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Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production. Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of "expert interviews + big data technology" for economic forecasting. Results: The forecasting accuracy on four provincial companies has reached over 90%. Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development. [ABSTRACT FROM AUTHOR]

Against the backdrop of increasing downward pressure on the global economy, domestic economic development also faces many uncertainties, especially the production of the industrial sector, which responds more quickly and sensitively to external shocks and internal adjustments, making it more difficult to predict industrial added value. Electricity, as an important terminal consumption energy, is a barometer of economic prosperity. Using a decomposition integration approach, a combination prediction method based on LSTM models with electricity consumption data is constructed, and short-term predictions of provincial industrial added value are made. The results show that the EMD-LSTM model with external data can achieve better short-term prediction results. [ABSTRACT FROM AUTHOR]

The management of industrial solid waste (ISW) and promoting sustainable circular development of the industrial economy is an urgent priority today. Therefore, this article constructs a sustainable circular model of 'generation-value-technology' of ISW management through the lens of industrial added value (IAV) and technology level. Also, the importance of the role of government is considered in the model. Based on actual data of China, this article simulates the future trend of the model using a system dynamics approach. The chief findings of the study are as follows: (1) under the current policy, China's future industrialization is increasing and the technological level of industrial enterprises is rising, but this is accompanied by a climb in ISW generation. (2) The win–win situation of ISW decrease and IAV increase can be achieved through enhanced information disclosure, technology innovation and government incentives. (3) Government subsidy should be oriented towards supporting technology innovation in industrial enterprises while reducing the proportion of incentives for ISW management results. Based on the results, this study proposes targeted policy implications for government and industrial enterprises. [ABSTRACT FROM AUTHOR]

Introduction: With the increasing fluctuations in the current domestic and international economic situation and the rapid iteration of macroeconomic regulation and control demands, the inadequacy of the existing economic data statistical system in terms of agility has been exposed. It has become a primary task to closely track and accurately predict the domestic and international economic situation using effective tools and measures to compensate for the inadequate economic early warning system and promote stable and orderly industrial production.Methods: Against this background, this paper takes industrial added value as the forecasting object, uses electricity consumption to predict industrial added value, selects factors influencing industrial added value based on grounded theory, and constructs a big data forecasting model using a combination of “expert interviews + big data technology” for economic forecasting.Results: The forecasting accuracy on four provincial companies has reached over 90%.Discussion: The final forecast results can be submitted to government departments to provide suggestions for guiding macroeconomic development.

China, as the world's largest energy consumer, is faced with the growing pressure of carbon emission reduction. Promoting the sustainable transformation of the economy and society has become a major concern for all sectors of society. This paper assesses the potential impacts of industrial added value on energy consumption and carbon dioxide (CO 2 ) emissions from 2010 to 2020 in China using a multi-objective, linear programming model. The results show that when the industrial energy consumption is 3695.17 Mtce and the industrial CO 2 emission is 9364.16 Mt, the goal of energy saving and emission reduction can be achieved. This corresponds to an annual average growth rate limit of industrial added value of 7.8%. In addition, we find that when the annual average growth rate of industrial added value is greater than 9.9%, changes in the annual average growth rate of industrial added value have no impact on economic development. However, industrial energy consumption intensity and industrial carbon emission intensity decrease with the increase in the annual average growth rate of sub-sectors. This study proposes that energy conservation, emission reduction, and a realization of the sustainable transformation of industry scheme need to be achieved by the Chinese government in order to to continue to promote the development of several sectors with low industrial energy consumption intensity and low industrial carbon emission intensity through policy implications. [ABSTRACT FROM AUTHOR]

Industrial added value is an important indicator to measure the operation of the real economy.In order to fully mine the value of power data in the current macroeconomic nowcasting, so as to serve the government policy making, the Bagging and Boosting algorithms in machine learning were applied to nowcast industrial added value based on electric power data as well as traditional statistical data.Firstly, traditional statistical data can significantly improve the forecasting effect of the ARIMA model.Secondly, the nowcasting ability of electric power data depends on the selection of electric power indicators, and the proper electric power index is helpful to predict industrial added value more timely and accurately.Thirdly, the prediction ability of electric power data to the industrial added value in the current period may be lower than that in the future, which means the power data is more likely to be used to predict ahead of time.

