摘要
Natural resource planning at all scales demands methods for assessing the impacts of resource development and use, and in particular it requires standardized methods that yield robust and unbiased results. Building from existing probabilistic methods for assessing the volumes of energy and mineral resources, we provide an algorithm for consistent, reproducible, quantitative assessment of resource development impacts. The approach combines probabilistic input data with Monte Carlo statistical methods to determine probabilistic outputs that convey the uncertainties inherent in the data. For example, one can utilize our algorithm to combine data from a natural gas resource assessment with maps of sage grouse leks and piñon-juniper woodlands in the same area to estimate possible future habitat impacts due to possible future gas development. As another example: one could combine geochemical data and maps of lynx habitat with data from a mineral deposit assessment in the same area to determine possible future mining impacts on water resources and lynx habitat. The approach can be applied to a broad range of positive and negative resource development impacts, such as water quantity or quality, economic benefits, or air quality, limited only by the availability of necessary input data and quantified relationships among geologic resources, development alternatives, and impacts. The framework enables quantitative evaluation of the trade-offs inherent in resource management decision-making, including cumulative impacts, to address societal concerns and policy aspects of resource development.
中文摘要
各种规模的自然资源规划都需要评估资源开发和使用影响的方法，特别是需要标准化的方法来产生稳健和公正的结果。 基于现有的评估能源和矿产资源量的概率方法，我们提供了一种算法，用于对资源开发影响进行一致、可重复、定量的评估。 该方法将概率输入数据与蒙特卡洛统计方法相结合，以确定传达数据固有不确定性的概率输出。 例如，可以利用我们的算法将来自天然气资源评估的数据与同一地区的鼠尾草沼泽地和 piñon-juniper 林地的地图相结合，以估计未来可能的天然气开发对栖息地可能产生的影响。 再举一个例子：可以将地球化学数据和山猫栖息地地图与同一地区的矿藏评估数据结合起来，以确定未来采矿对水资源和山猫栖息地可能产生的影响。 该方法可应用于广泛的正面和负面资源开发影响，例如水量或水质、经济效益或空气质量，仅受限于必要输入数据的可用性和地质资源、开发备选方案之间的量化关系、 和影响。 该框架能够对资源管理决策中固有的权衡进行定量评估，包括累积影响，以解决社会问题和资源开发的政策方面。
