MODELLING CONCEPTS: Co2 Emission Function

By Oghenemagan Kereokiye

     Abimbola Shode

     Kingsley Onuoha

     Yetunde Esan

     Nneka Iyabode Eze

THE MATHEMATICAL MODEL

For the mathematical model, we are looking at CO₂ emissions per capita as a function of the share of renewable energy in a country, the energy intensity of the country, European Union membership, and OECD membership.

For the regression we use a log – linear model.

Mathematically the model can be stated as;

Log(CO_2pc) =b₁+b₂REN + b₃EI + b₄EU + b₅OM + b₆REN(OM) + b₇EI(OM) + b₈EU(OM)

Where;

·                     REN means share of renewable energy in a country

·                     EI stands for energy intensity.

·                     EU stands for European union membership

·                     OM stands for OECD membership.

In the model we have two dummy variables namely;

1       if a country is part of the European union

EU =

                                                    0    otherwise

                                                    1   if a country is part of the OECD

OM =

0   otherwise

Also, we have introduced interaction between some of the variables like REN (OM), EI (OM), and EU (OM). The reason for the interaction is for us to determine whether being a member of an organization that is meant for developed or advanced economies, and the policies that are adopted by the countries who are members actually affect the level of carbon emissions in the country.

SIGN OF THE COEFFICIENTS

- For our model, we expect that the coefficient of the renewable energy variable will be negative which is in line with our chart in figure 3. As such we expect that as the share of renewable energy increases, the level of carbon emissions fall.

- The coefficient of EI is expected to have a positive sign just as was represented in figure 2. Thus we expect that as the energy intensity increases, the carbon emissions increase.

- The variables EU and OM should both have positive coefficients because they are economically developed countries which traditionally consume large amounts of energy and knowing that energy consumption is positively correlated to carbon emissions, as shown in figure1, their carbon emissions should be greater than those in other regions.

- The sign of the coefficient of REN(OM) is expected to be positive because most countries who are members of the OECD have a high energy consumption pattern which means that their carbon emissions are expected to be high.

- The interaction variable EI(OM) is also expected to be negative because we believe that being a member of the OECD and adopting their policies on energy efficiency can help to reduce the amount of CO₂ emissions for a country

THE ECONOMETRIC MODEL

Our econometric model is stated as;

Log(CO_2pc) =b₁+b₂REN + b₃EI + b₄EU + b₅OM + b₆REN(OM) + b₇EI(OM) + b₈EU(OM) + e_t where e_t stands for the error term.

The error term will take into account the fact that we may not have used all the relevant explanatory variables that could improve our model as well as errors that arise from the random nature of our dataset. For the regression panel data of the 30 members of the OECD, the 26 members of the EU, 17 African countries, 15 Asian countries, 15 South American countries, and 16 other European countries were selected, making a total of 90 countries as shown in tables 1 – 6 below;

Australia	Austria	Belgium	Canada	Czech Republic	Denmark
Finland	France	Germany	Greece	Hungary	Iceland
Ireland	Italy	Japan	Korea	Luxembourg	Mexico
Netherlands	New Zealand	Norway	Poland	Portugal	Slovakia
Spain	Sweden	Switzerland	Turkey	United Kingdom	United States

Table 1: OECD countries

Austria	Belgium	Bulgaria	Cyprus	CzechRepublic	Denmark	Estonia
Finland	France	Germany	Greece	Hungary	Ireland	Italy
Lithuania	Latvia	Luxembourg	Malta	Netherlands	Poland	Portugal
Romania	Slovakia	Spain	Sweden	United Kingdom

   Table 2: EU member countries

Algeria	Angola	Botswana	Senegal	Cameroon	Cote d’Ivoire
Egypt	Ghana	Kenya	Libya	Mozambique	Morocco
Namibia	Nigeria	South Africa	Tunisia	Zimbabwe

 Table 3: African Countries

China	Georgia	India	Indonesia	Iran
Malaysia	Philippines	Qatar	Russia	Singapore
Thailand	UAE	Saudi Arabia	Israel	Syria

 Table 4: Asian Countries

Argentina	Brazil	Chile	Costa Rica	Colombia	Cuba
Ecuador	Peru	Uruguay	Venezuela	Paraguay

  Table 5: South American Countries.

  Table 6: Other European Countries.

Nutshell:

This is the 2nd part of a 5-part series which seeks to employ a quantitative approach to investigating the impact of OECD and EU membership on Carbon (CO2) Emissions. In the first instalment, we explored four causal factors for carbon emissions. This layed the foundation for us to proceed with the next step.  If you are not mathematically inclined you should be getting scared by now; however you don't have to worry- In this instalment, Nneka simply explores the Carbon Emission function by expressing this relationship in form of two preliminary models; an econometric model and a mathematical model. This is with a view to outlining the base for which data shall be compiled in order to run several analyses of the causal relationships that exist- if any. This will lead us to very interesting results in the coming parts of the series. To view Nneka's professional profile and for more information on this article please click here:-->