Gains from emission trading under multiple stabilization targets and technological constraints

Fujimori, Shinichiro; Masui, Toshihiko; Matsuoka, Yuzuru

doi:10.1016/j.eneco.2014.12.011

Cited by 88 publications

(25 citation statements)

References 27 publications

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“…We used the Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model for this study, which has been widely used in global assessments of climate change mitigation and adaptation policies [10][11][12][13][14][15]. It is a recursive-dynamic general equilibrium model.…”

Section: Modelmentioning

confidence: 99%

Low-Carbon Energy Development in Indonesia in Alignment with Intended Nationally Determined Contribution (INDC) by 2030

et al. 2017

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This study analyzed the role of low-carbon energy technologies in reducing the greenhouse gas emissions of Indonesia's energy sector by 2030. The aim of this study was to provide insights into the Indonesian government's approach to developing a strategy and plan for mitigating emissions and achieving Indonesia's emission reduction targets by 2030, as pledged in the country's Intended Nationally Determined Contribution. The Asia-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model was used to quantify three scenarios that had the same socioeconomic assumptions: baseline, countermeasure (CM)1, and CM2, which had a higher emission reduction target than that of CM1. Results of the study showed that an Indonesian low-carbon energy system could be achieved with two pillars, namely, energy efficiency measures and deployment of less carbon-intensive energy systems (i.e., the use of renewable energy in the power and transport sectors, and the use of natural gas in the power sector and in transport). Emission reductions would also be satisfied through the electrification of end-user consumption where the electricity supply becomes decarbonized by deploying renewables for power generation. Under CM1, Indonesia could achieve a 15.5% emission reduction target (compared to the baseline scenario). This reduction could be achieved using efficiency measures that reduce final energy demand by 4%; This would require the deployment of geothermal power plants at a rate six times greater than the baseline scenario and four times the use of hydropower than that used in the baseline scenario. Greater carbon reductions (CM2; i.e., a 27% reduction) could be achieved with similar measures to CM1 but with more intensive penetration. Final energy demand would need to be cut by 13%, deployment of geothermal power plants would need to be seven times greater than at baseline, and hydropower use would need to be five times greater than the baseline case. Carbon prices under CM1 and CM2 were US$16 and US$63 (2005)/tCO 2 , respectively. The mitigation scenarios for 2030 both had a small positive effect on gross domestic product (GDP) compared to the baseline scenario (0.6% and 0.3% for CM1 and CM2, respectively). This is mainly due to the combination of two assumptions. The first is that there would be a great increase in coal-fired power in the baseline scenario. The other assumption is that there is low productivity in coal-related industries. Eventually, when factors such as capital and labor shift from coal-related industries to other low-carbon-emitting sectors in the CM cases are put in place, the total productivity of the economy would offset low-carbon investment.

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Section: Modelmentioning

confidence: 99%

Low-Carbon Energy Development in Indonesia in Alignment with Intended Nationally Determined Contribution (INDC) by 2030

et al. 2017

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“…Rather than making the model dynamic in a recursive dynamic manner as many literatures [21,22,[28][29][30], we choose a simple way to decompose the long-term target into a single-year target which could be modeled in a static model. The main reason is that it is difficult to decide the growth of related parameters across different regions making a multi-regional model dynamic, including capital stock, labor force, energy efficiency improvement, and the total factor productivity in different regions.…”

Section: Description Of the Indc Targets In Ce 3 Msmentioning

confidence: 99%

“…The ETS modelling in the CGE model is usually handled by implementing an endogenous carbon tax or treating allowance as a special input factor [21][22][23][24][28][29][30][42][43][44][45][46]. Although the impacts of implementing an ETS or an endogenous carbon tax on economies are the same theoretically, emissions trading is a decision optimization process for each trader under the principle of cost minimization.…”

Section: Modelling Approach For Etsmentioning

confidence: 99%

“…They further suggested that the impact of RES policy on emission mitigation should be considered when defining the long-term ETS targets, to avoid weakening the CO 2 price signal. Some studies applied the partial or computable general equilibrium model to evaluate the socio-economic impacts of the ETS or the combination of ETS and RES policies, as seen in Fujimori et al [21,22], Zhang et al [23], Hübler et al [24], Morris [25], Böhringer and Behrens [26], and Del Rio [27]. Cheng et al [28], Wang et al [29], and Wu et al [30] each assessed the ETS in Guangdong and Shanghai from a regional perspective, using a two-region CGE model.…”

Section: Introductionmentioning

confidence: 99%

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How Can China Achieve Its Nationally Determined Contribution Targets Combining Emissions Trading Scheme and Renewable Energy Policies?

