AI for Sufficiency: Monitoring Sufficiency and Related Concepts in Media Coverage

This challenge explores how AI can substantiate the perception of sufficiency – by analyzing how sufficiency and related concepts like post-growth are represented in public discourse, and transforming these insights into tools for policy and action.

Context

Sustainability is increasingly recognized as a core dimension of responsible AI and digital transformation. Beyond efficiency and technological innovation, there is growing attention on sufficiency – the question of how societies can reduce absolute resource use and consumption while maintaining a good quality of life.

Despite its importance, sufficiency remains:

• difficult to measure, and
• unevenly represented in public discourse.

At the same time, media and public narratives play a crucial role in:

• shaping political agendas,
• influencing societal norms, and
• enabling or constraining policy action.

Public discourse not only reflects sustainability debates, but also legitimizes, contests, or even marginalizes certain societal pathways, including growth-oriented and sufficiency-oriented models of development. It therefore plays a key role in determining whether we move towards growth-oriented, sufficiency-focused pathways.

Recent advances in AI, particularly in natural language processing (NLP) and data analysis, offer new opportunities to systematically analyse public discourse at scale.

However, there is currently a lack of:

• tools to monitor sustainability-related narratives in a structured way,
• metrics to assess the visibility and framing of sufficiency, and
• methods to connect discourse insights with environmental governance and policy-making.

Challenge Description

This challenge explores how AI can be used to monitor, analyse, and increase visibility on sustainability-related discourse, with a particular focus on sufficiency and post-growth narratives in Switzerland.

The goal is to develop practical, testable approaches that:

• detect and track sufficiency-related topics in media and public discourse,
• identify how concepts such as sufficiency, planetary boundaries and post-growth are framed,
• analyse trends over time and across sources, and
• make these insights accessible and usable for policymakers, researchers, and civil society.

Primary target user and decision focus (to avoid overly generic solutions)

To guide solution design, teams should prioritize one primary target group as their main user (e.g., policymakers, public administration, political decision-makers, etc. in Switzerland or/and globally). The prototype should be anchored in one or two concrete decision or application questions, for example:

• Agenda-setting: Which sufficiency-related topics are gaining traction and where?
• Communication strategy: Which frames dominate, and which counter-narratives emerge?

Scope and thematic focus

To keep solutions feasible, teams should focus their prototype on one concrete sufficiency-related thematic area and define an initial set of concepts accordingly. 

Example topic clusters include:

• housing / living space (e.g., living space reduction, densification, shared living)
• mobility (e.g., mobility reduction, modal shift, car-free policies)
• consumption and circularity (e.g., repair, reuse, sharing economy)
• energy demand reduction (e.g., heating, cooling, efficiency and sufficiency)

The chosen topic cluster should be clearly stated in the use case and will guide data selection, labeling, and evaluation.

The Data Reality: our recommended starting points

Teams should describe which sources they use and why. For prototyping, it is appropriate to start with a manageable subset (e.g., 2–3 outlets plus one social/media channel) and iterate.

Recommended media types / channels include:

• online news articles (Swiss outlets)
• press releases and position papers (administration, NGOs, industry associations) 
• parliamentary / policy texts (motions, consultations, policy documents)
• social media posts (optional, if feasible and compliant with access terms)

Languages: German, and if possible, French (DE / FR).

Minimal expectation: make data availability, access constraints (paywalls, licensing) and representation explicit.

Projects should go beyond purely analytical models and aim to create usable tools or systems that support:

• evidence-based policy-making,
• public understanding of sustainability debates, and
• environmental governance and strategic decision-making.

Key Problem Areas

Projects may address one or more of the following:

1. Topic detection and classification

• How can AI identify sufficiency-related themes in large volumes of media content?
• How can concepts like “sufficiency”, or post-growth, be detected despite:
  • indirect language,
  • varying terminology, and
  • contextual nuance

2. Discourse analysis and framing

• How are sufficiency-related topics framed in the media?
  • As opportunity, constraint, necessity, or trade-off?
• How do narratives evolve over time?
• Which actors and perspectives dominate the discourse?

Projects may also explore deeper structural dimensions of sustainability discourse, such as:

• which actors or stakeholder groups amplify or marginalise sufficiency-oriented perspectives,
• which narratives reinforce or challenge growth-dependent models of prosperity and consumption, and
• how high-consumption lifestyles and resource-intensive practices are normalized or contested?

3. Metrics for sustainability discourse

• How can we develop measurable indicators for:
  • visibility of sufficiency,
  • Types and prominence of sufficiency narratives, and
  • shifts in framing?
• How can these metrics be made:
  • robust,
  • comprehensible, and
  • policy-relevant?

4. Linking discourse to environmental governance

• How can discourse insights inform:
  • policy development,
  • regulatory priorities, and
  • public communication strategies?
• How can AI tools support decision-makers in navigating complex sustainability debates?
• How can discourse insights help identify:
  • narrative barriers to sufficiency-oriented policy-making,
  • dominant economic interests and competing policy frames, and
  • emerging societal support or resistance regarding resource reduction strategies?

Expected Outputs

Projects are expected to develop:

• A functional prototype (technical or socio-technical), such as:
  • a media monitoring tool
  • a discourse analysis platform
  • a visualisation or dashboard
• A clearly defined use case in the Swiss context
• Documentation and support, in synthesis of:
  • methodology (e.g. NLP approaches, data sources)
  • assumptions and limitations
  • potential biases