County Scale Research Articles

Integrating Ecosystem Health and Services for Assessing Ecological Risk and its Response to Typical Land-Use Patterns in the Eco-fragile Region, North China.

Changes in land-use patterns may increase the ecological risks faced by Eco-Fragile regions. It is vital for regional ecological restoration and management of Eco-Fragile regions to reasonably assess ecological risk and study its response to typical land-use patterns. Existing study on regional ecological risk largely ignored the internal representation of ecosystem health and ecosystem services to ecological risk, and also ignored the internal relationship between ecological risk and land use patterns. This study developed a regional ecological assessment model by describing the relationship between ecosystem health, ecosystem services and ecological risks. Among them, the ecosystem health assessment used the Net Primary Productivity, landscape index and ecosystem elasticity coefficient based on different land use patterns to build Vigor-Organization-Resilience (VOR) model, and the improved equivalent factor method was used to calculate the ecosystem service value. Taking the Fen River Basin (FRB), a typical Eco-Fragile region of the Loess Plateau, as a study region, spatial auto-correlation analysis was used to reveal the temporal and spatial changes and spatial clustering characteristics of regional ecological risk, and regression analysis was used to study the relationship between typical land use patterns and ecological risks, which was included in the consideration of ecological and environmental risk management strategies. The results show that the regions with high ecological risk are mainly distributed in the middle and southwest of the FRB; the regions with low ecological risk are mainly distributed in the north, east and west of the FRB. Both high-risk and low-risk areas show significant spatial clustering effects. The change of ecological risk in FRB is related to the land use patterns. The ecological risk is negatively related to the expansion of construction land and cultivated land at the county and patch scales. On this basis, the environmental management strategies at different scales are discussed. This study can helpful deepen the understanding of the impact of land use patterns on ecological risk, and can also provide important reference for regional ecological risk management and land use policies.

Spatiotemporal variation of land-use carbon emissions and its implications for low carbon and ecological civilization strategies: Evidence from Xiamen-Zhangzhou-Quanzhou metropolitan circle, China

• LUCE increased by 1284.78×10 4 tons from 2000 to 2020. • LCES scenario decreased LUCE with 128.74 × 10 4 tons than that in BAU scenario. • Area and management of arable land, forest land and construction land should be regulated. • The policy effectiveness and adaptation to local conditions should be ensured. This study investigated the spatiotemporal variation of historical and future land-use carbon emission (LUCE) in the Xiamen-Zhangzhou-Quanzhou metropolitan circle, China, to obtain policy implications for low carbon and ecological security by simulating land use change under low carbon and ecological security (LCES) scenario and business as usual (BAU) scenario in 2030. Our results showed that: (1) Under LCES scenario, the area of arable land and construction land was 1.17% and 0.40% less than that under BAU scenario, whereas the area of forest land was 1.90% more than that. (2) LUCE increased by 1284.78×10 4 tons totally in 2000–2020. Whereas under LCES scenario, LUCE was 128.74×10 4 tons less than that under BAU scenario, and the carbon absorption of forest land increased by 24.90×10 4 tons. (3) At city scale, LUCE in cities followed the order of Quanzhou>Zhangzhou>Xiamen in 2000–2030. The county scale witnessed rapid-growth and heavy LUCE in coast over time. LUCE at grid scale showed an expansion from coast to inland all the time, but it was alleviated under LCES scenario. The quantity and spatial structure of land use types should be optimized, and low-carbon and ecological classification management should be implemented at arable land, forest land and construction land strictly.