[Introduction] The primary issue of my country's data center industry is energy-saving assessment, that is, the issue of "energy evaluation". In recent years, the data center industry causes a lot of waste of investment due to the "energy evaluation" problem. At the same time, it severely restrictes the development of my country's data center industry. At the same time, it restricts the development of big data centers, artificial intelligence, industrial Internet and other fields in my country's "new infrastructure". [Method] This article combined the characteristics of the data center industry, through a systematic and comprehensive analysis of the energy-saving evaluation system, to find the key factors for the implementation of the system in the data center industry. The paper systematically elaborated the main concepts encountered in China's "energy evaluation", that is, the energy conservation assessment of fixed asset investment projects. [Result] The author systematically explains the origin of energy consumption indicators, the definition of standard coal, the standard coal coefficient of power grid conversion, the meaning and difference of power equivalent value and equivalent value, the change of data center industry classification and the calculation method of data center industrial added value and the calculation method of power grid carbon emissions. From a macro perspective, the author systematically interprets the relationship between energy consumption indicators and data centers. [Conclusion] For participants in the data center industry, understanding these key factors and the relationship between these factors can help understand my country’s current and future "data center" related policies. It can important strategic guiding significance for promoting the development of the data center industry and then promoting the development of "new infrastructure".

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The aims of this study were to analyze the spatial distributions of, contamination statuses of, and factors contributing to, heavy metals in cropland areas of different cities; thus, 55 agricultural soils were collected from 12 cities of Shandong Province, China. Concentrations of copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni) and zinc (Zn) were determined. Results showed that average contents of Cu, Pb, Cd, Cr, Ni and Zn were 24.13, 31.77, 0.16, 130.63, 22.13, and 71.19 mg·kg−1, respectively, and Pb and Cd had similar spatial distributions in those cities. Specifically, contents of Cr in cities of Weifang, Weihai, Yantai and Zibo were significantly higher than it in other cities; Weihai and Zibo also had significantly high contents of Zn. Moreover, concentrations of Cr in brown soils and cinnamon soils were significantly higher than that in fluvo-aquic soils, while other metals showed no significant differences among the soil types. Furthermore, Cu, Pb and Zn showed significant concentration decreases with respect to those measured in 2007. The correlation analysis and factor analysis indicated that the contamination of Pb and Cd was mainly caused by economic activities. In addition, the significantly correlated Cu/Ni/Zn and Pb/Cd indicated the inputs from different human activities, while Cr was prone to multiple sources. This study demonstrated that more attention should be given to the contamination by Cr, Pb and Cd, and that the management of human economic activities is vitally imperative for safety of surrounding cropland soils. [ABSTRACT FROM AUTHOR]

The aims of this study were to analyze the spatial distributions of, contamination statuses of, and factors contributing to, heavy metals in cropland areas of different cities; thus, 55 agricultural soils were collected from 12 cities of Shandong Province, China. Concentrations of copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni) and zinc (Zn) were determined. Results showed that average contents of Cu, Pb, Cd, Cr, Ni and Zn were 24.13, 31.77, 0.16, 130.63, 22.13, and 71.19 mg·kg−1, respectively, and Pb and Cd had similar spatial distributions in those cities. Specifically, contents of Cr in cities of Weifang, Weihai, Yantai and Zibo were significantly higher than it in other cities; Weihai and Zibo also had significantly high contents of Zn. Moreover, concentrations of Cr in brown soils and cinnamon soils were significantly higher than that in fluvo-aquic soils, while other metals showed no significant differences among the soil types. Furthermore, Cu, Pb and Zn showed significant concentration decreases with respect to those measured in 2007. The correlation analysis and factor analysis indicated that the contamination of Pb and Cd was mainly caused by economic activities. In addition, the significantly correlated Cu/Ni/Zn and Pb/Cd indicated the inputs from different human activities, while Cr was prone to multiple sources. This study demonstrated that more attention should be given to the contamination by Cr, Pb and Cd, and that the management of human economic activities is vitally imperative for safety of surrounding cropland soils.

