Researchers from the Department of Molecular Physics at the Fritz Haber Institute have demonstrated the first magneto-optical trap of a stable ‘closed-shell’ molecule: aluminum monofluoride (AlF). They were able to cool AlF with lasers and selectively trap it in three different rotational quantum levels - breaking new ground in ultracold physics. Their experiments open the door to advanced precision spectroscopy and quantum simulation with AlF. 

AI Adoption in the U.S. Adds ~900,000 Tonnes of CO₂ Annually, Equal to 0.02% of National Emissions

Abstract

Purpose – This paper represents the first attempt to examine investor behaviour for green stocks through the lens of return co-movement, and provides evidence indicating that green investment practices have gained traction after 2012.

Design/methodology/approach – We empirically test the hypotheses that the stock returns of firms with similar carbon dioxide emissions levels move together and, if so, whether this co-movement has increased over time as people become more “carbon-conscious.” Our baseline sample, based on carbon emissions data from public company disclosures, suffers from limited coverage, particularly before 2016, leading to low statistical power and sample selection bias. To address this, we employ machine learning methodologies to forecast the carbon emissions of firms that do not disclose such information, nearly quadrupling the sample size. Our findings indicate that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline and augmented data samples. Furthermore, this co-movement has increased during the 2012–2020 period compared to the 2004–2011 period, suggesting that green investment has gained traction over time.

Findings – We find that stocks with similar carbon emissions exhibit higher co-movement in stock returns in both the baseline sample and the augmented data sample, and the co-movement has increased in the 2012–2020 period compared to the 2004–2011 years, suggesting that green investment has gained traction over time.

Originality/value – (1) We use machine learning methodology to augment carbon emissions sample which goes back to 2004. Our approach almost quadruples the original data, enabling large-sample testing. (2) We are the first paper to examine how green companies’ stock returns co-move and thus provide complementary results on the research on expected returns and carbon emissions.

The increasing accumulation of discarded plastics has already caused serious environmental pollution. Simple landfills and incineration will inevitably lead to the loss of the abundant carbon resources contained in plastic waste. In contrast, photoconversion technology provides a green and sustainable solution to the global plastic waste crisis by converting plastics into hydrogen fuel and valuable chemicals. This review briefly introduces the advantages of photoconversion technology and highlights recent research progress, with a focus on photocatalyst design as well as the thermodynamics and kinetics of the reaction process. It discusses in detail the degradation of typical common plastic types into hydrogen and fine chemicals via photoconversion. Additionally, it outlines future research directions, including the application of artificial intelligence in catalyst design. Although photocatalytic technology remains at the laboratory stage, with challenges in catalyst performance and industrial scalability, the potential for renewable energy generation and plastic valorization is promising.