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Identifying the research topics of China's energy transition policy and predicting future research trends are crucial for policymakers to make informed decisions and advance the energy transition. This study applied the Latent Dirichlet Allocation (LDA) to extract 26 major research topics from the 13,976 abstracts of Chinese policy research papers in web of science (WOS), conceptualized the five most popular topics and used the ARIMA model to predict trends over the next 36 months. The results show that except for Economic and Environmental Impact of Renewable Energy Consumption (Topic13), which will first experience unanticipated fluctuations and then show strong growth capacity, all other topics will continue to remain positive growth trend. Research and Development (Topic3) is driven by the urgency of China's energy transformation. Economic and Environmental Impact of Renewable Energy Consumption (Topic13) is driven by the potential for the development of renewable energy. Economic Impact of Climate Change (Topic26) is driven by the climate change debate. Economic Growth, Urbanization, and Energy Consumption (Topic7) is driven by a discussion of their relationship and the mechanisms influencing them. Regional Efficiency and Productivity Analysis (Topic19) is driven by regional differences in China's energy policies. [ABSTRACT FROM AUTHOR]

• Introduce the state-of-art temporal fusion transformers (TFT) into economic system forecasting based on the high compatibility between deep learning and the nonlinear characteristics of socio-economic systems. • Propose a scientific economic system forecasting framework on the foundation of the extended gross final product (GFP). • Build a four-dimensional evaluation system including numerical error, direction accuracy, timing error and turning point accuracy. • Empirically forecast the monthly output indicators of the Chinese macroeconomic system. • TFT results have obvious advantages over benchmark models including ARIMA, BP, LSTM, TCN and N-BEATS through comparison analysis. Although helpful in reducing the uncertainty associated with economic activities, economic forecasting often suffers from low accuracy. Recognizing the high compatibility between deep learning and the nonlinear characteristics of socioeconomic systems, in this paper, we introduce state-of-the-art temporal fusion transformers (TFTs) into the field of economic system forecasting and predict the performance of the Chinese macroeconomic system. Based on an extended analysis of gross final product (GFP) and the intertemporal dynamic relationship between demand-side indicators and output indicators, we establish a scientific economic forecasting framework. To summarize the forecasting characteristics of the TFT algorithm, we compare its one-step and three-step modeling effects in forecasting output indicators with a series of representative benchmark models. According to our proposed four-dimensional evaluation system, the forecasts for China's macroeconomic system provided by the TFT model have obvious advantages in terms of overall stability, forecasting efficiency, reduction of numerical and timing errors, direction accuracy, and turning point accuracy. The forecast results show that China's economy faces a risk of slowing growth in the post-pandemic period. [ABSTRACT FROM AUTHOR]

Accurate and robust fine-grained expressway traffic volume simulation is a critical issue in intelligent transportation systems and real-time traffic-related applications. However, the fine-grained expressway traffic volumes aggregated from vehicle trajectories heavily rely on individual heterogeneity, making it challenging for modeling and accurate simulation. In order to eliminate the influence of individual heterogeneity on the modeling and simulation of the fine-grained expressway traffic volume, this paper proposes a novel method named Quantum Harmonic Oscillator Model for Fine-grained Expressway Traffic Volume Simulation (FGTVS-QHO). FGTVS-QHO adopts the wave function of the quantum harmonic oscillator model to describe the irregular evolution of the location of the vehicle. Then several optimization strategies are applied to the numerical solution of the wave function. FGTVS-QHO is validated with the expressway traffic volumes of six exits along the Nanjing-Shanghai Expressway in China. The simulation performance of FGTVS-QHO is compared with the methods of Long and Short-Term Memory (LSTM) networks and Autoregressive Integrated Moving Average (ARIMA). FGTVS-QHO shows higher simulation accuracy, less time cost, and lower parameter complexity. Especially, the average simulation accuracy of FGTVS-QHO is improved by 24.80% and 58.05% compared with the above two methods, respectively. The scale effects of FGTVS-QHO also indicate that it is adaptive to simulate the expressway traffic volume at fine time granularity. This paper provides a new potential method for fine-grained expressway traffic volume simulation with strong individual heterogeneity. • A traffic volume simulation model considering individual heterogeneity is proposed. • The irregular evolution of the location of vehicle is expressed by a wave function. • Our model can well simulate the complex fluctuation of fine-grained traffic volume. • Our model has the potential to model traffic with unobservable driving behavior. [ABSTRACT FROM AUTHOR]

