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Foodmiles: The Logistics of Food Chains Applied to Food Quality Schemes

Marion Drut, Federico Antonioli, Michael Böhm, Ruzica Brečić, Liesbeth Dries, Hugo Ferrer-Pérez, Lisa Gauvrit, Việt Hoàng, Kamilla Knutsen Steinnes, Apichaya Lilavanichakul, Edward Majewski, Orachos Napasintuwong, An Nguyễn ORCID logo, Konstadinos Mattas, Bojan Ristic, Burkhard Schaer, Torvald Tangeland, Marina Tomić Maksan, Peter Csillag, Áron Török, Efthimia Tsakiridou, Mario Veneziani, Gunnar Vittersø and Valentin Bellassen ORCID logo


This paper estimates the foodmiles (embedded distances) and transport-related carbon emissions of 27 Food Quality Scheme (FQS) products – Protected Designation of Origin (PDO), Protected Geographical Indications (PGI) and organic – and their reference products. It goes further than the existing literature by adopting a value chain perspective, instead of the traditional consumer perspective, and focusing on FQS products. The same methodology is applied across all the case studies. The article specifically investigates the determinants of differences between FQS and their references. FQS products travel significantly shorter distances (−30%) and generate significantly lower transport-related emissions (−23%) than conventional food products. The differences are even greater for vegetal and organic products. The relationship between distance and transport-related emissions is not exactly proportional and highlights the importance of transport modes and logistics, in particular for exports and imports. Finally, we stress the importance of the spatial distribution of the different stages in the value chains (e.g. production, processing). PDO technical specifications delimit a geographical area for production and processing, thereby limiting distances and transport-related emissions compared to conventional food products, but also compared to other types of FQS.

Corresponding author: Marion Drut, CESAER, AgroSup Dijon, INRAE, University Bourgogne Franche-Comté, Dijon, Bourgogne, France, E-mail:

Funding source: European Commission

Award Identifier / Grant number: 678024


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 678024. Partners listed as co-authors in this article were responsible for collecting data related to two case studies on average, i.e. two FQS products along with two reference products over the value chain. The authors would like to thank all the people and institutions who collected or provided raw data for this publication. Statistical analysis were conducted using the R software.


Appendix A

A detailed description of the variables used is presented below. These variables are to be completed for each transport mode used in a given section of the value chain. Indeed, different transport modes have different emission rates associated. Please refer to Bellassen et al. (2019) for more details.

  • Distance: either directly given in kilometers by case study conductors (usually from expert interviews) or computed by the corresponding author using online tools based on an origin point and a destination point. The shortest distance proposed is systematically selected when using online tools to estimate distances ( for road transport, for sea distances, and for flight distances).

  • Transport mode: the Excel data file provides the following options from a drop-down list: Road – Light Goods vehicle – Petrol; Road – Light Goods vehicle – Diesel; Road – Heavy Goods vehicle; Rail; Air; Ship – Small tanker; Ship – Large tanker; Ship – Very large tanker; Ship – Small BC; Ship – Large BC; Ship – Very large BC; Ship – Small CV; Ship – Large CV. Detailed description helps choosing the appropriate mode. Light goods vehicles are vehicles carrying loads less than 3.5 tons (3.9 US tons), while heavy goods vehicles are vehicles carrying loads greater than 3.5 tons (3.9 US tons). A tanker is a ship designed to transport or store liquids or gases in bulk. Small tankers carry about 1000 tons, while large tankers carry 20,000 tons and very large tankers carry 100,000 tons. BC states for Bulk Carrier, a ship especially designed to transport unpackaged bulk cargo, such as grains, coal, ore, and cement. Small bulk carriers carry up to 2000 tons, while large bulk carriers carry 15,000 tons and very large bulk carriers carry 70,000 tons. CV states for Container Vessel, a cargo ship that carries its load in truck-size intermodal containers, in a technique called containerization. Small container vessels carry 2500 tons, while large container vessels carry about 20,000 tons. When no further information is available about sea transport, the ship category “large CV” is selected. Indeed, today about 90% of non-bulk cargo worldwide is transported by container ships. Different sizes of transport modes correspond to different carbon emission rates.

