Table of Content

29 October 2023, Volume 35 Issue 3

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Identification Research of China's Potential Autonomous and Controllable Key Products

Wenping Wang, Laifeng Wu

2023, 35(3):  1-17. 

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To comprehensively prevent and resolve the potential risk of a "bottleneck" in the context of reverse globalization, sorting out China's potential autonomous and controllable key products systematically is an important issue. The paper constructs a product space network model according to the data of 243 countries' export products from 1995 to 2019 and measures the product coreness based on the revealed comparative advantage of products to identify global core products. On this basis, the close degree of grey incidence model is used to quantitatively analyze the close correlation degree of the overall revealed comparative advantage distribution of countries in the global product system. And through the analysis of the marginal contribution of revealed comparative advantage, the study also identifies China's potential autonomous and controllable key products. Among the 124 global core products in the top 10% of the coreness in 2019, the products whose revealed comparative advantage changes make a greater contribution to the distribution of China's overall revealed comparative advantage are potential autonomous and controllable key products in China. Among the top 31 kinds of core products with the top 25% contribution rate, China's potential autonomous and controllable key products are mainly distributed in the fields of mechanical and electrical equipment, vehicles, aircraft and ships, optical and medical precision instruments with high-tech characteristics, as well as in the field of chemical products with capital-intensive characteristics.  

A Multi-Model Grey Fusion Forecasting Procedure for China’s Ecommerce Service Industry

Jianhong Guo, Tianci Li, Yingyi Huang, Chejung Chang

2023, 35(3):  18-26. 

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During the Covid-19 pandemic, the e-commerce service industry has played a significant role in ensuring the development of e-commerce and promoting the innovation of new business models. Grasping the trend of the future development of the e-commerce service industry leads to pushing the government to formulate industrial development strategies and to ensure the rapid recovery of the postepidemic economy. This paper proposes a Multi-Model Grey Fusion (MGF) procedure to solve the reliability and robustness problems of predicting the future trend of the e-commerce service industry. The experimental results show that MGF performs well in handling the small data prediction problem and that it is more reliable and robust than the four single-base models, which are the grey model, linear regression, back-propagation neural network, and support vector regression. The forecast by combining the MGF with the rolling framework shows that China’s e-commerce service industry will keep an excellent development momentum. Moreover, the market size is expected to reach RMB 8.78 trillion by 2025, with an average annual growth rate of 8.8%.

Research on Grey Power Model Based on Periodic Wave Sequence and Its Application

Jiefang Liu, Rongrong Yang, Pumei Gao, Kun Yang

2023, 35(3):  27-38. 

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A new grey power model is proposed for modeling periodic small sample systems. In the modeling process, a trigonometric function is introduced to identify the periodicity characteristics of the data, and the specific time response formula and modeling steps are given. At the same time, in order to minimize the average relative error, the least square method and genetic algorithm are used to optimize and solve each parameter. Finally, the model is applied to forecast the traffic flow of the Hengda Expressway in Hebei Province and the PM2.5 content of air pollutants in Tianjin. The results are compared with those of other models. The results show that the proposed model has higher prediction accuracy, which further verifies the validity of the model. 

Forecasting Method Based on Attenuated LSTM for Time Series With Missing Data of Soil Organic Matter Based on Hyperspectral Data

Xin Liu, Yichao Ma, Jian Yu

2023, 35(3):  39-53. 

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Missing data presents a significant challenge when forecasting unknown data in time series. To address this issue, this paper proposes an Attenuated Long and Short-Term Memory model based on a decaying function, which is abbreviated as AD-LSTM, for forecasting unknown data in time series. As strong correlations exist in adjacent data, the Grey model is leveraged to predict and impute missing values in small-sample datasets. To improve the accuracy of forecasting in time series with missing data, we introduce subspace decomposition, which can correct errors in the memory state cell caused by missing elements, and a time decay function, which utilizes the number of adjacent continuous missing data as a weight to modify the memory cell and enables the model to detect the existence of missing elements, into the LSTM unit. These enhancements reduce the impact of imputed data on model training in time series analysis. Finally, to verify the effectiveness of our proposed method, we conduct experimental validation and comparative analysis using the Beijing house price dataset. Our results indicate that the AD-LSTM model performs better in reducing the impact of missing data and achieving lower forecast errors compared to the baseline models.  

A Grey Clustering Comprehensive Evaluation Methodology for Green Construction Based on Information Theory

Chenchen Jia, Jinfa Yao, Yongheng Wei, Bo Xie, Hazrat Bilal

2023, 35(3):  54-70. 

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With augmenting cognizance of environmental issues, there is a growing concern for sustainable construction due to the negative environmental impacts resulting from construction activities. Although green construction has been advocated globally, the lack of scientific and systematic green construction assessment tools is a key factor affecting the adoption of green construction. In this study, a methodology for green construction assessment was developed based on the grey system theory, which allows for a quantitative evaluation of environmental performance for construction activities. We integrate the advantages of both subjective and objective weighting methods to overcome the one-sidedness of a single weighting method. Besides, we resort to mutual information to determine a more reasonable index weight by eliminating the possible information overlap among indicators. The evaluation results of the simulation case demonstrate the effectiveness of the proposed approach for green construction evaluation. 

