RESUMO
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.
Assuntos
Luz , Urbanização , China , MeteorologiaRESUMO
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.
