Yangtze River Delta Urban Agglomeration Research Articles

Insights from land sparing and land sharing frameworks for land productivity degradation governance in the Yangtze River Delta urban agglomeration, China

AbstractLand degradation due to mismanagement is widespread globally and may threaten the achievement of several UN Sustainable Development Goals. Yet the differences in land productivity degradation under various land management patterns (land sparing vs. land sharing) are poorly known. In this research, we used remote sensing data to develop a machine learning model for assessing the risk of land productivity degradation and interpreted the model using state‐of‐the‐art interpretable artificial intelligence techniques. In 2018, the risk level of land productivity degradation in the agricultural production space of the Yangtze River Delta urban agglomeration (YRD) was 0.230. More than half of the area was at low risk (68.19% of the area), mainly in mountainous and hilly areas. The degradation risk of the land sharing management pattern is lower than that of the land sparing pattern, but there are significant differences among provinces/municipalities. The four most influential factors for land productivity degradation in YRD were Normalized Vegetation Difference Index, nighttime light, elevation, and nitrogen deposition, which together explained 72.75% of the degradation risk. This study provides a methodological framework for land degradation governance in emerging urban agglomerations. It strongly recommends that policymakers explore locally appropriate land management patterns based on regional contexts.

Does policy integration promote water–energy–food coordination? A quasi-natural experiment in the Yangtze River Delta Urban Agglomeration

Does policy integration promote water–energy–food coordination? A quasi-natural experiment in the Yangtze River Delta Urban Agglomeration

Exploring the spatial effects and influencing mechanism of ozone concentration in the Yangtze River Delta urban agglomerations of China.

Ground-level ozone (O3) pollution has emerged as a significant concern impacting air quality in urban agglomerations, primarily driven by meteorological conditions and social-economic factors. However, previous studies have neglected to comprehensively reveal the spatial distribution and driving mechanism of O3 pollution. Based on the O3 monitoring data of 41 cities in the Yangtze River Delta (YRD) from 2014 to 2021, a comprehensive analysis framework of spatial analysis-spatial econometric regression was constructed to reveal the driving mechanism of O3 pollution. The results revealed the following: (1) O3 concentrations in the YRD exhibited a general increasing and then decreasing trend, indicating an improvement in pollution levels. The areas with higher O3 concentration are mainly the cities concentrated in central and southern Jiangsu, Shanghai, and northern Zhejiang. (2) The change of O3 concentration and distribution is the result of various factors. The effect of urbanization on O3 concentrations followed an inverted U-shaped curve, which implies that achieving higher quality urbanization is essential for effectively controlling urban O3 pollution. Traffic conditions and energy consumption have significant direct positive influences on O3 concentrations and spatial spillover effects. The indirect pollution contribution, considering economic weight, accounted for about 35%. Thus, addressing overall regional energy consumption and implementing traffic source regulations are crucial paths for O3 pollution control in the YRD. (3) Meteorological conditions play a certain role in regulating the O3 concentration. Higher wind speed will promote the diffusion of O3 and increase the O3 concentration in the surrounding city. These findings provide valuable insights for designing effective policies to improve air quality and mitigate ozone pollution in urban agglomeration area.

Assessing Urban Water–Energy–Food Security: A Case of Yangtze River Delta Urban Agglomeration

Assessing Urban Water–Energy–Food Security: A Case of Yangtze River Delta Urban Agglomeration

