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首页> 外文学位 >Agent Based Modeling of Land Use Change: The Case of Shade Coffee in Mexico.
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Agent Based Modeling of Land Use Change: The Case of Shade Coffee in Mexico.

机译：基于主体的土地利用变化建模：墨西哥的Shade Coffee案例。

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摘要

This research focuses on addressing methodological issues that impact the performance of spatially explicit discrete choice agent-based land use models that are estimated with remotely sensed data. The empirical setting considers land use transitions between agroforests, perennial crops, grass and corn, and fallow lands during the period 1984 - 2006 in a Mexican coffee growing region in which relatively high deforestation rates were observed. As a starting point, a Mixed Conditional - Multinomial Logit model is implemented to highlight assumptions and limitations associated with this standard modeling approach. The results indicate that this model produces theoretically inconsistent parameter estimates for the revenue variable associated with three out of four land uses considered in the analysis. To investigate whether those counterintuitive marginal effects are generated from misclassified land use data, a Latent Multinomial Logit (LMNL) model is implemented. This approach allows the identification of land use observations that have a high likelihood of being wrongly classified. A reconfiguration of the dataset based on the LMNL model increased the magnitudes of the marginal effects of the analyzed land use drivers in the theoretically expected directions. Next, because static land use models require limiting assumptions that potentially oversimplify the behavioral process followed by landowners, a structural dynamic discrete choice model of land use decisions is implemented under the assumption that land managers are forward-looking and act to maximize their discounted flow of current and future expected utility within a stochastic environment. A comparison between static and dynamic models shows that the directions of the marginal effects corresponding to time-invariant parcel-specific variables generally have the expected directions independent of the selected modeling approach. More importantly, the marginal effect estimates for the revenue variables of the agroforestry and perennial crops categories have the expected direction in the dynamic model. By contrast the myopic modeling approaches generate counter-intuitive results for the revenue variable that corresponds to perennial crops production, which affects the validity of those results for policy design. Finally, a policy simulation exercise shows the sensitivity of welfare estimates to the discount factor selected as representative of the true value used by decision makers.
机译：这项研究的重点是解决影响以遥感数据估算的基于空间明确离散选择代理的土地利用模型的性能的方法论问题。该经验设置考虑了在1984年至2006年期间墨西哥咖啡种植地区的农用林，多年生作物，草和玉米以及休耕地之间的土地利用过渡，该地区的森林砍伐率相对较高。首先，实现混合条件-多项式Lo​​git模型以突出显示与该标准建模方法相关的假设和局限性。结果表明，该模型对与分析中考虑的四分之三土地用途相关的收入变量产生了理论上不一致的参数估计。为了调查这些错误的直觉边际效应是否是由错误分类的土地利用数据产生的，采用了潜在的多项式Lo​​git（LMNL）模型。这种方法可以识别很可能被错误分类的土地利用观测资料。基于LMNL模型的数据集重新配置在理论上预期的方向上增加了分析的土地利用驱动力的边际效应的幅度。其次，由于静态土地利用模型需要有限的假设，这些假设可能会过分简化地主的行为过程，因此，在土地管理者具有前瞻性并采取行动以最大化他们的贴现流量的假设下，实施土地使用决策的结构动态离散选择模型。随机环境中当前和将来的预期效用。静态模型和动态模型之间的比较表明，与时不变地块特定变量相对应的边际效应的方向通常具有与所选建模方法无关的预期方向。更重要的是，农林业和多年生作物类别的收入变量的边际效应估计在动态模型中具有预期的方向。相比之下，近视建模方法针对与多年生作物产量相对应的收入变量产生了违反直觉的结果，这影响了这些结果在政策设计中的有效性。最后，一项政策模拟练习显示了福利估计对于选择作为代表决策者使用的真实价值的折现因子的敏感性。

著录项

  • 作者

    Marcos Martinez, Raymundo.;

  • 作者单位

    University of California, Riverside.;

  • 授予单位 University of California, Riverside.;
  • 学科 Environmental Sciences.;Economics Environmental.;Natural Resource Management.
  • 学位 Ph.D.
  • 年度 2014
  • 页码 160 p.
  • 总页数 160
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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