Search Results (26)

Using multi-source precipitation datasets including NASA GPM (IMERG), GPCP, ECMWF ERA5, and station precipitation data from the China Meteorological Administration (CMA), along with ERA5 reanalysis fields for atmospheric circulation analysis, this study investigates the extreme precipitation events during the “Dragon-Boat Precipitation” period from 20 May to 21 June over South China in 2022 using the synoptic diagnostic method. The results indicate that the total precipitation during this period significantly exceeded the climatological average, with multiple large-scale extreme rainfall events characterized by high intensity, extensive coverage, and prolonged duration. The spatial distribution of precipitation exhibited a north-more-south-less pattern, with the maximum rainfall center located in the Nanling Mountains, particularly in the Shaoguan–Qingyuan–Heyuan region of Guangdong Province, where peak precipitation exceeded 1100 mm, and the mean precipitation was approximately 1.7 times the climatology from the GPM data. The average daily precipitation throughout the period was 17.5 mm/day, which was 6 mm/day higher than the climatological mean, while the heaviest rainfall on 13 June reached 39 mm/day above the average, exceeding two standard deviations. The extreme precipitation during the “Dragon-Boat Precipitation” period in 2022 was associated with an anomalous deep East Asian trough, an intensified South Asian High, a stronger-than-usual Western Pacific Subtropical High, an enhanced South Asian monsoon and South China Sea monsoon, and the dominance of a strong Southwesterly Low-Level Jet (SLLJ) over South China. Two major moisture transport pathways were established: one from the Bay of Bengal to South China and another from the South China Sea, with the latter contributing a little higher amount of water vapor transport than the former. The widespread extreme precipitation on 13 June 2022 was triggered by the anomalous atmospheric circulation conditions. In the upper levels, South China was located at the northwestern periphery of the slightly stronger-than-normal Western Pacific Subtropical High, intersecting with the base of a deep trough associated with an anomalous intense Northeast China Cold Vortex (NCCV). At lower levels, the region was positioned along a shear line formed by anomalous southwesterly and northerly winds, where exceptionally strong southwesterly moisture transport, significant moisture convergence, and intense vertical updraft led to the widespread extreme rainfall event on that day. Full article

Cropland serves as the most vital resource for agricultural production, while its security is primarily threatened by abandonment. Northeast Guangdong Province features a fragmented terrain and faces a significant issue of farmland abandonment. It is crucial to analyze the phenomenon of cropland abandonment to safeguard food security. However, due to limitations in data sources and attribution methods, previous studies struggled to comprehensively characterize the driving mechanisms of abandoned land. Using data from Sentinel time series remote-sensing images, we employed the land use change trajectory method to map cropland abandonment in Jiaoling County from 2019 to 2023. Furthermore, we proposed a novel analytical framework to quantify the influence pathways and interaction effects driving cropland abandonment. The results indicated that: (1) The overall accuracy of the abandoned land extraction was 79.6%. During the study period, the abandonment rate in Jiaoling County showed a trend of a “gradual rise followed by a sharp decline”, and the abandoned area reached its maximum in 2021. The abandonment phenomenon in the southeastern rural areas was serious and stubborn. (2) The slope has the greatest explanatory power for abandonment, followed by the total cultivated area, aggregation index of cropland, and distance to road. Each driving factor has a threshold effect. (3) Topography, location, and agriculture driving factors directly or indirectly affect the abandonment rate, with direct influences of 0.247, 0.255, and −0.256, respectively. The research findings offer valuable scientific guidance for managing abandoned land and deepen our understanding of its formation mechanisms. Full article

The chemical flame retardants represented by organophosphate esters (OPEs) are widely used and have a serious impact on the environment. In this study, we collected data on the exposure levels of ten OPEs in Chinese soils in recent years and performed an ecological risk assessment. The results showed that the levels of OPEs varied considerably throughout different regions of China, with high exposure levels in highly urbanized or industrialized areas such as Guangdong Province and Northeast China, where the mean value was >200 ng/g. The content of OPEs in the soil in industrial and commercial areas was significantly higher than in other regions, indicating that the concentration of OPEs in the soil is closely related to local economic development and the degree of industrialization. Meanwhile, the number of studies reporting on OPEs and their exposure concentrations have increased significantly since 2018. Through the ecological risk assessment, it was found that TCP, EHDPP and TEHP pose high ecological risks. Although some OPEs, such as TCIPP, have low ecological risk levels overall, their high exposure concentrations are still worthy of attention. This study details the general status of OPE contamination in Chinese soils, which can serve as a reference for ecological environmental supervision. Full article