Quantifying the direct effects of long-term dynamic land use intensity on vegetation change and its interacted effects with economic development and climate change in jiangsu, China

Vegetation change reflects sensitive responses of ecosystem environment to global climate change as well as land use. It is well known that land use type and its transformation affect vegetation change. However, how the changes in land use intensity (LUI) within different land use types impact vegetation and the interactions with other drivers remain poorly understood. We measured the LUI of Jiangsu Province, China, within the main land use types in 1995, 2000, 2005, 2010, 2015 and 2018 by combining remote sensing-based land use data with representative county scale economic and social indicators. Structural equation models (SEMs) were built to quantify the influences of long term LUI on vegetation change interacting with economic development, climate change and topographical conditions in transformed land, cropland, rural settlements and urbanized land, respectively. Seventy percent of significant vegetation change existed in non-transformed land use types. Although the area with a vegetation greening trend is larger than that with a vegetation browning trend, the vegetation browning areas is prominent in urbanized lands and some croplands in south basins. The constructed SEMs suggested the dominant negative effect of fast economic development regardless of land use types, while LUI played important and different direct and indirect effects on affecting vegetation change significantly interacting with economic development and climate change in different land use types. The LUI increasing led a vegetation greening in cropland, and stronger than climate warming with both positive direct and indirect effects for influencing climate change. The LUI change took negative effects on vegetation change in rural and urban areas, while a positive indirect effect of LUI increasing in urbanized land signaled the positive results of human managements. We then provided some land use-specific suggestions on basin scale for land management in Jiangsu. Our results highlight the necessity of long-term LUI quantification and promote the understanding of its effects on vegetation change interacted with other drivers within different land use types. This can be very helpful for sustainable land use and managements in regions with fast economic development.

U.S. decarbonization impacts on air quality and environmental justice

As policy organizations consider strategies to mitigate climate change, decarbonization initiatives can also reduce health-impacting air pollutants and may affect the associated racial disparities of adverse effects. With the U.S. Environmental Protection Agency CO-Benefits Risk Assessment Health Impacts Screening Tool (COBRA), we compare three decarbonization scenarios and their impacts at the regional and county scales. COBRA calculates changes in county-level ambient fine particulate matter (PM2.5), and associated mortality impacts, for each decarbonization scenario. We compare these patterns with demographic data to evaluate the relative exposure reduction benefit across race and ethnicity. Carbon-free electricity would reduce national average ambient PM2.5 concentrations by 0.21 μg m−3, compared with a 0.19 μg m−3 reduction associated with carbon-free industrial activity, and a 0.08 μg m−3 reduction associated with carbon-free light duty vehicle (LDV) transportation. Decarbonization strategies also vary in terms of the racial groups most benefitting from each scenario, due to regional and urban/rural patterns in emission sources and population demographics. Black populations are the only group to experience relative exposure reduction benefits compared to the total population in every scenario, with industrial decarbonization yielding 23% greater reductions in ambient PM2.5 concentrations for Black populations than for the total U.S. population. The largest relative reduction in PM2.5 exposure was found for Asian populations in the carbon-free LDV transportation scenario (53%). The magnitudes of total air quality improvements by scenario vary across regions of the U.S., and generally do not align with the decarbonization policy that achieves the largest equity goal. Only the transportation decarbonization scenario meets the criteria of the Justice40 Initiative nationwide, fulfilling the 2021 commitment by U.S. President Biden that federal investments in clean energy are designed to allocate at least 40% of benefits to disadvantaged communities.

Developing a Dual-Stream Deep-Learning Neural Network Model for Improving County-Level Winter Wheat Yield Estimates in China

Accurate and timely crop yield prediction over large spatial regions is critical to national food security and sustainable agricultural development. However, designing a robust model for crop yield prediction over a large spatial region remains challenging due to inadequate surveyed samples and an under-development of deep-learning frameworks. To tackle this issue, we integrated multi-source (remote sensing, weather, and soil properties) data into a dual-stream deep-learning neural network model for winter wheat in China’s major planting regions. The model consists of two branches for robust feature learning: one for sequential data (remote sensing and weather series data) and the other for statical data (soil properties). The extracted features by both branches were aggregated through an adaptive fusion model to forecast the final wheat yield. We trained and tested the model by using official county-level statistics of historical winter wheat yields. The model achieved an average R2 of 0.79 and a root-mean-square error of 650.21 kg/ha, superior to the compared methods and outperforming traditional machine-learning methods. The dual-stream deep-learning neural network model provided decent in-season yield prediction, with an error of about 13% compared to official statistics about two months before harvest. By effectively extracting and aggregating features from multi-source datasets, the new approach provides a practical approach to predicting winter wheat yields at the county scale over large spatial regions.

