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After some posts around “Global Warming” I wanted to know if there are some technologies to reduce greenhouse gas emissions. The quality of the provided links is interesting too. So I send the question to Phind, gpt-4 model, which seems to be appropriate for such a theme.

Phind answer | gpt-4 model

There are numerous technologies and methods available to help reduce greenhouse gas emissions. Here are some of them:

1. FuelGems(R): FuelGems produces a fuel additive that reduces greenhouse gas emissions and saves fuel. This additive uses tiny doses of carbon-based nanoparticles to reduce environmental damage. It can boost the cleanliness and performance of gasoline, diesel, and bio-fuel, and reduce greenhouse gas and dangerous emissions by as much as 50% ¹.

2. Climeworks(R): Climeworks provides direct air capture technology, which uses a filter designed to capture atmospheric carbon. This technology can be used in industrial applications and help reduce carbon emissions ¹.

3. Terramera(R): Terramera uses science, nature, and artificial intelligence to create technologies that help solve problems related to wasted food, which account for about 8% of all global CO₂ emissions. Its regenerative agriculture pulls carbon from the air and sequesters it in the soil, reducing greenhouse gas emissions ¹.

4. CarbonCure(R): CarbonCure technology reduces the carbon footprint of the concrete industry by introducing recycled CO₂ into fresh concrete. This process doesn’t compromise the performance of the concrete and increases economic and climate benefits for concrete producers ¹.

5. Digital Tools: Digital tools can help organizations achieve more accurate and comprehensive emissions measurements and greater emissions reduction. Automated digital solutions can measure, track, and reduce environmental footprints at scale, promoting transparency and supporting informed decision-making ².

6. Renewable Energy: The use of renewable energy sources such as wind, solar, and hydroelectric power can significantly reduce carbon emissions. These sources do not produce greenhouse gases when generating electricity ⁴.

7. Carbon Capture, Usage, and Storage (CCUS): CCUS technologies capture and store CO₂ emissions from industrial facilities, preventing them from being released into the atmosphere. Some applications of CCUS can also remove existing CO₂ from the atmosphere ⁴.

8. Hydrogen Technologies: Hydrogen is a clean, versatile energy carrier that can substitute for fossil fuels in certain areas of energy use that are costly or difficult to electrify. Low-carbon methods of producing hydrogen include electrolysis powered by low-carbon electricity ⁴.

Phind gpt-4 model summary

Each of these technologies and methods has its own set of advantages and challenges, and their effectiveness can depend on various factors such as geographical location, available resources, and government policies. Therefore, a combination of these technologies and methods is often needed to achieve significant reductions in greenhouse gas emissions.

Phind gpt-4 model sources with 15 citations

My statements

The presented companies or technologies

Phind gpt-4 model does not distinguish between company names and technologies. They are not in the same level. For me, this behavior points to a fundamental weakness of AI today: There exists no thesaurus, no structured keywords are available. Subheading 1 to 4 are companies with certain technologies. Only one links is presented, and this one is extremely weak. It is only advertising, has nothing to do with science.

Technologies under subheadings 4 to 8 may be relevant and more scientific. But the selected links 2 and 4 point to BCG respectively to Grantham Institute. They are significant as institutions, but again, there is no scientific information.

Comment to Phind gpt-4 model summary

The presented short text has a nice wording, but with respect to the content it is only blah blah.

Additional links: More global than detailed information

I selected the first 15 presented links of Phind. Only the links 1, 2 and 4 are directly used in the text above. The other links may have be used in preparing the text. They all together are no fake links, as they were in my experiments with gpt-2 a year ago. But only the links 10 and 12 are high quality links to the journals Nature or Science. The other links may be useful for gaining global information, but only for global aspects.

My conclusion

Phind gpt-4 model now is much better than the gpt-2 model a year ago. The last 35 years I was a fan of PubMed or Medline with the very advanced interface and the complete thesaurus, even with MESH. So I could perform extremely straight searches with Mesh and subheadings with valid results. This procedure is so logical for the user, and has a stable and open database. But not so while using Phind gpt-4.

After my example search “Technologies to reduce greenhouse gas emissions”, I would say: Phind gpt-4 model is only a nice playground. Until now, it is not reliable for structured or scientific searches. Sorry.