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Food loss and waste is increasingly becoming a topic of great public concern: in 2011, FAO presented the estimate that around one third of the world’s food was lost or wasted every year and SDG 12 (“Sustainable consumption and production”) from Agenda 2030 includes among its targets to “halve per capita global food waste” and to “reduce food losses”. The impact on environmental resources is significant: in particular, 24% of total freshwater resources used in food crop production are lost in the different stages of food loss and waste. While in high-income countries food is mainly wasted at the consumer level, low-income ones record losses concentrated in the agricultural and post-harvest stages. Globally, food markets are telecoupled and globalized, so wasted food has effects on water resources in the whole supply chain, propagating along the trade network up to the countries of initial production, where water resources have been utilized, often through irrigation, altering the local hydrological cycle. The reconstruction of such network is one of the most challenging aspects of tracing the impact of food waste on water resources. The difficulties are due to the numerous food re-exports and nested supply chains, to the different origins of food waste (from production to distribution and consumption), and to the marked variability of the country-specific unit water footprints. As a key hypothesis, we assumed that in each country the ratio between imports and domestic production would be the same in both domestic consumption and exports, to cope with re-export feedbacks in the network. Focusing on the emblematic case of wheat and its derivatives (e.g., flour, bread, pasta), we were able to reconstruct the complex global network that connects losses and wastes at any stage along the supply chain with the corresponding wasted water resources.Our results show that, for most countries, the network is very extensive and involves many states around the world. For example, in 2016 over 20 foreign countries employed their water resources to produce wheat which in turn was wasted as bread in Italy at the consumer level, accounting for around 15% of the bread’s water footprint (870 m3/t). This highlights how much water resources are now globalized and that the waste of food in a country can impact even very distant water resources. We also quantify the contribution of each waste component, from agriculture’s field losses to consumers’ household wastes. For Italy, 54% of losses related to bread are at the consumption stage, while only 6% occur at the agricultural stage. Eventually, we present how the relative importance of each component varies, depending on the network of countries involved in the production, storing, processing, distribution and consumption of food.

Direct model output forecasts by Numerical Weather Prediction models (NWPs) present some limitations caused by errors mostly due to sensitivity to initial conditions, sensitivity to boundary conditions and deficiencies in parametrization schemes (i.e. orography).These sources of error are unavoidable, and atmospheric chaotic dynamics make prediction errors spread rapidly in time in the course of the forecast, inducing both systematic and random errors.Nonetheless, in the last 50 years, NWPs had a significant decrease in the impact of these sources of errors, even in the long-term forecast, thanks for instance to an ever-increasing computational capability, but their relevance is still not neglectable.Moreover, different NWPs present specific different pros and cons which are findable empirically. For instance, in the case of precipitation forecast in north-west Italy, low-resolution models (e.g. ECMWF-IFS) are more reliable in terms of space and time in predicting the average precipitation, while high-resolution models (e.g. COSMO-2I) tend to forecast better the maximum precipitation. Research purposes apart, actual limitations must be seen in an operational context, where weather forecasts’ skillfulness and associated uncertainty are information of the utmost importance to the forecaster and in general to the user of a certain forecasts system.To tackle these limitations of NWPs and the need for an uncertainty-quantified meteorological forecast, we propose a machine learning-based multimodel post-processing technique for precipitation forecast. We focus on precipitation since it is the most important variable in the issue of spatially localized weather alert notice by the Italian Civil Protection system and at the same time it is one of the most challenging variables to forecast. We use a Convolutional Neural Network (CNN) to obtain deterministic and probabilistic forecast grids over 24h up to 48h focusing on North-West Italy, using several high and low-resolution deterministic NWPs as input and using high-resolution rain-gauge corrected radar observations for the training. The effect of the usage of different convolutional parameters (e.g. stride, padding) is taken into account. The deterministic output grid is chosen as the grid with the lowest mean square error obtained during the training, and it is compared with the linear regression of the input NWPs and with every single model. The probabilistic output grid is generated by considering the statistical ensemble of the twenty grids with the lowest mean square error obtained during the training, and it is compared with the logistic regression of the input NWPs and with ECMWF-EPS as a benchmark, both at different precipitation thresholds.