Spatial Differentiation and Tradeoff-Synergy of Rural Multifunction at the County Scale in Anhui Province in the China's Traditional Agricultural Areas.

The study of rural multifunction interaction has ignored the spatial differentiation characteristics, so it is necessary to reveal in-depth the spatial interaction relationship of rural multifunction interaction on the basis of type division and pattern recognition at the county scale. Taking Anhui as a case study, based on the county scale, the paper constructed an index system of rural multifunction evaluation, and comprehensively applied the improved entropy method, spatial autocorrelation model, and Spearman correlation coefficient to study the temporal-spatial characteristics and tradeoff–synergy relationship of rural multifunction from 2000 to 2020. The results showed that (1) the overall rural production function at the county level in Anhui showed an upward trend, showing a spatial pattern of “low in the south and high in the north”. The rural life function was gradually weakened, showing the characteristics of gradual decline from suburban to exurb. The rural ecological function increased, showing a high value distribution in areas with rich mountains and hills or a dense water network. (2) The spatial concentration of HH (High-High) and LL (Low-Low) positive correlation types of rural production, life, and ecological function was significant during the study period. The negative correlation types of HL (High-Low) and LH (Low-High) had low spatial agglomeration and showed discrete distribution. (3) There was a spatial synergistic relationship between rural production and life function at the county scale in Anhui Province. The tradeoff–synergy relationship between rural production and ecological function and between rural life and ecological function showed a trend of synergistic–tradeoff fluctuation. The spatial difference of the rural multifunction tradeoff–synergy relationship was significant during the study period. (4) In the future, we should change the situation that the expansion of rural production function compresses rural life and ecological function, and promote rural life and ecological function to promote the benign and coordinated development of rural multifunction at the county scale in traditional agricultural areas.

Exploring the Scale Effects of Trade-Offs and Synergies of Multifunctional Cultivated Land—Evidence from Wuhan Metropolitan Area

The purpose of this paper is to explore the trade-offs and synergies of multifunctional cultivated land (MCL) at multiple scales. The study area is Wuhan Metropolitan Area, China. The entropy method and the method of Spearman's rank correlation were employed for the analysis of combined land use/cover data, administrative division data, population data and statistical yearbook data, from the multi-scale perspectives of cities, counties and townships. The results showed that: (1) The multi-functionality of cultivated land had obvious spatial differences and its overall spatial patterns were relatively robust, which did not change very much at the single scale. (2) At each single scale, the MCL's trade-offs and synergies had spatial heterogeneity. (3) Scale effects existed in the MCL's trade-offs and synergies. From the prefecture-level city scale, to the county scale, and to the township scale, the MCL's trade-offs were changed to synergies, and some synergic relationships were enhanced. This article contributes to the literature by deepening the multiscale analysis of trade-offs and synergies of multifunctional cultivated land. The conclusions might provide a basis for helping policy-makers to implement protection measures for the multi-functionality of cultivated land at the right spatial scale, and to promote the higher-level synergies of multifunctional cultivated land to realize its sustainable use.

Spatiotemporal changes of soil and water loss (2012–2019) in three government-divided regions, Chongqing Municipality, China

AbstractUnderstanding the changes in soil and water loss is important to make the regional sustainable development strategies for administrative department. This study compiled a series of datasets from 2012 to 2019 at the county scale to investigate the changes in soil and water loss in three government divided regions, i.e., urban region (UR), northeast region (NER) and southeast region (SER) in Chongqing Municipality, China. The area percentage of soil and water loss over moderate degree (OMD) in each year and change rate for the area of soil and water loss (CR) on the basis in 2012 for each region were compared. The area of soil and water loss decreased by 22.3%, 18.7% and 14.2% in the UR, NER and SER, respectively, from 2012 to 2019. The area of soil and water loss in NER almost accounted for 50% of the total area of soil and water loss in Chongqing City. The OMD decreased from 66.06% in 2012 to 24.84% in 2019. The CR always presented significantly declined trend (P < 0.05) with the year in the UR, NER and SER, respectively. Our results are valuable to developing reasonable conservation strategies for the local government to control soil and water loss.

