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- Convenors:
-
Sung-Yueh Perng
(National Yang Ming Chiao Tung University)
Sophia Maalsen (University of Sydney)
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- Format:
- Combined Format Open Panel
Short Abstract:
Climate actions are urgent and enrol wide ranging algorithms, data and digital infrastructure. The combined panel invites contributions to explore, critically reflect, devise interventions or propose alternative approaches to knowing and making just algorithmic climate actions.
Long Abstract:
Actions targeting net-zero transitions are urgent and enrol wide ranging algorithms, data and digital infrastructure to accelerate their pace. From climate scenario modelling and carbon sink identification to the optimisation of electricity grids and factories, algorithms can be found recognising, categorising, calculating or regularising data about diverse social, geological, chemical, biological and environmental processes that are key to ushering net-zero transitions. In plans and policies adopted by local and national governments, enhancing algorithmic capabilities as a key measure to achieve net-zero transition goals in domains such as energy, home, transportation, finance and carbon capture and reuse, is high on their agenda.
However, it is equally important to examine the multiplication of algorithmic climate actions and the diverse practices, logics, imaginaries, materialities and politics such actions produce. Algorithms contribute differently to climate knowledge and decarbonisation technologies. They are entangled in different practices, imaginaries and infrastructures when pursuing carbon reduction goals. They can be significantly shaped by techno-solutionist strategies, ignoring that algorithms can be harmful, have unintended consequences and maintain and amplify structural inequalities. Additionally, just decarbonisation is crucial, but justice is differently perceived, articulated and enacted in different environmental, technological and social conditions.
In light of these issues, this combined panel invites contributions to explore, critically reflect, devise interventions or proposing alternative approaches to knowing and making just algorithmic climate actions. The panel will combine academic presentations and a dialogue session. Contributions are invited to address topics including but not limited to:
Data and ways of feeding climate algorithms
Algorithms and digital infrastructures for climate actions
Contested climate algorithmic operations and infrastructures
Potentials for algorithmic climate harm and care
Just decarbonised futures with algorithms
Knowledges in understanding algorithmic decarbonisations
Marginalised perspectives, practices and experiences of algorithmic climate actions
Accepted contributions:
Session 1Guillemette Legrand (Paris Sciences Lettres (PSL), University of Applied Sciences and Arts Northwestern)
Short abstract:
Through multi-modal research into the model Hector, this contribution shares theoretical and practical findings about how climate models formulate socio-technical and cosmological imaginaries by investigating and experimenting with its infrastructure, code, interface and applications.
Long abstract:
This contribution presents the results of multi-modal research into the climate model Hector; an open-source climate model available on GitHub that operates at reduced complexities. Its primary use is to model climate scenarios more efficiently (speed and energy) but with comparable results to large climate models (e.g.:Earth System Model). This research looks into Hector as a case study to inquire into how the algorithmic reading of climate has an impact on climate action and subsequently on Earth's geophysical condition. This research also aims to probe and experiment with how socio-technical and cosmological imaginaries of climate futures are formulated through the model and its operations. The research is conducted through software and infrastructure studies, as well as practice-based design methods. It focuses on four dimensions of the modelling of climate within the Hector algorithmic framework; the capture of data; the modelling of climate through the combination of climate science and socio-economics variables; the visualisation of climate scenarios; and the recursive effect of computational imagery on climate actions. The materials used to conduct theoretical and practical research on Hector include interviews with its developers (the Pacific Northwest National Laboratory); a literature review on the model including the contractual agreement between the laboratory and its commissioner (the US Department of Energy); visual and written analysis of the repository of the model available on GitHub; the mobilising of the model through its interface (pyhector); and a study of the scenario planning game Half Earth Socialism, which is backed by Hector.
Sola Kim (Arizona State University)
Short abstract:
This study uses generative AI to simulate the impact of Supreme Court decisions on carbon regulation adaptation in communities, emphasizing the need for balanced environmental justice advocacy.
Long abstract:
Considering shifting Supreme Court rulings on rising climate lawsuits over EPA's carbon regulations and the existing research gap regarding community adaptation to these shifts, this study aims to simulate and analyze the governance dynamics of modeled carbon-led and carbon-neutral communities, focusing on their interactions with U.S. Supreme Court decisions.
This research employs generative AI e.g., ChatGPT to create agents that mimic human behaviors across various governance. These agents “chat”, remember, reflect, and strategize within modeled communities but under government-set carbon emission limits. Community resilience is evaluated by introducing disruptions—from internal nonconformist members and external carbon limit reductions—and tracking the rate of carbon quota decline. Limited carbon availability necessitates choosing between self-resolution and court interventions for conflict resolution. However, Supreme Court decisions are also swayed by justices' ideologies, often reflecting government preferences.
