Spatial distribution of usable biomass feedstock and technical bioenergy potential in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Bioenergy will play an intimate and critical role in energy supply and carbon mitigation in the future. In recent years, “customizing the development of bioenergy to local conditions” and “prioritizing distributed utilization” have been the two key principles that have been released by the Chinese government to promote the national‐ and provincial‐level development of bioenergy. While many recognize the importance of bioenergy in achieving low‐carbon transition, little is known about the high‐resolution distribution of usable biomass feedstock and technical bioenergy potential in China, which brings about uncertainties and additional challenges for creating localized utilization plans. We propose a new assessment framework that integrates crop growth models, a land suitability assessment, and the geographic information systems to address these knowledge gaps. Distributions of 11 types of usable biomass feedstock and three kinds of technical bioenergy potential are mapped out through specific transformation technologies at 1 km resolution. At the national level, the final technical biogas potential is 1.91 EJ. The technical bioethanol potential (0.04–0.96 EJ) from the energy crop can supply 0.13–3.12 times the bioethanol demand for the consumption of E10 gasoline in 2015. The technical heat potential (1.06 EJ) can meet 20% of the demand for heating in all provinces (5.38 EJ). Most of the 2020 bioenergy goals can be achieved, excluding that for bioethanol, which will need to require more cellulosic ethanol from residues. At the provincial level, Henan and Inner Mongolia have the potential to develop clean heating alternatives via the substitution of agroforestry residues for coal. The results can provide a systematic analysis of the distribution of biomass feedstocks and technical bioenergy potential in China. With economic factors taken into consideration in further research, it can also support national and provincial governments in making bioenergy development plans in an effective and timely manner.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it