Effects of Land Consolidation and Precipitation Changes on the Balance of Water Supply and Demand in Western Jilin

As an important ecosystem service, water supply is closely related to human well-being. Maintaining the balance of water supply and demand is essential in the sustainable development of a regional economy and society. Taking western Jilin, where the difference between water supply and demand is increasingly prominent, as the research object, from the perspective of land consolidation and precipitation changes, this paper analyzes the temporal and spatial changes of water resources supply and demand at the regional and county scales in western Jilin from 2000 to 2018 by the InVEST model and the water resource demand model. The results show that water production in western Jilin did not change significantly before and after land consolidation. The change in planting structure made water demand increase from 3.03 billion m3 to 4.96 billion m3, which reversed the relationship between supply and demand. The impact of precipitation changes on water production is extremely significant. The annual production of water in wet years is 8.05 billion m3, and the annual water production in dry years is 1.08 billion m3. The amount of precipitation can directly change the relationship between the supply and demand of local water resources. The evolution of the water resources supply and demand pattern in western Jilin is the result of the combined effect of land consolidation and precipitation changes. Precipitation mainly affects supply, while land consolidation mainly affects demand.

Multiscale Characteristics and Drivers of the Bundles of Ecosystem Service Budgets in the Su-Xi-Chang Region, China.

Managing ecosystem services (ESs) to meet human needs is critical to achieving sustainable development in rapidly urbanizing regions. Identifying ES budget bundles and analyzing their drivers at a multiscale level can facilitate management decision-making; however, further research is required in areas undergoing rapid urbanization. This study quantified the supply, demand, and budgets of six typical ESs at the county, township, and village scales in the Su-Xi-Chang region in 2020. Additionally, the influence of natural environmental and socioeconomic factors on ES budget bundles was investigated based on K-means cluster analysis and the Geodetector model. The results showed that ESs on all three scales showed a mismatch between supply and demand. The similarity in the spatial pattern of supply, demand, and budgets of ESs at the township and village scales was higher than that at the township and county scales. The location and area of surplus, balance, and deficit varied with scale. We found that population density and the proportion of impervious surfaces are the main factors influencing the formation of the ES budget bundles at different scales. In addition, the diversity and degree of interpretation of drivers varied with scale. We believe that focusing on the overall situation on a large scale and implementing precise management on a small scale can make management decisions more effective. This study can provide a scientific basis for the sustainable utilization of ESs in the Su-Xi-Chang region, and the research results and methods can provide a reference for similar studies in other rapidly urbanizing areas in the world.

Analyzing Spatio-Temporal Characteristics of Cultivated Land Fragmentation and Their Influencing Factors in a Rapidly Developing Region: A Case Study in Guangdong Province, China

Cultivated land fragmentation (CLF) is a key obstacle to agricultural development and has a strong relationship with regional food security and global sustainable development. However, few studies have analyzed the spatio-temporal distribution pattern and evolution characteristics of CLF and the complex interactions among their influencing factors in rapidly developing regions. In this study, first, the GlobeLand30 datasets were used to obtain characteristic parameters of cultivated land in counties in Guangdong Province in 2000, 2010, and 2020. Then, the linear weighted comprehensive evaluation model based on the principal component analysis (PCA) was used to measure the extent of CLF. Finally, the exploratory spatial data analysis (ESDA) was used to analyze the spatio-temporal distribution pattern and evolution characteristics of CLF, and geodetector (GD) and random forest (RF) models were used to explore the factors influencing the spatial difference in CLF. The results showed that the spatial differences in the distribution of cultivated land resources in Guangdong Province are relatively large and the extent of agglomeration is generally low. The extent of CLF on the county scale is mainly medium and higher. The overall spatial distribution shows an increasing trend from the south to the north and from the west to the east, and the spatial distribution pattern with agglomeration and randomness remains relatively stable. From 2000 to 2020, the overall CLF continued to intensify and the evolution of CLF on the county scale mainly increased. The spatial difference in CLF is the result of that based on the natural environment and influenced by factors such as social, economic, and agricultural development. The interaction between influencing factors is very strong, dominated by nonlinear enhancement. The results are of great significance for promoting the intensive and efficient utilization of cultivated land resources and sustainable regional development.

