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The spatial dimension in environmental and resource economics

Published online by Cambridge University Press:  22 October 2010

ANASTASIOS XEPAPADEAS*
Affiliation:
Department of International and European Economic Studies, Athens University of Economics and Business and Beijer Fellow, 76 Patission Street, 104 34 Athens, Greece. Email: xepapad@aueb.gr

Abstract

Mechanisms generating patterns in spatial domains have been extensively studied in biology, but also in economics in the context of new economic geography. The Turing mechanism or Turing diffusion-induced instability has been central to the understanding of forces which endogenously generate spatial patterns, but in a context where agents do not explicitly optimize an objective. The present paper reviews tools to study, in the spirit of Turing's analysis, a mechanism generating optimal diffusion-induced instability where optimizing agents generate optimal agglomerations. By extending the maximum principle to the optimal control of partial differential equations, it is shown how under certain conditions it will be optimal to design controls so that the price-quantity system implied by the costate–state functions of the optimal control of distributed parameter systems induces optimal spatial patterns. These methods might be useful in studying pattern formation both in problems of resource management and of economic development.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010

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References

Brock, W.A. and Starrett, D. (2003), ‘Managing systems with non-convex positive feedback’, Environmental and Resource Economics 26: 575602.CrossRefGoogle Scholar
Brock, W. and Xepapadeas, A. (2005), ‘Spatial analysis in descriptive models of renewable resource management’, Swiss Journal of Economics and Statistics 141: 331354.Google Scholar
Brock, W. and Xepapadeas, A. (2008), ‘Diffusion-induced instability and pattern formation in infinite horizon recursive optimal control’, Journal of Economic Dynamics and Control 32: 27452787.Google Scholar
Brock, W. and Xepapadeas, A. (2010), ‘Pattern formation, spatial externalities and regulation in coupled economic-ecological systems’, Journal of Environmental Economics and Management 59: 149164.Google Scholar
Carpenter, S.R., Brock, W.A., and Hansen, J. (1999), ‘Ecological and social dynamics in simple models of ecosystem management’, Conservation Ecology, [Online], http://www.consecol.org/Journal/vol3/iss2/art4.CrossRefGoogle Scholar
Costello, C. and Polasky, S. (2008), ‘Optimal harvesting of stochastic spatial resources’, Journal of Environmental Economics and Management 56: 118.CrossRefGoogle Scholar
Derzko, N., Sethi, P., and Thompson, G. (1984), ‘Necessary and sufficient conditions for optimal control of quasilinear partial differential systems’, Journal of Optimization Theory and Applications 43: 89101.CrossRefGoogle Scholar
Fujita, M., Krugman, P., and Venables, A. (1999), The Spatial Economy, Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
Krugman, P. (1996), The Self-Organizing Economy, Cambridge, MA: Blackwell Publishers.Google Scholar
Kurz, M. (1968), ‘The general instability of a class of competitive growth processes’, Review of Economic Studies 35: 155174.CrossRefGoogle Scholar
Lenhart, S. and Bhat, M. (1992), ‘Application of distributed parameter control model in wildlife damage management’, Mathematical Models and Methods in Applied Sciences 4: 423439.Google Scholar
Levin, S. and Segel, L. (1985), ‘Pattern formation in space and aspect’, SIAM Review 27: 4567.Google Scholar
Lucas, R.E. (2001), ‘Externalities and cities’, Review of Economic Dynamics 4: 245274.Google Scholar
Lucas, R.E. and Rossi-Hansberg, E. (2002), ‘On the internal structure of cities’, Econometrica 704: 14451476.CrossRefGoogle Scholar
Mäler, K.-G., Xepapadeas, A., and de Zeeuw, A. (2003), ‘The economics of shallow lakes’, Environmental and Resource Economics 26: 603624.Google Scholar
Murray, J. (2003), Mathematical Biology, 3rd ed., Berlin: Springer.CrossRefGoogle Scholar
Okubo, A. and Levin, S. (eds) (2001), Diffusion and Ecological Problems: Modern Perspectives, 2nd ed., Berlin: Springer.Google Scholar
Quah, D. (2002), ‘Spatial agglomeration dynamics’, AEA Papers and Proceedings 92: 247252.Google Scholar
Sanchirico, J. (2005), ‘Additivity properties of metapopulation models: implications for the assessment of marine reserves’, Journal of Environmental Economics and Management 49: 125.CrossRefGoogle Scholar
Sanchirico, J. and Wilen, J. (1999), ‘Bioeconomics of spatial exploitation in a patchy environment’, Journal of Environmental Economics and Management 37: 129150.Google Scholar
Sanchirico, J. and Wilen, J. (2001), ‘A bioeconomic model of marine reserve creation’, Journal of Environmental Economics and Management 42: 257276.CrossRefGoogle Scholar
Sanchirico, J. and Wilen, J. (2005), ‘Optimal spatial management of renewable resources: matching policy scope to ecosystem scale’, Journal of Environmental Economics and Management 50: 2346.CrossRefGoogle Scholar
Smith, M., Sanchirico, J., and Wilen, J. (2009), ‘The economics of spatial-dynamic processes: applications to renewable resources’, Journal of Environmental Economics and Management 57: 104121.Google Scholar
Smith, M. and Wilen, J. (2003), ‘Economic impacts of marine reserves: the importance of spatial behavior’, Journal of Environmental Economics and Management 46: 183206.CrossRefGoogle Scholar
Turing, A. (1952), ‘The chemical basis of morphogenesis’, Philosophical Transactions of the Royal Society of London Series B 237: 3772.Google Scholar
Wilen, J. (2007), ‘Economics of spatial dynamic processes’, American Journal of Agricultural Economics 89: 11341144.Google Scholar