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There are debates on whether the implementation of the Belt and Road (BR) initiative could significantly increase CO2 emissions in participating countries. Nevertheless, the policy effect of the BR...

Using detailed Chinese manufacturing firm production and trade data from 2000 to 2006, this study finds that offshoring significantly increases firms’ average wages. First, using the quasi‐natural experiment of China's accession to the World Trade Organization, we investigate how a reduction in offshoring costs affects the manufacturing firm's wages and find that a productivity effect and a job‐relocation effect are two possible channels. Second, the dynamic decomposition of industry‐level wages indicates that the within‐firm effect is 0.547, accounting for 31.5 percent of the total variation. Finally, a Mincer‐type regression shows that offshoring also increases within‐firm skill premiums. Our findings have strong implications for the government related to framing appropriate industrial policies to raise wages and reduce income inequality.

Urban spatial structure is an important feature for assessing the effects of urban planning. Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments. Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures. However, these methods cannot efficiently reflect the influence of human activities. With the wide application of big data, analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning. In this study, we constructed a human-activity space network using the taxi trip big data. Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning. This method was applied to a case study based on one-month taxi trip data of Dongguan City. Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020, which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan. We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure. The analysis demonstrated that the taxi trip data are important big data on social spatial perception, and taxi data should be used for evaluating spatial structures in future urban planning.

Global land cover (GLC) information with fine spatial resolution is a fundamental data input for studies on biogeochemical cycles of the Earth system and global climate change. Although there are several public GLC products with 30 m resolution, considerable inconsistencies were found among them especially in fragmented regions and transition zones, which brings great uncertainties to various application tasks. In this paper, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing multiple existing land cover products based on the Dempster-Shafer theory of evidence (DSET). Firstly, we used more than 160,000 global point-based samples to locally evaluated the reliability of the input GLC products for each LC class within each 4°×4° geographical grid for the establishment of the basic probability assignment (BPA) function. Then, the Dempster’s rule of combination was used for each 30 m pixel to derive the combined probability mass of each possible land cover class from all the candidate maps. Finally, each pixel was determined with a land cover class based on a decision rule. Through this fusing process, each pixel is expected to be assigned with the land cover class that contributes to achieve a higher accuracy. We assessed our product separately with 34,987 global point-based samples and 144 global patch-based samples. Results show that, the GLC-2015 map achieved the highest mapping performance globally, continentally, and eco-regionally compared with the existing 30 m GLC maps, with an overall accuracy of 76.0 % (83.8 %) and a kappa coefficient of 0.715 (0.548) against the point-based (patch-based) validation samples. Additionally, we found that the GLC-2015 map showed substantial outperformance in the areas of inconsistency, with an accuracy improvement of 17.6 %–23.2 % in areas of moderate inconsistency, and 21.0 %–25.2 % in areas of high inconsistency. Hopefully, this improved GLC-2015 product can be applied to reduce uncertainties in the research on global environmental changes, ecosystem service assessments, and hazard damage evaluations, etc. The GLC-2015 map developed in this study is available at https://doi.org/10.6084/m9.figshare.19752856.v1 (Li et al., 2022).

By using the latest China population grid and land-use data, we assess the changing exposure of China’s population and land uses to the hazards of storm surges, droughts, earthquakes, floods, and landslides from 1995 to 2015. We found that the single-hazard areas and the multi-hazard areas covered 43% and 26% of China’s territory, respectively. Population grew faster in the hazard-prone areas than in the non-hazard areas. Built-up area expanded more rapidly in the areas prone to earthquakes and landslides. Cropland changed rapidly in many hazard-prone areas. The hazard-prone areas affected by floods featured the highest cropland loss rates, while the areas prone to earthquakes and landslides featured the highest cropland growth rates. We detected areas with significant exposure changes by using hot spot analysis. It was found that population and built-up land in the Pearl River Basin were increasingly exposed to storm surges, floods, and landslides. The Haihe River Basin and Huaihe River Basin also showed a consistent increase of population and built-up land exposure to droughts and earthquakes. These findings can provide a foundation for the design and implementation of protection and adaptation strategies to improve the resilience of Chinese society to natural hazards.

It is essential for quantitatively assessing the influences of climate change (CC) and human activities (HA) on cropland productivity to clarify the associated drive mechanisms. However, few studie...

