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This study innovatively develops a multi-objective Markal-Macro model, which simultaneously considers three objectives: minimizing carbon emissions from energy consumption, minimizing carbon emissions from production processes, and maximizing societal welfare. Based on the Cobb–Douglas production function, we construct a production function of carbon emission and use it as a coupling equation of the Markal-Macro model (Markal is the abbreviation of market allocation, and Macro is the abbreviation of macroeconomy). This enables the coupling of the endogenous variables of carbon emissions and those related to maximizing societal welfare. By collecting relevant data on energy consumption, production outputs, and key economic indicators, five different scenarios are established. To enhance the computational efficiency of the simulation, we introduce a Firefly Algorithm into the penalty function method. The objective of our simulation is to explore the optimal carbon peak pathway for China. The results indicate that under the baseline scenario, China can achieve its carbon peak by 2029, with the peak value reaching approximately 12.5 billion tons of carbon dioxide. Finally, based on the simulation results, this study provides specific policy recommendations for China's carbon peak pathway, addressing aspects such as industrial structure, energy consumption structure, the share of clean energy, economic growth targets, and the growth of emission reduction expenditures, while considering regional five-year plans and regional carbon peak strategies. From the aspect of the practical contributions, this article not only provides a set of methods for policymakers to make the Carbon Peak Implementation Plan but also offers an optimal path to improve the sustainable development for China. [ABSTRACT FROM AUTHOR]

In order to solve the problem of large-scale power grid, complex connection relationship, and large demand for electricity carbon factor calculation resources, this paper proposes an algorithm that is easy to model the power grid topology and has high computational efficiency to implement the application of "One Electricity Carbon Chart". In this paper, the graph computing component is used to realize the graph modelling, distributed storage, and high-performance calculation of the electric carbon factor for the data representing the topology of the power grid, such as the power grid table, AC line segment table, and AC line end table. This paper realizes the rapid grid topology graph modelling of EMS dispatching system data, so that the grid structure and active power flow can be intuitively displayed and dynamically studied. This paper also realizes the distributed parallel calculation of electric carbon factor in the case of a ring network, which solves the problem of difficult analysis and calculation of large-scale power grid topology. We conduct experiments on real datasets, and the experimental results demonstrate the effectiveness of the proposed method. In addition, the research on the "One Electricity Carbon Chart" makes the carbon emission of power plants depend on the power flow of the power grid and transfers the accounting method, realizing the calculation of the carbon emission factor of the power grid at the plant level, and provides a new perspective for the sharing of carbon emission responsibilities in the power system. [ABSTRACT FROM AUTHOR]

In the field of electric carbon, the mapping relationship between carbon emission flow calculation and power flow calculation was studied by combining techniques such as current trajectory tracking, carbon flow trajectory analysis, power system flow calculation methods, and electric network analysis theory. By delving into the mechanism between these two factors, a better understanding of the correlation between them can be achieved. In addition, by using time series data, graph attention neural networks (GNN), distributed computing technology, and spatiotemporal computing engines, carbon emission fluctuations can be decomposed and a high-frequency "energy-electricity-carbon" integrated dynamic emission factor can be achieved. Through the spatiotemporal distribution patterns of this dynamic factor in multiple dimensions, the carbon emissions from key industries in cities can be accurately calculated. In this paper, the LSTM-GAT model is used as the core to construct a key carbon emission prediction model for cities. The study focuses on the power plant, chemical industry, steel, transportation industry, and construction industry, which are high energy-consuming industries with an annual electricity consumption of more than 100 million kWh in a major city of China. By analyzing the entire life cycle from power generation to electricity consumption and conducting current flow analysis, monthly, weekly, and daily carbon emission calculations were performed. Additionally, other factors such as the industrial development index, GDP, coverage area of power generation enterprises, regional population, size, and type of power-consuming units were included in the comprehensive calculation to build a measurement system. By conducting experiments and analyzing historical data, we have found that the LSTM-GAT model outperforms the single models of GCN, GAT, LSTM, GRU, and RNN in terms of lower error values and higher accuracy. The LSTM-GAT model is better suited for predicting carbon emissions and related indicators with an accuracy rate of 89.5%. Our predictions show that the carbon emissions will exhibit a slow growth trend in the future, while the carbon emission intensity will decrease. This information can provide a scientific basis for government decision-making. [ABSTRACT FROM AUTHOR]

With the increasing prominence of the global carbon emission problem, the accurate prediction of carbon emissions has become an increasingly urgent need. Existing carbon emission prediction methods have the problems of slow calculation speed, inaccurate prediction, and insufficient deep mining of influencing factors when dealing with large-scale data. In this study, a comprehensive carbon emission prediction method is proposed. Firstly, multiple influencing factors including economic factors and demographic factors are considered, and a pathway analysis method is introduced to mine the long-term relationship between these factors and carbon emissions. Then, indirect influence terms are added to the multiple regression equation, and the variable is used to represent the indirect influence relationship. Finally, this study proposes the PCA-PA-MBGD method, which applies the results of principal component analysis to the pathway analysis. By reducing the data dimensions and extracting the main influencing factors, and optimizing the carbon emission prediction model by using a mini-batch stochastic gradient descent algorithm, the results show that this method can process a large amount of data quickly and efficiently, and realize an accurate prediction of carbon emissions. This provides strong support for solving the carbon emission problem and offers new ideas and methods for future related research. [ABSTRACT FROM AUTHOR]