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July 15, 2010

“Other” Factors Affecting Land Use Change (and Related GHG Impacts): Results from a Limited Literature Review. Michelle Manion, NESCAUM CARB Expert Workgroup Meeting Comparative and Alternative Modeling Subgroup. July 15, 2010.

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July 15, 2010

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  1. “Other” Factors Affecting Land Use Change (and Related GHG Impacts): Results from a Limited Literature Review Michelle Manion, NESCAUM CARB Expert Workgroup Meeting Comparative and Alternative Modeling Subgroup July 15, 2010

  2. Why Consider “Other” Factors Associated with Land Use Change (LUC) and GHG estimates? A key assumption of linked economic-biophysical modeling estimates of iLUC (eg. GTAP combined with land cover/emissions data) is that the models generate accurate estimates of LUC resulting from incremental demand for biofuels, all else being equal. Members of the Uncertainty and Comparative/Alternative Modeling Subgroups have asked: Could economic modeling in fact be confounding other factors that drive LUC with new demand for biofuels? Are estimates of carbon flux sufficiently capturing land and carbon dynamics? are modeling estimates over- or under-estimating the magnitude of iLUC associated with incremental biofuel demand? How can we establish a margin of error or maximum likelihood?

  3. Brief List of Factors Associated with LUC The literature on LUC reflects that it is and always has been a highly dynamic and complex phenomena… So, it is very difficult/impossible to hold other factors constant in order to conduct a controlled experiment on LUC drivers. A sampling of recent literature reveals a few examples of factors highly correlated with LUC (and associated carbon fluxes): Market distortions Land tenure Subsidies Scale of evaluation Ecological factors

  4. Market Distortions Land use markets, especially for agricultural products, are subject to a significant degree of official and unofficial interventions. Many of these interventions create distortions on the supply-side, demand-side, or both sides of land markets: Examples of supply-side distortions: Protected areas Ambiguous (or unenforced) land tenure Subsidized infrastructure (e.g., roads) Price supports Loan guarantees Examples of demand-side distortions: Collusion Price supports Import tariffs

  5. Ex. 1a: Land Tenure and Management No single governance structure has been shown to control overharvesting of forests; Formal ownership or legal protected status appear less important than on the ground rules and norms; Ostrom and Nagendra (2006) found a high correlation between forest density and the level of monitoring, esp. when local people participate in the monitoring; Field observations of cooperative monitoring that produce group benefits are contrary to classical economic model of individual, short-term profit-maximizing behavior.

  6. Ex. 1b: Government policy Policy change (early 2000s): Brazil’s National Institute for Colonization and Agrarian Reform (INCRA) began “legalizing” settlements of squatters (often on primary forest lands), and compensating ranchers for lost lands; Compensation paid is often higher than market price for land, so landowners do not discourage new settlements; Adds to existing challenge of controlling deforestation in Amazonas. (Fearnside 2005)

  7. Ex. 2: Scale of Evaluation/Data Aggregation Kaimowitz and Angelsen (1998) find little value in many regression models of deforestation, because of their tendency to “…lose sight of strong micro-level relationships, which evaporate in the process of aggregating data.” Many data on deforestation and reforestation are highly aggregated and over short timeframes; Few long-term studies of forest growth exist, and those few often rely upon different databases and/or time series;

  8. Ex. 3a: Ecological Factors “Edge effects”: Laurance and Laurance (1997) found that forest fragmentation can result in losses of above-ground dry biomass (AGBM) which are non-linear: Study area: 20km x 50km area 80 miles north of Manaus, Brazil; 39 study 1-ha plots censused from 1980 to 1997 Resulting losses in measured AGBM higher than expected, due primarily to deaths of large trees →Estimated carbon losses may be higher than anticipated when Amazonian forest fragments fall below 100 to 400 ha in area (depending on shape).

  9. Ex. 3b: Ecological Factors Fire: Nepstad et al. (1999, 2001) found significantly higher risks of fire in Amazonian forests that have been logged than those that have not Amazonian forest trees are not adapted to fire; These effects found to be further exacerbated during El Nino events and by burning for agriculture and ranching

  10. Sources Fearnside PM (2005) Conserv Bio: 680-688. Kaimowitz D, Angelsen A (1998) Economic Models of Tropical Deforestation: A Review (Center Intl For Res, Bogor Indonesia). Laurance WF, Laurance SG, Ferreira LV, Rankin de Merona J, Gascon C, Lovejoy T (1997) Science 287: 1117-1118. Nepstad D et al. (2006) Conserv Bio 20: 65-73. Ostrom, E and Nagendra H (2006) Proc Natl Acad Sci USA 103: 19224-19231. Turner II BL, Lambin EF, Reenberg A (2007) Proc Natl Acad Sci USA 104: 20666-20671. Waggoner PE, Ausubel JH (2002) Proc Natl Acad Sci USA 99: 7860-7865.

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