Monitoring public perceptions of contaminated sites based on social media

Contaminated sites have a negative impact on human health and the ecological environment, which can potentially lead to major pollution incidents, and frequently receive complaints and reports from the public. Therefore, monitoring public perceptions of contaminated sites is crucial for risk management. However, methods based on traditional questionnaire surveys are limited in terms of time, cost, and target audience size. The purpose of this study was to establish a method using social media to monitor public perceptions of contaminated sites in the Yangtze River Delta urban agglomeration. Thus, 6802 public opinions were collected from social media (mainly microblogs in China) and topic modeling and pollution and spatial mining techniques were employed. The topic modeling results indicated that public perceptions of contaminated sites mainly focused on the construction of prevention and control systems, law enforcement work, developments and challenges applicable to the coal industry, environmental public interest litigation (pertaining to pollution), pollution inspection and rectification, and green development and ecological governance, with intensities of 7.9%, 7.8%, 7.1%, 6.1%, 5.9%, and 4.8%, respectively. Three communities resulting from public opinions in the study area included “environment and pollution,” “enterprises,” and “environmental protection,” with proportions of 37.68%, 34.78%, and 27.54%, respectively. The field investigation results indicated that approximately 90% of the tweets in three typical cities (i.e., Taizhou City in Zhejiang Province and Wuxi and Changzhou Cities in Jiangsu Province) involved key industrial enterprises or contaminated sites located within 1 km of the surrounding areas. The emotional analysis indicated that >3401 tweets dealt with a pollution probability (i.e., the possibility of potentially contaminated sites mentioned in social media becoming contaminated sites) exceeding 0.90 for the period 2011–2021. This finding suggests that the pollution probability for the sites involved in these tweets was high. Our study provides methodological references for monitoring public perceptions of contaminated sites for large-scale, long-term, and high-resolution studies.

Evaluating the spatiotemporal dynamics of ecosystem service supply-demand risk from the perspective of service flow to support regional ecosystem management: A case study of yangtze river delta urban agglomeration

The inappropriate spatio-temporal distribution of natural capital during rapid urbanization has increased ecosystem service supply-demand (ESSD) risks, posing great challenges to the sustainable growth of human well-being, especially in urban agglomerations. Although methods focusing on the spatial match or temporal dynamics of ESSD in risk assessment have been established, a comprehensive understanding of the spatial dynamics underlying ecosystem service flows (ESF) is still missing. Due to the spatiotemporal heterogeneity of various ESSD risks within urban agglomerations, incorporating these elements into a unified spatial planning framework is still difficult. This study integrated the spatio-temporal analysis method of ESSD considering ESF and the spatial clustering method to propose an ESSD risk assessment and management framework: evaluating ES supply, demand and flow, incorporating ESF to quantify ESSD across various timeframes, assessing ESSD risks based on the current status and dynamic trends, and using Self-Organizing Map to identify optimal ESSD risk bundles (ESSDR_Bs). The results showed high ESSD risks of high temperature regulation, nitrogen purification, phosphorus purification, and food production in Yangtze River Delta urban agglomeration. While the ESSD risks of biodiversity conservation, carbon sequestration, soil retention, water yield, and tourism culture were relatively low, a concerning trend of decreasing surpluses was observed in general. In addition, six ESSDR_Bs were identified, and differentiated ecological management strategies were proposed for each bundle. This study provided a novel perspective for efficiently understanding and regulating the ESSD risks in urban agglomerations.

Contrasting Effects of Urban Land Cover Change and Anthropogenic Heat on Summer Precipitation Over the Yangtze River Delta of China: Analyses From an Atmospheric Moisture Budget Perspective

AbstractBoth urban land cover (ULC) change and anthropogenic heat (AH) emission are important causes of urban heat island, but their relative contributions to the changes in urban precipitation and the related mechanism remain unclear. Based on numerical simulations utilizing the latest realistic urban fraction and AH data over the Yangtze River Delta urban agglomeration, we found that ULC and AH resulted in nearly opposite effects on precipitation. Various dynamical and thermodynamic processes were involved according to the atmospheric moisture budget analyses. AH increased precipitation particularly during afternoon, and the increases were stronger during heavy precipitation events because of the enhanced moisture convergence effect together with the release of moisture storage previously accumulated in the atmosphere. Differently, ULC reduced mean precipitation mainly due to suppressed evaporation. During weak precipitation events, the suppressed evaporation was largely balanced by the intensified moisture convergence, but during heavy events, ULC caused more pronounced precipitation reduction because the moisture convergence response disappeared and failed to offset the evaporation effect. The relative contributions of different dynamical and thermodynamic processes such as those related to circulation, moisture gradient, and background moisture availability to the temporal variation in the total moisture convergence were further quantified. Overall, our results help better understand the relative roles of different aspects of urbanization on precipitation, and suggest that compared to ULC, reduction in AH emission that is tightly related to the energy consumption structure could be more efficient for mitigating the risk of extreme precipitation.