The construction industry plays a pivotal role in China’s achievement of its “dual carbon” goals. This study conducts a decomposition analysis of the carbon emissions from the construction industry (CECI) at both national and provincial levels for the period 2010–2020 and employs the ARIMA model to predict the short-term peak trends at the provincial level. The findings are as follows. (1) Inner Mongolia, Shandong, Sichuan, and Chongqing exhibit an N-shaped trend in CECI, while the northeast region shows an inverted U-shaped trend. (2) Labor productivity and energy intensity are identified as the largest and smallest drivers of national CECI growth, respectively, with the driving force of the study’s identified factors fluctuating between 1% and 60%. (3) Energy intensity significantly contributes to the growth of CECI in Tianjin and Zhejiang, while it aids in reducing CECI in western provinces. The “rebound effect” of building energy efficiency is particularly pronounced in provinces with strong resource endowments, such as Ningxia. (4) Between 2021 and 2025, CECI is predicted to decrease in the northern and economically developed provinces, while it is expected to increase in central and western provinces, with Heilongjiang, Shandong, Guangdong, Shanghai, and Shaanxi potentially reaching their peaks within the forecast period. The paper concludes with several recommendations. Full article

The rational flow of production factors is crucial for promoting benign interactions between urban and rural areas. To unveil the intrinsic mechanisms of factor flow pathways promoting mutual symbiosis between urban and rural areas, this study, based on symbiosis theory, takes total factor flow including land, technology, capital, and labor as inputs and urban–rural symbiosis level as output. Utilizing the Super-Efficiency Slack-Based Measure (SBM) model, this study calculates the urban–rural total factor flow efficiency of 27 provinces in China from 2011 to 2021 and explores specific improvement directions of urban–rural factor flow based on projection analysis. This study revealed the following findings: (1) The overall efficiency of urban–rural total factor flow in China shows a fluctuating upward trend but has not yet reached an effective state. There are significant regional disparities, with 8 provinces such as Guangdong and Fujian reaching Pareto optimality, while the remaining 19 provinces exhibit varying degrees of inefficiency. (2) Provinces with insufficient symbiotic production are mainly concentrated in the central and western regions and the northeast region, with 14 provinces including Inner Mongolia showing the inadequate transformation of urban–rural symbiosis. However, except for Hainan, the situation is gradually improving in other regions annually. (3) There is input redundancy in total factor, where land, labor, and capital redundancy are the main reasons for the inefficiency of urban–rural total factor flow in China. However, trends show that the redundancy of land, labor, and capital elements is improving annually, while technology redundancy is worsening. (4) Through a comprehensive analysis of input redundancy, output deficiency, symbiosis coefficient, and efficiency, this study categorizes the impact of factor flow on urban–rural symbiosis level into basic matching, redundancy, and comprehensive scarcity types. The research provides scientific guidance for promoting sustainable development through the rational flow of total factors and offers valuable insights for similar countries. Full article

The Golden Kaiser-I-Hind (Teinopalpus aureus Mell, 1923) is the only butterfly among Class I national protected animals in China and is known as the national butterfly. In this study, by accurately predicting the suitable habitat in China under current and future climate scenarios, the potential distribution area of T. aureus was defined, providing a theoretical basis for conservation and management. Based on species distribution records, we utilized the Biomod2 platform to combine climate data from the BCC-CSM2-MR climate model, future shared socio-economic pathways, and altitude data. The potential distribution areas of T. aureus in the current (1970–2000s) and future SSP1_2.6 and SSP5_8.5 climate scenarios in China in 2041–2060 (2050s), 2061–2080 (2070s), and 2081–2100 (2090s) were predicted. The AUC and TSS values of the combined model based on five algorithms were greater than those of the single models, and the AUC value of the receiver operating characteristic curve was 0.990, indicating that the model had high reliability and accuracy. The screening of environmental variables showed that the habitat area of T. aureus in China was mainly affected by annual precipitation, precipitation in the driest month, the lowest temperature in the coldest month, temperature seasonality, elevation, and other factors. Under the current circumstances, the habitat area of T. aureus was mainly located in southern China, including Fujian, Guangdong, Guangxi, Hainan, Zhejiang, Yunnan, Guizhou, Hunan, Taiwan, and other provinces. The suitable area is approximately 138.95 × 10⁴ km²; among them, the highly suitable area of 34.43 × 10⁴ km² is a priority area in urgent need of protection. Under both SSP1_2.6 and SSP5_8.5, the population centroid tended to shift southward in the 2050s and 2070s, and began to migrate northeast in the 2090s. Temperature, rainfall, and altitude influenced the distribution of T. aureus. In the two climate scenarios, the habitat area of T. aureus declined to different degrees, and the reduction was most obvious in the SSP5_8.5 scenario; climate was the most likely environmental variable to cause a change in the geographical distribution. Climate change will significantly affect the evolution and potential distribution of T. aureus in China and will increase the risk of extinction. Accordingly, it is necessary to strengthen protection and to implement active and effective measures to reduce the negative impact of climate change on T. aureus. Full article