Production Capacity Evaluation of Farmland Using Long Time Series of Remote Sensing Images

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale.

Integrated assessment of the supply–demand relationship of ecosystem services in the Loess Plateau during 1992–2015

ABSTRACT Landscape alterations and ecosystem services (ES) are crucial elements that affect the socio-ecological development of ecologically fragile regions. To provide scientific support for the land-use planning and regional ecological restoration to achieve sustainable development goals (SDGs) in the Loess Plateau from 1992 to 2015, this study proposes an integrated assessment framework for assessing the supply – demand match and equilibrium of ES combining potential relationships between landscape alterations, ES, and sustainable development at the county and regional scales. Results show that more than 85% of local counties have a supply – demand mismatch in terms of ecosystem services. Supply – demand distributions for provisioning and supporting services were relatively balanced compared to cultural services, which were imbalanced. Although the overall supply – demand relationship is relatively balanced because of the significant influence of ecological restoration, it shows an increasing trend toward imbalance as human – land conflict is prominent in some regions. SDGs emphasize on specific ES information such as ecosystem conservation, sustainable agriculture, and urban construction. Furtherly, positive impacts from landscape dynamics can improve the supply capacity of the ES and contribute to regional sustainability.

Probability of cultivating Se-rich maize in Se-poor farmland based on intensive field sampling and artificial neural network modelling

Selenium (Se) is a necessary micronutrient for humans, and its supplementation from crop grains is important to address the ubiquitous Se deficiency in people worldwide. Se uptake by crops largely depend on soil bioavailable Se rather than soil total Se content, which provides possibilities to explore the Se-rich crops in Se-poor area. Here, the possibility of cultivating Se-rich maize grains in Se-poor farmland was tested based on intensive field sampling and mathematical modelling. Sampling was conducted at county scale, and a total of 7779 topsoil samples and 109 maize samples with paired rhizosphere soils samples were collected. Results showed that although the soil Se content in the study county from southwestern China was at a low level (0.01–2.75 mg kg−1), 54.1% of the maize grain samples satisfied the standard for Se-rich products (0.02–0.30 mg kg−1). Soil organic matter, iron oxide, and phosphorus levels were correlated negatively with Se bioconcentration factor (BCF) of maize grain. Compared with the multivariate linear regression model, the artificial neural network (ANN) model was more accurate and reliable in predicting maize Se BCF. Prediction using the ANN model showed that 22.7% of the county's farmland was suitable for cultivating naturally Se-rich maize, which increased 21.3% growing areas than that from cultivation based on simply soil total Se. This study provided a new methodological framework for natural Se-rich maize production and verified the probability of cultivating naturally Se-rich maize in Se-poor farmland.

Impact of China's Rural Land Marketization on Ecological Environment Quality Based on Remote Sensing.

The market entry of rural collective operating construction land (MERCOCL) is an important way for the Chinese government to promote the marketization of rural land. However, the impact of China’s Rural Land Marketization on the ecological environment quality (EEQ) remains to be understood. Understanding these mechanisms is necessary for regional sustainable development and rational resource allocation. Therefore, a universal assessment model of China’s regional EEQ was built based on the Landsat 5/8 and the national ecological index (EI) provided by the Ministry of Ecology and Environment at the national district and county scale. A total of 229 counties (32 pilot counties and other counties in the pilot cities) in China from 2011 to 2018 were taken as the research object. This paper empirically studied the evolution process, driving mechanism and spatial heterogeneity of EEQ from the perspective of MERCOCL. The study shows that China’s EEQ presented a spatial distribution pattern of “high in the south, low in the north, high in the east and low in the west”. When a county implemented the MERCOCL policy, its EEQ index increased by 0.342, with the improvement effect occurring in the second year after the MERCOCL implementation. Regarding the mechanism, MERCOCL mainly improved the EEQ by promoting industrial structure optimization and increasing urban population aggregation. From the perspective of spatial heterogeneity, the improvement effect of MERCOCL on EEQ was more significant in regions with lower economic development levels and latitudes (southern China). This study will facilitate an understanding of the impact of China’s rural land marketization on the EEQ and provide scientific data support for government departments to formulate sustainable urban development policies that meet local conditions.

