Urban sustainable development has attracted widespread attention worldwide as it is closely linked with human survival. However, the growth of urban areas is frequently disproportionate in relation to population growth in developing countries; this discrepancy cannot be monitored solely using statistics. In this study, we integrated earth observation (EO) and statistical data monitoring the Sustainable Development Goals (SDG) 11.3.1: “The ratio of land consumption rate to the population growth rate (LCRPGR)”. Using the EO data (including China’s Land-Use/Cover Datasets (CLUDs) and the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data) and census, we extracted the percentage of built-up area, disaggregated the population using the geographically weighted regression (GWR) model, and depicted the spatial heterogeneity and dynamic tendency of urban expansion and population growth by a 1 km × 1 km grid at city and national levels in mainland China from 1990 to 2010. Then, the built-up area and population density datasets were compared with other products and statistics using the relative error and standard deviation in our research area. Major findings are as follows: (1) more than 95% of cities experienced growth in urban built-up areas, especially in the megacities with populations of 5–10 million; (2) the number of grids with a declined proportion of the population ranged from 47% in 1990–2000 to 54% in 2000–2010; (3) China’s LCRPGR value increased from 1.69 in 1990–2000 to 1.78 in 2000–2010, and the land consumption rate was 1.8 times higher than the population growth rate from 1990 to 2010; and (4) the number of cities experiencing uncoordinated development (i.e., where urban expansion is not synchronized with population growth) increased from 93 (27%) in 1990–2000 to 186 (54%) in 2000–2010. Using EO has the potential for monitoring the official SDGs on large and fine scales; the processes provide an example of the localization of SDG 11.3.1 in China.

Understanding the spatial distribution of populations at a finer spatial scale has important value for many applications, such as disaster risk rescue operations, business decision-making, and regional planning. In this study, a random forest (RF)-based population density mapping method was proposed in order to generate high-precision population density data with a 100 m × 100 m grid in mainland China in 2015 (hereafter referred to as ‘Popi’). Besides the commonly used elevation, slope, Normalized Vegetation Index (NDVI), land use/land cover, roads, and National Polar Orbiting Partnership/Visible Infrared Imaging Radiometer Suite (NPP/VIIRS), 16,101,762 records of points of interest (POIs) and 2867 county-level censuses were used in order to develop the model. Furthermore, 28,505 township-level censuses (74% of the total number of townships) were collected in order to evaluate the accuracy of the Popi product. The results showed that the utilization of multi-source data (especially the combination of POIs and NPP/VIIRS data) can effectively improve the accuracy of population mapping at a finer scale. The feature importances of the POIs and NPP/VIIRS are 0.49 and 0.14, respectively, which are higher values than those obtained for other natural factors. Compared with the Worldpop population dataset, the Popi data exhibited a higher accuracy. The number of accurately-estimated townships was 19,300 (67.7%) in the Popi product and 16,237 (56.9%) in the Worldpop product. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were 14839 and 7218, respectively, for Popi, and 18014 and 8572, respectively, for Worldpop. The research method in this paper could provide a reference for the spatialization of other socioeconomic data (such as GDP).

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Abstract:Tourism is an important sustainable industry in the economy that optimizes the industrial structure. Thus, as a core part of this market, tourism enterprises perform a key role in the effective operation of this industry. This paper applies data envelopment analysis (DEA) and Malmquist index (MI) models to calculate the efficiency of Chinese tourism enterprises between 2005 and 2014. Results showed that: (1) The efficiency and the total factor productivity change index (TFPC) of tourism enterprises remained low, and both have decreased. (2) The efficiency of regional tourism enterprises across China cloud be characterized as high in the east region, low in the central region, and high in both northeast and western regions. (3) The efficiency levels of the cities of Beijing and Shanghai were ahead of the country over the period of this study, while Chongqing, Tibet, Qinghai, and Ningxia all possess a number of obvious advantages in the western region. (4) Centers of overall tourism enterprise efficiency mainly moved in a southeast-to-northwest direction over the period of this research. (5) The spatial autocorrelation of tourism enterprise efficiencies is also assessed in this study, and the results show that the comprehensive efficiency (CE) of tourism enterprises in southeastern coastal regions of China tended to a certain spatial agglomeration effect, while the correlation between the central region and northern China was not significant. (6) The Geodetector model is applied to analyze the key factors driving the spatial differentiation of tourism enterprise efficiencies, and the results show that the degree of opening to the outside world, potential human capital, and traffic conditions were the most important factors driving spatial differentiation in the efficiency of tourism enterprises.

China′s bid for the 2022 Winter Olympic Games will promote rapid development of domestic ski tourism. More than 10 provinces in the northern China list ski tourism as a new source of growth in the tourism industry. Formulating a scientific evaluation system is an important foundation for ski resort development. By referring to the conditions of existing international ski resorts, a comprehensive index system for evaluating ski resort development was developed based on natural factors, human factors, athletic sports, and popular ski tourism. The system was composed of one target layer, two indices, seven sub-indices and twenty-five basic indices. Based on data collected during a field investigation in 2012 and on a statistical model, the development conditions of the following ski resorts were quantified: Yabuli Ski Resort in Heilongjiang Province and Beidahu Ski Resort in Jilin Province in Northeast China, Wanlong Ski Resort in Hebei Province and Nanshan Ski Resort in Beijing in the northern China, and Silk Road International Ski Resort in Uygur Autonomous Region of Xinjiang in the northwestern China. The resulting index values of development conditions at Beidahu, Yabuli, Wanlong, Nanshan, and Silk Road International Ski Resorts were 0.304, 0.278, 0.270, 0.214, and -0.025, respectively. Various natural and human factors exert positive and negative effects on the development potential of ski resorts. The development conditions of the five ski resorts were inferior to those of first-class international resorts. Therefore, the development conditions of the five ski resorts should be improved with future construction.

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Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

A number of countries are concerned, to a certain degree, about the prospects for the implementation of the Chinese strategic initiative for the joint creation of the "Silk Road Economic Belt" (SREB). These concerns relate to fears of the transfer from China to the "belt" countries of excessive capacities of the polluting primaries industries, possible environmental degradation, and the destruction of the traditional way of life as a result of the implementation of mega-projects, and the fragility and vulnerability of many ecosystems along the routes of the prospective throughways between the eastern provinces of China and Europe [Bezrukov, 2016]. Environmental problems are clearly of key importance for the prospects of China's initiative. The initiative's program documents have stressed the need to take into account the interests of all parties and act solely on the basis of mutual benefit. The authors briefly consider the variety of natural and socio-economic conditions in the SREB zone and the sharp differences in the degree of economic development of the territory, which require close attention and scientific justification for political and economic decisions. Particular differences include temperature regime, precipitation, modern atmospheric circulation, transport of particulate matter and contaminants, soils, vegetation, land use, and risks of desertification in the SREB zone. The potential of complementarity of the natural resources of China and a number of neighboring countries may be realized. The paper also discusses China's present policy in the transition to sustainable development and its underlying concepts and achievements, especially at the level of regions and cities, including the concept of "ecological civilization" and the six stages of greening of cities. The authors believe that tourism related activities should be coordinated specifically at the city level as part of "green development. " It is necessary to create free economic zones in the "economic corridors" along the planned transcontinental lines and utilize the existing national special zones. Such zones are particularly effective in border regions and cities. In conclusion, it is recommended to develop international research networks in the SREB zone, to establish an International Data Center, and to collect, organize, exchange, and publish jointly scientific information on the problems of transition to sustainable development.