Identifying marginal supplying countries of wood products via trade network analysis

WOOD AND OTHER RENEWABLE RESOURCES

Abstract

Purpose

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.

Methods

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.

Conclusions

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.

Keywords

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

Notes

Acknowledgements

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: https://github.com/massimopizzol?tab=repositories (RAR 83 kb)

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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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