Land use change driven by human activities plays a critical role in the terrestrial carbon budget through habitat loss and vegetation change. Despite the projections of the global population and economic growth under the framework of the Shared Socioeconomic Pathways (SSPs), little is known of land use/cover change (LUCC) at a fine spatial resolution and how carbon pools respond to LUCC under different SSPs. This study projected the future global LUCC with 1 km spatial resolution and a 10-year time step from 2010 to 2100 and then explored its direct impacts on aboveground biomass carbon (AGB) under SSPs. Scenario SSP3 yields the highest global cropland expansion, among which approximately 48% and 46% is expected to be located in the current forest land and grassland, respectively. Scenario SSP1 has the largest forest expansion and is mainly converted from grassland (54%) and cropland (30%). Due to the spatial change in land use/cover, global AGB loss is expected to reach approximately 3.422 Pg C in 2100 under scenario SSP3 and increases by approximately 0.587 Pg C under scenario SSP1. Africa is expected to lose 30% of AGB under the scenario SSP3. Aboveground biomass in Asia will fix 0.774 Pg C to reverse the AGB loss in 2100 under scenario SSP1. The global carbon loss estimated by the land use products with 10 km and 25 km resolution are less than that with 1 km by 1.5% (ranging from -11.2% in Africa to +34.0% in Oceania) and 2.9% (ranging from -11.8% in Africa to +24.0% in Oceania), respectively. These findings suggest that sufficient spatial details in the existing SSP scenario projections could reduce the uncertainties of AGB assessment, and reasonable land use development and management is a key measure to mitigate the negative impacts of LUCC on the biomass carbon pool.

Objective. Suanzaoren Decoction (SZRT) is a classic decoction to calm the nerves in traditional Chinese medicine (TCM). It has been extensively treated as an antianxiety drug in modern times, but the material basis and pharmacological mechanisms are still unclear. To explore the material basis and corresponding potential targets, as well as to elucidate the mechanism of SZRT, network pharmacology and molecular docking methods were utilized. Methods. The main chemical compounds and potential targets of SZRT were collected from the pharmacological database analysis platform (TCMSP). Anxiety targets were obtained from the GeneCards database. Then, a target compound network was established using overlapping genes and the corresponding potential compounds. Protein interaction analysis, GO enrichment, and KEGG pathway enrichment were performed using the STRING database, DAVID database, and KOBAS database. Finally, molecular docking was conducted between MAOB and its corresponding active compound in SZRT to further verify the results. Results. A total of 137 active components in SZRT were screened from the TCMSP database, and 210 corresponding targets were predicted. A total of 5434 anxiety-related targets were obtained from the disease target database, and finally 22 potential targets of SZRT on antianxiety were obtained. The constructed C-T network showed that the average degree of active components was 5.4, and four of them interacted with six or more targets. PPI analysis shows that key genes such as MAOA, MAOB, IL1B, TNF, NR3CI, and HTR3A were identified as potential therapeutic targets. A pathway analysis showed that SZRT may participate in neurotransmitter regulation and immunoregulation in a synergistic way to treat anxiety. The binding energy between the active compounds and MAOB was low, indicating good binding. The results of molecular docking showed that all the 10 active ingredients were able to successfully dock with MAOB, and the binding energy of coumaroyltyramine with MAOB was the lowest, that is, −9.6 kcal/mol, and the binding method was hydrogen bonding. Conclusions. SZRT produces antianxiety effects mainly by affecting the neurotransmitter release, transmission, and immunoregulation. This study provides a new approach to elucidating the molecular mechanism and material basis of SZRT in the treatment of anxiety, and it will also benefit the application of TCM in modern medicine.

