Guest Editorial: Incentivizing Energy and Climate Innovation
Bibliographic record
Abstract
Guest editorial In a quiet industrial park in suburban Toronto, there is a machine that eats carbon dioxide (CO2) and spits out fuel. This place, typically associated with its strip malls, ethnic and cultural diversity, and peaceful middle class life, might also soon be known as a hotbed of energy innovation. The project’s code name is “Pond.” A world away, at a world-class research institute in Bangalore, India, engineers have developed a completely different technology to convert CO2 into industrial chemicals. They are motivated by the desire to begin tackling rising global CO2 emissions. Their project code name is “Breathe.” Aside from a healthy obsession with carbon, what these two efforts have in common might surprise you. Rather than collaborating on an international science project or with a company’s industrial research and development, both are competing to win a global competition to transform CO2 from a liability into an asset and, in so doing, create a paradigm shift in the energy space. Both are competitors in the NRG COSIA Carbon XPRIZE. And the competition is just getting started. The XPRIZE Foundation relies on the growing power of exponential technologies and revolutionary science to catalyze radical breakthroughs. This means developments in science and technology such as robotics, artificial intelligence, nanotechnology, big data, and other disruptive forces have the potential to show exponential impact on grand challenges such as sustainable energy and climate change. By offering a suite of incentives in a prize competition, XPRIZE seeks to inspire the world’s scientists, technologists, and innovators to tackle seemingly intractable challenges. Transforming our energy systems may be the 21st century’s greatest challenge. Articulating a grand challenge does not solve any problems, but a clear and deep articulation of the problem demands an understanding of the complexity and nuance involved in the problem itself and in a vision for defining characteristics of a solution. In our approach to energy innovation, XPRIZE recognizes that the technological, sociopolitical, and economic changes occurring globally present innovators with a rare opportunity to apply truly groundbreaking research to challenges of worldwide importance. We operate at the intersection of audacious and achievable. The Carbon XPRIZE is a USD 20 million global competition to incentivize technologies that convert CO2 emissions into valuable products. The winning teams will convert the largest quantity of CO2 from actual flue gas from coal or natural gas power plants into one or more products with the highest net value. The 10 teams that survive the first two elimination rounds (proposal evaluation was in summer of 2016, and lab-scale demonstration from late 2016 through 2017) will use two brand new test centers adjacent to operating power plants in western Canada and the US state of Wyoming to demonstrate their solutions at industrial scale.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".