Forecast Of China Research Articles

The value of weather forecasts: Evidence from labor responses to accurate versus inaccurate temperature forecasts in China

Short-term weather forecasts, a common and popular public good in the modern world, affect labor decisions regarding time allocations. This study uses a novel dataset of city-level day-ahead weather forecasts in China, collected through video transcriptions of the country’s popular TV program spanning over 2000 days since 2010. I estimate the number of hours laborers worked in a day as flexible functions of the daily maximum temperature forecast under different historical levels of forecast accuracy (represented by half-year rolling daily maximum temperature forecast root-mean-squared-error, RMSE). The results suggest large-magnitude (up to 4.5 and 1.2 h per day) labor decreases under uncomfortable temperature forecasts (extreme heat above 30∘C and medium cold 15∘C–25∘C), but only when forecasts are accurate (RMSE≈1∘C). The economic value of accurate weather forecasts is assessed by modeling this labor adaptation to forecasts. Specifically, 930 Yuan (148 USD, in 2015 currency) is gained per worker per year, with each 1∘C decrease in the city forecast RMSE. For the entire country, an average 3.9% increase in city-level forecast accuracy for 2011–2015 generates a considerable social benefit of 25.3 billion Yuan (4.03 billion USD) annually from the labor sector alone, nearly covering the annual cost of the national weather forecasting system.

A balancing act between accuracy and timeliness: Evidence from analyst forecasts in China

AbstractThis paper investigates the impact of teamwork on analysts' ability to balance accuracy and timeliness. Surprisingly, team analysts demonstrate a lower ability compared to individual analysts. This trade‐off ability is even worse in diverse teams. Further, we find that Confucianism negatively affects this ability. However, this ability significantly improves when star analysts serve on the analyst team, or when analyst team members come from the province where the covered firm is headquartered, or when analyst team members graduate from the same college. Overall, these findings suggest that it is crucial to consider the two performance dimensions simultaneously.

Analysis and Forecast of China's Unemployment Rate

The unemployment rate is an important economic indicator that measures the proportion of the unemployed labor force. The natural unemployment rate is the normal unemployment rate based on economic fluctuations. Analyze the unemployment rate to determine its root causes. By forecasting the unemployment rate, people can obtain an estimate of the future conditions of the labor market. Not only can the government make policy adjustments based on this, people can use this prediction to make wise career choices and planning, or to learn the necessary skills. The risk of unemployment is closely related to everyone, especially now during the economic downturn due to the impact of global catastrophic events such as COVID-19. Understanding the unemployment rate is an important part of analyzing the characteristics of the current labor market. If we can make more connections about the labor market and even make rough predictions about future trends, college students will be able to make future career choices that are more suitable for them. This study predicts the unemployment rate in the next nine years based on China's unemployment rate from 2012 to 2022, which will fluctuate within a fixed range. The government can increase spending (such as on education) and use monetary and finance polices to ensure that unemployment does not exceed forecasts.

The future of solar forecasting in China

The current grid code in China in regard to solar forecasting is, in my opinion, underdeveloped, especially in contrast to the rate at which photovoltaics are being installed. As such, explaining the limitations of the grid code and resetting pathways to improve it are thought utilitarian for those scientists and policymakers who are responsible for or aware of the grid code but have not themselves worked on the problem of forecasting. In this perspective article, I should first explain with respect to China's grid code the perceived deficiencies in the current forecasting research and practices, and then outline a five-stage workflow that could completely mitigate the situation. Among other things, the over-reliance on accuracy as the basis for gauging the goodness of forecasts is identified as a root cause for the status quo, and thus, I advocate a holistic forecast verification procedure that encompasses consistency, quality, and value. With that in mind, the proposed workflow for better solar forecasting for grid integration purposes relies on the effective information flow among the weather department, grid operators, and individual plant owners, which is inline with the current grid code. What goes beyond this is that the proposal further introduces a couple of concepts called “hierarchical reconciliation” and “firm forecasting,” which are new but are able to eliminate forecast errors wholly, thus making solar power dispatchable on the system level. With a slight premium incurred, it is now possible to manage solar plants, or variable renewables in general, in the same style as managing conventional fire-powered generators.

