Pearl River Delta Urban Agglomeration Research Articles

Enhancing Urban Land Use Identification Using Urban Morphology

Urban land use provides essential information about how land is utilized within cities, which is critical for land planning, urban renewal, and early warnings for natural disasters. Although existing studies have utilized multi-source perception data to acquire land use information quickly and at low cost, and some have integrated urban morphological indicators to aid in land use identification, there is still a lack of systematic discussion in the literature regarding the potential of three-dimensional urban morphology to enhance identification effectiveness. Therefore, this paper aims to explore how urban three-dimensional morphology can be used to improve the identification of urban land use types. This study presents an innovative approach called the UMH–LUC model to enhance the accuracy of urban land use identification. The model first conducts a preliminary classification using points of interest (POI) data. It then improves the results with a dynamic reclassification based on floor area ratio (FAR) measurements and a variance reclassification using area and perimeter metrics. These methodologies leverage key urban morphological features to distinguish land use types more precisely. The model was validated in the Pearl River Delta urban agglomeration using random sampling, comparative analysis and case studies. Results demonstrate that the UMH–LUC model achieved an identification accuracy of 81.7% and a Kappa coefficient of 77.6%, representing an 11.9% improvement over a non-morphology-based approach. Moreover, the overall disagreement for UMH–LUC is 0.183, a reduction of 0.099 compared to LUC without urban morphology and 0.19 compared to EULUC-China. The model performed particularly well in identifying residential land, mixed-use areas and marginal lands. This confirms urban morphology’s value in supporting low-cost, efficient land use mapping with applications for sustainable planning and management.

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.

Toward dual carbon targets: Spatial correlation on comprehensive carbon emission index in urban agglomerations based on a new evaluation model

China is confronted with substantial pressure to mitigate its carbon emissions. Urban agglomerations assume a pivotal role in the attainment of carbon peaking and carbon neutrality. This study constructed a carbon emission evaluation model from the perspectives of total quantity, intensity, efficiency, population, and land. A modified gravity model and social network analysis method was used to analyze the spatial correlation characteristics of six urban agglomerations, clarified their carbon emission spillover effects, and explored their influencing factors using the quadratic assignment procedure method. The results are as follows. (1) The carbon emission correlations in the Yangtze River Delta, Pearl River Delta, and Chengdu-Chongqing urban agglomerations increased, while Beijing-Tianjin-Hebei, Central Plains, and Middle Reaches of Yangtze River urban agglomerations decreased. (2) From a macro perspective, the Yangtze River Delta and Pearl River Delta urban agglomerations were at the core of the network, and the “bridging” role of the Middle Reaches of Yangtze River urban agglomeration was evident. From a micro perspective, the cities in the middle of urban agglomerations were more likely to become network cores. (3) Spillover effects between plates were present in the correlation network, with a gradual balance between the reception and spillover relationships. Most urban agglomerations exhibited an uneven distribution of community correlation capacity. (4) Geographic adjacency and urbanization level significantly promoted carbon emission correlation, although the direction and magnitude of other factors’ influence varied. The findings contribute to the implementation of low-carbon planning, carbon reduction strategies and regional sustainable development.

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.

Urbanization intensifies the imbalance between human development and biodiversity conservation: Insights from the coupling analysis of human activities and habitat quality

AbstractIntensified human activities have been seriously threatening the structure and ecological processes of ecosystems, resulting in habitat degradation. Therefore, coordinating the coupling between human activities and habitat quality (HQ) is crucial for high‐quality sustainable regional development and human well‐being. This study evaluated the human activities and HQ in the Pearl River Delta (PRD) urban agglomeration in China from 2000 to 2020 using the human footprint index (HFI) and the integrated valuation of ecosystem services and tradeoffs model. Then, we employed bivariate spatial autocorrelation and a coupling coordination degree (CCD) model to explore the synergistic relationship between human activities and HQ. The results show that spatial changes in HQ were predominantly driven by human activities. The gradual outward urban expansion resulted in significant HQ degradation. Slight HQ improvement by ecological restoration in urban outskirts cannot offset HQ losses caused by urbanization. During the study period, high‐HQ low‐HFI clusters decreased by 1.02%, while low‐HQ high‐HFI clusters increased by 4.67%, the two main clustering types in the PRD. Despite the CCD between HFI and HQ increased after 2010, the continuous changes of CCD characteristics from the HFI significantly lagged type to the HQ lagged. HFI showed an inverted U‐shaped relationship with CCD. The CCD peaks during 2000–2020 corresponded to HFI decreasing from 0.711 to 0.566. This indicates that the risk of decoupling between human activities and HQ gradually increased. Furthermore, CCD levels and characteristics in different bivariate clusters exhibited varying changes over time. These results reveal that the spatiotemporal dislocation between urbanization and ecological restoration induced the spatial nonstationarity of the coupling relationship between human activities and HQ. Urbanization exacerbates the imbalance between human development and biodiversity conservation. Therefore, we suggest reasonably delimiting urban boundaries, controlling the scale of urban sprawl, and strengthening biodiversity protection in areas undergoing rapid urbanization. In addition, we advocate for the division of ecological barrier zones, urban development buffer zones, and urban built‐up areas, each with tailored management and protection measures. Our findings can provide an important reference for the ecological restoration of urban agglomerations.

