Abstract: Since the outbreak of the COVID-19 in early 2020, the epidemic has had a tremendous impact on China’s industrial economy, industrial organization and industrial structure. For example, measures such as mandatory quarantine are taken to avoid large-scale population movements and gatherings, which can severely impact service industries such as transportation, tourism, catering, retail and entertainment. In addition, due to the limited mobility of labor force and logistics transportation, manufacturing enterprises face difficulties in employment and transportation of raw materials in the short term, resulting in delayed order delivery and disruptions in the capital and supply chains. Overall, the COVID-19 pandemic has had the greatest negative impact on the tertiary industry, with a more profound effect on producer services than on consumer ones, and a greater influence on offline physical industrial activities than on online virtual industries. Therefore, quantifying the impact of the COVID-19 epidemic on China’s industrial structure and accurately predicting the change trend of China’s industrial structure in the post epidemic era have important practical significance for promoting the sustainable development of China’s economy.
In this paper, we first introduce a time breakpoint into the traditional GM(1,1) model to construct the Grey Breakpoint Prediction Model (GBPM(1,1)). Furthermore, we optimize this model to propose the Fractional Discrete Grey Breakpoint Model (FDGBM(1,1,t)). Second, drawing on traditional policy evaluation methods, we describe the application of the grey breakpoint model in intervention evaluation based on its characteristics and explain how to analyze the parameters of the grey breakpoint model. Third, we use data on the advancement and rationalization of China’s industrial structure to test the modeling accuracy of FDGBM(1,1,t) and other models. The new model is then employed to assess the impact of the COVID-19 pandemic on China’s industrial structure and that of its four major regions. Additionally, based on the disaggregated data of indicators, we model to identify the primary industries affected by the pandemic. Finally, we utilize FDGBM(1,1,t) to predict the future trends of advancement and rationalization in China’s industrial structure.
The results indicate that: first, by establishing time breakpoints and accurately estimating parameters, the grey breakpoint prediction models can effectively capture system changes caused by external environmental shifts, achieving precise system predictions. When compared to the GBPM(1,1,t), FDGM(1,1), and ARIMA models, the FDGBM(1,1,t) model demonstrates superior modeling accuracy. Second, the COVID-19 pandemic has exerted differential impacts on the advancement and rationalization of China’s industrial structure. Specifically, it has inhibited the current advancement of the industrial structure, primarily through its effects on the secondary and tertiary industries. Conversely, the pandemic has promoted the current rationalization of the industrial structure, mainly by influencing the secondary industry. Third, in the long term, the COVID-19 pandemic has accelerated the trend of industrial structure advancement but significantly hindered its rationalization. The rebound in these indicators is primarily attributed to the secondary industry, while the impact of the pandemic on the tertiary industry has, to some extent, facilitated the rationalization of the industrial structure. Fourth, regionally, the COVID-19 pandemic has exhibited similar effects on the industrial structures of China’s four major regions, with the most pronounced impacts observed in the northeast and western regions. Fifth, in the coming years, China and its four major regions are expected to witness a more rapid upward trend in the advancement of their industrial structures. However, the rationalization of the industrial structure in the eastern, central and western regions may experience stagnation in the later stages, while the northeast region may see a fluctuating trend in its industrial structure rationalization. Overall, the degree of industrial structure rationalization in China is projected to enter a period of stagnation.

Key words: grey model, breakpoint, industrial structure, FDGBM(1,1,t)

摘要： 为了准确评估和预测新冠疫情冲击下中国产业结构的变化,本文引入时间断点的概念,创建了灰色断点模型GBPM(1,1,t)和分数阶离散灰色断点模型FDGBM(1,1,t),并探讨了其建模过程和系数分析方法,利用新模型对中国及四大区域的产业结构高级化和合理化进行分析和预测。结果表明,凭借时间断点的建立和参数的精准估计,灰色断点预测模型都能够较好的捕捉外界环境改变所引起的系统变化,实现对系统的精准预测。对比GBPM(1,1,t),FDGM,ARIMA和BP模型,FDGBM(1,1,t)有着更优的建模精度。新冠疫情对中国产业结构的影响有好有坏,其中对西部和东北地区的影响最大。新冠疫情抑制了当期产业结构的高级化,却促进了当期产业结构的合理化。从长期来看,新冠疫情会加快产业结构的高级化趋势但抑制其合理化趋势。未来几年,中国产业结构高级化会呈现出更快的向上变化趋势,而产业结构合理化的进程会趋于平缓。

关键词: 灰色模型, 断点, 产业结构, FDGBM(1,1,t)