Consumers’ attitude towards honey consumption for its health benefits: first insights from an econometric approach

PurposeAs is well known, there are several aspects that characterise honey consumption and the reasons for purchasing it. Despite this, little attention has been paid to the drivers that move consumers towards the use of honey for its health benefits and therapeutic properties. The aim of this study is to define which drivers move the consumption of honey for its health benefits.Design/methodology/approachThe study, conducted on 640 Italian honey consumers, applied inferential tests (Chi-square) and an econometric model (logit regression), and showed that about 66% of the respondents stated that they consume honey for its health properties.FindingsThe main drivers of honey consumption are both among the intrinsic (Colour) and extrinsic (Origin and Organic certification) attributes of the product. What also emerges is that the propensity to consume honey for health purposes is influenced by the consumer characteristics and habits, such as Age cohort, Gender, BMI and Large retail buyer. Moreover, we observed that consumption is influenced by BMI but not by lifestyle characteristics such as sport and diet.Originality/valueThis study could be a support tool for policymakers who are interested in promoting good nutrition and improving public health, since there is great interest in the functional properties of foods and the need to enhance the value of products, while at the same time ensuring consumer protection.

Modeling Carbon Release of Brazilian Highest Economic Pole and Major Urban Emitter: Comparing Classical Methods and Artificial Neural Networks

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies.

Novel Model Based on Artificial Neural Networks to Predict Short-Term Temperature Evolution in Museum Environment

The environmental microclimatic characteristics are often subject to fluctuations of considerable importance, which can cause irreparable damage to art works. We explored the applicability of Artificial Intelligence (AI) techniques to the Cultural Heritage area, with the aim of predicting short-term microclimatic values based on data collected at Rosenborg Castle (Copenhagen), housing the Royal Danish Collection. Specifically, this study applied the NAR (Nonlinear Autoregressive) and NARX (Nonlinear Autoregressive with Exogenous) models to the Rosenborg microclimate time series. Even if the two models were applied to small datasets, they have shown a good adaptive capacity predicting short-time future values. This work explores the use of AI in very short forecasting of microclimate variables in museums as a potential tool for decision-support systems to limit the climate-induced damages of artworks within the scope of their preventive conservation. The proposed model could be a useful support tool for the management of the museums.

A Comprehensive Methodology to Support Decision-Making Towards Sustainability on Nanocomposite Materials in Additive Manufacturing Sector

Innovative nanocomposite materials and resultant additive manufacturing products are necessary to be assessed for their carbon footprint towards top priorities of EU for plastics, including the European Green Deal principles and the Action Plan for Circular Economy. Life Cycle Assessment (LCA) is widely applied standardized methodology that aims to study potential environmental impacts of novel products. Nano-scale materials (NM) are usually dispersed in polymer to enhance their limited functional properties resulting in a spectrum of end-products for multiple applications. However, little information exists on their environmental impact. Within this context, this study presents a ‘cradle-to-gate plus end-of-life’ LCA approach, studying different types of 3D printing nanocomposite filaments across the supply chain. Three different types of polymer matrixes were examined: polyamide (PA), polypropylene (PP) and polylactic acid (PLA), additivated with three different types of nanomaterial additives: multiwall carbon nanotubes (MWCNTs), graphene oxide (GO) forms and graphene nanoplatelets (GNPs), considering lab-scale production. In addition, several different EoL scenarios have been examined for the materials. Finally, LCA findings are coupled with the performance (taken here as conductivity) of these new materials to assist the decision-making process for selecting efficient scenarios with the least environmental impact. The outputs of this examination enable identification of potential sustainability issues for novel nanocomposite materials at an early design stage, while also assisting in the definition of actions to mitigate such issues. Thus, LCA studies can generate knowledge on the environmental impacts of nano-enabled materials, while also serving as a valuable decision support tool towards optimizing material sustainability aspects.

Modelling and simulating ‘informal urbanization’: An integrated agent-based and cellular automata model of urban residential growth in Ghana

The global urban population is expected to grow by 2.5 billion over the next three decades, and 90% of this growth will occur in African and Asian countries. Urban expansion in these regions is often characterised by ‘informal urbanization’ whereby households self-build without planning permission in contexts of ambiguous, insecure or disputed property rights. Despite the scale of informal urbanization, it has received little attention from scholars working in the domains of urban analytics and city science. Towards addressing this gap, we introduce TI-City, an urban growth model designed to predict the locations, legal status and socio-economic status of future residential developments in an African city. In a bottom-up approach, we use agent-based and cellular automata modelling techniques to predict the geospatial behaviour of key urban development actors, including households, real estate developers and government. We apply the model to the city-region of Accra, Ghana, drawing on local data collection, including a household survey, to parameterise the model. Using a multi-spatial-scale validation technique, we compare TI-City’s ability to simulate historically observed built-up patterns with SLEUTH, a highly popular urban growth model. Results show that TI-City outperforms SLEUTH at each scale, suggesting the model could offer a valuable decision support tool in similar city contexts.

Influencing factors of crude oil maritime shipping freight fluctuations: a case of Suezmax tankers in Europe-Africa routes

PurposeThe paper aims to identify the contributors to freight rate fluctuations in the Suezmax tanker market; this study selected the refinery output, crude oil price, one-year charter rate and fleet development as the main influencing factors for the market analysis.Design/methodology/approachThe paper used the VEC (vector error correction) model to evaluate the degree of impact of each influencing factor on Suezmax tanker freight rates, as well as the interplay between these factors.FindingsThe conclusion and results were tested using the 20-year data from 1999 to 2019, and the methodology and theory of this paper were proved to be effective. Results of this study provide effective reference for scholars to find the law of fluctuations in Suezmax tanker freight rates.Originality/valueThis paper provides a decision-making support tool for tanker operators to cope with fluctuation risks in the tanker shipping market.