Abstract: China is currently facing the threat of serious air pollution and has adopted a series of governance policies. However, the fact that PM 2.5 indicator in some regions often reached far higher than the official threshold since 2015 has reflected that China's current Air Quality Index system for measuring and evaluating air quality is still not perfect. Therefore, this paper extended the current air quality indicator system officially used by China. Using improved Entropy-weighted Factor Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) Method, we calculated the air quality scores of 26 cities in China's most economically developed region - the Yangtze River Delta region based on their daily average concentration numbers of main air pollutants from February 2015 to January 2018. Based on the air quality calculation result, we further studied the effectiveness of air pollution control policies of this region by using the Box-Jenkins Model with fuzzy strategy parameter adjustment. Our findings are: the pollution control policy is more effective to Shanghai, Jiangsu and Zhejiang. The air quality score of Shanghai has improved by 51.07% compared with its level before the policy. Meanwhile, the air quality improvement in Zhejiang province has shown the characteristics of "Campaign-style Governance" right before the G20 Summit, especially that in Hangzhou, the host city of the Summit. On the other hand, maybe due to late start of air pollution control programs, the cities of Anhui province haven't shown obvious improvements in air quality, and some cities have even seen deterioration in air quality scores during our study period. Graphical abstract Image 1 Highlights • We extended the current air quality indicator system officially used by China. • We used the Entropy-weighted Factor Method and TOPSIS Method for assessing. • We studied the control policies by Box-Jenkins Model with fuzzy strategy parameter. • Results and corresponding reasons were analyzed according to provinces and cities. • Policy recommendations for air pollution control in YRD were proposed. [ABSTRACT FROM AUTHOR]

Abstract China is the world's largest net importer of oil and the second largest oil consumer; consequently, changes of China's foreign oil dependence significantly impact both the Chinese and the international oil market. To enhance the forecasting ability of China's foreign oil dependence, this study combines the nonlinear metabolic grey model (NMGM) with the linear autoregressive integrated moving average model (ARIMA), thus obtaining the combined NMGM-ARIMA model. The proposed technique uses the linear ARIMA to correct NMGM forecasting residuals, thus improving forecasting accuracy. The proposed technique achieves a mean absolute error of 2.1–2.3%, reflecting its high reliability. The proposed NMGM-ARIMA was used to forecast China's foreign oil dependence for the period of 2017–2030 from two dimensions. For the first dimension, the gap between China's oil demand and supply was forecast. To fill this gap, China has to import oil; therefore, this gap is responsible for China's foreign oil dependence. For the second dimension, the change of China's foreign oil dependence level was directly forecast. Both dimensions indicate a similar conclusion, namely that the Chinese foreign oil dependence level will increase from 65% in 2016 to over 80% in 2030. A high level of 80% dependence on foreign oil would bring major concern to China. The policy recommendations given at the end of the paper will help China's decision makers respond appropriately. Highlights • The proposed forecasting technique uses the linear ARIMA to correct NMGM residuals. • The mean absolute percent error of the proposed technique is 2.1–2.3%. • Forecasting China's oil gap between production and consumption for 2017–2030. • Forecasting China's foreign oil dependence for 2017–2030. • Two-dimensional forecasting results show China's foreign oil dependence will exceed 80% by 2030. [ABSTRACT FROM AUTHOR]