In response to carbon dioxide (CO

To quantitatively estimate the divergences in the natural and anthropogenic dust emission fluxes among different climatic regions, the total dust emissions at the global scale from 2007 to 2010 were simulated in this study. Despite the widely scattered anthropogenic dust distribution, the total area of potential anthropogenic dust sources was found slightly higher than that of natural dust sources. The anthropogenic dust distribution area was 1.61 × 107 km2 in January and 1.54 × 107 km2 in July, respectively. The natural dust sources contributed 81.0% of the global dust emissions and the anthropogenic contributed 19.0% of the residual. The natural and anthropogenic dust emission flux was 6.34 ± 0.31 μg m−2 s−1 and 1.01 ± 0.07 μg m−2 s−1, respectively. Especially, natural and anthropogenic dust emissions situated in different climatic regions. Natural dust emissions mainly located in hyper-arid and arid regions such as the Sahara, Arabian and Taklimakan Desert where dust emission fluxes range from 1 to 50 μg m−2 s−1, accounted for 97.3% of natural dust emissions at the global scale. While anthropogenic dust emissions concentrate in semi-arid, sub-humid and humid regions and generally fluctuated between 0.1 and 10 μg m−2 s−1. In addition, natural and anthropogenic dust proportions in semi-arid regions were the most complicated due to the complex land cover types, including grasslands, urban areas, croplands and open shrub lands, resulting in 42.99% of the anthropogenic dust emissions in semi-arid regions. The complex interplay of natural and anthropogenic dust emissions contributing to the total dust loadings may be a crucial factor enhanced the warming over semi-arid regions. This study provided confidence for the further investigation of the climatic impacts of natural and anthropogenic dust in different climatic regions under the background of global warming, especially the strengthening warming in semi-arid regions.

Snow cover is an important component of land cover on the Earth’s surface and is also a good indicator of climate change. Hence, monitoring the spatial distribution and temporal variation of snow cover is of great significance to the study of the global water cycle and climate change. Traditional snow cover monitoring has been primarily based on in situ observations; however, the uneven and low-density distribution of meteorological stations made it difficult to reflect the overall picture of snow cover in some regions. To solve this problem, we used the Presence and Background Learning (PBL) algorithm to estimate the snow cover in China and obtained the 5 days (5 d) snow cover maps of the Special Sensor Microwave/Imager (SSM/I). The PBL model is a type of one-class classifier that needs no negative training sample in the training set. The cornerstone of this method is to combine SSM/I Brightness Temperature data and in situ observations to estimate the probability of the existence of snow cover based on the Artificial Neural Network (ANN). The estimation result indicates that the average annual overall accuracy of the PBL model in China is 0.88, which shows good agreement with the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover products. Compared with other snow cover products, such as MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid (MOD10A2) and AMSR-E/Aqua 5-Day L3 Global Snow Water Equivalent EASE-Grids(AE_5DSno), the performance of the PBL model is better at predicting the snow cover distribution in China. After obtaining the SSM/I 5d snow cover maps of China, we analysed the temporal and spatial variations of snow cover in China during 1992−2010 using the Mann-Kendall test, which included the variation of annual snow cover days, seasonal snow cover distribution and change characteristics, and the variation of the stable snow cover area. The results suggest that China’s snow cover is primarily distributed in the Tibetan Plateau, Xinjiang, northeast China, and Inner Mongolia, which are all high altitude or high latitude regions. From 1992−2010, the following occurred. (1) The number of snow cover days decreased significantly in the three major snow cover regions of China due to rising temperatures, while there was an observably upward trend in the northwestern Tibetan Plateau with the increase of precipitation. (2) The snow cover area of Xinjiang and Northeast-Inner Mongolia reached its maximum in winter and was relatively small in the spring and autumn. The Tibetan Plateau’s snow cover area is generally the largest in the spring, and it is also very large in the autumn and winter. Thus, the seasonal variation characteristics of snow cover there is not as obvious as that of the other two major snow regions. (3) In the four years of 1996, 2003, 2004 and 2006, the snow cover area in the spring in Xinjiang was much lower than that in the autumn, which indicates a “warmer winter” event caused by warmer than usual temperatures in the winter. (4) Although no significant change in the snow cover area was found in all regions during the study period, the change in snow cover on the Tibetan Plateau is particularly noteworthy due to its area having increased greatly since 2005, which might be the beginning of the increase of snow cover area in this region. (5) The stable snow cover area of China is 3.39 million km2. In the three major snow cover regions, the stable snow cover area of the Tibetan Plateau is the largest (1.68 million km2), the Northeast-Inner Mongolia region is the second largest (1.05 million km2), and the Xinjiang region is the smallest (0.63 million km2). The stable snow cover area had no significant variation trend in China during 1992−2010—only the snow cover days of the Tibetan Plateau had a larger interannual fluctuation.

