Global Change Biology Bioenergy最新文献

{"title":"Correction to “Corn and Switchgrass Derived Sustainable Aviation Fuel Carbon Intensity”","authors":"","doi":"10.1111/gcbb.70150","DOIUrl":"https://doi.org/10.1111/gcbb.70150","url":null,"abstract":"<p>Ramirez, S., M. Schmer, V. L. Jin, R. Mitchell, C. E. Stewart, D. Redfearn, J. Quinn, &amp; M. Afi. 2026. “Corn and Switchgrass Derived Sustainable Aviation Fuel Carbon Intensity.” GCB Bioenergy, 18, no. 6: e70121.</p><p>In Section 3.1, “In terms of theoretical ethanol production, both 120 N corn (2078 L·ha⁻¹) and 120 N SWG (1752 L·ha⁻¹) produced more SAF than 60 N SWG (1229 L·ha⁻¹) (<i>p</i> &lt; 0.0001)” was incorrect. This should have read: “In terms of theoretical ethanol production, both 120 N corn (<b>3293</b> L·ha⁻¹) and 120 N SWG (<b>2777</b> L·ha⁻¹) produced more SAF than 60 N SWG (<b>1949</b> L·ha⁻¹) (<i>p</i> &lt; 0.0001).”</p><p>In Section 4.1, “Under the same N fertilization rate (120 kg N·ha⁻¹·year⁻¹), switchgrass was more productive than corn (11.7 Mg biomass·ha⁻¹ and 7.21 Mg grain·ha⁻¹) and resulted in comparable potential SAF yields (875 and 931 L, respectively). However, SAF derived from switchgrass had a much lower WTWa carbon intensity (−1.20 ± 10.1 and 55.8 ± 2.02 gCO₂·MJ⁻¹ SAF)” was incorrect. This should have read: “Under the same N fertilization rate (120 kg N·ha⁻¹·year⁻¹), switchgrass was more productive than corn (<b>9.5</b> Mg biomass·ha⁻¹ and <b>6.5</b> Mg grain·ha⁻¹) and resulted in comparable potential SAF yields (<b>1752</b> and <b>2078</b> L·ha⁻¹, respectively). However, SAF derived from switchgrass had a much lower WTWa carbon intensity (<b>−14.5 ± 18.5</b> and <b>61.5 ± 3.3</b> gCO₂·MJ⁻¹ SAF).”</p><p>These errors were a drafting oversight and do not affect the results of the study.</p><p>We apologize for these errors.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methane Emissions Offsetting With Temporary Carbon Sinks","authors":"Hans-Peter Schmidt,&nbsp;Nikolas Hagemann","doi":"10.1111/gcbb.70143","DOIUrl":"https://doi.org/10.1111/gcbb.70143","url":null,"abstract":"<p>Methane accounts for more than 20% of current anthropogenic warming. While fossil methane emissions can likely be reduced substantially, biogenic methane from agriculture and waste will remain difficult to fully abate. Achieving climate neutrality therefore requires counterbalancing the warming effect of residual emissions. The rapid atmospheric decay of methane suggests that temporary carbon sinks can serve this purpose, yet current accounting approaches do not consistently quantify their time-dependent cooling effect. Here we develop a time-explicit method that enables offsetting the 100-year warming impact of a methane emission pulse with an equally sized cooling effect delivered by carbon dioxide removal over 20 years. Based on a CO₂ atmospheric impulse–response function (IRF), we define the total climate effect (TCE) as time-integrated warming and cooling of greenhouse gas emissions and dynamic carbon sinks expressed in CO₂-equivalent units. The developed method yields the amount of CO₂ removal and storage duration over a chosen time horizon to counterbalance methane-induced warming. For example, over a 20-year horizon, complete offsetting of 1 t CH₄ of biogenic origin requires 94.6 t CO₂e stored in a constant C-sink. The method allows differentiation between temporary and geologically persistent carbon dioxide removal by explicitly accounting for storage duration and time horizon. By providing a physically grounded basis for valuing temporary carbon storage—a question actively debated in the EU Carbon Removals and Carbon Farming Regulation and Article 6.4 of the Paris Agreement—the approach supports near-term climate mitigation and may bridge the decades required for scaling permanent CDR solutions by creating financial incentives for temporary carbon sink technologies.