Material Flow Analysis

  • David LanerEmail author
  • Helmut Rechberger
Part of the LCA Compendium – The Complete World of Life Cycle Assessment book series (LCAC)


Material flow analysis (MFA) is a tool to quantify the flows and stocks of materials in arbitrarily complex systems. MFA has been widely applied to material systems in providing useful information regarding the patterns of resource use and the losses of materials entering the environment. MFA and life cycle assessment (LCA) are traditionally different tools for environmental decision support. The two methods are basically different with respect to the definition of system boundaries and the actual subject of investigation. However, there are also overlaps between the tools. These overlaps highlight that MFA and LCA can complement each other and thereby increase the quality of studies in both domains. Thus, the combination of these tools offers the potential for more consistent and reliable decision support in environmental and resource management.

In this chapter, the authors aim at describing the state of the art in MFA and at highlighting the intertwined characters of MFA and LCA when it comes to the investigation of environmentally relevant material systems. Therefore, the main procedures, and the most important methodological approaches of MFA, are described in Sect. 2. Main applications of MFA to different problems and for different purposes based on selected cases from literature are dealt with in Sect. 3. In Sect. 4, the authors discuss the benefits of combining MFA and LCA including a brief outlook on the combined use of MFA and LCA in integrated assessments of environmentally relevant systems.


Application of material flow analysis Combining MFA and LCA Dynamic material flow analysis Eco-factors Ecological scarcity method LCA LCIA Life cycle assessment Life cycle impact assessment MFA Mass conservation Material flow analysis SFA Static material flow analysis Statistical entropy Substance flow analysis Uncertainty analysis 



