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Eco-efficiency is a key indicator to measure the level of regional sustainable development. The county is the basic spatial unit of socio-economic activities and sustainable policy implementation in China. Hence, this paper conducts eco-efficiency analysis at the county scale in order to provide reference for the central and local governments to formulate differentiated eco-efficiency enhancement policies, further promote Chinese ecological sustainable development, and advance the construction of ecological civilization with high quality. Based on the Super-Slacks-Based Measure (SBM) model and the Malmquist index, the paper constructed an eco-efficiency measurement method and analyzed the variation characteristics, influencing factors, spatial pattern of eco-efficiency in Chinese counties from 2000 to 2020. The results showed that: (1) The eco-efficiency of the county unit was generally low in China and was roughly distributed in a pyramid. The county-level eco-efficiency had a spatial distribution pattern of being high in the west and low in the east, and high in the south and low in the north, with significant non-equilibrium. There was a positive correlation between eco-efficiency of neighboring counties in China. (2) The per-capita GDP has a significant positive correlation with eco-efficiency, while energy consumption, arable land area and eco-efficiency have a negative correlation. The redundancy rate of input indicators was high in Chinese counties. (3) During the study period, the eco-efficiency of most counties displayed a fluctuating growth trend. The growth was mainly driven by technological progress.

With rapid urbanization and population growth, achieving equitable distribution of urban facilities in the city center has become a critical research focus due to limited land space and high population density. In this study, we propose a technical method to measure the spatial matching between urban service facilities and population at the grid resolution scale, using Baidu heat map and POI data. The method includes spatial heterogeneity analysis and spatial matching analysis between population density and service facilities. We apply the method to the main urban area of Lanzhou, a valley-type city in the upper reaches of the Yellow River, and measure the spatial matching between service facilities and population aggregation. Our results reveal the distribution characteristics of various service facilities and population aggregation in different time slots, and demonstrate that transportation facilities have the highest spatial matching with population aggregation, followed by real estate and education services, with rental business services exhibiting the lowest. The proposed method offers a new perspective for urban planners and decision-makers to understand the matching state between residents’ activity patterns and service facilities. Our findings can provide theoretical support for urban planning and optimize the layout of service facilities and regional function allocation.

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