Grey Prediction Model Research Articles

Equity and trends in general practitioners’ allocation in China: based on ten years of data from 2012 to 2021

BackgroundGeneral practitioners (GP) are the gatekeepers of residents' health, 2021 is the 10th year of the establishment of the GP system in China. This study aims to assess the equity and trends of GP allocation in China from 2012 to 2021, summarize the efforts and progress of GPs in China during the decade, predict the development trend of GPs in mainland China in the next 5 years to provide a reference for regional health planning and rational allocation of GPs in China.MethodsData from 2012 to 2021 on GPs in 22 provinces, 5 autonomous regions, and 4 municipalities directly under the central government in mainland China (excluding Hong Kong, Macao, and Taiwan) are collected by us. Gini coefficient, Lorenz curve and health resource agglomeration degree (HRAD) were used to analyze the equity of the allocation of GPs in China from different dimensions, a Grey prediction model was used to forecast the number of GPs in 2022–2026.ResultsThe number of GPs in mainland China increased from 109 794 to 434 868 from 2012 to 2021, with 3.08 GPs per 10 000 people in 2021. The Gini coefficient of GPs allocation by population in China decreased from 0.312 to 0.147 from 2012 to 2021, while the Gini coefficient of geographic dimension remained between 0.700 and 0.750. Compared with the degree of curvature of the Lorenz curve in the geographic dimension, the degree of curvature of the population and economic dimension were smaller. In 2021, the HRAD in the Eastern region was 4.618, the Central region was 1.493, with different degrees of imbalance among regions, the HRAD/PAD (population agglomeration degree) in the Eastern, Central and Western regions were 1.196, 0.880 and 0.821, respectively. GPs in the Eastern region is still concentrated, while the Central and Western regions were at a similar level, GPs were more scarce. The GM (1,1) model predicts that the number of GPs in mainland China will reach about 720 000 in 2026, the number of GPs per 10 000 people will reach 4.9.ConclusionAfter a decade of development, the number of GPs in China has increased significantly. It has reached the goal of the GP system when it was first established. However, the equity of the geographical dimension, both in terms of Gini coefficient and HRAD, has great differences between different regions. The average Gini coefficient at the geographic dimension is 0.723. The average HRAD index was 4.969 in the East and 0.293 in the West. The Western region has the problem of insufficient GP allocation in both population and geographical dimension. In the future, the number of GPs in China will continue to grow rapidly with the support of policies. The “2030” goal, proposed in 2018, is expected to be achieved by 2026. Due to certain factors (such as COVID-19), the actual situation may be different from the predicted results.

A Novel DGM(1, N) Model with Interval Grey Action Quantity and Its Application for Forecasting Hydroelectricity Consumption of China

This paper addresses the issue of the conventional DGM(1, N) model’s prediction results not taking into account the grey system theory pri1nciple of the “non-uniqueness of solutions”. Firstly, before presenting the interval grey action quantity, the practical significance of grey action quantity is examined. In the DGM(1, N) model, the grey action quantity is transformed into an interval grey action quantity. Then, the calculation of the parameters uses the least squares method. A DGM(1, N, ⊗c) model containing interval grey action is then built, and meanwhile, the program code for DGM(1, N, ⊗c) is provided. Lastly, the aforementioned model is used to forecast the hydroelectricity consumption of China. The findings indicate that it produces more rational outcomes than the traditional DGM(1, N) model. Overall, the research carries significant pragmatic implications for broadening the conceptual underpinnings of multivariate grey forecasting models and enhancing their structural arrangement.

Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model

Forecasting renewable energy generation with a novel flexible nonlinear multivariable discrete grey prediction model

Posterior Variance Test: Ex ante Evaluation of Grey Forecasting model

Scholars frequently use ex post evaluation metrics such as the Mean Absolute Percentage Error (MAPE) to estimate the forecast accuracy. However, ex ante metrics are essential to know whether a given forecasting model is suitable for a given variable irrespective of the outcome of ex post evaluations. The ex ante measures help us ensure that the forecast is accurate, not by chance. The current study presents the Posterior Variance Test (PVT), which can serve as an ex ante measure of grey forecast accuracy. The study forecasted the methane emissions from Australia and India using a grey forecasting model and found that even though the MAPE generated "accurate forecasts" for both cases, the PVT invalidated the model's suitability for one of the two cases. The data visualization also corroborated the outcome of the PVT.