This paper investigated the impacts of global crude oil price shocks on China's precious metals market using the meliorated autoregressive conditional jump intensity (ARJI) model (Chan and Maheu, 2002) and the extended autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) (Gronwald, 2012), based on the comparative analysis of gold and platinum markets. The results showed that discrete jumps existed in the crude oil market. Considering the volatility clustering of oil returns, the effect of oil price expected shocks on precious metals market was negative (−0.7025,-1.3341), due to the profitability of capital; While that of unexpected shocks was positive (0.0211,0.0645), which can be explained by the adaptive expectation theory. In terms of discrete jumps, the impact of current jumps on precious metals market was significantly negative (−1.4810,-2.6775), according to the risk transmission theory, and the responses tended to be characterized by “overreactions”. In addition, some evidence also suggested permanent volatility effects in China's precious metals market. [ABSTRACT FROM AUTHOR]

• A grey seasonal trend support vector machine for energy forecasting is proposed. • The model can efficiently identify the seasonal fluctuations in energy data. • The model is powerful to predict China's energy production. • The predictive performance of the new model is much superior to the benchmarks. This paper proposed a hybrid ensemble forecasting technique that incorporates the merits of the accumulation generation operation (AGO), least-square support vector regression (LSSVR), dummy variable, and time trend item to forecast the seasonal time series characterized by nonlinearity and uncertainty. This proposed framework, a grey seasonal trend least squares support vector machine (GSTLSSVR), can make it easy to add arbitrary seasonality to any feature, which improves model realism significantly. Furthermore, the grid search method and the last block validation are developed to identify the optimal hyperparameter, making it possible for the developed method to capture seasonal and nonlinear patterns of the original data. For illustration and verification, the quarterly petroleum coke production and monthly crude oil production in China are employed to construct diverse competing models, including the seasonal fluctuation grey model (SGM(1,1)), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters (HW), backpropagation neural network (BPNN), a grey seasonal least squares support vector machine (GSLSSVR), long short term memory (LSTM) and LSSVR. The forecasting values of quarterly petroleum coke production are 668.74, 682.68, 679.99, 704.35 for 2019 and 717.59, 734.99, 727.16, 741.53 for 2020, with the lowest MAPEP value (3.7566%). Moreover, the monthly predicted crude oil production for 2019 are 3113.9, 1645, 1562.2, 1625.4, 1599.3, 1619.1, 1627, 1576.9, 1629.6, 1583.6, and 1615.6, while those counterparts for 2020 are 3093.2, 1625, 1547.5, 1621.5, 1610.6, 1644.9, 1662.9, 1615.4, 1667.4, 1615, 1667.4, 1615, and 1640.2, with the lowest MAPEP value (1.0034%). Generally, the empirical results show that the proposed model obtains superior overall performance to other benchmarks in both case studies. Therefore, this proposed model exhibits promising prospects for identifying seasonal patterns in energy production sequences. [ABSTRACT FROM AUTHOR]

• We consider some indicators that have not been considered in previous studies, such as the impact of historical and social factors, which are very important and necessary in measuring national space exploration capabilities. • We introduce a more reasonable quantity (Equity Evaluation Index) to measure global equity, which is more objective for both stronger and weaker countries in space exploration. • The optimization model takes different aspects of the space industry into consideration (e.g., funding, technology, scientific research), providing suggestions for countries at different developing levels to take advantage of its strengths and cover its weaknesses. In this paper we introduce an equity evaluation index (EEI) to measure the global equity on asteroid mining, and present a global equity evaluation model by applying the analytic hierarchy process (AHP), entropy weight method and the technique for order preference by similarity to ideal solution (TOPSIS). Based on multi-objective optimization, we provide a global cooperative decision model to allocate the inputs and outputs among countries based on their strengths and demands. Furthermore, we analyze the the trends of EEI of each country under the influence of the outbreak events by using the autoregressive integrated moving average model. Our results show that among all countries with foundations in aerospace, China has the highest EEI while Russia ranks the lowest. Humans mining minerals in a cooperative manner will achieve a win-win situation. [ABSTRACT FROM AUTHOR]