Unveiling the complexities of sustainable urban phosphorus management based on lifecycle assessment and decomposition analysis

To address global phosphorus reduction challenges, we establish a quantitative framework for anthropogenic phosphorus emissions using a lifecycle model and decomposition analysis. Focusing on China's Pearl River Delta urban agglomeration, we highlight persistent difficulties in controlling phosphorus emissions from agricultural fertilizers and the emerging issue from industrial products. Emission intensity is a crucial factor for phosphorus reduction, yet gradually offset by affluence and population growth. Prioritizing agricultural control with supplementary industrial reduction, we delve into the decoupling challenge between phosphorus emissions, population expansion, and economic development. Proposing green economic development, our approach contributes to achieving sustainable phosphorus management goals.

Spatial-temporal differentiation of urban eco-efficiency and its driving factors: A comparison of five major urban agglomerations in China.

This paper utilizes an improved undesirable output DEA model to measure the eco-efficiency of cities in five major urban agglomerations in China during 2006-2020. It employs the Theil Index and Geodetector to investigate the spatial-temporal distribution differentiation characteristics and driving factors of urban eco-efficiency. The main findings are as follows. Firstly, the eco-efficiency of all urban agglomerations showed a fluctuating upward trend, but the eco-efficiency performance of different urban agglomerations in China shows a stratification characteristic. Specifically, the Pearl River Delta urban agglomeration consistently ranks first in China, while the mean values of the Yangtze River Delta urban agglomeration, Beijing-Tianjin-Hebei urban agglomeration, and Chengdu-Chongqing urban agglomeration are lower than the national average. Secondly, the overall differences in the urban eco-efficiency of all sample cities show a consistently fluctuating downward trend. The factor that affects the level differences of eco-efficiency in different cities is the intra-regional differences. Last but not least, the top three factors affecting the spatial distribution difference of urban eco-efficiency in the whole sample are environmental pollution control investments, innovation level, and environmental infrastructure investments. In the end, this paper proposes that reducing the intra-regional differences is the primary task to achieve the coordinated improvement of urban eco-efficiency in urban agglomerations, and then puts forward some policy suggestions to improve eco-efficiency for the five major urban agglomerations.

Forecasting electricity consumption in China's Pearl River Delta urban agglomeration under the optimal economic growth path with low-carbon goals: Based on data of NPP-VIIRS-like nighttime light

To reflect the trend of electricity consumption (EC) in China's Pearl River Delta (PRD) under a balanced environment and economy, this paper proposes an EC forecasting framework from the perspective of optimal economic growth. The historical GDP statistics of the PRD are calibrated with data from the new "NPP-VIIRS-like" nighttime light dataset from 2000 to 2020, and two simulation scenarios of optimal economic growth are constructed according to the carbon emission reduction rate. Finally, based on the electricity data from 2000 to 2017, an error correction model is constructed to predict and compare the trend of EC in the PRD under different carbon emission scenarios. The results demonstrate the following. (1) The economic growth path under low-carbon constraints is more closely aligned with the actual economic development of the PRD. (2) The electricity demand required to sustain optimal economic growth in the PRD under low-carbon constraints is projected to reach saturation around 2037, approximately a decade earlier than the scenario without carbon constraints. The results of the projections are expected to guide future work on power system planning and economic development assessment in the context of carbon reduction.

