The Value of Seasonal Productivity Forecasting in Biodiesel Plans

Crop productivity is commonly assumed as a deterministic function when developing agricultural plans. Actual data prove however that, even for the same soil at the same location, crop productivity can be better interpreted as a random variable due to the meteorological conditions of the specific year. For the production of biodiesel, crops are easily substitutable and the farmer can chose every year between various alternatives. Without information on the seasonal meteorology, the farmers select the crop to cultivate mainly on the basis of the expected productivity. However, changes in the meteorological situation may result in a reduction in crop profitability. As a result, a crop, that on average is less interesting, may become the best choice in a specific year. Given that seasonal forecasts based on long range climatic variables, such as ENSO, are becoming available, the paper examines their effectiveness in biodiesel production plans, with reference to an area in Mato Grosso, Brazil. We formulate and solve a mathematical programming problem to determine the most efficient crop plan under different scenarios: (i) no information about the seasonal meteorology, (ii) perfect information and (iii) meteorological forecasts with different precision. This allows us to quantitatively evaluate how important the availability of seasonal productivity forecasting might be and shows that even a rough seasonal forecast, if systematically applied, may improve the average production and reduce the risk of traditional agricultural decisions

Assessing Crop Portfolios: Diversification versus Monoculture for Biodiesel

The selection of crop patterns for agricultural areas is usually guided by the maximization of expected income. This variable is, however, influenced by the fluctuations of both crop productivity and prices. The annual variability is directly related to the risk of a crop portfolio and, according to the so called Modern Theory Portfolio (MTP), it is a fundamental aspect to be taken into account during the selection of crops. This is true especially in case of those farmers who are not wealthy. Crop diversification is considered an effective solution able to alleviate the abovementioned inter-annual fluctuations and to guarantee a safe minimum income. This being the context we assess different alternative crop portfolios for biodiesel production in Brazil, where many small and resource-poor ones co-exist with capital intensive and large-scale farms (units of less than 20 hectares constitute more than 60% of the total farm number). By adopting the MTP approach, we aim to compare two alternative and opposite strategies: monoculture and crop diversification for biofuel crop production. In particular, we evaluate the effectiveness of crop diversification in reducing the risk of crop portfolios and estimate possible losses in terms of expected incomes. The obtained results confirm that the choice of a mixed crop portfolio can guarantee the minimum risk in the majority of the analyzed cases, but the incomes are considerably lower than the ones obtained with monocultures. Nevertheless, the obtained outcomes vary considerably depending on the considered crop. Finally, an increment of diversity could have improved both expected income and risk of actual average national crop portfolio, which is close to soybean monoculture

One Day Ahead Prediction of Wind Speed Class by Statistical Models

This paper deals with the clustering of daily wind speed time series based on two features, namely the daily average wind speed and the corresponding degree of fluctuation. Daily values of the feature pairs are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes allows performing some useful statistics. Further, the problem of predicting the class of daily wind speed 1-step ahead is addressed by using both the Hidden Markov Models (HMM) and the Non-linear Auto-Regressive (NAR) approaches. The performances of the considered class prediction models are finally assessed in terms of True Positive rate (TPR) and True Negative rate (TNR), also in comparison with the persistent model

One Day Ahead Prediction of Wind Speed Class

This paper deals with the problem of clustering daily wind speed time series based on two features referred to as Wr and H, representing a measure of the relative daily average wind speed and the Hurst exponent, respectively. Daily values of the pairs (Wr ; H) are first classified by means of the fuzzy c-means unsupervised clustering algorithm and then results are used to train a supervised MLP neural network classifier. It is shown that associating to a true wind speed time series a time series of classes, allows performing some useful statistics. Further, the problem of predicting 1-step ahead the class of daily wind speed is addressed by introducing NAR sigmoidal neural models into the classification process. The performance of the prediction model is finally assessed

Assessing the value of systematic cycling in a polluted urban environment

The positive health effects of systematic cycling are weighted against the negative effects due to higher pollutant inhalation in the actual case of the city of Milan in northern Italy. The paper first evaluates the actual use of bikes in the city, and then considers why and how much such an active mobility style can be expanded. Two models are used to compare the outcome of cycling on the specific population sample with the equivalent path travelled by car. The first model computes the long term effects of the physical activity, and the second evaluates the exacerbation of some relevant diseases due to the exposure to high levels of pollutants, in the case at hand, mainly particulate matter with diameter smaller than 10 μm (PM10). According to these two models, the overall balance for public health is always in favour of systematic biking. Even the current level of biking, low in comparison to other European cities, allows a considerable economic advantage on the order of tens of millions euros per year. This may increase to hundreds of millions if the biking level of more bike-friendly cities is reached. Despite being much less relevant from the economic viewpoint, the study also estimates the reduction of pollution and greenhouse gas emissions corresponding to the assumed biking levels

Integrating Economy, Energy, Air Pollution in Building Renovation Plans

Residential buildings represent a considerable portion of the energy demand of a temperate country. Old European regions, where most of the buildings were often built in periods of low energy prices, have a large margin for improvement. The study shows how energy saving measures can be optimally planned at regional level, taking into account the specific features of the building stock, and what the consequences of an optimal choice are in economic and environmental terms

