Welcome back to topic 6, session 2, the last one of this Mooc.
Now, we will translate what we have discussed in the previous session into the MEDEAS language.
MEDEAS software needs an external spreadsheet file with the inputs required to define parameters,
constants, policy targets and scenarios.
First, we will get familiar with the scenarios interface in MEDEAS.
After that, we will run the world model an show some examples.
Finally, we will finish the course with some concluding remarks.
MEDEAS software needs an external spreadsheet file with the inputs required to define parameters,
constants, policy targets and scenarios.
This file is the one named inputs, low dash and the upper case initial of our geographical
model.
In this case, we will use ‘inputsw’ for MEDEAS world scenarios.
First of all, make sure the inputs file is in the same folder as your MEDEAS model file.
Otherwise, it won’t work.
To begin with, we should get familiar with the interface.
Here you can see the initial ‘readme’ sheet, where all the other sheets are briefly
described.
It’s important to note that they are only our self-defined scenarios, which do not necessarily
match other scenarios defined in the literature like the abovementioned ssps.
Some of them are for scenarios, but you can also check the different parameters and constants
that we have used for every module.
However, in this course, we are only focusing on scenarios and, particularly, on bau and
scen2 scenarios.
Because the business as usual scenario is based on current trends, we are learning scenario
management in MEDEAS through scenario 2, which we can say reflects a ‘green growth’ narrative,
and which usually describes a scenario with a deep energy transition along with high economic
growth rates.
Throughout these sheets you will be able to set different policy targets from economy
to energy, including your personal option on nuclear power or the desirability of an
afforestation programme.
Of course, for the sake of simplicity, we are only looking at the most representative
values in order to understand the rationale of scenario design in MEDEAS.
We will also run our scenarios, then changing some options to see how sensitive the model
is to the figures we choose and, finally, comparing two different scenarios.
As mentioned in previous topics of this course, MEDEAS is a demand-led model, activated by
gdp per capita and population scenarios.
So, we will first define economic and demographic growth here.
You can see, for both of them, different options in a grey background.
You can take either the ssp values, define your own time series year-by-year, set a constant
rate or an asymptotic target.
If you want to use ssps, just put a zero here and here.
For gdp per capita, only ssp2 is defined, so let’s put the same option for population
growth: a zero here and a 2 here.
In case you want to define your own time series, you only have to move to the right and set
up your final simulation year and your preferred annual rate, which can be increasing before
reaching a peak and then going down or whatever you define.
It’s up to you.
Don’t worry about having values set here, even though you don’t want that option.
As long as you have set the model to choose a particular option, it will disregard the
other ones.
It doesn’t matter if there are values here.
Therefore, if you want the model to take your own time series, you should put a one instead
of a zero here and here.
Scroll down and we can start setting scenarios for energy resources.
As mentioned throughout the course, and especially in the energy module lesson, MEDEAS is an
integrated assessment model that considers energy limits.
Not only for non-renewable energy sources, but also for renewables.
Here the maximum potential for different renewable energy sources is set based on the literature.
But, if you want to set higher values based on your own estimations, or on other literature
estimates, you can just change it.
Actually, if you think that renewable energies are not biophysically constrained at all,
you can also put a one here, they will be unlimited and it will disregard all the figures
below.
For our first run, we are setting all energy sources as unlimited, so we set a 1.
From row 31 on, you can set the expected capacity installed growth by technology.
You have hydropower, geothermic for electricity, solid bioenergy for electricity, oceanic,
onshore and offshore wind and solar photovoltaic.
Then you can also set values for techno-ecological potential bioenergy, a projection of different
bioenergy technologies, biogas, waste and heat.
Then, after deciding if you want to implement an afforestation program, which we have activated
by putting a one, you can select your options for non-renewable energy source extraction
curves.
You can take oil disaggregated into conventional and unconventional by putting a 1 here, or
aggregated as total oil by setting a zero.
