Review of climate change issues: A forcing function perspective in agricultural and energy innovation
Bibliographic record
Abstract
Climate change is observed globally, and the projections predict that the change will continue in the future for quite a long time. The mitigation and adaptation to climate change, however, are offering tremendous business opportunities around the world, especially for businesses operating in the agri-food, energy, finance, and health sectors, water infrastructure, built environments, and other relevant services. When the severity of heat waves is considered, for instance, it would become quite clear that the demand for cooling would accelerate, putting further stress on energy supply and increasing the risk of electricity black outs. Similarly, the projections also provide warnings about increased drought risk in many regions around the globe, and even worse, it should also be emphasized that 60% more food will be needed globally, while 100% more demand for food is projected in developing countries by the year 2050. While all these are being projected, we are experiencing progressively increasing stress on our global freshwater resources, which are worsened further by climate change-driven impacts and water pollution. Consequently, reducing agri-food production systems' susceptibility to climate change and strengthening the resilience of such systems are extremely important to sustain and improve the livelihoods of billions of people around the globe. Moreover, reducing emissions due to fossil fuels consumption and production is vital for the whole global population, and agri-food and energy sectors have tremendous potentials for reducing inefficiencies and emissions while simultaneously playing their crucial roles in food and energy security as well as poverty reduction. Both of these sectors are facing significant climate change-driven challenges, which provide ample opportunities for cutting-edge novel knowledge and innovative products, processes, services, and policies. And due to the reciprocal relationships between climate change and agri-food and energy innovations, in return, complementing the other forms, such innovations will speed up the climate change mitigation and adaptation processes.
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.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".