Search Results (32)

Green technology innovation (GTI) is pivotal for driving energy transition and low-carbon development in manufacturing. This study evaluates the spatiotemporal efficiency and predicts trends of GTI in China’s Yangtze River Economic Belt (YREB, 2010–2022) using a combined “input-desirable output-undesirable output” framework. Combining the SBM and super-efficiency SBM models, we evaluate regional GTI efficiency (2010–2022) and reveal its spatiotemporal patterns. An improved GM(1,N|λ,γ) model with a new information adjustment parameter (λ) and nonlinear parameter (γ) is applied for prediction. Key findings include: (1) The GTI efficiency remains generally low during the study period (provincial average: 0.7049–1.4526), showing an “east-high, west-low” spatial heterogeneity. Temporally, provincial efficiency peaked in 2016, with intensified fluctuations around 2020 due to policy iterations and external shocks. (2) Regional efficiency displays a stepwise decline pattern from downstream to middle-upstream areas. Middle-upstream regions face efficiency constraints from insufficient inputs and undesirable output redundancy, yet exhibit significant optimization potential. (3) Parameter analysis highlights that downstream provinces (γ ≈ 1) exhibit mature green adoption, while mid-upstream regions (e.g., Hubei) face severe technological lock-in and reliance on traditional energy. Additionally, middle and downstream provinces (e.g., Sichuan, Anhui) with low λ values show rapid policy responsiveness, but face efficiency volatility from frequent shifts. (4) The improved GM(1,N|λ,γ) model shows markedly enhanced prediction accuracy compared to traditional grey models, effectively addressing the “poor-information, grey-characteristic” data trend extraction challenges in GTI research. Based on these findings, targeted policy recommendations are proposed to advance GTI development. Full article

In this study, grey system theory is applied through the implementation of GM(1,1) modelling and Grey Relational Analysis (GRA) to forecast and evaluate sturgeon aquaculture production dynamics in Guizhou Province. The results demonstrate a marked temporal dependency in predictive efficacy, with GM(1,1) exhibiting a superior short-term forecasting performance that progressively diminishes with temporal extension. Utilizing 2018–2022 observational data, the GM(1,1) framework achieved Grade 2 precision (mean absolute percentage error, MAPE = 4.172%; 1% <$\overline{ ∆k}$ ≤ 5%), projecting sustained annual production growth. The decade-long forecast (2023–2032) yielded the following production estimates (×10³ tons): 32.3, 39.1, 47.3, 57.2, 69.2, 83.7, 101.2, 122.4, 148.1, and 179.2. GRA identified three principal determinants: the aquatic seed production value (X₉, r = 0.8336), freshwater fishery output (X₂, r = 0.8019), and per capita fisher income (X₅, r = 0.8003). Furthermore, technological promotion funding (X₆) and fishery workforce parameters (X₄), while demonstrating weaker correlations (r < 0.75), maintain critical roles in technological advancement and labour competency enhancement. This methodological framework provides empirical support for sustainable development strategies in Guizhou’s sturgeon aquaculture sector, emphasizing the necessity of temporal-scale considerations and multifactorial optimization in production management. Full article

This study investigates the level of sustainable development, evolution patterns, and obstacles in Qinghai Province. Considering the province’s unique characteristics and ecological significance, we have established an evaluation indicator system based on the DPSIR model. The entropy weight–TOPSIS model is used to assess the overall sustainability of Qinghai from 2008 to 2022. The grey GM(1,1) model is used to predict future sustainability trends, while the coupling coordination model quantifies the degree of coordination among subsystems. Furthermore, the barrier degree model is used to explore the factors hindering the improvement of Qinghai’s sustainable development. (1) The study finds that Qinghai’s overall sustainable development has shown a fluctuating upward trend, increasing from a weaker phase in 2008 to a stronger phase in 2022. All five subsystems in the sustainability evaluation system have shown gradual improvements in their index scores. This suggests that Qinghai’s sustainability level is expected to continue improving in the future. (2) From 2008 to 2022, the highest barrier degrees were observed in the pressure and state systems, with the barrier degrees of other systems gradually decreasing. Nine main factors, including the number of students in higher education, urban unemployment rate at year-end, and input–output ratio, have been identified as the obstacles to improving the province’s sustainable development level. (3) The coupling coordination degree of the five subsystems has shown a positive development trend, progressing through three stages: mild imbalance, basic coordination, and good coordination. The coordination type has shifted from deterioration to improvement. To achieve high-level sustainable development in Qinghai, leveraging the province’s advantageous environmental resources is crucial. Strengthening ecological protection, optimizing the industrial structure, accelerating urbanization, and emphasizing science and education are key pathways for Qinghai’s future development. Full article

