Assessment of the macroeconomic and sectoral effects of higher electricity and gas prices in the EU: A general equilibrium modeling approach

Capros, Pantélis; Paroussos, Leonidas; Charalampidis, Ioannis; Fragkiadakis, Kostas; Karkatsoulis, Panagiotis; Tsani, Stella

doi:10.1016/j.esr.2015.11.002

Cited by 16 publications

(14 citation statements)

References 12 publications

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“…Then, as shown in Appendix A (Tables A1 and A2), the database was aggregated into 6 countries/regions and 19 commodities. In addition, since the share of renewable and nuclear energy for Saudi Arabia was zero in the database, small shares were artificially introduced as recommended by The General Equilibrium Model for Economy-Energy-Environment (GEM-E3) [56]. That allows us to exogenously increase their contribution until the Saudi Arabia's goal in 2030 is met.…”

Section: Gtap-e Modelmentioning

confidence: 99%

“…The first nest consists of the electricity technologies (Technologies) and the distribution and transmission sector (TandD) with a substitution elasticity of zero, as suggested in the literature [56][57][58]. The Technologies nest, with a substitution elasticity of 5 as suggested by the OECD ENV-Linkages Model Version 3 [59], …”

Section: Gtap-e Modelmentioning

confidence: 99%

“…qf is demand for inputs into non-coal energy sub-production af is sector/region specific rate of factor i augmenting technology change ELFNCOAL is elasticity of substitution in non-coal energy sub-production pf is price of non-coal energy An important modification for the GTAP-E model, the addition of two new nests, was made to enable the use of renewable and nuclear technologies as shown in Figure 2. The first nest consists of the electricity technologies (Technologies) and the distribution and transmission sector (TandD) with a substitution elasticity of zero, as suggested in the literature [56][57][58]. The Technologies nest, with a substitution elasticity of 5 as suggested by the OECD ENV-Linkages Model Version 3 [59], includes eight new electricity producing technologies (elyoil, elygas, elycoal, wind, nuclear, solar, hydro, and elyother).…”

Section: Gtap-e Modelmentioning

confidence: 99%

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Ex-Ante Analysis of Economic, Social and Environmental Impacts of Large-Scale Renewable and Nuclear Energy Targets for Global Electricity Generation by 2030

Almutairi

Durand‐Morat

2018

Sustainability

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This study assesses the economic, social and environmental impacts of renewable and nuclear energy targets for global electricity generation by 2030. It examines different regions, as they might experience different impacts depending on the structures of their economies and their local natural resources, to understand the impact of these targets on their economics and well-being of their people. These regions are: Saudi Arabia, the United States (US), China, India, Europe and Rest of World (ROW). A well-known Computable General Equilibrium (CGE) model, the Global Trade Analysis Project (GTAP), is modified and used to predict global economic shifts that would be triggered by two scenarios. The business as usual (BAU) scenario assumes that the current electricity mix remains unchanged until 2030. The Renewable and Nuclear Energy (RNE) scenario is based on the International Energy Outlook (IEO) 2016 prediction. The analysis shows that the GDP value of all regions, except India, is affected negatively. The study shows a loss of 4.45 million jobs worldwide in the RNE compared to the BAU. Finally, the implementation of planned renewable and nuclear energy slightly benefits the environment but not enough to mitigate rise in global temperature.

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Section: Gtap-e Modelmentioning

confidence: 99%

Section: Gtap-e Modelmentioning

confidence: 99%

Section: Gtap-e Modelmentioning

confidence: 99%

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Ex-Ante Analysis of Economic, Social and Environmental Impacts of Large-Scale Renewable and Nuclear Energy Targets for Global Electricity Generation by 2030

Almutairi

Durand‐Morat

2018

Sustainability

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“…The macroeconomic marginal abatement cost curves in this paper are derived from simulations with the computable general equilibrium (CGE) model GEM-E3, which is extensively described in Capros et al (2013) and recently applied to assess international climate policy in Vandyck et al (2016). The simulations impose a range of global carbon prices uniformly across the regions up to the year 2030.…”

Section: Marginal Abatement Cost Curvesmentioning

confidence: 99%

Equity, Emissions Allowance Trading and the Paris Agreement on Climate Change

et al. 2017

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The Conference of the Parties to the UN Framework Convention on Climate Change (COP21), held in Paris in December 2015, resulted in voluntary greenhouse gas (GHG) reduction pledges, independent of each other, by 195 countries. The purpose of this paper is to analyze the equity implications of this "bottom-up" approach to climate change negotiations in two major ways. First we analyze the GHG reduction targets specified in the COP21 agreement prior to any emissions trading in terms of the Gini Coefficient equity metric. We also compare the pledges with specific equity principles proposed by developing countries for many years, such as the Egalitarian and Ability to Pay equity. Second, we analyze the equity outcomes after emissions trading takes place for a sample of countries/regions. We find that international policy coordination through a system of tradable GHG emissions allowances can greatly lower the cost to all participants of reducing atmospheric concentrations of greenhouse gases. An emissions trading system involving all countries and regions that made unconditional pledges at COP21 could reduce total GHG mitigation costs from an estimated $1.71 trillion to $0.4 trillion (in 2015 dollars), a savings of 77%. The ensuing allowance sales revenues would greatly enhance the capability of lower-income countries to meet their COP21 pledges. Moreover, the cost-savings in high-income countries would facilitate contributions to the promised fund of $100 billion per year to assist climate policy implementation in low-income countries.

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“…Within the first group, examples are given by the MIT-EPPA model (Paltsev et al, 2005) and the DICE and RICE model family (Nordhaus, 2012). The GEM-E3 model of the European Commission (Capros et al, 2013), the related GEMINI-E3 model (Bernard and Vielle, 2008), and the PACE modeling framework ) are examples for energy-related multi-regional models. Finally, the GENESwIS for Switzerland (Vöhringer, 2012) and the MIT U.S.…”

Section: Top-down Cge Modelsmentioning

confidence: 99%

Understanding the Human Factor of the Energy Transition: Mechanisms Underlying Energy-Relevant Decisions and Behaviors

Sander¹,

Brosch²,

Patel³

2016

Frontiers Research Topics

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Assessment of the macroeconomic and sectoral effects of higher electricity and gas prices in the EU: A general equilibrium modeling approach

Cited by 16 publications

References 12 publications

Ex-Ante Analysis of Economic, Social and Environmental Impacts of Large-Scale Renewable and Nuclear Energy Targets for Global Electricity Generation by 2030

Ex-Ante Analysis of Economic, Social and Environmental Impacts of Large-Scale Renewable and Nuclear Energy Targets for Global Electricity Generation by 2030

Equity, Emissions Allowance Trading and the Paris Agreement on Climate Change

Understanding the Human Factor of the Energy Transition: Mechanisms Underlying Energy-Relevant Decisions and Behaviors

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