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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]

This paper aims to investigate how the recent overheating economy and other economic factors influence the manufacturing industry in China. It makes use of Chinese historical data from 1950 to 2007 provided in the China Statistical Databases authorized by the National Bureau of Statistics of China for the development of econometric models. Regression analysis is used to investigate and verify the importance of and relationships among the variables, while exponential smoothing and autoregressive integrated moving average methods are applied for forecasting the important variables. This study provides empirical evidence to identify the main impact factors on the growth of the manufacturing industry, thereby enabling firms to take them into account when planning their manufacturing strategy. It also enables firms to get an insight into the development of China’s manufacturing industry in this dynamic, competitive economy. [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]

Drought is one of the major environmental stressors; drought is increasingly threatening the living environment of mankind. The standardized precipitation evapotranspiration index (SPEI) with a 12-month timescale was adopted to monitor dry–wet status over China from 1951 to 2021. The modified Mann–Kendall (MMK) and Pettitt tests were used to assess the temporal trend and nonlinear behavior of annual drought variability. The analysis focuses on the spatio-temporal structure of the dry–wet transition and its general connections with climate change processes. In addition, the seasonal autoregressive integrated moving average (SARIMA) model was applied to forecast the dry–wet behavior in the next year (2022) at 160 stations, and the hotspot areas for extreme dryness–wetness in China were identified in the near term. The results indicate that the dry–wet climate in China overall exhibits interannual variability characterized by intensified drought. The climate in the Northeast China (NEC), North China (NC), Northwest China (NWC), and Southwest China (SWC) has experienced a significant (p < 0.05) drying trend; however, the dry–wet changes in the East China (EC) and South Central China (SCC) are highly spatially heterogeneous. The significant uptrend in precipitation is mainly concentrated to the west of 100° E; the rising magnitude of precipitation is higher in Eastern China near 30° N, with a changing rate of 20–40 mm/decade. Each of the sub-regions has experienced significant (p < 0.01) warming over the past 71 years. Geographically, the increase in temperature north of 30° N is noticeably higher than that south of 30° N, with trend magnitudes of 0.30–0.50 °C/decade and 0.15–0.30 °C/decade, respectively. The response of the northern part of Eastern China to the warming trend had already emerged as early as the 1980s; these responses were earlier and more intense than those south of 40° N latitude (1990s). The drying trends are statistically significant in the northern and southern regions, bounded by 30° N, with trend magnitudes of −0.30–−0.20/decade and −0.20–−0.10/decade, respectively. The northern and southwestern parts of China have experienced a significant (p < 0.05) increase in the drought level since the 1950s, which is closely related to significant warming in recent decades. This study reveals the consistency of the spatial distribution of variations in precipitation and the SPEI along 30° N latitude. A weak uptrend in the SPEI, i.e., an increase in wetness, is shown in Eastern China surrounding 30° N, with a changing rate of 0.003–0.10/decade; this is closely associated with increasing precipitation in the area. Drought forecasting indicates that recent drying areas are located in NWC, the western part of NC, the western part of SWC, and the southern part of SCC. The climate is expected to show wetting characteristics in NEC, the southeastern part of NC, and the eastern part of EC. The dry–wet conditions spanning the area between 30–40° N and 100–110° E exhibit a greater spatial variability. The region between 20–50° N and 80–105° E will continue to face intense challenges from drought in the near future. This study provides compelling evidence for the temporal variability of meteorological drought in different sub-regions of China. The findings may contribute to understanding the spatio-temporal effect of historical climate change on dry–wet variation in the region since the 1950s, particularly in the context of global warming. [ABSTRACT FROM AUTHOR]

This study aimed to explore the epidemiological characteristics of breast cancer and establish an Exponential Smoothing (ETS) and Autoregressive Integrated Moving Average (ARIMA) models to predict the development of incidence in Shantou. This study has a large sample size, strong representativeness, and wide‐ranging and comprehensive medical insurance information, which can fill the gaps in basic epidemiological research on breast cancer in Shantou. Successful completion of this study is a helpful tool to understand the epidemiology of Guangdong Province and Southern China. This study also provides data and scientific references for the government and future research on breast cancer prevention and control. This retrospective study was conducted to describe the epidemic intensity, epidemic distribution, and epidemic trend of breast cancer in Shantou, Guangdong Province, from 2006 to 2017, gathered from the Shantou's Medical Security Bureau covers the whole districts of Shantou. ETS and ARIMA models were used to describe the regional distribution, time distribution, and population distribution of breast cancer in Shantou. Moreover, based on the ARIMA model and ETS model, the incidence trend of breast cancer was predicted during 2018–2022. This study included 5,681 breast cancer patients, majority of whom were aged 50–59 years. The male‐to‐female ratio of the breast cancer patients was about 1:107 (the same ratio of the insured population was 1:1). Female patients accounted for 98.61% of the total insured population. The incidence and mortality rates of female breast cancer were 16.42/100,000 and 0.66/100,000, respectively. Based on the ARIMA model or ARIMA and ETS model, a gradually decreasing trend in the incidence of breast cancer is expected in the future. Comparing the performances of the ARIMA model and ETS model, ARIMA (4, 0, 1) (0, 1, 0) model had a lower the root mean squared error and the mean absolute percentage error than ETS (M, N) model. This population‐based retrospective study showed that the high‐risk age for the age‐specific incidence of female breast cancer was 50–55 years. It is recommended that healthcare administration should strengthen program awareness and education regarding breast cancer prevention and control. It is also possible that feasibility of extrapolating the current methodology to other future studies or broader populations in which the cancer registry data are not available. [ABSTRACT FROM AUTHOR]

