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To curb the impacts arising from the agricultural sector, the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi-objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint 3.0 life cycle impact assessment method and the study is conducted "from cradle to farm gate". The model is applied to all the farms in Luxembourg, whose network is built utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e., when farmers' green consciousness is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as the Environmental Footprint single score) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried out using the single score impact in the objective function, though this results in an 8% reduction in profitability over 10 years. [ABSTRACT FROM AUTHOR]

Biowaste and manure are resources readily available as feedstock for biogas production. Possible scenarios with increased use of biowaste and manure for biogas production in the Grand Duchy of Luxembourg are investigated in this study using an Agent-Based Model (ABM) coupled with Life Cycle Assessment (LCA). ABMs are particularly suitable to simulate human-natural systems, since they allow modelers to consider behavioral aspects of individuals. On the other hand, when it comes to the assessment of a system's environmental sustainability, LCA is largely recognized as a sound methodology and widely used in research, industry, and policy making. The paper simulates three different scenarios that reproduce 10 years and can help policymakers building emission mitigation strategies. The aim is to increase the number of biogas plants or change the feedstock composition for anaerobic digestion in Luxembourg whilst observing the expected environmental impacts generated by these changes. The first scenario (Scenario A) is the baseline scenario, which simulates the current situation, with 24 operating biogas plants. The results of Scenario A show that, on average, 63.02 GWh of electricity production per year is possible from biogas. The second scenario (Scenario B) foresees an increase in the manure share (which is initially 63%) in the biogas feedstock composition along with an increase in the number of biogas production plants. The third scenario (Scenario C) only concerns increasing the amount of manure in the feedstock composition without the introduction of new plants. The results of Scenario C show that an 11% increase in electricity production is possible if more farms contribute to the production by bringing their excess manure to the biogas plant. This value is even higher (14%) in Scenario D where more biowaste is made available. The aggregated life-cycle impact assessment (LCIA) single scores, calculated with the ReCiPe method, show that Scenario C has the lowest impacts (although by only around 7% compared to the worst performing scenario, i.e., Scenario D), while Scenario D allows the highest electricity production (71.87 GWh in the last year of the simulation). As a result, the inclusion of more livestock farms into already established biogas cooperatives (as in Scenario C) can pave the way for an increase in electricity production from renewables and can bring a reduction in environmental impacts (more than 35% for the Terrestrial Ecotoxicity impact category and more than 27% in categories such as Agricultural Land Occupation, Marine Eutrophication and Water Depletion), thanks to the exploitation of manure for biogas production. [ABSTRACT FROM AUTHOR]

Simple Summary: The dairy industry is interested in developing a detection tool for milk produced from pasture to certify the protected designation of origin of certain dairy products. As the cattle's grazing influences the milk composition, the milk mid-infrared (MIR) spectra will be modified. Therefore, this study aims to develop a predictive model allowing one to use milk MIR spectrometry to classify milk as produced from a cattle diet either based on grass or not. To achieve this objective, the collection of grazing calendars from farms is needed. Unfortunately, this is hardly possible, as this information is rarely recorded. Therefore, the innovation of this research consists of using the usual farming practices developed in the southern part of Belgium combined with a large-scale milk database containing 48 milk composition-related traits. Indeed, in this geographical area, the cows are mainly on pasture between April and September. Therefore, the month of testing can be considered indirect information about their feeding. The developed models were able to distinguish with a precision of around 90% the supposed grass-based diet. Therefore, the probability of belonging to the GRASS class could be used in a tool counting the number of grazing days to confirm the labeling of dairy products. This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model's ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days. [ABSTRACT FROM AUTHOR]

Byline: Giorgio Beccali, Maurizio Cellura, Valerio Lo Brano, Antonino Marvuglia In order to meet the Italian Kyoto Protocol commitment, in 2002 the Interministerial Committee for Economic Planning (CIPE) approved the national allocation plan for the reduction of Greenhouse Gases (GHGs), envisioning various alternative scenarios. In this paper an extended impact assessment of the plan is accomplished and some further options are conceived and analysed in order to find an optimal trade-off among the different economic, environmental and social items at stake. After the selection of a set of independent indicators, nine different scenarios for the GHG reductions were evaluated and they were sorted according to the principles of the multicriteria analysis. The results of the evaluation process show that the preferable options are also the ones realising a good compromise between economic investment and reduction of GHG emissions. Although the model is necessarily affected by some subjective choices, it has a high level of transparency and scientific reliability.

