Search Results (80)

The promotion of new energy vehicles (NEVs) represents a critical strategy for mitigating carbon emissions and air pollution. To evaluate the CO₂ and air pollutant reduction potential of NEVs in the Beijing–Tianjin–Hebei region, this study developed an integrated framework combining gray correlation analysis (GRA) and bidirectional long short-term memory (BiLSTM), referred to as the GRA-BiLSTM model, to forecast the adoption trend of NEVs and calculate the CO₂ and air pollutant emission reduction. The GRA-BiLSTM model developed in this study shows optimal predictive performance. The results indicate that new energy vehicles (NEVs) have great potential for environmental collaborative emission reduction in the transportation sector: it is predicted that by 2035, the total number of NEVs will be nearly 11.88 million, with a cumulative reduction of 2.76 billion tons of carbon emissions and significant reductions in various key air pollutants. This study provides an important quantitative basis for formulating pollution reduction and carbon reduction policies in the transportation sector. Full article

This paper proposes an advanced forecasting model for predicting carbon dioxide (CO₂) emissions in Thailand’s electricity generation sector. The model integrates a multivariate gray model with the fminsearch optimization algorithm in MATLAB (R2025a) to address the critical challenge of accurate emission forecasting, a key driver of climate change. Historical data on CO₂ emissions, gross domestic product (GDP), peak electricity demand, and electricity user numbers are utilized to enhance predictive accuracy. Comparative analysis demonstrates that the optimized model significantly outperforms the conventional multivariate gray model, achieving mean absolute percentage error (MAPE) values of 7.74% for the training set and 1.75% for the testing set. The results highlight the effectiveness of the proposed approach as a robust tool for policymakers and stakeholders in Thailand’s energy sector, offering actionable insights to support informed decision-making in managing and reducing CO₂ emissions. Full article

This longitudinal study applies decade-spanning socioeconomic indicators (2013–2022) from the Beijing–Tianjin–Hebei urban agglomeration. An integrated analytical framework was developed, merging the super-efficiency slack-based measurement (SBM) methodology with entropic weighting techniques to quantify tourism efficiency and economic development. Subsequent phases employed a multi-method analytical cascade: coupling coordination assessment modeling for system interaction analysis, standard deviation ellipses for spatial dispersion characterization, and Markovian transition matrices for temporal pattern identification. The investigation concludes with evolutionary trajectory projections using gray system forecasting GM(1,1) modeling. The analytical findings reveal the following patterns: (1) Within the Beijing–Tianjin–Hebei metropolitan cluster, tourism efficiency demonstrates a consistent upward trajectory, manifesting spatial differentiation characteristics characterized by a dual-core structure centered on Tianjin and Baoding, with higher values observed in northwestern areas compared to southeastern regions. Concurrently, regional disparities exhibit progressive convergence over temporal progression. (2) The level of economic development in the Beijing–Tianjin–Hebei city cluster has been rising steadily, demonstrating a geospatial distribution of ‘central concentration with peripheral attenuation, with the north-east being better than the southwest’, and the gap between the regional differences has become broader over time. (3) The coupling between tourism efficiency and the level of economic development in the Beijing–Tianjin–Hebei city cluster has generally improved, with Beijing and Tianjin predominantly in a coordinated regime, and some cities in Hebei Province about to shift from dysfunctional to coordinated, and, spatially, the coupling and coordination in northern sectors demonstrate superior performance compared to southern counterparts nationally. (4) The coupling coordination degree of the Beijing–Tianjin–Hebei city cluster in the next eight years is predicted by the gray GM(1,1) prediction model and the overall continuation of the growth trend of the Beijing–Tianjin–Hebei city cluster over the past ten years, thus verifying the importance of the regional integrated policy frameworks in the system integration of the Beijing–Tianjin–Hebei metropolitan system. Full article

This study employs an accounting approach to quantitatively assess Chengdu’s ecological value, focusing on agriculture, forestry, animal husbandry, fisheries, climate regulation, water conservation, water quality purification, and air quality improvement. The value of each indicator is calculated and visualized using ArcGIS 10.8, with predictions made for four future time intervals. The analysis reveals the spatial distribution patterns of ecological value across Chengdu. The results indicate the following: (1) From 2015 to 2019, Chengdu’s ecological value indicators demonstrated a positive growth trend, with notable increases in recreation services (CNY 178.5 billion), agriculture, forestry, animal husbandry, and fisheries (CNY 32.88 billion), and water conservation (CNY 9.26 billion). Values exhibited a general decrease from the city center outward. (2) Water quality purification, air quality improvement, and pest control values exhibited slight declines in 2015, 2017, and 2019 compared to 2015. (3) Ecological values demonstrate spatial diversity, with lower values in central areas for categories such as soil conservation and a “high-low-high” pattern for water conservation. Recreation services exhibit a “high in the center, low around the edges” pattern. (4) The gray prediction model forecasts that by 2027, the values for agriculture, forestry, animal husbandry and fisheries, water conservation, and soil conservation will double relative to 2015. Climate regulation and air quality improvement values are predicted to triple, while water quality purification exhibits minimal change. Pest control is expected to decline to 67% of its 2015 value, while the value of recreation services will increase to 12 times its 2015 value. The results of this study reveal the evolutionary characteristics of the ecological value volume index in Chengdu, fill a gap in the field of ecological value volume measurement and prediction in the region, and provide scientific support for understanding the evolutionary trajectory of Chengdu’s ecological environment. Full article

