Potential of China's national policies on reducing carbon emissions from coal-fired power plants in the period of the 14th Five-Year Plan

Yang, Rui; Wang, Wensheng

doi:10.1016/j.heliyon.2023.e19868

Cited by 6 publications

(2 citation statements)

References 38 publications

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“…The transition towards a more sustainable and low-carbon socioeconomic system is a top policy priority, with the EU Green Deal aiming at climate neutrality by 2050, 4 the Inflation Reduction Act in the United States (Bistline et al 2023) and the ambitious renewable energy targets of China's 14th Five-Year Plan For Renewable Energy With Quantitative Targets For 2025 (Li et al 2019;Yang and Wang 2023). However, decarbonizing the economy by phasing out high-emission and energy-intensive industries will require radical transformations and profound structural change at the core of socioeconomic systems.…”

Section: Introductionmentioning

confidence: 99%

Economic complexity and the sustainability transition: a review of data, methods, and literature

Caldarola,

Mazzilli,

Napolitano

et al. 2024

J. Phys. Complex.

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Economic Complexity (EC) methods have gained increasing popularity across fields and disciplines. In particular, the EC toolbox has proved particularly promising in the study of complex and interrelated phenomena, such as the transition towards a greener economy. Using the EC approach, scholars have been investigating the relationship between EC and sustainability, proposing to identify the distinguishing characteristics of green products and to assess the readiness of productive and technological structures for the sustainability transition. This article proposes to review and summarize the data, methods, and empirical literature that are relevant to the study of the sustainability transition from an EC perspective. We review three distinct but connected blocks of literature on EC and environmental sustainability. First, we survey the evidence linking measures of EC to indicators related to environmental sustainability. Second, we review articles that strive to assess the green competitiveness of productive systems. Third, we examine evidence on green technological development and its connection to non-green knowledge bases. Finally, we summarize the findings for each block and identify avenues for further research in this recent and growing body of empirical literature.

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Section: Introductionmentioning

confidence: 99%

Economic complexity and the sustainability transition: a review of data, methods, and literature

Caldarola,

Mazzilli,

Napolitano

et al. 2024

J. Phys. Complex.

View full text Add to dashboard Cite

show abstract

“…A more particular point for the case of China is that, differs from other countries, its power market is supervised and regulated by the local governments besides the central government. The government manages the power industry through policies and optimizes management behavior by updating policies to achieve energy conservation and emission reduction goals (Yang & Wang, 2023), this makes China unique in both the reform of its power system and the process of achieving carbon neutrality. In addition, the energy resource endowment and demand in different regions of China vary, and the specific path to achieving carbon neutrality also varies from region to region.…”

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confidence: 99%

Towards carbon‐neutral society: Reconciling peak carbon strategy and thermal power generation via regional eco‐efficiency analysis

Jiang,

Zhang,

et al. 2023

Sustainable Development

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Transition towards low‐carbon and carbon‐neutral society requires multiple technological solutions. Thermal power generation is the main source of power generation in China and contributes to a large portion of carbon emission. Under the goal of “carbon peak”, thermal power generation efficiency will become an important factor affecting industrial development. Based on the directional distance function, this article assumes energy used as inputs and power generation as an output. Then, energy‐oriented and energy‐generation oriented power generation efficiency analysis models are proposed. The carbon emission is treated as an undesirable output to analyze the regional power generation efficiency with and without industrial transfer. The application focuses on panel data of three regions in China over the period 2013–2017, accounting for three energy inputs: solid, oil and gaseous fuel. The results show that in regard to the factors improving power generation efficiency, pure efficiency and scale efficiency change is positive for the eastern and central regions, while pure technical efficiency change is positive for the western region. By further analyzing the power generation potential, the results also show that the eastern region exhibits potential savings in gaseous fuel, the central region has potential in three fuels, but all not prominent, and the western region has potential in solid and oil fuels. Accordingly, the article puts forward policy recommendations from the perspective of technological innovation, differentiated development, and collaborative communication.

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Leveraging long short‐term memory networks and transfer learning for the soft‐measurement of flue gas flowrate from coal‐fired boilers

Lu,

Tang,

Duan

2024

Asia-Pacific J Chem Eng

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The dynamic operation and deep peak‐shaving of power‐generating units cause significant fluctuations in flue gas flowrate, thus affecting the accuracy of CO2 emissions measured by continuous emission monitoring systems (CEMS). This study established a long short‐term memory network with an attention mechanism (LSTM‐AM) for the soft measurement of the flue gas flowrate in real‐time. First, flue gas flowrate data and continuous operation parameters over 25 days were sampled from a typical 660 MW coal‐fired boiler in China. Then, a carbon balance model was established to verify the data reliability. The LSTM‐AM model was trained and testified at the 660 MW coal‐fired boiler. Results show that the LSTM‐AM model significantly surpassed the pristine LSTM model without attention, the convolutional neural network (CNN) with LSTM, and the static support vector regression (SVR) model in the real‐time prediction of flue gas flowrate. Finally, the LSTM‐AM model was generalized to a 630 MW coal‐fired power unit via transfer learning, which was further demonstrated to outperform the model re‐trained from scratch. This work manifests the feasibility of deep learning for the soft measurement of flue gas flowrate, which is promising to solve data‐lagging issues when measuring CO2 emissions from coal‐fired power plants.

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Potential of China's national policies on reducing carbon emissions from coal-fired power plants in the period of the 14th Five-Year Plan

Cited by 6 publications

References 38 publications

Economic complexity and the sustainability transition: a review of data, methods, and literature

Economic complexity and the sustainability transition: a review of data, methods, and literature

Towards carbon‐neutral society: Reconciling peak carbon strategy and thermal power generation via regional eco‐efficiency analysis

Leveraging long short‐term memory networks and transfer learning for the soft‐measurement of flue gas flowrate from coal‐fired boilers

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