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This study is concerned with the impact factors of electric carbon productivity change in China. Some influencing factors are identified by examining the time series decomposition of electric carbon productivity based on data from 2003 to 2015, where the usual Logarithmic Mean Divisia Index (LMDI) method is used but with the regional dimension taken into consideration. Moreover, this study analyzes the driving factors of electric carbon productivity change from the perspective of production and consumption in China's power industry, where the influences of power transfers among provinces, imports and exports, and transmission losses are considered. Based on the decomposition analysis of existing data in 30 provinces (including province-level municipalities), from the perspective of production, regional actual electric carbon productivity, and per capita GDP are the main influencing forces for the growth of electric carbon productivity, and the reciprocal of per capita electric carbon emissions, energy intensity, and energy emission intensity play dominate roles in the decline of electric carbon productivity. From the perspective of consumption, the main impact factors to improve electric carbon productivity are power transfers among provinces, imports and exports, the reciprocal of emission intensity of power consumption and regional electric carbon productivity, and the impact of energy consumption on thermal power generation, the proportion of thermal power to total electricity generation, and the effect of transmission losses. Finally, several conclusions are drawn that might be meaningful for the Chinese government to improve China's electric carbon productivity.

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The relief distributions after large disasters play an important role for rescue works. After disasters there is a high degree of uncertainty, such as the demands of disaster points and the damage of paths. The demands of affected points and the velocities between two points on the paths are uncertain in this article, and the robust optimization method is applied to deal with the uncertain parameters. This paper proposes a nonlinear location routing problem with half-time windows and with three objectives. The affected points can be visited more than one time. The goals are the total costs of the transportation, the satisfaction rates of disaster nodes, and the path transport capacities which are denoted by vehicle velocities. Finally, the genetic algorithm is applied to solve a number of numerical examples, and the results show that the genetic algorithm is very stable and effective for this problem.