DIGITAL INFRASTRUCTURES AND ENVIRONMENTAL JUSTICE: POLICIES, PRACTICES, AND VISIONS
Bibliographic record
Abstract
Environmental media scholars have long drawn attention to the physicality of digital systems, situating their work as part of the infrastructural turn (Larkin, 2013; Parks & Starosielski, 2015; Star, 1999). Contrary to the prevailing “cultural imagination of dematerialization” (Starosielski, 2015), digital supply chains – from data centers to AI systems to consumer electronics – depend on minerals, water, land, labour, and energy (Crawford, 2021; Cubitt, 2016; Hogan et al., 2022). This growth-based model of digital technology is based on assumed access to resources, implicating it in the extractive global economy shaped by ongoing colonial violence (Liboiron, 2021; Spice, 2018). Transdisciplinary scholarship on the intersection of digital technologies and the environment has looked at online organizing and digital climate change action (McLean & Fuller, 2016; Pearce et al., 2019), indigenous resistance and data sovereignty (Duarte, 2017; Kukutai & Taylor, 2016), the environmental impacts of large-scale data centers (Hogan, 2015; Velkova, 2016) and alternative social media (Laser et al., 2022), and what "responsible digitalization" could look like (Dwivedi et al., 2022). Building on already existing work that critically examines the material implications of digital infrastructures, this panel asks what environmental justice means in relation to digital technologies. Turning against the language of revolution that too often gets leveraged by Big Tech to describe the latest "disruptive" technology that is allegedly going to solve the world's problems (Geiger, 2020; Tabel, 2022), we foreground subversive practices, regulatory interventions, and grassroots organizing and vision building as emancipatory alternatives to a for-profit, monopolized internet. From a theory of change that seeks to understand and challenge the extractive nature of digital technology production from all angles, we shed light on reform, repair, refusal, and resistance as paths for transformation. Zooming in on Southeast Louisiana where hundreds of petrochemical processing and manufacturing facilities are located, the first paper examines how Internet access can be reimagined in landscapes shaped by extractive economies. The paper analyzes the challenges that activist and research groups face when using Internet of things (IoT) devices for real-time environmental sensing of air quality due to underdeveloped Internet infrastructures in a region that is becoming increasingly vulnerable to climate change. The second paper engages with the material footprint and environmental implications of computing hardware production. It looks at the "Right to Repair" as one approach that challenges corporate control over design and obsolescence of electronic devices. By comparing examples of recent legislation in the EU, India, and the US, and analyzing them through the lens of design justice and discard studies frameworks, it argues that Right to Repair needs to be complemented by a substantial change in industry norms and practices rather than simply attempting to delay the disposal through repair by consumers. The third paper examines community resistance to data centers in the United States. In the past years, activists have framed their resistance to data centers along three critiques, namely noise pollution, resource consumption, and lack of public input to permitting processes. The paper investigates how environmental justice activists use formal legal and regulatory processes such as public meetings, petitions, lawsuits, public records requests to organise against new data center developments, and the challenges they meet as part of their organising. The fourth paper presents a "feminist principle of the internet on the environment" that was developed over several years in transnational collaborative work by practitioners. It addresses the interconnections between gendered online violence against land and environmental defenders on large social media platforms and on-the-ground resistance to extractive industries and outlines a new emancipatory vision for a different internet that centers planetary care and justice for communities and ecosystems. The fifth paper presents an analysis of the Internet Architecture Board's (IAB) workshop on "Environmental Impact of Internet Applications and Systems", held online in December 2022. It uses an infrastructural lens to analyze which politics are embedded and missing from industry responses to the sector's environmental harms. While international regulatory bodies are slowly coming to terms with the environmental impacts of distributed digital networks, the paper argues that the proposed sustainability solutions are as of yet too narrow in scope.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".