Abstract Agriculture is one of the main assets of Pakistani and Indian economies, employing in both countries about 50% of the total labour force. Thus, improving agricultural sustainability in the Indo-Pak region has important implications for the local population as well as the rest of the world that relies on food imports from these countries. This article investigates the drivers and consequences of changes in crop production sustainability in India and Pakistan from an emergy-based perspective, from 2001 to 2011. However, due to the numerous crops cultivated in these regions, a detailed calculation of unit emergy values (UEVs) for each crop was not possible, therefore the paper presents a balance at country level (based on literature data for the crops' UEVs), rather than a canonical emergy accounting. The emergy perspective was chosen to holistically evaluate and compare the environmental pressures caused by crop production in both countries. Emergy-based indicators were calculated based on the real time series of input renewable and non-renewable sources. The major findings of the work revealed that purchased renewable inputs, such as irrigating water, and purchased non-renewable inputs, such as agricultural labor, are the largest contributors among the total inputs in both countries. Labor accounted for 46.79% and 60.59% of total emergy input for crop production in India and Pakistan respectively. Overall, the production efficiency in India was greater than that in Pakistan. Emergy of crop production in Pakistan witnessed an increase of only 23%, whereas India saw an increase of 42% during the study period. Despite the lack of data on each specific agricultural process, this trend is an evidence of the fact that, if on one side the agricultural activities in the two countries were intensified to supply an increasing population, on the other side, although India performed better than Pakistan, the sustainability of the agricultural practices (from a nature-oriented perspective as assessed with emergy analysis) in both countries did not improve.. Trends of carrying capacity indicated that intensive means of agricultural production are threatening natural resources in both countries. This study empirically demonstrates the need to conserve natural resources, especially water, which have been rapidly declining in these two countries. Since both countries share these resources, this study represents an evidence for the need to cooperate for transboundary natural resource management. [ABSTRACT FROM AUTHOR]

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The high-frequency on-line measurements coming from sensors installed in wastewater treatment plants (WWTPs) can be used for alert systems and decision support tools. Given the complexity of WWTPs, many authors developed efficient techniques for plant assessment. By contrast, few contributions are focussed on the identification of regime shifts in WWTPs on short-term, even if the early detection of occurring inefficiencies is a relevant information for plant managers. In order to fill this gap, the present paper uses a daily LCA + DEA analysis in order to monitor the potential deterioration of the eco-efficiency. This approach consists of an original combination of environmental life cycle assessment (LCA), data envelopment analysis (DEA), time series analysis and statistical tests. The main innovation in DEA algorithm consists of the set of decision-making units (DMUs), here composed of the 1-day operation datasets of a single WWTP. The results show a good performance in the regime shifts classification, for which the receiving operating characteristic (ROC) analysis returned a score of 0.8 (in a range 0–1). [ABSTRACT FROM AUTHOR]

Determining the relevance and importance of a technosphere process or a cluster of processes in relation to the rest of the industrial network can provide insights into the sustainability of supply chains: those that need to be optimized or controlled/safeguarded. Network analysis (NA) can offer a broad framework of indicators to tackle this problem. In this article, we present a detailed analysis of a life cycle inventory (LCI) model from an NA perspective. Specifically, the network is represented as a directed graph and the 'emergy' numeraire is used as the weight associated with the arcs of the network. The case study of a technological system for drinking water production is presented. We investigate the topological and structural characteristics of the network representation of this system and compare properties of its weighted and unweighted network, as well as the importance of nodes (i.e., life cycle unit processes). By identifying a number of advantages and limitations linked to the modeling complexity of such emergy-LCI networks, we classify the LCI technosphere network of our case study as a complex network belonging to the scale-free network family. The salient feature of this network family is represented by the presence of 'hubs': nodes that connect with many other nodes. Hub failures may imply relevant changes, decreases, or even breaks in the connectedness with other smaller hubs and nodes of the network. Hence, by identifying node centralities, we can rank and interpret the relevance of each node for its special role in the life cycle network. [ABSTRACT FROM AUTHOR]