Acidification potential


Air pollution control


Cumulative energy demand




Carbon dioxide


Cumulative energy demand




Global warming potential


Hydrogen chloride


Hydrogen fluoride


Life cycle assessment


Life cycle impact assessment


Material flow analysis


Municipal solid waste


Ozone depletion potential


Old scrap ratio




Residue derived fuel


Statistical entropy


Statistical entropy analysis


Substance flow analysis


Volatile organic compounds


  1. Andersen JK, Boldrin A, Christensen TH, Scheutz C (2010) Mass balances and life-cycle inventory for a garden waste windrow composting plant (Aarhus, Denmark). Waste Manag Res 28:1010–1020. doi: 10.1177/0734242x09360216 Google Scholar
  2. Andersson H et al (2012) WP4 background paper – identification of sources and estimation of inputs to the Baltic Sea. IVL Swedish Environmental Research Institute, StockholmGoogle Scholar
  3. Ayres RU (1995) Life cycle analysis: a critique. Resour Conserv Recycl 14:199–223. doi: CrossRefGoogle Scholar
  4. Ayres RU, Nair I (1984) Thermodynamics and economics. Phys Today 37:62–71CrossRefGoogle Scholar
  5. Baccini P, Bader HP (1996) Regionaler Stoffhaushalt: Erfassung, Bewertung und Steuerung. Spektrum Akademischer Verlag, HeidelbergGoogle Scholar
  6. Baccini P, Brunner PH (1991) Metabolism of the anthroposphere. Springer, BerlinCrossRefGoogle Scholar
  7. Baccini P, Brunner PH (2012) Metabolism of the anthroposphere: analysis, evaluation design, 2nd edn. MIT Press, Cambridge, MAGoogle Scholar
  8. Bai L, Qiao Q, Li Y, Wan S, Xie M, Chai F (2015) Statistical entropy analysis of substance flows in a lead smelting process. Resour Conserv Recycl 94:118–128. doi: CrossRefGoogle Scholar
  9. Björklund A (2002) Survey of approaches to improve reliability in LCA. Int J Life Cycle Assess 7:64–72. doi: 10.1007/BF02978849 CrossRefGoogle Scholar
  10. Bonnin M, Azzaro-Pantel C, Pibouleau L, Domenech S, Villeneuve J (2013) Development and validation of a dynamic material flow analysis model for French copper cycle. Chem Eng Res Des 91:1390–1402. doi: CrossRefGoogle Scholar
  11. Bringezu S, Moriguchi Y (2002) Material flow analysis. In: Ayres RU, Ayres LW (eds) Handbook of industrial ecology. Edward Elgar Publishing, CheltenhamGoogle Scholar
  12. Bringezu S, Schütz H, Moll S (2003) Rationale for and Interpretation of economy-wide materials flow analysis and derived indicators. J Ind Ecol 7:43–64. doi: 10.1162/108819803322564343 CrossRefGoogle Scholar
  13. Brunner PH (2010) Clean cycles and safe final sinks. Waste Manag Res 28:575–576. doi: 10.1177/0734242x10370987 CrossRefGoogle Scholar
  14. Brunner PH, Rechberger H (2004) Practical handbook of material flow analysis. CRC Press LCC, FloridaGoogle Scholar
  15. Brunner PH, Tjell JC (2012) Do we need sinks? Waste Manag Res 30:1–2. doi: 10.1177/0734242x11432367 CrossRefGoogle Scholar
  16. Buchner H, Laner D, Rechberger H, Fellner J (2014) In-depth analysis of aluminum flows in Austria as a basis to increase resource efficiency resources. Resour Conserv Recycl 93:112–123CrossRefGoogle Scholar
  17. Cencic O, Frühwirth R (2015) A general framework for data reconciliation. Part I: linear constraints. J Comput Chem Eng 75:196–208CrossRefGoogle Scholar
  18. Cencic O, Rechberger H (2008) Material flow analysis with software STAN. J Environ Eng Manag 18(1):3–7Google Scholar
  19. Chen W-Q (2013) Recycling rates of aluminum in the United States. J Ind Ecol 17:926–938. doi: 10.1111/jiec.12070 CrossRefGoogle Scholar
  20. Chen W-Q, Graedel TE (2012) Anthropogenic cycles of the elements: a critical review. Environ Sci Technol 46:8574–8586. doi: 10.1021/es3010333 CrossRefGoogle Scholar
  21. Daigo I, Igarashi Y, Matsuno Y, Adachi Y (2007) Accounting for steel stock in Japan. Tetsu-to-Hagane 93:66–70. doi: 10.2355/tetsutohagane.93.66 CrossRefGoogle Scholar
  22. Danius L, Burström F (2001) Regional material flow analysis and data uncertainties: can the results be trusted? In: Hilti LM, Giligen PW (eds) Sustainability in the information society. Metropolis Verlag, MarburgGoogle Scholar
  23. Do NT, Trinh DA, Nishida K (2014) Modification of uncertainty analysis in adapted material flow analysis: case study of nitrogen flows in the Day-Nhue River Basin. Vietnam Resour Conserv Recycl 88:67–75. doi: CrossRefGoogle Scholar
  24. Dos Santos M, Spitzbart M, Weinlich M, Leitner T, Laner D, Cencic O, Rechberger H (2012) MoveRec: On-line tool for estimating the material composition of WEEE input streams. In: Electronics Goes Green 2012+ (EGG), 9–12 Sept., IEEE, Berlin, pp 1–5Google Scholar
