The level of service-industry development has become an important symbol of the competitiveness and influence of cities. The study of the dynamic evolution characteristics and patterns of urban service-industry land use, the driving factors and their interactions is helpful to provide a basis for decision making in policy design and land use planning for the development of service economies. In this study we have conducted an empirical study of China, based on the methods of spatial cold- and hot-spot analysis, Tapio’s decoupling model, and GeoDetector. We found that: (1) the scales of land use, output efficiencies and development intensities of service-industries are increasing with a trend that takes the form of a “J”, “U” and “inverted U”, respectively; (2) Spatial variabilities and agglomerations are significant, with a stable spatial pattern of the scale of service-industry land use, and a gradient in the distribution of cold- and hot-spots. The dominant spatial units of output efficiency and development intensity have changed from low and lower to high and higher, and the cold- and hot-spots gather in clusters; (3) The development of service-industries is highly dependent on the input of land-resources, and only a few provinces are in a state of strong decoupling, while most are in a state of weak decoupling, with quite a few still in a state of expansive coupling, expansive negative decoupling, or even strong negative decoupling; (4) There are many driving factors for land use changes in the service-industry, with increasingly complicated and diversified relationships between each other, ranked in intensity as the scale effect > informatization > globalization > industrialization > urbanization.

Curbing carbon emissions by restricting economic growth could decrease human well-being across the world and especially in developing countries, suggesting that we need to find alternative approaches to reducing carbon emissions. Against this background, this paper investigates the relationship between urban spatial structure and carbon emissions in the Chinese context from 2002 to 2019. Specifically, urban spatial structure of 286 Chinese cities, represented by the two dimensions of polycentricity and compactness, are calculated based on the gridded (1 km × 1 km) LandScan dataset on population, while carbon emissions of these cities are aggregated from the gridded (1 km × 1 km) Open-source Data Inventory for Anthropogenic CO2 (ODIAC) dataset on carbon emissions. The empirical results based on different regression models find that overall (1) more dispersed and less monocentric (i.e., less compact and more polycentric) cities are often associated with lower levels of carbon emissions, ceteris paribus; (2) the impact of polycentricity on carbon emissions could be moderated by the economic development levels of Chinese cities. For cities with gross domestic product of more than 173 billion yuan, a more polycentric spatial structure is usually associated with a higher level of carbon emissions; (3) a city’s urban spatial structure could have positive spatial spillovers on carbon emissions of its neighboring cities.

With the arrival of the era of innovative economy, innovation is of great significance for the development of China, and even the world. Based on statistical data and taking 2009, 2014, and 2019 as node years, this study uses the entropy weight method and a modified gravity model to study the pattern and evolution characteristics of China’s interprovincial innovation network. The results showed that: 1. The weights of innovation output, innovation input, and innovation environment were 0.253, 0.340, and 0.407, respectively. Currently, innovation output is the most representative of the interprovincial innovation connections, but the weight of innovation environment has been increasing year by year, and its importance is constantly highlighted. 2. The overall spatial structure of China’s interprovincial innovation network shows a “core periphery” feature, radiating from coastal provinces such as Jiangsu, Beijing, and Zhejiang to inland provinces, and exhibiting an overall pattern of “strong in the east and weak in the west”. 3. In terms of evolutionary characteristics, Guangdong and Jiangsu ranked in the top two in terms of outward innovation scale from 2009 to 2019. The combined total innovation connections of the top five provinces in 2009, 2014, and 2019 accounted for 70.79%, 64.29%, and 64.24%, respectively. Although the phenomenon of uneven innovation connections exists, it has slowed down. In addition, China’s interprovincial innovation network is gradually becoming enriched, with the most significant change being the number of innovation links, with a gravity level increasing from four in 2009 to twenty-six in 2019. The interprovincial innovation links continue to strengthen, but the focus has not changed significantly; it is still concentrated in the Bohai Rim region and the Yangtze River Delta region. 4. In terms of maximum gravitational lines, Guangdong Province had the highest number of maximum gravitational lines in 2009, 2014, and 2019, with a total of six. The maximum gravitational line change from 2009 to 2019 took place from 2014 to 2019, transitioning from “Jilin-Liaoning” to “Jilin-Heilongjiang”.

Taking 27 cities in the Yangtze River Delta as an example, the time section data from 2009 to 2019 are selected, and the location entropy index and the modified E-G index are introduced to measure the collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta. The spatial weight matrix is constructed based on the highway mileage between cities. Using Moran’s index and Local Moran’s index, this article analyzes the spatial correlation of the collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta. The results show that: Firstly, the overall agglomeration degree of manufacturing industry of cities in the Yangtze River Delta shows a fluctuating downward trend. The agglomeration degree of manufacturing industry in Jiangsu and Zhejiang Provinces has decreased, that of most cities in Anhui Province has increased steadily. Secondly, the collaborative agglomeration level of manufacturing industry between Shanghai, Nanjing, Hangzhou and other cities in Jiangsu and Zhejiang Provinces showed a downward trend, while the collaborative agglomeration level with Anhui cities increased steadily. The collaborative agglomeration level of manufacturing industry between Wuxi, Suzhou, Nanjing and cities with obvious advantages in manufacturing industry agglomeration degree shows an upward trend, while the collaborative agglomeration level with other cities has declined. The collaborative agglomeration level of intercity manufacturing industry in Hefei has generally improved, especially the collaborative agglomeration quality and depth of manufacturing industry between Hefei and other cities in Anhui Province have significantly improved. Thirdly, the regions with high collaborative agglomeration level of intercity manufacturing industry in the Yangtze River Delta are still mainly concentrated in Jiangsu and Zhejiang Provinces. Although there has been a trend of westward transfer in the past decade, the overall change range is small, and the spatial correlation of the collaborative agglomeration level of intercity manufacturing industry shows a weakening trend as a whole.

This paper defines and explains the “lock-in effect” in China’s industrialization process using the county-level cities in southern Jiangsu while considering both internationalized and localized views. An empirical analysis of two typical representative indicators, per capita economic output and economic structure, using 2002, 2005, 2010, 2015 and 2019 official statistical data demonstrate that capital, labor and other traditional factors still play a role in the current “lock-in effect”. New elements, such as human capital, are manifesting their growing importance. Public services, such as science and technology, education, medical and health care, social safety and employment, are gradually exerting their influence to optimize the economic structure. This paper promotes pertinent development decisions associated with the “lock-in effect” that must be considered during current industrialization processes.