NN-based implicit stochastic optimization of multi-reservoir systems management

Multi-reservoir systems management is complex because of the uncertainty on future events and the variety of purposes, usually conflicting, of the involved actors. An efficient management of these systems can help improving resource allocation, preventing political crisis and reducing the conflicts between the stakeholders. Bellman stochastic dynamic programming (SDP) is the most famous among the many proposed approaches to solve this optimal control problem. Unfortunately, SDP is affected by the curse of dimensionality: computational effort increases exponentially with the complexity of the considered system (i.e., number of reservoirs), and the problem rapidly becomes intractable. This paper proposes an implicit stochastic optimization approach for the solution of the reservoir management problem. The core idea is using extremely flexible functions, such as artificial neural networks (NN), for designing release rules which approximate the optimal policies obtained by an open-loop approach. These trained NNs can then be used to take decisions in real time. The approach thus requires a sufficiently long series of historical or synthetic inflows, and the definition of a compromise solution to be approximated. This work analyzes with particular emphasis the importance of the information which represents the input of the control laws, investigating the effects of different degrees of completeness. The methodology is applied to the Nile River basin considering the main management objectives (minimization of the irrigation water deficit and maximization of the hydropower production), but can be easily adopted also in other cases

Balancing Externalities and Industrial Costs in Air Quality Planning

When adopting regional plans aimed at improving air quality, environmental authorities are often faced with the relevant costs that the adoption of abatement measures implies. On the other hand, scientific literature has well documented damages due to air pollution impact on human health and ecosystems. This paper proposes a tool that allows balancing these two viewpoints by defining the efficient set of measures in a multi-objective perspective. Despite both external (health related) and internal (industrial/emission abatement related) costs can be measured in the same unit, namely money, it appears unacceptable to add them together as in a cost-benefit analysis, since they pertain to quite different social groups. The tool proposed in this paper can thus be seen as a support to actual decision makers and allows them to compare in a ponderable way the pros and cons of any abatement policy. This contrasts what normally happens when air quality health impacts are simply defined as the satisfaction of a constraint at few specific points in space (coincident with the presence of measurement gauges). Indeed, both population and ecosystems are distributed in a non-uniform way on a territory and thus sparse point measurements of pollutant concentrations or other related air quality indicators may be only loosely related with the real impacts of air quality. An application of the tool to a European region (Lombardy, Italy) is presented with particular reference to PM10 and Ozone pollution problems. These are particularly difficult to cope with, since these pollutants are mainly formed in the atmosphere (secondary pollutants) and thus their concentration depends on chemical physical processes involving in different way on one side the emission of precursors and, on the other, the local meteorological conditions.JRC.H.2-Air and Climat

A model of the role of education in 2015 UN international migration data.

Migration on a global scale is clearly linked to a multiplicity of causes that, in addition to economic factors, include conflicts, natural disasters and local political conditions. However, it is possible to pinpoint what are the main variables that underlie migratory movements between macro areas of the planet. This study analyzes the 2015 UN report data and interprets them through a gravitational model, whose independent variables are indicators of the socio-economic situation of a population. Progressively eliminating the less significant contributions, the level of education of migrants emerges as one of the most important factors

The greatest air quality experiment ever: Policy suggestions from the COVID-19 lockdown in twelve European cities

COVID-19 (Coronavirus disease 2019) hit Europe in January 2020. By March, Europe was the active centre of the pandemic. As a result, widespread "lockdown" measures were enforced across the various European countries, even if to a different extent. Such actions caused a dramatic reduction, especially in road traffic. This event can be considered the most significant experiment ever conducted in Europe to assess the impact of a massive switch-off of atmospheric pollutant sources. In this study, we focus on in situ concentration data of the main atmospheric pollutants measured in twelve European cities, characterized by different climatology, emission sources, and strengths. We propose a methodology for the fair comparison of the impact of lockdown measures considering the non-stationarity of meteorological conditions and emissions, which are progressively declining due to the adoption of stricter air quality measures. The analysis of these unmatched circumstances allowed us to estimate the impact of a nearly zero-emission urban transport scenario on air quality in 12 European cities. The clearest result, common to all the cities, is that a dramatic traffic reduction effectively reduces NO2 concentrations. In contrast, each city’s PM and ozone concentrations can respond differently to the same type of emission reduction measure. From the policy point of view, these findings suggest that measures targeting urban traffic alone may not be the only effective option for improving air quality in cities.Peer ReviewedArticle signat per 19 autors/es: Marialuisa Volta 1, Umberto Giostra 2, Giorgio Guariso 3, Jose Baldasano 4, Martin Lutz 5, Andreas Kerschbaumer 5, Annette Rauterberg-Wulff 5, Francisco Ferreira 6, Luısa Mendes 6, Joana Monjardino 6, Nicolas Moussiopοulos 7, Christos Vlachokostas 7, Peter Viaene 8, Janssen Stijn 8, Enrico Turrini 1, Elena De Angelis 1, Claudio Carnevale 1, Martin L. Williams 9, Michela Maione 2,10 // 1 Dipartimento di Ingegneria Meccanica e Civile, Università di Brescia, Brescia, Italy; 2 Dipartimento di Scienze Pure e Applicate, Università di Urbino Carlo Bo, Urbino, Italy; 3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy; 4 Centro Nacional de Supercomputación, Universitat Politècnica de Catalunya, Barcelona, Spain; 5 Senatsverwaltung für Umwelt, Mobilität, Verbraucher-und Klimaschutz, Berlin, Germany; 6 Departamento de Ciências e Engenharia do Ambiente, Faculdade de Ciencias e Tecnologia Universidade Nova de Lisboa, Caparica, Portugal; 7 Aristoteleio Panepistemio Thessalonikes, Thessalonike, Greece; 8 VITO, Vision on Technology, Mol, Belgium; 9 Environmental Research Group, Imperial College, London, United Kingdom; 10 Istituto di Scienze dell’Atmosfera e del Clima, Consiglio Nazionale delle Ricerche, Bologna, ItalyPostprint (published version