Evaluation and influencing factors of green transformation in three major urban agglomerations in China

Since the 21st century, the world has increasingly focused on the issue of sustainable development, and the green transformation issues have become a new hot topic worldwide. Green and low-carbon transformation has become an international consensus. Urban agglomerations are important connections between urban development and regional coordination, as well as important spatial carriers for economic activities. They are not only the main source of carbon emissions, but also the main battlefield for energy conservation and emission reduction. As an important field for carbon reduction, the green transformation of cities is crucial for achieving the “dual carbon” goals. This article focuses on 48 cities in the three most mature and influential urban agglomerations in China from 2011 to 2019, namely, the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration. The three-stage DEA model and Malmquist index model are used to measure the green transformation efficiency of the three urban agglomerations from both dynamic and static perspectives, and a Tobit regression model is constructed to explore the influencing factors of green transformation efficiency in urban agglomerations. Research has found that: 1) From a static perspective, the overall efficiency of green transformation in the three major urban agglomerations is at a high level, but from a temporal perspective, it shows a downward trend. The Pearl River Delta urban agglomeration is known for its green development, with the highest average efficiency of green transformation, followed by the Yangtze River Delta urban agglomeration. The Beijing-Tianjin-Hebei urban agglomeration has the lowest level of green transformation; 2) From a dynamic perspective, technological progress is the main driving factor for improving the efficiency of green transformation in the three major urban agglomerations. Therefore, the government should pay special attention to the progressiveness of technology when formulating relevant policies to promote urban green transformation; 3) From the perspective of spatiotemporal differences, there are significant differences in the spatiotemporal characteristics of green transformation among the three major urban agglomerations, and there are significant differences in green transformation strategies among different urban agglomerations. Eliminating environmental factors and random interference is necessary for accurately measuring the efficiency of green transformation in urban agglomerations; 4) From the perspective of influencing factors, factors such as industrial structure upgrading, green innovation level, and environmental regulation intensity jointly affect the efficiency of green transformation in urban agglomerations. Based on this, we should pay attention to the differences between urban agglomerations and implement policies tailored to local conditions; Strengthen the network system of urban agglomerations and avoid conflicts between cities; Encourage green technology innovation, accelerate industrial structure upgrading, and so on. This article focuses on the issue of green transformation in urban agglomerations, and conducts research from three perspectives: efficiency measurement, influencing factors, and implementation mechanisms. A relatively systematic theoretical framework for green transformation in urban agglomerations is formed, and an evaluation index system for green transformation efficiency in urban agglomerations is constructed and optimized. The composition mechanism of the five dimensional evaluation system for transformation efficiency is analyzed, and the bottleneck and breakthrough direction of the three major urban agglomerations in China in the development process are grasped. It has a good demonstration effect on the green transformation of other urban agglomerations.

Effects of internal migrants' health on economic resilience in China's Yangtze River Delta urban agglomeration: moderating effects of basic public health services.