Spatio–Temporal Patterns and Driving Mechanisms of Urban Land High-Quality Use: Evidence from the Greater Pearl River Delta Urban Agglomeration

Efficient urban land use plays a crucial role in promoting regional development and ensuring economic growth. Analyzing the spatio–temporal pattern of urban land high-quality use (ULHU) and identifying its key influencing factors and pathways can contribute to enhancing land use efficiency and achieving high-quality development. This study established a comprehensive measurement indicator system for evaluating ULHU using the entropy method and investigated the spatio–temporal evolution pattern of ULHU in the Greater Pearl River Delta (PRD) urban agglomeration in the years 2005, 2010, 2015 and 2020. Furthermore, a multi-period fuzzy-set qualitative comparative analysis method was adopted to explore the concurrent driving mechanisms that impact ULHU from the configuration perspective. The findings reveal that the level of ULHU has exhibited a significant improvement, increasing from 0.1150 in 2005 to 0.2758 in 2020, with an annual growth rate of 8.739%. The spatial pattern in the PRD region exhibits an incremental distribution, characterized by higher values in the central region and lower values in the peripheral area, as this pattern reveals significant heterogeneity across the region. The configurations for ULHU in the PRD were identified, such as Population-industry-talent driven, Openness-fiscal-talent driven, Population-led, and Population-industry driven. Among these configurations, population density and industrial structure were identified as the core driving factors, while the role of talent resources is gradually diminishing. This study provides practical guidance for promoting the ULHU and optimizing national spatial planning.

Analysis of carbon emissions from social water cycle in the Pearl River Delta of COVID-19: Perspectives from water-energy-carbon nexus

Carbon emissions from various sectors during COVID-19 pandemic have become a hot research topic. However, the impact of COVID-19 on carbon emissions from social water cycle (SWC) processes is unclear. This study uses the Pearl River Delta (PRD) urban agglomeration as the research object and considers the water system carbon emissions in the study region pre- (2018, 2019) and post-COVID-19 (2020, 2021). First, the findings indicate that carbon emissions from SWC systems in PRD cities increased during the COVID-19 pandemic. Guangzhou had the most significant growth of 2.868 Mt, and Zhongshan had the highest annual growth rate of approximately 9%. Second, carbon emission intensity of each subsystem in 2021 is higher than that in other years, with the water use system emitting much more carbon than the other systems. Finally, the amount of water in the SWC showed a significant positive relationship with carbon emissions and average carbon emission intensity. Guangzhou and Shenzhen released more carbon emissions than other cities, and Zhaoqing and Huizhou had a lower average carbon intensity. This study's conclusion differs from other research findings that, indicate a declining carbon emission trend during the COVID-19 pandemic. This study is the first to analyze the changing patterns of carbon emissions from SWC systems during the COVID-19 pandemic. The results can provide carbon recommendations for SWC system development in response to major public health emergencies.

Multiscale effects and drivers of landscape heterogeneity for water‐related ecosystem services in urban agglomerations

AbstractUnderstanding the multiscale impacts and drivers of urban agglomeration landscape patterns for ecosystem services (ESs), especially water‐related ecosystem services (WESs), is essential for the development of regional ecological management. However, the multiscale impacts and driving mechanisms of urban agglomeration landscape patterns for ESs have not been adequately explained. In this study, multivariate data were employed, and the InVEST model, trend test method, coupled GeoDetector and geographically and temporally weighted regression (GTWR) method were utilized to comprehensively explore the spatial and temporal changes in landscape patterns and WESs in the Pearl River Delta urban agglomeration (PRDUA) at various grid and administrative scales from 1990 to 2020 and to determine the driving mechanisms affecting WESs. The results indicated that the variation characteristics of landscape patterns and WESs in the PRDUA were consistent, forming a binary spatial structure of core and peripheral areas in an inverted “U” shape around the estuary of the Pearl River. The relationship between landscape patterns and WESs weakened with the increase of scale, and the correlation coefficient decreased by approximately 0.10 from 5 km to 10 km grid scale. Additionally, precipitation (PRE) was the main factor controlling WESs changes in the PRDUA, explaining more than 50% of the changes in WESs, and the regression coefficients ranged from 0.0825 to 0.1584. Changes in WESs were the result of the combined effects of natural factors, including PRE, landscape pattern, elevation, slope, and socioeconomic factors, such as population and gross domestic product (GDP). Overall, these findings could contribute to optimizing regional landscape patterns and fostering sustainable development of the ecological environment in urban agglomerations.

