Zoekresultaten

Research results of a new governance method in climate adaptation

Cities are becoming increasingly vulnerable to climate change and there is an urgent need to become more resilient. This research involves the development of the City Climate Scan methodology to measure, map, scan and assess different parameters that provide insight into the vulnerability of urban areas and neighborhoods. The research involved the development of a set of measurement tools that can be applied in different urban neighborhoods in a low-cost low-tech approach with teams of stakeholders and practitioners. The City Climate Scan method was tested in different cities around the globe with groups of young professionals and stakeholders in rapid urban appraisals.For the Rotterdam City Climate Scan (September 2017), the following challenges were selected: risk of flooding, heat stress, water quality (micro-pollutants and plastic waste) and air quality. The Rotterdam climate scan is evaluated with their triple helix partners (public, private and academic partners). The conclusion is that the City Climate Scan approach helps policy makers and practitioners to gather valuable data for decision makers in a rapid appraisal at the neighborhood and city level. The results of the City Climate Scan methodprovides insights, creates awareness and brings together stakeholders. The most valuable deliverable is the concrete and tangible results. The participatory approach brings residents and practitioners together and provides insight into local problems, while at the same time the method facilitates the collection of valuable data about the robustness of neighborhoods. As a result of this positive evaluation, the City Climate Scan will be up scaled to a number of cities in Europe and Asia in the upcoming months.

Research results of a new combined method in climate adaptation

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?

Networked Content Analysis

Climate change is one of the key societal challenges of our times, and its debate takes place across scientific disciplines and into the public realm, traversing platforms, sources, and fields of study. The analysis of such mediated debates has a strong tradition, which started in communication science and has since then been applied across a wide range of academic disciplines.So-called ‘content analysis’ provides a means to study (mass) media content in many media shapes and formats to retrieve signs of the zeitgeist, such as cultural phenomena, representation of certain groups, and the resonance of political viewpoints. In the era of big data and digital culture, in which websites and social media platforms produce massive amounts of content and network this through hyperlinks and social media buttons, content analysis needs to become adaptive to the many ways in which digital platforms and engines handle content.This book introduces Networked Content Analysis as a digital research approach, which offers ways forward for students and researchers who want to work with digital methods and tools to study online content. Besides providing a thorough theoretical framework, the book demonstrates new tools and methods for research through case studies that study the climate change debate with search engines, Twitter, and the encyclopedia project of Wikipedia.

Networked Content Analysis

Content Analysis has been developed within communication science as a technique to analyze bodies of text for features or (recurring) themes, in order to identify cultural indicators, societal trends and issues. And while Content Analysis has seen a tremendous uptake across scientific disciplines, the advent of digital media has presented new challenges to the demarcation and study of content. Within Content Analysis, different strategies have been put forward to grapple with these dynamics. And although these approaches each present ways forward for the analysis of web content, they do not yet regard the vast differences between web platforms that serve content, which each have their own ‘technicities,’ e.g. carry their own (often visually undisclosed) formats and formatting, and output their own results and rankings. In this dissertation I therefore develop Networked Content Analysis as a term for such techniques of Content Analysis that are adapted specifically to the study of networked digital media content. The case in question is climate change, one of the major societal challenges of our times, which I study on the web and with search engines, on Wikipedia as well as Twitter. In all, my contribution provides footing for a return to the roots of Content Analysis and at the same time adds to its toolkit the necessary web- and platform-specific research techniques for creating a fine-grained picture of the climate change debate as it takes place across platforms.

Enhancing the Inclusivity and Effectiveness of European Climate Action through Lessons from Participatory Action Research with Activist Groups

The climate crisis is an urgent and complex global challenge, requiring transformative action from diverse stakeholders, including governments, civil society, and grassroots movements. Conventional top-down approaches to climate governance have proven insufficient (e.g. UNFCCC, COP events), necessitating a shift towards more inclusive and polycentric models that incorporate the perspectives and needs of diverse communities (Bliznetskaya, 2023; Dorsch & Flachsland, 2017). The independent, multidisciplinary approach of citizen-led activist groups can provide new insights and redefine challenges and opportunities for climate governance and regulation. Despite their important role in developing effective climate action, these citizen-led groups often face significant barriers to decision-making participation, including structural, practical, and legal challenges (Berry et al., 2019; Colli, 2021; Marquardt et al., 2022; Tayler & Schulte, 2019).