Internal migrants constitute a significant generality in the socioeconomic development of developing countries. With the frequent occurrence of major public health emergencies, obstacles to labor supply due to health issues among internal migrants not only affect their livelihood stability but also urban economic resilience. Moreover, the design of basic public health service systems tends to favor local residents over internal migrants, further exacerbating the health and employment risks of internal migrants. As a result, urban economic resilience faces significant challenges. The objective of this study was to deconstruct economic resilience into economic resistance and recovery abilities, investigate the net effect and its heterogeneity of internal migrants' health on economic resilience in China's Yangtze River Delta urban agglomeration (CYRD), and the mediating effect from labor participation rate and labor time supply, as well as the moderating effect of basic public health services. Based on the China Migrants Dynamic Survey data (CMDS), the study empirically estimated the effects of internal migrants' health on economic resilience in CYRD through microeconometric analysis methods, mediating and moderating effect model. Our findings indicate that internal migrants' health has a positive effect on economic resilience in CYRD. For each unit increase in migrants' health, it will drive up the average economic resistance ability by 0.0186 and the average recovery ability by 0.0039. Secondly, the net effects of migrants' health on economic resilience show significant structural differences, industry and city heterogeneity. The effect of migrants' health on economic resistance ability is significantly higher than that on economic recovery ability; The effect of migrants' health on economic resilience of the secondary industry is higher than that of the tertiary industry; The cities with high economic resistance and recovery abilities have more prominent positive effect from migrants' health. Thirdly, migrants' health not only has a direct effect on the economic resistance and recovery abilities, but also has a mediating effect on which through labor participation rate and labor time supply. Enhancing the accessibility and quality of basic public health services is beneficial for enhancing the positive effects of internal migrants' health on economic resilience.

Identifying building function using multisource data: A case study of China's three major urban agglomerations

As crucial geographical components shaping urban landscapes, defining the functions of buildings plays one of the pivotal roles in planning urban development and facilitating socio-economic human activities. Existing studies in large-scale mapping mainly ignore the environment around buildings, leading to insufficient meeting the demands of large-scale building-level mapping in certain disciplines. To abridge this gap, this article calculates the geometric characteristics of building footprint, the distances from buildings to adjacent objects, and the kernel densities of Point-of-Interest (POIs) to identify building main functions in three Chinese urban agglomerations (Beijing-Tianjin-Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and Pearl River Delta Urban Agglomeration). Leveraging XGBoost, our models attained accuracies of 0.936, 0.934, and 0.940 in the three urban agglomerations, accompanied by kappa coefficients of 0.883, 0.868, and 0.891 respectively, suggesting their capability of identifying building functions in large-scale mapping. Furthermore, the conducted experiments on varying feature combinations and transferability highlight the significance of the built environment in enhancing classification accuracy. Additionally, the analysis of the classification results of buildings in the three urban agglomerations shows harmonized development strategies for Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration, while cities within the Beijing-Tianjin-Hebei Urban Agglomeration do not show the same development strategies. All results demonstrate the feasibility of employing multi-source accessible data to efficiently categorize building functions over large areas in a cost-effective manner. This methodology can be replicated in China and even on a larger scale to offer insights into complex urban spaces and to help urban planners provide policy support such as old city renovations and new city planning.

China’s urbanisation evolution and metropolitan area expansion, based on the Prolonged Artificial Nighttime-light Dataset (PANDA, 1984–2020)

ABSTRACT China’s massive urbanisation development will undoubtedly serve as a global reference. Due to the uncertainty of statistical data, nighttime light (NTL) data have emerged as alternative data for urbanisation evaluation. Here, the application of the Prolonged Artificial NTL Dataset of China (1984–2020) has been newly expanded. Total-value statistical, complex NTL index (CNLI), NTL concentration degree, rank-size rule, and Markov transfer matrix were used to systematically mine information about urban change from multiple perspectives. China’s urbanisation exhibited rapid growth and outwards expansion. The total percentage increase in NTL brightness and NTL area was 409.38% and 302.58%, respectively. The Yangtze River Delta urban agglomeration of East China had the fastest urbanisation (CNLI Trend = 0.0057/a, p < 0.01). Low NTL areas transitioned to medium NTL areas, and high and extremely-high NTL areas diffused to medium NTL areas. The cities in Eastern China and Southern China typically exhibited extremely-high type NTL areas, whereas other cities primarily exhibited medium NTL areas. The gaps between city sizes decreased over time (q Trend = – 0.0200/a, p < 0.01). Lowest-ranked cities exhibited the highest stability (>95%) in city size type transition. The spatiotemporal changes in NTL obtained were of great significance for monitoring urban expansion patterns, making government decisions, and quantifying China’s sustainable development.