Spatial-temporal pattern of ecosystem service supply-demand and coordination in the Ulansuhai Basin, China

• Ecosystem services supply, demand, match degree and coordination dynamics were assessed at basin scale and county scale. • Spatially correlations between influencing factors and coupling coordination degree were investigated by mixed effects model. • Different development policies at county scale according to local dynamic of coupling coordination degree and total ES supply-demand match degree were formulated. Accurately quantifying the spatiotemporal patterns in ecosystem service (ES) 1 1 ES-Ecosystem service. supply and demand and their coupling coordination dynamics, as well as identifying the spatial heterogeneity of the influencing factors for coupling coordination degree are necessary for regional sustainable development. This study used the Ulansuhai Basin in Inner Mongolia Province of China as a case to quantify the supply, demand, and supply-demand ratio of ES (meat production, grain production, carbon storage, water yield, nitrogen purification, and phosphorus purification) from 2000 to 2020. The synthesized coefficient of ES supply (ESCI) 2 2 ESCI-Synthesized coefficient of ES supply. and demand (ESDI), 3 3 ESDI-Synthesized coefficient of ES demand. the match degree of total supply and demand (MD), 4 4 MD-Match degree of total ES supply and demand. and the coupling coordination degree (CD) 5 5 CD-Coupling coordination degree between total ES supply and demand. were taken as indices to explore the relationship between the ESCI and ESDI and the coordination relationships. A mixed effects model was used to determine the spatial heterogeneity response of CD to different influencing factors. The results showed that the total ES supply in the Ulansuhai Basin always met the demand. The area with the highest ESCI was always observed in the Urad Front Banner, while Linhe district had the highest ESDI. Although incoordination improved from mild to superior from 2000 to 2020 at the basin scale, significant spatial variations were observed at the county scale. In addition, the variation in CD was significantly correlated with population, followed by precipitation, and cropland area. The findings comprehensively explore the match and coordination between ES supply and demand, and the spatial heterogeneity of the effects of the influencing factors on CD. Our study can contribute to the formulation of future policies for regional ecological restoration and sustainable development.

Characteristics and influencing factors of chemical fertilizer and pesticide applications by farmers in hilly and mountainous areas of Southwest, China

Studying the characteristics and driving factors of chemical fertilizer and pesticide application of farmers is helpful for the prevention and control of regional non-point source pollution (NPSP). In this study, 320 farmers in Chongqing were surveyed, and the characteristics and main factors influencing chemical fertilizer and pesticide applications were analyzed by spatial analysis and partial least squares regression (PLSR). The results can be showed that: (1) Aaverage unit area applications of N, P2O5 and pesticide were 6.01 kg/ha/yr, 3.78 kg/ha/yr and 107.89 g/ha/yr. Among them, the fertilizer and pesticide application rates of large-scale households are 8.46 kg/ha/yr/, 5.82 kg/ha/yr and 287.61 g/ha/yr, and the application rates of general farmers are 4.54 kg/ha/yr, 2.55 kg/ha/yr and 40.21 g/ha/yr. Larger amount were applied to orchard and vegetable by large-scale farmer, while larger amounts were applied to grain by general farmers. (2) Fertilizer were spatially agglomerated on a household scale, while pesticide were spatially discrete on a county scale. (3) Average age (AA) of farmers and planting scale (PL) are the main factors affecting fertilizer amounts. Education level (EL), policy guidance (PG), and awareness of environmental effects (AEE) are the main factors influencing pesticide applications. Governments need to formulate rural labor policies and promote land transfer, while also strengthening fertilizer and pesticide reduction policies to enhance farmers’ environmental awareness.

Spatial Analysis of Cultivated Land Productivity, Site Condition and Cultivated Land Health at County Scale.