  25. Do-Thu N, Morel A, Nguyen-Viet H, Pham-Duc P, Nishida K, Kootattep T (2011) Assessing nutrient fluxes in a Vietnamese rural area despite limited and highly uncertain data. Resour Conserv Recycl 55:849–856. doi: 10.1016/j.resconrec.2011.04.008 CrossRefGoogle Scholar
  26. Dubois D, Fargier H, Ababou M, Guyonnet D (2014) A fuzzy constraint-based approach to data reconciliation in material flow analysis. Int J General Syst:1–23. doi: 10.1080/03081079.2014.920840 Google Scholar
  27. Feketitsch J, Buchner H, Lederer J, Laner D, Fellner J (2013) Material flow analysis of plastic products in Austria: emphasis on data uncertainties in consumption sectors and solid waste management. Paper presented at the ISWA World Congress, 7–10 Oct 2013, Vienna, Austria, pp 1–11. ISBN: 978-3-200-03229-3Google Scholar
  28. Fellner J, Aschenbrenner P, Cencic O, Rechberger H (2011) Determination of the biogenic and fossil organic matter content of refuse-derived fuels based on elementary analyses. Fuel 90:3164–3171. doi: CrossRefGoogle Scholar
  29. Frändegård P, Krook J, Svensson N, Eklund M (2013) A novel approach for environmental evaluation of landfill mining. J Clean Prod 55:24–34. doi: CrossRefGoogle Scholar
  30. Frischknecht R, Büsser Knöpfel S (2013) Swiss eco-factors 2013 according to the ecological scarcity method. Methodological fundamentals and their application in Switzerland, vol 1330. Federal Office for the Environment, BernGoogle Scholar
  31. Georgescu-Roegen N (1971) The entropy law and the economic process. Harvard University Press, Cambridge, MACrossRefGoogle Scholar
  32. Glöser S, Soulier M, Tercero Espinoza LA (2013) Dynamic analysis of global copper flows. Global stocks, postconsumer material flows, recycling indicators, and uncertainty evaluation. Environ Sci Technol 47:6564–6572. doi: 10.1021/es400069b CrossRefGoogle Scholar
  33. Gottschalk F, Scholz RW, Nowack B (2010) Probabilistic material flow modeling for assessing the environmental exposure to compounds: methodology and an application to engineered nano-TiO2 particles. Environ Model Softw 25:320–332. doi: CrossRefGoogle Scholar
  34. Graedel TE et al (2004) Multilevel cycle of anthropogenic copper. Environ Sci Technol 38:1242–1252. doi: 10.1021/es030433c CrossRefGoogle Scholar
  35. Grinberg M, Ackermann R, Finkbeiner M (2012) Ecological scarcity method: adaptation and implementation for different countries. Environ Climate Technol 10:9–15. doi: 10.2478/v10145-012-0019-5 Google Scholar
  36. Udo de Haes HA, van der Voet E, Kleijn R (1997) Substance flow analysis (SFA), an analytical tool for integrated chain management. Paper presented at the ConAccount workshop, Leiden, The Netherlands, 21–23 January 1997Google Scholar
  37. Hedbrant J, Sörme L (2001) Data vagueness and uncertainties in urban heavy-metal data collection water. Water Air Soil Pollut Focus 1:43–53. doi: 10.1023/a:1017591718463 CrossRefGoogle Scholar
  38. Huang D-B, Bader H-P, Scheidegger R, Schertenleib R, Gujer W (2007) Confronting limitations: new solutions required for urban water management in Kunming City. J Environ Manag 84:49–61. doi: CrossRefGoogle Scholar
  39. ISO 14044 (2006) Environmental management – life cycle assessment − requirements and guidelines. Geneva, SwitzerlandGoogle Scholar
  40. Itsubo N (2015) Weighting. Chapter 15 “Life Cycle Impact Assessment” (Hauschild M, Huijbregts MAJ eds). In: LCA compendium – the complete world of life cycle assessment (Klöpffer W, Curran MA, series eds). Springer, Dordrecht, pp 301–330Google Scholar
  41. Johansson N, Krook J, Eklund M, Berglund B (2013) An integrated review of concepts and initiatives for mining the technosphere: towards a new taxonomy. J Clean Prod 55:35–44. doi: 10.1016/j.jclepro.2012.04.007 CrossRefGoogle Scholar
  42. Kaufman S, Krishnan N, Known E, Castaldi M, Themelis N, Rechberger H (2008) Examination of the fate of carbon in waste management systems through statistical entropy and life cycle analysis. Environ Sci Technol 42(22):8558–8563CrossRefGoogle Scholar
  43. Kral U (2014) A new indicator for the assessment of anthropogenic substance flows to regional sinks. PhD thesis, Vienna University of TechnologyGoogle Scholar
  44. Kral U, Kellner K, Brunner PH (2013) Sustainable resource use requires ‘clean cycles’ and safe ‘final sinks’. Sci Total Environ 1:461–462. doi: Google Scholar
