دانلود مقاله ISI انگلیسی شماره 12139
عنوان فارسی مقاله

اثر آستانه از نرخ رشد اقتصادی بر توسعه انرژی های تجدید پذیر از تغییر در قیمت انرژی: شواهدی از کشورهای OECD

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
12139 2009 7 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Threshold effect of the economic growth rate on the renewable energy development from a change in energy price: Evidence from OECD countries
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Energy Policy, Volume 37, Issue 12, December 2009, Pages 5796–5802

کلمات کلیدی
- انرژی های تجدید پذیر - رشد اقتصادی - اثر آستانه
پیش نمایش مقاله
پیش نمایش مقاله اثر آستانه از نرخ رشد اقتصادی بر توسعه انرژی های تجدید پذیر از تغییر در قیمت انرژی: شواهدی از کشورهای OECD

چکیده انگلیسی

This paper uses a panel threshold regression (PTR) model to investigate the influence that energy prices have on renewable energy development under different economic growth rate regimes. The empirical data are obtained from each of the OECD member-countries over the period from 1997 to 2006. We show that there is one threshold in the regression relationship, which is 4.13% of a one-period lag in the annual gross domestic product (GDP) growth rate. The consumer price index (CPI), in so far as it relates to variations in energy, is significantly positively correlated with the contribution of renewables to energy supply in the regime with higher-economic growth, but there is no relationship in the regime with lower economic growth. Therefore, countries characterized by high-economic growth are able to respond to high energy prices with increases in renewable energy use, while countries characterized by low-economic growth countries tend to be unresponsive to energy price changes when they come to their level of renewable energy.

مقدمه انگلیسی

Greenhouse gases, such as carbon dioxide (CO2) emissions, have resulted in a serious global-warming problem that is believed to have caused extreme changes in the world's climate. In addition, the world is dealing with the dual problems of fossil energy exhaustion and the impact of inflation on economic growth. In the historical statistics on energy CPI and crude oil prices, energy CPI grew rapidly (from 137.4 to 159.7 in 2004), while the West Texas Intermediate Cushing Oil spot price rocketed from $32.81 to $56.17.1 In addition, the IEA (2004) reported that a $10 oil price increase would reduce global GDP by 0.5% thereby giving rise to $225 billion in losses over several years. These shocks have seriously affected macroeconomic growth by raising inflation and unemployment.2 Policy-makers have therefore attempted to implement various fiscal and monetary policies around the world to stabilize macroeconomic conditions. In particular, the promotion of renewable energy has become one of the important strategies introduced in order to mitigate CO2 emissions and find substitutes for fossil energy in the transportation and power generation sectors. Moreover, renewable energy can help serve as a substitute for fossil fuels and thereby reduce man-made emissions of greenhouse gases upon meeting the future energy needs of developing countries. On January 23, 2008, the European Commission put forth an integrated proposal for climate action, which includes a directive that sets an overall binding target for the European Union of 20% renewable energy and a 10% minimum target for the share of biofuels in overall gasoline and diesel consumption in the transportation sector by 2020. However, because the utilization of renewable energy resources (e.g., wind power, solar or photovoltaic cells, and biomass) has often given rise to cost disadvantages, their successful performance frequently depends on support schemes. Therefore, most advanced countries have introduced various incentive mechanisms to increase their production and utilization of renewable energy. These mechanisms include feed-in tariffs, renewable portfolio standards, financing preferences, tax credits, and investment and research subsidies. As far as past studies are concerned, investments in renewable energy technologies will result in an increase in GDP. However, the exploitation of massive renewable energy resources will dampen economic activities through the mechanism of the consumer price index, which will be pushed upwards by higher energy prices. Awerbuch and Sauter (2006) argue that the wealth released by avoiding the negative relationship between oil prices and GDP provides sources of deployment of renewable energy. If this statement is true, low-growth countries are incapable of maintaining renewable energy incentive policies when economic circumstances become unfavorable over a long period of time. The statement also implies that high-growth countries can more easily handle the pressure from steep increases in energy prices than can low-growth countries when meeting their obligations to reduce greenhouse gas emissions. From the above discussion, another question arises. What kinds of economic growth measures are necessary to change renewable energy development in the future? It is important for policy-makers to have access to knowledge regarding the critical value of energy development. Our empirical results are helpful in interpreting why policy-makers ignore powerful cost reductions and other benefits associated with renewable energy and efficiency. In its empirical work, this paper adopts the panel threshold regression (PTR) method proposed by Hansen (1999) to examine whether the threshold effect of GDP influences renewable energy supply when countries face greater oil price volatility and higher energy prices. The PTR model is developed for non-dynamic panels with individual-specific fixed-effects, which is advantageous to our work in several ways. First, traditional regression functions do not capture variations between OECD countries in terms of renewable energy policies. In particular, every OECD country is a significantly developed economy in its respective region, and every one exists in a different geographic location with different policy-maker attitudes. Therefore, the PTR model can help us to understand that individual observations can be grouped into classes based on the values of a key variable. Second, we also employ a non-standard asymptotic theory of inference and a bootstrap method, which allow for the construction of confidence intervals in assessing the statistical significance of the threshold effect and the testing of hypotheses. The PTR model illustrates that errors tend to be heteroskedastic. This implies that adopting conventional OLS models may lead to incorrect inferences. Finally, the PTR model's sample can be split and thereby more accurately examine whether OECD countries possess significantly different threshold effects. Thus, the PTR model helps us to effectively understand issues related to the problem of renewable energy policy. The panel dataset is drawn from data for the OECD member-countries,3 and the research period extends from 1997 to 2006. From the estimated results, the overall findings show that: (1) a single threshold effect exists, where the optimal threshold effect value is found to be 4.13% for a one-period lag of annual growth, measured as a percentage of GDP; and (2) the threshold effect segments countries into groups with lower- and higher-growth rates. A complete reversal in the estimated slope coefficient exists in the two regimes, such that the CPI of energy variation is significantly and positively correlated with the contribution of renewables to energy supply in the higher-growth regimes, but is insignificantly and negatively correlated with the contribution of renewables to energy supply in the lower-growth regimes. These results also indicate that the different countries’ economic growth during the previous period plays an important role in reforming energy policy. In other words, because of cost considerations, the low-economic growth countries are less willing to enhance their renewable energy development. Conversely, if the previous economic growth rate reaches 4.13% or more, those countries will have an economic surplus to deploy their renewable energy resources. As a consequence, the high-economic growth countries are able to respond to energy price impacts by changing their use of renewable energy, while the low-economic growth countries cannot. Our findings are also consistent with Awerbuch and Sauter (2006) view that, in high-growth countries, a substitution effect arises from renewable energy use so as to avoid the negative relationship between oil prices and GDP. Less-restricted countries, on the other hand, have the ability to adjust to the impact of abrupt changes in energy prices and to maintain sustainable renewable energy development. Furthermore, if a country's wealth is limited, it may tend to concentrate its resources on other fiscal policies and on decreasing renewable energy consumption in order to mitigate the impact of rising energy prices. The remainder of this paper is organized as follows. The next section reviews previous studies on the relationships among renewable energy, economic growth, and energy prices. Section 3 describes the PTR model's estimation methodology. Section 4 describes how the panel data are constructed. Section 5 presents the empirical results of the estimation, which are robust with respect to the threshold effect. The final section summarizes our empirical findings and enables us to draw conclusions.

