Hemp as A Sustainable Carbon Negative Plant: A Review of Its Properties, Applications, Challenges and Future Directions
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
Hemp is a versatile plant from the Cannabis sativa species, that has gained significant attention in recent years due to its potential to contribute to sustainable development and climate change mitigation. Hemp has the remarkable ability to absorb and store carbon dioxide not just during its growth phase, but also during its application and thus has the potential to be carbon negative. With the alarming global increase in carbon emissions and its implications, the cultivation and application of hemp can be a valuable tool in mitigating climate change. Although hemp is a versatile plant with many countries like Canada and China leading the way in its cultivation, it still faces challenges in Australia in terms of its acceptance, cultivation and widespread application. Much more needs to be done in terms of gaining a better understanding of the potential of hemp, growth opportunities, future prospects and challenges in further developing the industry. This review paper aims to provide a comprehensive overview of hemp's properties, applications, challenges, and future directions in the context of its role as a sustainable carbon-negative plant. The review begins by exploring the unique properties of hemp that make it an ideal candidate for carbon sequestration. The review also examines the diverse range of applications for hemp across multiple industries, ranging from construction materials, paper and packaging to biofuels and edible oil. The review has also identified several challenges and barriers to hemp's widespread adoption as a sustainable carbon-negative plant.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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