Identifying marginal supplying countries of wood products via trade network analysis

  • Massimo Pizzol
  • Marco Scotti



The consequential inventory modeling approach for life cycle assessment implies that an increase in the demand for a specific product is met by the marginal suppliers within the market. The identification of marginal suppliers is however complicated by difficulties in defining appropriate geographical market delimitations. In this study, an advanced system thinking approach is proposed to address this challenge in the identification of marginal supplying countries of wood products.


Groups of countries which represent geographical markets are identified from trade data by using a network analysis-based clustering technique. Within these markets, marginal supplying countries are selected based on positive historical increments. The analysis covers 12 different products and all countries in the world using trade data for the period 1998–2013.

Results and discussion

Global indices allow differentiating how product-specific trade networks are separated into communities and how interconnected these networks are. Large differences between products and minor differences between trade years are observed. Communities identified for each product tend to overlap with existing geographical regions and seem thus realistic. By combining this information with product-specific production increment rankings, marginal supplying countries of wood products were identified.


The identified geographical market delimitation is a key for proper consequential life cycle assessment (LCA) inventory modeling in areas such as timber-based construction and biomass-based energy production. The method can in principle be applied to any product for which trade network data are available and ideally should be accompanied by a detailed analysis of technological constrains within the identified supplying country.


Consequential life cycle assessment Network analysis Network clustering Network communities Trade Wood products 



Thanks to Matthias Buyle, Bo Weidema, and Stefano Merciai for providing insightful and useful comments on draft versions of this manuscript. We acknowledge three anonymous reviewers for the constructive suggestions and the stimulating discussion. This work was funded by the research grant no. 1305-00030B of the Danish Strategic Research Council.

Supplementary material

11367_2016_1222_MOESM1_ESM.rar (2.4 mb)
SI 1: The archive reports for all 38,400 networks under analysis (16 years * 12 products * cutoff yes/no * 100 iterations): total network weight, number of vertices, number of edges, percent of total network weight maintained after cutoff, modularity, weighted modularity, and density. Plots are provided as well, summarizing this information at a glance. (RAR 2429 kb)
11367_2016_1222_MOESM2_ESM.rar (39 kb)
SI 2: The archive reports detailed results of the statistical testing: ANOVA statistics, group means, and Tukey HSD results for weighted modularity and modularity calculated on the mean and mode of the 100 iterations respectively. (RAR 38 kb)
11367_2016_1222_MOESM3_ESM.rar (4.2 mb)
SI 3: The archive reports the composition of all communities identified in each of the 38,400 iterations, i.e. the countries included in each community. (RAR 4292 kb)
11367_2016_1222_MOESM4_ESM.rar (409 kb)
SI 4: The archive reports 16 product-specific contingency tables. (RAR 409 kb)
11367_2016_1222_MOESM5_ESM.rar (83 kb)
SI 5: The archive reports 16 product-specific production increment rankings. R scripts for reproducing the results can be retrieved at: (RAR 83 kb)


