Shrinking cities—cities suffering from population and economic decline—has become a pressing societal issue of worldwide concern. While night-time light (NTL) data have been applied as an important tool for the identification of shrinking cities, the current methods are constrained and biased by the lack of using long-term continuous NTL time series and the use of unidimensional indices. In this study, we proposed a novel method to identify and classify shrinking cities by long-term continuous NTL time series and population data, and applied the method in northeastern China (NEC) from 1996 to 2020. First, we established a long-term consistent NTL time series by applying a geographically weighted regression model to two distinct NTL datasets. Then, we generated NTL index (NI) and population index (PI) by random forest model and the slope of population data, respectively. Finally, we developed a shrinking city index (SCI), based on NI and PI to identify and classify city shrinkage. The results showed that the shrinkage pattern of NEC in 1996–2009 (stage 1) and 2010–2020 (stage 2) was quite different. From stage 1 to stage 2, the shrinkage situation worsened as the number of shrinking cities increased from 102 to 162, and the proportion of severe shrinkage increased from 9.2% to 30.3%. In stage 2, 85.4% of the cities exhibited population decline, and 15.7% of the cities displayed an NTL decrease, suggesting that the changes in NTL and population were not synchronized. Our proposed method provides a robust and long-term characterization of city shrinkage and is beneficial to provide valuable information for sustainable urban planning and decision-making.

Disordered urban expansion has encroached on a large amount of ecological land, resulting in the steady degradation of urban ecology, which has an adverse effect on the sustainable development of the region. An ecological security pattern can effectively control urban expansion, and it is of great significance to balance urban development and ecological protection. In order to analyze the impact of ecological security patterns on urban expansion, Hangzhou was taken as an example, the CA-Markov model and FLUS model were used to simulate the urban expansion pattern in 2030 under the natural development scenario and the ecological security scenario. The results showed that (1) the ecological source area in the study area is 630.90 km² and was mainly distributed in the western mountainous area. There are 14 ecological corridors, primarily composed of valleys and rivers. Ecological nodes are mainly distributed on the north and south sides of the main urban area. (2) From 2000 to 2018, the annual increase index (AI) of construction land decreased in the northeast and southeast directions but increased in the northwest and southwest directions, and in the northeast direction the value was always the highest. Except for the southwest direction, the average annual growth rate (AGR) of construction land in the other directions decreased. At a distance from the city center of 30 km, AI was relatively higher and was increasing, while AGR was declining. At a distance of 30–45 km, both AI and AGR were increasing, indicating that the focus of construction land was moving outwards. (3) From 2018 to 2030, under both natural development scenario and ecological security scenario, construction land would keep expanding, but the construction land area, proportion, AI, and AGR of the latter would both be smaller than the former, indicating that the ecological security pattern can effectively curb urban expansion. Because of a large amount area of ecological sources, the expansion of construction land in the southwest direction would be constrained, especially under the ecological security scenario. The methods and results of this study can provide theoretical and application references for urban planning and green development in metropolises.

Science parks are considered to be key drivers of innovative economic activities and are important tools for countries and regions to achieve sustainable development. However, there still exists controversy about the positive effect of the science parks on the companies’ innovative performance. In this study, we constructed six hypotheses according to previous studies and tested them in the Shanghai Zhangjiang National Innovation Independent Demonstration Zone to answer two major research questions, i.e., (1) “Do science parks promote companies’ innovative performance?” and (2) “What factors in science parks affect the likelihood and intensity of companies’ innovative performance?”. Specifically, we selected 911 companies within the park and 861 companies outside the park using the coarsened exact matching method and applied the zero-inflated negative binomial model to identify the relationship between the company’s presence within the science park and the company’s innovative performance. Then, we applied the Heckman two-step model to further explore the key impact factors affecting the intensity of the innovation activities of the companies in the park. The results confirmed our first hypothesis that science parks can promote companies’ innovative performance. Moreover, we obtained two other findings. First, if a company is located within a science park, it can greatly improve the probability of innovation of that company, but it does not have any significant impact on the intensity. In other words, science parks promote more innovation among companies lacking innovation experience than those with innovation experience. Secondly, the reason why science parks can promote innovation is the knowledge spillover of innovation supporting institutions and high-tech enterprises in the parks, which is limited to a small geographical range. Our study provided new evidence on the positive role of science parks on companies’ innovative performance and offered a valuable sample for the research of science parks in developing countries. In addition, the policy suggestions we raised have reference value for developing countries to take full advantage of science parks to achieve sustainable development of their innovative economy.