Empirical analysis of green finance and high-quality economic development in the Yangtze River Delta based on VAR and coupling coordination model

The Yangtze River Delta region is one of the most active regions in China’s economic development and financial innovation, and its green finance is at the leading level in the country. To promote the integrated development of green finance in China’s Yangtze River Delta and boost high-quality economic development, this paper uses the data of three provinces and one city in the Yangtze River Delta from 2010 to 2021, constructs the green finance development index and the high-quality economic development index by entropy weight method, and constructs the VAR model to analyze the interaction between the two. The coupling coordination model is used to analyze the coupling coordination degree between green finance and high-quality economic development, and the correlation between green finance and high-quality economic development is deeply analyzed by using the gray correlation method. Finally, the gray prediction model is used to predict the coupling coordination between green finance and high-quality economic development. The research shows that green finance and high-quality economic development have short-term mutual promotion effects, and the coupling and coordination are good, but there are regional differences. The overall degree of correlation between the two is medium to upper, and the interaction force is strong. From the forecast results, the coupling coordination and coupling priority of the Yangtze River Delta region show positive development, and Anhui Province has more room for improvement. Based on the above research, this paper proposes to establish a green finance integrated development mechanism to promote regional green finance synergistic development, promote green finance infrastructure interconnection to better realize resource allocation and information sharing, attract more professionals to participate in green finance innovation, and build a favorable external development environment for green finance.

Chinese Provincial Air Pollutant Concentration Prediction over the Long Term

Although China’s urban air quality has improved, there are still many cities that do not meet China’s ambient air quality standards and experience serious air pollution problems, causing tremendous damage to people’s health and sustainable social development. For the sake of obtaining the specific time when China’s ambient air quality will reach the standard, the annual mean air pollutant concentrations of 27 Chinese provinces are predicted and analyzed. Based on original data from air pollutant concentrations in 27 Chinese provinces from 2017 to 2021, a gray prediction model with fractional order accumulation is established to analyze and predict the concentration of pollutants in 27 provinces. The applicability of the model is then validated by mean absolute percentage error values. According to the forecast results, by 2026, the concentrations of six pollutants, PM2.5, PM10, SO2, NO2, CO, and O3, will all meet Class II air quality standards in 25 Chinese provinces, namely Beijing, Chongqing, Shanghai, Hebei, Jiangsu, Shanxi, Zhejiang, Liaoning, Anhui, Jilin, Fujian, Heilongjiang, Jiangxi, Hubei, Qinghai, Hunan, Guangdong, Hainan, Guangxi, Guizhou, Shaanxi, Gansu, Yunnan, Inner Mongolia, and Xinjiang (corrected for the effect of sandstorms). Tianjin, Sichuan, and Xinjiang (not corrected for the effect of sandstorms) still exceed the standard in the annual mean concentration of PM2.5, NO2, and PM10, respectively. Sichuan and Tianjin are, respectively, expected to meet Class II air quality standards in 2027 and 2030, and Xinjiang (not corrected for the effect of sandstorms) is expected to fail to meet Class II standards in the next 15 years. Finally, the current situation with respect to China’s ambient air quality in 27 Chinese provinces is analyzed, and corresponding suggestions are put forward to offer an explicit direction for relevant departments.

Research on component analysis and identification based on clustering and prediction model

To better study the ancient relics of glass types and content of chemical composition, this article first USES no sequence grey prediction model to predict and distribution rules of cultural relics of the weathering before chemical component content, and then USES the K Means clustering will classify glass, finally set up the BP neural network model, based on the experimental data respectively set up the training set and validation set, Predicting the type of glass artifacts. It solves the problem of composition analysis and identification of glass cultural relics.