Timely and accurate delineation of global urban land is fundamental to the understanding of global environmental changes. However, most of the contemporary global urban land maps have coarse resolutions and are available for one or two years only. In this study, we developed the multi-temporal global urban land maps based on Landsat images for the 1990–2010 period with a five-year interval (‘Urban land’ in these maps refers to ‘impervious surface’, i.e., artificial cover and structures such as pavement, concrete, brick, stone and other man-made impenetrable cover types). We proposed the method of Normalized Urban Areas Composite Index (NUACI) and utilized the Google Earth Engine to facilitate the global urban land classifications from an extensive number of Landsat images. The global level's overall accuracy, producer's accuracy and user's accuracy for our mapping results are 0.81–0.84, 0.50–0.60 and 0.49–0.61, respectively. The Kappa values are 0.43–0.50 at the global level, and ~0.33 (in China) and ~0.42 (in the U.S.) at the country level. By analyzing the presented dataset, we found that the world's urban land area had increased from 450.97 ± 1.18 thousand km2 in 1990 to 747.05 ± 1.50 thousand km2 in 2010, reaching a global coverage of 0.63%. China, the United States and India together (14% of the world's terrestrial area in total) contributed almost 43% of the total increase of global urban land area. A free download link for these data is attached at the end of this paper.

In this paper, the unified gas-kinetic wave-particle (UGKWP) method is further developed for diatomic gas with the energy exchange between translational and rotational modes for flow study in all regimes. The multiscale transport mechanism in UGKWP is coming from the direct modeling in a discretized space, where the cell's Knudsen number, defined by the ratio of particle mean free path over the numerical cell size, determines the flow physics simulated by the wave particle formulation. The non-equilibrium distribution function in UGKWP is tracked by the discrete particle and analytical wave. The weights of distributed particle and wave in different regimes are controlled by the accumulating evolution solution of particle transport and collision within a time step, where distinguishable macroscopic flow variables of particle and wave are updated inside each control volume. With the variation of local cell's Knudsen number, the UGKWP becomes a particle method in the highly rarefied flow regime and converges to the gas-kinetic scheme (GKS) for the Navier-Stokes solution in the continuum flow regime without particles. Even targeting on the same solution as the discrete velocity method (DVM)-based unified gas-kinetic scheme (UGKS), the computational cost and memory requirement in UGKWP could be reduced by several orders of magnitude for the high speed and high temperature flow simulation, where the translational and rotational non-equilibrium becomes important in the transition and rarefied regime. As a result, 3D hypersonic computations around a flying vehicle in all regimes can be conducted using a personal computer. The UGKWP method for diatomic gas will be validated in various cases from one dimensional shock structure to three dimensional flow over a sphere, and the numerical solutions will be compared with the reference DSMC results and experimental measurements., Comment: 35 pages, 15 figures, 1 table

Land use and land cover change (LUCC) simulation models are effective and reproducible tools for analyzing both the causes and consequences of future landscape dynamics under various scenarios. Current simulation models primarily focus on the evolution of specific land use types under the influence of human activities, but they rarely consider background climatic effects. However, these background climate changes significantly affect the landscape dynamics and should be incorporated into long-term LUCC simulations under various human-climate-included scenarios. In this paper, we propose a future land use simulation (FLUS) model that explicitly simulates the long-term spatial trajectories of multiple LUCCs. The top-down system dynamics and bottom-up cellular automata were interactively coupled during the projection period, which improved the model’s ability to accurately simulate future land use patterns. A self-adaptive inertia and competition mechanism was developed within the CA model to process the complex competitions and interactions between the different land use types. The proposed model was applied to an LUCC simulation in China from 2000 to 2010. The results show promising grid-to-grid agreement compared to actual land use, and the simulation accuracy is higher than other well-accepted models, such as CLUE-S and CA models. The model was further applied to the simulation of four scenarios from 2010 to 2050 that depict different development strategies by considering various socio-economic and natural climatic factors. The simulation results and findings demonstrate that the proposed model is effective for future LUCC simulation under variously designed scenarios. FLUS is available for free download at http://www.geosimulation.cn/FLUS.html .

Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human–environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-k...