Decrypting resilience: The spatiotemporal evolution and driving factors of ecological resilience in the Yangtze River Delta Urban Agglomeration

Can a system be reconstructed for greater resilience to complex disturbances? We provide answers to this question by demystifying ecological resilience (ER). We innovatively constructed a “resistant, absorptive, and restorative capacity” framework based on evolutionary resilience theory, the Dagum Gini coefficient, and the panel Tobit model to systematically determine what ER is and how to enhance it. The results indicate that during 2005–2021, minimal longitudinal changes with significant horizontal variations occurred in the ER of the Yangtze River Delta Urban Agglomeration (YRDUA). ER showed an “east strong, west weak” pattern, with average values from 0.124 to 0.665. The ER of Southern Jiangsu ranked high, while that of the seven cities in Anhui ranked low. The inequity of ER showed an initial increase followed by a fluctuating decreasing trend; the overall average Gini coefficient was 0.262. The intra-regional differences showed Zhejiang > Jiangsu > Anhui, with a contribution rate of 55.46%. Green patents and the proportion of cultivated land were the most significant positive and negative driving forces for ER, respectively. Additionally, when these factors worked together, they increased the explanatory power of ER, primarily via nonlinear enhancement. Urbanization rate and green patents enhanced the joint action of driving forces. The contribution of our study is to compensate for the lack of theoretical traceability in previous studies and provide an inspiring analytical framework for ER.

Occurrence and health risk assessment of organic amine pesticides in drinking water of the Yangtze River delta urban agglomeration

Organic amine pesticides (OAPs) are widely used in modern agriculture and these compounds can contaminate drinking water sources in different ways. However, there is a lack of data on the occurrence of OAPs in drinking water and their potential human health risks. In this study, tap water (TW) and bottled water (BW) samples were collected from eight cities in the Yangtze River Delta urban agglomeration in China, and their OAP levels were analyzed using high-throughput organic analysis testing coupled with high-volume solid-phase extraction (Hi- throat/Hi-volume SPE techniques). This study is the first to systematically characterize the trace levels of OAPs in drinking water in China. Our findings indicated that the total concentration of OAPs (∑OAPs) in TW (average 11.06 ± 4.99 ng/L) was 29.4% higher than in BW (average 8.55 ± 3.98 ng/L) and fewer kinds (7) of OAPs were detected in BW. Furthermore, the long-term intake of TW in some areas was linked to carcinogenic risks even at an acceptable OAP range, particularly in males, with molinate being the major contributor (61.3%) to OAP exposure. Further analysis revealed that the occurrence and health risks of OAPs in drinking water were mainly influenced by the quality of water sources and the technologies adopted in drinking water treatment plants (DWTPs). Furthermore, our findings demonstrated that advanced treatment technologies such as nanofiltration could more effectively remove OAPs in raw water (up to 87.5%). Therefore, our findings highlighted the importance of selecting appropriate advanced treatment technologies in DWTPs.

A novel framework for urban flood resilience assessment at the urban agglomeration scale

With global climate change and continuous urbanization exacerbating floods, urban flood resilience (UFR) has become a key to cope with floods. However, few studies target frameworks of UFR assessment at the urban agglomeration scale over a longer time span. This study, taking the Yangtze River Delta urban agglomeration as a case study, developed an evaluation framework to detail the building of a final evaluation index system of UFR, to analyze UFR's driving factors and spatiotemporal features based on the SSA-PP-KL-TOPSIS (projection pursuit based on sparrow search algorithm-Kullback-Leibler-technique for order of preference by similarity to ideal solution) model. From the perspectives of comparing numerical values and spatial distribution results, the evaluation indicators and method proposed in this article perform better. The case results showed that UFR displayed an overall growth trend and significant spatial heterogeneities. The economic, social, and infrastructure resilience showed a similar growth trend, while the environmental resilience demonstrated a decreasing trend. Environmental resilience has become a weak link in improving resilience. Higher resilience levels were concentrated in the central metropolis, provincial capitals, and industrial cities. The findings could be of use to researchers and practitioners, and the framework presented would be of reference to other flood-stricken areas.

