Revealing the dynamic characteristics of the temporal and spatial evolution of electricity power consumption (EPC) is of great significance for realizing the scientific allocation and rational utilization of electricity power resources. Therefore, based on the EPC data extracted from the DMSP/OLS nighttime light data, this paper takes the Yangtze River Economic Belt (YREB) as an example, and uses various methods such as coefficient of variation, Kernel density analysis, rank-scale rule, trend analysis, and standard deviation ellipse. The evolution characteristics of the spatiotemporal pattern of EPC at the provincial, prefecture and county levels in the YREB were analyzed. The results show that: (1) Through the coefficient of variation (CV), we found that the coefficient of variation (CV) of EPC in the YREB showed a downward trend at the provincial, prefecture and county levels. Specifically, the county-level EPC has the largest difference, followed by the provincial and prefecture-level. (2) Through the kernel density analysis, we found that the EPC agglomeration degree in the YREB obviously shows the characteristics of decreasing from the east to the central and western regions. (3) Through the rank-scale rule, it is found that the |q| value of the YREB has been in a downward trend during the research period at the provincial, prefecture and county scales, and the |q| value is constantly approaching 1. It directly shows that the scale and quantity distribution of EPC in the YREB at the provincial, prefecture and county levels are becoming more and more reasonable. (4) Through trend analysis, we found that the changes in EPC in the YREB at the provincial, prefecture and county scales all obviously showed a decreasing trend from the east to the central and western regions. (5) Through the standard deviation ellipse (SDE), we found that the standard deviation ellipse of the EPC in the YREB obviously shows the spatial distribution direction of “Southwest-Northeast”, and the directionality to the Yangtze River Delta is very obvious, which directly indicates that the promotion of the YREB that the main driving force behind the growth in EPC is the increase in EPC in the east-west direction.

In this study, we applied an innovative trend analysis (ITA) technique to detect the annual and seasonal trends of solar photovoltaic resource potential (R_s) in East Asia during 1961-2010 based on the Global Energy Balance Archive (GEBA) data. The Mann-Kendall (M-K) trend test and linear regression method (LRM) were compared with the ITA technique. The results showed that the annual R_s in China presented a significant decreasing trend (D<-0.5 and P<0.01, where P is the P-value and D is the trend indicator of ITA) using these three techniques. The seasonal R_s generally showed a significant decreasing trend (D<-0.5) using the ITA technique in China, however, a slightly increasing trend was observed in Japan. The R_s values were further divided into four groups (“low”, “moderate”, “high” and “very high”) to detect the sub-trends using the ITA technique. The results indicated that the decreasing annual R_s in China was mainly due to reductions in the “high” and “very high” R_s values. The most probable causes of the trends in the variation in China were the decreasing sunshine duration and increasing anthropogenic aerosol loadings; while the trends in Japan were probably driven by the increasing sunshine and declining cloud optical thickness. Moreover, the similarities and differences between the M-K test and ITA technique results were compared and evaluated, and the ITA technique proved to be superior to the M-K test.

The impact of spatial heterogeneity on industrial outputs is a new important topic in economic geography. A considerable amount of research literature has accumulated, but the academic community lacks a systematic and comprehensive review and consensus on this topic. This study carried out research by mining the relevant classical literature. This investigation first combed the connotation of spatial heterogeneity, which is both corresponding to and related to spatial dependence. Theorists generally acknowledge that there is spatial heterogeneity in the process of industrial outputs. Then this study summarizes the logical basis, relationship coordination, measurement and other aspects of the effect of spatial heterogeneity on industrial outputs. In analyzing the impact of spatial heterogeneity on industrial outputs, we should not ignore the spatial dimension, but must also pay attention to the heterogeneity of individual enterprises. Industrial output analysis needs to be based on the relationship between spatial heterogeneity and spatial dependence. The influence of spatial heterogeneity on industrial outputs and the degree of differences among observation objects can be measured by econometric methods. The common indicators for measuring and quantitatively describing the impact of spatial heterogeneity on industrial outputs mainly include semivariogram, the spatial expansion model and the geographical weighted regression model. Finally, some directions of future research are pointed out in order to provide useful ideas for future theoretical research and industrial practice.