Chenhan Huang 2024a - Revision history

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← Older revision		Revision as of 04:34, 29 October 2024
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	==2 Current status of the study==		==2 Current status of the study==
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−	In recent years, in the prediction of carbon emission accounting, gray model, LMDI method, Environmental Kuznets Curve (EKC), regression analysis method, KAYA decomposition method, STIRPAT model, BP neural network and other models have been widely used by scholars at home and abroad [8] .
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−	The team of Assistant Professor Michael R. Davidson at the University of California, San Diego, in collaboration with the team of Professor Xiliang Zhang and Associate Professor Da Zhang at the Institute of Nuclear Energy and New Energy Technology of Tsinghua University (INERT), the team of Professor Xu Lu at the School of Environment of Tsinghua University, and the team of Academician Xiaohui Zhang at the Chinese Academy of Meteorological Sciences (CAMS), have participated in the development of the Renewable Energy Siting and Power-system Optimization Model (RESPO). This model is designed to optimize the layout of renewable energy sources and power systems with a high degree of spatial and temporal accuracy. The RESPO model can be used to study the optimal layout of renewable energy development within the power system, supporting the realization of carbon neutrality[9].
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−	The Lawrence Berkeley National Laboratory: In-depth research on carbon emissions from electric power systems and development of models and analytical tools to assess and analyze carbon emissions from electric power production, transmission and consumption, providing a basis for policymaking and energy planning [10].
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−	Imperial College, UK, researchers have conducted an in-depth study of carbon emission modeling in the power system, focusing on the impact of factors such as power market mechanisms and energy policies on carbon emissions. Through modeling, they analyze the carbon emission trends and reduction potential of the power sector under different policy scenarios, providing decision support for the energy transition in the UK and Europe [11] .
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−	The Karlsruhe Institute of Technology (KIT), Germany, has a high level of excellence in energy system modeling and analysis, and its research team has developed a variety of electricity-carbon models for assessing the carbon emissions of the German power system and the impact of renewable energy development on carbon emissions. The institute is also actively involved in European energy research programs and collaborates with research institutes in other European countries to promote Europe's energy transition [12] .
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−	Xie Di et al. Analyzing and constructing a prediction model of future carbon emissions in Qinghai Province using the STIRPAT model and socio-economic development data[13]. Li Qigeng et al. assessed the energy saving and emission reduction effects of environmental regulatory policies in Shanxi by constructing a system dynamics model of industrial energy consumption and pollutant emissions[14]; Ma Yuheng et al.analyzed the current status of carbon emissions in the Northeast region by constructing the STIRPAT model, Using the LMDI decomposition method to investigate the factors influencing carbon emissions in the Northeast region, and then employing scenario analysis to predict the peak of carbon emissions in this area, it is concluded that the three northeastern provinces will achieve their carbon emission peak targets under the "energy-saving scenario" by 2030 [15].
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−	After the launch of the national carbon emissions trading market, the electric power industry was the first to be included in the carbon market. In order to intuitively understand the dynamics of carbon emissions, the implementation of carbon reduction action grid enterprises actively open "electric carbon" model, "electric carbon index" model, electric carbon ecological map and many other studies. State Grid Fujian Xiamen Power Supply Company established a carbon and electricity intensity analysis model, and initially completed the drawing of Xiamen's electricity and carbon ecological map[16]. State Grid Zhoushan Power Supply Co. State Grid Zhoushan Power Supply Company in Zhejiang Province has developed the "Electricity and Carbon Index" model, which integrates the energy consumption data of the production and operation of enterprises in various industries, such as electricity, gas, coal, oil, etc., and converts them into carbon emissions, resulting in the value of the "Electricity and Carbon Index" [17].The State Grid Sichuan Electric Power Research Institute (SGSPRI) has deployed a dynamic emission factor calculation model, which realizes the dynamic and localized calculation of the emission factor by integrating the production data and energy consumption data of different types of power plants, as well as the data of inward and outward power transfer in the region.
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−	To summarize, this paper proposes to establish five kinds of electricity-carbon models to assess the carbon emissions of 38 ceramic enterprises in Fujian Province. The five models are: a linear regression electricity-carbon model with a univariate polynomial that only considers the relationship between electricity consumption and carbon emissions; a linear regression electricity-carbon model with a univariate quadratic polynomial; a linear electricity-carbon model with a decomposition of carbon emission sources that takes into account the combustion of fossil fuels, emissions from the production process, the net purchased electricity, the net purchased heat, and so on. The purpose of this model is to verify the performance of various models, including the linear EC model for decomposition of carbon emission sources considering fossil fuel combustion, production process emissions, net purchased electricity, net purchased heat, etc.; the linear EC model based on the production correlation volume considering ceramic production; and the linear EC model based on the multi-parameter "electricity-energy-carbon" considering "electricity, natural gas, and carbon emissions" for carbon emission estimation. The purpose of this study is to verify the accuracy of carbon emissions from various electricity-carbon models and to provide reference for enterprises when choosing electricity-carbon models for accounting. The accurate measurement and management of electricity-carbon models can help to improve the effectiveness of carbon regulation and provide a basis for participation in the national carbon market.

