This paper develops a conceptual model and an indicator system for measuring economic resilience of resource-based cities based on the theory of evolutionary resilience and the related concepts of persistence, adaptation, and transformation. Nineteen resource-based cities in Northeast China were analyzed using the indicator system. The results showed that Liaoning and Jilin provinces had higher economic resilience than Heilongjiang Province. Panjin, Benxi, and Anshan in Liaoning Province were the top three cities, while Shuangyashan and other coal-based cities in Heilongjiang Province ranked last. Metals-and petroleum-based cities had significantly higher resilience than coal-based cities. The differences in persistence, adaptability, transformation, and resilience among resource-based cities decreased since the introduction of the Northeast Revitalization Strategy in 2003. Forestry-based cities improved the most in terms of resilience, followed by metals-based and multiple-resource cities; however, resilience dropped for coal-based cities, and petroleum-based cities falling the most. The findings illustrate the importance and the way to develop a differentiated approach to improve resilience among resource-based cities.
Promoting active travel behavior and decreasing transport-related carbon dioxide(CO_2) emissions have become a priority in many Chinese cities experiencing rapid urban sprawl and greater automobile dependence. However, there are few studies that holistically examine the physical and social factors associated with travel CO_2 emissions. Using a survey of 1525 shoppers conducted in Shenyang, China, this study estimated shopping-related travel CO_2 emissions and examined how the built environment and individual socioeconomic characteristics contribute to shopping travel behavior and associated CO_2 emissions. We found that, firstly, private car trips generate nearly eight times more carbon emissions than shopping trips using public transport, on average. Second, there was significant spatial autocorrelation with CO_2 emissions per trip, and the highest carbon emissions were clustered in the inner suburbs and between the first and second circumferential roads. Third, shopping travel CO_2 emissions per trip were negatively correlated with several built environment features including population density, the quantity of public transport stations, road density, and shop density. They were also found to be significantly related to the individual socio-economic characteristics of car ownership, employment status, and education level using a multinomial logistic regression model. These empirical findings have important policy implications, assisting in the development of measures that contribute to the sustainability of urban transportation and meet carbon mitigation targets.
