We analyze how two different ecosystem services: forest-based amenity values and carbon storage benefits, affect spatial targeting for conservation investment for protected area acquisition using selected forest clusters in Knox County, Tennessee in the United States. We determine return-on-investment (ROI) for these two different forest-based ecosystem services by estimating amenity values and carbon storage amounts using hedonic price model and dynamic Terrestrial Ecosystem Model (TEM), respectively, and corresponding acquisition costs at the forest cluster level. Our findings of the optimal protected area acquisition in the form of forest clusters serve as an empirically informed knowledge base to help both the conservation and planning agencies in prioritizing acquisition of potential protected areas depending on their preferences. By using carbon and amenity ROIs in spatial targeting of forest clusters within the multi-objective optimization set up, we not only addressed the spatial heterogeneity in the carbon storage benefits and amenity values but also the spatial heterogeneity in cluster acquisition costs. We also found that selection decisions were dictated by the weakly negative correlation (−0.16) between the carbon and amenity ROIs instead of weakly positive correlation (0.14) between carbon storage benefits and amenity values. Since the spatial distribution of carbon and amenity ROIs were weakly negatively correlated, there were apparent conflicts between the objectives of maximizing forest carbon storage and amenity value. This resulted in concave frontiers with tradeoffs between the objectives implying variation in spatial distribution of the selected forest clusters letting the conservation and planning agencies decide combination of strategies which best fit their preferences.

我们分析了两种不同的生态系统服务：基于森林的舒适度价值和碳储存效益，如何使用美国田纳西州诺克斯县的选定森林集群影响保护区收购的保护投资的空间目标。我们通过分别使用享乐价格模型和动态陆地生态系统模型 (TEM) 估计舒适度价值和碳储存量，以及森林集群的相应收购成本来确定这两种不同的基于森林的生态系统服务的投资回报 (ROI)等级。我们以森林集群的形式获得最佳保护区的发现作为经验丰富的知识库，帮助保护和规划机构根据他们的喜好优先收购潜在的保护区。通过在多目标优化设置中将碳和舒适度投资回报率用于森林集群的空间定位，我们不仅解决了碳储存效益和舒适度价值的空间异质性问题，还解决了集群获取成本的空间异质性问题。我们还发现，选择决策是由碳和舒适度 ROI 之间的弱负相关 (-0.16) 决定的，而不是碳储存效益和舒适度值之间的弱正相关 (0.14)。由于碳的空间分布与舒适度投资回报率呈弱负相关，因此最大化森林碳储量的目标与舒适度价值之间存在明显的冲突。