Spillover effects and influencing factors of tourism eco‐efficiency for sustainable development: A case study of cities in the Yangtze River Delta

AbstractTourism often brings about negative environmental impacts. Tourism eco‐efficiency (TEE) and its spatial coordination are of great significance for sustainable tourism development and environmental protection in the Yangtze River Delta (YRD). TEE index system was established and applied in this study. The inter‐city TEE spatial correlation network is established by the modified gravity model and analyzed by social network analysis (SNA). Spatial Durbin model (SDM) was used to analyze the spatial spillover effects of the two centrality indicators of the network. Based on fuzzy set qualitative comparative analysis, we analyzed TEE enhancement configuration. The results show that: TEE in 26 cities in the study area has improved overall; the phenomenon of unbalanced development within the region still exists. In addition, the TEE of each city in the spatial correlation network of TEE in the YRD urban agglomeration is connected with each other, and there is a significant negative spillover effect. TEE is mainly affected by the level of economic development, industrial status of the tourism industry, tourism industry concentration, tourism investment level, tourism resource endowment, urban landscaping, and information development level. The combination of these factors and TEE can form five configuration patterns for TEE enhancement. This study combines SNA and SDM for the first time, and the conclusions obtained can more deeply describe the spatial correlation between different urban eco‐efficiency in the YRD. Aiming at the sustainable development of regional tourism, this study provides help for strengthening the protection of tourism ecological environment in the YRD.

Evaluation and application of local fiscal science and technology expenditure efficiency in the Yangtze River Delta urban agglomeration based on AHP-TOPSIS and SOM clustering

This study selected 18 indicators from four dimensions: the scale of regional fiscal science and technology expenditure, the social impact of science and technology, the efficiency of science and technology expenditure, and the environment for scientific and technological innovation, and constructed an evaluation index system for the efficiency of local fiscal science and technology expenditure in the Yangtze River Delta urban agglomeration. The AHP-TOPSIS model was used to determine the weights of evaluation indicators, and the efficiency ranking of fiscal science and technology expenditures in various cities of the Yangtze River Delta urban agglomeration was quantified. Furthermore, using the SOM algorithm for clustering analysis, the level division of innovation capability in the Yangtze River Delta urban agglomeration was clarified. This study provides a scientific tool and method for evaluating the efficiency of local financial science and technology expenditure in the Yangtze River Delta urban agglomeration, and provides solid theoretical support and practical guidance for promoting the coordinated development of regional science and technology innovation.

Towards sustainability: The spatiotemporal patterns and influence mechanism of urban sprawl intensity in the Yangtze River Delta urban agglomeration

Urban sprawl has been a great challenge to current sustainable development goals, especially in urban agglomeration areas. Based on the nighttime light and WorldPop population grid data from 2011 to 2020, this study established a multidimensional evaluation system of urban sprawl intensity in the Yangtze River Delta Urban Agglomeration (YRDUA) with a "scale-density-structure" framework. The Dagum Gini coefficient, spatial Markov chain, and econometric analysis were applied to analyze urban sprawl intensity. The results suggested that: (1) the urban sprawl intensities of the cities in the YRDUA became more imbalanced over the decade; (2) the mild sprawl cities were dominant in the study area, but the moderate and severe sprawl cities were mostly located in the west and the only inland province--Anhui; (3) the spatial distribution of urban sprawl intensity indicated "club convergence" and strong spatial correlation effect, but poor stability; and (4) land resource mismatch and population urbanization were the main explanatory variables for urban sprawl intensity, with large cities more sensitive to the former, and medium cities to the latter. These findings can provide important policy implications to sustainable development and compact growth.

