The Effect of Governance on Carbon Dioxide Expansion in the G8 Countries: A Panel Quantile Regression Approach

Document Type : مقالات پژوهشی

Authors

• Emad Kazemzadeh ¹
• nooshin karimi alavijeh ¹
• taghi ebrahimi salari ²

¹ PhD student of Economics Ferdowsi University of Mashhad

² Assistant Professor of Economic Ferdowsi University of Mashhad

https://doi.org/10.22067/erd.v26i18.77394

Abstract

Introduction
Good governance is an index designed by the World Bank to classify governments in terms of their attention to people and their social and economic status. One of the most important factors in implementing good governance in many countries has been environmental decision making, in today's world, the environment is one of the most important issues facing people. Various factors affecting the environment include economic variables (industrialization, trade and technological inequality), political variables (democracy and despotism), social variables (urbanization and literacy rate) and government (size and quality of government). In this study, the effect of good governance on CO2 emissions in G8 countries is investigated.
 
Theoretical framework
Governance has a broad meaning that is directly related to domains such as the economic environment or, in other words, economic security, politics, society and rights. The World Bank has introduced governance indicators to reflect the institutional quality of countries. These indicators include voice and accountability, political stability, control of corruption, regulatory burden, government effectiveness and rule of law. Various aspects of governance, both direct and indirect, affect carbon dioxide emissions.
 
Methodology
Quantile regressions id based on a symmetric and asymmetric loss function and calculated similarly to the estimation of parameters in the Ordinary Least Squares (OLS) regression. The general definition of quantile regression is that if the linear regression model is assumed to be the following:
= +    ,    0                                                                                 (1)
) =                                                                                         (2)
Equation (2), the condition quantile function τ distribution of y shows to the condition of random variables x, where the following condition holds.
) =
To estimate the coefficients of the model, minimize the value of the absolute value of the errors is used with proper weighing:
 
 
Equation (3) obtains answers by linear programming. In this study, a panel quantile regression method with fixed effects is used. Consider the following fixed-effect panel quantile regression model:
 
 
Where is the conditional 100 quantile of   ,  is the fixed-effects parameters correlated with  , which exhibits the unobservable effects of each specific country and  is the slope coefficient at the 100 quantile. The unique feature of this method is that a penalty term in minimizing to address the computing problem introduces a sum of parameters; Estimates of the parameters are as follows:
min_(α,β) (5)
In relation (5), i indicates the number of countries (N), T time period, K, the level of quantiles, x the matrix of explanatory variables and  the quantile loss function. In addition, shows the relative weight for k^th quantile. In this paper, = 1/K is considered. λ is the adjustment parameter that reduces the individual effects of to zero to improve the performance of β.
 
Results & Discussion
The model presented in this study is as follows:
The description of variables is as follows:
 
 
 
 
Table (1) - Introduction of variables

Variable                                         Definition

CO₂                         Carbon dioxide emissions (metric tons)
ENC                        Energy use (kg of oil equivalent per capita)           
GDP                         Economic growth (GDP per capita constant at 2010 prices)          
TRADE                   Trade openness  
GOVERN               Governance Index

URB                        Urbanization (urban population/total population)
INDUS                    Industrialization

 
The proposed model is estimated at various quantiles (0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95) and the results are presented in Table (2). Also, for comparative analysis, the results of estimating the OLS method are reported in the last column of Table (2).
 
Table (2) – Panel quantile regression results





Variable                                   Quantiles
ـــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــــ




 


5^th


10th


20th


30th


40th


50th


60th


70th


80th


90th


95th


Ols fixed




ENC


.0190^***


.0193^***


.0191^***


.01919^***


.0189^***


.0191^***


.0188^***


.01905^***


.01923^***


.0191^***


.0192^***


0.022^***




GDP


-.0136


-.0128


-.037^**


-.076^*


-.062*


-.062^*


-.040^**


-.0294^*


-0.004


-.010


-0.009^**


-0.012^**




TRADE


.0296^***


.0236^***


.019^***


.008


.002


.002


-.009


-.011^**


-.020^***


-.019^***


-.0183^***


0.084^***




GOVERN


-1.008^***


-.992^***


-1.550^***


-2.175^***


-1.208^**


-1.013^*


-.763^***


-.556^**


-.151


-.1256


-.0738


-2.276^***




URBAN


0.030^***


0.0292^***


0.029^***


0.030^***


0.018^***


0.018^***


0.016^***


0.014^**


0.011^***


0.012^***


0.012^***


0.101^***




INDUS


.253^***


.270^***


.277^***


.271^***


.082^*


.041


.044^**


.0343^**


.0403^***


.035^***


.032^***


0.156^***




Constant


-14.391^***


-14.402^***


-15.438^***


-15.614^***


-4.730^**


-2.625


-1.409


-.3849


.884


1.547^***


1.730^***


3.512^***




Pseudo R²


0.736


0.728


0.718


0.705


0.720


0.756


0.795


0.832


0.854


0.867


0.874


0.90





Notes ;***significance at the 1% level, **significance at the 5% level, *significance at the 10% level.
 
