City Research Online

Abstract

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

Publication Type:	Book Section
Additional Information:	© Copyright © 2023 Namhyun Kim, Patrick Wongsa-art and Ian J. Bateman. This AAM is provided for your own personal use only. It may not be used for resale, reprinting, systematic distribution, emailing, or for any other commercial purpose without the permission of the publisher.
Publisher Keywords:	Agro-environmental policy, land-use, multivariate Tobit, system of censored equation, spatial model, error component model
Subjects:	G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography H Social Sciences > HB Economic Theory Q Science > QA Mathematics > QA75 Electronic computers. Computer science S Agriculture
Departments:	School of Policy & Global Affairs School of Policy & Global Affairs > Economics
SWORD Depositor:	Symplectic Administrator

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