Improving tools
Climate Change Mitigation in Developing Countries
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.
Responding to mitigation challenges
There are limitations within our existing toolsets and ways of thinking how we might address the mitigation gap. We need to look more closely at the interface of economic and development pathways and to question the way in which climate change mitigation professionals are approaching this huge and complex climate and development problem. Responses to these challenges include linking of economic and mitigation model to better understand the interconnectedness of mitigation policies and economic development, and involves out-of-the-box thinking when imagining climate and development solutions of the future. This week we hope to inspire innovations and responses to challenges in the climate mitigation and development community.
Improving tools6:20
Looking at science8:49
Professor Harald Winkler
Energy Research Centre, University of Cape Town
In this lesson, we're going to look and
acknowledge production challenge that face the researchers in the MAPS countries.
As Martin mentioned in week four, we face, as developing countries and
as MAPS countries, a two-full challenge.
We have development needs and priorities on the one hand,
and we also need to transition to a low carbon society on the other hand.
The question for us is how do we move forward?
Given that a medication central analysis view point is
often not that relevant to decision makers and policy makers in countries.
We need to have a deep understanding of how the mitigation decisions or
actions taken by developing country governments affect the sectors and
their economy as a whole in countries.
Because any effort to curb greenhouse gas emissions
will have ripple effects throughout the country.
And this gives uncertainty to policy makers and it concerns them.
Brit in week three introduced us to computer modeling programs and
how they can be a very useful tool for us to provide various
possible scenarios of what will happen
when policy makers make decisions regarding mitigation actions.
But the problem is that as these models stand now, either using a sectoral model
on its own, or a CGE or economic model on its own, it provides us with
very simple information about what the possibility of this future will be.
And what decision makers and policy makers want a more complex and
nuanced information.
So the researchers in the maps countries have embarked on a journey
of linking these two models, the sectoral and the economy-wide models.
And we're going to turn to Brake now, who's going to give us a little
bit more information about how they went about linking the models,
what the added benefits are of linking the two models rather than using them
separately, and also what some of the disadvantages are.
In week three I presented to you a number of different types of models.
And I spoke to you briefly about models that might be Sectoral models.
And here we had a look at models that might exist in the electricity sector.
[SOUND] And might exist in the waste sector and
the land use sector, et cetera.
And then also I mentioned to you that we had models which we turned economic
models, often computational general equilibrium models.
And these models are used to tell us what the implications might be of implementing
mitigation actions in the individual sectors on the economy as a whole.
However, these links are often static links.
One takes information from here and then feeds it into a CGE model and
looks at what the implications are for the economy.
In linking, what one does is one live-links these models.
So instead of it being a one-way flow of information,
we have a two-way flow of information that,
events that happen in our economy wide model feed back into our sector models.
And our sector models might feed into our economy wide models.
First of all this linking might be a live linking where you're having a constant
feedback flow of information between the two types of models and
they run these iterations automatically
until some sort of equilibrium or situation is achieved.
Whereby one ends up in a final result.
One might also run this in a static fashion.
So not in a dynamic fashion.
But that you take electricity from model information, you feed it into your economy
wide model, and then you take economy wide information, and
you feed it back into your different sectoral models.
That's obviously more labor and time intensive, but
might be easier to design models, which do not have dynamic feedbacks between them.
A hybrid approach might be that you take certain information and
feed it forwards and then manually feed information backwards.
Or you might have certain sectoral models that talk to your economy wide models,
but other sectors that are less sophisticated that can't talk directly.
So mineeds happen in the situation where you would have a combination of
the dynamic feedbacks and the static feedbacks.
So indeed these types of models do take a lot more work to develop and
could be argued to be more open for error or more open, for
interpretation or subject to a wide arrange of assumptions
that need to be made by the models or the modeling teams.
However, in both Brazil where they may used quite extensively of what was
termed imikimi model, and in South Africa by linking our times
model with a computational general equilibrium model in a dynamic fashion.
We've been able to get much deeper insights into the types of
implications that climate mitigation can have for the economy as a whole.
So, in both of these cases there has been demonstrated to be benefit or
additional information that can be generated from these models.