  1. Bergstrand JH (1985) The gravity equation in international trade: some microeconomic foundations and empirical evidence. Rev Econ Stat 67:474–481CrossRefGoogle Scholar
  2. Bodini A, Bondavalli C, Allesina S (2012) Cities as ecosystems: growth, development and implications for sustainability. Ecol Model 245:185–198CrossRefGoogle Scholar
  3. Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323:892–895CrossRefGoogle Scholar
  4. Caberlotto L, Lauria M, Nguyen T-P, Scotti M (2013) The central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: a multifactor network analysis. PLoS One 8:e78919CrossRefGoogle Scholar
  5. CEPII (2016) BACI World trade database:2016Google Scholar
  6. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:66111CrossRefGoogle Scholar
  7. Csardi G, Nepusz T (2006) The igraph software package for complex network researchGoogle Scholar
  8. Dale BE, Kim S (2014) Can the predictions of consequential life cycle assessment Be tested in the real world? Comment on “using attributional life cycle assessment to estimate climate-change mitigation...”. J Ind Ecol 18:466–467CrossRefGoogle Scholar
  9. De Benedictis L, Tajoli L (2011) The world trade network. World Econ 34:1417–1454CrossRefGoogle Scholar
  10. De Rosa M, Schmidt J, Brandão M, Pizzol M (2016) A flexible parametric model for a balanced account of forest carbon fluxes. Int J Life Cycle Assess. doi: 10.1007/s11367-016-1148-z Google Scholar
  11. Deng Y, Tian Y (2015) Assessing the environmental impact of flax fibre reinforced polymer composite from a consequential life cycle assessment perspective. Sustainability 7:11462–11483CrossRefGoogle Scholar
  12. Ekvall T, Weidema BP (2004) System boundaries and input data in consequential life cycle inventory analysis. Int J Life Cycle Assess 9:161–171CrossRefGoogle Scholar
  13. Enders W (2014) Applied econometric time series, 4th edition. WileyGoogle Scholar
  14. Eshun JF, Potting J, Leemans R (2010) Inventory analysis of the timber industry in Ghana. Int J Life Cycle Assess 15:715–725CrossRefGoogle Scholar
  15. FAOSTAT (2016) Website of the food and agriculture organization of the United Nations.
  16. Fath BD, Scharler UM, Ulanowicz RE, Hannon B (2007) Ecological network analysis: network construction. Ecol Model 208:49–55CrossRefGoogle Scholar
  17. Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174CrossRefGoogle Scholar
  18. Grinde M (2011) Environmental assessment of scenarios for products and services based on forest resources in Norway. Institutt for energi- og prosessteknikkGoogle Scholar
  19. Hänninen R, Hetemäki L, Hurmekoski E (2014) European forest industry and forest bioenergy outlook up to 2050: A synthesisGoogle Scholar
  20. Hausmann R, Hidalgo CA (2011) The network structure of economic output. J Econ Growth 16:309–342CrossRefGoogle Scholar
  21. Heijungs R (2012) Spatial differentiation, GIS-based regionalization, hyperregionalization, and the boundaries of LCA. In: Ioppolo G (ed) Environment and energy (editorial series of Italian commodity science academy and engineering Association of Messina). FrancoAngeli, Milano, pp. 165–176Google Scholar
  22. Helin T, Sokka L, Soimakallio S et al (2012) Approaches for inclusion of forest carbon cycle in life cycle assessment—a review. GCB Bioenergy 5:475–486CrossRefGoogle Scholar
  23. Hetemäki L (2014) Future of the European forest-based sector: what science can tell us. Grano OyGoogle Scholar
  24. Hidalgo CA, Hausmann R (2009) The building blocks of economic complexity. Proc Natl Acad Sci U S A 106:10570–10575CrossRefGoogle Scholar
  25. Huang J, Ulanowicz RE (2014) Ecological network analysis for economic systems: growth and development and implications for sustainable development. PLoS One 9:e100923CrossRefGoogle Scholar
  26. Hurmekoski E (2016) Long-term outlook for wood construction in Europe. School of Forest Sciences, Faculty of Science and Forestry, University of Eastern FinlandGoogle Scholar
  27. Jørgensen S, Hauschild M (2013) Need for relevant timescales when crediting temporary carbon storage. Int J Life Cycle Assess 18:747–754CrossRefGoogle Scholar
  28. Kim H, Holme P (2015) Network theory integrated life cycle assessment for an electric power system. 7:10961–10975Google Scholar
  29. Levasseur A, Lesage P, Margni M et al (2010) Considering time in LCA: dynamic LCA and its application to global warming impact assessments. Environ Sci Technol 44:3169–3174CrossRefGoogle Scholar
  30. Lund H, Mathiesen B, Christensen P, Schmidt J (2010) Energy system analysis of marginal electricity supply in consequential LCA. Int J Life Cycle Assess 15:260–271CrossRefGoogle Scholar
