بررسی رابطه عملکردی بین تولید گازهای گلخانه ای CO2 و توسعه اقتصادی با استفاده از روش، مدل مختلط افزودنی
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|14679||2012||10 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 9179 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
|ترجمه تخصصی - سرعت عادی||هر کلمه 90 تومان||14 روز بعد از پرداخت||826,110 تومان|
|ترجمه تخصصی - سرعت فوری||هر کلمه 180 تومان||7 روز بعد از پرداخت||1,652,220 تومان|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Economic Modelling, Volume 29, Issue 4, July 2012, Pages 1328–1337
Researchers have suggested that the relationship between the emission of carbon dioxide per capita and the real gross domestic product per capita follows an inverted-U-shaped (so-called environmental Kuznets) curve. Studies have generally used polynomial regression (quadratic or cubic form) to investigate this relationship. It has been recognised that polynomials are not that flexible and that, by choosing the degree of the polynomial, researchers make a priori assumptions. In this paper, we investigate the environmental Kuznets curve hypothesis using a flexible approach from additive mixed models. Such models are well-suited to handle nonlinear covariate effects flexibly and to simultaneously deal with temporal error structure. We consider the following countries: Australia, Austria, Canada, Denmark, Finland, France, Italy, Spain and Switzerland. Our results show the existence of the classic environmental Kuznets curve for France and Switzerland, and of a nonlinear (increasing) relationship for Australia, Italy and Spain. For Austria, the evidence reveals a weak N-shaped relationship. New nonlinear shapes are found for Finland (inverted-L-shape relationship), Canada (a special case of the inverted-L-shape relationship), and Denmark (M-shape relationship). Our findings are complemented by the calculation of the elasticity of the carbon dioxide emission per capita as a percentage of each level of real gross domestic product per capita.
Carbon dioxide (CO2) is one of the most abundant greenhouse gases in the earth's atmosphere. This is one of the main reasons why, during the last two decades, a number of studies have appeared explaining how the excessive emission of CO2 from fossil fuels is contributing to important climatic and environmental changes, such as the increase in average global temperatures and sea levels (e.g., Davis et al., 2010, Lacis et al., 2010 and Magnus et al., 2011). Some scholars have shown that the change in the global mean temperature due to CO2 emissions is irreversible on human timescales (e.g., Eby et al., 2009 and Frolicher and Joos, 2010), and Gillett et al. (2011) predicted that changes in temperature and precipitation will continue to worsen for many centuries after a complete cessation of CO2 emissions. The results of these studies sound like an ‘alarm bell’. Thus, economists and policy-makers should pursue strategies based on a path of innovation and ‘green growth’ (with a resource-efficient and low-carbon economy) to preserve the earth's ecosystems (e.g., Parson and Keith, 1998). In this regard, for example, the EU Heads of State and Government set three key targets to be met by 2020, known as the ‘20-20-20’ targets: ‘(a) a reduction in European Union (EU) greenhouse gas emissions of at least 20% below 1990 levels, (b) 20% of EU energy consumption to come from renewable resources, (c) a 20% reduction in primary energy use compared with projected levels, to be achieved by improving energy efficiency’ ( European Commission, 2008 and European Commission, 2010).
نتیجه گیری انگلیسی
In this study, we investigated the existence of an EKC for CO2 emissions for each of the following countries: Australia, Austria, Canada, Denmark, Finland, France, Italy, Spain and Switzerland. We proposed a flexible model specification from the class of the AMMs as opposed to the classical polynomial regression. The main advantages of using an AMM approach can be summarised as follows: we did not impose any structure on the model specification (e.g., linear, quadratic or cubic); we used a flexible approach employing penalised regression splines with automatic smoothing parameter selection; we accounted for the temporal autocorrelation in the residuals during the model fitting process. The RLRT test, employed to determine whether the use of the proposed approach was supported by the data, suggested that the classic polynomial regression approach is in fact not adequate for the study considered here.