Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-gtxcr Total loading time: 0 Render date: 2024-04-25T04:57:49.149Z Has data issue: false hasContentIssue false

3 - Estimating spatial interactions in deforestation decisions

Published online by Cambridge University Press:  11 August 2009

Juan A. Robalino
Affiliation:
PhD Candidate, Department of Economics, Columbia University, USA
Alexander Pfaff
Affiliation:
Associate Professor in Economics & International Affairs, School of International and Public Affairs and Department of Economics Columbia University, USA
Arturo Sanchez-Azofeifa
Affiliation:
Associate Professor, Earth and Atmospheric Sciences Department University of Alberta, Edmonton, Alberta, Canada
Andreas Kontoleon
Affiliation:
University of Cambridge
Unai Pascual
Affiliation:
University of Cambridge
Timothy Swanson
Affiliation:
University College London
Get access

Summary

Introduction

Ongoing decreases in the stock of tropical forest have long been a major concern, due to their implications for biodiversity loss and provision of ecosystem services. Ecological research also provides evidence that even if the stock is held constant, the spatial pattern of forest affects the level of services generated (McCoy and Mushinsky 1994; Twedt and Loesch 1999; Diaz et al. 2000; Parkhurst et al. 2002; Coops et al. 2004; Scull and Harman 2004). A highly fragmented forest made up of small patches may not provide the minimum habitat size that some organisms require. Thus it may offer less protection for species than the same amount of unfragmented forest. It is then important to understand the effects of human activities that fragment standing forest and, as a result, alter the size, the shape, and also the spatial arrangement of habitat. These properties of habitat affect extinction rates of local populations.

Standard economic models of rural land use (e.g. agriculture/forest frontiers) will generate predictions of spatial pattern down to the level of detail that their data permit. However, a focus on spatial pattern highlights a question these models do not address: are there spatial dynamics per se? If we look behind observed spatial correlation, do one's land-use choices actually have any causal impacts upon those made by one's neighbours? This chapter presents a model of such spatial interactions and then discusses a method to empirically test for their presence using observed deforestation behaviour.