This paper presents a novel method for delineating urban functional areas based on building-level social media data. Our method assumes that social media activities in buildings of similar functions have similar spatiotemporal patterns. We subsequently apply a dynamic time warping (DTW) distance based k-medoids method to group buildings with similar social media activities into functional areas. The proposed method is applied in the Yuexiu District, Guangzhou, China. We carry out two clustering experiments with k = 2 and k = 8. In the experiment with k = 2, buildings are separated into two groups based on density values. Buildings with higher density are situated mainly within the traditional city core and urban villages in the northern part of study area. The results for k = 8 suggest that most buildings have mixed functions. In addition, heterogeneity can be discerned even in the clusters with similar urban functions. Concentric spatial structures are observed in urban villages that were previously deemed disordered. We also assess the diversity of urban functions at the community level and identify several potential ‘central places’ based on hot spot analysis. Our analysis provides an alternative way of characterizing intra-city urban spatial structure and could therefore inform future planning and policy evaluation.

Since the late 1990s, there are growing studies on the development of cellular automata (CA) as a simulation tool for assisting urban and regional planning in China. Rapid urban development is the main reason that this country has become one of the best places to test the methodology of CA and analyze the effectiveness of using these models. This paper attempts to summarize the experiences and issues of using CA to solve various environmental and planning problems in China. The analysis is based on the literature review using the search engines of ISI Web of Science and Google Scholar. These experiences could be important for those who want to apply CA in developing countries. For example, which environmental and ecological problems can be solved by using this bottom-up approach? What are the data inputs to these models and how can they be calibrated? Our analyses indicate that CA have the great potential to support land-use planning and policy analysis for fast-growing regions. Some specific feat...

High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones. The world keeps urbanizing. This study finds that since 1985 global urban lands have expanded four times faster than previously recognized and faster than population is growing.

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50–63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1–4%, corresponding to the annual food needs for a certain crop of 122–1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production., Shared socioeconomic pathways (SSPs) is a crucial scenario describing the potential of future socio-economic development. The authors here investigate long-term effects of various government policies suggested by different SSPs on urban land and reveal the impact of future urban expansion on other land and food production.

Increasing water scarcity in China is further exacerbated by the rapid socio-economic development and uneven spatial distribution of water resources. Current studies on water footprint have mainly focused on historical accounting and trend analysis at the provincial scale. However, a comprehensive exploration of future water footprint would be vital to a better understanding of future water shortage challenges, and more importantly, would allow the mitigation of water scarcity and inequal water distribution. In this paper, we present an approach to project the future water footprint of China at a fine resolution (0.125 arc-degree) under the shared socio-economic pathway (SSP) scenario framework, which described five future alternative socio-economic development pathways over the 21st century. We first simulated the future spatial patterns of built-up land using the Future Land Use Simulation (FLUS) model and derived the future population growth and urbanization rate from the population projection provided by the National Center for Atmospheric Research (NCAR). Then future water footprint was projected according a log-transformed linear regression calibrated with historical data during 2007-2012. We found that the total volume of China's water footprint will increase significantly in the future under the SSP1, SSP4 and SSP5 scenarios, reaching up to nearly 400 billion m3 in 2050, equivalent to almost 40% increase compared to that in 2010. The spatial patterns of future water footprint show dramatic increase (up to 100-130%) in the eastern provinces (Shandong, Henan, and Hebei), and slight decrease were found in the western provinces (Xinjiang, Ningxia, and Qinghai). In addition, the future water footprints were found to share very similar spatial patterns at local pixel scale among different SSP scenarios in three of the largest metropolitan areas of China (Beijing-Hebei-Tianjin, Yangtze River Delta, and Pearl River Delta). These findings provide extensive knowledge of the future water footprint and suggest a more severe water scarcity in the future from a consumption-oriented perspective. More effective water management policies are urgently needed to mitigate future water resource scarcity and inequality.

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.

The Chinese government has been using annual quotas to control the amount of farmland that can be converted for urban uses. Using an analysis sample of more than 1.5 million land-lease transactions during 2007–2016, we document facts on land conversion for urban development in China. We present evidence that land conversion quotas have been increasingly misallocated across cities in that a growing share of land conversion is occurring in less productive cities. A city-level production function is estimated for counterfactual analysis. Based on estimated parameters, we assess the economic losses from misallocation of land conversion quotas across cities in China and calculate the potential gains from reallocating land quotas to cities where urban land is more productive.

