Absorbing shocks: Designing an agriculture vegetative waste management system resilient to final product price fluctuations
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1 Athens th International Conference on Sustainable Solid Waste Management Absorbing shocks: Designing an agriculture vegetative waste management system resilient to final product price fluctuations Dani Broitman 1, Orna Raviv 2, Ofira Ayalon 2 and Iddo Kan 3 1 Faculty of Architecture and Town Planning, Technion - Israel Institute of Technology, Haifa, Israel 2 Department of Natural Resources & Environmental Management, University of Haifa, Haifa, Israel 3 Department of Environmental Economics and Management, and the Center for Agricultural Economics Research; Robert H. Smith Faculty of Agriculture, Food and Environment, Hebrew University of Jerusalem, Rehovot, Israel
2 Research questions The agricultural sector in Israel produces annually 1,481,421 tons of vegetative waste 1,167,492 tons of foliage waste (around 79%) 172,421 tons of woody waste (around 11%) 141,509 tons of fruits and vegetables waste (around 10%) There are 6 existing technologies that were found economically feasible Each one can treat specific types of vegetative waste Investment and operational costs are known Prices of final outputs can be assessed but can fluctuate widely and are hard to predict
3 Research questions What is the optimal Waste Management System (WMS) for vegetative waste if entrepreneurs are indifferent to the risk of final product price fluctuation? (i.e., are risk-neutral) What is the optimal WMS for vegetative waste under a risk-averse assumption?
4 Basic model Risk neutrality Treatment technologies, inputs and main outputs: Treatment technology Main output Foliage Woody F&V Total cost* Market price* Profit* Torrefaction Charcoal X Pyrolysis Biochar X X Animal feed Mixing X X RDF RDF X X Composting Compost X X Anaerobic Biogas X The solution is: Torrefaction is the most profitable technology but it treats only woody waste. All this type will be allocated to it. Pyrolysis is the second best. Woody waste is already treated so all the foliage will be allocated to it. Animal feed is the most profitable technology for F&V waste, and all F&V will be allocated to it.
5 Basic model Risk neutrality What is the sensitivity of the model to final price fluctuations? Torrefaction Pyrolysis Animalfeed RDF Composting Anaerobic Torrefaction Pyrolysis Animal-feed RDF Composting Anaerobic The diagonal shows the original prices of each technology. The other figures are the break-even prices between each couple.
6 Δ Δ Risk-aversion model Risk-aversion regarding uncertain final product prices means that the marginal utility of higher prices diminishes (the utility function is concave) Assume fluctuating prices with equal chance of increasing or Certainty Utility equivalent decreasing relative to the mean If price goes up, the utility increase is less than the utility Risk Premium Price decrease when price goes down A risk-averse agent will be willing to get in certain a price lower than the average one, if the certain price provides the same utility as the average utility. The certainty equivalent price equals the average price minus a risk premium.
7 The Certainty Equivalent (CE) is defined as Price minus Cost minus Risk Premium And that Risk Premium is related to the Coefficient of Variation (CV) of the final prices What is the CV? A measure of final prices dispersion Defined as the ratio of the standard deviation to the mean (average). Average ~ 200 Std Dev ~ 12 CV = (very low)
8 The Certainty Equivalent (CE) is defined as Price minus Cost minus Risk Premium And that Risk Premium is related to the Coefficient of Variation (CV) of the final prices What is the CV? A measure of final prices dispersion Defined as the ratio of the standard deviation to the mean (average). Average ~ 200 Std Dev ~ 28 CV = (higher)
9 The Certainty Equivalent (CE) is defined as Price minus Cost minus Risk Premium And that Risk Premium is related to the Coefficient of Variation (CV) of the final prices What is the CV? A measure of final prices dispersion Defined as the ratio of the standard deviation to the mean (average). Average ~ 200 Std Dev ~ 50 CV = (much higher)
10 The Certainty Equivalent (CE) is defined as Price minus Cost minus Risk Premium And that Risk Premium is related to the Coefficient of Variation (CV) of the final prices Conclusion 1: A higher CV means a larger price distribution and therefore more risk. So the Risk Premium need to be larger Conclusion 2: There is a CV value so high that the Risk Premium associated with it zeroes the Certainty Equivalent and the technology is no longer viable.
11 The Certainty Equivalent (CE) is defined as Price minus Cost minus Risk Premium And that Risk Premium is related to the Coefficient of Variation (CV) of the final prices CV s of several recycled materials (*) : Product CV Recycled glass Recycled paper and board Plastic waste * EUROSTAT, Recycling secondary material price indicator.
12 We use a risk-aversion estimate from the literature (*) Torrefaction Pyrolysis Animal-feed RDF For example, if the CV of the charcoal price is within the range [0, 1.519), torrefaction is still viable. If CV is higher, the technology is too risky to be implemented. Composting Anaerobic * Bar-Shira Z, Just RE, Zilberman D. Estimation of farmers' risk attitude: an econometric approach. Agricultural Economics (1997)
13 Using CVs we can compare the Certainty Equivalents of any pair of technologies Assume n is a more profitable technology than m, we can find a CV of n and a CV of m that causes their Certainty Equivalent to be equal In other words, even if n is more profitable than m, a riskaverse entrepreneur will choose technology m if the fluctuation of its final products price is moderate compared with the fluctuation expected for the final prices of n.
14 Torrefaction If the CV of Torrefaction s final prices is higher than (the black thick line) the technology is too risky. If the CV of Torrefaction s final prices is lower, still other technologies can compete with it (although being on average the most profitable one ) If the CV of Pyrolysis is 0.4, any CV higher than 1.25 for Torrefaction makes Pyrolysis preferable to a risk-averse agent. Suitable waste types can be allocated to technologies based on forecasted CVs.
15 Pyrolysis If the CV of pyrolysis s final prices is higher than 1.12 (the black thick line) the technology is too risky. Any pair of CVs below the colored lines means that pyrolysis is the best option 1.1 But if 1.07 Anaerobic digestion becomes the best option 0.35 And if 0.6 Animal feeding becomes the best option Then remaining (and suitable) waste types can be allocated
16 Animal-feed And so on... The model allows the hierarchical allocation of vegetative waste types among competing technologies while taking into account: 1- Observed mean final prices 2- The CVs of final prices 3- The risk-aversion level
17 RDF And so on... The model allows the hierarchical allocation of vegetative waste types among competing technologies while taking into account: 1- Observed mean final prices 2- The CVs of final prices 3- The risk-aversion level
18 Composting And so on... The model allows the hierarchical allocation of vegetative waste types among competing technologies while taking into account: 1- Observed mean final prices 2- The CVs of final prices 3- The risk-aversion level
19 Summary We collected data about types of vegetative waste, their quantities and feasible treatment technologies Investing and operational cost for each technology were calculated The optimal (profit maximizing) WMS was designed by means of a linear programming model We performed a sensitivity analysis assuming risk-neutral and risk-aversion perspectives The risk-aversion perspective takes into account mean prices and a measure of their fluctuation, using the coefficient of variance The model can be applied to other types of WMS in which their final products (or recycling) prices are uncertain
20 Further research Spatial analysis of waste management under risk-aversion, referring to geographical areas and their different availability of vegetative waste Collection of final product price data for the considered technologies from all over the world in order to calibrate the model using expected coefficients of variance Thank you for your attention! Questions? Comments?
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