Mechanical properties and microscopic characteristics of steel fiber coal gangue concrete

Incorporation of coal gangue in the concrete mixes can realize utilization of the solid waste and reduce extraction/use of natural aggregates. To improve the mechanical properties of coal gangue concrete, this paper studies use of steel fibre together with coal gangue coarse aggregate, coal gangue fine aggregate/sand in various concrete mixes. The effect of volume dosages of steel fibre and different levels of replacing nature coarse aggregate and river sand with coal gangue aggregates on concrete compressive strength was first investigated. Then, a design of experiment using orthogonal test was adopted to study concrete mixes with 3 factors, namely, coal gangue coarse aggregate, coal gangue sand and steel fibre, and each at 3 levels. Through multidimensional statistical data analysis of the test results, the primary and secondary factors and the optimal composition of the steel fibre reinforced coal gangue concrete were identified, and a grey prediction model for compressive strength of the concrete mixes established. The microstructural characteristics and failure mechanism of steel fiber reinforced coal gangue concrete was also studied and discussed.

RESEARCH ON ONLINE PREDICTION OF SOFT TISSUE MECHANICAL RESPONSE BASED ON GREY MODEL

Soft tissue is an important operation object in robot-assisted surgery and its mechanical response is of great significance to the precision of surgical operation. Accurate prediction of soft tissue mechanical properties can effectively avoid the potential damage of biological tissue caused by excessive operating force. In this paper, three typical mechanical responses of soft tissue were obtained by no-slip compression experiments of liver tissue. Second, the prediction model of soft tissue mechanical response based on gray prediction was established, and the influence of key parameters of the model on the precision of mechanical response prediction was analyzed. The results show that the gray prediction model can accurately predict the mechanical response of soft tissue, and the prediction accuracy is the highest when the number of historical data is 7. The prediction method of soft tissue mechanical response proposed in this paper will provide important data reference for accurate operation of surgery.

Marine and land economy–energy–environment systems forecasting by novel structural-adaptive fractional time-delay nonlinear systematic grey model

Rapid economic growth and rising energy consumption have inevitably led to environmental problems, which is a cutting-edge issue of sustainable development. To distinguish the internal structure of marine and land economy–energy–environment (3E) systems and the non-linear relationships between the variables, a novel structural-adaptive fractional time-delay nonlinear systematic grey prediction model (FTDNSGM(1,m)) is established. Additionally, the solving process of each layer model of FTDNSGM(1,m) is described in detail, and the derived models of underlying layer FBTDGM(1,1) and upper layer FBTDGM(1,N) are deduced respectively, which constitute the nested grey difference equations. Furthermore, combined with comparative analysis and Monte-Carlo simulation, optimal non-linear parameters are determined by Beluga Whale Optimization (BWO) algorithm with robustness. Empirically, based on five case datasets under the national–regional dimensions, the FTDNSGM(1,m) model is scientifically verified to have superlative validity and stability. The results demonstrate that the average MAPEs of the proposed model in training and test sets are 0.38%, 0.28%, 0.34%, 0.26%, 0.21%, and 0.38%, 0.88%, 0.47%, 1.36%, 0.67%. Ulteriorly, the proposed model is of applicability to assess the impact on 3E systems attributed to the COVID-19 pandemic in 2020 and 2021. Predictably, from 2022 to 2025, with sustained economic growth, marine and land energy consumption will rise within reasonable limits, while coastal water quality will continue to grow and NOx emissions will keep going down.

