Justice & Paix

Current energy projections often envision an expansion of nuclear capacities to decarbonize future energy systems. However, this contrasts with the historic and current status of the nuclear industry, marked by techno-economic challenges for both light-water and non-light-water reactor technologies. Regardless, projections of strong nuclear growth have persisted since the 1970s. This paper investigates the “nuclear energy paradox” which shows the recurring divergence between historical projections and actual developments. A data compilation of long-term energy projections from international organizations such as the IAEA and the IEA as well as energy system models like GCAM and MESSAGE, as used in the IPCC, reveal a recurring pattern of high-growth projections for nuclear power. Such projections often rest on techno-economic assumptions such as substantial cost reductions. We propose the concept of nuclear imaginaries to show that these assumptions are embedded into techno-economic visions of nuclear power de

Les systèmes d’intelligence artificielle générative, qui parlent si bien, ne comprennent pas encore le monde. De nouvelles méthodes physiques ou statistiques comme les world models, ou « modèles de monde », permettraient de les doter d’une forme de sens commun, qui leur servirait à mieux simuler la réalité et de mieux interagir avec elle.

Scenarios serve as a critical tool in climate change analysis, enabling the exploration of future evolution of the climate system, climate impacts, and the human system (including mitigation and adaptation actions). This paper describes the scenario framework for ScenarioMIP as part of CMIP7. The design process has involved various rounds of interaction with the research community and user groups at large. The proposal covers a set of scenarios exploring high levels of climate change (to explore high-end climate risks), medium levels of climate change (anchored to current policy), and low levels of climate change (aligned with current international agreements). These scenarios follow very different trajectories in terms of emissions, with some likely to experience peaks and subsequent declines in greenhouse gas concentrations in this century. An important innovation is that most scenarios are intended to be run, if possible, in emission-driven mode, providing a better representation of the Earth system uncert

States and financial bodies using modelling that ignores shocks from extreme weather and climate tipping points

Climate models that give a low warming from increases in greenhouse gases do not match satellite measurements. Future warming will likely be worse than thought unless society acts, according to a new study published in Science.

Real world measurements of how much extra heat the Earth is trapping are well beyond most climate models. That’s a real problem.

Global warming is moving faster than the best models can keep a handle on.

EPFL scientists developed a tool to evaluate climate models, revealing that some predict a much hotter future due to high carbon sensitivity, suggesting current emission reduction efforts may be inadequate.

Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed. Taking into account all known factors, the planet warmed 0.2 °C more last year than climate scientists expected. More and better data are urgently needed.

A major reason for the growth in the use of renewable energy is the fact that if a person looks at them narrowly enough--such as by using a model--wind and solar look to be useful. They don't burn fossil fuels, so it appears that they might be helpful to the environment. Energy modeling misses important points. I believe that profitability signals are much more important.

We investigate the potential implications of large language models (LLMs), such as Generative Pretrained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15%