Green hydrogen and ammonia are forecast to play key roles in the deep decarbonization of the global economy. Here we explore the potential of using green hydrogen and ammonia to couple the energy, agriculture, and industrial sectors with India’s national-scale electricity grid. India is an ideal test case as it currently has one of the most ambitious hydrogen programs in the world, with projected electricity demands for hydrogen and ammonia production accounting for over 1500 TWh/yr or nearly 25% of India’s total electricity demand by 2050. We model the ambitious deep decarbonization of India’s electricity grid and half of its steel and fertilizer industries by 2050. We uncover modest risks for India from such a strategy, with many benefits and opportunities. Our analysis suggests that a renewables-based energy system coupled with ammonia off-take sectors has the potential to dramatically reduce India’s greenhouse emissions, reduce requirements for expensive long-duration energy storage or firm generating capacity, reduce the curtailment of renewable energy, provide valuable short-duration and long-duration load-shifting and system resilience to inter-annual weather variations, and replace tens of billions of USD in ammonia and fuel imports each year. All this while potentially powering new multi-billion USD green steel and maritime fuel export industries. The key risk for India in relation to such a strategy lies in the potential for higher costs and reduced benefits if the rest of the world does not match their ambitious investment in renewables, electrolyzers, and clean storage technologies. We show that such a pessimistic outcome could result in the costs of green hydrogen and ammonia staying high for India through 2050, although still within the range of their gray counterparts. If on the other hand, renewable and storage costs continue to decline further with continued global deployment, all the above benefits could be achieved with a reduced levelized cost of hydrogen and ammonia (10–25%), potentially with a modest reduction in total energy system costs (5%). Such an outcome would have profound global implications given India’s central role in the future global energy economy, establishing India’s global leadership in green shipping fuel, agriculture, and steel, while creating an affordable, sustainable, and secure domestic energy supply. Full article

Recently, health risk assessment and early warning systems for high-temperature events have become critical concerns. However, current high-temperature warning systems primarily focus on temperature alone, which fails to accurately reflect the actual heat exposure levels and associated health risks. Therefore, this paper proposes an improved AHP (analytic hierarchy process) combined with a multi-level gray evaluation method for assessing human health risks during high-temperature conditions. A comprehensive early warning system is developed, incorporating various indicators, including human status, building conditions, and weather forecasts, making it more holistic than traditional temperature-based warning systems. A case study shows that the highest evaluation score for young individuals is 3.41, while elderly males receive the highest score of 2.5. Furthermore, the highest evaluation score for males is 3.41, while for females the highest score of 3.1. The warning results indicate that for young individuals, no alert is issued; for the elderly, a red alert is triggered; and for middle-aged individuals, the system issues orange and yellow alerts based on varying levels of risk. This study can be used to monitor health risk and provide alert message to humans. Based on the proposed early warning system, people can be able to predict health risk in time. Full article

Post-disaster rapid recovery and reconstruction, along with the evaluation of these efforts, are crucial for affected areas. They not only facilitate the swift repair of vulnerable systems but also reflect whether the recovery work has enhanced regional coordinated development. This is vital for achieving sustainable development post-reconstruction. This study addresses two main questions: (1) How effective were the recovery and reconstruction efforts in Mianyang, Deyang, and Guangyuan post-Wenchuan earthquake from a socio-economic–ecological system perspective? (2) What are the temporal and spatial changes in the Coordinated Development Index (CDI) of key affected counties? By constructing a framework to assess post-disaster coordinated development, this study utilized the entropy weight method and mean-variance method for the comprehensive weighting of evaluation indicators. The gray system prediction model G(1,1) was used to forecast the coordinated development levels of the three cities from 2019 to 2025. The findings reveal the following: (1) From 2005 to 2018, the CDI of Deyang, Guangyuan, and Mianyang showed a significant upward trend. Post-earthquake reconstruction measures like land planning and ecological restoration notably enhanced regional resilience and promoted coordinated development among social, economic, and ecological systems. (2) Despite overall success in reconstruction, disparities in development levels persist among Mianyang, Deyang, and Guangyuan. Predictions suggest that Deyang, Mianyang, and Guangyuan will achieve high-quality coordinated development in the next 5, 2, and 1 years, respectively. (3) Although significant achievements have been made through industrial restructuring, land reuse planning, and ecological restoration, more precise disaster prevention and mitigation strategies are needed to foster coordinated development among social, economic, and ecological systems. In summary, this study evaluates the post-disaster recovery effects in the hardest-hit areas of the Wenchuan earthquake and forecasts future development, providing a reference for similar post-disaster reconstruction areas in assessing and predicting coordinated development. Full article