Type
Chapter
Information
Biodiversity Economics
Principles, Methods and Applications
, pp. 92 - 114
Publisher: Cambridge University Press
Print publication year: 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Anderson, L., Granger, C., Reis, E., Weinhold, D. and Wuder, S. 2002. The Dynamics of Deforestation and Economic Growth in the Brazilian Amazon. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Ando, A., Camm, J., Polasky, S. and Solow, A. 1998. Species distributions, land values, and efficient conservation, Science. 279 (5359). 2126–2128.CrossRefGoogle ScholarPubMed
Anselin, L. 1988. Spatial Econometrics: Methods and Models. Boston: Kluwer Academic Publishers.CrossRefGoogle Scholar
Bayer, P. and Timmins, C. 2003. Estimating equilibrium models of sorting across locations. Yale University. Economic Growth Center. Discussion Paper No. 862. 1–31.
Blume, L. E. 1993. Statistical mechanics of strategic interaction, Games and Economic Behaviour. 5. 387–424.CrossRefGoogle Scholar
Brock, W. A. and Durlauf, S. N. 2001a. Discrete choice with social interactions. Review of Economic Studies. 68 (2). 235–260.CrossRefGoogle Scholar
Brock, W. A. and Durlauf, S. N. 2001b. Interactions-based models. In J. J. Heckman. and E. Leamer (eds.). Handbook of Econometrics. Amsterdam: Elsevier. 3329–3371.
Case, A. 1992. Neighborhood influence and technological change. Regional Science and Urban Economics. 22 (3). 491–508.CrossRefGoogle Scholar
Chaudhuri, S. 1999. Forward-looking behavior, precautionary savings, and borrowing constraints in a poor, agrarian economy: tests using rainfall data. Working Paper 9899–10. Columbia University.Google Scholar
Chomitz, K. M. and Gray, D. A. 1996. Roads, land use, and deforestation: a spatial model applied to Belize. World Bank Economic Review. 10. 487–512.CrossRefGoogle Scholar
Cocks, K. D. and Baird, I. A. 1989. Using mathematical-programming to address the multiple reserve selection problem – an example from the Eyre peninsula, South Australia. Biological Conservation. 49 (2). 113–130.CrossRefGoogle Scholar
Conley, T. G. and Topa, G. 2002. Socio-economic distance and spatial patterns in unemployment. Journal of Applied Econometrics. 17 (4). 303–327.CrossRefGoogle Scholar
Cooper, R. and John, A. 1988. Coordinating coordination failures in Keynesian models. Quarterly Journal of Economics. 103 (3). 441–463.CrossRefGoogle Scholar
Coops, N. C., White, J. D. and Scott, N. A. 2004. Estimating fragmentation effects on simulated forest net primary productivity derived from satellite imagery. International Journal of Remote Sensing. 25 (4). 819–838.CrossRefGoogle Scholar
Costello, C. and Polasky, S. 2004. Dynamic reserve site selection. Resource and Energy Economics. 26 (2). 157–174.CrossRefGoogle Scholar
Cropper, M. and Griffiths, C. 1994. The interaction of population-growth and environmental-quality. American Economic Review. 84 (2). 250–254.Google Scholar
Diaz, J. A., Carbonell, R., Virgos, E., Santos, T. and Telleria, J. L. 2000. Effects of forest fragmentation on the distribution of the lizard psammodromus algirus. Animal Conservation. 3. 235–240.CrossRefGoogle Scholar
Ellison, G. 1993. Learning, local interaction, and coordination. Econometrica. 61 (5). 1047–1071.CrossRefGoogle Scholar
Evans, W. N., Oates, W. E. and Schwab, R. M. 1992. Measuring peer group effects – a study of teenage behavior. Journal of Political Economy. 100 (5). 966–991.CrossRefGoogle Scholar
Geoghegan, J., Villar, S. C., Klepeis, P., Mendoza, P. M., Ogneva-Himmelberger, Y., Chowdhury, R. R., Turner, B. L. and Vance, C. 2001. Modeling tropical deforestation in the southern Yucatan peninsular region: comparing survey and satellite data. Agriculture Ecosystems and Environment. 85. 25–46.CrossRefGoogle Scholar
Glaeser, E. and Scheinkman, J. 2001. Measuring social interactions. In S. N. Durlauf. and H. P. Young. (eds.). 83–132.
Greene, W. H., 2003. Econometric Analysis. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Irwin, E. G. and Bockstael, N. E. 2002. Interacting agents, spatial externalities and the evolution of residential land use patterns. Journal of Economic Geography. 2 (1). 31–54.CrossRefGoogle Scholar
Kirkpatrick, J. B. 1983. An iterative method for establishing priorities for the selection of nature reserves – an example from Tasmania. Biological Conservation. 25 (2). 127–134.CrossRefGoogle Scholar
Kok, K. and Veldkamp, A. 2001. Evaluating impact of spatial scales on land use pattern analysis in Central America. Agriculture Ecosystems and Environment. 85. 205–221.CrossRefGoogle Scholar
Maddala, G. S. 1983. Limited-dependent and Qualitative Variables in Econometrics. Cambridge, New York: Cambridge University Press.CrossRefGoogle Scholar
McCoy, E. D. and Mushinsky, H. R. 1994. Effects of fragmentation on the richness of vertebrates in the Florida scrub habitat. Ecology. 75 (2). 446–457.CrossRefGoogle Scholar
Moffitt, R. 2001. Policy Interventions, low-level equilibria, social interactions. In S. N. Durlauf. and H. P. Young. (eds.). Social Dynamics. Cambridge, MA: The MIT Press. 45–82.
Munshi, K. 2003. Networks in the modern economy: Mexican migrants in the U. S. labour market. Quarterly Journal of Economics. 118 (2). 549–599.CrossRefGoogle Scholar
Nelson, G. C. and Hellerstein, D. 1997. Do roads cause deforestation? Using satellite images in econometric analysis of land use. American Journal of Agricultural Economics. 79. 80–88.
Panoyotou, T. and Sungsuwan, S. 1989. An econometric study of the causes of tropical deforestation: the case of northeast Thailand. Development Paper 284. Harvard Institute for International Development.Google Scholar
Parkhurst, G. M., Shogren, J. F., Bastian, C., Kivi, P., Donner, J. and Smith, R. B. W. 2002. Agglomeration bonus: an incentive mechanism to reunite fragmented habitat for biodiversity conservation. Ecological Economics. 41 (2). 305–328.CrossRefGoogle Scholar
Pfaff, A. S. P. 1999. What drives deforestation in the Brazilian Amazon? Evidence from satellite and socioeconomic data. Journal of Environmental Economics and Management. 37 (1). 26–43.CrossRefGoogle Scholar
Pfaff, A. S. P. and Sanchez-Azofeifa, G. A. 2004. Deforestation pressure and biological reserve planning: a conceptual approach and an illustrative application for Costa Rica. Resource and Energy Economics. 26 (2). 237–254.
Polasky, S., Camm, J. D., Solow, A. R., Csuti, B., White, D. and Ding, R. G. 2000. Choosing reserve networks with incomplete species information. Biological Conservation. 94 (1). 1–10.CrossRefGoogle Scholar
Rudel, T. K. 1989. Population, development, and tropical deforestation – a cross-national-study. Rural Sociology. 54 (3). 327–338.Google Scholar
Schelling, T. C. 1971. Dynamic models of segregation. Journal of Mathematical Sociology. 1 (2). 143–186.CrossRefGoogle Scholar
Scull, P. R. and Harman, J. R. 2004. Forest distribution and site quality in southern Lower Michigan, USA. Journal of Biogeography. 31 (9). 1503–1514.CrossRefGoogle Scholar
Serneels, S. and Lambin, E. F. 2001. Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model. Agriculture Ecosystems and Environment. 85. 65–81.CrossRefGoogle Scholar
Stavins, R. N. and Jaffe, A. B. 1990. Unintended impacts of public-investments on private decisions – the depletion of forested wetlands. American Economic Review. 80 (3). 337–352.Google Scholar
Tubbs, C. and Blackwood, J. 1971. Ecological evaluation of land for planning purposes. Biological Conservation. (3). 169–172.CrossRefGoogle Scholar
Turner, M. A. 2005. Landscape preferences and patterns of residential development. Journal of Urban Economics. 57 (1). 19–54.CrossRefGoogle Scholar
Twedt, D. J. and Loesch, C. R. 1999. Forest area and distribution in the Mississippi alluvial valley: implications for breeding bird conservation. Journal of Biogeography. 26 (6). 1215–1224.CrossRefGoogle Scholar
Walsh, S. J., Crawford, T. W., Welsh, W. F. and Crews-Meyer, K. A. 2001. A multiscale analysis of LULC and NDVI variation in Nang Rong district, northeast Thailand. Agriculture Ecosystems and Environment. 85 (1–3). 47–64.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×