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This paper aims to analyze the urban human settlements of 21 megacities and supercities from 2011 to 2020 in China and improve urban human settlements planning and management. First, the data envelopment analysis (DEA)–Malmquist–Tobit model is used to conduct a static and dynamic evaluation of the efficiency of urban human settlements and analyze the main factors that affect it. Then, the coupling coordination degree model is utilized to quantitatively evaluate the coupling coordination degree between urban human settlements and high-quality economic development. Finally, the GM (1, 1) model is established to predict each city's average input–output index during the 14th Five-Year Plan period. It is hoped that this paper could promote sustainable urban development. The empirical results are as follows: (1) the technical efficiency (TE) from CRS DEA of human settlements in China's megacities and supercities is low. Management and technology are the shortcomings in the development of urban human settlements; (2) total factor productivity (TFP) has shown negative growth. Technical progress efficiency (TC) is the key to improving the efficiency of urban human settlements; (3) urban infrastructure has the most significant role when promoting the efficiency of urban human settlements; (4) the coupling degree between the efficiency of human settlements and high-quality economic development is mainly high-level coupling. The degree of coordination is gradually rising; and (5) during the 14th Five-Year Plan period, the investment in human settlements in both types of cities will continue to increase. With the continuous expansion of investment, the output will be imbalanced. [ABSTRACT FROM AUTHOR]

In the grey prediction, the nonlinear Grey Bernoulli model NGBM (1, 1) is an important type. The NGBM (1, 1) has good adaptability to data fitting and then small prediction errors, and thus has been applied widely. However, if we improve the modelling method, the prediction precision shall be improved to some extent. The important factors of prediction error are the approximation of background value and the approximation of power exponent. Therefore, the paper tries to combine the optimisation of background value with the optimisation of the power exponent of NGBM (1, 1) model and then improves the model from parameter estimation. The paper gives three methods for the following three cases respectively: the background value in the form of exponential curve, the background value in the form of the polynomial curve and the background value in the form of interpolation function, to combine background value optimisation with power exponent optimisation for parameter optimisation. The final section of the paper builds the NGBM (1, 1) models of China's GDP and energy consumption with three improvement methods. The simulation and prediction results show the three improvement methods all have high precision. The methods given offer good approaches for the in-depth study on nonlinear grey Bernoulli model, enrich the method system of grey modelling and can be applied to the studies on other grey models to promote the study and wide application of the grey model. [ABSTRACT FROM AUTHOR]

In recent years, the consumption of natural gas in China has dramatically increased. The consumption of natural gas in 2013 is six times as much as that in 2003. Hence, reasonably forecasting the supply and demand of China's natural gas has an important significance for Chinese government to formulate energy policy. In this paper, a novel grey model named TPGM(1,1) is proposed to simulate and forecast the supply and demand of natural gas in China. Firstly, the unbiased parameter estimation method of TPGM(1,1) was studied by Cramer's rule; secondly the optimal method of the initial value of TPGM(1,1) was deduced; thirdly, the TPGM(1,1) for the output and consumption of natural gas in China was then built, and simulated and predicted results were compared with those of other models using known data. Finally, the supply and demand of natural gas in China during 2015-2020 are then forecasted by the novel model, and the results show 67.61 percent of the consumption of natural gas in 2020 will depend on foreign imports due to a surge in demand for natural gas in China. [ABSTRACT FROM AUTHOR]

Discharge from the largest karst spring in north China, the Niangziguan Springs, has been declining since the 1950s. This paper examines the response of these springs to climatic change and anthropogenic influence by attempting a model-based discrimination between phases in the stream discharge record. In Niangziguan Springs Basin, the exploitation of karst groundwater began in 1979. Accordingly, the spring discharge data were divided into two phases: pre-1979 and post-1979. In the first phase (1957-78) the spring discharge was believed to be affected solely by climate change, and in the second phase (1979-2007) the spring discharge was influenced by both climate change and human activities. Using grey system theory, a discharge model was estimated for the first phase. Extrapolating the model, we obtained a projection of the spring discharge during the second phase. Using a water balance calculation, we discerned the respective effects of climate change and human activities on depletion of spring discharge for the second phase. The results show that the contribution of climate change to depletion of Niangziguan Springs is 2.30m3/s and the contribution of anthropogenic activities ranges from 1.89 to 2.90 m3/s, although this assumes a constant for the climate change effect. Accordingly, the anthropogenic effects have been approaching and surpassing the effects of climate change during the second phase. With respect to the impact of human activities on spring discharge, groundwater abstraction accounts for only about 34-52% of the declines; 48-66% of the declines are related to other human activities, such as dewatering from coal mining, dam building and deforestation. [ABSTRACT FROM AUTHOR]

Abstract: Electricity demand forecasting plays an important role in electricity systems expansion planning. In this paper, we present a trigonometric grey prediction approach by combining the traditional grey model GM(1,1) with the trigonometric residual modification technique for forecasting electricity demand. Our approach helps to improve the forecasting accuracy of the GM(1,1) and allows a reasonable grey prediction interval to be obtained. Two case studies using the data of China are presented to demonstrate the effectiveness of our approach. [Copyright &y& Elsevier]