An interchange is a pivotal traffic facility that connects highways and controls access. It is necessary to study their dynamic response characteristics to analyze the operational safety of ramp bridges on interchanges. Based on the numerical simulation results of the finite element model of the Fuxing Interchange Bridge, non-destructive measurement techniques were used to conduct field dynamic load tests on the bridge, including ramp strain testing and acceleration testing. These tests aimed to study the dynamic response characteristics of the ramp bridge under moving loads. Due to the design speed limitation of the ramp bridge, the grey prediction GM(1, 1) model was used to predict the maximum dynamic deflection, maximum dynamic strain, and vibration acceleration when the vehicle speed was 60 km/h. Subsequently, finite element software was used to simulate the dynamic deflection under vehicle speeds ranging from 30 to 60 km/h. The simulated value was compared with the predicted value, and the difference between the simulated value and the predicted value was slight. This model can evaluate the operational safety performance of off-ramps at different speeds. Full article

For time series forecasting, multivariate grey models are excellent at handling incomplete or vague information. The GM(1, N) model represents this group of models and has been widely used in various fields. However, constructing a meaningful GM(1, N) model is challenging due to its more complex structure compared to the construction of the univariate grey model GM(1, 1). Typically, fitting and prediction errors of GM(1, N) are not ideal in practical applications, which limits the application of the model. This study presents the neural ordinary differential equation multivariate grey model (NMGM), a new multivariate grey model that aims to enhance the precision of multivariate grey models. NMGM employs a novel whitening equation with neural ordinary differential equations, showcasing higher predictive accuracy and broader applicability than previous models. It can more effectively learn features from various data samples. In experimental validation, our novel model is first used to predict China’s per capita energy consumption, and it performed best in both the test and validation sets, with mean absolute percentage errors (MAPEs) of 0.2537% and 0.7381%, respectively. The optimal results for the compared models are 0.5298% and 1.106%. Then, our model predicts China’s total renewable energy with lower mean absolute percentage errors (MAPEs) of 0.9566% and 0.7896% for the test and validation sets, respectively. The leading outcomes for the competing models are 1.0188% and 1.1493%. The outcomes demonstrate that this novel model exhibits a higher performance than other models. Full article

The development height and settlement prediction of water-conducting fracture zones caused by coal seam mining play an important role in the stability of overburden aquifers and the safety of roadways. Based on the engineering geological data of the J60 borehole in the Daliuta Coal Mine and the mining conditions of the 2⁻² coal seam, China, this study established a similar material test model of mining overburden. The deformation characteristics of overlying strata in the mining process of coal seam were studied by using distributed optical fiber sensing technology, and the development height of water flowing fractured zone was determined. According to the equidistant sampling characteristics of Brillouin optical time domain reflection technology and the principle of the grey theory model, the settlement prediction model of the water-conducting fracture zone was established. By analyzing and comparing the prediction accuracy of the GM (1, 1) model, grey progressive model, and metabolic model, the optimal method for settlement prediction of the water-conducting fracture zone was discussed. The results show that, for the metabolic model, with the increase in the number of test sets and the decrease in the number of prediction sets, the mean square error ratio c and the small error probability p of the prediction accuracy evaluation parameters display a downward trend. The accuracy is related to the sudden change in the settlement of the water-conducting fracture zone caused by the breaking of the key stratum of the overlying rock. The optimal time of test sets selected for the best settlement prediction model is 7~8, and that of prediction sets selected is 5~6. For the GM (1, 1) model and the grey progressive model, the prediction accuracy of mining overburden subsidence is grade 4, which is not suitable for settlement prediction of water-flowing fractured zones. Full article

