Gains associated with linking the EU and Chinese ETS under different assumptions on restrictions, allowance endowments, and international trade

Winkler, Malte; Peterson, Sonja; Thube, Sneha

doi:10.1016/j.eneco.2021.105630

Cited by 20 publications

(12 citation statements)

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“…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. Then, after forward-calibration of the model based on International Energy Outlook (IEO) [29] and World Energy Outlook (WEO) [30] projections until 2030, marginal abatement cost curves (MACCs) are derived.…”

Section: Introductionmentioning

confidence: 90%

“…For the sake of result tractability and numerical efficiency, the regional disaggregation of the EU in this paper is built on those used in Winkler et al [24] (see Table 2), except that Ireland is disaggregated from Great Britain and is aggregated with Iceland and Liechtenstein, collectively forming ILI. Therefore, the EU is disaggregated into nine regions: Germany; France; BLX (including Belgium, Luxembourg, and The Netherlands); EEU (east European countries); ILI (including Ireland, Iceland, and Liechtenstein); SCA (Scandinavian countries); SEU (south European countries); GBR (Great Britain); REU (the rest of Europe which do not participate in the current EU ETS).…”

Section: Regions and Sectorsmentioning

confidence: 99%

“…The Core EMF36 demonstrates that the results on the EU notably depend on the model in hand [6]. While the Core EMF36 reports on the EU as an aggregate region, there are individual contributions that consider further disaggregating the EU [22][23][24]. Analyzing the impacts of Norway joining the EU ETS, Faehn and Yonezawa [22] model scenarios where two emission trading systems, with one international price each, co-exist.…”

Section: Introductionmentioning

confidence: 99%

“…Their focus is on Norway, a small-open economy, while the rest of the EU is aggregated into one region. Disaggregating the EU into eight regions, Winkler et al [24] look into the circumstances under which ETS linking between the EU and China is most beneficial to the EU or China, but they do not consider two concurrent emission trading systems. Moreover, Kriegler et al [25] states that the EU reliance on coal and natural gas imports can decline more rapidly than oil imports-oil imports pose itself as a bottleneck, where the inefficiencies due to oil phase-out can create more elevated production costs.…”

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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Section: Introductionmentioning

confidence: 90%

Section: Regions and Sectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Although numerous studies have discussed the possible outcome of institutionally linking independent ETSs at a global level (Li et al, 2019 ; Li and Duan, 2021 ; Winkler et al, 2021 ), the real spillover effects are seldom tested, as currently, there is a lack of practices to do so. Earlier empirical studies focusing on the EU ETS (European Union Emission Trading Scheme) case can only test the spillover effects between EU carbon markets and energy markets (Alberola et al, 2008 ; Kim et al, 2010 ; Creti et al, 2012 ; Aatola et al, 2013 ; Reboredo, 2014 ; Yu et al, 2015 ; Zhang and Sun, 2016 ; Chang et al, 2017 , 2018b ; Zhao et al, 2018 ; Ji et al, 2018a ;).…”

Section: Introductionmentioning

confidence: 99%

Spillover effect among independent carbon markets: evidence from China’s carbon markets

Yan

Liang

Wang

2022

Econ Change Restruct

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Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen , Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei , Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design.

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A Dynamic Baseline Calibration Procedure for CGE models

Ziesmer

Jin²,

Thube

et al. 2022

Comput Econ

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Baseline assumptions play a crucial role in conducting consistent quantitative policy assessments for dynamic Computable General Equilibrium (CGE) models. Two essential factors that influence the determination of the baselines are the data sources of projections and the applied calibration methods. We propose a general, Bayesian approach that can be employed to build a baseline for any recursive-dynamic CGE model. We use metamodeling techniques to transform the calibration problem into a tractable optimization problem while simultaneously reducing the computational costs. This transformation allows us to derive the exogenous model parameters that are needed to match the projections. We demonstrate how to apply the approach using a simple CGE and supply the full code. Additionally, we apply our method to a multi-region, multi-sector model and show that calibrated parameters matter as policy implications derived from simulations differ significantly between them.

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Gains associated with linking the EU and Chinese ETS under different assumptions on restrictions, allowance endowments, and international trade

Cited by 20 publications

References 12 publications

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

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

Spillover effect among independent carbon markets: evidence from China’s carbon markets

A Dynamic Baseline Calibration Procedure for CGE models

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