Matching degree evaluation between new urbanization and carbon emission system in China: a case study of Anhui Province in China

In order to reveal the relationship between new urbanization and carbon emission to provide reference opinions for the construction of low-carbon urbanization, an evaluation system between new urbanization and carbon emission was constructed. Then their matching degree relationship was analyzed by coupling coordination degree model based on the data from 2012 to 2021 in Anhui Province, and their development trend from 2023 to 2032 was predicted by gray prediction model. The results show that: (1) New urbanization and carbon emission have the co-trend effect, and the consistency of core impact factors is relatively significant. Among them, the level of new urbanization increases from 0.058 in 2012 to 0.699 in 2021 and carbon emission development increases from 0.023 in 2012 to 0.165 in 2021, which both showing an upward trend. Meanwhile, social urbanization and population carbon emission are the core influencing factors. (2) The coupling coordination degree between new urbanization and carbon emission is low, but the synergy trend is optimistic and there is a large room for improvement. Among them, the coupling coordination coefficient of the coupling system rises from 0.136 in 2012 to 1.412 in 2021 (antagonistic phase), and then reaches 0.820 by 2032 (highly coordinated phase) by forecast. It shows that their current development is unbalanced, but the development trend is good, and there is a chance for improvement. This paper deepens the understanding of the logical correlation between new urbanization and carbon emission, and the following views are formed: (1) Low-carbon development is still the mainstream of new urbanization; (2) The coordination development of new urbanization and carbon emission reduction should be strengthened.

Forecast of natural gas consumption in 30 regions of China under dual carbon target.

Natural gas is an environmentally friendly and low-carbon clean energy. Its replacement of coal and other fossil energy sources will be important in China's carbon peaking and carbon neutrality goals. The Chinese government has also introduced many policies to encourage the development of natural gas. Therefore, it is of great significance to forecast the natural gas consumption. The grey prediction model has the unique advantage that it can perform well in the case of inadequate sample size. In this paper, the fractional cumulative grey model (FGM(1,1)) is used to forecast the natural gas consumption of 30 areas (provinces, cities, and autonomous regions) in China from 2022 to 2030. According to the reasonable forecast results, except for a few special areas, the consumption in other areas of China will continue to rise in recent years. By analyzing the results, it can also be clearly concluded that the natural gas consumption has regional characteristics. The consumption in 19 regions shows a rapid growth trend, 8 regions show a steady growth trend, and 3 regions show a downward trend. The prediction results and analysis will provide some reference for different regions to formulate natural gas-related policies.

How critical mineral supply security affects China NEVs industry? Based on a prediction for chromium and cobalt in 2030

As an important means to enhance energy security and air pollution pressure, new energy vehicles (NEVs) are regarded as a strategic emerging industry in China, as its industrial development is severely constrained by the availability of critical minerals. Nevertheless, the existing literature lacks empirical studies on the impact of supply security of critical minerals on the development of China's NEVs industry. In this paper, we investigated the influence mechanism of chrome and cobalt supply security on the development of China’s NEVs industry by using the multivariate stepwise regression method and predicted China’s NEVs long-term production scale in 2030 by adopting the gray prediction model. The results indicate that: (1) The supply security of chrome in China has a positive contribution to the production of NEVs; (2) As an important raw material for battery production, the growth rate of NEVs increases significantly when the supply of cobalt is secured, and is expected to be 30 times higher by 2030 than in 2020; and (3) Driven by the government's energy-saving and environmental protection policies, the production of NEVs is on a sharp upward trend, and it is expected that NEVs output will reach 485.883 million units in 2030. Finally, relevant policy recommendations are proposed based on these findings. The government should take various measures to expand the supply channels, improve the comprehensive utilization of mineral resources to ensure the safe supply of cobalt, chrome, and other mineral resources, and promote the growth of NEVs.