  45. Krook J, Svensson N, Eklund M (2012) Landfill mining: a critical review of two decades of res. Waste Manag 32:513–520. doi: CrossRefGoogle Scholar
  46. Laner D, Brunner PH (2008) Kriterien zur Trennung von Siedlungsabfall aus Industrie und Gewerbe als Voraussetzung zur Zuordnung zu Behandlungsverfahren. Institut für Wassergüte, Ressourcenmanagement und Abfallwirtschaft, WienGoogle Scholar
  47. Laner D, Cencic O (2013) Comment on “Solid recovered fuel: materials flow analysis and fuel property development during the mechanical processing of biodried waste”. Environ Sci Technol 47:14533–14534. doi: 10.1021/es403403u CrossRefGoogle Scholar
  48. Laner D, Rechberger H (2007) Treatment of cooling appliances: interrelations between environmental protection, resource conservation, and recovery rates. Resour Conserv Recycl 52:136–155CrossRefGoogle Scholar
  49. Laner D, Pomberger R, Scherübl T, Brunner PH (2009) Voraussetzungen für eine zielorientierte Bewirtschaftung hausmüllähnlicher Gewerbeabfälle – eine Analyse am Beispiel der Steiermark. Müll Abfall 9:2–10Google Scholar
  50. Laner D, Rechberger H, Astrup T (2014) Systematic evaluation of uncertainty in material flow analysis. J Ind Ecol 18(6): 859–870Google Scholar
  51. Laner D, Rechberger H, Astrup T (2015) Applying fuzzy and probabilistic uncertainty concepts to the material flow analysis of palladium in Austria. J Ind Ecol 19(6):1055–1069Google Scholar
  52. Lassen C, Hansen E (2000) Paradigm for substance flow analyses. Guide for SFAs carried out for the Danish EPA. Environmental Project No. 577, Danish Environmental Protection Agency, CopenhagenGoogle Scholar
  53. Laurent A, Hauschild MZ (2015) Normalization. Chapter 14 “Life Cycle Impact Assessment” (Hauschild M, Huijbregts MAJ eds). In: LCA compendium – the complete world of life cycle assessment (Klöpffer W, Curran MA, series eds). Springer, Dordrecht, pp 271–300Google Scholar
  54. Lederer J, Rechberger H (2010) Comparative goal-oriented assessment of conventional and alternative sewage sludge treatment options. Waste Manag 30:1043–1056. doi: 10.1016/j.wasman.2010.02.025 CrossRefGoogle Scholar
  55. Lederer J, Laner D, Fellner J (2014) A framework for the evaluation of anthropogenic resources: the case study of phosphorus stocks in Austria. J Clean Prod 84:1–848. doi: CrossRefGoogle Scholar
  56. Lifset RJ, Eckelman MJ, Harper EM, Hausfather Z, Urbina G (2012) Metal lost and found: dissipative uses and releases of copper in the United States 1975–2000. Sci Total Environ 417–418:138–147. doi: 10.1016/j.scitotenv.2011.09.075 CrossRefGoogle Scholar
  57. Lopes Silva DA, de Oliveira JA, Saavedra YMB, Ometto AR, Rieradevall i Pons J, Gabarrell Durany X (2015) Combined MFA and LCA approach to evaluate the metabolism of service polygons: A case study on a university campus. Resour Conserv Recycl 94:157–168CrossRefGoogle Scholar
  58. Mastellone ML, Brunner PH, Arena U (2009) Scenarios of waste management for a waste emergency area. J Ind Ecol 13:735–757. doi: 10.1111/j.1530-9290.2009.00155.x CrossRefGoogle Scholar
  59. Montangero A, Belevi H (2007) Assessing nutrient flows in septic tanks by eliciting expert judgement: a promising method in the context of developing countries. Water Res 41:1052–1064. doi: CrossRefGoogle Scholar
  60. Müller D (2006) Stock dynamics for forecasting material flows—case study for housing in the Netherlands. Ecol Econ 59:142–156. doi: CrossRefGoogle Scholar
  61. Müller E, Hilty LM, Widmer R, Schluep M, Faulstich M (2014) Modeling metal stocks and flows: a review of dynamic material flow analysis methods. Environ Sci Technol 48:2102–2113. doi: 10.1021/es403506a CrossRefGoogle Scholar
  62. O’Rourke D, Conelly L, Koshland CP (1996) Industrial ecology: a critical review. Int J Environ Pollut 6(2/3):89–112Google Scholar
  63. Ott C, Rechberger H (2012) The European phosphorus balance. Resour Conserv Recycl 60:159–172. doi: 10.1016/j.resconrec.2011.12.007 CrossRefGoogle Scholar
  64. Pauliuk S, Wang T, Müller DB (2013) Steel all over the world: estimating in-use stocks of iron for 200 countries. Resour Conserv Recycl 71:22–30. doi: CrossRefGoogle Scholar
  65. Rechberger H, Brunner PH (2002) A new, entropy based method to support waste and resource management decisions. Environ Sci Technol 34(4):809–816CrossRefGoogle Scholar
  66. Rechberger H, Graedel TE (2002) The European copper cycle: statistical entropy analysis. Ecol Econ 42(1–2):59–72CrossRefGoogle Scholar
