Examining the relationship between economic growth, energy consumption and Co2 emission using inverse function regression

Zaidi Isa, R. M A Ahmed, K. S. Siok

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Recently, the relationship between carbon dioxide emission (CO2), aggregate energy consumption (EC) and economic growth (GDP) has been widely studied by many researchers using different approaches but the results were conflicting. Such controversy may due to the efficiency of the applied statistical approaches and using different dataset. The main objective of this experimental study is to examine the relationship between CO2, EC, and GDP using different data transformation forms (natural logarithm versus inverse form) in reducing the heteroscedasticity in panel data. The panel data consist of 29 countries from two different economic levels of countries, 17 developed versus 12 developing countries. The data spanning from 1960 to 2008. A panel data approach is applied and estimations based on three models. First of all, the estimations are conducted by constructing three different models; First model is estimated by using the original data without any transformation, while the second and third model use the natural logarithm (Log) and inverse form to transform the data. Those two transformation forms are applied to reduce the heteroscedasticity problem. The main findings show a strong relationship between the three variables. The model with inverse function transformation is superior to the other two models using original data and Log transformation, as it has the highest R2 which illustrates that more than 84% of CO2 emission can be explained by GDP and EC. Since EC and GDP are influential on the CO2 emissions, higher EC and lower GDP may lead to environmental problems such as air and water pollution. Therefore, prevention action should be taken to minimize the environmental degradation.

Original languageEnglish
Pages (from-to)473-484
Number of pages12
JournalApplied Ecology and Environmental Research
Volume15
Issue number1
DOIs
Publication statusPublished - 2017

Fingerprint

energy use and consumption
economic development
economic growth
Gross Domestic Product
carbon dioxide
panel data
heteroskedasticity
environmental degradation
air pollution
water pollution
energy consumption
developing countries
atmospheric pollution
transform
experimental study
researchers
developing world
economics

Keywords

  • Economic growth
  • Heteroscedasticity
  • Inverse form transformation
  • Panel data approach

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Agronomy and Crop Science

Cite this

Examining the relationship between economic growth, energy consumption and Co2 emission using inverse function regression. / Isa, Zaidi; Ahmed, R. M A; Siok, K. S.

In: Applied Ecology and Environmental Research, Vol. 15, No. 1, 2017, p. 473-484.

Research output: Contribution to journalArticle

@article{afc726e30a38431db25442b02eb691d7,
title = "Examining the relationship between economic growth, energy consumption and Co2 emission using inverse function regression",
abstract = "Recently, the relationship between carbon dioxide emission (CO2), aggregate energy consumption (EC) and economic growth (GDP) has been widely studied by many researchers using different approaches but the results were conflicting. Such controversy may due to the efficiency of the applied statistical approaches and using different dataset. The main objective of this experimental study is to examine the relationship between CO2, EC, and GDP using different data transformation forms (natural logarithm versus inverse form) in reducing the heteroscedasticity in panel data. The panel data consist of 29 countries from two different economic levels of countries, 17 developed versus 12 developing countries. The data spanning from 1960 to 2008. A panel data approach is applied and estimations based on three models. First of all, the estimations are conducted by constructing three different models; First model is estimated by using the original data without any transformation, while the second and third model use the natural logarithm (Log) and inverse form to transform the data. Those two transformation forms are applied to reduce the heteroscedasticity problem. The main findings show a strong relationship between the three variables. The model with inverse function transformation is superior to the other two models using original data and Log transformation, as it has the highest R2 which illustrates that more than 84{\%} of CO2 emission can be explained by GDP and EC. Since EC and GDP are influential on the CO2 emissions, higher EC and lower GDP may lead to environmental problems such as air and water pollution. Therefore, prevention action should be taken to minimize the environmental degradation.",
keywords = "Economic growth, Heteroscedasticity, Inverse form transformation, Panel data approach",
author = "Zaidi Isa and Ahmed, {R. M A} and Siok, {K. S.}",
year = "2017",
doi = "10.15666/aeer/1501_473484",
language = "English",
volume = "15",
pages = "473--484",
journal = "Applied Ecology and Environmental Research",
issn = "1589-1623",
publisher = "Corvinus University of Budapest",
number = "1",

}

TY - JOUR

T1 - Examining the relationship between economic growth, energy consumption and Co2 emission using inverse function regression

AU - Isa, Zaidi

AU - Ahmed, R. M A

AU - Siok, K. S.

PY - 2017

Y1 - 2017

N2 - Recently, the relationship between carbon dioxide emission (CO2), aggregate energy consumption (EC) and economic growth (GDP) has been widely studied by many researchers using different approaches but the results were conflicting. Such controversy may due to the efficiency of the applied statistical approaches and using different dataset. The main objective of this experimental study is to examine the relationship between CO2, EC, and GDP using different data transformation forms (natural logarithm versus inverse form) in reducing the heteroscedasticity in panel data. The panel data consist of 29 countries from two different economic levels of countries, 17 developed versus 12 developing countries. The data spanning from 1960 to 2008. A panel data approach is applied and estimations based on three models. First of all, the estimations are conducted by constructing three different models; First model is estimated by using the original data without any transformation, while the second and third model use the natural logarithm (Log) and inverse form to transform the data. Those two transformation forms are applied to reduce the heteroscedasticity problem. The main findings show a strong relationship between the three variables. The model with inverse function transformation is superior to the other two models using original data and Log transformation, as it has the highest R2 which illustrates that more than 84% of CO2 emission can be explained by GDP and EC. Since EC and GDP are influential on the CO2 emissions, higher EC and lower GDP may lead to environmental problems such as air and water pollution. Therefore, prevention action should be taken to minimize the environmental degradation.

AB - Recently, the relationship between carbon dioxide emission (CO2), aggregate energy consumption (EC) and economic growth (GDP) has been widely studied by many researchers using different approaches but the results were conflicting. Such controversy may due to the efficiency of the applied statistical approaches and using different dataset. The main objective of this experimental study is to examine the relationship between CO2, EC, and GDP using different data transformation forms (natural logarithm versus inverse form) in reducing the heteroscedasticity in panel data. The panel data consist of 29 countries from two different economic levels of countries, 17 developed versus 12 developing countries. The data spanning from 1960 to 2008. A panel data approach is applied and estimations based on three models. First of all, the estimations are conducted by constructing three different models; First model is estimated by using the original data without any transformation, while the second and third model use the natural logarithm (Log) and inverse form to transform the data. Those two transformation forms are applied to reduce the heteroscedasticity problem. The main findings show a strong relationship between the three variables. The model with inverse function transformation is superior to the other two models using original data and Log transformation, as it has the highest R2 which illustrates that more than 84% of CO2 emission can be explained by GDP and EC. Since EC and GDP are influential on the CO2 emissions, higher EC and lower GDP may lead to environmental problems such as air and water pollution. Therefore, prevention action should be taken to minimize the environmental degradation.

KW - Economic growth

KW - Heteroscedasticity

KW - Inverse form transformation

KW - Panel data approach

UR - http://www.scopus.com/inward/record.url?scp=85013196827&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85013196827&partnerID=8YFLogxK

U2 - 10.15666/aeer/1501_473484

DO - 10.15666/aeer/1501_473484

M3 - Article

VL - 15

SP - 473

EP - 484

JO - Applied Ecology and Environmental Research

JF - Applied Ecology and Environmental Research

SN - 1589-1623

IS - 1

ER -