Longitudinal analysis reveals variances in conflict resolution due to different governance and court decisions. This emphasizes the importance of balanced advocacy for environmental justice by each governance entity. Although communities face rulings causing climate distress or promoting just transition, resilient ones can sidestep distress-causing actions or forgo court resolutions. This study highlights the need for balanced environmental justice advocacy within each governance entity amid the climate change era.
Dorian Groll (École des Hautes Études en Sciences Sociales)
Short abstract:
According to proponents of climate services, algorithms coupled with environmental data produced by all-seeing satellites could provide useful tools for managing climate. But what is the nature of such technical devices? Where do they come from? And on what assumptions and data are they based?
Long abstract:
Satellite data are routinely used for research purposes, but a long-standing discourse originating from the space sector has emphasised their potential for so-called “operational” uses for environmental management, through climate services and decision-making tools. In Europe, the Earth observation system Copernicus embodies a distinctive attempt from the European Commission to both produce such data and try to favour the emergence of a market for climate services that leverage remote sensing data and algorithms to create “smart” solutions against climate-related threat – for instance detecting and predicting urban heat islands, floods, or forest fires. Moreover, such services are also presented as scalable – meaning that the algorithmic solutions could easily be tailored to other geographic areas and scales that the ones they were built for. Yet, within the black box of these algorithmic solutions lie a number of assumptions regarding the malleability and neutrality of satellite data, the possibility to manage climate as a technical problem and even the influence of data and knowledge on policy-making. To reflect on the nature of such devices, this communication will trace, within the European context, the origins of the “operationalisation” of satellite data through sophisticated algorithms and the efforts from the space sector to champion space-based climate services. As part of an ongoing PhD inquiry on Copernicus and the production, diffusion and use of satellite data for climate governance, this contribution will be based on archival analysis and interviews with producers of satellite-based climate services.
Claudio Coletta (University of Bologna)
Short abstract:
Which are the futures produced through digital twinning? The contribution delves into the new logics of urban digital twinning in future-making processes via à vis the "climate-neutral regime”, following them in action during the actual development and experimentation.
Long abstract:
The contribution examines Digital Twins (DTs) in EU cities within the framework of the "twin transitions” (JRC 2022). We contend that climate-digital transitions introduce a new regime of anticipation, knowledge production, and governance, which interferes with the previously established regime centered around "smart urbanism” focused on efficiency, public-private partnerships, and real-time service delivery.
Theoretically, the contribution delves into the new logics of urban digital twinning in future-making processes (Anderson 2010) in relation to what we term the "climate-neutral regime”. These logics are considered part of a contested "timescape" (Adam 1998; Kitchin 2023) specifically generated within the friction of the promissory and future-oriented nature of technological innovation (Rip 2018), the "mission-oriented" approach (Mazzucato 2018) at the core of the EU Green Deal, and the emerging AI paradigm based on correlation (Mayer-Schönberger and Cukier 2013).
Empirically, the contribution follows these logics in action during the ongoing development and experimentation of a DTs in an Italian city part of the "100 Climate-neutral Cities by 2030 – by and for the citizens" program. Here the author is engaged in the design process as researchers, which are observed through qualitative methods and analysed adopting discourse analysis.
Considering the gaps and frictions arising from the interaction between digital and climate in actual urban digital twinning, the contribution aims to understand how technopolitical processes, knowledge infrastructures (Edwards 2011), and innovation interact and are shaped by the climate crisis, ultimately influencing the remaking of futures.
Sung-Yueh Perng (National Yang Ming Chiao Tung University) Sophia Maalsen (University of Sydney)
Short abstract:
Computing and algorithmic capabilities have been in rapid development to meet urgent decarbonisation demands. We unpack the multiplicity of algorithm associations in generating knowledge and innovation by drawing upon research conducted in Taiwan and Australia.
Long abstract:
There has been an intensifying development of computing and algorithmic capabilities in response to urgent demands of decarbonising social and industrial activities, principally in the pursuit of better understanding the changing climate and developing plans for effective decarbonisation. From the UK Royal Society’s emphasis on ‘climate computing’ to many national net-zero strategies featuring artificial intelligence, algorithms have become a critical actor in shaping how plans and strategies are drawn in anticipation of uncertain climatic and societal futures and with hopes of reducing carbon emissions in the meantime. However, questions pertaining to what these algorithms are, what associations they have established with others and what consequences have emerged to facilitate or discourage decarbonisation initiatives, have rarely been asked and not been adequately explored. The main purpose of the paper is to make explicit the multiplicity of algorithm associations. We investigate the emergent and diverse ways in which algorithms act (not necessarily in tandem) with other actors, lives, environments, institutions, infrastructures and decarbonisation strategies and the consequences arising from these associations. Drawing on interviews with scientists and engineers in the academic and public/private research institutions in Taiwan and Australia, we unpack how algorithms have been assembled differently in the process of generating knowledge and innovation for transitioning towards a decarbonised future. We pay specific attention to how the multiplicity of algorithm associations sheds light on the uneven paces of decarbonisation in different societal domains.