On the decision of whether to continue to implement the industrial support policy, two scenarios are set to predict the market penetration rate of China’s electric vehicles (EVs) (In this paper, the term Electric Vehicles (EVs) refers to both full-battery EVs and plug-in hybrids). In order to weaken the disturbance caused by international oil prices and industrial policies, the grey buffer operator was firstly applied, to preprocess the original data series. The sales data for EVs and fuel vehicles were buffered for second order and first order, respectively. Based on the obtained buffer data sequence, the GM (1, 1) model was used to predict the sales of EVs and fuel vehicles between 2022 and 2025 in China. The results demonstrate a significantly improved fit compared to directly modeling the raw data. This method is suitable for studying the market penetration rate prediction of China’s EVs. If the industry support policies continue (Scenario I), an EV market penetration rate of 22.45% can be achieved in 2024, and the expected target can be achieved one year ahead of schedule. Even if the corresponding industrial support policies are no longer implemented (Scenario II), the EV market penetration rate will reach 20.58% in 2025, and the set target of 20% will be achieved on schedule. Full article

Research on the geographical aspects of the digital economy is valuable. We base our study on 10 consecutive years of panel data from 2011–2020 for 31 Chinese provinces. First, we measure the Digital Economy Index using the entropy weight method and analyze its spatiotemporal heterogeneity characteristics using the Exploratory Spatial Data Analysis (ESDA) method. Next, the Grey Model (GM) is utilized to conduct time series predictions of each geographical unit. Finally, we use the GM predicted values and Geographic Weighted Regression (GWR) model to explore the spatial heterogeneity effects of external factors. This study finds that: (1) The overall development shows a trend of vigorous growth, with significant spatial heterogeneity. The gradient difference shows a decreasing trend from the eastern coastal areas to the western inland areas. (2) There is an obvious “digital divide” and a “Matthew effect” in regional development, with agglomeration and spillover effects gradually increasing. (3) Considering the influencing factors, technological progress has a positive impact, and the technology-oriented spatial spillover is obvious, showing a pattern of high in the south and low in the north. The industrial structure is significantly positive, and increases year by year, showing a distribution characteristic of high in the north and low in the south in general, with a clear effect of reducing the “bipolar” distribution. The marginal effects of government support and foreign investment are reduced and there is spatial non-stationarity. This study provides a scientific basis for further research on the spatial development of the digital economy. Full article

This study calculated CO₂ emissions related to the consumption of primary energy by five sectors in the Yangtze River Delta region over 2000 to 2019. The Logarithmic Mean Divisia Index (LMDI) decomposition method was used to establish the factor decomposition model of CO₂ emissions change. The LMDI model was modified to assess the impact of five influencing factors, namely energy structure, energy intensity, industrial structure, economic output, and population size, on CO₂ emissions in the Yangtze River Delta region over the study period. The empirical results show that economic output has the largest positive effect on the growth in CO₂ emissions. Population size is the second most important factor promoting the growth in CO₂ emissions. Energy intensity is the most inhibitory factor to restrain CO₂ emissions, with a significant negative effect. Energy structure and industrial structure contribute insignificantly to CO₂ emissions. Using data on CO₂ emissions in the Yangtze River Delta region from 2000 to 2019, the GM (1, 1) model was applied for future forecasts of primary energy consumption and CO₂ emissions. Specific policy suggestions to mitigate CO₂ emissions in Yangtze River Delta region are provided. Full article

The tandem metal inert gas (MIG) process uses two wires that are continuously fed through a special welding torch and disbursed to form a single molten pool. Within the contact tip of the modern approach, the wires are electrically insulated from one another. This study identified the effect of welding electrode spacing on the distortion of AA5052 aluminum plates and different mechanical properties including hardness and thermal cycle using grey relational analysis. Plate distortion was subsequently predicted using the grey prediction model GM (1, 6). This research used a pair of 400 mm × 75 mm × 5 mm of AA5052 plates and electrode distances of 18, 27, and 36 mm. The welding current, voltage, welding speed, and argon flow rate were 130 A, 23 V, 7 mm/s, and 17 L/min, respectively. The temperature was measured using a type-K thermocouple at 10, 20, 30, and 40 mm from the center of the weld bead. The smallest distortion at an electrode distance of 27 mm was 1.4 mm. At an electrode distance of 27 mm, the plate may reach a proper peak temperature where the amount of heat input and dissipation rate are similar to those for electrode distances of 18 mm and 36 mm. The highest relative VHN of 57 was found in the BM, while the lowest, 46, was found in the WM, showing good agreement with their respective grain sizes. Six parameters were designed using grey relational analysis (GRA) and subsequently employed in the grey prediction model GM (1, 6). Process evaluation results show that predictions for welding distortions are consistent with actual results, thus, the GM (1, 6) model can be used as a predictive model for welding distortions of 5052 aluminum plates. Full article

