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In recent years, the rapid development of the rare earth industry has had a serious impact on the environment. Some enterprises have taken measures to improve the production process. In order to explore the sustainability of this industry and these improvements’ environmental benefits, this paper combines emergy analysis and lifecycle assessment to evaluate and compare the production process of rare-earth oxides considering the three aspects of emergy flow, pollutant emissions, and emergy-based indicators. Changes in the emergy of pollutant emissions before and after improvement of the production process are discussed. The results show that the greatest inputs in the mining and beneficiation stage and smelting separation stage are labor force and service and non-renewable resources, respectively. These two production stages are highly dependent on external input and have weak competitiveness. Both stages place great pressure on the environment, so the bastnasite production process would be unsustainable in the long term. After the improvement, the environmental impact of the production process for bastnaesite changed significantly, indicating that the improvement effect of the wastewater treatment facilities and the change of fuel from coal to natural gas is remarkable.

Innovation ability has become one of the core elements in the pursuit of China’s green growth, and high-tech industries are playing a leading role in technological innovation in China. With the rapid development of China’s high-tech industries, their innovation efficiency has attracted widespread attention. This article aims to illustrate a shared inputs two-stage network Data Envelopment Analysis (DEA), to measure the innovation efficiency of high-tech industries in China’s 29 provinces from 1999 to 2018. The results indicate that there are obvious differences in the innovation efficiency of the provinces. The technology development efficiency, the technical transformation efficiency, and the overall innovation efficiency of the developed east coast provinces are generally higher than those of the backward central and western provinces. This article further applies the spatial econometrics model to analyze the factors influencing the innovation efficiency of high-tech industries. We have found that government support, R&D input intensity, industries aggregation, economic extroversion, and the level of development of the modern service industries cause varying degrees of impact on innovation efficiency.

AbstractThere is close relationship between science & technology inputs and economic growth. Based on the data of science & technology input and economic growth, the paper makes the econometric models analysis on the economic growth and science & technology input of the three major coastal economic regions of China. The paper analyzes and compares the contribution rate of science & technology inputs to economic growth of the three major economic regions.

To understand the effects of physicochemical factors on nitrite transformation by microalgae, a lipid-rich Chlorella with high nitrite tolerance was cultured with 8 mmol/l sodium nitrite as sole nitrogen source under different conditions. The results showed that nitrite transformation was mainly dependent on the metabolic activities of algal cells rather than oxidation of nitrite by dissolved oxygen. Light intensity, temperature, pH, NaHCO3 concentrations, and initial cell densities had significant effects on the rate of nitrite transformation. Single-factor experiments revealed that the optimum conditions for nitrite transformation were light intensity: 300 μmol/m(2); temperature: 30°C; pH: 7-8; NaHCO3 concentration: 2.0 g/l; and initial cell density: 0.15 g/l; and the highest nitrite transformation rate of 1.36 mmol/l/d was achieved. There was a positive correlation between nitrite transformation rate and the growth of Chlorella. The relationship between nitrite transformation rate (mg/l/d) and biomass productivity (g/l/d) could be described by the regression equation y = 61.3x (R(2) = 0.9665), meaning that 61.3 mg N element was assimilated by 1.0 g dry biomass on average, which indicated that the nitrite transformation is a process of consuming nitrite as nitrogen source by Chlorella. The results demonstrated that the Chlorella suspension was able to assimilate nitrite efficiently, which implied the feasibility of using flue gas for mass production of Chlorella without preliminary removal of NOX.

The development of advanced computational models for improving the accuracy of streamflow forecasting could save time and cost for sustainable water resource management. In this study, a locally weighted learning (LWL) algorithm is combined with the Additive Regression (AR), Bagging (BG), Dagging (DG), Random Subspace (RS), and Rotation Forest (RF) ensemble techniques for the streamflow forecasting in the Jhelum Catchment, Pakistan. To build the models, we grouped the initial parameters into four different scenarios (M1–M4) of input data with a five-fold cross-validation (I–V) approach. To evaluate the accuracy of the developed ensemble models, previous lagged values of streamflow were used as inputs whereas the cross-validation technique and periodicity input were used to examine prediction accuracy on the basis of root correlation coefficient (R), root mean squared error (RMSE), mean absolute error (MAE), relative absolute error (RAE), and root relative squared error (RRSE). The results showed that the incorporation of periodicity (i.e., MN) as an additional input variable considerably improved both the training performance and predictive performance of the models. A comparison between the results obtained from the input combinations III and IV revealed a significant performance improvement. The cross-validation revealed that the dataset M3 provided more accurate results compared to the other datasets. While all the ensemble models successfully outperformed the standalone LWL model, the ensemble LWL-AR model was identified as the best model. Our study demonstrated that the ensemble modeling approach is a robust and promising alternative to the single forecasting of streamflow that should be further investigated with different datasets from other regions around the world.

In recent years, food security, especially supply, has been an important issue in China’s agricultural production. The stability of grain prices is related to the stability and development of the grain market. Based on agricultural production data from 1970 to 2015, this paper explores the influence of agricultural product price fluctuation on grain production by using the cobweb theory and vector error correction (VEC) model. The results show that changes in grain production in China are affected by fluctuations in agricultural product prices, that the production change lags behind the price change, and that there is a long-term equilibrium relationship between grain yield and agricultural product price. A Granger causality test shows that the change in agricultural product price is the Granger cause of grain yield change.

Biogas production is a clean renewable energy source that can improve lives in developing countries. However, winter temperatures in some areas are too low to enable enough biogas production in small unheated digesters to meet the energy requirements of households. Low-cost, high yield reactors adapted to the local climate are needed in those situations. A decision-support model was developed to assist in the design of biogas reactors capable of meeting households’ year-round energy needs. Monthly biogas production relative to household energy needs was calculated for the scenario of suburban Hanoi, Vietnam. Calculations included pig number, slurry (manure water mixture) dilution, retention time and biogas/solar heating. Although using biogas to heat the digester increased biogas production, it did not lead to an energy surplus, particularly with the 1:9 slurry dilution rate commonly used on pig farms. However, at a 1:3 slurry dilution, the use of solar heating to provide 90% and biogas 10% of the heat required to heat the digester to 35 °C improved the biogas production by 50% compared to psychrophilic production. The energy needs of an average five-person family throughout the year required 17 fattening pigs. This model can establish the best solution for producing sufficient energy throughout the year.