Traditional regionalization methods allow estimating hydrological variables in stationary conditions: in the context of climate change new techniques are sought, which take into account the non-stationarity of climate variables. As part of a project in collaboration between universities and the Po basin authority, different approaches including regionalization procedures are used to characterize the hydrological extremes in the Po River basin. In particular, we use the Spatially Smooth Regional Estimation method, which is based on multiregressive estimation of L-moments that do not require the definition of homogeneous regions. The regression models are based on morpho-climatic descriptors including climate variables such as the mean annual precipitation, and the coefficients of a model for the IDF curves. By analyzing the multi-year variability of the climatic variables in each basin, with this work we aim at: (i) comparing the trends of the climatic variables and the trend of discharges associated with different return periods, (ii) analyzing the sensitivity of the regression equations to changes in time of these variables. Moreover, we compare the rainfall and flood quantiles for each sub-basin, to evaluate the percentage change of the standardised flood discharge for the percentage change in extreme rainfall. This approach allows us to investigate the effects of rainfall mechanisms and catchment characteristics on flood probabilities in the Po River basin.

Innovation and technological progress are the main drivers of sustainable development and economic growth, and they both play a role in addressing climate change and related actions. These drivers rely on Research and Development (R&D), i.e., the systematic creative work aiming to increase the stock of knowledge and devoted to the creation and development of new products and procedures. Against the existing literature that differently addresses the economic implications of the R&D sector, in this work, we introduce a novel quantification of the R&D content embedded in countries’ export baskets. Considering the current need to understand the dynamics of CO2 emissions and their nexus with the economic aspects, the R&D content in nations’ export baskets is related to the country-specific terrestrial carbon emissions. To this aim, we refer the CO2 emissions embedded in nations' export baskets to the dollars the country at hand exports, defining a country-specific CO2 export intensity; in this way, we can compare economies of different sizes (for example, Germany and Paraguay). Our results show that as countries export products with an increasing R&D content, their CO2 export intensity decreases. Germany, Japan, and the United States are examples of countries exporting high R&D products and having low CO2 export intensity. China stands as an example of elevated CO2 intensity despite having a high R&D content embedded in its export basket. Fuel exporting economies (such as the Russian Federation) and the majority of developing countries have low R&D-oriented export baskets, with high CO2 export intensities. Our work provides a novel perspective of the R&D-CO2 emissions nexus, highlighting the R&D centrality in the green transition and decarbonization process.

Fossil fuels are not distributed evenly throughout the world, and hence the countries rely heavily on international trade to secure energy supply. Characterization of the energy trade network is needed to conduct long-term assessments of energy security. This study proposes a modeling framework to assess the evolution of energy trade under current conditions as well as under future scenarios up to 2050. The total trade of each country is estimated with trade predictive models (TPMs) using key variables. Subsequently, a matrix-balancing method (RAS) is used to estimate the annual bilateral trades. The projected energy trade network in 2050 varies under each shared socioeconomic pathway (SSP) of the future, with annual fossil fuel global trades among countries ranging between 538 and 215 EJ. Canada, USA, Venezuela, and China are projected to dominate the global trade network, with Canada-USA remaining the most dominant fossil fuel trade link up to 2050.

The virtual water (VW) trade associated to food is composed by the quantity of water utilized for the production of the crops exchanged on the global market. In assessing a country’s water abundance or scarcity when entering the international VW trade, scholars consider only physical water availability, neglecting economic water scarcity, which indicates situations in which socio-economic obstacles impede the productive use of water. We weight the global VW trade associated to primary crops with a newly proposed composite water scarcity index (CWSI) that combines physical and economic water scarcity. 39% of VW volumes is exported from countries with a higher CWSI than the one of the destination country. Such unfair routes occur both from low- to high-income countries and among low- and middle-income countries themselves. High-income countries have a predominant role in import of CWSI-weighted VW, while low- and middle-income countries dominate among the largest CWSI-weighted VW exporters. For many of them economic water scarcity dominates over physical scarcity. The application of the CWSI elicits also a status change from net exporter to net importer for some wealthy countries and viceversa for some low- and middle-income countries. The application of CWSI allows one to quantify to what extent VW exchanges flow along environmentally and economically unfair routes, and it can inform the design of compensation policies.

