In this study, the authors apply water ecological footprint measurement methods to the field of gray water footprint research and provide a method for calculating gray water ecological footprints. Based on the extended Kaya identity and the Logarithmic Mean Divisia Index (LMDI) technique decomposition methods, we calculated the gray water ecological footprint of 31 provinces and the per capita gray water ecological footprint in China from 2000 to 2014. We then measured and decomposed the values of the driving effects of per capita gray water ecological footprint change. From these results, we selected five of the more important economic and environmental factors to apply as capital factors to gray water research. This method included measurements of the effects of the working population, capital stocks, capital output coefficients, water ecological footprint intensity, and gray water ecological footprint emission coefficients. The five driving effects were economic activity, capital deepening, capital efficiency, footprint intensity, and environmental efficiency. The values of the driving effects on changes during the time period are discussed and analyzed based on the Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) clustering model for spatial clustering of the effects. The results showed that the Chinese per capita gray water ecological footprint output changed because of the interaction of these five factors. Among them, the incremental capital deepening effect was obvious, as a large amount of capital has been invested to promote rapid development of the regional economy and to employ a rapidly growing population. Economic activity can also promote the characteristics and the size of regional economies as population growth will increase regional demands for water. The other capital deepening effect is a decline in the output efficiency of capital. With the rapid development of its economy, China is in a period of industrialization and urbanization. The economic structure has gradually changed from a population-intensive to a capital-intensive structure. Industry, especially heavy industry, had a higher initial investment and a lower output efficiency, which is why the effect of capital efficiency continues to decrease. The most obvious effect on footprint intensity was the effect of per capita gray water ecological footprint reduction. With the improvement of economic activity and advances in science and technology, the water ecological footprint intensity has been greatly reduced. As a result of the effective control of pollutant emissions, the per capita gray water ecological footprint in most provinces and cities has shown a decreasing trend. A reduction in environmental efficiency is an inevitable consequence of a decrease in the water ecological footprint intensity. With a decrease in water ecological footprint intensity, more water is reused and we can improve water efficiency in most provinces to reduce the gray water ecological footprint. This study discusses the relationship between gray water ecological changes and capital factors; the reported results have value as a reference for the adjustment of environmental policies and the sustainable utilization of water resources.