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2. Competitiveness Evaluation and Obstacle Factor Analysis of Urban Green and Low-Carbon Development in Beijing-Tianjin-Hebei Cities.
- Author
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Yuan, Kai, Hu, Biao, Niu, Tingyun, Zhu, Binglong, Zhang, Liang, and Guan, Yuqiong
- Subjects
SUSTAINABLE development ,FACTOR analysis ,URBAN growth ,EMISSIONS (Air pollution) ,CARBON nanofibers ,GREEN technology - Abstract
Based on the PSR conceptual evaluation model of urban green and low-carbon development competitiveness, this paper evaluates the green and low-carbon development competitiveness of 13 cities in Beijing-Tianjin-Hebei from 2010 to 2019 and reveals their spatial effects and obstacle factors combined with the spatial autocorrelation and obstacle model. The results show the following: (1) In terms of the overall situation, the evaluation index of urban green and low-carbon development competitiveness in the Beijing-Tianjin-Hebei region shows a fluctuating upward trend as a whole from 2010 to 2019. Among them, the comprehensive evaluation indexes of Beijing, Zhangjiakou, and Tianjin are very competitive, while Hengshui, Handan, and Xingtai are relatively weak. (2) In terms of spatial data analysis, the global and local Moran's I of Beijing-Tianjin-Hebei cities from 2010 to 2019 were positive and passed the 1% significant level test. This shows that the comprehensive evaluation index of urban green low-carbon development competitiveness of Beijing-Tianjin-Hebei cities has a positive spatial correlation, and the spatial agglomeration effect is significant. On the whole, it presents a spatial agglomeration pattern of "high in the north, partial jump, and low in the south". (3) In the aspect of obstacle factor analysis, the proportion of the tertiary industry output value in GDP, energy consumption of ten thousand yuan of GDP, the urban green technology innovation level, and industrial soot emission of ten thousand yuan of GDP are the main obstacle factors restricting the improvement of urban green low-carbon development competitiveness in Beijing-Tianjin-Hebei cities. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
3. A Novel Structure-Adaptive Fractional Bernoulli Grey Model for Solar Photovoltaic Forecasts.
- Author
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Huang, Ying, Huang, Weilong, and Ding, Song
- Subjects
SOLAR technology ,PARTICLE swarm optimization ,FORECASTING ,CARBON emissions ,MACHINE learning - Abstract
Since the limitation of carbon emissions, China's photovoltaic (PV) industry has developed vigorously, while some traditional heavy industries have been violently hit. Therefore, the industrial production data exhibits significant nonlinear and complexity characteristics, which may affect prediction accuracy, thus hindering the corresponding department's decision-making. Consequently, a novel structure-adaptive fractional Bernoulli grey model is presented in this paper to surmount this toughie, and the core innovations can be summarized as follows. Initially, a novel time function term is utilized to depict the accumulative time effect, which can smoothly represent the dynamic variations and significantly strengthen the robustness of the new model. Besides, the fractional-order accumulation technique, which could effectively improve the predicting accuracy, is employed in the proposed model. Furthermore, the adaptability and generalizability of the proposed model can be enhanced by the self-adaptive parameters optimized by the Particle Swarm Optimization. For illustration and verification purposes, experiments on forecasting the annual output of Photovoltaic modules in China and the annual output of steel in Beijing are compared with a range of benchmarks, including the classic GM (1, 1), conventional econometric technology, and machine learning methods. And the results confirmed that the proposed model is superior to all benchmark models, which indicates that the novel model is indeed suitable for industrial production forecasting. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Methodology and Application of Fiscal and Tax Forecasting Analysis Based on Multi-Source Big Data Fusion.
