In recent decades, the carbon sink provided by the U.S. forest sector has offset a sizable portion of domestic greenhouse gas (GHG) emissions. In the future, the magnitude of this sink has important implications not only for projected U.S. net GHG emissions under a reference case but also for the cost of achieving a given mitigation target. The larger the contribution of the forest sector towards reducing net GHG emissions, the less mitigation is needed from other sectors. Conversely, if the forest sector begins to contribute a smaller sink, or even becomes a net source, mitigation requirements from other sectors may need to become more stringent and costlier to achieve economy wide emissions targets. There is acknowledged uncertainty in estimates of the carbon sink provided by the U.S. forest sector, attributable to large ranges in the projections of, among other things, future economic conditions, population growth, policy implementation, and technological advancement. We examined these drivers in the context of an economic model of the agricultural and forestry sectors, to demonstrate the importance of cross-sector interactions on projections of emissions and carbon sequestration. Using this model, we compared detailed scenarios that differ in their assumptions of demand for agriculture and forestry products, trade, rates of (sub)urbanization, and limits on timber harvest on protected lands. We found that a scenario assuming higher demand and more trade for forest products resulted in increased forest growth and larger net GHG sequestration, while a scenario featuring higher agricultural demand, ceteris paribus led to forest land conversion and increased anthropogenic emissions. Importantly, when high demand scenarios are implemented conjunctively, agricultural sector emissions under a high income-growth world with increased livestock-product demand are fully displaced by substantial GHG sequestration from the forest sector with increased forest product demand. This finding highlights the potential limitations of single-sector modeling approaches that ignore important interaction effects between sectors.

近几十年来，美国林业部门提供的碳汇抵消了国内温室气体（GHG）排放的很大一部分。未来，这一汇的规模不仅对参考案例下预计的美国温室气体净排放量产生重要影响，而且对实现给定缓解目标的成本也产生重要影响。林业部门对减少温室气体净排放量的贡献越大，其他部门需要的减排量就越少。相反，如果林业部门开始贡献较小的汇，甚至成为净源，其他部门的缓解要求可能需要变得更加严格和成本更高，以实现整个经济范围的排放目标。众所周知，美国林业部门提供的碳汇估计存在不确定性，这归因于对未来经济状况、人口增长、政策实施和技术进步等因素的预测存在较大差异。我们在农业和林业部门经济模型的背景下研究了这些驱动因素，以证明跨部门互动对排放和碳封存预测的重要性。使用该模型，我们比较了详细的情景，这些情景在农业和林业产品需求、贸易、（郊区）城市化率以及受保护土地上木材采伐的限制方面的假设有所不同。我们发现，在其他条件不变的情况下，假设更高的需求和更多的林产品贸易会导致森林生长增加和更大的温室气体净封存，而在其他条件不变的情况下，更高的农业需求会导致林地转变和人为排放增加。 重要的是，当高需求情景同时实施时，在畜产品需求增加的高收入增长世界中，农业部门的排放完全被林产品需求增加的林业部门的大量温室气体封存所取代。这一发现凸显了单部门建模方法的潜在局限性，该方法忽略了部门之间重要的相互作用影响。