Toxicity characterization of chemical emissions in Life Cycle Assessment (LCA) is a complex task which usually proceeds via multimedia (fate, exposure and effect) models attached to models of dose–response relationships to assess the effects on target. Different models and approaches do exist, but all require a vast amount of data on the properties of the chemical compounds being assessed, which are hard to collect or hardly publicly available (especially for thousands of less common or newly developed chemicals), therefore hampering in practice the assessment in LCA. An example is USEtox, a consensual model for the characterization of human toxicity and freshwater ecotoxicity. This paper places itself in a line of research aiming at providing a methodology to reduce the number of input parameters necessary to run multimedia fate models, focusing in particular to the application of the USEtox toxicity model. By focusing on USEtox, in this paper two main goals are pursued: 1) performing an extensive exploratory analysis (using dimensionality reduction techniques) of the input space constituted by the substance-specific properties at the aim of detecting particular patterns in the data manifold and estimating the dimension of the subspace in which the data manifold actually lies; and 2) exploring the application of a set of linear models, based on partial least squares (PLS) regression, as well as a nonlinear model (general regression neural network — GRNN) in the seek for an automatic selection strategy of the most informative variables according to the modelled output (USEtox factor). After extensive analysis, the intrinsic dimension of the input manifold has been identified between three and four. The variables selected as most informative may vary according to the output modelled and the model used, but for the toxicity factors modelled in this paper the input variables selected as most informative are coherent with prior expectations based on scientific knowledge of toxicity factors modelling. Thus the outcomes of the analysis are promising for the future application of the approach to other portions of the model, affected by important data gaps, e.g., to the calculation of human health effect factors. [ABSTRACT FROM AUTHOR]

Abstract: Luxembourg aims at complying with the EU objective of attaining a 14% use of bioenergy in the national grid by 2020. The increase of biomethane production from energy crops could be a valuable option in achieving this objective. However, the overall environmental benefit of such option is yet to be proven. Consequential Life Cycle Assessment (CLCA) has shown to be a useful tool to evaluate the environmental suitability of future energy scenarios and policies. The objective of this study was, therefore, to evaluate the environmental consequences of modifying the Luxembourgish agricultural system to increase maize production for biomethane generation. A total of 10 different scenarios were modelled using a partial equilibrium (PE) model to identify changes in land cultivation based on farmers' revenue maximisation, which were then compared to the baseline scenario, i.e. the state of the agricultural sector in 2009. The results were divided into three different consequential decision contexts, presenting differing patterns in terms of land use changes (LUCs) but with minor shifts in environmental impacts. Nevertheless, energy from maize production would imply substantially higher environmental impacts when compared with the current use of natural gas, mainly due to increases in climate change and agricultural land occupation impacts. The results are discussed based on the consequences they may generate on the bioenergy policy, the management of arable land, the changes in import–export flows in Luxembourg and LUCs in the domestic agricultural system. In addition, the specific PE+LCA method presented intends to be of use for other regional studies in which a high level of site-specific data is available. [Copyright &y& Elsevier]

Abstract: Emergy analysis is an environmental accounting approach which evaluates the memory of the geobiosphere exergy (environmental work) supporting economic systems, e.g. to make a product or service available via the use of natural resources. This paper presents the software SCALE (Software for CALculating Emergy based on life cycle inventories), which allows a rigorous and replicable calculation of the emergy associated to any product or service. The software exploits an ad hoc algorithm, fulfilling the emergy algebra rules, which requires as input the matrix describing a given system of interconnected processes, containing the amount of natural resources used by the output product and its linked technological processes. Starting from this matrix the algorithm performs a graph search across the corresponding network and records the output emergy associated with each of the explored paths linking the input nodes of the network (i.e. the emergy sources) to the output nodes (i.e. the studied products). The algorithm is specifically devised to be used with data coming (in matrix form) from any available life cycle inventory (LCI) database used in life cycle assessment (LCA) studies. Two case studies are presented to illustrate the features of the software: the first one deals with heat generation from grape marc pellets, the second with drinking water production. The use of large LCI databases for emergy calculation using SCALE could contribute to extend the application of emergy systems evaluation in the practice of sustainability science. To our knowledge, this is the first software tool which allows exact calculation of the emergy of products using LCI databases, and therefore it achieves an operational integration between the well-known and standardized LCI methodology and emergy evaluation. [Copyright &y& Elsevier]