China’s rapid economic growth has inevitably led to serious resource depletion, environmental degradation, and a decline in social welfare. As such, establishing total-factor energy green efficiency (TFEGE) and exploring its factors are of paramount importance to bolster comprehensive energy efficiency and foster sustainable development. In this research, we deployed the spatial lag model (SLM) and data envelopment analysis (DEA), using energy, capital and labor as input indicators, GDP and social dimension metrics as desirable outputs, and “three wastes” as undesirable outputs, to assess the TFEGE across 30 provinces in China from 2001 to 2020. Employing the exploratory spatial data analysis (ESDA) method, we analyzed the spatial autocorrelation of TFEGE at national and provincial levels. Simultaneously, we examined the influencing factors of TFEGE using a spatial econometric model. Our study reveals that, throughout the examined period, the TFEGE in China has generally shown a steady decline. The TFEGE dropped from 0.630 to 0.553. The TFEGE of all regions in China also showed a downward trend, but the rate of decrease varied significantly across different regions. Among them, the TFEGE of the eastern region fluctuated between 0.820 and 0.778. The TFEGE of the northeast region decreased significantly from 0.791 to 0.307. The TFEGE of the western region decreased from 0.512 to 0.486. The TFEGE of the central region decreased from 0.451 to 0.424. Beijing, Guangdong, Hainan, Qinghai, and Ningxia showed an effective TFEGE, while for other provinces, it was ineffective. The TFEGE in all four major regions failed to achieve effectiveness. Its distribution pattern was east > west > northeast > central. The TFEGE across the 30 provinces showed positive spatial autocorrelation, indicating a strong spatial clustering trend. We found that while transportation infrastructure and technological progression exert a positive impact on TFEGE, elements such as industrial structure, energy composition, and foreign direct investment negatively influence TFEGE. Full article

The low–carbon transition of farmland use (LCTFU) is an effective measure to coordinate the development of farmland and the environment to meet China’s “dual carbon” and green agricultural transformation goals. We studied the spatial–temporal evolution of the LCTFU and further explored the driving factors of the LCTFU by applying a geographically weighted regression model (GWR) to the coastal cities of Guangdong Province from 2000 to 2020. The results show that (1) temporally, the comprehensive, spatial, functional, and mode transitions of farmland use in coastal areas of Guangdong Province generally declined. The LCTFU level in most counties was low, and the difference in the LCTFU level among counties was narrowing. (2) Spatially, the LCTFU generally followed a high–to–low spatial distribution pattern, with high LCTFU values in the east and west and low values in the center. (3) The hotspots of the comprehensive, spatial, functional, and mode transitions were mainly concentrated in the eastern part of the study area, while the cold spots were in the central region, which is generally consistent with the spatial distribution of high– and low–value areas of the LCTFU. (4) The spatial migration path of the LCTFU migrated from northeast to southwest, with the main body of the standard deviation ellipse in the middle of the study area, displaying a C–shaped spatial pattern with weak expansion. (5) Economic, social, and environmental factors jointly contributed to the spatial–temporal evolution of the LCTFU, with social factors being the strongest driver. Full article

The East Asian summer monsoon (EASM) was extremely strong in 2018, which substantially affected surface ozone (O₃) in China. Taking 2018 and the average synthesis of 2003 and 2010 to represent the strong and weak EASM cases, respectively, GEOS-Chem with constant anthropogenic emission was employed to investigate the impact of the EASM on surface O₃ in the east of China. Simulations show that surface O₃ decreased in the northeast and the eastern coast of China and increased in most of the remaining regions during strong EASM. The difference in surface O₃ between strong and weak EASM was around −15~7 ppbv. After analyzing relevant meteorological fields, it is found that the decrease in northeast China was mainly attributed to the large increase in vertical upward transport. The considerable decrease in the Huang-Huai-Hai region depended on the dilution and diffusion of eastward anomalous horizontal circulation. The increase in Hunan-Hubei-Guangdong Province was largely due to input from the north. In addition, the vast areas between the Yangtze River and the Yellow River were supported by higher temperatures and stronger shortwave solar radiation that promoted photochemical reactions. The reasons for changes in Shanxi-Sichuan-Yunnan Province were relatively more complex and thus require more in-depth exploration. Full article