The coupling of and coordination between science and technology innovation (STI) and sustainable development (SD) is a basic requirement for Anhui Province’s economic high-quality development. According to panel data of 16 prefecture-level cities in Anhui Province from 2010 to 2021, the entropy method was applied to quantify the comprehensive development level of the two systems. The models of coupling coordination degree, grey GM (1, 1), and ARIMA prediction were constructed to analyze the spatiotemporal dynamic evolution features of the two systems’ coupling coordination. In the time series, the two systems’ comprehensive development showed a steady increase, a high level of coupling, and an increasing overall trend of coupling coordination. Moreover, the two systems’ coupling and coordination levels show the gradient spatial differentiation characteristics of “central > east > west.” The prediction shows that the two systems’ coupling coordination degree exhibits a monotonic increasing trend and reaches the optimal coupling coordination state around 2030. This study provides a decision-making reference for the implementation of the innovation-driven development strategy of Anhui Province. Full article

During spinning, the chemical component content of natural fibers has a great influence on the mechanical properties. How to rapidly and accurately measure these properties has become the focus of the industry. In this work, a grey model (GM) for rapid and accurate prediction of the mechanical properties of windmill palm fiber (WPF) was established to explore the effect of chemical component content on the Young’s modulus. The chemical component content of cellulose, hemicellulose, and lignin in WPF was studied using near-infrared (NIR) spectroscopy, and an NIR prediction model was established, with the measured chemical values as the control. The value of R_C and R_CV were more than 0.9, while the values of RMSEC and RMSEP were less than 1, which reflected the excellent accuracy of the NIR model. External validation and a two-tailed t-test were used to evaluate the accuracy of the NIR model prediction results. The GM(1,4) model of WPF chemical components and the Young’s modulus was established. The model indicated that the increase in cellulose and lignin content could promote the increase in the Young’s modulus, while the increase in hemicellulose content inhibited it. The establishment of the two models provides a theoretical basis for evaluating whether WPF can be used in spinning, which is convenient for the selection of spinning fibers in practical application. Full article

Dust pollution arising out of building demolition has serious health implications on workers, as well as the neighboring communities. Existing research has shown that regulatory and engineering control methods are the most popular for dust pollution control on demolition sites. Though engineering control methods are effective in suppressing dust pollution, they have enormous cost implications for demolition companies. Therefore, accurate prediction of dust treatment costs is an important element of the demolition planning process. However, very little information is available in the existing research about treatment costs. In addition, there has not been any attempt to develop a model which can accurately predict the cost of dust treatment during building demolition. To overcome this knowledge gap, a grey prediction model is built according to the information obtained from twenty previous demolition projects. The historical trend of demolition project cost is combined to establish the prediction model based on GM (1, 1), which can be used to obtain the dust treatment cost of a project with very high accuracy. To further improve the prediction accuracy, this paper also builds a Single Function Residual Identifiability (SFRI) model. The relative error between the actual and predicted dust treatment costs from 2013 to 2021 ranges from 0.003% to 0.077%. Through detailed assessment of various treatment measures using a case study, it was found that the results obtained by the prediction model are very close to the actual costs incurred, which verifies the accuracy of the proposed model. Full article

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide. Full article

This paper uses the idea of fractional order accumulation instead of the form of grey index, and applies the fractional order accumulation prediction model to the economic growth prediction of the member states of the Group of Seven from 1973 to 2016. By comparing different evaluation indexes such as $R^2$, MAD and BIC, it is found that the prediction performance of fractional order cumulative grey prediction model (GM($\alpha$,1)) is significantly improved in the medium and long term compared with the traditional grey prediction model (GM(1,1)). Full article