Pearl River Delta Urban Agglomeration Research Articles

Measurement and Analysis of Carbon Emission Efficiency in the Three Urban Agglomerations of China

China aims to reduce its carbon emissions to achieve carbon peaking and neutrality. Measuring the carbon emission efficiency of three urban agglomerations in China, exploring their spatiotemporal characteristics, and investigating the main influencing factors are crucial for achieving regional sustainable development and dual carbon goals. Using the super-slack-based measurement (super-SBM) model, we calculated the carbon emission efficiency of the Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2011 to 2021 and explored the spatiotemporal non-equilibrium characteristics of carbon emission efficiency and its influencing factors. The results indicated that: (1) Overall, the carbon emission efficiency showed an N-type trend, with the PRD having the highest average efficiency. Regional differences between the YRD and BTH regions gradually increased. (2) The efficiency hotspots shifted from the PRD to the YRD, whereas the cold spots were mainly concentrated in the BTH region. The variation in the standard deviation ellipse radius of carbon emission efficiency in the urban agglomerations was clear, and the spatial disequilibrium was significant. (3) Economic level and opening up had positive impacts on carbon emission efficiency, whereas energy intensity and industrial structure had negative impacts. The effects of population size, government intervention, and technological level varied among the regions.

Analysis of Spatiotemporal Predictions and Drivers of Carbon Storage in the Pearl River Delta Urban Agglomeration via the PLUS-InVEST-GeoDetector Model

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA.

Spatiotemporal Evolution and Influencing Factors of Coupling and Coordination between the Ecosystem Service Value and Economy in the Pearl River Delta Urban Agglomeration of China

In the context of pursuing high-quality development, the coupling and coordination of the ecosystem and economy has become the fundamental goal and inevitable choice for achieving the sustainable development of urban agglomerations. Based on remote sensing and statistical data for the Pearl River Delta (PRD) region from 2005 to 2020, in this paper, we construct an index system of the ecological and economic levels to assess the ecosystem service value (ESV). We use the equivalent factor method, entropy method, coupling coordination model, and relative development model to systematically grasp the spatial pattern of the levels of the two variables, analyse and evaluate their spatial and temporal coupling and coordination characteristics, and test the factors influencing their coupling and coordination using the geographical and temporal weighted regression (GTWR) model. The results show that ① the ESV in the PRD exhibited a fluctuating decreasing trend, while the level of the economy exhibited a fluctuating increasing trend; ② the coordination degree of the ESV and economy in the PRD exhibited a fluctuating increasing trend, and the region began to enter the basic coordination period in 2007; ③ in terms of the spatial distribution of the coordination degree, there was generally a circular pattern, with the Pearl River Estuary cities as the core and a decrease in the value towards the periphery; ④ the coordinated development model is divided into balanced development, economic guidance, and ESV guidance, among which balanced development is the major type; ⑤ the results of the GTWR reveal that the influencing factors exhibited significant spatial–temporal heterogeneity. Government intervention and openness were the dominant factors affecting the coordination, and the normalised difference vegetation index was the main negative influencing factor.

Analysis of disequilibrium and driving factors of carbon emission efficiency: Evidence from five major urban agglomerations in China

Urban agglomerations (UAs) are not only highly integrated economic communities but also communities of shared destiny with integrated environmental protection and ecological construction. Although low-carbon development within UAs has garnered significant attention, a lack of comprehensive analysis remains regarding the imbalances and driving factors influencing carbon emission efficiency (CEE) in these regions. This study investigates the CEE of China's five national UAs from 2006 to 2020 by incorporating the meta-frontier framework into the super slack-based model. The global Malmquist index analyzes dynamic trends in CEE by decomposing the total factor productivity of carbon emissions to examine the catch-up, innovation, and technological leadership effects. In addition, this paper employs the Theil index to explore the disequilibrium of CEE among the five UAs. Outcomes reveal that, first, CEE has regional heterogeneity, with the Pearl River Delta UA (PRDUA) having the highest CEE and the Beijing–Tianjin–Hebei UA (BTHUA) having the lowest CEE. The gap between BTHUA's CEE and that of other UAs is widening. Second, the overall CEE holds improvement room, with its disequilibrium resulting from uneven city growth within the UA. Third, the innovation effect is the key to improving CEE. Fourth, PRDUA serves as the technical leader among all UAs, while the two UAs in the inland area possess the potential for technical leadership. Finally, drawing from the empirical results, this paper provides specific suggestions for advancing industrial transformation, boosting innovation capabilities, and minimizing internal imbalances within UAs. The goal is to steer UAs in China toward low-carbon development.