  • Logistics: the Excel data file provides the following options from a drop-down list: single journey; returning empty. The logistics variable indicates when a transport mode runs back empty or not. Only road transport modes may run back empty, air and sea transport are assumed to travel single journeys and to run back or further to another destination full of another commodity. When no information is available on logistics for road transport, we assume trucks run back empty.

  • Share of the product concerned: the share of the product traveling with a given transport mode and between two steps of the value chain. This variable is used to weight the distance when several modes are used for a given segment of the value chain, or when the value chain divides into several segments at a given level (e.g., exports and domestic market at retail level).

  • Product concentration: the final product ratio variable applies to value chains with at least one processing or packing stage. It gives an idea of the product concentration and of the degree of embeddedness of distance and emissions into the final product. For instance, a product ratio of 0.1 indicates that 10 units of raw product are required to produce 1 unit of final product. In this case, when raw products travel x kilometers upstream, there will be 10 times x kilometers embedded in the final product. When there are more than one processing stage, the product concentration for the (raw) product concerned at a given level is indicated with respect to the final product.

  • Co-product value: the value of co-products compared to the value of final products. This variable allows to weight the foodmiles and transport-related carbon emissions by removing the foodmiles and emissions attributable to co-products.

  • Energy: default hypothesis are made in Cool Farm Tool (e.g., heavy goods vehicles are necessarily powered by diesel) and the energy variable only makes a difference for road light goods vehicles. When no further information is available on the energy used by light goods vehicles, the national energy mix is considered.

Appendix B

Table 5 presents the most important data sources on which each case studied and reference product rely, as well as a detailed description of the reference products used along the value chain.

Table 5:

Data sources and reference products.

Case studiedType of FQSCountryReference productMost important data sources
Buon Ma Thuot coffeePGIVietnam
  • U3-P1 = conventional unsorted green coffee beans from Dak Lak province in Vietnam

  • P1-P2 = conventional sorted green coffee beans from Dak Lak province in Vietnam

  • P2-D1 = conventional ground and roasted coffee from Dak Lak province in Vietnam

  • Exports (P1-D1) = conventional sorted green coffee beans from Dak Lak province in Vietnam

Interviews, accountancy data
Dalmatian prosciuttoPGICroatiaConventional prosciutto made from pigs raised in CroatiaInterviews
Olive oilPDOCroatiaConventional olives and conventional olive oil produced in CroatiaFQS: Interviews

REF: Interviews, FAOSTAT (2015), Croatian Bureau of Statistics (2015)
Saint-Michel bay bouchot musselsPDOFrance
  • U1-U3 = conventional Bouchot mussels in France

  • U3-P1 = conventional Bouchot mussels in France

  • P1-D1 = mussel sector in France

  • Exports = mussel sector in France

FQS: Interviews

REF: Interviews, FranceAgriMer (2016)
Organic flourOrganicFrance
  • U3-P1 = conventional soft wheat

  • P1-D1 = conventional flour

  • Exports = conventional soft wheat, conventional soft wheat flour, conventional bread

FQS: Interviews, ANMF (2016)

REF: ANMF (2016), Passion Céréales (2017), France Export Céréales (2017)
Comte cheesePDOFrance
  • U3-P1 = national average from the cheese industry in France

  • Exports = Emmental cheese, France.

FQS: Interviews, CIGC (2018)

REF: CNIEL (2016), Maison du Lait (2016)
Camargue riceOrganicFrance
  • U3-P1 = conventional rice from Camargue, France.

  • P1-D1 = conventional rice from Camargue, France.

  • Exports = conventional rice from France.

