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Rapidly changing climate issues and increasingly severe carbon emissions are great challenges to the carbon peaking and carbon neutrality strategy. Analyzing the impact of future land use changes on carbon emissions can provide an important basis and reference for scientifically constructing a low-carbon and sustainable territorial spatial planning, as well as realizing the goal of the dual-carbon strategy. Based on land use data, agricultural production activity data, and energy consumption statistics, this study simulated the land use changes of the Yangtze River Delta region (YRDR) from 2030 to 2060 under the natural development (ND) scenario and sustainable development (SD) scenario by using the Patch-generating Land Use Simulation (PLUS) model and analyzed the impacts of future land use changes on carbon emissions. The results showed that: (1) The land use simulation results obtained by using the PLUS model under the sustainable development scenario were highly consistent with the actual land use with an OA value of 97.0%, a Kappa coefficient of 0.952, and a FoM coefficient of 0.403; (2) Based on the simulated land use under the SD scenario from 2030 to 2060, the quantity of construction land was effectively controlled, and the spatial distributions of cropland and forests were found to dominate in the north and south of the Yangtze River, respectively; (3) Anhui Province was the major contributor (accounted for 49.5%) to the net carbon absorption by cropland while Zhejiang Province was the major contributor (accounted for 63.3%) to the net carbon absorption by forest in the YRDR during the period 2020–2060 under the SD scenario; (4) Carbon emissions from construction land were the main source of carbon emissions from land use in the YRDR during the period 2020–2060 with proportions higher than 99% under both the ND and SD development scenarios. These findings underscore the urgent need for the government to take measures to balance the relationships between cropland and ecological protection and economic development, which provides a reference for the optimization of land use structure and policy formulation in the future. [ABSTRACT FROM AUTHOR]

Online texts and images have become an important data source for investigating tourism emotion. However, tourism emotion extracted from online data cannot reflect the emotions in the real world. Few scholarly attempts have focused on the complex biases in tourism emotion based on online data. By comparing online and offline emotion, this study quantified biases in tourism emotion and explored patterns among biases. Facial expressions and texts within Disney Resort were collected. Based on facial expression recognition and text mining, several indices were proposed to measure tourism emotion and biases. Our results reveal that tourists tend to share stronger happiness, increased anger in commerce areas and exaggerated disgust in waiting areas. In addition, males show "surprise-suppress"; while females show "surprise-exaggeration" in recreation areas. Our research can help scholars reexamine previous conclusions based on online tourism emotion and provides a theoretical basis for improving the quality of online tourism emotion. [ABSTRACT FROM AUTHOR]

In recent years, non-grain production of cultivated land (NGPCL) has become increasingly prominent in China, seriously affecting food production and threatening the country's food security. However, there is a lack of large-scale and high-precision methods for remote sensing identification of NGPCL. From the perspective of effective management of cultivated land resources, the characteristics of the spatial patterns of NGPCL, both on a large scale and at a patch scale, need to be further studied. For solving this problem, this paper uses the Google Earth engine (GEE) cloud computing platform and multi-source remote sensing data with a machine learning algorithm to determine the occurrence of NGPCL in Anhui province in 2019, and then uses nine selected landscape pattern indexes to analyze the spatial patterns of NGPCL from two aspects, specifically, economic development level and topography. The results show that: (1) terrain features, radar features, and texture features are beneficial to the extraction of NGPCL; (2) the degree of separation obtained by using an importance evaluation approach shows that spectral features have the highest importance, followed by index features with red edges, texture features, index features without red edges, radar features, and terrain features; and (3) the cultivated land in Anhui province in 2019 is mainly planted with food crops, and the phenomenon of NGPCL is more likely to occur in areas with high economic development levels and flat terrain. Aided by the GEE cloud platform, multi-source remote sensing data, and machine learning algorithm, the remote sensing monitoring approach utilized in this study could accurately, quickly, and efficiently determine NGPCL on a regional scale. [ABSTRACT FROM AUTHOR]