- Southeast Asia and the Global South
are some of the regions that face
the highest impacts of climate change,
impacting communities, impacting the economy.
And these are multiple hazards that impact these countries,
from sea-level rise to storms to extreme heat.
Now these are extremely complex systems,
and these systems, we are getting into a phase
where these impacts are increasing in frequency,
they're increasing in severity,
and they're becoming more unpredictable.
So this is where the power of analytics, data,
become very helpful to help narrow that range
of uncertainty that decision makers face.
And AI plays a critical part in this.
It's an extremely helpful tool in a wide variety
of toolkits that we need to apply to help these countries,
these communities build resilience.
Let's take an example of coastal communities.
In Southeast Asia, there are numerous coastal communities
that the economies and people just live near the coast,
and they're all exposed to extreme climate events.
There's impact to vulnerable populations,
to the economy, to lives, livelihoods.
And governments in this part of the world
have limited resources to build resilience--
whether that's in technical capacity and funding--
and they'll need to make some big choices.
With AI and advanced analytics,
and applying that with the latest climate science
and with local context,
you can start to unpack the cost of inaction.
If you did nothing, what will be the impact
on vulnerable communities?
With that better understanding
through this analysis and these scenarios,
decision makers in government, for example,
can then decide what are the optimal responses.
Now do you move certain communities?
Do you build protection around parts of the economy?
Do you make choices between
green nature-based solutions for resilience
or do you invest in hard infrastructure?
There are multiple choices,
and having a good understanding
of what is the cost of inaction,
what are the different scenarios
when you mix these responses, allows decision makers
to deploy those limited resources in the optimal way.
Now there are multiple benefits to use AI
as part of the toolkit to build resilience.
Now that said, there are some practical constraints.
So for example, in a survey of public
and private sector leaders,
78% of them said that they don't have
the technical capacity to use these tools.
67% said that they don't have confidence
in being able to get the data and hence have confidence
on the analysis that comes out from this data.
So these are some examples of the type of roadblocks.
There's capacity constraints,
there's access to data constraints,
there's constraints associated
with access to the available solutions--and these are developing and moving fast
- -and having them deployed in the places
where they're most needed.
These are all constraints,
but they're addressable constraints.