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Abstract Background 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. Results 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. Conclusion 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.

Residential and non‐residential buildings are a major contributor to human well‐being. At the same time, buildings cause 30% of final energy use, 18% of greenhouse gas emissions (GHGE), and about 65% of material accumulation globally. With electrification and higher energy efficiency of buildings, material‐related emissions gain relevance. The circular economy (CE) strategies, narrow, slow, and close, together with wooden buildings, can reduce material‐related emissions. We provide a comprehensive set of building stock transformation scenarios for 10 world regions until 2060, using the resource efficiency climate change model of the stock–flow–service nexus and including the full CE spectrum plus wood‐intensive buildings. The 2020–2050 global cumulative new construction ranges from 150 to 280 billion m2 for residential and 70‐120 billion m2 for non‐residential buildings. Ambitious CE reduces cumulative 2020–2050 primary material demand from 80 to 30 gigatons (Gt) for cement and from 35 to 15 Gt for steel. Lowering floor space demand by 1 m2 per capita leads to global savings of 800‐2500 megatons (Mt) of cement, 300‐1000 Mt of steel, and 3‐10 Gt CO2‐eq, depending on industry decarbonization and CE roll‐out. Each additional Mt of structural timber leads to savings of 0.4‐0.55 Mt of cement, 0.6‐0.85 Mt of steel, and 0.8‐1.8 Mt CO2‐eq of system‐wide GHGE. CE reduces 2020–2050 cumulative GHGE by up to 44%, where the highest contribution comes from the narrow CE strategies, that is, lower floorspace and lightweight buildings. Very low carbon emission trajectories are possible only when combining supply‐ and demand‐side strategies. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Current implementations of hybrid life cycle assessment (LCA) mostly do not fully exploit valuable information from multi‐regional input–output databases by aggregating regional supply chains to the lower geographical resolution of process LCA databases. We propose a method for sampling the various individual regions within the aggregated regional scope of LCA processes. This sampling maximizes the information content of hybrid LCA footprint results by preserving the regional variance, and it allows for regional price distributions from BACI/UN‐COMTRADE international trade statistics to be used to simultaneously improve the accuracy of the hybrid model. This work makes the previously hidden regional and price variance explicit and analyzes uncertainty of the hybrid carbon (global warming potential 100, GWP100) and land use footprints arising from these variances, both separately and in combination. We find that the median process footprint intensity increases by 7−3+18%$7^{+18}_{-3}\%$ for the GWP100 due to hybridization, and 90−23+143%$90^{+143}_{-23}\%$ for the land use footprint. Results show that the magnitude of the footprint uncertainty strongly depends on the product sector of the LCA process and environmental impact considered. In a case study of Swiss household consumption, we find truncation error estimates of 8.4−2.7+9.2%$8.4^{+9.2}_{-2.7}\%$ for the GWP100 and 36−14+64%$36^{+64}_{-14}\%$ for the land use footprint. Our results highlight the importance of regionalization of process LCA databases, as it has the potential to significantly improve both the precision and accuracy of derived hybrid LCA models. This article met the requirements for a gold/gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]