How to Apply the CLA Foresight Technique to Better Understand a Climate Finance Issue

This post is part of a series called Exploring Future Scenarios in Climate Finance: A Case Study using Foresight Lab, where we want to showcase how our AI-powered foresight platform can help you to analyze complex data, extrapolate future scenarios and better create strategies today.

See other posts:

Step 2: How to Use AI to Apply Environmental Scanning Foresight Methodology on Climate Change Research

Step 3: Imagining the Future of Climate Finance Using AI and Four Futures Scenarios Methodology

In the realm of strategic planning and decision-making, foresight tools have always played a crucial role. These tools, designed to anticipate and shape future events, have been instrumental in navigating the complexities of our ever-changing world. Among these tools, Causal Layered Analysis (CLA) stands out for its unique approach to understanding the depth and breadth of issues. Originating from the field of futures studies, CLA has been widely adopted for its ability to delve beyond the surface of problems and explore the underlying layers. However, as we move further into the digital age, the potential for enhancing these tools through automation and artificial intelligence is becoming increasingly apparent. This text explores the intersection of CLA and generative AI, focusing on how this powerful combination can revolutionize our approach to foresight.

Understanding Causal Layered Analysis (CLA)

Causal Layered Analysis (CLA) is a technique used in strategic planning and futures studies to examine the different layers of a complex issue. Developed by Sohail Inayatullah, a political scientist and futurist, CLA is designed to delve deeper into problems, going beyond the surface to explore the underlying causes and drivers.

CLA operates on four levels:

1. Litany: This is the surface level, where observable events and trends are discussed. It’s often characterized by quantitative data and is typically the focus of mainstream media and politics.
2. Social causes: This level delves into the social, economic, and political factors contributing to the surface-level events. It involves a systemic understanding of structures and policies that lead to the observed events.
3. Worldview/Discourse: This level examines the cultural, ideological, and belief system factors that give rise to the systemic structures. It’s about understanding the narratives and discourses that shape how we perceive and approach the issue.
4. Myth/Metaphor: The deepest level, it uncovers the unconscious dimensions of an issue. It involves the collective unconscious, archetypes, myths, and metaphors that influence our worldviews.

The power of CLA lies in its ability to uncover the different layers of reality, enabling us to understand an issue from multiple perspectives. It encourages us to question our assumptions, challenge dominant narratives, and envision alternative futures.

In the next section, we’ll explore how CLA could be used to understand a real-world event: the need for climate finance in Africa’s agricultural sector.

Wanna see more about CLA? Check this great TED Talk about CLA.

Access the CLA — Causal Layered Analysis skill with Generative AI at https://foresightlab.eu/s/bJz4pbt0XrofQUXpSfehemLNXze9zshy

Applying CLA to Understand Climate Finance in Africa’s Agriculture

We've chosen this sign of change https://businessfightspoverty.org/africa-farmers-climate-finance/ to show how can we use AI to quickly generate instances of CLA on this subjects to broaden our perspective of the issues beyond the tip of the iceberg.

The text “Africa’s Farmers Need Climate Finance. The Private Sector Must Step Up” highlights the urgent need for climate financing solutions that empower smallholder farmers on the frontlines of climate change. Let’s apply the layers of CLA to this issue:

Litany

The inability to deliver pledged financing for climate change solutions has left smallholder farmers, particularly in Africa, at risk. Despite the dire need for funding for both climate change mitigation and adaptation practices, there are significant gaps. Further, the financing provided is disproportionately directed towards mitigation, leaving adaptation measures under-resourced. The agricultural sector in Africa, which is responsible for the majority of the continent’s carbon emissions and supports the livelihoods of over 60% of its population, is under severe threat due to climate change.

Systemic Causes

The systemic causes behind the challenges faced by African farmers in relation to climate change include:

• Limited access to climate finance and investment opportunities for adaptation and mitigation strategies.
• Inadequate infrastructure, such as irrigation systems, that could help farmers cope with changing climate conditions.
• Lack of knowledge and awareness among farmers about climate change and its implications on agriculture.
• Inequitable distribution of resources and power dynamics that perpetuate vulnerability among smallholder farmers.

Worldview

The worldview surrounding Africa’s farmers and climate change is characterized by:

• A focus on short-term economic gains often prioritized over long-term sustainability.
• A belief that technological solutions alone can address the challenges posed by climate change in agriculture.
• An emphasis on individualism and competition, which may hinder collective action and collaboration among farmers.
• A lack of recognition and understanding of the interdependence between human activities and the Earth’s ecosystems.

Myth and Metaphor

The myth and metaphor that shape the understanding of the relationship between Africa’s farmers and climate change include:

• The myth of progress, which perpetuates the idea that economic development must come at the expense of environmental sustainability.
• The metaphor of the “green revolution,” which promotes the idea that technological advancements in agriculture can solve the challenges caused by climate change.
• The myth of self-sufficiency, which hinders the acknowledgment of the interconnectedness between different regions and the need for cooperation in addressing climate change impacts.
• The metaphor of resilience, which often portrays farmers as individuals who can overcome any obstacle without considering the systemic disadvantages they face.

Outcome and Reflection

The analysis reveals that Africa’s farmers are confronted with the compounding effects of climate change and systemic factors that hinder their ability to adapt and mitigate its impacts. Limited access to climate finance, inadequate infrastructure, and a lack of awareness contribute to their vulnerability.

The prevailing worldview prioritizes short-term economic gains and technological solutions, often disregarding the long-term sustainability of agriculture. Additionally, the myth of progress and the metaphor of the “green revolution” shape perceptions of agricultural development and potential solutions.

To address these challenges, it is crucial for the private sector to step up and provide climate finance and investment opportunities to support African farmers. This would enable the development of climate-resilient infrastructure and promote knowledge-sharing and capacity-building initiatives. Moreover, there is a need to challenge the prevailing worldview and myths, fostering a paradigm shift towards a more holistic and sustainable approach to agriculture and climate change adaptation.

Turning CLA into a Skill Powered by Generative AI

So, what does it mean to turn CLA into a skill powered by generative AI? In essence, it involves using the capabilities of generative AI to collaborate with professional futurists, innovators, strategists to tap into the huge potential of CLA insights to inform strategy decisions. Here’s how it could work:

1. Data Analysis: Generative AI can analyze large amounts of data at a speed and scale that humans cannot match. It can sift through vast datasets to identify trends, patterns, and anomalies, providing valuable input for the litany layer of CLA.
2. Insight Generation: By training generative AI models on relevant data, they can generate insights across the different layers of CLA. For instance, they could identify social causes, dominant discourses, and even underlying myths or metaphors related to a particular issue.
3. Continuous Learning: Generative AI models learn from each interaction, continuously improving their understanding and generating increasingly accurate and relevant outputs. This makes them a powerful tool for ongoing foresight analysis.

The CLA powered by generative AI holds significant potential. It can make the process more efficient, scalable, and capable of handling complex, multi-dimensional issues. However, it’s important to note that this doesn’t eliminate the need for human oversight. While generative AI can provide valuable insights, human experts are still needed to sensemaking these insights, validate their relevance, and apply them in decision-making processes.

Next step: Environmental Scanning

In the next post, we'll show how we used Environmental Scanning foresight technique with Foresight Lab, allowing us to identify potential changes and trends in the external environment that might impact it.

Any thoughts, feedback, ideas? Get in touch on the comments and let’s create a better future together!