The estimation results in Table (2) show that energy consumption at all quantile levels from 5% to 95% has a positive and significant effect. As shown in Figure (1), the effect of energy consumption on CO₂ emissions in all countries has a constant trend. GDP has a negative effect on CO₂ emissions at all quantile levels, the results of the OLS estimates also confirm the negative relationship between GDP and carbon dioxide emissions. The results of different quantile levels show a strong and positive effect on the relationship between governance and CO₂ expansion.
Trade openness in the quantile of 5% to 20% has a positive and significant effect on carbon dioxide emissions, but for the quantile levels above 70%, the increase in trade has a negative impact on CO₂ emissions. The relationship between urbanization and carbon dioxide emissions is positive and significant at all quantile levels. The relationship between industrialization and carbon dioxide expansion is also positive and significant at all quantile levels.
 
 
Chart (1)-Estimation results of panel quantile regression
 
Conclusions & Suggestions
This study investigates the effect of governance on carbon dioxide emissions in the G8 countries over the period 1996 to 2016 using the panel quantile regression method. The results show that the effect of the good governance index, which shows the quality of a country's public institutions and the ability of the government to perform its duties, has a negative and significant effect on the amount of carbon dioxide emissions that represent environmental status in all quantiles. However, this effect is stronger in the lower quantiles and reduces the movement to the higher quantiles.
 

Keywords

• Governance
• energy
• Carbon Dioxide
• Quantile Panel Regression

References

[1]      Acemoglu, D., Johnson, S., & Robinson, J. A. (2005). Institutions as a fundamental cause of long-run growth. Handbook of economic growth, 1, 385-472.

[2]      Ahmad, N., Du, L., Lu, J., Wang, J., Li, H.-Z., & Hashmi, M. Z. (2017). Modelling the CO2 emissions and economic growth in Croatia: is there any environmental Kuznets curve? Energy, 123, 164-172.

[3]      Alexander, M., Harding, M., & Lamarche, C. (2011). Quantile regression for time-series-cross-section data. International Journal of Statistics Management System, 6(1-2), 47-72.

[4]      Alizade, S., & Bayat, M. (2016). The Effect of Good Governance on the Environment in Middle-Income Countries Journal of Environmental Science and Technology, 18(3), 501-513. (In persian).

[5]      Amadeh, h., shakeri, a., & mohammadeyan, f. (2012). Government size, Government quality and environmental quality Case study of OECD and OIC countries Journal of Applied Economics Studies in Iran, 1(2), 27-60. (In persian).

[6]      Aron, J. (2000). Growth and institutions: a review of the evidence. The World Bank Research Observer, 15(1), 99-135.

[7]      Bameni Moghadam, M., & Khoshgooyan Fard, A. (2005). The Application of the Quantile Regression in Finding the Distribution of Expected Welfare Social Welfare, 4(15), 43-56.

[8]      Bouznit, M., & Pablo-Romero, M. d. P. (2016). CO2 emission and economic growth in Algeria. Energy Policy, 96, 93-104.

[9]      Cai, Y., Sam, C. Y., & Chang, T. J. J. o. c. p. (2018). Nexus between clean energy consumption, economic growth and CO2 emissions. 182, 1001-1011.

[10]  Canay, I. A. (2011). A simple approach to quantile regression for panel data. The Econometrics Journal, 14(3), 368-386.

[11]  Chen, P.-Y., Chen, S.-T., Hsu, C.-S., & Chen, C.-C. (2016). Modeling the global relationships among economic growth, energy consumption and CO2 emissions. Renewable Sustainable Energy Reviews, 65, 420-431.

[12]  Dadgara, Y., & Nazari, R. (2017). The impact of good governance on environmental pollution in South West Asian Countries. Iranian Journal of Economic Studies, 5(1), 49-63.

[13]  Damette, O., & Delacote, P. (2012). On the economic factors of deforestation: What can we learn from quantile analysis? Economic Modelling, 29(6), 2427-2434.

[14]  Davino, C., Furno, M., & Vistocco, D. (2013). Quantile regression: theory and applications (Vol. 988): John Wiley & Sons.