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148174152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating County-Level Spatial Resolution in Maize Life Cycle Emissions Analysis in Illinois, USA","authors":"Frank G. Dohleman,&nbsp;Ken Copenhaver,&nbsp;Clifford P. Haefke,&nbsp;Steffen Mueller","doi":"10.1111/gcbb.70142","DOIUrl":"https://doi.org/10.1111/gcbb.70142","url":null,"abstract":"<p>Maize is a major biofuel feedstock, accounting for 64% of the global share of ethanol production. Recent improvements in life cycle analyses (LCAs) have accounted for temporal changes in the improved efficiency of farming practices, with reductions in the carbon intensity (CI) of maize grain production over time. There has been little progress on understanding the spatial variation of the CI of maize grain production. The average life cycle CI of US maize production at the farm gate converts to a contribution of 251 g CO₂e/kg of maize produced, used in policy calculations related to biofuel production. The objective of this work was to understand the spatial variation in CI scores using Illinois, USA as a case study to conduct life cycle analysis for modeling county-level CI score. The average CI score of maize produced in Illinois has a range of 9%–12% lower than the national average CI when averaged across the state using county-level yield and fertilizer application data. Maize grown within 56 km of ethanol facilities in Illinois has scores 14%–17% lower than the national average, when adjusted for ethanol output. This is mainly due to the high yields in Illinois and more efficient use of fertilizer across counties delivering to ethanol plants. There was spatial variation in the impact of cover crop seeding and conservation tillage across a transect of counties. Further refinement of life cycle models will provide more accurate assessments of the carbon intensity of maize production at smaller spatial scales than national averages.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148100401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Greenhouse Gas Reduction Potential Through Bioheat for Remote Communities in Northeastern Ontario","authors":"Sabrina M. Desjardins,&nbsp;Michael T. Ter-Mikaelian,&nbsp;Terrence Sauve,&nbsp;Sebnem Madrali,&nbsp;Jiaxin Chen,&nbsp;Scott Miller,&nbsp;Vincent Chamberland","doi":"10.1111/gcbb.70146","DOIUrl":"https://doi.org/10.1111/gcbb.70146","url":null,"abstract":"<p>Switching from high-emitting fossil fuel to lower-emitting biomass fuels is one approach to help to lower greenhouse gas (GHG) emissions. Most remote communities in Canada are primely positioned to take advantage of fuel switching for space heating as they are closer to sustainable biomass resources than urban areas. Furthermore, bioheat in remote communities can improve both environmental and socio-economic outcomes by allowing communities to gain energy independence and security when using local renewable resources. This study examines the GHG emissions reduction potential of substituting fossil fuels with woody residues to produce heat in remote communities in northeastern Ontario, Canada. We examined both hydronic and electric heat using forest harvest and sawmill residues as biomass fuel types. We found the largest emission reductions in the electric bioheat scenarios, with the scenario using forest harvest residues that would have otherwise been burned showing the greatest emissions reduction potential (−256.32 gCO₂eq·MJ_heat⁻¹ by 2124). Biomass fuel type rankings for all scenarios, regardless of heating system or fossil fuel substitution, are: forest harvest residues from burn slash piles &gt; forest harvest residues from decay slash piles &gt; sawmill residues. All scenarios were carbon neutral in the first year of bioheat production and remained so for the 100-year study period. While our study showed the environmental benefit of fuel switching, social, economic, and operational risks of a project such as this should not be overlooked.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70146","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148097298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linking Plant and Microbial Traits to Soil Carbon for Reliable and Resilient Bioenergy Systems","authors":"Aubrey K. Fine,&nbsp;Anis Mahmud Khokon,&nbsp;Ilenne Del Valle Kessra,&nbsp;John L. Field,&nbsp;Laura E. Bartley,&nbsp;Larry M. York,&nbsp;Melissa A. Cregger","doi":"10.1111/gcbb.70122","DOIUrl":"https://doi.org/10.1111/gcbb.70122","url":null,"abstract":"<p>Bioenergy systems in the United States offer a dual opportunity to supply renewable feedstocks while enhancing ecosystem services such as hydrologic regulation, erosion control, and soil carbon (C) storage. National assessments highlight the potential to grow perennial energy crops to improve soil function and ecosystem resilience. Realizing this potential requires understanding the ecological mechanisms that govern how C is added, transformed, and stabilized in soils. Plant traits determine the quantity, depth, and chemistry of organic inputs, while microbial processes—including carbon use efficiency, necromass formation, and trophic interactions—mediate