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Climate futures

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?

Alumni impact of Dutch knowledge institutions on dairy chain governance in Kenya and Ethiopia

This booklet presents twenty-one impact stories of Ethiopian and Kenyan alumni of Dairy (Livestock) trainings in the Netherlands. The Dutch trainings consists of Master courses conducted by Van Hall Larenstein University of Applied Sciences (VHL) in Wageningen or Velp, short courses or diploma courses organised by Practical Trainings Centre (PTC+) in Oenkerk or Barneveld / Dairy Trainings Centre (DTC) in Oenkerk and short courses organised by Centre of Development Innovation – part of Wageningen University and Research Centre (WUR) in Wageningen. Most refresher course participants attended the Dutch trainings between 2010 and 2018. These alumni attended the Refresher Course entitled “Assessing the impact of Dutch knowledge institutions on performance of Netherlands alumni on dairy value chain governance in Kenya and Ethiopia” organised by Van Hall Larenstein University of Applied Sciences (VHL), Agrikom (alumni organisation) and Egerton University on 7-16 July 2019 in Nairobi and Nakuru, Kenya. Part of the refresher course was dedicated to a write shop about the impact of the Dutch training. It was written in collaboration with the professorship Climate Smart Dairy Value Chains.

Research results of a new combined method in climate adaptation

Turnover among young adults in the hospitality industry

Purpose As a step toward more firmly establishing factors to promote retention among younger employees in the hospitality industry, this study aims to focuses on fun in the workplace (fun activities, manager support for fun and coworker socializing) and training climate (organizational support, manager support and job support) as potential antecedents of turnover in a European context. Design/methodology/approach Logistic regression was used to analyze the impact of fun and training climate on turnover with a sample of 902 employees from Belgium, Germany and The Netherlands. Data on fun and training climate were obtained through surveys, which were paired with turnover data from organizational records. Findings With respect to fun in the workplace, group-level manager support for fun and coworker socializing were significantly related to turnover, but not fun activities. With respect to training climate, individual-level job support was significantly related to turnover, but not organizational support and manager support. Research limitations/implications As the data were obtained from employees from one organization, further research would be valuable with additional samples to substantiate the generalizability of the results. Practical implications Given the challenge of turnover, organizations should foster informal aspects of fun in the workplace and learning opportunities to promote retention. Originality/value The study examined the fun–turnover relationship in a context outside of the USA where previous fun–turnover research has been conducted, and it examined fun relative to training climate, which has not been studied heretofore. This study also investigated group- and individual-level effects of both fun and training climate on turnover.

The Impact of Future Climate Change and Human Activities on Hydro-climatological Drought, Analysis and Projections

Both climate change and human activity are the important drivers that can change hydrological cycle routs and affect the features of hydrological drought in river basins. The current study selects the Zayandeh Rud river Basin as a case study region in which to evaluate the influences of climate alteration and human activity on meteorological and hydrological drought based on the Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) on different time scales. The generated local climatic data of future years (2006–2040), (2041–2075) and (2076–2100) under the severest scenario (RCP 8.5) from the CMIP5 climate model are selected and used for the hydrology model and water allocation model of WEAP to construct hydrological drought which also consider human activities. The results indicate that significant meteorological drought is expected to occur in the winter and spring months of January to June. However, the driest month for hydrological drought is in the summer and autumn (July to December) (e.g. no changes in seasonality of droughts compared to historic period). It is concluded that, in the results of this work, the human influences on projected hydrological drought have been outlined; they had been missed in many projections for future hydrological drought. However, this study confirms the previous study (Bierkens et al. 2012) which mentioned that human influences can account for future hydrological drought in areas of Asia, the Middle East and the Mediterranean. The results attained in this study are beneficial for examining how hydrological drought characterizations respond to climate alteration and human activity on several time scales, thereby providing scientific information for drought predicting and water resources management over various time scales under non-stationary circumstances.

Research results of a new combined method in climate adaptation

Cities are becoming increasingly vulnerable for climate change and there is an urgent needto become more resilient. This research involves the development of the City climate scanRotterdam (September 2017) methodology to measure, map, scan and assess differentparameters that together give insight in the vulnerability of urban areas and neighborhoods.The research at recent City climate scan / Sketch your city in April 2018 used storytelling andsketching1 as main method to connect stakeholders, motivate action, evoke recognition in ajointly formulated goal, such as taking climate action. The city climate scan also involved thedevelopment of a set of measurement tools that can be applied in different urbanneighborhoods in a low-cost low-tech approach with teams of stakeholders andpractitioners. The city climate scan method was tested in different cities around the globe(Rotterdam, Manila and Cebu) in groups of young professionals and stakeholders in rapidurban appraisals.