低碳经济外文翻译(编辑修改稿)

低碳经济外文翻译(编辑修改稿)内容摘要：

). However, recent evaluations of the literature (IPCC, 2020) have shown the increasing convergence of these model categories as each group of modelers adopts the strengths of the alternate approach. There is a long track record of energy models underpinning major energy policy initiatives, producing a large and vibrant research munity and a broad range of energy modeling approaches (Jebara and Iniyan, 2020). Particularly in recent years, energy models have been directly applied by policy makers for longterm decarburization scenarios (IEA, 2020。 Das et al., 2020。 European Commission, 2020), with further academic modeling collaborations directly feeding into the global policy debate on climate change mitigation (Weyant, 2020。 Strachan Neil et al., 2020). Before deriving any particular conclusion from the scenarios presented in this paper, it is important to consider the modeling approach and the way the scenarios have been implemented with E3MG. E3MG being a macroeconometric model of the global economy has the advantage of examining policies at global and at national level, which is more important in cases of international efforts. The 40%, 60% and 80% reduction targets are not realistic options if implemented only by UK because they would not lead to a significant reduction in climate change and because no single country would easily take a decision moving towards such policies on its own. For these reasons we assume that the emissions reduction targets for the UK are implemented as part of international reduction targets. Based on the facts that the Obama USA Administration is mitted to finding solution to climate change issue and the major developing countries are reluctant to adopt such policies in the medium term, a G8 reduction target of 40%, 60% and 80% by 2050 pared to 1990 levels seems to be a more realistic framework. The E3MG model adopts a hybrid approach. The aggregate and disaggregate energy demand is estimated using econometric techniques, allowing for fuel switching for the 12 different fuel types and for the 19 fuel users, while the power sector is simulated using a probabilistic approach which considers the economic, technical, environmental characteristics of the power units but considers also the history. The electric system expansion is modeled by using parameters for the different technologies based on historical data on learning rates, which allows new technologies to gain a share in the market even when their cost is higher than conventional technologies. Moreover the dispatch of the different technologies to meet the electric demand, although using the cost optimization approach paring the peration of the different technologies, takes historical data as its starting point. Both the energy demand system and the energy technology options are implemented so as to model market imperfections which exist in all markets and are not usually considered in the classical cost optimization techniques. These market imperfections, resulting either from sociopolitical factors or from the presence of oligopolies that speculate on the electricity price, cause differentiation in the electricity mix across countries, and lead in many cases to significantly different profiles from those projected from models assuming perfect market conditions. The scenarios are implemented in this framework, allowing the cumulative investment at global level for alternative technologies so their faster peration provides solutions with a more diverse electric mix. It is also important to mention that the emission reduction scenarios are modeled not。