نتیجه گیری انگلیسی

The adoption of a renewable energy policy is one important strategy for the mitigation of CO2 emissions and the substitution for fossil energy use in the transportation and power generation sectors. One view that has been put forward is that investment in renewable energy technologies serves to promote macroeconomic growth. An opposing view holds that the exploitation of renewable energy is unfavorable in that it has a depressing effect on economic growth because of productive cost inflation. In the face of such debates, we have employed a PTR model to examine the phenomenon of threshold effects for economic growth that distinguish between energy price impacts and renewable energy development when countries face fluctuations in energy prices. We employed a panel dataset formed across all OECD member-countries over the period from 1997 to 2006. The analytical results are summarized as follows: (1) a single threshold effect was found, with the optimal value for that threshold being found to be 4.13%, based on a one-period lag of the annual GDP growth rate, and (2) the threshold effect is between lower- and higher-growth regimes. The synchronous GDP growth rate did not influence the contribution of renewables to energy supply. This empirical result implies that enhancing the utilization of renewable energy resources does not necessarily accompany economic growth. The CPI of energy variation exhibits a significantly positive relationship with the contribution of renewables to energy supply in the higher-growth regimes, but is insignificant and negative in the lower-growth regimes. The countries with low-economic growth rates in the previous period are found to be less willing to enhance their renewable energy development in order to mitigate the impact of the rise in energy prices if energy prices rise sharply. Conversely, if the previous period's economic growth rate is at least as high as 4.13%, those countries will have an economic surplus to deploy toward renewable energy resources. In conclusion, these countries with high rates of economic growth can respond to energy price impacts by changing their use of renewable energy. By contrast, these countries with low rates of economic growth tend to be unresponsive to energy price changes in the renewable energy use. Then the contribution of renewable energy is price inelastic in low-growth countries. A high-economic growth environment thus provides a buffer against energy price impacts and is beneficial to renewable energy development. We therefore recommend that policy-makers incorporate the level of economic development into their investment decisions regarding renewable energy sources in order to stabilize their economic environments.

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