Topic: Projection for the future emission for non energy sectorSubscribe | Previous | Next
Kyawmoe Aung 6/11/2021
Dear LEAP,
I am considering to create branches similar to Asiana one to calculate GHG emission from Agriculture, Waste, Industry and Energy (Fugitive emission). I have activity data from 2005 onwards with all required parameters and emission factor. The aim is to project future BAU, build mitigation scenario (conditional and unconditional scenario) until 2040 or may be 2050. I would like to ask three questions as follow-
1. Can I create time-series historical data assuming my current account year is 2015 for example and
2. If yes, how to use growthas() function which I would like to use for two variables such as population and GDP growth assuming future emission will depend on these two variables. Can I do like that with LEAP. Or
3. Should I create annually calculated emission of each variables such as enteric fermentation, manure management, rice cultivation, residue burning, residue burning, etc. at key assumption tab and project using growth() function?
Thank you for any hint on this.
Kyaw Moe Aung
Project Coordnator
SNC-BUR
Utsavshree Rajbhandari 6/19/2021
Dear Kyaw,
How you create a model totally depends upon data you have and kind of analysis you seek. Having said that, the answer to all three question could be Yes.
1. Yes. you can input historical data and forecast, extrapolate , linearly or exponentially any one using time-series function
2. Growthas function basically works with two components, the prior year value of same and another dependent variable ( along with its, elasticity) and Yes, you can even use multivariate equations, be it linear or polynomial or logarithmetic.
3. Yes, you can, but I do not see a reason you should. The Asiana model is basically built upon all parameters as per IPCC guidelines. But if you have any other extra elements you can always add your own using User Variables.
I would suggest you to go through Function Wizard (Ctrl+F) to explore the LEAP's in-built capabilities.
Best,
Utsav
Kyawmoe Aung 6/30/2021
Dear Utsav,
Thank you very much for your answers to my questions. Now I am trying to analyse GHG emissions as a function of population growth and GDP.
Regarding the Growth functions, I am exploring GrowthAs, growth and HistoricalGrowth to fit our situation.
As far as I know, Asiana is not fully following the IPCC 2006 GLs. Sometimes I found creating variables for IPCC 2006 Equations easy for me. for example, I am adding Iron and Steel production under the Non-energy sector because my model is Top-Down so I can not use energy intensity for iron and steel under the demand sector.
I am exploring GHG categories which cover almost all of source categories for - Energy (fugitive), Industry (iron and steel, mineral products, etc., Livestock, Agriculture, Forestry and Waste.
Is there any suggestions to set BAU among 1. GDP growth, 2. Population Growth and 3. combination of GDP and Population with multiple regression analysis?
Thank you for sharing about Function Wizard which I will explore further please let me raise questions if any in the future.
Thank you again
Best regards,
Kyaw Moe Aung
Utsavshree Rajbhandari 7/6/2021
Hello Kyaw Moe,
Being you model a top down approach, yes it would not fit Asiana model, specially for Energy. I am not sure what is your model structure, but i will give you 2 examples in simplest form
Even though you model is Top-Down, LEAP is flexible enough such that you can still use total energy in place of energy intensity with one little change - set the units of main branch to "No Data". On doing this now you can enter total energy value in the Parametric Tab "Final Energy Intensity"
as you can see, there won't be Per column in this case. Thus now you can put your equation in this expression bar.
if you have model for each type of fuel, you can do it as shown in screen shot above,
if not, and have only fuel share, you can use the category with energy intensity, where you can put the energy value or equation and further below, give share of each types of fuels.
On further notes, you can add Iron and Steel energy in both demand and non-energy module, as per data available and relate them both.
Further more, you can create different scenario for each regression model, but which suits best is dependent on statistical analysis.
I hope this helps,
Best,
Utsav