Evolution Mode, Influencing Factors, and Socioeconomic Value of Urban Industrial Land Management in China

(1) Background: Accurate measurement of the matching relationship between urban industrial land change and economic growth is of great value for industrialized and re-industrialized countries to perform land resource management in territorial spatial planning. (2) Methods: Based on the combination of the Boston Consulting Group matrix, Geodetector, and decoupling model, we constructed a new method integrating “model evolution + driving mechanism + performance evaluation + policy design” in this paper, and conducted an empirical study on the economic value of urban industrial land management in the Yangtze River Delta. (3) Results: The evolution modes of urban industrial land in the Yangtze River Delta are divided into four types: stars, cows, dogs, and question, distributed in structures ranging from an “olive” shape to a “pyramid” shape, with high spatial heterogeneity and agglomeration and low autocorrelation. The government demand led by driving economic growth and making large cities bigger is the key factor driving the change in urban industrial land and the influence of transportation infrastructure and the business environment has remained stable for a long time. The mechanisms of industrialization, globalization, and innovation are becoming increasingly complicated. Industrial land change and value-added growth in most cities have long been in a state of strong and weak decoupling, with progressive decoupling occurring alongside the unchanged stage and regressive decoupling. The government outperforms the market in terms of urban industrial land management, and the degradation of the synergy between urban industrial land and corporate assets emerges as a new threat to sustainable and high-quality development of the region. (4) Conclusions: This paper establishes a technical framework for zoning management and classification governance of urban industrial land to divide the Yangtze River Delta into reduction-oriented transformation policy zoning, incremental high-quality development zoning, incremental synchronous growth zoning, and reduction and upgrading development zoning. It also proposes an adaptive land supply governance strategy for quantitative and qualitative control, providing a basis for territorial spatial planning and land resource management

Evolution Characteristics, Eco-Environmental Response and Influencing Factors of Production-Living-Ecological Space in the Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau (QTP) is a major “river source” and “ecological source” in China, as well as South Asia and Southeast Asia, and is a typical plateau region. Studying the evolution characteristics and ecological effects of the production-living-ecological space (PLES) of the QTP is of great practical significance and theoretical value for strengthening its ecological construction and environmental protection. Based on 30 m × 30 m land use/cover data of the QTP at five time-points of 1980, 1990, 2000, 2010, and 2020, this paper investigates the PLES evolution characteristics, transfer characteristics, eco-environmental response, and influencing factors of the eco-environmental quality index (EEQI) in the region of China of the QTP from 1980 to 2020 by land use transfer matrix, eco-environmental response model, hot spot analysis, and geographically weighted regression (GWR). The results show that: (1) from 1980 to 2020, the ecological space of the QTP decreased, while the production and living space saw an increase. The PLES pattern of the QTP showed a clear shift from 2000 to 2010, while there was no significant change from 1980 to 2000 and from 2010 to 2020. (2) From 1980 to 2020, the EEQI of the QTP decreased from 0.5634 in 1980 to 0.5038 in 2010, and then increased to 0.5044 in 2020, showing a changing trend of first decreasing and then increasing; the degradation of grassland ecological space to other ecological space was the main cause leading to ecological environment deterioration. (3) From 1980 to 2000, the EEQI was high in the midwestern and southeastern parts of the QTP, presenting a double-center distribution. From 2010 to 2020, the EEQI decreased in the western part, while the high value area in the eastern part increased significantly, obviously low in the west and high in the east. The spatial variation characteristics of hot and cold spots and EEQI are generally similar. (4) Natural ecological and socioeconomic factors have significant differences on the spatial distribution of EEQI in the QTP, and natural ecological factors are the main driving factors, with topographic relief having the strongest effect on EEQI as a natural ecological factor, and population density having the strongest effect as a socioeconomic factor

Satellite remote sensing of atmospheric particulate matter mass concentration: Advances, challenges, and perspectives

Mapping the mass concentration of near-surface atmospheric particulate matter (PM) using satellite observations has become a popular research niche, leading to the development of a variety of instruments, algorithms, and datasets over the past two decades. In this study, we conducted a holistic review of the major advances and challenges in quantifying PM, with a specific focus on instruments, algorithms, datasets, and modeling methods that have been developed over the past 20 years. The aim of this study is to provide a general guide for future satellite-based PM concentration mapping practices and to better support air quality monitoring and management of environmental health. Specifically, we review the evolution of satellite platforms, sensors, inversion algorithms, and datasets that can be used for monitoring aerosol properties. We then compare various practical methods and techniques that have been used to estimate PM mass concentrations and group them into four primary categories: (1) univariate regression, (2) chemical transport models (CTM), (3) multivariate regression, and (4) empirical physical approaches. Considering the main challenges encountered in PM mapping practices, for example, data gaps and discontinuity, a hybrid method is proposed with the aim of generating PM concentration maps that are both spatially continuous and have high precision

Causal association between snoring and stroke: a Mendelian randomization study in a Chinese populationResearch in context

Summary: Background: Previous observational studies established a positive relationship between snoring and stroke. We aimed to investigate the causal effect of snoring on stroke. Methods: Based on 82,339 unrelated individuals with qualified genotyping data of Asian descent from the China Kadoorie Biobank (CKB), we conducted a Mendelian randomization (MR) analysis of snoring and stroke. Genetic variants identified in the genome-wide association analysis (GWAS) of snoring in CKB and UK Biobank (UKB) were selected for constructing genetic risk scores (GRS). A two-stage method was applied to estimate the associations of the genetically predicted snoring with stroke and its subtypes. Besides, MR analysis among the non-obese group (body mass index, BMI <24.0 kg/m2), as well as multivariable MR (MVMR), were performed to control for potential pleiotropy from BMI. In addition, the inverse-variance weighted (IVW) method was applied to estimate the causal association with genetic variants identified in CKB GWAS. Findings: Positive associations were found between snoring and total stroke, hemorrhagic stroke (HS), and ischemic stroke (IS). With GRS of CKB, the corresponding HRs (95% CIs) were 1.56 (1.15, 2.12), 1.50 (0.84, 2.69), 2.02 (1.36, 3.01), and the corresponding HRs (95% CIs) using GRS of UKB were 1.78 (1.30, 2.43), 1.94 (1.07, 3.52), and 1.74 (1.16, 2.61). The associations remained stable in the MR among the non-obese group, MVMR analysis, and MR analysis using the IVW method. Interpretation: This study suggests that, among Chinese adults, genetically predicted snoring could increase the risk of total stroke, IS, and HS, and the causal effect was independent of BMI. Funding: National Natural Science Foundation of China, Kadoorie Charitable Foundation Hong Kong, UK Wellcome Trust, National Key R&D Program of China, Chinese Ministry of Science and Technology