• A novel multivariate probabilistic forecasting method. • The impacts of probabilistic forecasting's performance on hydro-wind dispatch. • Appropriate trade-off between running time and benefit. • Case study that optimizes the long-term optimal dispatch scheme. There are two difficulties in long-term optimal dispatch of hydro-wind hybrid systems. First, monthly runoffs and wind speeds have the dynamic characteristics such as variability, instability, seasonality, heteroscedasticity, linear and nonlinear dynamic correlations. Second, hydro-wind hybrid systems have highly non-convex nonlinear constraints. To overcome the problem, this research develops a novel X-12 seasonal adjustment, vector autoregressive integrated moving average (VARIMA), component generalized autoregressive conditional heteroscedasticity (C-GARCH) and dynamic copula mixed model to estimate the joint probability distribution of runoffs and wind speeds. And then, this paper builds a multistage stochastic mixed-integer linear programming (MILP) with the help of several linearization methods. Finally, the paper compares several probabilistic forecasting models' performances and analyzes their impacts on the dispatch of the hydro-wind hybrid system under different hydrological years. A hydro-wind hybrid system in southwest China is taken as an example. The case study leads to the following conclusions: 1) the more sufficient to capture the dynamic characteristics of variables, the higher benefit will be; 2) it is necessary to increase the scale of scenario tree to reduce the electricity shortfall during the dry year; 3) serious spilled water can be caused by insufficient interregional transmission capacity under the wet year and it is the most appropriate to expand the capacity to 8000 MW; 4) the model proposed in this paper can increase the economic benefit by 0.466 × 10 9 C N Y , 1.775 × 10 9 C N Y and 0.400 × 10 9 C N Y during the normal, dry and wet year, respectively. [ABSTRACT FROM AUTHOR]

Statistical and structural characteristics provide us with a multi-dimensional picture of time series. Rooting the properties in a unified scheme is the preliminary step to develop a model that can reproduce most part or even the whole of the picture. In this paper, we proposed a concept called multi-scale transition matrix, which is a series of transition matrices describing the jumping probabilities between states after specified numbers of jumps each. The eigenvectors corresponding to the unitary eigenvalues are identical with the probability distribution function. The second largest eigenvalues depict the upper-boundary curve of the persistence. The change speed of the eigenvector corresponding to the second largest eigenvalue along with time scale displays the relaxation behavior of the time series. The multi-scale matrix maintains the structure of autocorrelation in the original time series and its evolution, which are merged by the averaging procedure in the statistical properties. These predictions are confirmed by using the series generated with the Auto-Regressive Conditional Heteroskedasticity Model, the Auto-Regression Model and the fractional Brownian motion, and the empirical records for Shenzhen Component index in Mainland China and the word length series of the novel entitled Remembrance of Things Past written by Marcel Proust. Hence, the concept is a good candidate of bridges between the multi-dimensional picture and the dynamical models of the time series. [ABSTRACT FROM AUTHOR]

Abstract: This paper compares the predictive performance of ARIMA, artificial neural network and the linear combination models for forecasting wheat price in Chinese market. Empirical results show that the combined model can improve the forecasting performance significantly in contrast with its counterparts in terms of the error evaluation measurements. However, as far as turning points and profit criterions are concerned, the ANN model is best as well as at capturing a significant number of turning points. The results are conflicting when implementing dissimilar forecasting criteria (the quantitative and the turning points measurements) to evaluate the performance of three models. The ANN model is overall the best model, and can be used as an alternative method to model Chinese future food grain price. [Copyright &y& Elsevier]

