The role of tourism in China’s economic system and growth. A social accounting matrix (SAM)-based analysis

After the opening policy in 1978, China’s tourism increasingly took relevance, up to become an important industry in the last two decades. Despite this, no analysis has been conducted at macroeconomic level to check both tourism industry interdependencies and wealth creation. To fill this gap, in this paper we elaborated an innovative conceptual model for the theory-based analysis of the tourism phenomenon in China, having the Keynesian macroeconomic theory as the background and using an SAM as the model accounting representation, and conducted an original, comprehensive methodological analysis of China’s tourism industry. As the database, we used a purposively elaborated 2015 SAM for China with 19 industries, on whose basis we identified endogenous and exogenous accounts, set up an innovative impact multiplier model adjusted to them and conducted an economic analysis of tourism interdependencies never performed so far. Evidence shows that manufacturing, agriculture and trade industries provide a relevant support to tourism services production, and that tourism greatly contributes to value added/GDP and household income creation. Overall, tourism industry has direct policy management implications, representing a sector on which enterprises and government can profitably base their decisions, with exogenous tourism demand shocks positively activating China’s economic system and growth

Food waste reduction and economic savings in times of crisis: The potential of machine learning methods to plan guest attendance in Swedish public catering during the Covid-19 pandemic

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of <0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system

Exploring the Italians' food habits and tendency towards the Mediterranean diet

At first sight, the Mediterranean diet appears to be the best and most well-balanced diet to follow as it links environmental and human health. Unfortunately, it seems that Mediterranean countries are replacing the traditional Mediterranean diet with other less healthy eating habits and orienting their food choices towards products typical of the Western diet which is characterised by a high intake of animal products, refined grains, saturated fats without taking into consideration health issues and environmental sustainability. By using repeated cross-sections of the ISTAT “Aspects of daily-life” survey over the period 1997-2012, we assess the Italians’ prevailing food pattern and explore how it has changed over time and across regions in Italy. Moreover, the role of the socio-demographic and lifestyle factors are investigated

Mapping by spatial predictors exploiting remotely sensed and ground data: a comparative design-based perspective

This study was designed to compare the performance – in terms of bias and accuracy – of four different parametric,semiparametric and nonparametric methods in spatially predicting a forest response variable using auxiliary information from remote sensing. The comparison was carried out in simulated and real populations where the value of response variable was known for each pixel of the study region. Sampling was simulated through a tessellation stratified design. Universal kriging and cokriging were considered among parametric methods based on the spatial autocorrelation of the forest response variable. Locallyweighted regression and k-nearest neighbor predictors were considered among semiparametric and nonparametricmethods based on the information from neighboring sites in the auxiliary variable space. The study was performed from a design-based perspective, taking the populations as fixed and replicating the sampling procedurewith 1000Monte Carlo simulation runs. On the basis of the empirical values of relative bias and relative root mean squared error it was concluded that universal kriging and cokriging were more suitable in the presence of strong spatial autocorrelation of the forest variable, while locally weighted regression and k-nearest neighbors were more suitable when the auxiliary variables were well correlated with the response variable. Results of the study advise that attention should be paid when mapping forest variables characterized by highly heterogeneous structures. The guidelines of this study can be adopted even for mapping environmental attributes beside forestry