This paper examines a paradoxical issue in tourism's adaptation to climate change and emissions reduction demands. Operators increasingly take tourists to destinations threatened by climate change, with Antarctica and other polar regions as favourites and cruise ship and aircraft as main transport modes. The selling point is to see a destination before it disappears, a form of last chance tourism. This has been claimed to increase the environmental awareness of tourists and make them "ambassadors" for conservation and the visited destination. Antarctic cruise ship passengers tripled from 2000 to 2007. The paper finds that high levels of greenhouse gas emissions are created by cruise ship tourists in general, and especially high levels for those visiting the Antarctic, up to approximately eight times higher per capita and per day than average international tourism trips. A survey found no evidence for the hypothesis that the trips develop greater environmental awareness, change attitudes or encourage more sustainable future travel choices. Of the Antarctic cruise passengers surveyed, 59% felt that their travel did not impact on climate change; fewer than 7% had or might offset their emissions. Alternative opportunities for visitation to glacial/polar destinations that comply with the desire to reduce future emissions are discussed.
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This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
Sustainable Open Solutions Climate Waterfront is an interdisciplinary project that aims to explore waterfronts in Europe facing extreme situations under the threat of climate change, eg. heat, too much absent rain and sea level rise with all its consequences. The central goal is to exchange adaptive strategies for sustainable solutions for infrastructure and urban planning. The multidisciplinary perspective in cooperation with all possible partners, stakeholders and citizens, leads to a better understanding of the challenges and adaptation strategies.The participating parties are six coastal cities: Lisbon, Rome, Thessaloniki, Gdansk, Stockholm and the Amsterdam region. All these cities, except ‘Amsterdam’, are represented by a university. The Amsterdam area is represented by a multidisciplinary, educated but not necessarily academically employed delegation.