Forecast of China’s natural gas demand based on the double-logarithmic model with stepwise regression method

ABSTRACT In the current world’s energy system, natural gas, as a clean energy source, is gradually becoming the main energy source. However, the volume of natural gas demand could be able to be influenced by numerous factors, in order to forecast the future trend of the requirement more precisely, it is necessary to further explore effective driving factors influencing the volume of demand and construct a brand new function model of forecasting natural gas demand. In this study, the relative gray relational degree is used to quantitatively describe the correlation among variables, diagnosing whether there are serious multicollinearity problems among these factors. According to the principle of correlation degree: first and then small, a stepwise regression analysis is adopted to refine the analysis of factors influencing natural gas demand. On the basis of statistical significance testing, in-depth studying is conducted on the effective influencing factors of natural gas demand. This helps to develop a more accurate prediction model. In this study, we find that: (1) China’s energy consumption structure and economic development influence its natural gas demand. (2) The stepwise regression double logarithmic demand function model based on the effective drivers has good forecasting performance. Researchers can use it to forecast the natural gas demand for medium- and long-term in different regions, and the forecasting results can be considered an important reference basis for scientific natural gas policy formulation. (3) Over the coming 15 years, Natural gas demand in China will continue to grow. It will reach about 6910 × 10 8 m 3 by 2035. It is concluded that the development of China’s natural gas demand would be promoted in an increasing scale trend by striving to promote China’s high-quality economic development, carefully building a new pattern of high-quality energy development, and increasing the proportion of natural gas consumption.

Analysis and forecast of China's carbon emission: evidence from generalized group method of data handling (g-GMDH) neural network

Analysis and forecast of China's carbon emission: evidence from generalized group method of data handling (g-GMDH) neural network

Analysis of the current state of national mobile games markets

The work includes a general analysis of the global market of mobile games and a comparative analysis of the largest national markets of mobile games. The key indicators of the mobile games market in different countries of the world were chosen as the object of the study. Since the beginning of the 2010s, the market has been growing steadily by 10% every year and has trends for further development. Such a process has many prerequisites: the development of mobile technologies, the improvement of the standard of living of the population, the increase of free time and the popularization of interactive entertainment. The work offers a brief description of the formation of the mobile game market and its advantages among competitors, a full description of the global mobile game market and its segregation into the largest centers and promising markets. The characteristics of the largest world markets: China, the USA, Japan, South Korea, the reasons for their success and current statistics are given. The most promising world regions are singled out: Europe, Africa and South America, and the main challenges for each of them are given. The findings provide numerical figures for developed and developing regions and the reasons why this difference exists. The graphs of the number of mobile game applications in the App Store, the number of players by region of the world, the dynamics of the market volume and the forecast for China and the USA were constructed and analyzed. Comparative tables and separated data for each of the considered regions were built. The results of the study showed that the main global markets have a common trend of growth in both the number of players and the revenue per player. In addition, the main challenge of the future can be considered approaching the maximum number of players and the limitation of their free time. Another important trend is the constant rapid development of technologies, which makes mobile games a real competitor for computer and console counterparts. The results of the research can be used to make decisions for mobile companies about entering new markets, as well as to analyze the key development trends for the coming years for the entertainment industry as a whole.

The Agricultural Economic Value of Weather Forecasting in China

Agriculture is one of the areas most sensitive to climate change. Extreme climate events can directly affect agricultural production and development. Based on the data from the statistical yearbooks of 30 provinces in China from 2009 to 2019 and the survey data of meteorological forecasting, this paper uses the fixed-effect model to empirically test the impact of meteorological forecasting on agricultural economic value. The results show that meteorological forecasting has a significant promoting effect on agricultural economic value, in that an increase in the accuracy of the meteorological forecast by one percentage point will increase agricultural economic value by 0.500 percentage points, and the results still hold after a series of robustness tests. Further study showed that there were some regional differences in the agricultural economic value of the meteorological forecasting, and the meteorological forecasting had the strongest promoting effect on the agricultural economic value of the western region, followed by the eastern region and the central region.