In response to carbon dioxide (CO2) emissions, numerous studies have investigated the link between CO2 emissions and urban structures, and pursued low-carbon development from the standpoint of urban spatial planning. However, most of previous efforts only focused on urban structures in term of two-dimensional space, whereas the vertical influence of urban buildings (three-dimensional space) plays an important role in CO2 emissions. To address this issue, we took the cities in mainland China as study case to quantitatively explore how the three-dimensional urban structure affects CO2 emissions. First, we collected the city-level CO2 emission data from a greenhouse gas emission dataset released by the China City Greenhouse Gas Working Group. Then, a series of spatial metrics were established to quantify three-dimensional urban structures based on urban building data derived from Baidu Map. On the strength of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, an extended approach and ridge regression analysis were finally utilized to investigate the consequences of three-dimensional urban structures on CO2 emissions at the city level. The results indicate that the total building volume is the largest driving force accelerating CO2 emissions due to the massive consumption of energies for human activities during rapid urbanization. Besides, urban buildings with taller height and large heat dissipation area also have significant positive effects on promoting CO2 emissions. Although a compact coverage of urban buildings at a two-dimensional scale contributes to the reduction of CO2 emissions, urban structure characterized by an intense and congested pattern in three-dimensional space can lead to more CO2 emissions because of the adverse impacts from surrounding environment and traffic congestion. Additionally, an irregular pattern of three-dimensional urban structure would help reduce CO2 emissions to some extent. Such study results highlight the importance of urban planning for the development of a low-carbon city, and suggest the compact patterns of three-dimensional urban structures should be controlled within a reasonable range to avoid more CO2 emissions caused by excessive centralization and aggregation.

Entropy-based measures (e.g., mutual information, also known as Kullback-Leiber divergence), which quantify the similarity between two signals, are widely used as similarity measures for image registration. Although they are proven superior to many classical statistical measures, entropy-based measures, such as mutual information, may fail to yield the optimum registration if the multimodal image pair has insufficient scene overlap region. To overcome this challenge, we proposed using the symmetric form of Kullback-Leiber divergence, namely Jeffrey’s divergence, as the similarity measure in practical multimodal image registration tasks. Mathematical analysis was performed to investigate the causes accounting for the limitation of mutual information when dealing with insufficient scene overlap image pairs. Experimental registrations of SPOT image, Landsat TM image, ALOS PalSAR image, and DEM data were carried out to compare the performance of Jeffrey’s divergence and mutual information. Results indicate that Jeffrey’s divergence is capable of providing larger feasible search space, which is favorable for exploring optimum transformation parameters in a larger range. This superiority of Jeffrey’s divergence was further confirmed by a series of paradigms. Thus, the proposed model is more applicable for registering image pairs that are greatly misaligned or have an insufficient scene overlap region.

s The accurate mapping of housing rent is crucial to the understanding of residential dynamics. In this study, we proposed the use of online rental listings as a new reliable data source for mapping housing rent. With the collected individual rental information from an online platform, we attempted to produce the fine-scale spatial pattern of housing rent in the metropolitan area of Guangzhou, China, at the neighborhood committee (NC) level. This involves the task of estimating the housing rent for areas with no observation data of housing rent. To this end, we evaluated six numeric prediction methods of machine learning. We further enhanced their performance through ensemble learning, an approach which can form new classifiers with even better performance than any of the individual constituent classifiers. We implemented ensemble learning through ways of bagging and stacking, and selected the most accurate ensemble classifier to produce the spatial pattern of housing rent at the NC-level. In the resulting housing rent pattern, we identified a distance decay relationship between the housing rent and the distance from the city center. The data sources and the ensemble learning platform in this application of housing rent mapping are generally open access. Therefore, the proposed approach in this study can provide useful hints for housing rent mapping in other geographical areas. Our mapping results can also be integrated with additional information to support the studies of urban residential problems in China.