Fan

Xia

2017

Energies

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Abstract:The adoption of emissions trading scheme (ETS) and renewable energy sources (RES) policies have been essential to achieving China's national targets for reducing CO 2 emissions and developing non-fossil energy sources. The combination of ETS and RES policies raises an important issue: What is the effect of combining ETS and RES policies on the existing carbon market and economy? Focusing on the design of the nationwide carbon market, this paper uses a multi-regional computable general equilibrium (CGE) model to analyze the economic impacts of ETS policy when combined with RES policies in China. The results show that China's annual ETS emissions cap should decrease by 0.3% to maintain stable CO 2 prices and achieve the targets in China's intended nationally determined contribution (INDC). It is estimated that the CO 2 price on the nationwide carbon market would decrease by 11-64% when the renewable energy subsidy rate increases from 20 to 100%, and the total trading volume would decrease by 3-25%. The results also show that the combination of an ETS and a feed-in tariff (FIT) results in greater GDP cost and welfare loss in all Chinese regions, increasing the total social cost by 0.01-0.06%.

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“…In these models, the travel demand is computed with linear or quadratic correlations between traffic activities and GDP per capita, or an exogenously determined modal share, and therefore it is not suitable to incorporate behavioral and technological factors to assess their influences on passenger transport and mitigation potential. Compared with the bottom-up type model, in top-down integrated assessment models such as AIM/Computable General Equilibrium (AIM/CGE), which has been widely used for climate change mitigation and impact assessment [22][23][24][25][26][27][28], travel demand is dynamically estimated with relative prices and the elasticity of substitution. However, AIM/CGE represents transport at a highly aggregated level, but technological details and behavioral determinants such as travel cost, travel time, modal split, and preference are not incorporated.…”

Section: Introductionmentioning

confidence: 99%

Contribution of the transport sector to climate change mitigation: Insights from a global passenger transport model coupled with a computable general equilibrium model

et al. 2018

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A passenger transport model, Asia-pacific Integrated Model (AIM)/Transport, incorporating travelers' mode of choice and transport technological details was developed in this study. This AIM/Transport was coupled with the AIM/Computable General Equilibrium (AIM/CGE) to capture interactive mechanisms between the transport sector, energy consumption, greenhouse gas (GHG) emissions, and the macro-economy. This paper presents the model structure and mathematical formulation of AIM/Transport, and explains how it was integrated with the CGE model by an iterative algorithm, taking into consideration the feedback between AIM/Transport and AIM/CGE. A numerical simulation proved that the integration of AIM/CGE and AIM/Transport can achieve a convergence after 13 iterations. A business-as-usual (BaU) scenario and a mitigation scenario were created to test the robustness of the model integration and how the mitigation potential and cost would be modified by coupling AIM/Transport. The key finding was that the carbon price and mitigation cost were modified with the coupled CGE-Transport model.

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Gains from emission trading under multiple stabilization targets and technological constraints

Cited by 88 publications

References 27 publications

Low-Carbon Energy Development in Indonesia in Alignment with Intended Nationally Determined Contribution (INDC) by 2030

Low-Carbon Energy Development in Indonesia in Alignment with Intended Nationally Determined Contribution (INDC) by 2030

How Can China Achieve Its Nationally Determined Contribution Targets Combining Emissions Trading Scheme and Renewable Energy Policies?

Contribution of the transport sector to climate change mitigation: Insights from a global passenger transport model coupled with a computable general equilibrium model

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