- Author
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Zhu, Lin
- Subjects
BIG data ,MULTISENSOR data fusion ,INCOME tax ,CORPORATE taxes ,BUSINESS tax ,INTERNAL revenue - Abstract
With the advent of the big data era, the use of computers has spread to all walks of life, and the finance and taxation industry is also in the middle of it. The current taxation system is huge and complex, and different tax types are inevitably linked to different economic indicators at a deep level, so tax forecasting requires personalised forecasting analysis for different tax types. This paper selects several tax types that account for a large proportion of tax revenue for prediction analysis, respectively, and conducts fusion research on multi-source big data, including business tax, corporate income tax, and personal income tax. Based on the multi-source big data fusion method, the prediction research on fiscal taxation tax types is conducted, and experiments are conducted with the taxation data of Beijing from 1995 to 2020 to predict the three tax types from 2017 to 2020. The results show that the deviation of the forecast data from the real tax data is small, controlling the forecast deviation to within 14%, indicating the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
5. Research on Public Management Application Innovation Based on Spark Big Data Framework.
- Author
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Liu, Zhi
- Subjects
PUBLIC administration ,INNOVATION management ,BIG data ,URBAN planning ,BIOINDICATORS ,TECHNOLOGICAL innovations ,DIFFUSION of innovations - Abstract
Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to the public management of Beijing. The results indicate that the public management platform based on Spark big data framework can improve the public management level of the city and help to build an intelligent city. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
6. Research on Optimization of Power Emergency Material Dispatching for Beijing Winter Olympics.
- Author
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Zhao, Yue, Hou, Hanping, He, Ting, Li, Dandan, and Fang, Jiaqi
- Subjects
- *
EMERGENCY power supply , *OLYMPIC Winter Games , *ELECTRIC power failures , *ELECTRIC power distribution grids , *POWER resources , *SYSTEM safety - Abstract
The Beijing Winter Olympic Games is an extremely important event, and the supply of electricity is the basis for it. In order to prevent the sudden loss of power to the event, it is necessary to carry out power repair and restore normal power supply in the shortest possible time. Contemporary research is less focused on the emergency repair of power systems. This thesis studies the dispatching of power emergency materials in two stages and in the first stage, the minimum gap rate and the shortest time for material dispatching and transportation are mainly considered, and the required types of power emergency materials are dispatched from the storage near the Olympic venues to various power failure points. In the second stage, aiming at the shortage of power emergency materials at each fault point, the power emergency materials will be dispatched from the rear national power grid regional material reserve to the power emergency materials reserve near the Olympic venues and then dispatched to each power fault point through the power emergency materials reserve. Finally, this paper verifies the effectiveness of the combination of the primary and secondary dispatching models in an example, providing a reference for further improving the emergency response capability of the Olympic venues and improving the safety system of the power system. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
7. Unsafe Behavior Analysis and Risk Measurement of Traffic Accidents in Mountainous Highway Tunnel.
- Author
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Zhou, Xiaoxiang, Huang, Chengfeng, and Zhou, Yixiang
- Subjects
TUNNELS ,TRAFFIC flow measurement ,BEHAVIORAL assessment ,RISK assessment ,AT-risk behavior ,TRAFFIC accidents ,TRAFFIC safety ,RAILROAD tunnels - Abstract
Traffic accidents in mountainous highway tunnels have resulted in significant negative effects and losses. Among the potential hazards that can lead to fatal injuries, human-related hazards have been recognized as the leading cause. Determining the risk management effectively and prioritizing unsafe human behavior are the basis for preventing and controlling traffic accidents in mountainous highway tunnels. Therefore, hazards that could potentially cause highway tunnel traffic accidents were identified by using a tail-biting fish diagram combined with the fault tree method. A risk assessment model was constructed based on probability, degree of importance, and loss. Furthermore, the probability can be calculated by assessing the degree of unreliability, which can be obtained by assessing the degree of importance of unsafe behavior in the fault tree, and the loss can be acquired from the authority. The case of 8–10 traffic accidents that occurred in the Qinling No. 1 Tunnel of the Ankang section of the Beijing-Kunming expressway was studied, and the values of the unsafe behaviors were assessed. According to the risk values, the priority for controlling unsafe behaviors can be acquired and tailored measures can be taken to prevent and control the risks, which provides a theoretical basis and new method for the effective control of mountainous highway tunnel traffic accidents. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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