The industrial added value is an important indicator of the effectiveness of the real economy. With the increase of the uncertainty of China5s economic development, more timely forecast of its growth is particularly important. In this paper, firstly an advanced mixed frequency structure vector autoregression model is established; then the mathematical expressions are derived for the prediction of industrial added value and the forward rolling prediction is given. And the prediction model is transformed into state space structure, and Bootstrap importance sampling step is used to test the robustness of the model. Based on the monthly data from January 1996 to March 2016, the empirical research shows that the prediction model is reasonable, and the parameter estimation is convergent and robust. In addition to the impact of its own inertia of the industrial added value, the 1~2 lags monetary policy M2 and consumer spending have some positive impact on the half-month frequency in the industrial added value growth rate. The full sample prediction, partial sample prediction and predictive extrapolation are reasonable and feasible, and the high frequency half-month forecast is an effective early warning to growth fluctuations of the industrial added value. Finally, relevant policy implications and prospects are put forward. [ABSTRACT FROM AUTHOR]

Han, L., 2019. The relationship between energy consumption intensity and the proportion of industrial added value in Jilin Province. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 582–584. Coconut Creek (Florida), ISSN 0749-0208. The intensity of energy consumption reflects the degree of dependence of economic development on energy consumption. The greater the value of energy consumption intensity, the more economic development depends on energy, and the low efficiency of energy use. Conversely, the lower the intensity of energy consumption, the higher the efficiency of energy use and the better coordination of economy and energy. After analysis, Jilin Province, as an old industrial base in Northeast China, has a close relationship between energy consumption intensity and industrial weight. Therefore, it is recommended to strengthen investment in science and technology and environmental protection when developing industry, and strive to match the intensity of energy consumption with international standards. [ABSTRACT FROM AUTHOR]

The intensity of energy consumption reflects the degree of dependence of economic development on energy consumption. The greater the value of energy consumption intensity, the more economic development depends on energy, and the low efficiency of energy use. Conversely, the lower the intensity of energy consumption, the higher the efficiency of energy use and the better coordination of economy and energy. After analysis, Jilin Province, as an old industrial base in Northeast China, has a close relationship between energy consumption intensity and industrial weight. Therefore, it is recommended to strengthen investment in science and technology and environmental protection when developing industry, and strive to match the intensity of energy consumption with international standards. [ABSTRACT FROM AUTHOR]

Han, L., 2019. The relationship between energy consumption intensity and the proportion of industrial added value in Jilin Province. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 582–584. Coconut Creek (Florida), ISSN 0749-0208. The intensity of energy consumption reflects the degree of dependence of economic development on energy consumption. The greater the value of energy consumption intensity, the more economic development depends on energy, and the low efficiency of energy use. Conversely, the lower the intensity of energy consumption, the higher the efficiency of energy use and the better coordination of economy and energy. After analysis, Jilin Province, as an old industrial base in Northeast China, has a close relationship between energy consumption intensity and industrial weight. Therefore, it is recommended to strengthen investment in science and technology and environmental protection when developing industry, and strive to match the intensity of energy consumption with international standards. [ABSTRACT FROM AUTHOR]

This study attempted to use GARCH-Vine copulas model to analyze the dependence structure between the value of imports and exports with exchange rate on the economic development of Yunnan Province. Since it is difficult to apply the traditional copula analysis in standard multivariate copulas, when the dependence structure is inflexible or sensitive with parameters, in this research, we consider Vine copulas via its inherent advantage of computation. Comparing the AIC with four constructed copula vines, we can see that the Clayton dependence structure exhibits a better explanatory than the student-t dependence structure. In the Clayton copula that there is a strongly significant correlation on C34∣12 in C-vine, as industrial added value and exchange rates increase, the demand of goods and imports, the production and exports will increase too; therefore, positive relationships between industrial added value and exchange rates with international trade. In D-vine, we find that all parameters vary considerably and significantly, respectively, in student-t copula model. Finally, in the Clayton copula, we conclude a strongly significant correlation copula in exportimport. [ABSTRACT FROM AUTHOR]

Ma, Y.; Wang, Q.; Bai; X., and Chen, G., 2020. Marine Economic Prediction Based on Mathematical Models. In: Li, L. and Huang, X. (eds.), Sustainable Development in Coastal Regions: A Perspective of Environment, Economy, and Technology. Journal of Coastal Research, Special Issue No. 112, pp. 224-227. Coconut Creek (Florida), ISSN 0749-0208. The mathematical forecasting model has the characteristics of low data requirements, but high accuracy and good effect on medium and short-term forecasts. Its application range is very wide. Based on the marine economic data from 2010 to 2014, establish the mathematics of the marine economic added value. The model was tested and verified to prove that the model was available, and the marine economic added value was predicted. By comparing the predicted value with the initial value data, it was found that the added value did not match the increase of the marine production added value. The rate of increase in value is slower than that of marine production, which shows that when attracting marine economic talents, capital, technology, etc., the corresponding marine scientific research, education, and management services are not paid enough attention. Neither the absolute amount nor the relative proportion does not match. Finally, conclusions and suggestions are given. [ABSTRACT FROM AUTHOR]