Forestry plays a major role in climate change mitigation. However, which intensity of logging is best suited for that task remains controversial. We contribute to the debate by quantitatively analyzing three different forest management scenarios in Germany—a baseline scenario which represents a continuation of current forest management practice as well as an intensive and an extensive logging scenario. We assess whether increased carbon storage in wood products and substitution of other emission-intensive materials can offset reduced carbon stocks in the forest due to increased harvesting. For that, we calculate annual required displacement factors (RDF)—a dimensionless quantity that indicates the minimal displacement factor (DF) so that intensive forestry outperforms extensive forestry from a climate perspective. If the intensive forest management scenario is included in the comparison, the RDF starts off with relatively high values (1 to 1.5) but declines over time and eventually even reaches negative values. Comparing the extensive scenario to a baseline yields RDF values between 0.1 and 0.9 with a slightly increasing trend. Compared to RDFs, expected future DFs are too low to favour the intensive forestry scenario and too high to favour the extensive forestry scenario, during the first 25 years of the modeling period. However, towards the end of the modeling period, the relationship between DFs and RDF is turned around in both comparisons. In the comparison between intensive and extensive forest management RDF values are very similar to future DF trajectories. RDFs are a useful tool for comparing annual climate impacts of forest growth scenarios and can be used to benchmark material and energy substitution effects of wood. Our results indicate that the baseline scenario reflects an effective compromise between carbon stocks in the forest and carbon displacement by wood use. For a longer modeling period, however, this might not be the case. Which of the alternative scenarios would be best suited for climate change mitigation is heavily dependent on future DF trajectory. Hence, our findings highlight the necessity of robust projections of forest dynamics and industry decarbonization pathways.

中文翻译：

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评估和比较德国集约型和粗放型林业的气候影响所需的位移因子

林业在减缓气候变化方面发挥着重要作用。然而，哪种记录强度最适合该任务仍然存在争议。我们通过定量分析德国三种不同的森林管理情景来为辩论做出贡献——基线情景代表当前森林管理实践的延续，以及集约和广泛的伐木情景。我们评估增加木材产品中的碳储存和替代其他排放密集型材料是否可以抵消因增加采伐而减少的森林碳储量。为此，我们计算了年度所需位移因子 (RDF)——一个表示最小位移因子 (DF) 的无量纲量，因此从气候角度来看，集约型林业优于粗放型林业。如果在比较中包括集约化森林经营方案，RDF 开始时的值相对较高（1 到 1.5），但随着时间的推移而下降，最终甚至达到负值。将广泛的情景与基线进行比较，得到的 RDF 值介于 0.1 和 0.9 之间，并有略微增加的趋势。与 RDF 相比，在建模期的前 25 年，预期的未来 DF 太低不利于集约林业情景，而太高不利于粗放林业情景。然而，在建模期结束时，DF 和 RDF 之间的关系在两个比较中都发生了转变。在集约型和粗放型森林管理之间的比较中，RDF 值与未来的 DF 轨迹非常相似。RDF 是比较森林生长情景的年度气候影响的有用工具，可用于衡量木材的材料和能源替代效应。我们的结果表明，基线情景反映了森林碳储量和木材使用引起的碳置换之间的有效折衷。然而，对于较长的建模周期，情况可能并非如此。哪种替代情景最适合减缓气候变化在很大程度上取决于未来的 DF 轨迹。因此，我们的研究结果强调了对森林动态和行业脱碳路径进行强有力预测的必要性。我们的结果表明，基线情景反映了森林碳储量和木材使用引起的碳置换之间的有效折衷。然而，对于较长的建模周期，情况可能并非如此。哪种替代情景最适合减缓气候变化在很大程度上取决于未来的 DF 轨迹。因此，我们的研究结果强调了对森林动态和行业脱碳路径进行强有力预测的必要性。我们的结果表明，基线情景反映了森林碳储量和木材使用引起的碳置换之间的有效折衷。然而，对于较长的建模周期，情况可能并非如此。哪种替代情景最适合减缓气候变化在很大程度上取决于未来的 DF 轨迹。因此，我们的研究结果强调了对森林动态和行业脱碳路径进行强有力预测的必要性。