U-Net: A deep-learning method for improving summer precipitation forecasts in China

A deep-learning method named U-Net was applied to improve the skill in forecasting summer (June–August) precipitation for at a one-month lead during the period 1981–2020 in China. The variables of geopotential height, soil moisture, sea level pressure, sea surface temperature, ocean salinity, and snow were considered as the model input to revise the seasonal prediction of the Climate Forecast System, version 2 (CFSv2). Results showed that on average U-Net reduced the root-mean-square error of the original CFSv2 prediction by 49.7% and 42.7% for the validation and testing set, respectively. The most improved areas were Northwest, Southwest, and Southeast China. The anomaly same sign percentages and temporal and spatial correlation coefficients did not present significant improvement but maintained the comparable performances of CFSv2. Sensitivity experiments showed that soil moisture is the most crucial factor in predicting summer rainfall in China, followed by geopotential height. Due to its advantages in handling small training dataset sizes, U-Net is a promising deep-learning method for seasonal rainfall prediction.摘要本研究应用了名为U-Net的深度学习方法来提高中国夏季 (6–8月) 降水的预报技能, 预报时段为1981–2020年, 预报提前期为一个月. 将位势高度场, 土壤湿度, 海平面气压, 海表面温度, 海洋盐度和青藏高原积雪等变量作为模型输入, 本文对美国NCAR气候预报系统第2版 (CFSv2) 的季节性预报结果进行了修正. 结果显示, 在验证集和测试集上, U-Net平均将原CFSv2预测的均方根误差分别减少了49.7%和42.7%. 预报结果改善最大的地区是中国的西北,西南和东南地区. 然而, 同号率和时空相关系数没有得到明显改善, 但仍与CFSv2的预测技巧持平. 敏感性实验表明, 土壤湿度是预测中国夏季降雨的最关键因素, 其次是位势高度场. 本研究显示了U-Net模型在训练小样本数据集方面的优势, 为我国汛期季节性降雨预测提供了一种有效的深度学习方法.

Analogue Ensemble Averaging Method for Bias Correction of 2-m Temperature of the Medium-Range Forecasts in China

The 2-m temperature is one of the important meteorological elements, and improving the accuracy of medium- and long-term forecasts of the 2-m temperature is important. The similarity forecasting method is widely used as a calibration technique in the statistical postprocessing of numerical weather prediction (NWP). In this study, the analogue ensemble averaging method is used to correct the deterministic forecast of the 2-m temperature with a forecast lead time from 180 h to 348 h using the CMA-GEPS model. The bias, mean absolute error (MAE), and root mean square error (RMSE) are used as the evaluation metrics. In comparison with NWP, the systematic error of the model for 2-m temperature is effectively reduced during each forecast period when using the analogue ensemble averaging method. In addition, the differences in forecast errors between regions are reduced, and the accuracy of 2-m temperature forecasts over complex terrain, especially in Southwest China, Northwest China, and North China, is improved using this method. In the future, there is certainly potential to apply the analogue ensemble averaging method to the bias correction of medium- and long-term forecasts of more meteorological elements.

Does the Tone in Corporate Social Responsibility Reports Misdirect Analysts’ Forecasts in China?

With increasing emphasis being placed on corporate social responsibility, the number of companies furnishing corporate social responsibility (CSR) reports is increasing. This study investigates the impact of abnormal positive tone in CSR reports on analysts’ earnings forecast bias. The textual analysis of CSR reports of Chinese listed companies between 2006 and 2016 reveals that an abnormal positive tone significantly and positively relates to an optimistic bias in analysts’ forecasts. This effect is pronounced among companies with poor financial transparency and those operating in regions where culture is stakeholder-oriented. Further analysis confirms that the poorer the company’s CSR performance, the more it tends to mislead analysts using an abnormal positive tone in its CSR report. Based on these findings, this study suggests that firms may greenwash using an abnormally positive tone in their CSR reports.