Brightness temperature (BT), which is remotely sensed by the space-borne microwave radiometer, is widely used in snow cover monitoring for its long time series imaging capabilities in all-weather conditions. Traditional linear fitting and stand-alone methods are usually uncertain with respect to the spatial distribution and temporal variation of derived snow cover, as they rarely consider local conditions and scene characteristics but fit the model with static empirical coefficients. In this paper, a novel method utilizing daily ground in situ observations is proposed and evaluated, with the purpose for accurate estimation of long-term daily snow cover. To solve the challenge that ground snow-free records are insufficient, a one-class classifier, namely the Presence and Background Learning (PBL) algorithm, is employed to identify daily global snow cover. Benefiting from daily ground in situ observations on a global scale, the proposed method is temporally and spatially dynamic such that estimation errors are globally independent during the entire study period. The proposed method is applied to the estimation of global daily snow cover from 1987 to 2010; the results are validated by ground in situ observations and compared with available optical-based and microwave-based snow cover products. Promising accuracy and model stability are achieved in daily, monthly and yearly validations as compared against ground observations (global omission error 0.82 in China region, and keep stable in monthly and yearly averages). The comparison against the MODIS daily snow cover product (MOD10C1) shows good agreement under cloud-free conditions (Cohen's kappa = 0.715). The comparison against the NOAA daily Interactive Multisensor Snow and Ice Mapping System (IMS) dataset suggests promising agreement in the Northern Hemisphere. Another comparison against the AMSR-E daily SWE dataset (AE_DySno) demonstrates the efficiency of the proposed method regarding to the overestimation problem in thin snow cover region.

The Chinese government has been using annual quotas to control the amount of farm-land that can be converted for urban uses in cities. Using a sample of more than 1.5 million land-lease transactions during 2007-2016, we document facts on land conversion for urban development in China. We present evidence that land conversion quotas have been increasingly misallocated across cities in that a growing share of land conversion is occurring in less productive cities. A city-level production function is estimated for counterfactual analysis. Based on estimated parameters, we assess the economic losses from misallocation of land conversion quotas across cities in China and calculate the potential gains from reallocating land quotas to cities where urban land is more productive.

Dust emissions refer to the spatial displacement of dust particles from wind forcing, which is a key component of dust circulation. It plays an important role in the energy, hydrological, and carbon cycles of the Earth's systems. However, most dust emission schemes only consider natural dust, neglecting anthropogenic dust induced by human activities, which led to large uncertainties in quantitative estimations of dust emissions in numerical modeling. To fully consider the mechanisms of anthropogenic dust emissions, both indirect and direct anthropogenic dust emission schemes were constructed and developed in the study. Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) retrievals were used to constrain the simulations at global scale. The results showed that the schemes reasonably reproduced the spatio-temporal distributions of anthropogenic dust from 2007 to 2010. The high centers of anthropogenic dust emission flux appeared in India, eastern China, North America, and Africa range from 0.9 to 11 μg m−2 s−1. Compared with natural dust emissions, indirect anthropogenic dust emissions have indistinctive seasonal variation, with differences less than 3.2 μg m−2 s−1. Pasturelands contribute higher anthropogenic dust emissions than croplands, with emissions of approximately 6.8 μg m−2 s−1, accounting for 60 % of indirect anthropogenic dust emissions. Moreover, average anthropogenic dust emissions in urban areas have a value of 13.5 μg m−2 s−1, which is higher than those in rural areas (7.9 μg m−2 s−1). This study demonstrates that the environmental problems caused by anthropogenic dust in urban areas cannot be ignored.

Cellular automata (CA) are considered to be effective models to simulate the behavior of oil spills for overcoming the difficulty of obtaining parameters in numerical models of oil spills. Besides, CA models are convenient to combine geographic information system (GIS) to display the simulation results. This paper presents a new oil spill simulation based on logistic-regression CA model, which easily obtain the weights of the impact factors. The model also can simulate the dynamic changes of oil spill using only a few inputs, such as the initial image, impact factors, and their weights. It was applied to simulate the oil spill in DeepSpill project using five factors, the distance factor, wind, current, temperature, and salinity. Experiments showed that the simulation results are consistent with the verification image with the total accuracy and Kappa coefficient of simulation results as high as 96.8% and 0.834 respectively. We also study the influence of sampling ratio on simulation results. The accuracy improves with the increasing ratio. However, the performances improve only slightly when the ratio reaches 20%. We also analyze the sensitivity of temperature, salinity, winds, currents, and distance. Experiments showed that the simulation results will only expanse around the original area without considering the current and wind. The simulation results will have big model error without considering distance factor. However, less model error occurs in the simulation results without using temperature and salinity.