Silicon vs Carbon: Balancing scientific aspirations with environmental costs.

The impacts of climate change are particularly strong in Pacific Small Island Developing States. However, empirical data on mental health and well-being in the context of climate change in this region remain limited. To fill in some of these gaps, we interviewed 71 Indigenous and traditional Fijian adults from seven coastal, coastal hinterland, and river delta rural villages. Data were analysed using an indicative latent thematic analysis. The experience of ecological grief was evident in this population. In particular, grief experiences were related to losses of species and resources, which impacted ways of life and led to the loss of culture, traditions, and customs. Anticipatory grief was also evident, relating to the loss of lifestyle for future generations, and the loss of traditional and ancestral homes through potential migration. The results highlight the experience of ecological grief among Indigenous Pacific Islanders and underscore the potential mental health risks in these populations as losses continue to accumulate with advancing climate change. 

To date, several academics, including academic historians, have failed to fully confront the climate and biodiversity crises, often engaging in disavowal of the problems and our contribution to them in the course of our historical work. This talk will explain how historians and other academic researchers tend to put certain things into a box labelled ‘environment’, without acknowledging that in fact, everything, including ourselves, belongs inside the box. Intellectuals have an important role to play in raising awareness among the diverse communities with whom they engage – from other academics to funders to students to the general public – that nothing we do happens outside of the living earth’s systems. 

Superconducting MRIs and the energy demands of Graphics Processing Units (GPUs) used in artificial intelligence (AI) development and deployment contribute significantly to greenhouse gas emissions and waste. MRI systems also depend on helium, a non-renewable resource derived from fossil fuels, while the compute requirements for supercomputers powering AI models—including the growing demands of training Foundation Models and large language models (LLMs) in imaging—further exacerbate environmental impacts. This talk will explore the potential role of AI and low-field or helium-free MRI in enhancing the sustainability of research and clinical imaging. We will discuss practical, easy-to-implement strategies, such as powering down MRI scanners when not in use, minimizing low-value imaging, adopting efficient low/zero-gadolinium protocols, and shortening MRI examination times. Additionally, we will consider how AI might transform lab environments by potentially eliminating the need for energy-intensive image viewing workstations if disease detection and diagnosis can be performed more accurately on raw and DICOM data by AI than by human interpretation.