Climate impact of diverting residual biomass to cement production
Bibliographic record
Abstract
Abstract Co‐firing residual lignocellulosic biomass with fossil fuels is often used to reduce greenhouse gas (GHG) emissions, especially in processes like cement production where fuel costs are critical and residual biomass can be obtained at a low cost. Since plants remove CO2 from the atmosphere, CO2 emissions from biomass combustion are often assumed to have zero global warming potential (= 0) and do not contribute to climate forcing. However, diverting residual biomass to energy use has recently been shown to increase the atmospheric CO2 load when compared to business‐as‐usual (BAU) practices, resulting in values between 0 and 1. A detailed process model for a natural gas‐fired cement plant producing 4200 megagrams of clinker per day was used to calculate the material and energy flows, as well as the lifecycle emissions associated with cement production without and with diverted biomass (supplying 50% of precalciner energy demand) from forestry and landfill sources. Biomass co‐firing reduced natural gas demand in the precalciner of the cement plant by 39% relative to the reference scenario (100% natural gas), but the total demands for thermal, electrical, and diesel (transportation) energy increased by at least 14%. Assuming values of zero for biomass combustion, cement's lifecycle GHG intensity changed from the reference (natural gas only) plant by −40, −23, and − 89 kg CO2/Mg clinker for diverted biomass from slash burning, forest floor and landfill biomass, respectively. However, using the calculated values for diverted biomass from these same fuel sources, the lifecycle GHG intensities changes were −37, +20 and +28 kg CO2/Mg clinker, respectively. The switch from decreasing to increasing cement plant GHG emissions (i.e., forest floor or landfill feedstocks scenarios) highlights the importance of calculating and using the factor when quantifying lifecycle GHG impacts associated with diverting residual biomass to bioenergy use.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".