Finding the balance between economic development and environmental protection is a major problem for many countries around the world. Air pollution caused by economic growth has caused serious damage to humans’ living environment, and as improving energy and resource efficiencies is the first priority, many countries are targeting to move towards a sustainable environment and economic development. This study uses the modified dynamic SBM (slack-based measure) model to explore the economic efficiency and air pollutants emission efficiency in Taiwan’s counties and cities from 2012 to 2015 by taking labor, motor vehicles, and electricity consumption as inputs and average disposable income as output. Particulate matter (PM2.5), nitrogen oxide emissions (NO2), and sulfur oxide emissions (SO2) are undesirable outputs, whereas factory fixed assets are a carry-over variable, and the results show the following: (1) the regions with the best overall efficiency between 2012 and 2015 include Taipei City, Keelung City, Hsinchu City, Chiayi City, and Taitung County; (2) in counties and cities with poor overall efficiency performance, the average disposable income per household has no significant relationship with air pollutant emissions; (3) in counties and cities where overall efficiency is poor, the average efficiency of each household’s disposable income is small; and (4) except for the five counties and cities with the best overall performance, the three air pollutants in the other fourteen counties and cities are high. Overall, the air pollution of most areas needs improvement.

In order to better identify the spatial influence between adjacent parts of road networks, the paper introduces the spatial autocorrelation theory in evaluating the operation performance of urban road networks. The research proposes several spatial correlation validation indicators to verify the spatial characteristics among the road networks. Based on the analysis of spatial characteristics, the relationship between operation performance and influencing factors under the impact of spatial effect is examined. Furthermore, a spatial autocorrelation based influence models at three traffic flow levels is developed by using the data from a partial urban road network in Beijing. The model analysis shows that the spatial autocorrelation model is more effective in analyzing the urban road network operation performance under the influence of various factors. This model will be beneficial in identifying traffic network problems and improving traffic operations of the urban road network.

In order to improve the reliability of urban road network operation evaluation, the road network regional Partition methods were launched in this paper. The geographic grid was introduced first, and a 4-level road network model was defined. Then, the spatial analysis based urban road network division method was proposed by analyzing the characteristics of road network operation. This method can reflect the influence between adjacent regional units, and improve the reliability of urban road network division. Finally, this research took a certain area in Beijing as a case study, and divided the road network as several regional units. Macroscopic evaluation result shows that it is effective for scientifically describing the road network operation status.