The Impact of Renewable Energy Sources on Economic Growth and CO2 Emissions - a SVAR approach

Abstract

We analyze how an increasing share of Renewable Energy Sources on Electricity generation (RES-E) affects Gross Domestic Product (GDP) and Carbon Dioxide (CO2) emissions using a 3 variable Structural Vector Autoregressive (SVAR) methodology. We used a sample of four countries with different levels of economic development and social and economic structures but a common effort of investment in RES in the last decades. The period considered was 1960 to 2004. The existence of unit roots was tested to infer the stationarity of the variables. Through the impulse response functions (IRF), the SVAR estimation showed that, for all countries in the sample, except for the USA, the increasing RES-E share had economic costs in terms of GDP per capita. There was also an evident decrease of CO2 emissions per capita. The variance decomposition showed that a significant part of the forecast error variance of GDP per capita and a relatively smaller part of the forecast error variance of CO2 per capita were explained by the share of RES-E.

Assessing the Future of Renewable Energy Consumption for United Kingdom, Turkey and Nigeria

“…This result is in line with a priori expectations. This outcome is also consistent with the literature on the relationship between economic growth and RE consumption [Apergis, Payne, 2014Silva et al, 2012;Sadorsky, 2011].…”

“…He pointed out that increases in income and CO2 emissions are the major drivers for increases in RE consumption in the long run. Silva et al [Silva et al, 2012] analyzed how an increasing share of RE sources in power generation affects economic growth and carbon emissions using structural VAR approach over the period 1960 to 2004 for Denmark, Portugal, Spain, and the United States. Their findings show that economic costs emerged with the increase of RE in terms of GDP per capita and the decrease of CO2 emissions per capita was ensured.…”

“…RE is the electricity generated from wind, solar, geothermal, biomass, hydropower, tidal, and wave sources. This paper uses electricity price, as opposed to oil price (the most pervasive energy source), because of the strong penetration of the RE sources and the fact that the electricity price has added significance in the energy balances of most countries [Silva et al, 2012]. GDP per capita comes from the World Bank database and is measured in current US dollars.…”

“…Therefore, some steps must be taken to ensure that the VAR is stable while the IRFs are used to interpret the results [Sadorsky, 2011]. The IRF indicates how a residual shock to one of the innovations in the model influences the contemporaneous and future values of all endogenous variables [Silva et al, 2012]. Significance was determined at the 95% confidence intervals.…”

Heterogenous Energy Consumption Behavior by Firm Size: Evidence from Korean Environmental Regulations

“…However, Magazzino [16] has shown that the energy sector has negligible effects on GHG emissions. Silva et al [21] and Maslyuk and Dharmarathna [22] have found that renewable energy shares in electricity generation increase GHG emissions; in contrast, Shabbir et al [23] and Yu and Kim [24] have concluded that the rise in renewable energy consumption could decrease GHG emission levels by substituting non-renewable energy. However, Menyah and Wolde-Rufael [13] and Tiwari [14] have shown that renewable energy consumption could not contribute to the reduction of GHG emissions.…”