Evaluating the role of renewable energy, economic growth and agriculture on CO₂ emission in E7 countries

ABSTRACT

This study examines the dynamic links between per capita CO₂ emission, economic growth, agricultural value added, renewable and non-renewable energy consumption and investigates the existence of Environmental Kuznets Curve (EKC) hypothesis for a panel of E7 countries spanning the period 1990–2014. The estimates indicate that there is a positive relationship between CO₂ emissions and real GDP, non-renewable energy consumption and agricultural value added in the long run, whereas a negative relationship is represented between CO₂ emissions and square of real GDP and renewable energy consumption. The results of long-run estimates support the inverted U-shape EKC in these selected countries. Regarding the Granger causality analysis, bi-directional Granger causality exists between non-renewable energy consumption and CO₂ emissions in the long run. In regards policy implications and recommendations, E7 countries should keep on increasing the share of renewable energy for the sake of growth purposes in the agricultural sector, thereby reducing fossil energy consumption for environmental improvements.

KEYWORDS:

• Renewable energy
• agricultural value added
• carbon dioxide emissions
• panel data analysis

JEL CLASSIFICATION:

• C33
• Q20
• Q42
• Q43
• Q54

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The period of this study is selected according to the data availability.

2 The data are available at the dataset website https://data.worldbank.org/.

3 According to the World Energy Outlook 2018 (WEO 2018), the share of global energy demand from developing countries increases from 64% to 70%.

4 The Emissions Database for Global Atmospheric Research reports that E7 countries are responsible for 45.95% of fossil carbon emissions in 2017 (JRC 2018).

5 Before testing the stationarity properties of each variable by employing first-generation unit root tests, namely Levin, Lin, and Chu (2002), Im, Pesaran, and Shin (2003), Fisher-ADF and Fisher-PP tests (Maddala and Wu 1999, Choi 2001), Pesaran’s (2004) CD test, Friedman (1937) LM statistics and Frees’ (1995) Q statistic have to be applied to detect whether each variable is cross-sectionally independent or not.