The aim of the study is to investigate the impact of income inequality on environmental quality in Turkey within the Environmental Kuznets Curve framework. In order to observe the short-run and long-run effects of income inequality on environmental quality, an autoregressive distributed lag bounds test on CO2 emission has been employed for the period 1963–2011 of Turkey. The results of the analysis reveal that there is a negative association between CO2 emission level and income inequality, which implies that increasing income inequality reduces environmental degradation in Turkey. Hence, a greater inequality in the society leads to less aggregate consumption in the economy due to lower propensity to emit in the richer households resulting in better environmental quality. The findings confirm an argument in the existing literature, which suggests that for developing countries, until a certain level of development, environmental degradation increases as income inequality in the society decreases. The results also confirm the Environmental Kuznets Curve hypothesis.
BoyceJK.Inequality as a cause of environmental degradation. Ecol Econ1994;
11: 169–178.
2.
JorgensonAKSchorJBHuangX.Income inequality and residential carbon emissions in the United States: a state level analysis, 1997–2012. Ecol Econ2017;
134: 40–48.
3.
RavallionMHeilMJalanJ.Carbon emissions and income inequality. Oxf Econ Pap2000;
52: 651–669.
4.
BorghesiS. Income inequality and the environmental Kuznets curve. In: M. Basili, M. Franzini & A. Vercelli (Eds.), Environment, inequality and collective action. London: Routledge, 2006, pp. 33–51.
5.
BaekJGweisahG.Does income inequality harm the environment? Empirical evidence from the United States. Energy Policy2013;
62: 1434–1437.
6.
JorgensonAKSchorJBKnightKWet al.
Domestic inequality and carbon emissions in comparative perspective.Sociol Forum2016;
31: 770–786.
7.
KasugaHTakayaM.Does inequality affect environmental quality? Evidence from major Japanese cities. J Clean Prod2017;
142: 3689–3701.
8.
HeJZhuangTXieX.Energy consumption, economic development and environmental improvement in China. Energy Environ2014;
25: 1345–1357.
9.
GolleyJMengX.Income inequality and carbon dioxide emissions: the case of Chinese urban households. Energy Econ2012;
34: 1864–1872.
10.
ZhangCZhaoW.Panel estimation for income inequality and CO2 emissions: a regional analysis in China. Appl Energy2014;
136: 382–392.
11.
HaoYChenHZhangQ.Will income inequality affect environmental quality? Analysis based on China’s provincial panel data. Ecol Indic2016;
67: 533–542.
12.
GrunewaldNKlasenSMartínez-ZarzosoIet al. Income inequality and carbon emissions. Courant Research Centre, Poverty, Equity and Growth. Discussion Papers, No. 92, 2011.
13.
GuoL.Cross-country income disparity and its effect on carbon emissions. Chin J Popul Resour Environ2013;
11: 33–50.
14.
GrunewaldNKlasenSMartínez-ZarzosoIet al.
The trade-off between income inequality and carbon dioxide emissions. Ecol Econ2017;
142: 249–256.
15.
UcalM.Energy-saving behavior of Turkish women: a consumer survey on the use of home appliances. Energy Environ2017;
28: 775–807.
16.
MaGAndrews-SpeedPAmd ZhangJ.Chinese consumer attitudes towards energy saving: the case of household electrical appliances in Chongqing. Energy Policy2013;
56: 591–602.
17.
EngleRFGrangerCW.Co-integration and error correction: representation, estimation, and testing. Econometrica1987;
55: 251–276.
JohansenS.Likelihood-based inference in cointegrated vector autoregressive models.
Oxford:
Oxford University Press, 1995.
20.
JohansenSJuseliusK.Maximum likelihood estimation and inference on cointegration – with applications to the demand for money. Oxf Bull Econ Statist1990;
52: 169–210.
21.
PesaranMHShinY.An autoregressive distributed lag modelling approach to cointegration analysis. In: StromS (ed.) Econometrics and economic theory in the 20th century.
Cambridge:
Cambridge University Press, 1999, pp. 371–413.
22.
PesaranMHShinYSmithRJ.Bounds testing approaches to the analysis of level relationships. J Appl Econ2001;
16: 289–326.
23.
PanopoulouEPittisN.A comparison of autoregressive distributed lag and dynamic OLS cointegration estimators in the case of a serially correlated cointegration error. Econom J2004;
7: 585–617.
24.
BaekJKimHS.Is economic growth good or bad for the environment? Empirical evidence from Korea. Energy Econ2013;
36: 744–749.
25.
GrangerCWJ.Some recent developments in a concept of causality. J Econom1988;
39: 199–211.
26.
GrangerCWNewboldP.Spurious regressions in econometrics. J Econom1974;
2: 111–120.
27.
DickeyDAFullerWA.Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc1979;
74: 427–431.
28.
PhillipsPCBPerronP.Testing for a unit root in time series regression. Biometrika1988;
75: 335–346.
29.
ShahbazMKhanSTahirMI.The dynamic links between energy consumption, economic growth, financial development and trade in China: fresh evidence from multivariate framework analysis. Energy Econ2013;
40: 8–21.
30.
PerronP.The great crash, the oil price shock, and the unit root hypothesis. Econometrica1989;
57: 1361–1401.
31.
ZivotEAndrewsDWK.Further evidence on the great crash, the oil-price shock, and the unit-root hypothesis. J Bus Econ Stat2002;
20: 25–44.