A novel nonlinear grey multivariate prediction model based on energy structure and its application to energy consumption

Energy consumption can effectively reflect not only the regional economic and industrial structure but also the regional energy utilization efficiency. Effective analysis of energy consumption structures and further scientific understanding of energy consumption are crucial to the formulation of energy development and utilization policies. This paper establishes a nonlinear differential equation for the diversified development of oil-gas, coal, and other energy sources for energy conservation, consumption reduction, and optimization of energy structure and establishes a nonlinear grey multivariate prediction model for energy structure using the differential equation and grey difference information. Specifically, the relationships among several differential functions in the system are analyzed, and specific modeling steps are given. By solving the parameter estimates and time response functions of the model, the consumption of oil-gas and other energy sources can be predicted simultaneously, and the novel model's effectiveness is demonstrated from three different perspectives. The results show that the novel model can effectively predict the consumption of oil-gas and other energy sources as well as the price and consumption of coal in China from 2021 to 2026, and its prediction results can provide effective information for maintaining a sustainable state of the energy system and formulating reasonable and effective control measures.

Combined forecasting approach for product quality based on support vector regression and gray forecasting model

Forecasting product quality by incorporating customer satisfaction perception factors is an intriguing research area, which can promote the sustainable development of enterprises. To address small-sample random time series data, this study proposes a combined forecasting approach (CFA) for the product quality index that considers perception factors. The proposed approach is based on the support vector regression (SVR) and an improved gray forecasting model (GFM). First, the study constructs a system of perception factors related to defect parts per million (DPPM). Then, the key perception factors (KPF) are selected using the gray entropy relational degree, which is derived from gray relational analysis and information entropy. Then, a multivariable GFM is proposed based on the weighted Markov and the derived form of the gray model to reduce the forecasting error. Finally, a CFA is constructed considering KPF and optimized based on the SVR and the proposed GFM to forecast the DPPM. A case study of liquid crystal display is conducted to demonstrate the feasibility of the proposed CFA. The forecast error of the proposed CFA is 3.2%, which is better than those of GFM, SVR, and ARIMA (4.01%, 6.21%, and 9.89%, respectively). The comparison and discussion of methods demonstrate the superiority of the proposed approach for forecasting product quality.

Time-lead nonlinear grey multivariable prediction model with applications

Time lags or leads and non-linearity of complex systems are critical to address in accurate scientific prediction. However, few studies have focused on time-lead and nonlinear models with limited information. Therefore, this paper aims to develop a novel grey multivariate model with time-lead operator τ and nonlinear factor r from the perspective of this problem. Next, the solution of the proposed model is deduced through a derivation method to reduce the morbidity of the model. Then, the parameter identification of the model is given on the basis of the least-squares method. The grey prediction evolution algorithm is employed to find the optimal value of parameters τ and r to meet the minimum errors. Finally, house prices of Wuhan, Beijing and Shanghai in China are performed to verify the validity and reliability of the new model. The results demonstrate the model we proposed has satisfactory prediction precision, demonstrating that time-lead and nonlinear factor in the model is reasonable. At the end of the paper, the house prices of these three cities from 2022 to 2028 are also predicted. The average annual growth rate in the three regions is 2.49%, 3.57% and 2.97%, respectively. These results are useful to policy analysis regarding house price correlations. They provide insights for market investors and policy makers.

Performance predictions for sustainability governance of firms: implications to select Indian firms

PurposeThe author aims to study and predict the sustainability governance performances of firms using an advanced grey prediction model. The case implication of the prediction model is also studied considering select firms in the Indian context.Design/methodology/approachThe author has proposed an advanced grey prediction model, the first-entry grey prediction model (FGM (1, 1)) for forecasting the sustainability governance performances of firms. The proposed model is tested using the periodic data of sustainability governance performances of 10 Indian firms.FindingsThe author observes that the majority of firms (6 out of 10) show dipping performances for sustainability governance for the future predicted period. This throws insights into the direction of improving good governance practices for Indian firms.Practical implicationsThe idea and motivation for sustainability-focussed governance need a bi-directional focus from the side of managers that act as the agents and from the side of shareholders that act as the principals, as seen from an agency theory perspective for sustainability governance.Social implicationsSustainability governance culture can be inculcated to a firm at the strategic level by having a bi-directional focus from managers and shareholders, so as to enhance the social and environmental sustainability performances.Originality/valueThe governance performance evaluations for firms particularly in developing countries were not dated back more than a decade or two. Hence, the author implements a prediction model that can be best suited, when there are small periodic data sets available for prediction.

