Market allocation factor

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Market allocation factor is a method to balance out market situations when either the demand or supply increase or decrease. To be able to predict this, elasticity, market allocation factor, or other methods need to be used.


How to predict market balances when either the demand or supply increases or decreases?


How many per cent does the supply or demand change if the price of the product increases 1 €/product unit?

Market allocation factor(relative change per (€/unit))
1Fuel woodPetroleum refineriesInputRenewables and wasteMWh1
2Fuel woodCHP plantsInputRenewables and wasteton1
3Fuel woodHeatingOutputRenewables and wasteMWh1
4Fuel woodOther energy consumptionOutputRenewables and wasteMWh1
5Traffic fuelPetroleum refineriesOutputPetrochemical productsMWh1
6Traffic fuelFinal energy useOutputPetrochemical productsMWh1
7Traffic fuelPetroleum refineriesOutputPetrochemical productsMWh1
8Traffic fuelPetroleum refineriesOutputPetrochemical productsMWh1
9Global oilPetroleum refineriesInputCrude oilMWh1
10Global oilCHP plantsInputCrude oilton1
11PeatCHP plantsInputCoal and peatton1
12District heatCHP plantsOutputHeatMWh1
13National electricityCHP plantsOutputElectricityMWh1
14Emission tradeCHP plantsOutputCO2eton1
15Emission tradeFinal energy useOutputCO2eton1
16CardboardOther energy consumptionOutputCardboard productston1

⇤--#: . The input/output column does not make sense. The meaning depends on the part of the energy balance table (supply, transformation, use) where the activity is. Can it be dropped? --Jouni 12:09, 24 April 2012 (EEST) (type: truth; paradigms: science: attack)

+ Show code


Explanations of the calculations

The logic of the code is the following:

  • Energy contains information about the total amounts of energy in a given Activity * Fuel pair per time unit (typically a year). (For example, 54 ktoe/a petrochemical products are used in road transport.)
  • Trans contains descriptions of all activities that result in the production or use of energy. (For each ktoe used in road transport, 1 ktoe is imported.
  • When these two are merged, we get a table that explains where the energy came from and where the energy went, assuming that all energy flow goes through that activity.
  • The ratio of the actual energy stock to the energy stock in the activity table is used as the factor to scale the whole activity (not only the energy). This is done by merging the data.frame with factors again with the activities (tran) and multiplying the factor with the activities. This gives the BAU situation.
  • We assume that current energy production and use is distributed in such a way that the cost curves are in balance. If the price changes, both the supply and demand will change according to their cost curves. The new balance can be found from \Sigma f_i(p) = 0 where fi are the cost curves (supply functions producing positive values and demand functions negative values. When p is solved from the equation, values of all supply and demand functions can be solved. If all functions

f_i(p) = a_i p^2 + b_i p + c_i,

then individual functions and also the sum of all functions can be expanded and then easily be solved by using the equation for polynomial roots:

\Sigma f_i(p) = \Sigma (a_i p^2 + b_i p + c_i) = 0

p = \frac{-\Sigma b_i \pm \sqrt{(\Sigma b_i)^2 - 4 \Sigma a_i \Sigma c_i}} {2 \Sigma a_i}

Cost curves are parameterised in the way that the current price p = 0 and therefore the current supply or demand is ci.

When a demand or supply changes, a new price p must be calculated based on fi, and then the supplies and demands of other actors can be calculated, resulting in a new balance.

Taxes are complicating this picture. They do not actually change the shape of a cost curve (if it is proportional to energy content) but they shift a supply curve to right (or a demand curve to left if the tax is payed by the buyer and not the seller) by the amount of the tax. In other words, instead of p we solve \Sigma f_i(p - t) = 0, i.e.

p = \frac{-\Sigma (b_i - 2 a_i t) \pm \sqrt{(\Sigma b_i - 2 t \Sigma a_i)^2 - 4 \Sigma a_i (\Sigma c_i + t^2 - t \Sigma b_i)}} {2 \Sigma a_i},

where t is the amount of tax to the suppliers. With the same formula, it is also easy to calculate particular taxes, where the tax per unit energy is different to different suppliers. Then we simply use ti instead of t.

Cost curves example.png

How to estimate parameters a, b, and c?

Let's start from a simple approach. If the cost curve is linear, then a = 0. If all suppliers or all buyers change their behaviour in the same way (in relative terms) to price changes, then bi are proportional to ci. If we don't care about the absolute values yet, we can simply start from these assumptions and say: a = 0, b = c, c = the current amount of demand or supply, and p = 0.

The curves can be generalised to that they are polynomials with higher degree than just 2. The computations are more laborious, but the computer solves that problems with no additional burden to the user.

----#: . We could use a tool that estimates the parameters based on some points that a user gives on a graph about his/her personal cost curve. --Jouni 06:53, 2 February 2012 (EET) (type: truth; paradigms: science: comment)

See also

Urgenche research project 2011 - 2014: city-level climate change mitigation
Urgenche pages

Urgenche main page · Category:Urgenche · Urgenche project page (password-protected)

Relevant data
Building stock data in Urgenche‎ · Building regulations in Finland · Concentration-response to PM2.5 · Emission factors for burning processes · ERF of indoor dampness on respiratory health effects · ERF of several environmental pollutions · General criteria for land use · Indoor environment quality (IEQ) factors · Intake fractions of PM · Land use in Urgenche · Land use and boundary in Urgenche · Energy use of buildings

Relevant methods
Building model · Energy balance · Health impact assessment · Opasnet map · Help:Drawing graphs · OpasnetUtils‎ · Recommended R functions‎ · Using summary tables‎

City Kuopio
Climate change policies and health in Kuopio (assessment) · Climate change policies in Kuopio (plausible city-level climate policies) · Health impacts of energy consumption in Kuopio · Building stock in Kuopio · Cost curves for energy (prioritization of options) · Energy balance in Kuopio (energy data) · Energy consumption and GHG emissions in Kuopio by sector · Energy consumption classes (categorisation) · Energy consumption of heating of buildings in Kuopio · Energy transformations (energy production and use processes) · Fuels used by Haapaniemi energy plant · Greenhouse gas emissions in Kuopio · Haapaniemi energy plant in Kuopio · Land use in Kuopio · Building data availability in Kuopio · Password-protected pages: File:Heat use in Kuopio.csv · Kuopio housing

City Basel
Buildings in Basel (password-protected)

Energy balances
Energy balance in Basel · Energy balance in Kuopio · Energy balance in Stuttgart · Energy balance in Suzhou


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