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Analysis of the coupling coordination and obstacle factors between green low-carbon systems and digital economy in the nine provinces(autonomous regions)along the Yellow River
WANG Jing, JIANG Mingyue, ZHAO Lin
Shandong Science    2025, 38 (2): 89-99.   DOI: 10.3976/j.issn.1002-4026.2025011
Abstract27)   HTML3)    PDF(pc) (2815KB)(3)       Save

The nine provinces(autonomous regions)along the Yellow River are important supporting regions for the economic and social development of China. The coordinated advancement of their green low-carbon and digital economies holds strategic significance for China to achieve its “dual-carbon” goals and high-quality economic development. Considering the nine provinces(autonomous regions) along the Yellow River from 2013 to 2022 as the research object, this study constructs an evaluation index system for the digital economy and green low-carbon systems from six dimensions: resource consumption, pollution emissions, governance effectiveness, communication capabilities, the Internet, and the information industry. The spatiotemporal coupling relationship and obstacle factors between the two were analyzed using entropy weight method, coupling coordination degree model, obstacle degree model, and gray prediction. The results demonstrated that the green low-carbon level of the nine provinces(autonomous regions) along the Yellow River has significantly improved, following a spatial pattern of “upper reaches green low-carbon level<lower reaches green low-carbon level<middle reaches green low-carbon level”. The evaluation indices of the digital economy systems exhibit regional imbalances, showing a gradually increasing trend from top to bottom. The coupling coordination degree of the digital economy and green low-carbon systems as a whole shows a steady upward trend, and the overall state of the nine provinces(autonomous regions) along the Yellow River has changed from being on the verge of disorder to an initially coordinated state. The information industry is the main obstacle to coupling coordination. The insufficient development of the digital information industry in the Yellow River Basin severely hinders the coordinated development of the digital economy and green low-carbon systems in these provinces(autonomous regions). Gray prediction analysis suggests that the coupling state of the digital economy and green low-carbon systems in the nine provinces(autonomous regions) along the Yellow River from 2025 to 2029 will steadily improve to an intermediate coordination level. On the basis of the research results, targeted suggestions are put forward to promote the coupling coordination of the green low-carbon systems and digital economy in the nine provinces(autonomous regions) along the Yellow River for them to advance to a higher stage of development. These insights provide a scientific basis and decision-making reference for the sustainable development of the region.

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An automatic method based on a POT model for the identification of high-value points of oil and gas loss in the process of oilfield surface production
YUAN Zishang, WAN Yong, ZHAN Zihao, FAN Lu, DAI Yongshou
Shandong Science    2025, 38 (2): 100-108.   DOI: 10.3976/j.issn.1002-4026.20240099
Abstract19)   HTML1)    PDF(pc) (2489KB)(4)       Save

Under the “dual carbon” goals, high carbon emitting enterprises in industries, such as petrochemicals, are undergoing a low-carbon transformation and are reducing their carbon emissions, which have grown to become an important development trend. Oil and gas losses account for a large proportion of the total energy consumption in the petrochemical industry. In this study, focusing on the inability to automatically identify the high-value points of loss because of the change in the law of oil and gas loss data, a method for automatically identifying the high-value points of oil and gas loss based on the peak over threshold (POT) model is proposed. First, accounting of oil and gas losses was conducted in 35 links of an oil producing reservoir in Shengli Oilfield. Second, according to the leptokurtic distribution characteristics of the accounting data, the oil and gas loss tail data were segmented using the POT model and fitted with its probability distribution function (PDF). The corresponding high-value points of oil and gas losses in the PDF were identified according to the 3σ principle. Finally, the results determine the threshold of high-value points to be 869.34 m3/d, and the identification accuracy of high-value points to be 0.986, accuracy greater than other traditional methods. Therefore, the proposed method is conducive to the efficient development of loss control methods.

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