  31. Mathiesen BV, Munster M, Fruergaard T et al (2009) Uncertainties related to the identification of the marginal energy technology in consequential life cycle assessments. J Clean Prod 17:1331–1338CrossRefGoogle Scholar
  32. Navarrete-Gutiérrez T, Rugani B, Pigné Y et al (2015) On the complexity of life cycle inventory networks: role of life cycle processes with network analysis. J Ind Ecol 20:1094–1107CrossRefGoogle Scholar
  33. Neupane B, Halog A, Dhungel S (2011) Attributional life cycle assessment of woodchips for bioethanol production. J Clean Prod 19:733–741CrossRefGoogle Scholar
  34. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:26113CrossRefGoogle Scholar
  35. Nguyen T-P, Scotti M, Morine MJ, Priami C (2011) Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Syst Biol 5:1752–1509Google Scholar
  36. Nuss P, Chen W-Q, Ohno H, Graedel TE (2016) Structural investigation of aluminum in the U.S. economy using network analysis. Environ Sci Technol 50:4091–4101CrossRefGoogle Scholar
  37. Pizzol M, Scotti M, Thomsen M (2013) Network analysis as a tool for assessing environmental sustainability: applying the ecosystem perspective to a Danish water management system. J Environ Manag 118:21–31CrossRefGoogle Scholar
  38. R Core Team (2005) R: a language and environment for statistical computingGoogle Scholar
  39. Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E. doi: 10.1103/PhysRevE.74.016110 Google Scholar
  40. Reichardt J, Bornholdt S (2007) Clustering of sparse data via network communities—a prototype study of a large online market. Journal of Statistical Mechanics: An IOP and SISSA Journal. doi: 10.1088/1742-5468/2007/06/P06016 Google Scholar
  41. Reinhard J, Weidema B, Schmidt J (2010) Identifying the marginal supply of wood pulp. 2.-0 LCA Consultants, AalborgGoogle Scholar
  42. Rodriguez MA, Pepe A (2008) On the relationship between the structural and socioacademic communities of a coauthorship network. J Informetr 2:195–201CrossRefGoogle Scholar
  43. Schaubroeck T, Staelens J, Verheyen K et al (2012) Improved ecological network analysis for environmental sustainability assessment; a case study on a forest ecosystem. Ecol Model 247:144–156CrossRefGoogle Scholar
  44. Schaubroeck T, Alvarenga RAF, Verheyen K et al (2013) Quantifying the environmental impact of an integrated human/industrial-natural system using life cycle assessment; a case study on a forest and wood processing chain. Environ Sci Technol 47:13578–13586CrossRefGoogle Scholar
  45. Schmidt JH (2010) Comparative life cycle assessment of rapeseed oil and palm oil. Int J Life Cycle Assess 15:183–197CrossRefGoogle Scholar
  46. Schmidt JH (2015) Life cycle assessment of five vegetable oils. J Clean Prod 87:130–138CrossRefGoogle Scholar
  47. Scott-Boyer MP, Lacroix S, Scotti M et al (2016) A network analysis of cofactor-protein interactions for analyzing associations between human nutrition and diseases. Sci Rep 6:19633CrossRefGoogle Scholar
  48. Singh S, Bakshi BR (2011) Insights into sustainability from complexity analysis of life cycle networks: a case study on gasoline and bio-fuel networks. Proceedings of the 2011 I.E. International Symposium on Sustainable Systems and TechnologyGoogle Scholar
  49. Tukker A, de Koning A, Wood R et al (2013) EXIOPOL—development and illustrative analyses of a detailed global MR EE SUT/IOT. Econ Syst Res 25:50–70CrossRefGoogle Scholar
  50. Wasserman S, Faust K (2016) Social network analysis—methods and applications. Cambridge University Press, CambridgeGoogle Scholar
  51. Weidema B, Frees N, Nielsen A-M (1999) Marginal production technologies for life cycle inventories. Int J Life Cycle Assess 4:48–56CrossRefGoogle Scholar
  52. Weidema B, Ekvall T, Heijungs R (2009) Guidelines for application of deepened and broadened LCA—Deliverable D18 of work package 5 of the CALCAS project. ENEA, The Italian National Agency on new Technologies, Energy and the EnvironmentGoogle Scholar
  53. Wood R, Stadler K, Bulavskaya T et al (2015) Global sustainability accounting—developing EXIOBASE for multi-regional footprint analysis. Sustainability 7:138CrossRefGoogle Scholar
  54. Zamagni A, Guinée J, Heijungs R et al (2012) Lights and shadows in consequential LCA. Int J Life Cycle Assess 17:904–918CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of Development and PlanningAalborg UniversityAalborgDenmark
  2. 2.GEOMAR Helmholtz Centre for Ocean Research KielKielGermany

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