Baselines for land-use change in the tropics: application to avoided deforestation projects
- 501 Downloads
Although forest conservation activities, particularly in the tropics, offer significant potential for mitigating carbon (C) emissions, these types of activities have faced obstacles in the policy arena caused by the difficulty in determining key elements of the project cycle, particularly the baseline. A baseline for forest conservation has two main components: the projected land-use change and the corresponding carbon stocks in applicable pools in vegetation and soil, with land-use change being the most difficult to address analytically. In this paper we focus on developing and comparing three models, ranging from relatively simple extrapolations of past trends in land use based on simple drivers such as population growth to more complex extrapolations of past trends using spatially explicit models of land-use change driven by biophysical and socioeconomic factors. The three models used for making baseline projections of tropical deforestation at the regional scale are: the Forest Area Change (FAC) model, the Land Use and Carbon Sequestration (LUCS) model, and the Geographical Modeling (GEOMOD) model. The models were used to project deforestation in six tropical regions that featured different ecological and socioeconomic conditions, population dynamics, and uses of the land: (1) northern Belize; (2) Santa Cruz State, Bolivia; (3) Paraná State, Brazil; (4) Campeche, Mexico; (5) Chiapas, Mexico; and (6) Michoacán, Mexico.
A comparison of all model outputs across all six regions shows that each model produced quite different deforestation baselines. In general, the simplest FAC model, applied at the national administrative-unit scale, projected the highest amount of forest loss (four out of six regions) and the LUCS model the least amount of loss (four out of five regions). Based on simulations of GEOMOD, we found that readily observable physical and biological factors as well as distance to areas of past disturbance were each about twice as important as either sociological/demographic or economic/infrastructure factors (less observable) in explaining empirical land-use patterns.
We propose from the lessons learned, a methodology comprised of three main steps and six tasks can be used to begin developing credible baselines. We also propose that the baselines be projected over a 10-year period because, although projections beyond 10 years are feasible, they are likely to be unrealistic for policy purposes. In the first step, an historic land-use change and deforestation estimate is made by determining the analytic domain (size of the region relative to the size of proposed project), obtaining historic data, analyzing candidate baseline drivers, and identifying three to four major drivers. In the second step, a baseline of where deforestation is likely to occur–a potential land-use change (PLUC) map—is produced using a spatial model such as GEOMOD that uses the key drivers from step one. Then rates of deforestation are projected over a 10-year baseline period based on one of the three models. Using the PLUC maps, projected rates of deforestation, and carbon stock estimates, baseline projections are developed that can be used for project GHG accounting and crediting purposes: The final step proposes that, at agreed interval (e.g., about 10 years), the baseline assumptions about baseline drivers be re-assessed. This step reviews the viability of the 10-year baseline in light of changes in one or more key baseline drivers (e.g., new roads, new communities, new protected area, etc.). The potential land-use change map and estimates of rates of deforestation could be re-done at the agreed interval, allowing the deforestation rates and changes in spatial drivers to be incorporated into a defense of the existing baseline, or the derivation of a new baseline projection.
KeywordsAvoided deforestation Carbon sequestration Land-use change Forestry GEOMOD LULUCF Tropics
Funding for this work was provided by a Cooperative Agreement between Winrock International (WI) and the US Environmental Protection Agency (USEPA) (ID No. CR 827293-01-0 and XA-83052101; Sandra Brown, Principal Investigator and Ken Andrasko Project Officer) and by US Agency for International Development (Contract No. PCE-I-00-96-00002-00 Task Order 844, through the Environmental Policy and Institutional Strengthening Indefinite Quantity Contract [EPIQ] Consortium, and by Contract No. 523-C-00-02-00032-00). We thank David Antonioli for his support, guidance, valuable suggestions, and insights throughout the work supported by AID. We also thank David Shoch, Gil Pontius, Charlie Hall, Alejandro Flamenco, Heather Huppe, Miguel-Angel Castillo, Larry Gorenflo, Kim Batchelder, Billy Turner, Daniel Juhn, and Stephen Ambagis for their input.