FQS: Interviews

REF: Interviews, Eurostat (2015)
Gyulai sausagePGIHungaryConventional (generic) sausage from Gyulai region, in HungaryFQS: Interviews, company data

REF: interviews, industry data, World Bank (2017)
Kalocsai paprika powderPDOHungary
  • U1-U3 = conventional dried paprika from raw paprika produced abroad

  • U3-P1 = conventional dried paprika from raw paprika produced abroad

  • P1-D1 = conventional paprika powder

  • Exports = conventional paprika powder

Interviews, World Bank (2017)
Parmigiano Reggiano cheesePDOItalyBiraghi cheese (similar non-PDO cheese)Interviews, ISTAT (2016)
Kastoria applesPGIGreeceConventional apples produced by the cooperative Kissavos, in Agia, GreeceInterviews
Zagora applesPDOGreeceConventional apples produced by the cooperative Kissavos, in Agia, GreeceInterviews
Phu Quoc fish saucePDOVietnamConventional fish sauce from Phu Quoc island in VietnamInterviews
Organic pastaOrganicPolandConventional cereals produced by the 14 model conventional farmsInterviews
Kaszubska StrawberryPGIPolandConventional strawberries from PolandInterviews
Organic porkOrganicGermanyConventional pork from GermanyISN (2016)
Organic raspberriesOrganicSerbiaConventional raspberries from SerbiaInterviews, National Institute of Statistics of Serbia (2016)
Sjenica cheesePDOSerbiaConventional cow cheese produced in SerbiaInterviews, National Institute of Statistics of Serbia (2016)
Organic tomato from Emilia RomagnaOrganicItaly
  • U3-P1 = conventional processed tomato from Northern Italy (Emilia Romagna region).

  • P1-D1 = conventional processed tomato from Northern Italy (Emilia Romagna region).

  • Exports = processed tomato from Northern Italy (Emilia Romagna region).

Interviews, ISTAT (2017), ANICAV (2017)
Organic yoghurtOrganicGermany
  • U3-P1 = natural cow milk yoghurt (unflavored) produced in Germany

  • P1-D1 = natural cow milk yoghurt (unflavored) produced in Germany (both conventional and organic)

  • Exports = natural cow milk yoghurt (unflavored and flavored) produced in Germany (both conventional and organic)

  • D1-D2 = natural cow milk yoghurt (unflavored) produced in Germany (both conventional and organic)

Interviews, AMI (2018), Müller-Lindenlauf et al. (2014), Destatis (2017)
Opperdoezer Ronde potatoesPDOThe NetherlandsConventional fresh consumption potato from The NetherlandsInterviews, Central Bureau of Statistics (2016)
Lofoten stockfishPGINorway
  • P1-D1 = clipfish produced in More og Romsdal, Norway

  • Exports = clipfish produced in Norway

Interviews, Seafood Council (2016)
Organic salmonOrganicNorway
  • U3-P1 = conventional salmon in Norway

  • P1-D1 = conventional salmon in Norway

  • Exports = salmon in Norway

FQS: Interviews

REF: Interviews, Seafood Council (2016)
Sobrasada of MallorcaPGISpain
  • U3-P1 = no data (assumptions)

  • P1-P2 = no data (assumptions)

  • Exports = both PGI Sobrasada de Mallorca, Mallorca, Spain and PGI Sobrasada de Mallorca de Porc Negre, Mallorca, Spain

Interviews, DGAR
Ternasco de AragonPGISpainConventional lamb from Aragon region, in SpainFQS: Interviews, MAGRAMA (2016)

REF: Interviews, DataComex (2016)
Thung Kula Rong-Hai (TKR) Hom Mali ricePGIThailand
  • U2-U3 = conventional rice seeds, Thailand.

  • U3-P1 = conventional paddy rice produced in the TKR region, Thailand.

  • P1-D1 = conventional milled rice produced in the TKR region, Thailand.

  • Exports = conventional milled rice produced in the TKR region, Thailand.

FQS: Interviews

REF: Interviews, Potchanasin et al. (2017), MOC (2017)
Doi Chaang CoffeePGIThailand
  • U3-P1 = conventional coffee cherries produced in Doi Phahee in Chiang Rai province, Thailand

  • P1-D1 = conventional roasted coffee beans produced in Doi Phahee in Chiang Rai province, Thailand


  1. NB: REF = reference product.


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Received: 2019-09-13
Accepted: 2020-09-12
Published Online: 2020-10-08

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