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.

High water availability enhances agricultural performance and food security. However, many countries where water is abundant according to hydrological indicators face difficulties in the utilization of water in agriculture, being in a situation of economic water scarcity (EWS), due to lack of institutional and material means for water management and governance. EWS faces a stronger challenge of measurability, if compared to physical water scarcity. Since the Sustainable Development Goal Indicator on Integrated management of domestic and transboundary water resources (IWRM) is a unique attempt to quantify information on water management at a national level, we explore whether it can represent a valid metric for EWS measurement. We first show that a high level of water management is neither necessarily associated to high economic power of the country nor to low physical water availability. Then, we analyze whether the indicator can predict typical EWS situations such as low agricultural productivity and inefficient water use. Although the importance of water institutions for agriculture is well known through case studies at the local level, we make the first attempt to quantify the strengths of this relation at a global scale for different crops in climatic diverse countries. We detect a positive and significant association between IWRM level and yield, and consequently a negative and equally significant association between the IWRM level and the crop water footprint. Statistical significance holds also when potentially confounding variables are included in a multiple regression analysis. We infer from this analysis that good water management, as detectable through the IWRM indicator, improves land productivity and water saving, in turn mitigating EWS. Our findings pave the way toward the use of the IWRM indicator as a valuable tool for measuring EWS in agriculture, bridging the measurability gap of economic water scarcity, with straightforward policy implications in favour of investments in water management as a lever for enhancing food security and development.

The world is currently witnessing a rapid exacerbation of the effects of climate change on anthropic and environmental systems. Through the latest Assessment Report 6, the Intergovernmental Panel on Climate Change (and so the EU with the Climate Change Adaptation strategy) has launched an urgent call to action to implement mitigation and adaptation strategies, to improve the resilience of these systems. Climate models are complex, requiring multi-disciplinary knowledge about climatology, physics, hydrology, hydraulics, mathematics and statistics, among others, to be conceived and implemented. Complexity is not limited to the model preparation and functioning, but it extends to the interpretation of the outputs by the users. Models’ assumptions and the uncertainties related to the outcomes pose an issue of accessibility and usability in the short period, with consequences on the decision-makers' (corporations, and governments) ability to correctly address the issues at hand.Several requirements to conduct climate risk assessment have been and are being developed by governmental and non-governmental organizations, particularly for infrastructure projects, and this is creating a demand for new services besides the traditional engineering and scientific services. Golder Associates is a global consulting firm providing services to governments and corporations, with a particular emphasis on the energy and infrastructure sectors. Golder has seen an increase in demand for Climate Risk Assessment services, requiring up-to-date climate data and projections to determine the current and future exposure, hazard, and vulnerability to climate change of its clients’ assets and activities. The firm stands as an example of the challenges in translating the results and uncertainties of climate models and data into adaptation and mitigation strategies, often leading to an increase in uncertainties in major capital investments.To address this issue, we are developing a decision-support toolbox named CLOUDS (CLimate OUtputs for Decision Support) to help identify and calculate a set of key performance indicators and variables. The aim of CLOUDS is to provide a more straightforward representation of the complexity of the climate models’ outputs, still maintaining the accuracy of the estimates of climate-change effects but addressing the needs of decision-makers. CLOUDS consist of methodologies and routines, derived from the available suite of global circulation models, a set of indicators useful to decision-makers in preparing climate risk assessment analysis of existing assets and future infrastructure projects. The indicators are chosen considering their ability to define the exposure, hazards, and vulnerability to climate change in various contexts, and their connection with the output of the models. The advantage of creating such a toolbox in cooperation and collaboration with a consultancy firm stands in the opportunity to test and adapt the toolbox on a wide range of projects in different business sectors, geographic conditions, and sizes. Therefore, this allows us to study the effectiveness of CLOUDS and by comparing its performances in terms of time and cost with projects using other decision-making tools. Finally, CLOUDS fosters the transfer of knowledge between the academic, the governmental, and the business communities, required to face the consequences of climate change.

