A Concordia study found that Montreal’s electric buses consume 26% more energy in winter due to colder temperatures, road friction, and the need for interior heating. Despite this seasonal drop in efficiency, they remain 40–60% cheaper to operate than diesel buses and continue to reduce greenhouse gas emissions.

Can you imagine a future where insulin releases itself—without sensors or human intervention?

Researchers at ESPOL have developed an advanced mathematical model that simulates a device capable of doing just that. Based on glucose sensitive hydrogels, this system could revolutionize diabetes treatment, offering a more autonomous, safe, and biocompatible approach.

A synthesis of four studies showcasing how to use artificial intelligence technologies to design collaborative learning scenarios that have implications.

A collaborative research team from institutions including China Agricultural University and Solomon Islands National University has addressed this question through in-depth analysis using a multi-country general equilibrium model. By leveraging trade data from 2001 to 2022 and combining it with a structural multi-country trade model, the team simulated the impact of China–Africa agricultural trade liberalization on the welfare and food security of both parties. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025617).

Recently, a study led by Professor Qiran Zhao from the College of Economics and Management, China Agricultural University, for the first time incorporated physical fitness tests into the evaluation system. By analyzing the implementation effect of China’s Nutrition Improvement Program (NIP) for rural compulsory education students, the study provides a new perspective for optimizing SFPs worldwide, especially in African countries. The related article has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025611).

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.

Abstract

Purpose – This study examines the relationships between herding behaviour, market overreaction and financial stability in developed and Brazil, Russia, India and China (BRICS) markets from 1 January 2017 to 31 December 2023. It identifies the significant differences in these phenomena across different market types and their implications for financial stability.

Design/methodology/approach – This study employs panel data regression, quantile regression, Granger causality tests and the Baron and Kenny mediation model to analyse the data. These methods are used to explore the extent to which herding behaviour exacerbates market overreaction and affects financial stability.

Findings – The results reveal that herding behaviour exacerbates short-term market overreaction, leading to increased financial instability, particularly in BRICS markets. In contrast, herding behaviour does not significantly impact intermediate-term overreactions in developed markets. The study also finds that market overreaction significantly mediates the relationship between herding behaviour and financial stability.

Practical implications – These findings have practical implications for policymakers. Understanding how herding behaviour and market overreaction impact financial stability can help formulate strategies to enhance market stability and mitigate systemic risks, particularly in more volatile BRICS markets. Social implications– Enhanced financial stability has broad social implications, including improved investor confidence and economic growth. Policymakers can use these insights to create more stable financial environments, which can lead to more robust economic development and reduced vulnerability to financial crises.

Originality/value – This study provides new insights into the differential impact of herding behaviour and market overreaction on financial stability in developed and BRICS markets. By confirming the mediating role of market overreaction, this study enhances our understanding of financial market anomalies and contributes to the literature on financial stability.