their transformation and partitioning among soil carbon pools. These biological pathways are shaped by soil physical and chemical properties, including aggregation, texture, and mineralogy, and by environmental drivers such as temperature, moisture, and disturbance, leading to context-dependent outcomes across landscapes. Management practices that diversify feedstocks, minimize disturbance, and maintain soil cover can promote both biomass production and C retention, while microbial amendments and rhizosphere engineering offer emerging, but often context-dependent, tools to optimize plant–microbe interactions. Trade-offs between biomass yield and soil carbon storage may arise when systems favor rapid aboveground productivity at the expense of belowground inputs and microbial processing, underscoring the importance of trait combinations that support both functions. Advances in monitoring, reporting, and verification—spanning precision agriculture, remote sensing, and biosensing—are improving predictive capacity through microbial-explicit process models and model–experiment (ModEx) frameworks. By connecting soil, plant, and microbial processes with advances in modeling and biosensing, this review outlines research priorities focused on trait-based parameterization and ModEx integration. These priorities will support the design of bioenergy systems that are both reliable and resilient, enhancing renewable energy production and ecosystem sustainability.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70122","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148099900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biochar and Sediment Amendments Alter Soil Nitrogen Mineralization: Microbial Biomass and Extracellular Enzyme Activity","authors":"Waqas Ahmed,&nbsp;Yunting Wang,&nbsp;Sajid Mehmood,&nbsp;Sehrish Ali,&nbsp;Fengyue Qin,&nbsp;Lun Tan,&nbsp;Weidong Li,&nbsp;Md Ashraful Alam","doi":"10.1111/gcbb.70145","DOIUrl":"https://doi.org/10.1111/gcbb.70145","url":null,"abstract":"<p>This study investigates the effects of biochar and sediment amendments, particularly Mn-modified biochar (Mn-BC), on soil nutrient dynamics, microbial activity, and nitrogen (N) mineralization. Sediments were collected from a fishpond in San Jiang, Hainan Province, and biochar was produced from bamboo and modified with manganese (Mn) to enhance its properties. A 90-day incubation experiment was conducted with six treatments: control (T1), sediment alone (T2), soil with 10% sediment (T3), soil with 3% biochar (T4), sediment with 3% biochar (T5), and soil with 10% sediment and 3% biochar (T6). Results showed that biochar and sediment amendments significantly increased soil pH, organic carbon content (SOC), and microbial biomass carbon (SMBC) and nitrogen (SMBN) with T6 exhibiting the highest increases in pH (9.51) and SOC (7.82 g kg⁻¹), compared to the control (5.51 pH and 5.58 g kg⁻¹ SOC). SMBC and SMBN were also significantly higher in T6, with increases of 37.32% and 76.11%, respectively. Additionally, N-acquiring enzyme activities, particularly N-acetylglucosaminidase (NAG) and leucine aminopeptidase (LAP), were enhanced in the biochar-sediment treated soils, with T6 showing a remarkable increase compared to the control. N mineralization was significantly improved by the amendments, with T6 showing a 60.84% increase in total mineralized nitrogen. The N mineralization potential (N_o) in T6 was 61.87 mg kg⁻¹, compared to 39.20 mg kg⁻¹ in the control, and the mineralization rate constant (k) was highest in T6 (0.22 d⁻¹). These results highlight the potential of Mn-modified biochar and sediment as sustainable soil amendments that improve soil fertility, microbial activity, nitrogen cycling, and overall soil health in agricultural systems.</p>","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/gcbb.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148093716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 6","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148120870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 6","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148116351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 6","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148104701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":55126,"journal":{"name":"Global Change Biology Bioenergy","volume":"18 6","pages":""},"PeriodicalIF":4.1,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148132110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}