  67. Rechberger H, Cencic O, Frühwirth R (2014) Uncertainty in material flow analysis. J Ind Ecol 18:159–160CrossRefGoogle Scholar
  68. Reck BK, Graedel TE (2012) Challenges in metal. Recycl Sci 337:690–695. doi: 10.1126/science.1217501 Google Scholar
  69. RPA (2012) Study on data needs for a full raw materials flow analysis. Risk & Policy Analysts Limited, BrusselsGoogle Scholar
  70. Schaffner M (2007) Applying a material flow analysis model to assess river water pollution and mitigation potentials. PhD Thesis, University of BernGoogle Scholar
  71. Schaffner M, Bader H-P, Scheidegger R (2009) Modeling the contribution of point sources and non-point sources to Thachin River water pollution. Sci Total Environ 407:4902–4915. doi: CrossRefGoogle Scholar
  72. Schneider L, Berger M, Finkbeiner M (2011) The anthropogenic stock extended abiotic depletion potential (AADP) as a new parameterisation to model the depletion of abiotic resources. Int J Life Cycle Assess 16:929–936. doi: 10.1007/s11367-011-0313-7 CrossRefGoogle Scholar
  73. Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27:379–423, 623–656, July, OctoberCrossRefGoogle Scholar
  74. Sobańtka A, Rechberger H (2013) Extended statistical entropy analysis (eSEA) for improving the evaluation of Austrian wastewater treatment plants. Water Sci Technol 67(5):1051–1057CrossRefGoogle Scholar
  75. Sobańtka AP, Zessner M, Rechberger H (2012) The extension of statistical entropy analysis to chemical compounds. Entropy 14:2413–2426CrossRefGoogle Scholar
  76. Stumm W, Davis J (1974) Kann Recycling die Umweltbeeinträchtigung vermindern? In: Recycling: Lösung der Umweltkrise? Gottlieb Duttweiler-Institut für wirtschaftliche und soziale Studien (ed), Stuttgart, Zürich (in German)Google Scholar
  77. Tonini D, Martinez-Sanchez V, Astrup TF (2013) Material resources, energy, and nutrient recovery from waste: are waste refineries the solution for the future? Environ Sci Technol 47:8962–8969. doi: 10.1021/es400998y Google Scholar
  78. Udo deHaes HA, van der Voet E, Kleijn R (1997) Substance flow analysis (SFA), an analytical tool for integrated chain management. Paper presented at the ConAccount workshop, Leiden, The Netherlands, 21–23 January 1997Google Scholar
  79. Vadenbo CO, Boesch ME, Hellweg S (2013) Life cycle assessment model for the use of alternative resources in iron making. J Ind Ecol 17:363–374. doi: 10.1111/j.1530-9290.2012.00543.x CrossRefGoogle Scholar
  80. Vadenbo C, Guillén-Gosálbez G, Saner D, Hellweg S (2014a) Multi-objective optimization of waste and resource management in industrial networks. Part II: model application to the treatment of sewage sludge. Resour Conserv Recycl 89:41–51. doi: CrossRefGoogle Scholar
  81. Vadenbo C, Hellweg S, Guillén-Gosálbez G (2014b) Multi-objective optimization of waste and resource management in industrial networks. Part I: model description. Resour Conserv Recycl 89:52–63. doi: CrossRefGoogle Scholar
  82. van der Voet E (2002) Substance flow analysis methodology. In: Ayres RU, Ayres LW (eds) A handbook of industrial ecology. Edward Elgar Publishing Ltd, CheltenhamGoogle Scholar
  83. Winterstetter A, Laner D, Rechberger H, Fellner J (2015) Framework for the evaluation of anthropogenic resources: a landfill mining case study – Resource or reserve? Resour Conserv Recycl 96:19–30CrossRefGoogle Scholar
  84. Wolman A (1965) The metabolism of cities. Sci Am 213:179–190CrossRefGoogle Scholar
  85. Wu H, Yuan Z, Zhang Y, Gao L, Liu S, Geng Y (2014) Data uncertainties in anthropogenic phosphorus flow analysis of lake watershed. J Clean Prod 69:74–82. doi: CrossRefGoogle Scholar
  86. Yue Q, Lu ZW, Zhi SK (2009) Copper cycle in China and its entropy analysis. Resour Conserv Recycl 53(12):680–687CrossRefGoogle Scholar
  87. Zeltner C, Bader HP, Scheidegger R, Baccini P (1999) Sustainable metal management exemplified by copper in the USA. Reg Environ Chang 1:31–46. doi: 10.1007/s101130050006 CrossRefGoogle Scholar
  88. Zoboli O, Laner D, Zessner M, Rechberger H (2015) Added value of time series in MFA. The Austrian phosphorus budget from 1990 to 2011. J Ind Ecol. doi: 10.1111/jiec.12381, open access, Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Institute for Water Quality, Resource and Waste ManagementVienna University of TechnologyViennaAustria
  2. 2.Christian Doppler Laboratory for Anthropogenic ResourcesVienna University of TechnologyViennaAustria

Personalised recommendations