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Hermine Van Coppenolle

@herminevc.bsky.social

Postdoctoral research associate at the Department of Land Economy, University of Cambridge. PhD in political science from Ghent University. (Climate) Nerd, observer, thinks a lot about the 2015 Paris Agreement. 🏳️‍🌈

15 Posts188 Follower450 Folgt
Jana Gheuens@janagheuens.bsky.social · 2y ago

What to make of COP29? Join this excellent roundtable to get a better understanding of COP29 outcomes and what they mean for tackling the climate crisis!

GreenDeal-NET@greendealnet.eu · 2y ago

🌍What’s next for global climate governance after COP29? Join our roundtable w/ @oberthuerseb.bsky.social to discuss COP29 outcomes, the future of climate action, international climate finance & the role of non-state actors in tackling the global climate crisis 👉 www.greendealnet.eu/Reflections-...

Hermine Van Coppenolle@herminevc.bsky.social · 2y ago

Caroline Bertram and I just published a new paper! In this open access article, we explore the role of EU trade agreements in bolstering the Paris Agreement's obligations (a wordy🧵) link.springer.com/article/10.1...

Strengthening the Paris Agreement through trade? The potential and limitations of EU preferential trade agreements for climate governance - International Environmental Agreements: Politics, Law and Ec...

Since 2019, a commitment has been included in the European Union’s (EU) preferential trade agreements to effectively implement the Paris Agreement. This commitment now exists in nine ratified or pendi...

link.springer.com

George Monbiot@georgemonbiot.bsky.social · 2y ago

1. Here's why I'll never use AI to assist my research or writing: A. I don't want to know exactly what I'm looking for. I mean that when I start researching a topic, I want to remain open to its contradictions and paradoxes, and to the unexpected paths that might lead off it. 🧵

Ryan Katz-Rosene, PhD@ryankatzrosene.bsky.social · 2y ago

👂what's that? You want to start your week with a thread about how the Jevons Paradox is often misused to imply that technological innovations will necessarily result in greater environmental damage? I got you covered! THREAD!

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Paul Lehmann@plehmann.bsky.social · 2y ago

Highlighting the efficacy and revenue recycling potential of carbon pricing increases support, especially in emerging market economies. Moreover, people adopt more negative views of subsidies for renewable energy and low-carbon technologies when the salience of their costs is increased.

Does information change public support for climate mitigation policies?

Carbon pricing policies can play an important role in reducing global greenhouse gas emissions. However, policymakers often cite the lack of public support as a major obstacle to adopting and expan...

tandfonline.com

Ketan Joshi@ketanjoshi.co · 2y ago

nice paragraph on environmental impacts in this! It is the aggressive, panicked and wide-eyed force-injection of chatbots into literally everything that is driving the ballooning energy depravity of this technology.

3.2. Worrying environmental impacts The emphasis on scale in AI also comes with consequences to the planet, since training and deploying AI models requires raw materials for manufacturing computing hardware, energy to power infrastructure, and water to cool evergrowing datacenters. The energy consumption and carbon footprint of AI models has grown with models such as LLMs emitting up to 550 tonnes of CO2 during their training process (Strubell et al., 2019; Luccioni & Hernandez-Garcia, 2023; Luccioni et al., 2022) . But the most serious sustainability concerns relate to inference, given the speed at which AI models are being deployed in user-facing applications. It is hard to gather meaningful data regarding both the energy cost and carbon emissions of AI inference because this varies widely depending on the deployment choices made (e.g. type of GPU used, batch size, precision, etc.). However, high-level estimates from Meta attribute approximately one-third of their AI-related carbon footprint to model inference (Wu et al., 2021), whereas Google attributes 60% of its AI-related energy use to inference (Patterson et al., 2022). Comparing estimates of the energy required for inference for LLMs (Luccioni et al., 2024) to ChatGPT’s 10 million users per day (Oremus, 2023) reveals that within a few weeks of commercial use, the energy use of inference overtakes that of training. Given the many companies and investors working to make AI more ubiquitous, these numbers could go up by many orders of magnitude. In fact, we are already seeing chatbots applied in consumer electronics such as smart speakers and TVs, as well as appliances such as refrigerators and ovens. The increased use of AI in userfacing products is putting tech companies’ climate targets at text classification question answering fill text mask token classification image classification object detection text generation summarization image captioning image generation task 1mWh 10mWh 100mWh 1Wh 10Wh Energy used fo…
Meredith Whittaker @meredithmeredith.bsky.social · 2y ago

Now that "scale is all we need" has predictably faltered... 1. Small AI models often perform better in context. 2. Obsession w bigness has bad consequences, from climate, to power concentration, to research capture. Me + @gaelvaroquaux.bsky.social @sashamtl.bsky.social arxiv.org/abs/2409.14160

Hermine Van Coppenolle@herminevc.bsky.social · 2y ago

Finally jumping ship in search of a new algorithm! I'm a PhD researcher working on global climate governance, the Paris Agreement, and climate ambition. Looking forward to follow anyone working on climate, politics, or science communication in general!