- Bolin B, Sukumar R (2000) Global perspective. In: Watson RT, Noble IR, Bolin B, Ravindranath NH, Verardo DJ, Dokken DJ (eds) Land use, land-use change, and forestry. Special Report of the IPCC, Cambridge University Press, Cambridge, UK, pp 23–51Google Scholar
- Brown S (Principal Investigator) (2002b) Land use and forests, carbon monitoring, and global change. Cooperative Agreement between Winrock International and the EPA, ID# CR 827293-01-0. Winrock International. Available at http://www.winrock.org/ecosystems/files/Summary%20of%20project–Brown%202002.pdf
- Brown S (project coordinator) (2003) Finalizing avoided deforestation project baselines. Final report to US Agency for International Development, Contract No. 523-C-00-02-00032-00. Available at http://www.winrock.org/ecosystems/files/Deforestation-baselines-Report-ENG.pdf
- Brown S, Masera O, Sathaye J (2000b) Project-based activities. In: Watson RT, Noble IR, Bolin B, Ravindranath NH, Verardo DJ, Dokken DJ (eds) Land use, land-use change, and forestry. Special Report to the Intergovernmental Panel on Climate Change, Cambridge University Press, UK, Ch. 5, pp 283–338Google Scholar
- Castillo-Santiago MA, Hellier A, Tipper R, DeJong BHJ (2006) Carbon emissions from land-use change: an analysis of causal factors in Chiapas, Mexico. Mitig Adapt Strateg Climate Change (this volume)Google Scholar
- Dale V (ed) (1994) Effects of land use change on atmospheric CO2 concentrations. South and Southeast Asia as a case history. Springer Verlag, New York, NYGoogle Scholar
- Faeth P, Cort C, Livernash R (1994) Evaluating the carbon sequestration benefits of forestry projects in developing countries. World Resource Institute, WashingtonGoogle Scholar
- FAO (1993) Forest resources assessment 1990 – tropical countries. Forestry Papers 112, Rome, ItalyGoogle Scholar
- Hall M, Dushku A, Brown S (2006) Methods for examining scale issues in projecting land-use change in the tropics and their application to developing a deforestation baseline for the region of the Noel Kempff Mercado climate action project, Bolivia. In: LeClerc G, Hall C (eds) Making development work: a new role for science. University of New Mexico Press, Albuquerque, NM, Ch. 18Google Scholar
- Hall MHP, Hall CAS, Taylor MR (2000) Geographical modeling: the synthesis of GIS and simulation modeling. In: Hall CAS (ed) Quantifying sustainable development: the future of tropical economies. Academic Press, San Diego, CA, Ch. 7Google Scholar
- IDRISI Project (2003) Kilimanjaro edition, Clark Labs, Clark University, Worcester, MA. http://www.clarklabs.org/Home.asp
- Kaimowitz D, Angelsen A (1998) Economic models of tropical deforestation: a review. Center for International Forestry Research, Bogor, Indonesia, 139 ppGoogle Scholar
- Kerr S (2001) Seeing the forest and saving the trees: tropical land use change and global climate policy. Can carbon sinks be operational? Resources for the Future Workshop Proceedings, April 30, 2001. RFF web site. http://www.rff.org/disc_papers/PDF_files/0126.pdf
- Moura-Costa P (2001) Elements of a certification system for forestry-based greenhouse gas mitigation projects. Can carbon sinks be operational? Resources for the Future Workshop Proceedings, April 30, 2001. Resources For the Future web site. http://www.rff.org/disc_papers/PDF_files/0126.pdf
- OECD/IEA (2003) Forestry projects: lessons learned and implications for CDM modalities. OECD/IEA Information Paper (COM/ENV/EPOC/IEA/SLT(2003)1)Google Scholar
- Pontius RG Jr (2000) Quantification error versus location error in comparison of categorical maps. Photogram Eng Remote Sensing 66(8):1011–1016Google Scholar
- Pontius RG Jr (2002) Statistical methods to partition effects of quantity and location during comparison of categorical maps at multiple resolutions. Photogram Remote Sensing 63(10):1041–1049Google Scholar
- Sathaye J, Makundi W, Dale L, Chan P, Andrasko K (2006) Estimating global forestry GHG mitigation potential and costs: a dynamic partial equilibrium approach. Energy J (in press)Google Scholar
- Sciotti R (1991) Estimating and projecting forest area at global and local level: a step forward. FAO FRA-1990 project report, FAO, Rome, ItalyGoogle Scholar
- Sciotti R (2000) Demographic and ecological factors in FAO tropical deforestation modeling. In: Palo M, Vanhanen H (eds) World forests from deforestation to transition. Kluwer Academic Publishing, The NetherlandsGoogle Scholar
- Tipper R, DeJong B (1998) Quantification and regulation of carbon offsets from forestry: comparison of alternative methodologies, with special reference to Chiapas, Mexico. Commonwealth Forestry Review, 77(3), 14 ppGoogle Scholar
- Tipper R, DeJong BHJ, Ochoa-Gaona S, Soto-Pinto ML, Castillo-Santiago MA, Montoya-Gómez G, March-Mifsut I (1998) Assessment of the cost of large scale forestry for CO2 sequestration: evidence from Chiapas, Mexico. IEA Greenhouse Gas R&D Programme, 84 ppGoogle Scholar
- World Resources Institute/World Business Council for Sustainable Development (2003) The greenhouse gas protocol: project quantification standard. Road Test Draft, September, 143 pp. Available via www.ghgprotocol.orgGoogle Scholar