Spatial Differentiation and Influencing Factors of Tertiary Industry in the Pearl River Delta Urban Agglomeration

The tertiary industry has become the main driving force for China’s economic development, and the adjustment and optimization of its structure are important prerequisites for achieving high-quality economic development. Existing studies have mostly focused on the spatial layout and influencing factors of the tertiary industry, with insufficient exploration of its internal structure. In this study, the PRD urban agglomeration is selected as the study area. On the basis of classifying the tertiary industry, the Dagum Gini coefficient, kernel density estimation, and local spatial autocorrelation are used to explore the spatial differentiation of various tertiary industries. The influencing factors are analyzed using geographical detectors, and suggestions for future development strategies are proposed. The results show that in terms of regional differentiation, the agglomeration of various tertiary industries in Guangzhou and Shenzhen is the most significant, but there is insufficient spillover to surrounding cities. In terms of development structure, the level of agglomeration of the consumptive tertiary industry is higher, the public tertiary industry tends to be more evenly distributed, and the productive tertiary industry is relatively dispersed. In terms of influencing factors, the interaction between population and employment dominates the spatial differentiation and evolution of the tertiary industry in the PRD urban agglomeration. Therefore, in the future, the tertiary industry in PRD urban agglomeration should promote the optimization of industrial structure and regional coordinated development under the guidance of the government.

How can the Pearl River Delta urban agglomeration achieve the carbon peak target: Based on the perspective of an optimal stable economic growth path

The Pearl River Delta (PRD) has high concentrations of energy consumption and carbon emissions, and the conflict between its economic development and environmental protection is relatively prominent. Beginning from the ideal path of economic growth, this study proposes a framework for carbon emission prediction under the optimal economic growth path of the PRD via the cross-analysis of low-carbon economic analysis and artificial intelligence prediction. First, the generalized Divisia index method (GDIM) is used to decompose carbon emissions to quantify the contribution rate of each influencing factor. Then, the genetic algorithm (GA) is introduced to optimize the backpropagation (BP) neural network to construct a GA-BP combined prediction model. Finally, the optimal economic growth rate models under different scenarios are constructed, and the carbon peak in the PRD under stable economic growth is accurately predicted. The results show that under the optimal economic growth path, the carbon peak in the PRD under the scenario with carbon constraints will be reached in 2029, while that under the scenario without carbon constraints will only begin to appear around 2035. Thus, the achievement of the carbon peak target in the PRD still requires insisting on stringent carbon emission reduction measures.

Unveiling the impact mechanism of urban resilience on carbon dioxide emissions of the Pearl River Delta urban agglomeration in China

With the increasingly severe global climate change situation, the construction of low-carbon and resilient cities is expected to be an effective means of accelerating the achievement of carbon peak and carbon neutrality goals. As a major global economic and carbon-emitting power, it is necessary to advocate for China to accelerate the construction of low-carbon and resilient cities. We have formulated a comprehensive methodological framework to appraise the effects of urban resilience (UR) on carbon dioxide (CO2) emissions. This study first constructs a “social-economic-institutional-ecological-engineering” UR assessment framework. Based on this framework, we establish an evaluation system for the UR of the urban agglomeration to explore the dynamic evolution of its spatial and temporal patterns. And then this study uses the SBM-DEA method that includes undesirable outputs to measure the carbon emission efficiency of the urban agglomeration. Finally, the spatial Durbin model is used to analyze the spatial spillover effects of UR on CO2 emissions, and the mechanism of CO2 emission spatial effects is theoretically explained by introducing geographical laws. The results show that the overall UR level in the region increased from 0.107 in 2000 to 0.318 in 2020, with an average carbon emission efficiency of 0.82, but still at a relatively low level. Moreover, there are significant differences in the regional level, with Guangzhou and Shenzhen and their surrounding cities showing significantly higher levels compared to other cities. Regarding the spillover effects analysis, CO2 emissions exhibit significant spatial spillover effects between cities. Improving UR can reduce urban CO2 emissions, but it may also have negative spillover effects on surrounding cities. This research can provide a new perspective for improving the study of regional CO2 emissions reduction and its influencing mechanisms while also offering important references for accelerating the construction of UR and achieving regional carbon peak targets.