• EMD is applied to reduce the complexity of the prediction subsequence. • The SE of the decomposed sub-sequence is induced to improve the prediction precision of wind speed. • LSTM is applied to predict the high frequency sub-sequences. • The ARIMA is employed to predict the low frequency sub-sequences and one residual. • A hybrid EMD-LSTM-ARIMA model is successfully proposed for wind speed prediction. Wind speed is the key factor of wind power generation. With the increase of the proportion of wind power generation in total power generation, the accurate prediction of wind speeds plays an important role in the stable operations of power grids. However, the strong randomness of wind speeds makes it difficult to accurately predict wind speeds. Thus, a wind speed prediction model combining empirical mode decomposition (EMD) with some novel recurrent neural networks (RNN) and the autoregressive integrated moving average (ARIMA) is proposed to solve the problem. The selected RNNs are long short-term memory network (LSTM) and the gated recurrent unit (GRU) network. In this model, EMD is used to decompose the wind speed sequence to reduce the complexity and non-stationary of the series. The entropy of the samples of the sub-sequences after decomposition is calculated. Consequently, LSTM is applied to predict the high frequency sub-sequences with large entropy while the ARIMA is employed to predict the remaining low frequency sub-sequences and one residual. Finally, the prediction results of each sub series are combined to obtain the final prediction results. To verify the accuracy and stability of the model, four wind speed data sets form Inner Mongolia, China, are used to test the proposed methods. Five models are established in four practical cases and their performances are compared with the performances of the proposed model. The results in this paper show the following: (1) the EMD method can improve the wind speed prediction performance when it is combined with LSTM; (2) after decomposition, LSTM is suitable for predicting high complexity subsequences and the ARIMA is suitable for effectively predicting low complexity subsequences based on the different sample entropies; and (3) the root mean squared errors (RMSEs) of the hybrid model on the four wind speed data sets are 0.4163, 0.2085, 0.1613, and 0.2790, respectively, which are basically lower than those of the five models compared. Therefore, it is feasible to apply the hybrid model to wind speed prediction. [ABSTRACT FROM AUTHOR]

Developing more effective carbon mitigation strategies requires accurate forecasting of carbon emissions. This is especially true for China, the United States and India, which produce over half of the world's carbon emissions. China, the United States, and India represent three different types of carbon emissions data. The first data type is typical for China: rapid grows which is thereafter slowing down. The US carbon emissions data show volatile growth and decline. The third data type is common for India's data: accelerated growth. To deal with the volatility of the data and to improve the forecasting accuracy, this paper combines Metabolic Nonlinear Grey Model (MNGM) with Autoregressive Integrated Moving Average (ARIMA) to develop the combined MNGM-ARIMA model, and MNGM with Back Propagation Neural Network (BPNN) to develop the new combined MNGM-BPNN model. In this way, ARIMA and BPNN could correct the modelling residual error of MNGM, to decrease the forecasting error. Through the quantitative data study of the countries in focus, we have made the following findings: First, the mean relative percent errors of the proposed MNGM-ARIMA and MNGM-BPNN models are 1.35% and 1.57%, which are lower than the traditional MNGM, ARIMA, and BPNN models. Besides, it is projected that during the period of 2019–2030, the US′ carbon emissions will keep a downward trend, while carbon emissions in China and India will continue to grow. Finally yet importantly, in the future periods of time, the carbon emissions growth rate in India will be faster than that for China. The above findings have led us to the following conclusions: (1) Error values of those two proposed approaches have proven that the strategy of correcting the forecasting error of the previous models with the latter models can effectively improve the forecasting accuracy. (2) The proposed two combined forecasting techniques have shown sound performance for the three types of data. It is reasonable to believe that new approaches will help to improve insight into better forecasting carbon emissions in other countries and regions of the world. (3) China and India will remain the major sources of global carbon emissions, and should therefore continue their efforts on carbon emission reduction. Image 10221 • China, the United States and India contribute half of global carbon emissions. • Forecasting the carbon emissions is conducive to global carbon emission reduction. • Strategy of "error correction + quadratic modeling" and new models are proposed. • Mean relative errors of the two proposed models achieved 1.35% and 1.57%. • Future carbon emissions growth will be concentrated in developing countries. [ABSTRACT FROM AUTHOR]