Capturing key energy and emission trends in CGE models: Assessment of Status and Remaining Challenges

IF 2.2 Q2 ECONOMICS Journal of Global Economic Analysis Pub Date : 2020-06-25 DOI:10.21642/JGEA.050106AF
Taran Fæhn, G. Bachner, Robert H. Beach, Jean Château, S. Fujimori, M. Ghosh, M. Hamdi-Chérif, E. Lanzi, S. Paltsev, Toon Vandyck, Bruno S. L. Cunha, Rafael Garaffa, K. Steininger
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引用次数: 15

Abstract

Limiting global warming in line with the goals in the Paris Agreement will require substantial technological and behavioural transformations. This challenge drives many of the current modelling trends. This article undertakes a review of 17 state-of-the-art recursive-dynamic computable general equilibrium (CGE) models and assesses the key methodologies and applied modules they use for representing sectoral energy and emission characteristics and dynamics. The purpose is to provide technical insight into recent advances in the modelling of current and future energy and abatement technologies and how they can be used to make baseline projections and scenarios 20-80 years ahead. Numerical illustrations are provided. In order to represent likely energy system transitions in the decades to come, modern CGE tools have learned from bottom-up studies. Three different approaches to baseline quantification can be distinguished: (a) exploiting bottom-up model characteristics to endogenize responses of technological investment and utilization, (b) relying on external information sources to feed the exogenous parameters and variables of the model, and (c) linking the model with more technology-rich, partial models to obtain bottom-up- and pathway-consistent parameters.
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在CGE模型中捕捉关键的能源和排放趋势:现状和剩余挑战评估
根据《巴黎协定》的目标限制全球变暖需要进行实质性的技术和行为变革。这一挑战推动了当前许多建模趋势。本文对17个最先进的递归动态可计算一般均衡(CGE)模型进行了综述,并评估了它们用于表示部门能源和排放特征和动态的关键方法和应用模块。其目的是提供技术见解,了解当前和未来能源和减排技术建模的最新进展,以及如何使用这些技术来做出20-80年前的基线预测和情景。提供了数字插图。为了代表未来几十年可能发生的能源系统转型,现代CGE工具从自下而上的研究中学习。可以区分三种不同的基线量化方法:(a)利用自下而上的模型特征来内生技术投资和利用的反应,(b)依靠外部信息源来提供模型的外生参数和变量,以及(c)将模型与更丰富的技术联系起来,部分模型,以获得自下而上和路径一致的参数。
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CiteScore
5.60
自引率
12.00%
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