Evaluation of the Dynamical–Statistical Downscaling Model for Extended Range Precipitation Forecasts in China

In order to focus on pentad-scale precipitation forecasts, we investigated the coupling relationship between 500 hPa geopotential height (Z500) anomalies and precipitation anomalies using the China Meteorological Administration Global Land Surface ReAnalysis Interim (CRA40/Land) gridded precipitation dataset from 1999 to 2018 and the National Centers for Environmental Prediction 1 reanalysis dataset for Z500. We obtained a dynamical–statistical downscaling model (DSDM) on the pentad scale and used the daily Z500 forecast product for sub-seasonal to seasonal forecasts (15–60 days) of the FGOALS-f2 model as the predictor. Our results showed that pentad-scale prediction of precipitation is the key to bridging the current deficiencies in sub-seasonal forecasts. Compared with the FGOALS-f2 model, the pentad DSDM had a higher skill for prediction of precipitation in China at lead times longer than four pentads throughout the year and of two pentads in the summer months. FGOALS-f2 had excellent precipitation predictability at lead times less than three pentads (15 days), so the proposed pentad DSDM could not perform better than FGOALS-f2 in this period. However, at lead times greater than four pentads, the precipitation prediction scores (such as the anomaly correlation coefficient (ACC), the temporal correlation coefficient (TCC) and the mean square skill score (MSSS)) of the pentad DSDM for the whole of China were higher than those of the FGOALS-f2 model. With the rate of increase ranging from 76% to 520%, the mean ACC scores of pentad DSDM were basically greater than 0.04 after a lead time of five pentads, whereas those of the FGOALS-f2 were less than 0.04. An analysis of the Zhengzhou “720” super heavy rainstorm event showed that the pentad DSDM also had better predictability for the distribution of precipitation at lead times of three pentads than the FGOALS-f2 model for the extreme precipitation event.

Evidence for Intensification in Meteorological Drought since the 1950s and Recent Dryness–Wetness Forecasting in China

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.

Forecast of China’s Annual Carbon Emissions Based on Two-Stage Model

China’s carbon emissions are a major global concern. China has proposed a defined “dual-carbon” aim, with the first target being to attain the carbon emissions peak by 2,030. To address this issue, this study provides a two-stage method for forecasting China’s annual carbon emissions, which is paired with pertinent carbon emissions data to predict China’s annual carbon emissions. We discovered the associated aspects affecting China’s carbon emissions through the research of this article, and we predicted the carbon emissions data from 2017 to 2020 using the two-stage technique based on these factors. When compared to the actual data of China’s annual emissions from 2017 to 2020, the prediction intervals from this method encompass the actual data well. This method, on the one hand, identifies the main affecting factors for estimating carbon emissions data, and on the other hand, it validates the method’s performance. It provides support for further policy development and change based on the outcome of this method.

The Effect of Conditional Management Earnings Forecast Mandates on Voluntary Disclosure and Analyst Forecast Properties

This study examines the effect of a conditional management earnings forecast mandate on voluntary disclosure and the properties of analyst forecasts in China, where firms are required to provide earnings forecasts when they expect to either make a loss, turn from loss into profit, or experience a large change in earnings. We find that firms’ propensity to issue voluntary management earnings forecasts is higher when they anticipate the disclosure of mandatory management earnings forecasts in the future. This relation is stronger for government‐controlled firms. We also find a significant improvement in the quality of analyst earnings forecasts (measured by forecast accuracy and dispersion) for firms that provide voluntary management earnings forecasts, while the disclosure of mandatory management earnings forecasts is negatively associated with analyst earnings forecast quality. We document that this differential effect on analyst earnings forecast quality is attributable to the higher quality of voluntary management earnings forecasts. Further analyses suggest that the positive effect of voluntary management earnings forecasts on analyst earnings forecast quality is larger for firms with a relatively weak existing information environment. Our findings suggest that the conditional mandatory disclosure of management earnings forecasts can incentivize managers to provide voluntary forecasts, and that these voluntary forecasts play a greater role than mandatory management forecasts in improving firms’ external information environment in the Chinese capital market.