The synergistic relation among land resources, water resources, and food production plays a crucial role in sustainable agricultural development. This research constructs a coupling coordination assessment system of the land–water–food (LWF) nexus from 2005 to 2020 for 31 provinces (municipal cities, autonomous regions) in China, and explores the current development status of land, water, and food systems at multiple scales as well as the coupling coordination characteristics of the LWF nexus. The exploring spatial data analysis and spatial Tobit model are used to explain the spatial correlations and influencing factors of coupling coordination development on the LWF nexus. On that basis, the gray GM (1,1) model is used to forecast the future development of the LWF nexus in China. The results show that the comprehensive development indexes of the land system, water system, food system, and LWF nexus are on the rise, but the land system lags behind the water system and food system. The coupling coordination degree of the LWF nexus in different regions ranges from 0.538 to 0.754, and the coupling coordination development of the LWF nexus in China has reached the preliminary coupled coordination type, with an evolutionary process similar to that of its comprehensive development level. Further empirical research shows that there is a significant positive spatial correlation between coupling coordination development levels for the LWF nexus in China. The level of urbanization and agricultural industry agglomeration have negative effects, while economic development, ecological environment, and scientific and technological progress have positive effects. The prediction results indicate that the coupling coordination degree of the LWF nexus in China will show a stable upward trend from 2024 to 2025, and most provinces will reach the intermediate coupled coordination type in 2025. This study can inform decision-making for policy-makers and practitioners and enrich the knowledge hierarchy of the LWF nexus’ sustainable development on the national and regional scales. Full article

Managing foliar corn diseases like northern leaf blight (NLB) and gray leaf spot (GLS), which can occur rapidly and impact yield, requires proactive measures including early scouting and fungicides to mitigate these effects. Decision support tools, which use data from in-field monitors and predicted leaf wetness duration (LWD) intervals based on meteorological conditions, can help growers to anticipate and manage crop diseases effectively. Effective crop disease management programs integrate crop rotation, tillage practices, hybrid selection, and fungicides. However, growers often struggle with correctly timing fungicide applications, achieving only a 30–55% positive return on investment (ROI). This paper describes the development of a disease-warning and fungicide timing system, equally effective at predicting NLB and GLS with ~70% accuracy, that utilizes historical and forecast hourly weather data. This scalable recommendation system represents a valuable tool for proactive, practicable crop disease management, leveraging in-season weather data and advanced modeling techniques to guide fungicide applications, thereby improving profitability and reducing environmental impact. Extensive on-farm trials (>150) conducted between 2020 and 2023 have shown that the predicted fungicide timing out-yielded conventional grower timing by 5 bushels per acre (336 kg/ha) and the untreated check by 9 bushels per acre (605 kg/ha), providing a significantly improved ROI. Full article

Well-production forecasting plays a crucial role in oil and gas development. Traditional methods, such as numerical simulations, require substantial computational effort, while empirical models tend to exhibit poor accuracy. To address these issues, machine learning, a widely adopted artificial intelligence approach, is employed to develop production forecasting models in order to enhance the accuracy of oil and gas well-production predictions. This research focuses on the geological, engineering, and production data of 435 fracturing wells in the North China Oilfield. First, outliers were detected, and missing values were handled using the mean imputation and nearest neighbor methods. Subsequently, Pearson correlation coefficients were utilized to eliminate linearly irrelevant features and optimize the dataset. By calculating the gray correlation degrees, maximum mutual information, feature importance, and Shapley additive explanation (SHAP) values, an in-depth analysis of various dominant factors was conducted. To further assess the importance of these factors, the entropy weight method was employed. Ultimately, 19 features that were highly correlated with the target variable were successfully screened as inputs for subsequent models. Based on the AutoGluon framework, model training was conducted using 5-fold cross-validation combined with bagging and stacking techniques. The training results show that the model achieved an R² of 0.79 on the training set, indicating good fitting ability. This study offers a promising approach for the development of oil and gas production forecasting models. Full article