Industry is widely valued as an important contributor to carbon emissions. Therefore, it is of great significance to analyze the industrial carbon emissions (ICE) in Guangdong, the strongest industrial province in China. We have adopted the carbon emission accounting model and standard deviational ellipse analysis model to analyze the temporal and spatial characteristics and evolution trends of the industry carbon emission amount and intensity in Guangdong from 1998 to 2013. The study results include: (1) Due to the rapid development of industry, Guangdong’s ICE showed a steady growth trend; (2) The distribution characteristics of ICE were characterized by the trend of taking the Pearl River Delta (PRD) region as the center and gradually spreading to the surrounding areas. From the perspective of industrial sectors, it can be divided into steady growth type, fluctuant growth type, basically stable type, and decrease type; (3) The spatial pattern of the ICE in Guangdong is basically the same as that of the total industrial output value, that is, the southwest-northeast pattern. This work is helpful for China’s carbon peak, especially for the formulation of industrial carbon peak policy and the sustainable development of the environment. Full article

With the rapid development of society and economy, people’s demand for ecosystem services is constantly increasing. All countries support this demand by vigorously developing green finance. The coordinated development of green finance and ecosystem service demand is of great significance for sustainable development. Most of the existing studies separately study green finance or ecosystem service demand, separating the relationship between the two. At present, there is still a lack of clear understanding of the coupling relationship between green finance and ecosystem service demand. In addition, in the existing coupling relationship calculation models, the setting of relevant parameters is subjective. Therefore, based on the green finance and ecosystem service demand database of 30 provinces in China from 2010 to 2017, this paper firstly evaluates the green finance and ecosystem service demand quantitatively, and then analyzes the coupling coordination relationship between them by using an improved coupling coordination degree model. The results show that: (1) compared with the traditional coupling coordination degree model, the contribution coefficient of each subsystem in the improved coupling coordination degree model has a more sufficient basis, and more objective evaluation results; (2) from 2010 to 2017, the level of green finance in China’s provinces increased significantly, showing a spatial pattern of “high in the east and low in the west”; the ecosystem services demand increased first and then decreased, with an increase in nearly two-thirds of provinces; (3) the coupling coordination relationship between green finance and ecosystem service demand in China’s provinces was optimized continuously from 2010 to 2017, showing the spatial differentiation of “eastern China > central China > northeast China > western China”; (4) in 2017, the coupling coordination degree of green finance and ecosystem service demand in Guangdong Province was the highest, reaching a high level of coordination, while Qinghai Province was the lowest, as a result of a serious level of incoordination. It is worth noting that the comprehensive development level of green finance in China is still low and seriously lags behind the development level of ecosystem services demand. In the future, green and low-carbon transformation should be accelerated to promote the sustainable development of financial ecology. Full article

The Yangtze River Delta, located in East China, is an important passage on the eastern pathway of the northward migration of fall armyworm Spodoptera frugiperda (Smith) in China, connecting China’s year-round breeding area and the Huang-Huai-Hai summer maize area. Clarifying the migration dynamics of S. frugiperda in the Yangtze River Delta is of great significance for the scientific control and prevention of S. frugiperda in the Yangtze River Delta, even in the Huang-Huai-Hai region and Northeast China. This study is based on the pest investigation data of S. frugiperda in the Yangtze River Delta from 2019 to 2021, combining it with the migration trajectory simulation approach and the synoptic weather analysis. The result showed that S. frugiperda migrated to the Yangtze River Delta in March or April at the earliest, and mainly migrated to the south of the Yangtze River in May, which can be migrated from Guangdong, Guangxi, Fujian, Jiangxi, Hunan and other places. In May and June, S. frugiperda migrated further into the Jiang–Huai region, and its source areas were mainly distributed in Jiangxi, Hunan, Zhejiang, Jiangsu, Anhui and Hubei provinces. In July, it mainly migrated to the north of Huai River, and the source areas of the insects were mainly distributed in Jiangsu, Anhui, Hunan, Hubei and Henan. From the south of the Yangtze River to the north of the Huai River, the source areas of S. frugiperda were constantly moving north. After breeding locally, S. frugiperda can not only migrate to other regions of the Yangtze River Delta, but also to its surrounding provinces of Jiangxi, Hunan, Hubei, Henan, Shandong and Hebei, and even cross the Shandong Peninsula into Northeast China such as Liaoning and Jilin provinces. Trajectory simulation showed that the emigrants of S. frugiperda from the Yangtze River Delta moved northward, westward and eastward as wind direction was quite diverse in June–August. This paper analyzes the migration dynamics of S. frugiperda in the Yangtze River Delta, which has important guiding significance for the monitoring, early warning and the development of scientific prevention and control strategies for whole country. Full article

