Reasons for the gap: technical perspective

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Climate Change Mitigation in Developing Countries

173 classificações

University of Cape Town

Climate Change Mitigation in Developing Countries

173 classificações

This course challenges you to consider how one might lift societies out of poverty while also mitigating greenhouse gas emissions. We explore the inherent complexity of developing country governments wanting to grow their economies in a climate friendly way. You will be introduced to an approach with which to address this challenge. The approach consists of a facilitated process whereby academic researchers and high-level influential actors within society co-produce knowledge. You will track this process in four Latin American countries - Brazil, Chile, Colombia, Peru, and South Africa. You will hear from various professionals about their contexts and the different challenges and opportunities the process includes. This course will cover topics such as facilitation process techniques, energy modeling, scenario building, innovation and policy making. You will have the opportunity to respond to these topics with ideas and reflection from your own context. Whether you are a climate change practitioner, work in development or are simply curious about how climate mitigation is understood, this course will give you insights into the complexity of how countries from the South pursue development goals while addressing climate mitigation. The course is free to enroll and take. You will be offered the option of purchasing a certificate of completion, which you become eligible for if you successfully complete the course requirements. This can be an excellent way of staying motivated! Financial Aid is also available.

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Minding the mitigation gap

What happens when your best efforts are not good enough? We will look at the ‘gaps’ between where we would like to be and where we are.The direction emission trends are headed is a function of everything put into the model (such as population, growth and GDP, and technology). Yet what is required by science is driven by considerations such as how we need to reduce emissions to keep temperature rises below two degrees. This week, in exploring some of the potential reasons for this gap we consider technical reasons and other pushbacks, like vested-interests, political or inherent human behaviour.

Reasons for the gap: technical perspective7:47

Reasons for the gap: political perspective (part 1)6:59

Reasons for the gap: political perspective (part 2)10:27

Reflection: learning from failures5:02

Conheça os instrutores

Harald Winkler
Professor
Energy Research Centre, University of Cape Town

Hello, now we have to unpack what this mitigation gap is and

we know we have to do much more.

What we going to look at this video is so why.

Why can't we get as far as we wanted?

So there are several factors, and we group them here into two the technical ones and

what we call more the political ones.

To start with the technical ones,

I'm going to give three main examples of three main barriers that we found.

The first one is on poor data or even lack of data.

Low carbon modeling status require huge amount of data,

because of its nature, socioeconomic nature, etc.

And what we also to know is that in the global south.

Unfortunately, the quality of the data that we got is not as good as

we would like.

So, that was a hard constraint.

And because of the rigor and the standards of these models,

if the data was not good enough, then the intervention could not be modeled and

therefore was excluded from that big picture.

So no data, sometimes no interventions and

we leave behind some ideas that we cannot capture in our approach.

But even if we would have more time to go and research and get more data and

investigate and improve the quality of our dataset, there's always unknown stuff.

So that is again, the main learning to recognize that

we have this limitation of things that we don't know.

And one we could say, we'll, we have a gap, but

this gap don't worry in ten years time.

We'll know how to cover it,

but we don't know it today.

One of the main criticism that we received were related

to some parameters that we used a input for this model.

For example, the sanction for the GDP projection or

the sanction for the price of food projection.

So, it's very important when you

try to find the credibility of the result.

Try to consider the uncertainties that you can

have in the input of your models.

So, we've talked about the known information.

We've talked about the lack of data and

there's also the limitations of our models, and

using these very rigid tools to determine what we can do what we cannot do.

There are lots of advantages and disadvantages of choosing different

models, but Brett explained what are the important parameters to choose it, and

in the end you will have to make a choice, and they will always comes with tradeoffs.

What I want to highlight here in terms of limitations

is this inability to be able to make a case or

demonstrate certain interventions are not going to ruin your economy.

Another important thing that is very

important when you try to use models to analyze

is that sometimes to use a very good model to analyze

a very complicated model is sometimes is not

necessarily the best solution to be ambitious.

Now with case, for example, we work with a very complex and

compete model to evaluate the economic

impact of the different mitigation actions.

And however, at the end of the project,

some people who didn't know did a lot of

the questions related to this that we used and

that it could be very complicated thing in a project like this.

Maybe sometimes more important to be very clear with the main

assumption that you did to model and

that could be sometimes more important than to have a very complex model.

And that is very important in a project

with a participatory process,

because all the results will probably be reviewed by a lot of people.

So also very important are just the pragmatic constraints.

The skills we have, the time constraints that we had,

the resources constraints we had.

So these processes took three, four years and

there was a huge amount of data developed.

But one would say,

especially the researchers if they would have had more time.

Of course, they would have been able to provide more textures to some of these

scenarios, to spend more time identifying all the interventions that could

have been adopted into our model.

So one can clearly say that investing more time on research,

on improving the data quality would have beneficial results.

And probably would also see the gap diminishing, but

what was important from these processes was to deliver on time.

When the policy maker had to take a decision, we had to had the results there.

There was no all the benefit in extending the research work without and

missing that policy window.

And not just that, MAPS is about co-production of knowledge.

So that means it's about research, but it's also It's about process.

Let's ask Hernan who was in charge of the process of MAPS Chile to see what were

the challenges that emerge from prolonging the work from the process perspective.

This, as I said at the beginning is a four-year process.

Almost, I would say is almost a five-year process.

And of course, to keep the momentum and to keep all stakeholders,

all the relevant stakeholders on board is not really easy.

And I guess, stakeholders have to have a perception that they

are being influential on the one hand.

And maybe on the other hand, gain.

Gaining something out of all this process.

So, we can have some fears.

I would say that in the end and with hindsight,

the project in this final year has some change and

it has changed towards more implementation analysis, implementation emphasis and

I think that has been appreciated by the stakeholders.

In the end, we've seen a lot of loyalty, which has actually surprised us.

But we've seen as I'm saying, a lot of loyalty from stakeholders and

I think they've seen that what we are doing right now is actually useful and

that's very important.

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