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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Bergstrand JH (1985) The gravity equation in international trade: some microeconomic foundations and empirical evidence. Rev Econ Stat 67:474–481
Bodini A, Bondavalli C, Allesina S (2012) Cities as ecosystems: growth, development and implications for sustainability. Ecol Model 245:185–198
Borgatti SP, Mehra A, Brass DJ, Labianca G (2009) Network analysis in the social sciences. Science 323:892–895
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:e78919
CEPII (2016) BACI World trade database:2016
Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:66111
Csardi G, Nepusz T (2006) The igraph software package for complex network research
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 18:466–467
De Benedictis L, Tajoli L (2011) The world trade network. World Econ 34:1417–1454
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
Deng Y, Tian Y (2015) Assessing the environmental impact of flax fibre reinforced polymer composite from a consequential life cycle assessment perspective. Sustainability 7:11462–11483
Ekvall T, Weidema BP (2004) System boundaries and input data in consequential life cycle inventory analysis. Int J Life Cycle Assess 9:161–171
Enders W (2014) Applied econometric time series, 4th edition. Wiley
Eshun JF, Potting J, Leemans R (2010) Inventory analysis of the timber industry in Ghana. Int J Life Cycle Assess 15:715–725
FAOSTAT (2016) Website of the food and agriculture organization of the United Nations. http://faostat.fao.org/
Fath BD, Scharler UM, Ulanowicz RE, Hannon B (2007) Ecological network analysis: network construction. Ecol Model 208:49–55
Fortunato S (2010) Community detection in graphs. Phys Rep 486:75–174
Grinde M (2011) Environmental assessment of scenarios for products and services based on forest resources in Norway. Institutt for energi- og prosessteknikk
Hänninen R, Hetemäki L, Hurmekoski E (2014) European forest industry and forest bioenergy outlook up to 2050: A synthesis
Hausmann R, Hidalgo CA (2011) The network structure of economic output. J Econ Growth 16:309–342
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. 165–176
Helin T, Sokka L, Soimakallio S et al (2012) Approaches for inclusion of forest carbon cycle in life cycle assessment—a review. GCB Bioenergy 5:475–486
Hetemäki L (2014) Future of the European forest-based sector: what science can tell us. Grano Oy
Hidalgo CA, Hausmann R (2009) The building blocks of economic complexity. Proc Natl Acad Sci U S A 106:10570–10575
Huang J, Ulanowicz RE (2014) Ecological network analysis for economic systems: growth and development and implications for sustainable development. PLoS One 9:e100923
Hurmekoski E (2016) Long-term outlook for wood construction in Europe. School of Forest Sciences, Faculty of Science and Forestry, University of Eastern Finland
Jørgensen S, Hauschild M (2013) Need for relevant timescales when crediting temporary carbon storage. Int J Life Cycle Assess 18:747–754
Kim H, Holme P (2015) Network theory integrated life cycle assessment for an electric power system. 7:10961–10975
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 44:3169–3174
Lund H, Mathiesen B, Christensen P, Schmidt J (2010) Energy system analysis of marginal electricity supply in consequential LCA. Int J Life Cycle Assess 15:260–271
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 17:1331–1338
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 20:1094–1107
Neupane B, Halog A, Dhungel S (2011) Attributional life cycle assessment of woodchips for bioethanol production. J Clean Prod 19:733–741
Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:26113
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 5:1752–1509
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 50:4091–4101
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 118:21–31
R Core Team (2005) R: a language and environment for statistical computing
Reichardt J, Bornholdt S (2006) Statistical mechanics of community detection. Phys Rev E. doi:10.1103/PhysRevE.74.016110
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
Reinhard J, Weidema B, Schmidt J (2010) Identifying the marginal supply of wood pulp. 2.-0 LCA Consultants, Aalborg
Rodriguez MA, Pepe A (2008) On the relationship between the structural and socioacademic communities of a coauthorship network. J Informetr 2:195–201
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 247:144–156
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 47:13578–13586
Schmidt JH (2010) Comparative life cycle assessment of rapeseed oil and palm oil. Int J Life Cycle Assess 15:183–197
Schmidt JH (2015) Life cycle assessment of five vegetable oils. J Clean Prod 87:130–138
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 6:19633
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 Technology
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 25:50–70
Wasserman S, Faust K (2016) Social network analysis—methods and applications. Cambridge University Press, Cambridge
Weidema B, Frees N, Nielsen A-M (1999) Marginal production technologies for life cycle inventories. Int J Life Cycle Assess 4:48–56
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 Environment
Wood R, Stadler K, Bulavskaya T et al (2015) Global sustainability accounting—developing EXIOBASE for multi-regional footprint analysis. Sustainability 7:138
Zamagni A, Guinée J, Heijungs R et al (2012) Lights and shadows in consequential LCA. Int J Life Cycle Assess 17:904–918
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.
Responsible editor: Yi Yang
Electronic supplementary material
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)
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)
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)
The archive reports 16 product-specific contingency tables. (RAR 409 kb)
About this article
Cite this article
Pizzol, M., Scotti, M. Identifying marginal supplying countries of wood products via trade network analysis. Int J Life Cycle Assess 22, 1146–1158 (2017). https://doi.org/10.1007/s11367-016-1222-6
- Consequential life cycle assessment
- Network analysis
- Network clustering
- Network communities
- Wood products