Price and the Supply of REDD Credits : A review of the literature

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1 Price and the Supply of REDD Credits : A review of the literature Dr. Doug Boucher Tropical Forest and Climate Initiative Union of Concerned Scientists Washington, DC, USA dboucher@ucsusa.org Forest Day Bali COP/MOP 8 December 2007

2 o o What will it cost to reduce emissions from deforestation and forest degradation? Answer is generally based on paying more than the opportunity cost the most valuable alternative to maintaining the forest Price vs. quantity graph (supply curve) shows emissions reduced (t CO 2 eq, x-axis) for a given price ($, y-axis)

3 Typical supply curve, based on global partial equilibrium model From Sohngen, B Marginal cost curves

4 Two approaches to estimating prices: Global top-down models of the forest, agriculture and sometimes energy sectors to simulate the world economy ( global ) Regional bottom-up studies, dividing opportunity cost ($/ha) by carbon density of the forest (t CO 2 eq/ha) to get price ($/tco 2 eq) ( regional )

5 Review of the published literature REDD in tropical countries only 24 studies, providing 33 data points Data converted to comparable units: Prices in $ US, 2005 Emissions in tco 2 eq. Comparisons: global vs. regional studies, continents, alternate land uses used to calculate opportunity costs

6 Frequency distribution of prices ($ US/tCO 2 eq, 2005) from regional studies Frequency $- $2.00 $4.00 $6.00 $8.00 $10.00 $12.00 $14.00 Price ($ US/tCO2 - label shows upper limit) n = 28; mean = $ 2.56, S.D. = $ 3.05

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8 Global equilibrium models give much higher price estimates than regional studies Regional Global Mean Standard Dev. Ratio of means $ 2.56 $ 3.05 $ $ fold ANOVA, Regional vs Global prices: F = 77.9, P <.0001

9 Prices differ consistently among continents, although much more strongly in models Africa Americas Asia Ratio (high/low) Regional $ 2.22 $ 2.48 $ fold Global $ $ $ fold ANOVA, difference among continents: F = 0.102, P =.90

10 Use of intensive ( modern, commercial ) agriculture for opportunity cost comparisons, gives much higher prices Mean price Ratio of means Intensive $ fold Non-intensive $ 0.58 Paired t-test (one tailed) of intensive vs. non-intensive prices, n = 14: P =.012

11 Deterring the highest-value kinds of land use ( the last tons ) increases the price quite substantially Brazilian Amazon (Nepstad et al. 2007, this session): $ 1.49 for 100% of emissions vs. $ 0.76 for 94% of emissions Brazil (Vera Diaz and Schwartzman 2005): $ 5.44 including soy, $ 2.34 not including soy Sumatra (Tomich et al. 2005): $ for commercial logging, range of $ to $ 5.22 for crops Indonesia (van Nordwijk et al. 2007): considerably higher prices in high-access areas

12 Why the large differences between global and regional Possible explanations: prices? Global models include the timber sector, which is typically high-value relative to ranching and crop production Model points are at the upper-right corner of the supply curve (choke prices, last tons) Some other fundamental difference in assumptions? Which should we use to estimate real-world costs?

13 How to reconcile the estimates? o From Chapter 9 of Working Group III, IPCC AR4: Further research is required to narrow the gap in the potential estimates from global and regional assessments (medium agreement, medium evidence)." o I suggest that this should involve intensive collaboration, not just doing more modeling and more regional studies

14 Will REDD be limited by opportunity costs, or other factors? Possible factors that could keep real-world REDD credit supply well below supply curves: Limited numbers of countries participating, or delay in important countries entering Difficulties in administering national REDD credit systems Reduction in deforestation emissions in some countries due to running out of forest

15 The impact of bioenergy If bioenergy and its demand for land increases in the tropics, this will increase opportunity costs, perhaps substantially The result would be a decrease in the supply of REDD credits Thus, bioenergy may have negative impacts on forests via markets, beyond the effects of direct displacement (e.g. rain forest oil palm for biodiesel)

16 Conclusions Despite considerable published literature, we still do not have a consensus on how to estimate real-world REDD supply Nevertheless, some consistent differences (e.g. among continents) are clear Costs could be reduced substantially by not trying to use REDD credits to deter the highest-value land uses ( the last tons )

17 Acknowledgements Geoff Heal, Columbia University Rachel Cleetus, UCS Peter Frumhoff, UCS Chris Busch, UCS Dan Nepstad, Woods Hole Research Center Meine van Noordwijk, World Agroforestry Center

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