Using AI to tackle Climate-Induced Migrations

COP28 adopted a resolution today that calls for transitioning away from fossil fuel. It is a major development compared to the previous 27 resolutions. But still, what we need to consider is that climate reacts slowly to changes. The patterns that have already been established have most likely baked in a large average temperature increase by 2050.

Whatever is done now, even if emissions are frozen at their current state and then reduced slowly, we are likely to experience 2–2.5 degrees increase. To avoid the litany of disasters that are already unfolding it is more important than ever to focus on climate adaptation and resilience. AI will play a prominent role in these efforts.

In a previous article I shared a session with ChatGPT that demonstrated how using Large Language Models (LLMs) can provide insight into climate trends and help focus on selecting relevant adaptation efforts. In this article I look at another problem that is related to climate change: mass migrations with an emphasis on Europe. The goal of the investigation was to understand some of the patterns of climate-induced migration to Europe and point out some practical steps that can be taken to ameliorate the challenges migration presents. On the way we also look at some policy area that need to be addressed currently to begin preparing for a climate uncertain future.

From an AI perspective, the question being asked by this article is: Can LLM systems like ChatGPT provide practical actionable steps in a complex area like migration? You be the judge of whether ChatGPT succeeded in doing this by the end of this article (and you are welcome to share your views in the comments).

As a general note, AI technology has evolved to a degree that nearly every business and organisation can benefit from improvements in automation, content creation, and engagement. What was once the domain of large corporations is now available at all levels. Although the topics addressed in this article are broad and of a large scale, the same process can be applied to much narrower niches.

When working with large language models (LLMs), like ChatGPT, it is important to be patient when creating the prompts. The more details one includes in the prompt, with an emphasis on specifying what form the reply should take, the more likely the response will be satisfactory. In investigating this topic I found that it made sense to break every step into a separate prompts that specified what was the structure of the response I was looking for.

We will begin the journey by delving into the geography of climate-induced migrations.

The Geography of Climate-Induced Migrations

After going through the various areas of climate adaptation activities I asked the system to focus on the expected climate-induced mass migration events that will occur. ChatGPT identified the three top causes of climate-induced migration to Europe.

1. Environmental Degradation and Resource Scarcity: In regions like the Sahel in Africa and parts of the Middle East, environmental degradation, including desertification and diminishing water resources, severely impacts agriculture, leading to food insecurity and economic instability
2. Extreme Weather Events: Frequent and intense droughts, floods, and storms in parts of Africa and the Middle East disrupt livelihoods and increase the vulnerability of populations. For example, increasing droughts in the Horn of Africa and severe storms in North Africa push people to migrate
3. Temperature Extremes: Increasing temperatures and heatwaves, particularly in the Middle East, can make regions uninhabitable and adversely affect working conditions, health, and productivity.

From a demographic perspective, migrating populations are expected to initially by mostly young men, and as the route gains in popularity families and other individuals follow.

Climate-induced migrants often move alongside those fleeing conflict or seeking better economic opportunities, making it challenging to distinguish the primary drivers of migration.

Crafting A European Policy to Manage the Challenge of Migration

Having identified the key drivers of migration, we now turn our attention to potential solutions. One of the strengths of a GPT tool is the ability to look through a large volume of documents and harmonise their content. It is a perfect tool to help craft policy on complex topics.

To address the climate-induced migration challenge European policies need to balance humanitarian, environmental, and security concerns. They also require regional cooperation between Europe, African nations, and the Middle East, focusing on climate adaptation and sustainable development. Given the current political climate in Europe this goal is very ambitious at the present.

I asked ChatGPT to suggest a policy framework for Europe that can manage climate-induced migrations process in a humane economically sustainable way. I was pleasantly surprised by the breadth and depth of ChatGPT’s responses. This reassured me that AI can play a role in improving humanity’s response to climate change.

Without going into too many details the main policy points around migration were divided into five areas:

• Policy Development and Analysis: data analysis, scenario modelling, and policy drafting assistance
• Monitoring and Evaluation: real-time monitoring and climate impact assessment
• Communication and Public Engagement: public information campaigns and stakeholder engagement
• Legal and Humanitarian Assistance: legal aid to migrants and crisis response coordination
• Integration and Social Adaptation: language and integration tools and job matching and skills assessment

There were many more details regarding the specific policies. One of the areas was around engaging now with countries in Africa and the Middle East to improve climate adaptation and resilience efforts to reduce future migration pressures. I am certain that a simple cost/benefit analysis will show that any funds that invested currently in developing countries that are likely to be the source of migrants in the future are very well spent. The future financial and social costs of tens of millions of migrants fleeing from adverse climate circumstances to Europe will much higher than any investments that can be currently made to reduce the need for these migrations.

Practical Tools That Assist with the Challenge of Migration

Equipped with a policy framework, the next step is to explore practical tools that assist in tackling the challenge of migration. To do that I prompted the system to focus on two sectors: Integration and Social Adaptation and Communications and Public Engagements. For each idea the prompt included instructions on the information that is required.

This is the prompt I used:

I want to focus on two areas: Integration and Social Adaptation, and Communication and Public Engagements. For each of these areas provide examples of real applications of LLMs in implementing migration policy. For each of the implementations provide the following details: 1. Name 2. One line description 3. One paragraph description 4. AI tech used 5. Delivery platform (phone app/computer/or any other) 6. Expected impact 7. Complexity of development using a set of criteria you will develop for assessing that 8. Potential commercial case, if existing, for this solution 9. estimated number of potential users

ChatGPT came up with four ideas. This is the full response for the first one:

Name: Language Bridge

One Line Description: AI-powered language learning tool tailored for migrants.

Description: Language Bridge offers personalized language learning modules to help migrants adapt to their new environment. It uses natural language processing to adapt lessons to the learner’s proficiency level and cultural context, making learning more effective and engaging.

AI Tech Used: Natural Language Processing (NLP), Machine Learning (ML).

Delivery Platform: Smartphone app.

Expected Impact: Facilitates language acquisition, aiding in social integration and employment opportunities.

Complexity of Development: Moderate, requiring advanced NLP capabilities and user interface design.

Commercial Case: Subscription model for advanced features; partnerships with governments and NGOs.

Potential Users: Thousands to millions of migrants in Europe.

The other three examples were:

• JobMatch AI: AI-driven employment matching platform for migrants
• InfoMigrant Portal: Multilingual information portal for migrants
• Community Connect: Social platform fostering connections between migrants and local communities

To me all of these seem like quite good ideas. In fact, given the challenges European countries are facing now with migrants these tools can be beneficial today, not at some future mass migration point.

Final Thoughts

In general, AI can provide wide ranging support to improving climate adaptation and resilience. In this article I focused on LLMs, but the impact of AI includes better modelling, more efficient disaster management, and anymore.

My goal today of coming up with practical solutions to some of the problems posed by migration has been met. The tools presented by ChatGPT can make a difference today. Each of these tools is an opportunity.

This type of usage of ChatGPT is driven by user prompts. Many of these activities can be automated using various AI automation tools and accelerate and improve processes tremendously.

I invite you to share your thoughts in the comments. What has been your experience?

I would love to talk to you about whether AI may benefit your company. You are welcome to message me to begin a discussion.