不确定性与空间存量外部性条件下的最优地下水开采
Optimal Groundwater Extraction under Uncertainty and a Spatial Stock Externality.
作者信息
Merrill Nathaniel H, Guilfoos Todd
机构信息
Environmental economist at the U.S. Environmental Protection Agency, Atlantic Ecology Division.
Assistant professor in the Environmental and Natural Resource Economics Department at the University of Rhode Island.
出版信息
Am J Agric Econ. 2018 Jan 1;100(1):220-238. doi: 10.1093/ajae/aax057.
We introduce a model that incorporates two important elements to estimating welfare gains from groundwater management: stochasticity and a spatial stock externality. We estimate welfare gains resulting from optimal management under uncertainty as well as a gradual stock externality that produces the dynamics of a large aquifer being slowly exhausted. This groundwater model imposes an important aspect of a depletable natural resource without the extreme assumption of complete exhaustion that is necessary in a traditional single cell (bathtub) model of groundwater extraction. Using dynamic programming, we incorporate and compare stochasticity for both an independent and identically distributed as well as a Markov chain process for annual rainfall. We find that the spatial depletion of the aquifer is significant to welfare gains for a parameterization of a section of the Ogallala Aquifer in Kansas, ranging from 2.9% to 3.01%, which is larger than those found previously over the region. Surprisingly, the inclusion of stochasticity in rainfall increases welfare gains only slightly.
我们引入了一个模型，该模型包含估算地下水管理带来的福利收益的两个重要因素：随机性和空间存量外部性。我们估算了不确定性下最优管理所带来的福利收益，以及产生大型含水层被缓慢耗尽动态变化的渐进存量外部性。这个地下水模型体现了可耗竭自然资源的一个重要方面，而无需传统地下水开采单单元（浴缸）模型中完全耗尽这一极端假设。利用动态规划，我们纳入并比较了年降雨量的独立同分布以及马尔可夫链过程的随机性。我们发现，对于堪萨斯州奥加拉拉含水层一部分的参数化，含水层的空间枯竭对福利收益具有显著影响，范围从2.9%到3.01%，这比该地区先前发现的影响更大。令人惊讶的是，降雨随机性的纳入只会略微增加福利收益。
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