662 Journal of Reviews on Global Economics, 2019, 8, 662-671 E-ISSN: 1929-7092/19 © 2019 Lifescience Global Does Education Reduce CO2 Emmisions? Empirical Evidence of The Environmental Kuznets Curve in Indonesia Rodhiah Umaroh * Faculty of Economics and Bussines, Universitas Gadjah Mada (UGM), Special Region of Yogyakarta, Indonesia Abstract: The purpose of this study is to analyze whether education has a role in energy use in society using the Environmental Kuznets Curve (EKC) hypothesis during the period 1972-2016 in Indonesia. The paper applied the Autoregressive Distrubuted Lag (ARDL) Bound Test approach to identify co-integration relationships among variables in the model. The results confirmed the evidence that education initially increased CO2 emmisions and at some point education reduced co2 in the short run but not in the long run. In addition, i also found conclusive evidence to support the Inverted U-shaped EKC hypothesis of the relationship between GDP per capita and environmental degradation. The stability test has conducted in estimated model and the result indicated that estimated model is stable over time. Keywords: CO2 Emmision, Environmental Kuznets Curve, Education. 1. INTRODUCTION Study on the relationship between per capita income and environmental degradation has been undertaken by many researchers (Baek & Choi, 2017; Dinda, 2004; Sehar & Khan, 2013; Shahbaz, Dube, Ozturk, & Jalil, 2015) who modeled through the Kuznets Environment Curve hypothesis (EKC) which was first introduced by Grossman & Krueger (1991) on the study of potential impact of NAFTA. According to the Environmental Kuznets Curve hypothesis in the early stages of economic growth, environmental degradation and pollution are increasing but at certain per capita income levels the trend is reversing. This indicates that the environmental impact indicator is an inverted U-shaped function of per capita income (Stern, 2003). Increased environmental degradation resulting from income inequality and then declines in line with development outcomes. The environmental indicators in EKC are modeled on the logarithmic quadratic revenue function on environmental hazard emissions although many researchers add new variables in the model specification to improve estimation results such as urbanization and income inequality ( Bond & Farzin, 2004; Shahbaz et al., 2015). Not only economic indicators are used but human capital indicators are also considered as environmental quality is also determined from the quality of human resources. Education is one of the most widely used human capital indicators despite the issue of endogenity *Address correspondence to this author at the Faculty of Economics and Bussines, Universitas Gadjah Mada (UGM), Special Region of Yogyakarta, Indonesia; E-mail: dhiah.basuki@gmail.com between education and income (Gangadharan & Rebecca, 2001). However, the limited number of studies that take account of education cannot find consistent evidence (Balaguer & Cantavella, 2018). Research conducted by Bond & Farzin (2004) found out that education has a negative impact on environmental quality. While some studies have found no effect of education on pollution production (Williamson, 2017). This study focuses on the importance of education on environmental degradation shown by CO2 emissions. This is based on the assumption that the energy used in the community is determined by the quality of the citizen. As the level of education of citizen increases, it is believed to increase the level of environmental awareness through individual performance and policy makers so that the harmful emissions can be reduced. In addition, a high level of education can create more sophisticated and better technology in production output and emissions (Williamson, 2017). Therefore, based on the Environmental Kuznets Curve hypothesis it is believed that educational enhancement, ceteris paribus, will shift the downward curve due to a decrease in pollution. The purpose of this study is to analyze the role of education on environmental quality in Indonesia in 1972-2016. Other variables used are trade openness, population and industrial value added. The research method used is Autoregressive Distributed Lag (ARDL) Bound Testing which can estimate short and long term relationship in estimated model. This research is structured as follows: in section 2 encompassing the literature review, section 3 is a research methodology and in sections 4 and 5 includes results and conclusions.