When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance

Yao, Xilong; Liu, Yang; Qu, Shiyou

doi:10.1016/j.apenergy.2015.04.094

Cited by 54 publications

(13 citation statements)

References 19 publications

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“…Learning curve approach has been incorporated into many energy models to project cost reductions from investment in new energy generation or conversion [10]. Yao et al [11] used a learning curve approach while investigating financing options to support grid parity for wind electricity in China. Their results showed that a learning rate of 8.9% would be necessary to make wind electricity competitive.…”

Section: Introductionmentioning

confidence: 99%

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

2017

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a b s t r a c tMarket penetration of energy-efficient technologies can be estimated using energy optimization models that minimize cost; however, such models typically estimate the minimum cost of optimal pathways under a certain set of non-dynamic assumptions, so technology penetrations determined for the longterm do not fully respond to changing circumstances or costs. In this study, investment costs of energy-efficient technologies are modeled dynamically in the Industrial Sector Energy-Efficiency Model (ISEEM) using a technological learning formula. Results from 24 energy-efficient technologies -14 existing, 10 emerging -selected from the United States (U.S.) iron and steel sector show that when technological learning is incorporated into the model, total energy consumption of this sector is expected to decrease by 13% (180 PJ) in 2050 compared to energy consumption in a non-learning scenario. Average energy intensity of the steel production improves from 12.3 GJ/t in the non-learning scenario to 10.7 GJ/t in the learning scenario in 2050. This decrease represents a cost savings of US$1.6 billion and a carbon dioxide emissions reduction potential of 14.9 billion tonnes. Results discussed in this paper focus on the U.S. iron and steel sector, but the proposed framework can be applied to study new technology development in any other industrial processes and regions.

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Section: Introductionmentioning

confidence: 99%

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

2017

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“…The learning coefficient ( in Annex 2) is estimated to be 0.066, which leads to a learning α rate of 4.4% (Yao et al 2015). Our estimate is in the low range of "rule-of-thumb" learning estimates for renewable energy technologies.…”

Section: Numerical Simulation Of Optimal Social Welfarementioning

confidence: 75%

Market and Non-market Policies for Renewable Energy Diffusion: A Unifying Framework and Empirical Evidence from China’s Wind Power Sector

Liu¹,

Wei²

2016

The Energy Journal

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We provide a comprehensive framework of analyzing the diffusion process of renewable technology, incorporating epidemic and pecuniary effects. Relying on a panel dataset consisting of information from 1207 CDM wind projects in thirty provinces over the period 2004-2011, we find strong evidence on the dominant role of the epidemic effect and new evidence on pecuniary effects that generate a diminishing marginal effect of profitability in inducing technology adoption. Our numerical simulation demonstrates that the epidemic effect can play a quantitatively important role in the spread of renewable energy technology and markedly enhance the optimal social welfare. Our findings convey important policy implications for regulators when choosing policy instruments to enhance the diffusion and adoption of clean technology. Price instruments should be complemented by a wide range of non-market instruments to address non-market barriers. Policy interventions should be taken using a systemic approach.

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“…As Q t 2 , Q t 1 , and C t 1 are obtained, the key issue for applying equation to estimate the future cost of wind systems is the learning rate ( b ), which is related to the historical variation trend in the cost and the cumulative installed capacity of wind turbines, and the cost reduction potentials of key components. Previous studies focusing on China's wind power have investigated different types of learning curve models, indicating that learning rate ranges from 4.1% to 11% . Here, a medium learning rate of 7% is adopted in this paper, while the analysis of learning rate is not our focus.…”

Section: Methodology and Datamentioning

confidence: 99%

Marginal abatement cost curve for wind power in China: a provincial‐level analysis

Xiong

Yang

Wang

et al. 2016

Energy Science & Engineering

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Wind power has seen a remarkable growth in China since the Renewable Energy Law came into force in the beginning of 2006. However, the contribution and the economic cost of wind power development to reduce carbon emission at the provincial level are still unclear. This research combined geographic information system analysis and levelized production cost calculation to provide provincial-level supply curve of carbon dioxide (CO 2 ) mitigation by wind power in China. The results showed that wind power could be a very competitive mitigation option in China, with a potential to contribute 500 million tons of CO 2 emission mitigation at an average abatement cost of 75 RMB/t of CO 2 mitigation. The emission abatement cost and the potential to reduce carbon emission in different provinces may be used for national planning as well as for the allocation of investments. 246

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When will wind energy achieve grid parity in China? – Connecting technological learning and climate finance

Cited by 54 publications

References 19 publications

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

Modeling technological change and its impact on energy savings in the U.S. iron and steel sector

Market and Non-market Policies for Renewable Energy Diffusion: A Unifying Framework and Empirical Evidence from China’s Wind Power Sector

Marginal abatement cost curve for wind power in China: a provincial‐level analysis

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