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).

The EAT-LancetCommission has proposed a global benchmark diet to guide the shift towards healthy and sustainable dietary patterns. Yet, it is unclear whether consumers’ choices are convergent with those guidelines. Applying an advanced statistical analysis, we mapped the diet gap of 15 essential foods in 174 countries over 1961-2018. We found that countries at the highest level of development have an above-optimal consumption of animal products, fats, and sugars, but a sub-optimal consumption of legumes, nuts, and fruits. Countries suffering from limited socioeconomic progress primarily rely on carbohydrates and starchy roots. A gradual change towards healthy and sustainable dietary targets can be observed for seafood, milk products, poultry, and vegetable oils. We show that if all countries adopted the EAT-Lancetdiet, water footprint would fall by 12% at the global level but increase for nearly 40% of the global population.

Global food prices are typically analysed in a time-series framework. We complement this approach by focusing on the spatial price dispersion of the country-pair bilateral trade in the international food trade network (IFTN), for ten relevant commodities. The main purposes are to verify if the Law of One Price (LOP) holds and to investigate the emergence of randomness in the price-formation mechanism. We distinguish between the “internal” variance, which indicates the magnitude of price discrimination, and the “external” variance, that is a measure of price dispersion. We find that, for some commodities, spatial price dispersion is remarkable and persistent over time (i.e., failure of the LOP) and that there exists a strict correlation between price spikes and peaks in spatial price variability. We test whether the price distribution can be replicated through a stochastic process of extraction. Surprisingly, the actual distribution of prices, for several commodities, is well described by a random distribution. Then, the process of data aggregation is not neutral because the information at the micro-level scale might be lost at the macro-scale, due to the complexity of the IFTN. Finally, we discuss some possible economic explanations of these outcomes and the main methodological, environmental, and policy consequences.

The virtual water (VW) trade associated to food is composed by the quantity of water utilized for the production of the crops exchanged on the global market. In assessing a country’s water abundance or scarcity when entering the international VW trade, scholars consider only physical water availability, neglecting economic water scarcity, that indicates situations in which socio-economic obstacles impede the productive use of water. We weight the global VW trade associated to primary crops with a newly proposed composite water scarcity index (CWSI) that combines physical and economic water scarcity. Almost half of VW volumes is exported from countries with a higher CWSI than the one of the destination country. Such unfair routes occur both from low- to high-income countries and among low- and middle-income countries themselves. High-income countries have a predominant role in import of CWSI-weighted VW, while low- and middle-income countries dominate among the largest CWSI-weighted VW exporters. For many of them economic water scarcity dominates over physical scarcity. The application of the CWSI elicits also a status change from net exporter to net importer for some wealthy countries and viceversa for some low- and middle-income countries. The application of CWSI allows one to quantify to what extent VW exchanges flow along environmentally and economically unfair routes, and it can inform the design of compensation policies.

