GTAP- Power Database: Version 10

Chepeliev, Maksym

doi:10.21642/jgea.050203af

Cited by 30 publications

(27 citation statements)

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“…The core economic dataset used to calibrate the C-PLAN model is Version 10 of the GTAP-Power Database (Peters, 2016;Chepeliev, 2020). This database extends the standard GTAP Database (Aguiar et al, 2016;Aguiar et al, 2019) to represent electricity generation and transmission and distribution in greater detail and provides a snapshot of the global economy in 2014 (and some previous years).…”

Section: Model Calibration 41 Economic Datamentioning

confidence: 99%

The Climate PoLicy ANalysis (C-PLAN) Model, Version 1.0

Winchester¹,

White²

2022

Energy Economics

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Section: Model Calibration 41 Economic Datamentioning

confidence: 99%

The Climate PoLicy ANalysis (C-PLAN) Model, Version 1.0

Winchester¹,

White²

2022

Energy Economics

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“…The reference data (quantities, prices, and exogenous elasticities) specify the free parameters of the model's functional forms. In this paper, the reference data are based on the GTAP 10 Power database with detailed accounts of regional production, consumption, bilateral trade flows, energy uses, and CO 2 emissions for the year 2014 [27,28]. The main elasticity values, such as elasticities between energy inputs and non-energy inputs, elasticities between production factors (labor, capital, and resources), the elasticities in international trade (so-called Armington elasticities), and the supply elasticities of fossil fuels (coal, gas, and oil), are presented in Table 1.…”

Section: 𝑲𝑳mentioning

confidence: 99%

“…As a comparison of the selected regions in the benchmark, Table 3 shows CO 2 emission (MtCO 2 ), GDP (B$), emission intensity (MtCO 2 /B$), and percentage of emission by EITE and NEIT sectors in 2014 using the GTAP 10 Power database [27,28]. The last two rows in Table 3 As shown in Table 3, the EU regions vary in the percentage of emission by sector.…”

Section: Regions and Sectorsmentioning

confidence: 99%

“…The underlying CGE model that Khabbazan and von Hirschhausen [11] is based upon has also been used for the model intercomparison studies in Böhringer et al [6] and Akın-Olçum et al [4]. Using the GTAP10 Power data set [27,28], the EU region is further disaggregated into nine regions. The regional disaggregation of the EU is similar to that of Winkler et al [24], except that Ireland is disaggregated from Great Britain-since 2021, the UK has not been part of the EU ETS anymore.…”

Section: Introductionmentioning

confidence: 99%

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The EU’s Gain (Loss) from More Emission Trading Flexibility—A CGE Analysis with Parallel Emission Trading Systems

Khabbazan

2022

Journal of Open Innovation: Technology, Market, and Complexity

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The EU has established the world’s first cross-border emission-trading systems (ETS) for greenhouse gas (GHG) emissions, currently covering aviation, emission-intensive sectors, and electricity (EITE). The EU Commission has offered to apply emissions trading in new sectors where emissions from maritime transport will be incorporated into the current EU ETS, while a separated emissions trading system will cover emissions from road transport and the building sector. This paper employs a multi-regional multi-sectoral computable general equilibrium (CGE) model with two simultaneous international emission permit markets. After examining the abatement costs for the EU regions, various policy scenarios are implemented to study the welfare effects of forming an ETS covering the sectors other than EITE (NEIT) and its linking with the EITE sectors under two different baselines and four emission reduction targets. The results provide several important insights: (i) Marginal abatement costs in Germany and the Eastern European Union region (EEU) are significantly lower than in the rest of the EU regions. (ii) The carbon price in the emission permit market covering NEIT is significantly higher than the carbon price in the emission permit market covering EITE. (iii) Germany and EEU appear as notable suppliers of emission permits in both markets. (iv) There is a significant aggregate welfare gain under the scenario in which the ETS covering NEIT co-exists parallel with the ETS covering EITE. (v) The aggregate welfare in the EU under the full integration of EITE and NEIT may fall below its value under the scenario with two parallel emission permit markets.

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“…They found that the supply chain losses were dependent on the number of countries imposing travel restrictions, while a longer containment that might control the epidemic could impose smaller losses [ 41 ]. This study built the global supply chain network using the Global Trade Analysis Project (GTAP) database [ 42 ], which is subject to a subscription fee. Maliszewska et al also used GTAP data and previous episodes of global epidemics to simulate the impact of the COVID-19 pandemic on gross domestic product and trade, and drew similar conclusions [ 43 ].…”

Section: Assessing the Economic Trade And Supply Chain Impactmentioning

confidence: 99%

Data science approaches to confronting the COVID-19 pandemic: a narrative review

Zhang

Gao

et al. 2021

Phil. Trans. R. Soc. A.

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During the COVID-19 pandemic, more than ever, data science has become a powerful weapon in combating an infectious disease epidemic and arguably any future infectious disease epidemic. Computer scientists, data scientists, physicists and mathematicians have joined public health professionals and virologists to confront the largest pandemic in the century by capitalizing on the large-scale ‘big data’ generated and harnessed for combating the COVID-19 pandemic. In this paper, we review the newly born data science approaches to confronting COVID-19, including the estimation of epidemiological parameters, digital contact tracing, diagnosis, policy-making, resource allocation, risk assessment, mental health surveillance, social media analytics, drug repurposing and drug development. We compare the new approaches with conventional epidemiological studies, discuss lessons we learned from the COVID-19 pandemic, and highlight opportunities and challenges of data science approaches to confronting future infectious disease epidemics. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

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GTAP- Power Database: Version 10

Cited by 30 publications

References 0 publications

The Climate PoLicy ANalysis (C-PLAN) Model, Version 1.0

The Climate PoLicy ANalysis (C-PLAN) Model, Version 1.0

The EU’s Gain (Loss) from More Emission Trading Flexibility—A CGE Analysis with Parallel Emission Trading Systems

Data science approaches to confronting the COVID-19 pandemic: a narrative review

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