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Environmental Pressures Embodied in the French Cereals Supply Chain
Author(s) -
Courtonne JeanYves,
Longaretti PierreYves,
Alapetite Julien,
Dupré Denis
Publication year - 2016
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12431
Subject(s) - greenhouse gas , supply chain , production (economics) , consumption (sociology) , work (physics) , asset (computer security) , natural resource economics , environmental science , markov chain , order (exchange) , business , economics , microeconomics , computer science , engineering , mechanical engineering , ecology , social science , computer security , marketing , sociology , biology , finance , machine learning
Summary France is the second largest exporter of cereals in the world. Although the cereal supply chain is an asset to the country's economy and employment, it is at the same time responsible for a number of pressures on the local and global environment including greenhouse gases (GHG) emissions and stresses on water quality and quantity. This article aims at evaluating this situation from an environmental point of view by linking production occurring in French regions with consumption occurring in France and abroad. Based on previous work on material flow analysis, we use an absorbing Markov chain model to study the fate of French cereals and link worldwide consumption to environmental pressures along the supply chain, that is, induced by production, transformation, or transport. The model is based on physical supply and use tables and distinguishes between 21 industries, 22 products, 38 regions of various spatial resolution (22 French regions, ten countries, six continents), and four modes of transport. Energy use, GHG emissions, land use, use of pesticides, and blue water footprint are studied. Illustrative examples are given in order to demonstrate the versatility of the results produced, for instance: Where and in what form does local production end up? How do regions compare relative to their production and consumption footprints? These results are designed to be a first step toward scenario analysis for decision‐aiding that would also include socio‐economic indicators. Examples of such scenarios are discussed in the conclusion.