The Agenda 2030 of sustainable development introduced in 2015 by the United Nations is a call for action to address the major challenges the world faces [1]. To tackle these challenges, the Agenda defines the 17 Sustainable Development Goals (SDGs), which have been conceived with respect to five pillars (planet, people, prosperity, peace and partnership), thus creating synergies and trade-offs among the Goals. The Agenda also addresses the need for more targeted policy implementations, totaling 169 targets across the Goals. Moreover, indicators have been defined to measure progresses in each target, and so, Goal.To create aggregated scores of such countries’ performance indicators is a recurrent and crucial issue within the SDGs framework, where several methodologies have been proposed to create a ranking of countries which can provide insights about the fulfillment of all of the Agenda’s objectives and principles (see, e.g., Sachs et. al. [2] and Biggeri et al. [3]). In light of the complex nature of the Agenda (as pointed out by LeBlanc [4]), we argue that the use of multidisciplinary tools is essential to help shed light on how to address efforts in global sustainable development. In particular, network theory can be used to create several aggregated scores that can actually account for the complex nature of the Agenda, the synergies and trade-offs among the Goals and, no less, of the role of countries toward the achievement of SDGs.In this work, we recast the data concerning the performances of countries in each Goal’s indicators as the incidence matrix of a bipartite system constituted of two sets: countries and Goals, connected by the performances of countries within each Goal. We exemplify our framework using the data taken from the 2020 SDG Index and Dashboard by Sachs et al. [2]. We show that, framed within network science, the SDG Index coincides with measuring the degree centrality of countries within this bipartite system and that such measure neglects the heterogeneity of countries in tackling the Goals and their responsibilities at the global scale. More informative centrality measures, and so, aggregated scores, can be obtained by the adoption of the economic complexity theory, in particular, the GENEPY framework [5]. The GENEPY rationale defines a data-driven weighting scheme in which relative countries’ performances of all SDGs are considered to define a more comprehensive ranking of countries.References:[1] Transforming our world: the 2030 Agenda for Sustainable Development. Division for Sustainable Development Goals: New York, NY, USA, 2015.[2] Sachs, J., et al. . 2020. The Sustainable Development Goals and COVID-19. Sustainable Development Report 2020. Cambridge: Cambridge University Press.[3] Biggeri, M., et al. (2019). Tracking the SDGs in an ‘integrated’ manner: A proposal for a new index to capture synergies and trade-offs between and within goals. World Development, 122, 628-647.[4] Le Blanc, D. (2015). Towards integration at last? The sustainable development goals as a network of targets. Sustainable Development, 23(3), 176-187.[5] Sciarra, C., et al. (2020). Reconciling contrasting views on economic complexity. Nature Communications, 11(1), 1-10.

Through the international trade of agricultural goods, water resources that are physically used in the country of production are virtually transferred to the country of consumption. Food trade leads to a global redistribution of freshwater resources, thus shaping distant interdependencies among countries. Recent studies have shown how agricultural trade drives an outsourcing of environmental impacts pertaining to depletion and pollution of freshwater resources, and eutrophication of river bodies in distant producer countries. What is less clear is how the final consumer – being an individual, a company, or a community- impacts the water resources of producer countries at a subnational scale. Indeed, the variability of sub-national water footprint (WF in m3/tonne) due to climate, soil properties, irrigation practices, and fertilizer inputs is generally lost in trade analyses, as most trade data are only available at the country scale. The latest version of the Spatially Explicit Information on Production to Consumption Systems model (SEI-PCS) by Trase provides detailed data on single trade flows (in tonne) along the crop supply chain: from local municipalities- to exporter companies- to importer companies – to the final consumer countries. These data allow us to capitalize on the high-resolution data of agricultural WF available in the literature, in order to quantify the sub-national virtual water flows behind food trade. As a first step, we assess the detailed soybean trade between Brazil and Italy. This assessment is relevant for water management because the global soybean flow reaching Italy may be traced back to 374 municipalities with heterogeneous agricultural practises and water use efficiency. Results show that the largest flow of virtual water from a Brazilian municipality to Italy -3.52e+07 m3 (3% of the total export flow)- comes from Sorriso in the State of Mato Grosso. Conversely, the highest flow of blue water -1.56e+05 m3- comes from Jaguarão, in the State of Rio Grande do Sul, located in the Brazilian Pampa. Further, the analysis at the company scale reveals that as many as 37 exporting companies can be identified exchanging to Italy; Bianchini S.A is the largest virtual water trader (1.88 e+08 m3 of green water and 3,92 e+06 m3 of blue water), followed by COFCO (1,06 e+08 m3 of green water and 6.62 m3 of blue water) and Cargill ( 6.96 e+07 m3 of green water and 2.80 e+02 m3 of blue water). By building the bipartite network of importing companies and municipalities originating the fluxes we are able to efficiently disaggregate the supply chains , providing novel tools to build sustainable water management strategies.