Mechanism of single variable grey forecasting modelling: Integration of increment and growth rate

Grey forecasting models as a kind of time series model have been widely applied in different areas, which have achieved high-precision performance for different series with evolutionary characteristics. This paper aims to deconstruct its modelling mechanism from the perspective of data characteristics. First, classical increment and growth rate relationship is analysed and a unified representation of the mixed relationship with increment and growth rate is given. To establish the relationship between grey forecasting models and traditional models, the mechanism is analysed by deconstructing discrete structural components. Then, the series of evolutionary characteristics in main grey forecasting models are linked and summarized. The affine properties of parameter estimation and predicted value are analysed before simulation. Furthermore, through numerical simulations, the performance of grey forecasting models for different feature sequences and the difference between single variable models is demonstrated. Finally, results in the real case show that MAPEs of grey forecasting models have controlled under 4% for different sequences, which are suitable for mixed feature sequences.

Temporal trends and projections of gynecological cancers in China, 2007–2030

BackgroundGynecological cancer will become a more important public health problem in future years but limited evidence on gynecological cancer burden in China.MethodsWe extracted age-specific rate of cancer cases and deaths during 2007–2016 from the Chinese Cancer Registry Annual Report, and estimated age-specific population size using the data released by National Bureau of Statistics of China. Cancer burden were calculated by multiplying the rates with the population size. Temporal trends of the cancer cases, incidence, deaths, and mortality during 2007–2016 were calculated by JoinPoint Regression Program, and from 2017 to 2030 were projected by grey prediction model GM (1,1).ResultsIn China, total gynecological cancer cases increased from 177,839 to 241,800, with the average annual percentage change of 3.5% (95% CI: 2.7–4.3%) during 2007–2016. Cervical, uterine, ovarian, vulva, and other gynecological cancer cases increased by 4.1% (95% CI: 3.3–4.9%), 3.3% (95% CI: 2.6–4.1%), 2.4% (95% CI: 1.4–3.5%), 4.4% (95% CI: 2.5–6.4%), and 3.6% (95% CI: 1.4–5.9%) respectively. From 2017 to 2030, projected gynecological cancer cases are changing from 246,581 to 408,314. Cervical, vulva and vaginal cancers showed evident upward trend, while uterine and ovarian cancer cases are slightly increasing. The increases for age-standardized incidence rates were similar with that of cancer cases. Temporal trends of cancer deaths and mortality were similar with that of cancer cases and incidence during 2007–2030, except that uterine cancer deaths and mortality were declined.ConclusionsWith the aging of population and other increased risk factors, the burden of gynecological cancers in China is likely to be grew rapidly in the future, comprehensive gynecological cancer control should be concerned.

Forecasting CO2 emissions from road fuel combustion using grey prediction models: A novel approach

This paper proposes an optimized wavelet transform Hausdorff multivariate grey model (OWTHGM(1,N)) that addresses some of the weaknesses of the conventional GM(1,N) model such as inaccurate prediction and poor stability. Three improvements have been made: First, all inputs are filtered using a wavelet transform; second, a new time response function is established using the Hausdorff derivative; and finally, the use of Rao's algorithm to optimise the model's parameters as well as the ξ-order accumulated value of the observation data described by the Hausdorff derivative. In order to demonstrate the effectiveness of OWTHGM(1,N), it is applied to predict CO2 emissions from road fuel combustion. The new model scores 1.27% MAPE and 79.983 RMSE, and is therefore more accurate than competing models. OWTHGM(1,N) could therefore serve a reliable forecasting tool and used to monitor the evolution of CO2 emissions in Cameroon. The forecast results also serve as a sound foundation for the formulation of energy consumption strategies and environmental policies.• Modification, extension and optimization of grey multivariate model is done.• The model is very generic can be applied to a wide variety of energy sectors.• OWTHGM(1,N) is a valid forecasting tool that can be used to track CO2 emissions.