[15]  Djankov, S., La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2002). The regulation of entry. The quarterly journal of economics, 117(1), 1-37.

[16]  Galvao Jr, A. F. (2011). Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics, 164(1), 142-157.

[17]  Gani, A. (2012). The relationship between good governance and carbon dioxide emissions: Evidence from developing economies. Journal of Economic Development, 37(1), 77.

[18]  Halkos, G. E., & Tzeremes, N. G. (2013). Carbon dioxide emissions and governance: A nonparametric analysis for the G-20. Energy Economics, 40, 110-118.

[19]  Jalalian, k., & Pajohian, j. (2009). Effect of green taxes and good governance on environment. Journal of Financial Economics, 3(7), 37-55.

[20]  Joskow, P. L., & Schmalensee, R. J. T. j. o. l. (1998). The political economy of market-based environmental policy: the US acid rain program. The journal of law economics Letters, 41(1), 37-84.

[21]  khani, f., & hoshmand, m. (2018). Evaluation of the Financial Development Effect on Environmental Pollution of the selected Petroleum Exporting Countries with the Emphasis on Good Governance. FINANCIAL MONETARY ECONOMICS, 25(16), 133-158. (In persian).

[22]  Koenker, R. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74-89.

[23]  Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica: journal of the Econometric Society, 33-50.

[24]  Lamarche, C. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157(2), 396-408.

[25]  Lancaster, T. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391-413.

[26]  Lashkarizadeh, m., & Ali ashrafi, m. (2011). The Effect of Good Governance on Environmental Quality in Developing Countries. Paper presented at the First International Conference on Tourism Management and Sustainable Development. https://www.civilica.com/Paper-TMSD01-TMSD01_047.html

[27]  Mikayilov, J. I., Galeotti, M., & Hasanov, F. J. (2018). The impact of economic growth on CO2 emissions in Azerbaijan. Journal of Cleaner Production, 197, 1558-1572.

[28]  mohamadzade, y., & ghahramani, h. (2017). Good governance role and size of government on environmental performance in selected countries. Journal of Environmental Studies, 43(3), 477-496. (In persian). doi:10.22059/jes.2017.209037.1007248

[29]  Mohammadzadeh Asl, N., Seifi Pour, R., & Mehrabian, A. (2017). The Impact of the Return of Research and Development on Economic Growth (Using Regression Quintiles Model). Scientific Journal Management System, 8(15), 1-14.

[30]  Neyman, J., & Scott, E. L. (1948). Consistent estimates based on partially consistent observations. Econometrica, 16(1), 1-32.

[31]  Olson, M. (1996). Big bills left on the sidewalk: why some nations are rich, and others poor. Journal of Economic Perspectives, 10(2), 3-24.

[32]  Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25(3), 303-325.

[33]  rajabzadeh moghani, n., falahi, m. a., & khodaparast mashhadi, m. (2017). The Impact of Good Governance on the Relationship between Resource Abundance and Financial Development in Oil Countries. FINANCIAL MONETARY ECONOMICS, 24(14), 90-114. (In persian).

[34]  Sarwar Lateef, K. (1991). Comment on “Governance and Development,” by Boeninger. The World Bank Economic Review, 5(suppl_1), 295-298.

[35]  Shahbazi, K., Hekmati Farid, S., & Rezaei, H. (2016). The Effect of Government Size and Good Governance on Energy Consumption Intensity: A Case Study of OPEC Countries Quarterly Journal of Applied Theories of Economics, 2(4), 23-48. (In persian).

[36]  Solakoglu, E. G. (2007). The effect of property rights on the relationship between economic growth and pollution for transition economies. Eastern European Economics, 45(1), 77-94.

[37]  Varoudakis, A., Tiongson, E. R., & Pushak, T. (2007). Public finance, governance, and growth in transition economies: Empirical evidence from 1992-2004: The World Bank.

[38]  Welsch, H. (2004). Corruption, growth, and the environment: a cross-country analysis. Environment Development Economics, 9(5), 663-693.

[39]  Xu, B., & Lin, B. (2016). A quantile regression analysis of China's provincial CO2 emissions: Where does the difference lie? Energy Policy, 98, 328-342.

[40]  Zhang, Y.-J., Jin, Y.-L., Chevallier, J., & Shen, B. (2016). The effect of corruption on carbon dioxide emissions in APEC countries: a panel quantile regression analysis. Technological Forecasting Social Change, 112, 220-227.

[41]  Zhu, H., Duan, L., Guo, Y., & Yu, K. (2016). The effects of FDI, economic growth and energy consumption on carbon emissions in ASEAN-5: evidence from panel quantile regression. Economic Modelling, 58, 237-248.