Achieving rural revitalization is the aim of building a strong modern socialist country. However, regional heterogeneity exists in rural development in general, and studying regional differences in rural development quality is an important prerequisite for developing specific policies for rural revitalization. This paper takes 20 prefecture-level cities in Guangdong Province as the research objects and constructs a rural-development-quality-evaluation system based on four dimensions: industrial revitalization, rural affluence, social development, and environmental livability; combines the entropy value method, hierarchical analysis method, and TOPSIS to complete the evaluation process; uses a spatial autocorrelation model and cold–hot spot analysis to explore the characteristics of regional heterogeneity of rural development in Guangdong Province; and relies on stepwise regression analysis to clarify the main influencing factors. The results show the following: (1) The average value of rural development quality in the province is 0.342, with “high in the middle and low in the surrounding area” as the main spatial characteristic. (2) The average value of environmental livability dimension is 0.580, and the high-value area is found in the northeastern part of the province. There is an outer circle distribution structure with Dongguan City as the core of the high-value area, and the score gradually decreases outward, while the average value of all other dimensions is less than 0.350. The mean value of all other dimensions is less than 0.350. (3) The social development dimension shows a cold–hot spot distribution of “hot in the northeast and low in the middle”, the rural development quality and other development dimensions show a cold–hot spot spatial pattern of “high value gathering in the middle and low value gathering in the northeast”, and there is no cold spot gathering in the environmental livability dimension. (4) The average collective assets and the construction rate of science-based communities are the main driving factors of rural development, while the coverage rate of service institutions and the Engel coefficient are the main hindering factors. This paper enriches the rural development level measurement system, clarifies the spatial differentiation and main influencing factors of rural development in Guangdong Province, and helps to provide scientific support and a theoretical basis for the differentiated promotion of rural revitalization. Full article

In recent years, with the deepening of the reform of rural economic systems, the demand for disaster risk governance in land production and management is increasing, and it is urgent for the state to develop agricultural insurance to improve land production recovery capacity and ensure national food security. The study develops a quantitative model to determine the agricultural premium rate for each county in China based on disaster risk level in order to refine agricultural insurance. The results show that: (a) in terms of the disaster situation, most of northeast and central China, part of southwest, north, and northwest China are seriously affected; (b) regarding the integrated natural disaster risk level, there are 129 counties with extremely high disaster risk in China; (c) as for agricultural premium rates based on the integrated natural disaster risk index, some counties in Inner Mongolia, Shanxi, Liaoning, Jilin, Shandong, Anhui, Jiangxi, Zhejiang, Guangdong, Hubei, and Hunan Province had extremely high rates, out of a total of 63 counties. The above results reveal regional differences in disaster risk levels and premium rates between counties, providing a reference for improving the accuracy of agricultural premium rates. This contributes to the creation of security for further improving land production capacity and promoting the intensification and sustainable development of agricultural production. Full article

Rural tourism resources are the core carriers of rural tourism. It is, therefore, beneficial to further optimize the layout of rural tourism and to explore the spatial differentiation of rural tourism resources and their influencing factors. Taking 4670 rural tourism resources in Guangdong Province in China as the research object, this study explores the spatial distribution patterns of rural tourism resources through the nearest neighbor index, grid dimension analysis, kernel density analysis, and standard deviation ellipse method. Geodetectors are used to identify the influencing factors of the spatial heterogeneity of these resources in Guangdong Province. The findings reveal the following: (1) The distribution of rural tourism resources in Guangdong Province shows a tendency of agglomeration along the Tropic of Cancer, and the spatial distribution is unbalanced. The hot and cold spots show a “northeast-southwest” distribution trend. Furthermore, most of the hotspots form three high-density core areas, the sub-dense stretch zones connect into a w-shaped belt, and the sub-cold areas and sub-hot areas show a large expansion trend, thus forming five radiation areas. (2) The distribution of rural tourism resources in Guangdong Province is affected by multiple factors. In particular, the force of agricultural resource endowment, tourism resource endowment and transportation location are relatively strong, and social economy and tourist source market are the weak factors. Full article