Urban-Rural Fringe Long-Term Sequence Monitoring Based on a Comparative Study on DMSP-OLS and NPP-VIIRS Nighttime Light Data: A Case Study of Shenyang, China.

Urban-rural fringes, as special zones where urban and rural areas meet, are the most sensitive areas in the urbanization process. The quantitative identification of urban-rural fringes is the basis for studying the social structure, landscape pattern, and development gradient of fringes, and is also a prerequisite for quantitative analyses of the ecological effects of urbanization. However, few studies have been conducted to compare the identification accuracy of The US Air Force Defence Meteorological Satellite Program's (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data from the same year, subsequently enabling long time series monitoring of the urban-rural fringe. Therefore, in this study, taking Shenyang as an example, a K-means algorithm was used to delineate and compare the urban-rural fringe identification results of DMSP and VIIRS nighttime light data for 2013 and analyzed the changes between 2013 and 2020. The results of the study showed a high degree of overlap between the two types of data in 2013, with the overlap accounting for 75% of the VIIRS data identification results. Furthermore, the VIIRS identified more urban and rural details than the DMSP data. The area of the urban-rural fringe in Shenyang increased from 1872 km2 to 2537 km2, with the growth direction mainly concentrated in the southwest. This study helps to promote the study of urban-rural fringe identification from static identification to dynamic tracking, and from spatial identification to temporal identification. The research results can be applied to the comparative analysis of urban-rural differences and the study of the ecological and environmental effects of urbanization.

The Effect of Urban Shrinkage on Carbon Dioxide Emissions Efficiency in Northeast China.

Climate change caused by CO2 emissions is a controversial topic in today's society; improving CO2 emission efficiency (CEE) is an important way to reduce carbon emissions. While studies have often focused on areas with high carbon and large economies, the areas with persistent contraction have been neglected. These regions do not have high carbon emissions, but are facing a continuous decline in energy efficiency; therefore, it is of great relevance to explore the impact and mechanisms of CO2 emission efficiency in shrinking areas or shrinking cities. This paper uses a super-efficiency slacks-based measure (SBM) model to measure the CO2 emission efficiency and potential CO2 emission reduction (PCR) of 33 prefecture-level cities in northeast China from 2006 to 2019. For the first time, a Tobit model is used to analyze the factors influencing CEE, using the level of urban shrinkage as the core variable, with socio-economic indicators and urban construction indicators as control variables, while the mediating effect model is applied to identify the transmission mechanism of urban shrinkage. The results show that the CEE index of cities in northeast China is decreasing by 1.75% per annum. For every 1% increase in urban shrinkage, CEE decreased by approximately 2.1458%, with urban shrinkage, industrial structure, and expansion intensity index (EII) being the main factors influencing CEE. At the same time, urban shrinkage has a further dampening effect on CEE by reducing research and development expenditure (R&D) and urban compactness (COMP), with each 1% increase in urban shrinkage reducing R&D and COMP by approximately 0.534% and 1.233%, respectively. This can be improved by making full use of the available built-up space, increasing urban density, and promoting investment in research.

How does China's industrial wastewater shadow price evolve? The perspective of spatiotemporal characteristics and differences decomposition.

To provide a reference for reducing the cost of industrial wastewater treatment and alleviate the pressure on water environment governance in China, we use the non-parametric dual evaluation linear analysis framework to estimate the shadow price of China's urban industrial wastewater (IWSP) with consideration of multiple inputs based on the data of 267 cities in China from 2003 to 2016. Then, we investigate the spatiotemporal characteristics of IWSP and analyze its sources of differences. Main conclusions are as follows: (1) Mean of China's urban IWSP increased from 645.54 yuan/ton in 2003 to 5662.64 yuan/ton in 2016, implicating the significant results and increasing difficulty of emission reduction policies. In addition, the Moran's I index of IWSP decreased from 0.056 to 0.002, implicating declining spatial correlation and differentiated green production processes in various regions. (2) From stock perspective, the σ convergence result shows that the IWSP of the country and each region gradually diverges, and the ß convergence results from incremental perspective show that the IWSP of a single region tends to converge in a steady state. Furthermore, regions with lower average shadow prices converge faster than regions with higher average shadow prices. (3) Using the Dagum Gini coefficient method, we find that the overall difference of IWSP dropped from 0.5758 to 0.3568. The intra-regional differences in each region continued to decline, as well as inter-regional differences. And the contribution rate of intensity of transvariation has risen from 33.71 to 60.80%, becoming the main reason for the imbalanced distribution of IWSP.

Assessing e-commerce impacts on China's CO2 emissions: testing the CKC hypothesis.

This paper made the first attempt to summarize the rules from a regional perspective and use panel data to explore the carbon Kuznets curve (CKC) between e-commerce and carbon dioxide emissions. The impact of online shopping on carbon emission has mixed conclusions. No CKC tests set mainly focuses on the e-commerce sector, which can help this research determine the relationship between e-commerce and carbon emissions. From a macro point of view, we examine both developed and developing regions by testing the CKC hypothesis. We try to explain it by exploring the econometric relationship between e-commerce and CO2 emissions. At first, we attempt to accurately measure the CO2 emissions by carefully distinguishing the carbon emission increments caused by the primary energy resulting from the secondary energy. Then, we use panel data collected from different Chinese cities during 2001-2017. The analyzed variables are stationary at their first differences with the LLC test, IPS test, Fisher-ADF test, Fisher-PP test, CADF, and CIPS unit root tests. The analyzed variables are cointegrated by employing the Pedroni panel cointegration test, the Kao panel cointegration test, and the Westerlund panel cointegration test. Using the DOLS, we also find that increases in trade openness decrease carbon emissions while increases in foreign direct investment (FDI) and market size contribute to the level of emissions. The quadratic-shape CKC hypothesis is supported for China, Eastern China, and Western China, and it is an inverted "U" shape. The cubic-form CKC is supported for Central China, and it is an "N" shape. Our study provides important insights for enacting regional and country-level e-commerce regulations and reducing carbon dioxide emissions.