Forecasting and Analysis of Energy Consumption in China

Energy is essential to the development of an economy and society. In recent years, China's rapid economic development has created the "China Miracle", but it has also led to a sharp increase in energy consumption in China. To ensure the achievement of the ambitious goal of reaching the carbon peak by 2030, it is of great significance to study the total energy consumption in China in order to promote the national energy conservation and emission reduction actions. This paper constructs models GM(1,1), DGM(1,1), and gray Verhulst model based on the original data of China's total energy consumption from 2001 to 2020, and constructs a combined forecasting model by the least squares method to make an economic forecast of China's energy consumption in the next five years. It provides a theoretical basis for making a reasonable energy planning.

An improved version of Search and Rescue algorithm for electrical power energy production and the demand of China

A new technique is suggested in this paper for optimal electrical power energy production and demand forecasting in China. The paper provides a new improved design of the Search and Rescue algorithm for better optimization of the forecasting models. This study is performed based on three scenarios on the electrical power energy production and demand in the period 2020–2050. The study utilizes linear and quadratic models for electricity energy production and demand prediction of the analyzed scenarios. The improved Search and Rescue algorithm in the study is designed based on import and export, gross domestic product (GDP), and population. All of the models based on the scenarios are applied for optimal prediction of electrical power energy generation and demand. The results show that the proposed optimal quadratic models based on the Improved Search and Rescue algorithm provides the fittest results with economic indicators.

Observing system simulation experiment (OSSE)-quantitative evaluation of lidar observation networks to improve 3D aerosol forecasting in China

In order to evaluate the potential impact of an aerosol lidar monitoring network in China on improving air quality prediction, an observing system simulation experiment (OSSE) was conducted using the Weather Research and Forecasting Chemistry (WRF-Chem) model and three-dimensional variational (3DVAR) data assimilation (DA) system. The impact of lidar numbers and locations on DA performance was evaluated using sensitivity analysis. Three ground-based lidar monitoring networks with 590, 310, and 99 lidars were constructed based on the distribution of existing surface air pollution monitoring stations in China to study the added value of lidar measurements. Four assimilation experiments were conducted using varying aerosol extinction coefficient data (AEXT) from three lidar monitoring networks and surface particulate matter (PM) concentration data over a four-week period. The results showed that compared with only assimilating surface PM2.5, the correlation coefficient (CORR) of PM2.5 was significantly higher, the root mean square error (RMSE) and mean fraction error (MFE) were significantly lower, and the mean mass concentration was closer to the simulated observation after adding lidar profile measurements to the 3DVAR DA system. Additionally, the evaluation of DA with 590 stations indicated spatial differences in the improvement of the positive effect of assimilation. Overall, the improvement effects of assimilation in Central and East China were better than those in other regions. Finally, it was observed that the positive effect of DA on PM2.5 prediction increased with an increase in the lidar number. However, with an increase in the number of lidars, the improvement of the positive effect of DA decreased gradually. The results indicate that it is necessary to balance the forecasting effect and economic cost while establishing a lidar observation network.

Forecast of China’s carbon emissions under the background of carbon neutrality

Forecast of China’s carbon emissions under the background of carbon neutrality

A Study on Carbon Emission Forecasting in China Based on PSO-BP Neural Network

To address the problem that the traditional BP(Back-Propagation) neural network is prone to fall into the local optimal situation for carbon emission prediction, and thus the prediction results have large errors, this paper proposes to improve the BP neural network by using particle swarm optimization algorithm, and conducts simulation experiments with national carbon emission data. The results show that the model has a 12.1% reduction in error compared with the traditional BP neural network model, and has a better prediction effect on carbon emissions.