rainfall-based models in both their deterministic and stochastic formulations outperform models that do not account for rainfall. In fact, classical SIRB models are not able to reproduce the second epidemiological peak, thus suggesting that it was rainfall-driven. Moreover we found stronger support across model types for rainfall acting on increased exposure rather than on exacerbated water contamination. Although these results are context-specific, they stress the importance of a systematic and comprehensive appraisal of transmission pathways and their environmental forcings when embarking in the modelling of epidemic cholera.

A change of perspective in network centrality.

Typing "Yesterday" into the search-bar of your browser provides a long list of websites with, in top places, a link to a video by The Beatles. The order your browser shows its search results is a notable example of the use of network centrality. Centrality measures the importance of the nodes in a network and it plays a crucial role in several fields, ranging from sociology to engineering, and from biology to economics. Many centrality metrics are available. However, these measures are generally based on ad hoc assumptions, and there is no commonly accepted way to compare the effectiveness and reliability of different metrics. Here we propose a new perspective where centrality definition arises naturally from the most basic feature of a network, its adjacency matrix. Following this perspective, different centrality measures naturally emerge, including degree, eigenvector, and hub-authority centrality. Within this theoretical framework, the effectiveness of different metrics is evaluated and compared. Tests on a large set of networks show that the standard centrality metrics perform unsatisfactorily, highlighting intrinsic limitations for describing the centrality of nodes in complex networks. More informative multi-component centrality metrics are proposed as the natural extension of standard metrics.