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As CO 2 emissions are quantified by allowances and traded in markets, wise trading strategies will bring emitting companies higher profits or lower costs. Based on the big data of Community Independent Transaction Log (CITL), this article hereby presents a micro study on the emitting companies' efforts in increasing profits and saving costs during the allowances trading in the first two phases of the European Union Emission Trading Scheme (EU ETS). The efforts are measured by an after-action factor of trading performance, which is built on a series of behaviour and monetary variables. By comparison, demanders of the emitting companies are more inclined to reach a higher trading performance, while that inclination is heterogeneous among the suppliers. In addition, emitting companies with lower emission levels had a better trading performance. With a higher proportion of low-emitting companies, the manufacturing sector had a better trading performance than the energy sector. The effect of the trading requirement on trading performance are investigated via a quantile regression mode. Results suggest that: (1) the selling requirement of suppliers has a positive effect on their trading performance, while the effect becomes weaker when the selling requirement increases; (2) the buying requirement has a positive effect on the demanders' trading performance only when the requirement is high, and the effect becomes stronger as the requirement increases; and (3) when the buying requirement is at a lower level, demanders' trading performance becomes worse as the requirement grows. The conclusion is that the emission level, industrial sector and trading requirement do have influences on the trading performance of emitting companies in emission trading. [ABSTRACT FROM AUTHOR]

As a well-known numerical approximation method, the least squares fitting method is widely applied in surveying engineering. The most important and difficult problem in this method is to determine the type of base function to be used, and polynomial is the most frequently used type. But if the degree of polynomial is too high (>=7), the normal equations are usually ill-conditioned. When we choose the uniform B-spline as the basis, there are two problems: (1) if we require the known data points to be an equidistant grid, it can only be applied in limited field; and (2) if the known data points are scattered, it is very difficult to obtain satisfactory results by using the uniform B-spline method. The purpose of this paper is to present the least squares fitting method based on a bivariate nonuniform B-spline. The principles and algorithms of this least squares fitting method are developed. Based on this method, two numerical simulations and one application in regional gravity field approximation are discussed. [ABSTRACT FROM AUTHOR]