Environment Kuznets curve for CO2 emissions: A cointegration analysis for China

Abstract

This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975-2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO2 emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO2 emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO2 emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO2 emissions. © 2009 Elsevier Ltd. All rights reserved

Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis

“…In addition, the panel Granger causality analysis revealed bidirectional long-run causalities running: from land urbanization to economic growth and CO 2 emissions; and from economic growth to emissions. The heterogeneous panel Granger causality testing further verified the existence of unidirectional short-term causalities running from economic growth to urbanization and from urbanization (or economic growth) to CO 2 emissions in China, which are in agreement with previous studies (Jalil and Mahmud, 2009;Wang et al, 2011). At the regional scale, there exist only unidirectional short-term causal nexus running from land urbanization to CO 2 emissions and from economic growth to emissions in the eastern and central regions.…”

“…previous studies (Wang et al, 2011;Wang et al, 2012a;Du et al, 2012). In addition to the differences as summarized in Table 10, a major difference is that the models we employed are different from those used by Jalil and Mahmud (2009), which may have an impact on the validity of EKC hypothesis. The quadratic model in the regression equations was used in the study by Jalil and Mahmud (2009).…”

“…In addition to the differences as summarized in Table 10, a major difference is that the models we employed are different from those used by Jalil and Mahmud (2009), which may have an impact on the validity of EKC hypothesis. The quadratic model in the regression equations was used in the study by Jalil and Mahmud (2009). More importantly, the studies by Wang et al (2012a), and Jalil and Mahmud (2009) were based on the time series data while our study performed a panel data analysis using the China's provincial data.…”

“…The quadratic model in the regression equations was used in the study by Jalil and Mahmud (2009). More importantly, the studies by Wang et al (2012a), and Jalil and Mahmud (2009) were based on the time series data while our study performed a panel data analysis using the China's provincial data. It is generally acknowledged that panel data models have several major advantages over conventional cross-sectional or time series data models (Wang et al, 2014).…”

The relationship between economic growth, energy consumption, and CO2 emissions: Empirical evidence from China

“…Using error-correction-based Granger causality models, Acaravci and Ozturk (2010) found a one-way causal relationship to exist between economic growth and energy consumption. The same result was arrived at by Jalil and Mahmud (2009) in their study of China. Results of the study taken by Pao and Tsai (2010) indicated bidirectional strong causality between energy consumption-emissions, and bidirectional long-run causality between energy consumption-output, along with unidirectional causalities between emissions-output and energy consumptionoutput (both strong and short-run, respectively.).…”