Formulating a scientifically sound and efficient approach to allocating carbon quota aligned with the carbon peaking goal is a fundamental theoretical and practical challenge within the context of climate-oriented trading in the power sector. Given the highly irrational allocation of carbon allowances in China’s power sector, as well as the expanding role of renewable energy, it is essential to rationalize the use of green energy in the development of carbon reduction in the power sector. This study addresses the risk of “carbon transfer” within the power industry and develops a predictive model for CO₂ emission based on multiple influential factors, thereby proposing a carbon quota distribution scheme adapted to green energy growth. The proposed model employs a hybrid of the gray forecasting model-particle swarm optimization-enhanced back-propagation neural network (GM-PSO-BPNN) for forecasting and allocating the total carbon quota. Assuming consistent total volume control through 2030, carbon quota is distributed to regional power grids in proportion to actual production allocation. Results indicate that the PSO algorithm mitigates local optimization constraints of the standard BP algorithm; the prediction error of carbon emissions by the combined model is significantly smaller than that of the single model, while its identification accuracy reaches 99.46%. With the total national carbon emissions remaining unchanged in 2030, in the end, the regional grids received the following quota values: 873.29 million tons in North China, 522.69 million tons in Northwest China, 194.15 million tons in Northeast China, 1283.16 million tons in East China, 1556.40 million tons in Central China, and 1085.37 million tons in the Southern Power Grid. The power sector can refer to this carbon allowance allocation standard to control carbon emissions in order to meet the industry’s emission reduction standards. Full article

In order to solve the difficulties that the uncertain characteristics of PV output, such as volatility and intermittency, will bring to the development of microgrid scheduling plans, this paper proposes a Transformer–Bidirectional Long Short-Term Memory (BiLSTM) neural network PV power generation forecasting fusion model based on the Newton–Raphson optimization algorithm (NRBO) and Variational Modal Decomposition (VMD). Firstly, the principle of the VMD technique and the Gray Wolf Optimization (GWO) algorithm’s key parameter optimization method for VMD are introduced. Then, the Transformer decoder partially fuses the BiLSTM network and retains the encoder to obtain the body of the prediction model, followed by explaining the principle of the NRBO algorithm. And finally, the VMD-NRBO-Transformer-BiLSTM prediction model and hyperparameter selection are evaluated by the NRBO algorithm. The algorithm sets up a multi-model comparison experiment, and the results show that the prediction model proposed in this paper has the best prediction accuracy and the optimal evaluation index. Full article

(1) Background: In the current competitive market environment, accurately forecasting user needs is crucial for business success. By analyzing user-generated content (UGC) on social network platforms, enterprises can mine potential user needs and discern shifts in these needs, thereby enabling more efficient and precise product design that aligns with user needs. For newly launched products with a limited presence in the market, the scarcity of UGC poses a challenge to businesses seeking to predict user needs from small datasets. (2) Methods: To address this challenge, this paper proposes a model using correlation analysis (CA) and linear regression (LR) combined with multidimensional gray prediction (a CA-LR-GM (1, N) model) to help enterprises use small sample data to predict user needs. Using the UGC of the Xiaomi SU7 as a case study, this paper demonstrates the prediction of user needs for the vehicle and refines the prediction outcomes through an optimization design informed by the principle of optimal key feature distribution. (3) Results: The findings validate the feasibility of the proposed theoretical framework, offering a technical solution for the identification and prediction of user need trends. (4) Conclusions: This research puts forward strategic recommendations for enterprises regarding the optimization of their products. Full article

Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales. Full article

As cracks are a precursor landslide deformation feature, they can provide forecasting information that is useful for the early identification of landslides and determining motion instability characteristics. However, it is difficult to solve the size effect and noise-filtering problems associated with the currently available automatic crack detection methods under complex conditions using single remote sensing data sources. This article uses multidimensional target scene images obtained by UAV photogrammetry as the data source. Firstly, under the premise of fully considering the multidimensional image characteristics of different crack types, this article accomplishes the initial identification of landslide cracks by using six algorithm models with indicators including the roughness, slope, eigenvalue rate of the point cloud and pixel gradient, gray value, and RGB value of the images. Secondly, the initial extraction results are processed through a morphological repair task using three filtering algorithms (calculating the crack orientation, length, and frequency) to address background noise. Finally, this article proposes a multi-dimensional information fusion method, the Bayesian probability of minimum risk methods, to fuse the identification results derived from different models at the decision level. The results show that the six tested algorithm models can be used to effectively extract landslide cracks, providing Area Under the Curve (AUC) values between 0.6 and 0.85. After the repairing and filtering steps, the proposed method removes complex noise and minimizes the loss of real cracks, thus increasing the accuracy of each model by 7.5–55.3%. Multidimensional data fusion methods solve issues associated with the spatial scale effect during crack identification, and the F-score of the fusion model is 0.901. Full article