Objectives: This study was conducted to estimate road traffic deaths and to forecast short-term road traffic deaths in China using the Elman recurrent neural network (ERNN) model.Methods: An ERNN model was developed using reported police data of road traffic deaths in China from 2000 to 2017. Different numbers of neurons of the hidden layer were tested and different combinations of subgroup datasets have been used to develop the optimal ERNN model after normalization. The mean absolute error (MAE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE) were measures of the deviation between predicted and observed values. Predicted road traffic deaths from the ERNN model and the seasonal autoregressive integrated moving average (SARIMA) model were compared using the MAPE.Results: By comparing the MAE, RMSE and MAPE of different numbers of hidden neurons and different ERNN models, the ERNN model provided the best result when the input neurons were set to 3 and hidden neurons were set to 10. The best validated neural model (3:10:1) was further applied to make predictions for the latest 12 months of deaths (MAPE = 4.83). The best SARIMA (0, 1, 1) (0, 1, 1)12 model was selected from various candidate models (MAPE = 5.04). The fitted road traffic deaths using the two selected models matched closely with the observed deaths from 2000 to 2016. The ERNN models performed better than the SARIMA model in terms of prediction of 2017 deaths.Conclusions: Our results suggest that the ERNN model could be utilized to model and forecast the short-term trends accurately and to evaluate the impact of traffic safety programs when applied to historical road traffic deaths data. Forecasting traffic crash deaths will provide useful information to measure burden of road traffic injuries in China. [ABSTRACT FROM AUTHOR]

The forecast of CO2 emissions is very crucial, especially for Pakistan as it is one of the top victims of climate change. A univariate model, (ARIMA) autoregressive integrated moving average (ARIMA), was used to forecast CO2 emissions for Pakistan. The CO2 emissions scenarios were developed for Pakistan till 2020, forecasting them further to 2030. The scenarios developed include China–Pakistan Economic Corridor (CPEC) scenario, where CO2 emissions from high‐priority energy projects under the CPEC were considered. The scenarios attempt to estimate the impactful emission reduction percentage, which the country needs to adopt along with other necessary changes in the existing policies of the country. The forecast results clearly indicate that the emissions are bound to increase under business as usual and CPEC scenarios and the country would fail to meet the Nationally Determined Contributions pledged at COP21. In other scenarios, where we assumed the country has adopted mitigatory strategies to curb the emissions, the forecast shows decreased CO2 emissions for Pakistan. The mean absolute percentage error for all the forecasts was found to be less than 10%, making the forecast highly accurate. © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Tourist volume forecasting is an ongoing theme in tourism research. Current methods rely too much on the previous tourist arrivals data. Based on tourism system perspective, we propose a visiting probability model composed of five independent variables: the attractiveness of a destination, the travel time from a origin to the destination, the traffic expense to and from the destination, the physical fatigue travel time and the per capita disposable monthly income of the origin. The model provides a new method for forecasting the number of tourists from a specific origin without historical tourist arrivals data. [ABSTRACT FROM AUTHOR]

China, the largest developing country, is the world largest cement producer and the largest cement-consuming nation. Although China's cement output reached its peak in 2014, regions, i.e., Fujian and Yunnan provinces, were no peaking until 2016. At the same time, rare studies referred to China's cement consumption and CO2 emissions from the perspective of cement consumption at the provincial level. We developed the S-Logistic, polynomial model, and ARIMA model to study the peaking time of cement consumption at the provincial level, and we also projected China's cement consumption and CO2 emissions toward 2030. Meanwhile, the discrepancies of peaking time and cumulative cement consumption per capita (CCCPC) among provinces were also studied based on GDP per capita and urbanization rate (UR). The results are that the CCCPC respectively in the range of 22–34 ton, 18–25 ton, and 17–27 ton in the eastern, intermediate, and western zone when cement consumption reached its peak. We draw the following conclusions that the CCCPC in 2030 could reach ~ 43 ton and the projected cement consumption is ~ 1252.72 Mt, which accounts for 50% of that in 2017, and cement CO2 emissions are at the range of 488.19–510.90 MtCO2 in 2030. Furthermore, capacity replacement, controlling new capacity and eliminating backward capacity are significant of greenhouse gas emission reduction not only for China, but also for the global cement industry. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is one of the most common communicable diseases in China, and current climate change had been recognized as a significant contributor. Nevertheless, no reliable models have been put forward to predict the dynamics of HFMD cases based on short-term weather variations. The present study aimed to examine the association between weather factors and HFMD, and to explore the accuracy of seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting HFMD. Weather and HFMD data from 2009 to 2014 in Huainan, China, were used. Poisson regression model combined with a distributed lag non-linear model (DLNM) was applied to examine the relationship between weather factors and HFMD. The forecasting model for HFMD was performed by using the SARIMA model. The results showed that temperature rise was significantly associated with an elevated risk of HFMD. Yet, no correlations between relative humidity, barometric pressure and rainfall, and HFMD were observed. SARIMA models with temperature variable fitted HFMD data better than the model without it (s R increased, while the BIC decreased), and the SARIMA (0, 1, 1)(0, 1, 0) offered the best fit for HFMD data. In addition, compared with females and nursery children, males and scattered children may be more suitable for using SARIMA model to predict the number of HFMD cases and it has high precision. In conclusion, high temperature could increase the risk of contracting HFMD. SARIMA model with temperature variable can effectively improve its forecast accuracy, which can provide valuable information for the policy makers and public health to construct a best-fitting model and optimize HFMD prevention. [ABSTRACT FROM AUTHOR]

Background: Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease. Methods: The autoregressive integrated moving average (ARIMA) model and the generalized regression neural network (GRNN) model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention) from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE) and mean square error (MSE), were used to compare the performance among the three models. Results: The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2)(1,1,1)12 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models. Conclusions: The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County. [ABSTRACT FROM AUTHOR]