The production and distribution of food are among the hot topics debated in the context of sustainable development. Short food supply chains (SFSCs) are now widely believed to be more sustainable in comparison to mass food delivery systems. To date, very little quantitative evidence exists on the impacts of various types of food supply chains. Using a cross-sectional quantitative approach, this study assesses the sustainability of distribution channels in short and long food supply chains based on 208 food producers across seven countries: France, Hungary, Italy, Norway, Poland, the United Kingdom, and Vietnam. Ten distribution channel types are used in this study. To provide a comprehensive sustainability assessment, a set of economic, social, and environmental indicators are applied. Indicators commonly used in the literature are used, supported by original indicators constructed specifically for the present study. In total, 486 chains are examined and the study confirms that individual producers participate simultaneously in several, short and long chains. Participation in SFSCs is beneficial for producers from an economic perspective. SFSCs allow producers to capture a large proportion of margin otherwise absorbed by different intermediaries. It appears, however, that ’longer’ supply channels generate lower environmental impacts per unit of production when measured in terms of food miles and carbon footprint. Finally, ambiguous results are found regarding social dimension, with significant differences across types of chains.
The present food system faces major challenges in terms of sustainable development along social, economic and environmental dimensions. These challenges are often associated with industrialised production processes and longer and less transparent distribution chains. Thus, closer distribution systems through Short Food Supply Chains (SFSCs) may be considered as a sustainable alternative. This study explores the role of different types of SFSCs and their contribution to sustainability through participants' (consumers, retailers and producers) views and perceptions. As part of the European H2020 project "Strength2Food" we conducted a cross-case analysis and examined 12 European SFSC cases from six countries: France, Hungary, Italy, Norway, Poland and the UK. We applied a mixed method approach including primary data collection, via in-depth interviews and customer surveys, as well as desk research. The findings suggest that, irrespective of the type of SFSC, a strong agreement among the participants were found on the contribution of SFSCs to social sustainability. However, participants' views considerably differ regarding the economic and environmental dimensions of sustainability. These differences relate to the way the SFSCs were organised and to some degrees to regional differences attributed to the significance of SFSC in different parts of Europe. The article concludes that the spatial heterogeneity of SFSCs, including supply chain actor differences, different types and organisational forms of SFSCs as well as regional and territorial characteristics, must be taken into account and further emphasised in future policies aimed at strengthening European food chain sustainability.
Methods of interpolation, whether based on regressions or on kriging, are global methods in which all the available data for a given study area are used. But the quality of results is affected when the study area is spatially very heterogeneous. To overcome this difficulty, a method of local interpolation is proposed and tested here with temperature in France. Starting from a set of weather stations spread across the country and digitized as 250 m-sided cells, the method consists in modelling local spatial variations in temperature by considering each point of the grid and the n weather stations that are its nearest neighbours. The procedure entails a series of steps: recognition of the n stations closest to the cell to be evaluated and subdivision of the study area into polygons defined by a neighbourhood rule, elaboration of a local model by multiple regression for each polygon, and application of the parameter estimate from the regression to obtain a predicted value of temperature at each point of the polygon under consideration.These results are compared with results from three global interpolation methods: (1) regression, (2) ordinary kriging, and (3) regression with kriging of residuals. We then develop the original results from local interpolation such as mapping of the coefficients of determination and of the parameter estimate related to altitude and to distance to the sea. These developments highlight the processes that dictate the spatial variation of climate.
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