Then, in each one, you can select the extraction curve based on different literature estimates.
They are all the same for gas, coal and uranium.
Take oil for instance.
Here, we have selected total oil aggregated extraction curves and the estimates from laherrere
(2018).
You don’t believe in the limited nature of non-renewable energy sources?
You can just make any of them unlimited by putting a 1 in the applicable cell, or all
of them at the same time by putting a 1 here.
And that’s what we are doing for our first run.
Please note that, if you put a 1 here, it will disregard everything else.
But, if you put a zero, you can still make any of the non-renewable energy sources unlimited.
More options.
If you scroll down to row 199, you will find the energy intensity options.
Energy intensity is trickier that this, but just to make it simple, we will only play
with these values.
If you don’t want the energy intensities to decrease or, in other words, you want no
energy efficiency gains, put a one, otherwise: a zero.
Here you can set how low the energy intensity can go.
In this case, we are imposing a minimum of 30% of its initial value.
Here you can decide to change intensities by sectors or only by energy sources.
Put a zero if you want to make it globally for energy sources.
Then, you can change here, for each final energy source, the maximum rate that energy
intensity improvement can reach for a year.
Here, we have set a maximum 1.5% decrease.
In row 317 you can activate or deactivate climate change impacts.
Here, we are keeping climate impacts deactivated, a rather conservative choice.
Finally, in row 337, we can change the option for the economic structure represented by
the technical coefficients matrix, the so-called a matrix, as explained in the economic module
lesson.
Put a zero here if you want to keep it constant with the last observation’s value.
Put a one and copy-paste the a matrix that you prefer and, finally, put a two if you
want the a matrix to evolve according to historical trends.
For option one, it can be a completely made up matrix, or you can use an actual country
a matrix.
In order to show you an example, the bau scenario is set with a 2, evolving according to historical
evolution, and scen2 takes the a matrix from a modern, renewable energies-supporter like
denmark.
Now, let’s run the model and see the results for scen2.
Remember that the purpose of this module is not to discuss outcomes, but to learn how
to define and run scenarios and MEDEAS.
So just take these results as examples.
We will call our first simulation ‘test’.
We can run it by clicking here.
After a few seconds, we will be ready to see some examples.
In order to see the graphics, you need to click on any variable and then here.
Please remember that all energy sources are unlimited in this simulation.
We can see some examples for gdp, the share of renewable energies in the energy mix and
total co2 emissions.
You can see how gdp rises steeply, which apparently should be good news.
If we have a look at the share of renewable energy sources, we can see that it’s not
very impressive and co2 emissions just skyrocket.
Why does this happen if we have set high values for the installed capacity of renewables,
a modern economic structure of a renewable energies supporter and a high drop in energy
intensity?
Because high economic growth, together with the unlimited nature of non-renewable energy
sources made it possible to rely on them for a longer period while the energy transition
was being deployed.
We can run the model with energy limits in order to assess the differences.
Should energy limits be considered, economic growth would be stopped at a certain point,
because energy scarcity made it possible to grow only at lower rates.
Conversely, we find better results for renewable-energies share and emissions.
We can see that, in MEDEAS, given the scenario values that we have chosen, a sort of trade-off
between high economic growth and energy and climate goals appears.
Finally, we can compare these outcomes with a hypothetical business as usual scenario.
We will run it with limits to make both scenarios comparable.
Here, we can see different outcomes for the same variables regarding these scenarios.
In MEDEAS, we can also impose a transition scenario to begin policies earlier or later
on the simulation period.
The results might vary substantially considering the delay to implement the transition policy
targets.
We can do this by changing the initial year cell that appears next to the different policies
in the scenario sheets in our inputs file.
To summarise, we can highlight the following conclusions:
Integrated assesment models (iams) are enriched by scenarios.
The shared socioeconomic pathways (ssps) is the most common sccenarios framework.
MEDEAS accounts with a flexible scenarios structure for the user
to decide.