Estimation of Economic Spillover Effects under the Hierarchical Structure of Urban Agglomeration Based on Time-Series Night-Time Lights: A Case Study of the Pearl River Delta, China

Urban agglomerations are becoming increasingly important in driving economic development in China. Accurate representation of the economic development status and spillover effects of cities within an urban agglomeration is the foundation of and an effective approach for promoting the coordinated development of that agglomeration. However, current studies of economic spillovers tend to focus on urban agglomerations as a whole, and there is a lack of scrutiny and validation of research data. Therefore, this study proposes a framework for detecting economic spillover effects within an urban agglomeration based on a prolonged night-time light dataset. Firstly, we explored the most suitable night-time light index to characterize the economic status. Then, we used this index to construct the economic network and hierarchical structure of the urban agglomeration. Finally, we explored the heterogeneity of spillover effects under the hierarchical structure. The results of a case study in the Pearl River Delta (PRD) urban agglomeration show that (1) the total night-time light in built-up areas (BNTL) has the highest Pearson correlation coefficient with GDP, which is 0.82; (2) there is an obvious hierarchical structure within the PRD; (3) there are significant and sustained economic spillover effects among the core cities, with Guangzhou–Foshan and Shenzhen–Dongguan having more obvious spillover effects; and (4) the economic spillover effects within the three metropolitan areas have different characteristics. The Guangzhou–Foshan–Zhaoqing metropolitan area is closely linked, to apparent differences in the pace of spillover effects. The Shenzhen–Dongguan–Huizhou metropolitan area has strong close linkages, with strong synchronization of spillover effects. The Zhuhai–Zhongshan–Jiangmen metropolitan area has not yet formed a stable synergistic development relationship. Overall, the framework can effectively reveal the hierarchical structure and different characteristics of economic spillovers within urban agglomerations, which can provide a scientific reference for policy making related to the coordinated development of such agglomerations.

Effect of Planning and Construction of Intercity Railways on the Economic Development of the Pearl River Delta Urban Agglomeration: An Analysis Based on the Spatial Durbin Model

Efficient transportation is essential for regional economic development. Currently, the Pearl River Delta urban agglomeration is accelerating the construction of intercity railways in the region to strengthen the links between cities. To provide a more detailed account, this study examined data spanning the years 2010–2021 across nine key cities in the Pearl River Delta. Employing a spatial Durbin model, we sought to uncover specific spatial patterns associated with intercity railways, particularly their pronounced impact on neighboring cities. Our quantitative findings, derived from the spatial Durbin model, revealed a statistically significant positive contribution of intercity railways to overall regional development. Contrary to initial concerns, the results indicate that intercity railways contribute significantly without exacerbating existing imbalances. However, according to the decomposition analysis of effects, most coefficients of the explanatory variables with a direct effect were found to be positive and significant, indicating their positive impact, while those with an indirect effect were found to be nonsignificant, suggesting that the effect of planning and construction of intercity railways on the economic development of the Pearl River Delta urban agglomeration is currently at a stage of factor concentration. In conclusion, our study emphasizes the need for prioritizing regional connections in intercity railway construction. This strategy can facilitate production factor flow and foster the proactive development of transportation infrastructure, thereby ensuring a balanced and sustainable regional economic growth trajectory.

Research on the Spatial Disparities and Convergence of Guangdong’s Urban Economy Based on Industrial Agglomeration and Industrial Proximity

Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and the Theil index to analyze the spatial distribution characteristics and regional disparities of urban economy. Then, a spatial econometric model was constructed to analyze the convergence and influencing factors of Guangdong’s urban economy. The results indicate that from 2006 to 2020, Guangdong’s urban economy grew rapidly and the degree of economic agglomeration gradually weakened, but its economic pattern always maintained the “Core-Edge” structural feature. The interval disparities between the Pearl River Delta Urban Agglomeration (PRD) and the edge area have always been greater than the intra-regional disparities, so they are main source of disparities in Guangdong. In Guangdong’s urban economy, σ-convergence and β-convergence coexist. The conditional β-convergence rate is 0.96~1.53%, and the half-life cycle is 45.4~72.36 years. Compared to the PRD, the economic disparities in the edge area are smaller but the convergence speed is faster and the half-life cycle is shorter. Both industrial agglomeration and industrial proximity have a significant impact on the economic convergence of Guangdong’s cities. Among them, industrial agglomeration has a positive impact, while industrial proximity has a negative impact. There is spatial heterogeneity in the impact of industries on economic development. Industrial agglomeration has a positive impact on the overall economic development of Guangdong, but it is not significant within the regions. Industrial proximity has significant negative externalities in the PRD region, and its impact is not significant in the edge area.