	==3 Research objectives==		==3 Research objectives==

← Older revision		Revision as of 04:33, 29 October 2024
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	==1 Background of the study==		==1 Background of the study==
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−	Climate change is a global challenge, with greenhouse gas emissions leading to rising global temperatures and frequent extreme weather events. The power sector is one of the major sources of global carbon emissions, and achieving carbon peak and carbon neutrality in this sector is a key initiative for countries to fulfill their commitments under international climate conventions such as the Paris Agreement. For example, China has proposed a "dual-carbon" goal, where the low-carbon transformation of the power sector has far-reaching implications for global climate governance, demonstrating the responsibility of a major country. [1] The power sector's low-carbon transition not only demonstrates the responsibility of a major power but also helps to promote a common global response to climate change. Reducing carbon emissions from the power sector can directly decrease the growth rate of greenhouse gases such as sulfur dioxide, nitrogen oxides, and particulate matter in the atmosphere, which are among the main causes of environmental problems like acid rain and haze.
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−	Utilizing the electricity-carbon model, it is possible to accurately assess the carbon emissions of different power generation technologies (e.g., traditional coal power generation, natural gas power generation, and emerging solar power generation, wind power generation, nuclear power generation, etc.) at various stages. Through detailed data analysis, it is possible to determine when and at what rate to phase out high-carbon emitting power generation technologies, while simultaneously vigorously developing low-carbon or zero-carbon power generation technologies. For example, the electricity-carbon model can simulate that over the next 10-20 years, if carbon peaking is to be achieved, the installed capacity of solar and wind power generation will need to be expanded at a certain growth rate per year, while gradually reducing the operating hours of thermal power units. This will help the power industry to formulate a scientific and reasonable energy transition strategy, avoiding blind investment and resource wastage.At the same time, the electricity-carbon model can take into account the real-time operation of the power system, including changes in load demand and the generation characteristics of different power sources. On the premise of ensuring the reliability of power supply, optimized scheduling through the model can prioritize the use of low-carbon emission power sources for power generation. Additionally, the government and enterprises will introduce various policies and technical measures to promote carbon peaking and carbon neutrality, such as carbon tax policies, renewable energy subsidy policies, and the promotion of carbon capture, utilization, and storage (CCUS) technology. The electricity-carbon model can quantitatively assess these policies and technical measures. The results from the model can serve as a scientific basis to demonstrate to the public, investors, and the international community the specific action plans and progress of the power industry in addressing climate change. Transparent quantitative analysis of carbon emissions and planning of emission reduction pathways can enhance social confidence in the low-carbon transformation of the power industry and provide strong data support for international climate negotiations, proving the actual contributions made by the country or region in the field of electricity to combat climate change.
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−	Estimation and management of carbon emissions has become a key issue in scientific research and policy development[2]. The Carbon emission estimation modeling, as a major tool for assessing and predicting corporate carbon emissions, plays a crucial role in formulating emission reduction strategies and achieving carbon neutrality goals[3,4]. electricity-carbon model, also known as "electric-carbon analysis model" or "electric-carbon calculation model", is based on the correlation between electricity consumption and carbon emissions from consumption of other energy varieties and industrial production, and makes use of the advantages of electricity big data, such as wide coverage, real-time, high resolution and high degree of digitization, to construct an innovative model to estimate the carbon emissions of enterprises. It is based on the correlation between electricity consumption and carbon emissions from other energy sources and industrial production, and utilizes the advantages of wide coverage, strong real-time, high resolution and high degree of digitization of big data of electricity to innovatively construct the calculation method of "electricity-carbon calculation"[5]. The electric power industry is a major concern for the nation's economy and people's livelihood. The power industry is a basic energy industry that has a major impact on the people's livelihood and national security, and power consumption is also an indicator of carbon dioxide emissions，The electricity consumption is also an indicator of carbon dioxide emissions[6]. Therefore, the development of enterprise "electricity-carbon model", the use of accurate measurement of electricity, real-time access, unified management and other characteristics of the carbon dioxide emissions of emission-control enterprises accurate prediction, real-time and effective mapping of the province's carbon assets, carbon emissions, is not only conducive to enriching the means of carbon regulation, but also conducive to the provision of a reference basis for the prediction of the carbon price, which will help local enterprises to better participate in the national carbon market transactions. It not only helps to enrich the means of carbon regulation, but also helps to provide a reference basis for carbon price prediction, which in turn helps local enterprises better participate in the national carbon market. Accurate carbon emission estimation not only helps the government and enterprises to formulate more scientific and reasonable emission reduction strategies and policies, but also lays a solid foundation for realizing the goal of global carbon neutrality!The carbon emissions estimate will also lay a solid foundation for achieving the global carbon neutrality goal [7].

	==2 Current status of the study==		==2 Current status of the study==

← Older revision	Revision as of 02:38, 29 October 2024
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