Spatial Network Analysis of Coupling Coordination between Digital Financial Inclusion and Common Prosperity in the Yangtze River Delta Urban Agglomeration

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing’s siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and “small-world” properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region.

Unveiling the Impact of Urbanization on Net Primary Productivity: Insights from the Yangtze River Delta Urban Agglomeration

Urbanization has significantly altered the carbon cycle of the terrestrial environment, particularly in relation to net primary productivity (NPP). Gaining a more comprehensive comprehension of how NPP is affected by urbanization is crucial for obtaining fresh perspectives on sustainable urban landscape design and decision making. While there is a significant body of research examining the geographical and temporal patterns of NPP supply capacity, there are only a few studies that have investigated the spatial relationships between NPP and urbanization, particularly at the grid scale. This research investigated the temporal and geographical features and patterns of NPP and their impact mechanisms. In order to estimate NPP and the level of urbanization in the Yangtze River Delta Urban Agglomeration (YRDUA), we used a combination of different models and datasets. To evaluate the geographical correlations and dependence between NPP and urbanization, we utilized local bivariate autocorrelation methods and spatial regression models to describe and visualize these relationships. The findings revealed that there was a consistent negative relationship between NPP and urbanization on a global scale from 1990 to 2020. However, when examining the local scale, the geographical correlations could be classified into four distinct categories: areas with both low NPP and low urbanization, areas with high NPP and high urbanization, areas with low NPP and high urbanization, and areas with high NPP and low urbanization. Our analysis showed that spatial regression models are more suitable for quantifying the spatial relationship between NPP and urbanization due to their ability to include the impacts of spatial Moran’s I techniques. Due to the growing urbanization, the highest NPP value was recorded in 2005, followed by 2000, 2020, and 2010. Conversely, the smallest association was observed in 2015. Examining the geographical connection between NPP and urbanization offers theoretical and practical insights for urban planning that prioritizes human needs and promotes sustainable development. It also aids in the development of reasonable methods for organizing ecological functional systems.

Monitoring the Invasion of S. alterniflora on the Yangtze River Delta, China, Using Time Series Landsat Images during 1990–2022

Spartina alterniflora (S. alterniflora) has grown rapidly in China since its introduction in 1979, showing the trend of alien species invasion, which has seriously affected the ecosystem balance of coastal wetlands. The temporal and spatial expansion law of S. alterniflora can be obtained through remote sensing monitoring, which can provide a reference and basis for S. alterniflora management. This paper presents a method for extracting and mapping S. alterniflora based on phenological characteristics. The coastal areas of the Yangtze River Delta Urban Agglomeration are selected as the research area, and the Landsat time series data from 1990 to 2022 on the Google Earth Engine (GEE) platform are used to support the experiment in this paper. Firstly, the possible growing area of S. alterniflora was extracted using the normalized differential moisture index (NDMI), normalized differential vegetation index (NDVI), and normalized differential water index (NDWI); Then, the time series curve characterizing the phenological characteristics of vegetation was constructed using the vegetation index to determine the difference phase of phenological characteristics between S. alterniflora and other vegetation. Finally, a decision tree was constructed based on the phenological feature difference phase data to extract S. alterniflora, and it is applied to the analysis of temporal and spatial changes of S. alterniflora in the study area from 1990 to 2022. The results show that the area of S. alterniflora increased from ~1426 ha in 1990 to ~44,508 ha in 2022. However, the area of S. alterniflora began to show negative growth in 2015 due to the construction of nature reserves and ecological management. The results of correlation analysis showed that the growth of C. japonicum was significantly affected by temperature stress and weakly affected by precipitation. This study verified that Landsat time series images can effectively extract vegetation phenological information, which has strong feasibility for extraction and dynamic monitoring of S. alterniflora and provides technical support for the management and monitoring of invasive plants in coastal wetlands.