Abstract: The estimation of a wind farm’s power curve, which links the wind speed to the power that is produced by the whole wind farm, is a challenging task because this relationship is nonlinear and bounded, in addition to being non-stationary due for example to changes in the site environment and seasonality. Even for a single wind turbine the measured power at different wind speeds is generally different than the rated power, since the operating conditions on site are generally different than the conditions under which the turbine was calibrated (the wind speed on site is not uniform horizontally across the face of the turbine; the vertical wind profile and the air density are different than during the calibration; the wind data available on site are not always measured at the height of the turbine’s hub). The paper presents a data-driven approach for building an equivalent steady state model of a wind farm under normal operating conditions and shows its utilization for the creation of quality control charts at the aim of detecting anomalous functioning conditions of the wind farm. We use and compare three different machine learning models – viz. a self-supervised neural network called GMR (Generalized Mapping Regressor), a feed-forward Multi Layer Perceptron (MLP) and a General Regression Neural Network (GRNN) – to estimate the relationship between the wind speed and the generated power in a wind farm. GMR is a novel incremental self-supervised neural network which can approximate every multidimensional function or relation presenting any kind of discontinuity; MLPs are the most widely used state-of-the-art neural network models and GRNNs belong to the family of kernel neural networks. The methodology allows the creation of a non-parametric model of the power curve that can be used as a reference profile for on-line monitoring of the power generation process, as well as for power forecasts. The results obtained show that the non-parametric approach provides fair performances, provided that a suitable pre-processing of the input data is accomplished. [Copyright &y& Elsevier]

Implementing nature-based solutions (NBSs) in cities, such as urban forests, can have multiple effects on the quality of life of inhabitants, acting on the mitigation of climate change, and in some cases also enhancing citizens' social life and the transformation of customer patterns in commercial activities. Assessing this latter effect is the aim of this paper. An agent-based model (ABM) was used to assess change in commercial activities by small and midsize companies in retail due to the development of parks. The paper focuses on the potential capacity of NBS green spaces to boost retail companies' business volumes, thus increasing their revenues, and at the same time create a pleasant feeling of space usability for the population. The type of NBS is not specified but generalized into large green spaces. The simulation contains two types of agents: (1) residents and (2) shop owners. Factors that attract new retail shops to be established in an area are simplified, based on attractor points, which identify areas such as large green spaces within and around which shops can form. The simulations provided insights on the number of retail shops that can be sustained based on the purchasing behavior of citizens that walk in parks. Four European cities were explored: Szeged (Hungary), Alcalá de Henares (Spain), Çankaya Municipality (Turkey) and Milan (Italy). The model allowed analyzing the indirect economic benefit of NBSs (i.e., large green spaces in this case) on a neighborhood's economic structure. More precisely, the presence of green parks in the model boosted the visits of customers to local small shops located within and around them, such as cafés and kiosks, allowing for the emergence of 5–6 retail shops (on average, for about 800 walking citizens) in the case of Szeged and an average 12–14 retail shops for a simulated population of 2900 persons that walk in parks in the case of Milan. Overall, results from this modeling exercise can be considered representative for large urban green areas usually visited by a substantial number of citizens. However, their pertinence to support for local policies for NBS implementation and other decision-making related activities of socioeconomic nature is hampered by the low representativeness of source data used for the simulations. [ABSTRACT FROM AUTHOR]

In a context where digital giants are increasingly influencing the actions decided by public policies, smart data platforms are a tool for collecting a great deal of information on the territory and a means of producing effective public policies to meet contemporary challenges, improve the quality of the city, and create new services. Within the framework of the Smarter Together project, the cities of Lyon (France), Munich (Germany), and Vienna (Austria) have integrated this tool into their city's metabolism and use it at different scales. Nevertheless, the principle remains the same: the collection (or even dissemination) of internal and external data to the administration will enable the communities, companies, not-for-profit organizations, and civic administrations to "measure" the city and identify areas for improvement in the territory. Furthermore, through open data logics, public authorities can encourage external partners to become actors in territorial action by using findings from the data to produce services that will contribute to the development of the territory and increase the quality of the city and its infrastructure. Nevertheless, based on data that is relatively complex to extract and process, public data platforms raise many legal, technical, economic, and social issues. The cities either avoided collecting personal data or when dealing with sensitive data, use anonymized aggregated data. Cocreation activities with municipal, commercial, civil society stakeholders, and citizens adopted the strategies and tools of the intelligent data platforms to develop new urban mobility and government informational services for both citizens and public authorities. The data platforms are evolving for transparent alignment with 2030 climate-neutrality objectives while municipalities strive for greater agility to respond to disruptive events like the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