An Extended Grey Bass Diffusion Model With Dynamic Market Capacity

Xiansheng Fan, Zhengxin Wang, Lingling Pei

2023, 35(3):  71-81. 

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The structure of the classical Bass diffusion model reveals that the maximum number of imitators is achieved when the cumulative number of adopters equals half the maximum number of adopters in the market. But this situation does not necessarily correspond to the actual activities. To more accurately represent where the number of imitators reaches its peak, a new grey Bass diffusion model for cases with small samples is proposed by introducing a parameter to the classical Bass diffusion model to improve the model. In addition, the solution of the grey Bass diffusion model is derived by integrating the grey Bass diffusion model representation, and the parameter-solving process of the new grey Bass diffusion model is given by using the ordinary least squares method. Finally, an example of new energy vehicle sales forecasting shows that the new grey Bass diffusion model is more accurate than the traditional grey Bass diffusion model. The new grey Bass diffusion model further broadens the use of the Bass diffusion models and enriches the mathematical methods of innovation diffusion.

Forecasting New Energy Vehicle Sales in China Based on a Novel Grey Lotka-Volterra Model and Assessing Its Environmental Impact  

Wuyong Qian, Tingting Zou, Yuhong Wang, Chunyi Ji, Minghao Ran

2023, 35(3):  82-99. 

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With China's vigorous implementation of the two-carbon policy, the automotive industry is transitioning from traditional to alternative energy sources. Accurate forecasting of new energy vehicle sales can provide statistical support for policy planning. This paper proposed a grey Lotka-Volterra model based on an HP filter to forecast new energy vehicle sales trends. The proposed model overcomes the limitations of the original model in forecasting cyclical data and improves its applicability in small sample data systems. And the competitive forecasting model allows us to explore the market competition or cooperation between various vehicle types. Compared to the two existing models, the new model performs better in forecasting vehicle sales. The study further applies the LCA method to assess the scale of energy consumption and GHG emissions in the automotive sector. The prediction and evaluation results show that new energy vehicle sales will surpass traditional fuel vehicles by 2035 and dominate the vehicle market before 2050. Unfortunately, vehicle electrification has not significantly reduced the transportation industry's environmental concerns. To fully utilize the potential for energy-savings and emission-reduction of new energy vehicles, it is urgent to pursue technological advancements in battery production and power-generating structure. 

Grey Clustering Evaluation of the Science and Technology Innovation Platform Considering Technology Readiness Level

Huyi Zhang, Lijie Feng, Jinfeng Wang, Kuoyi Lin, Tiancong Zhu

2023, 35(3):  100-116. 

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Technological innovation is the primary driving force leading development. The science and technology innovation platform is important for enhancing innovation capabilities. Aiming at the operational performance of the science and technology innovation platform, it can be evaluated through the technology readiness level and the grey clustering evaluation model. First, we take the technology readiness level as the entry point to divide the science and technology innovation platform. On this basis, the operation evaluation index system of the science and technology innovation platform is constructed. After that, we construct the grey clustering evaluation model and conduct a case study to verify the feasibility of the study. Finally, we focus on the evaluation indicators and put forward relevant reference suggestions to improve the operational performance of the science and technology innovation platform.  

Research on the Supply and Demand Matching of Care Services for Disabled Elderly Individuals in the Community

Yun Fan, Jun Liu, Na Zhang, Zhigeng Fang, Sifeng Liu, Xiaojun Guo, Xin Lin, Zhihao Huang

2023, 35(3):  117-138. 

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To address the unreasonable matching of care service resources for disabled elderly individuals in the community, we first identified uncertainties in the expression of care needs by disabled elderly individuals and their families and the expression of care service resources by care institutions, defined the matching of the supply and demand of care service resources for disabled elderly individuals, and explored the expected demand evaluation values of different types of disabled elderly individuals and care service institutions. A grey bilateral matching model for care service resources in the case of incomplete supply and demand information was then constructed by considering the different degrees of disability of disabled elderly individuals, extending from the original one-to-one matching model to a one-to-many matching model. Finally, MATLAB simulation was used to obtain the optimal match between disabled elderly individuals and care service institutions, thereby providing an accurate and efficient match between the supply and demand of care service resources. 

A Contribution-based Model for Space Launch Systems Allocation

Fei Deng, Sifeng Liu, Luyun Qiu, Huan Wang, Yingjie Yang

2023, 35(3):  139-156. 

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Cost allocation is a critical part of the space launch system R&D stage of cost control. There exists a central challenge that the efficiency (also called input-output ratio) is difficult to obtain due to the uncontrollable input, long R&D cycle, and unpredictable output. Therefore, in order to obtain effective cost allocation strategies in practice, this paper proposes an improved contribution-based cost allocation method according to the measurable contribution of each team, bypassing calculating the uncertainty efficiency. First, a grey relational clustering-based evaluation system is developed to assess the contribution of the R&D team. Then, the real circumstances are examined, and four popular categories of launch tasks are identified. Next, the BWM method is employed to identify cost allocation strategies that align with the requirements of each of the four launch missions. Finally, comparative analyses with DEA and Costing method demonstrate the superiority of the proposed method in terms of accuracy and efficiency.