Cultivated land is a fundamental factor related to the social stability and sustainable development of the whole country. However, the safety of quantity and quality of cultivated land has decreased year by year, resulting in great challenges to the sustainable development of cultivated land. Cultivated land productivity, site conditions, and soil health jointly determine the sustainable development potential of cultivated land. Analyzing and calculating the coupling and cooperative relationship between these three subsystems can provide a theoretical and methodological reference for protecting and zoning cultivated land resources. Using Jiangyou City as a case study, this paper constructs a coupling coordination degree model of cultivated land productivity, site conditions, and soil health assessment systems in different geomorphic regions, and comprehensively analyzes the level of sustainable development of cultivated land in the study area. The results show that there are differences in the development potential of cultivated land resources in the mountainous regions in the north, the hilly regions in the center, and the plain regions in the south of Jiangyou City. The coupling coordination index of the three regions were calculated as 0.34, 0.51, and 0.63, respectively, for which the overall average coupling coordination index is 0.57; notably, it only reaches the “barely coordination” level. Based on our analysis results, the cultivated lands in Jiangyou City are classified into the following zones: core protection zone, dominant remediation zone, and key regulation zone. The cultivated land located in the core protection zone has a high coupling coordination index, which can be used as the preferred area for the delimitation of high standard basic farmland and permanent basic farmland. For the cultivated land located in the dominant remediation zone, the development of its subsystems is unbalanced. Comprehensive land improvement projects can be carried out in this zone to improve the overall quality. For the cultivated land located in the key regulation zone, it is recommended to implement projects such as returning farmland to forests to improve land use efficiency. In particular, the evaluation index system constructed in this paper is sufficiently representative, as it can support the classification, quality improvement, and sustainable use of cultivated land. Thus, other similar countries and regions can learn from the evaluation system constructed in this paper.

Spatio-Temporal Dynamics and Driving Forces of Multi-Scale CO2 Emissions by Integrating DMSP-OLS and NPP-VIIRS Data: A Case Study in Beijing-Tianjin-Hebei, China

The emission of greenhouse gases, especially CO2, is the main factor causing global warming. Due to incomplete statistical data on energy consumption at and below the urban scale of Beijing-Tianjin-Hebei (BTH), in this study, Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) data were combined, and a neural network model and weighted average method based on DN (Digital Number) value were used to obtain CO2 emissions at the municipal and county scales with a resolution of 1 km × 1 km from 2000–2019. Next, a spatial-temporal analysis model and spatial econometric model were used to study the CO2 emissions at different scales of BTH. This study also solved the problem that STIRPAT analysis cannot be carried out due to insufficient urban statistical CO2 emissions data. The results show that the energy CO2 emissions in BTH present a distribution pattern of “East greater than West”, with a trend of first rising and then slowing down. Moreover, the rapid growth areas are mainly located in Chengde and Tianjin. The degree of regional spatial aggregation decreased year by year from 2000–2019. Population, affluence and technology factors were positively correlated with CO2 emissions in Tianjin and Hebei. For Beijing, in addition to foreign investment, factors such as urbanization rate, energy intensity, construction and transportation factors all contributed to the increase in CO2 emissions. Among them, the growth of population is the main reason for the increase of CO2 at the urban scale in BTH. Finally, based on the research results and the specific situation of the cities, corresponding policies and measures are proposed for the future low-carbon development of the cities.

Understanding China’s urban functional patterns at the county scale by using time-series social media data

ABSTRACT Understanding urban functions help with government planning and resource allocation to promote economic development. Few studies have reflected the influence of population movement and migration on urban functions overtime on a large scale. This study adopted the time-series social media data and points of interest to analyse urban functions’ distribution at the county scale. By employing dynamic time warping distance and K-Medoids to cluster cities, the result of China’s hourly population distribution over different periods achieves a high accuracy (Pearson’s R = 0.821, R2 = 0.668). From the clustering results, time-series population data effectively reflect cities’ socioeconomic characteristics and identify the spatial distribution of China’s urban functional patterns. We select four representative urban agglomerations to deeply analyse their urban function patterns. Furthermore, urban functions can also influence changes in time-series populations. This study explores the correlations between time-series population mobility and urban functions, which could help analyse urban functions and urban socioeconomic conditions.