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An evolutionary modelling of recycling and product-life extension

Eric Brouillat eric.brouillat@u-bordeaux4.fr GREThA UMR CNRS 5113 Montesquieu - Bordeaux IV University, France. An evolutionary modelling of recycling and product-life extension. EMAEE 2007 Globalisation, Services and Innovation: The Changing Dynamics of the Knowledge Economy

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An evolutionary modelling of recycling and product-life extension

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  1. Eric Brouillat eric.brouillat@u-bordeaux4.fr GREThA UMR CNRS 5113 Montesquieu - Bordeaux IV University, France An evolutionary modelling of recycling and product-life extension EMAEE 2007 Globalisation, Services and Innovation: The Changing Dynamics of the Knowledge Economy 17 – 19 May 2007

  2. Plan • Outline of the context • Model description • Simulations results • Conclusions

  3. Evolution of waste streams • In the OECD countries, domestic waste streams have increased by 40% in volume between 1980 and 1997. • In France, the emission of domestic waste per person actually doubled between 1960 and 1995. Growth rate in French domestic waste emissions has been about 2,5% par year for decades.

  4. Closing the “material loop” • Short loops = product-life extension by means of re-using products as well as by maintenance, repair, reconditioning and technological upgrading (ex: retreading of tyres, the Xerox life-cycle design programme for photocopiers, the Siemens-Albis AG portable operating system BS2000, etc.) • Long loops = post-consumption recycling : collecting waste, sorting it to recover materials which may be recycled, and then transforming these materials (physically and/or chemically) to obtain new raw materials.

  5. Closing the “material loop” • Short loops are much more complex to implement than long ones. • product-life extension => slow down product replacement speed by consumers => lower sales. • product life extension => slow down technical progress. Firms are usually reluctant to adopt a product-life extension strategy. The tendency is rather to see a decrease in product-lifetime. Firms tend mainly to concentrate more on recyclability.

  6. Issues studied • Investigate firm's economic incentives to extend product life and market recyclable products. • Identify the conditions favouring diffusion of products with a low environmental impact: easily recyclable products with a long lifetime (green products). • study the impact of such diffusion on waste management and ecological variables.

  7. Model structure • Model based on the "History-friendly" model by Malerba, Nelson, Orsenigo and Winter (1999). • Focus on the environmental dimension of the product => introduction of the post-consumption phase.

  8. Model structure

  9. Recyclability (R) Design for recycling Design for recycling Long lifetime design backed up with services Long lifetime design backed up with services Initial design Product’s characteristics Rmax2 Rmax1 Lifetime (LT). LTmax1 LTmax2

  10. Firms’ R&D investment  with 0 ≤ μi ≤ 1. with 0 ≤ δ i ≤ 1

  11. The innovation process Innovation probabilities with Rmax = Rmax1 if Ri,t-1 ≤ Rmax1 and Rmax = Rmax2 if Ri,t-1 > Rmax1. with LTmax = LTmax1 if LTi,t-1 ≤ LTmax1 and LTmax = LTmax2 if LTi,t-1 > LTmax1.

  12. Supply - demand interactions Product’s visibility Non-green consumers : β3β1 + β2 Green consumers : β3 < β1 + β2

  13. Product manufacturing • two categories of perfectly substitutable inputs: recycled inputs and virgin inputs • Share of recycled inputs = parameter • Recycled inputs provided by the recycler, virgin inputs by suppliers external to the model

  14. Selling price If LTi,t ≤ LTmax1 If LTi,t > LTmax1

  15. The recycler • The recycler collects the complete range of end of life products which he recycles and sells to the producers as recycled inputs • Starting from a unit of product, the recycler recycles, manufactures and sells R units of recycled inputs

  16. Experimental set-up • LSD 5.5 simulation platform • 10 firms, 1000 consumers and 1 recycler • neither entries nor exits of firms and consumers • Monte-Carlo method : 10 000 simulations. The number of periods is randomly chosen between 1 and 200. The results of the last period of each simulation are studied. Simulations initialized with a randomly drawn vector of values for the main parameters => 10 000 observations covering quite a diversified subset of the parameter space

  17. Firm level results Determinants of market shares in terms of users

  18. Firm level results

  19. Market level results Determinants of product quantities sold

  20. Market level results Determinants of recycled waste flows

  21. Market level results Box plots of virgin inputs flows

  22. Conclusions • Very simple model • New model under development with - an explicit technological dimension; - adaptive firms’ innovation strategies; - new design => adoption costs; - exit of firms; - innovation activities of the downstream actors in the supply chain; - environmental regulations (extended producer responsability).

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