• Green roofs are nature-based solutions with an important role in buildings' indoor temperature regulation. • Green roofs act on lowering the damaging effects of heatwaves on human health. • We simulated with a spatial microsimulation model a 1.5 °C to 3 °C indoor temperature reduction. • Simulations take into account future outdoor air temperature patterns until 2090. • The simulations showed a variable impact of heatwave-induced mortality reduction across cities. Embedding nature-based solutions (NBS) in cities is expected to bring quantifiable benefits, including resilience to flooding, drought, and heatwaves, and air quality improvement. Among NBS, green roofs have an important role in temperature regulation in buildings and in lowering the damaging effects of heatwaves on human health. In this paper a spatial microsimulation model is implemented to simulate temperature impacts of green roofs installations in cities and their capacity to attenuate the effects of heatwave episodes. Particularly vulnerable to heatwaves are elderly people with limited mobility, who have limited means to seek cooling and create cooler indoor environments. The model, implemented using the Netlogo platform (version 6.0.4), considers as agents the elderly citizens in a city area and simulates the heatwave-related health impacts, which are measured in mortality likelihood. In particular, the model simulates a generalised 1.5 °C to 3 °C indoor temperature reduction range induced by green roofs (based on inferences from green roof literature) in four different European cities: Szeged (Hungary), Alcalá de Henares (Spain), Metropolitan City of Milan (Italy) and Çankaya municipality (Turkey). The simulation utilises a ceteris paribus modelling approach, meaning that the relationships of the observed phenomenon (mortality induced by heatwaves) with other possible influencing factors (e.g. level of sport and physical activities practiced by people) are not taken into account. In the case of Szeged, Alcalá de Henares, and Çankaya municipality a substantial reduction in mortality is found to occur associated with green roofs roll out. In the case of the Metropolitan city of Milan, green roofs installations show a low mitigation effect in some scenarios. The underlying factor is the temperature threshold parameter of the model, above which heatwave mortality occurs. This parameter was inferred from the literature (Baccini M., et al., 2008) and it resulted to be substantially higher in the Metropolitan city of Milan (31.8 °C) than in the other cities. The simulation helps in obtaining results which are specific to a given city and particular scenarios therein, and provides additional insights, such as expected temperature mitigation effect induced by green roofs under climate change conditions, or the indoor temperature reduction targets that are needed for a particular city to have a maximum desired heatwave mitigation impact. However, the model parameters have to be carefully selected, after an accurate study of the domain literature. [ABSTRACT FROM AUTHOR]

Abstract: This paper reports the emergy-based evaluation (EME) of the ecological performance of four water treatment plants (WTPs) using three different approaches. The results obtained using the emergy calculation software SCALE (EMESCALE) are compared with those achieved through a conventional emergy evaluation procedure (EMECONV), as well as through the application of the Solar Energy Demand (SED) method. SCALE's results are based on a detailed representation of the chain of technological processes provided by the lifecycle inventory database ecoinvent®. They benefit from a higher level of details in the description of the technological network as compared to the ones calculated with a conventional EME and, unlike the SED results, are computed according to the emergy algebra rules. The analysis delves into the quantitative comparison of unit emergy values (UEVs) for individual technospheric inputs provided by each method, demonstrating the added value of SCALE to enhance reproducibility, accurateness and completeness of an EME. However, SCALE cannot presently include non-technospheric inputs in emergy accounting, like e.g. human labor and ecosystem services. Moreover, SCALE is limited by the approach used to build the dataset of UEVs for natural resources. Recommendations on the scope and accuracy of SCALE-based emergy accounting are suggested for further steps in software development, as well as preliminary quantitative methods to account for ecosystem services and human labor. [Copyright &y& Elsevier]

This research aims to develop a predictive model to discriminate milk produced from a cattle diet either based on grass or not using milk mid-infrared spectrometry and the month of testing (an indirect indicator of the feeding ration). The dataset contained 3,377,715 spectra collected between 2011 and 2021 from 2449 farms and 3 grazing traits defined following the month of testing. Records from 30% of the randomly selected farms were kept in the calibration set, and the remaining records were used to validate the models. Around 90% of the records were correctly discriminated. This accuracy is very good, as some records could be erroneously assigned. The probability of belonging to the GRASS modality allowed confirmation of the model's ability to detect the transition period even if the model was not trained on this data. Indeed, the probability increased from the spring to the summer and then decreased. The discrimination was mainly explained by the changes in the milk fat, mineral, and protein compositions. A hierarchical clustering from the averaged probability per farm and year highlighted 12 groups illustrating different management practices. The probability of belonging to the GRASS class could be used in a tool counting the number of grazing days.