How to achieve the goal of carbon peaking by the energy policy? A simulation using the DCGE model for the case of Shanghai, China

Hu, Haisheng; Zhao, Laijun; Dong, Wanhao

doi:10.1016/j.energy.2023.127947

Cited by 10 publications

(2 citation statements)

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“…In order to simulate the long-term impacts of carbon emission trading and renewable energy policies on the economy, environment, and energy, we have transformed the static CGE model into a dynamic CGE model using a recursive approach [30,46,47]. In general, the neoclassical macro-closure condition is preferred over Keynesian macro-closure and Lewis macro-closure in the long run, which means that all factors of production and goods are clear in the long run.…”

Section: Dynamicsmentioning

confidence: 99%

“…To conduct our analysis, we will employ a computable general equilibrium (CGE) model, which is a widely-used method for evaluating the economic impacts of policy changes at both micro and macro levels [30][31][32]. What sets the CGE model apart from other approaches is its ability to capture the complex interactions and feedback effects between various economic sectors, including energy markets, and measure the distributional impacts across different income groups [33,34].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Carbon Emission Trading on Renewable Energy: A Comparative Analysis Based on the CGE Model

Huang,

Du,

Jin

et al. 2023

Sustainability

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This study examines the effects of carbon emission trading on renewable energy consumption in China. The research applies the CEEEA2.0 model to simulate the economic, energy, and environmental impacts of carbon trading from 2018 to 2030. The CEEEA2.0 model is a recursive dynamic computable general equilibrium model that incorporates multiple households, sectors, and an energy and environment module. Four scenarios are considered: the Business as Usual (BaU) scenario, the Emission Trading Scheme (ETS)-benchmark scenario, and the ETS-strengthened and ETS-enhanced scenarios. The findings reveal that carbon emission trading positively influences electricity consumption, resulting in a higher preference for renewable energy due to reduced price disparities between renewable sources and fossil fuels. Consequently, electricity generation from renewable sources increases in all scenarios compared to the BaU scenario. However, the share of renewable energy is not substantially affected by carbon emission trading due to the complex interplay of factors, including substitution and income effects. The study further highlights that carbon trading significantly reduces coal usage and partially increases the overall proportion of renewable energy. These results underscore the significance of establishing ambitious carbon reduction targets and continual efforts to shift towards clean energy sources.

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Section: Dynamicsmentioning

confidence: 99%