Given the importance of food imports for food security and the role of exports in income generation, food trade is an indispensable component of most countries’ development strategies. Global and regional agreements set the rules for trade policies between countries. In this context, we investigate the impact of trade agreements on the trade network of agricultural products. We study whether the ratification of agricultural-oriented trade agreements has an influence on the topology of the cereal trade network (link establishment) and the variation of flows through existing links.Our analysis differs from previous studies for three main reasons. Firstly, it is a data-driven analysis, based on a dataset that combines the trade agreement structure provided by the World Bank and cereal trade flow data from FAOSTAT. Secondly, the analysis focuses on a global scale, considering data for all countries where information is available. Finally, we carried out the analysis at the level of aggregated cereals, both from a monetary (US$) and diet-based (Kcal) perspective, over the period from 1993 to 2015. This time interval includes the most important recent reforms in the agricultural sector.The results show that a new trade agreement between two countries increases the probability of activating a grain trade link by 7.3% in the year after the agreement is ratified. In the case where trade agreements are not considered, the probability of triggering a new link between two countries drops to 1.3%.Regarding the volume of flows, we classify variations into three categories: flow decrease (negative variation of the flux), mild increase (50% increase).The results obtained, both in economic value (US$) and in quantitative variations (Kcal), show that the entry force of a trade agreement has two main effects: in flows covered by trade agreements, there is a significant increase in the percentage of flows experiencing a sharp increase, and a reduction of the percentage of flows experiencing a negative variation. We, therefore, provide here global-scale, data-based evidence. Previous results suggest that trade agreements are facilitators of the connections between different countries and, therefore, facilitators in terms of global food trade accessibility. This work aims to be a first attempt to investigate the impacts of international agreements simultaneously on the topology of the agricultural product trade network, and on the increase of existing link flows. Our intention is to dedicate further analysis about which trade agreements perform better, increasing the traded volume, to explore the role of trade liberalization at a worldwide level.

Clean water scarcity plagues several hundred million people worldwide, representing a major global problem. Nearly half of the total population lacking access to safe and drinkable water lives in Africa. Nonetheless, the African continent has a remarkable yet untapped potential in terms of renewable energy production, which may serve to produce clean water from contaminated or salty resources and for water extraction and distribution. In this view, the analysis of possible scenarios relies on data-driven approaches due to the scale of the problem and the general lack of comprehensive, direct on-site experience. In this work, we aim to systematically review and map the renewable potentials against the freshwater shortage in Africa to gain insight on perspective possible policies and provide a readily usable and well-structured framework and database for further analyses. All reported datasets are critically discussed, organized in tables, and classified by a few metadata to facilitate their usability in further analyses. The accompanying discussion focuses on regions that, in the near future, are expected to significantly exploit their renewable energy potentials, and on the reasons at the basis of the local water shortage, including technological and distribution problems.

Increasing population and changing diets toward larger proportion of meat products have driven agricultural production increase over the past decades and will probably push it in the upcoming years. The analysis of the agricultural production increase is at the centre of the international debate since the 1800-century Malthusian prediction of exponentially growing population outstripping linearly increasing production.In this study, we show how agriculture has changed over the past decades through the concept of a newly developed land productivity (LP) indicator, which measures the amount of calories, proteins, and fats produced per hectare of land and merges the variegate macronutrients spectrum of a 140-crops production basket. Land productivity indicator adds to the more widespread (crop specific) yield indicator the nutrient content of each product.We find that the global LP has increased by 2.6-2.9% per year over 1961-2016 for calorie and protein, and 3.7% for fat. This confirms an important boost of the global productive regime whose growing rate has been able to overcome that of population. Humans can rely on larger amounts of calories (+1640 kcal/cap/day), proteins (+69 g/cap/day), and fats (+55 g/cap/day) supply. In this global picture, different macro-regions exhibit relevant heterogeneities. In particular, we found that Eastern Asian and Latin American countries could escape the Malthusian trap around the Nineties through both LP increase and ad hoc variation in the composition of their basket of products. However, this transition seems far to happen in Sub-Saharan Africa and South Asia, where the daily productive regime has remained stable since the Sixties, despite the variation of the basket composition.