New research has shed light on the vast and largely unmonitored trade of wildlife around the world, revealing alarming threats to biosecurity and the survival of many species.

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.