The driving factors of recreational utilization of ecological space in urban agglomerations: The perspective of urban political ecology

Understanding factors driving the recreational utilization of ecological space in urban agglomerations is crucial for the sustainable management of regional systems. However, existing literature focuses on the provision, distribution, and accessibility of urban green space at small scales, lacking explorations into ecological recreational spaces at an urban agglomeration scale. The current study explores the recreational utilization of ecological space in the Pearl River Delta Urban Agglomeration in China by using panel data regression and field investigations from the perspective of urban political ecology. The results show that the forest area proportion, natural reserve area proportion, fiscal revenue, and tourist numbers all have a positive effect on the recreational utilization rate of ecological space; the fixed assets investment exerts unstable prohibitive impacts on the RUoES rate, and the wetland area proportion presents a negative effect on the RUoES rate; land use rights and regional governance structure exacerbate the imbalanced distribution of the rate across different cities. The research contributes to the existing literature by researching urban ecological space from a recreational perspective and at a broader spatial scale, thereby promoting the theoretical integration of recreational geography and political ecology.

An urban metabolism framework purpose to assess the dynamics of linear alkylbenzenes (LABs) discharge flows and driving forces at the city level in Guangdong province, China

Linear alkylbenzenes (LABs) are a class of molecular markers derived from anthropogenic activities. A comprehensive understanding of the mechanism that determines their entry into anthroposphere, in terms of magnitude and pathway, is the prerequisite to establish effective mitigation measures. This study develops a methodology framework to analyze the source-sink interactions and driving factors of the direct and indirect LAB discharges from production and living activities in Guangdong Province, China from 2004 to 2017. Results indicated that the total LAB discharges of Guangdong into the environment were averaged at 2.9 kt yr−1, of which 61.9% originated from the Pearl River Delta (PRD) urban agglomeration. An average proportion of 76.0% was discharged into water bodies with the remaining released into land bodied. From 2014 to 2017, the LAB discharges increased by seven times, resulting from the steady increase of urban residential sources, while contribution from industrial sources continuously declined during the studied period. Meanwhile, the discharging hotspots expanded from Guangzhou city to other super-cities around it, including Shenzhen and Dongguan. The other cities exhibited a decreasing trend in discharges as a function of distance from these hotspot cities. The multisectoral sources of LABs differed considerably among cities, and the source contribution of each city changed significantly with progressive urbanization. The factor decomposition analysis indicated that LAB discharges in PRD cities primarily contributed by the pollutant concentration and reflected the treatment structure, while peripheral cities of the PRD mainly contributed by the per capita consumption and pollutant discharge per unit of GDP. Overall, our results provide a scientific database and supports for the regional co-remediation of anthropogenic pollution.

Unleashing the convergence between SDG 9 and SDG 8 towards pursuing SDGs: Evidence from two urban agglomerations in China during the 13th five-year plan

Cities play a pivotal role in China's sustainable economic progress, serving as focal points for various developmental elements. This study offers a novel perspective, delving into two crucial dimensions of urban advancement: industrial infrastructure and economic growth. This research assesses the sustainable development efficiency of 36 cities within the Yangtze River Delta urban agglomeration (YRDUA) and the Pearl River Delta urban agglomeration (PRDUA), using specific indicators aligned with SDG9 and SDG8. To conduct this evaluation, the study employs the Meta-Frontier Window Network DDF Data Envelopment Analysis considering both the stability and enhancements in urban sustainable development efficiency over a five-year policy span. The key findings are as follows: 1) Urbanization emerges as a driver of sustainable development efficiency. However, while Shenzhen, Dongguan, and Zhuhai have attained optimal levels, their internal efficiency stability remains a concern; 2) During the 13th Five-Year Plan, Nantong, Changzhou, and Wuxi showcased significant performance improvements. In contrast, Jiangmen experienced a notable decline of 64.72%; 3) Focusing on subsystems, the Resilient Industrial and Infrastructure Subsystem displays superior efficiency compared to the Sustainable Economic Growth Subsystem. Yet, it's essential to note that neither subsystem has achieved optimal efficiency across most regions; 4) Geographically, the urban sustainable efficiency of YRDUA appears evenly distributed, with ample room for improvement in most cities. In contrast, PRDUA demonstrates an efficiency increase from its periphery to its core. Through this comprehensive exploration, the study provides valuable insights into the intricate relationship between urbanization, sustainable development efficiency, and regional disparities.