Changing patterns and determinants of China's interprovincial migration: Evidence from the 2020 census

AbstractInterprovincial migration in China has changed in recent years due to the socioeconomic transformation. Yet, less attention has been paid to the current spatial patterns of interprovincial migration, especially the spatial heterogeneity of the determinants. Based on the newly released 2020 census data, this study explored the spatial patterns and determinants of interprovincial migration in China using a spatial analysis and geographically weighted regression. The results revealed multiple trends in interprovincial migration, including flow from developing provinces in the central region to eastern and western regions and returning flow from developed to developing provinces in the central and western regions. The Beijing‐Tianjin‐Hebei, Yangtze River Delta, and Pearl River Delta urban agglomerations remained centres of population inflow. Moreover, key western development areas, such as the Chengdu‐Chongqing area and Xinjiang, emerged as centres of population inflow. Economic and social development, social safety, and living environment significantly and positively affected interprovincial migration. Furthermore, significant spatial heterogeneity in factors influencing migration was detected. The findings provide a systematic understanding of China's interprovincial migration in 2020 and the spatial heterogeneity of determinants can guide the population development strategy design of governments.

Identification of Spatial Distribution of Afforestation, Reforestation, and Deforestation and Their Impacts on Local Land Surface Temperature in Yangtze River Delta and Pearl River Delta Urban Agglomerations of China

Forest change affects local and global climate by altering the physical properties of the land surface. Accurately assessing urban forest changes in local land surface temperature (LST) is a scientific and crucial strategy for mitigating regional climate change. Despite this, few studies have attempted to accurately characterize the spatial and temporal pattern of afforestation, reforestation, and deforestation to optimize their effects on surface temperature. We used the China Land Cover Dataset and knowledge criterion-based spatial analysis model to map urban forestation (e.g., afforestation and reforestation) and deforestation. We then analyzed the impacts of these activities on LST from 2010 to 2020 based on the moving window strategy and the spatial–temporal pattern change analysis method in the urban agglomerations of the Yangtze River Delta (YRD) and Pearl River Delta (PRD), China. The results showed that forest areas declined in both regions. Most years, the annual deforestation area is greater than the yearly afforestation areas. Afforestation and reforestation had cooling effects of −0.24 ± 0.19 °C and −0.47 ± 0.15 °C in YRD and −0.46 ± 0.10 °C and −0.86 ± 0.11 °C in PRD. Deforestation and conversion of afforestation to non-forests led to cooling effects in YRD and warming effects of 1.08 ± 0.08 °C and 0.43 ± 0.19 °C in PRD. The cooling effect of forests is more evident in PRD than in YRD, and it is predominantly caused by reforestation. Moreover, forests demonstrated a significant seasonal cooling effect, except for December in YRD. Two deforestation activities exhibited seasonal warming impacts in PRD, mainly induced by deforestation, while there were inconsistent effects in YRD. Overall, this study provides practical data and decision-making support for rational urban forest management and climate benefit maximization, empowering policymakers and urban planners to make informed decisions for the benefit of their communities.

Estimation Model and Spatio-Temporal Analysis of Carbon Emissions from Energy Consumption with NPP-VIIRS-like Nighttime Light Images: A Case Study in the Pearl River Delta Urban Agglomeration of China

Urbanization is growing at a rapid pace, and this is being reflected in the rising energy consumption from fossil fuels, which is contributing significantly to greenhouse gas impacts and carbon emissions (CE). Aiming at the problems of the time delay, inconsistency, uneven spatial coverage scale, and low precision of the current regional carbon emissions from energy consumption accounting statistics, this study builds a precise model for estimating the carbon emissions from regional energy consumption and analyzes the spatio-temporal characteristics. Firstly, in order to estimate the carbon emissions resulting from energy consumption, a fixed effects model was built using data on province energy consumption and NPP-VIIRS-like nighttime lighting data. Secondly, the PRD urban agglomeration was selected as the case study area to estimate the carbon emissions from 2012 to 2020 and predict the carbon emissions from 2021 to 2023. Then, their multi-scale spatial and temporal distribution characteristics were analyzed through trends and hotspots. Lastly, the influence factors of CE from 2012 to 2020 were examined with the OLS, GWR, GTWR, and MGWR models, as well as a ridge regression to enhance the MGWR model. The findings indicate that, from 2012 to 2020, the carbon emissions in the PRD urban agglomeration were characterized by the non-equilibrium feature of “high in the middle and low at both ends”; from 2021 to 2023, the central and eastern regions saw the majority of its high carbon emission areas, the east saw the region with the highest rate of growth, the east and the periphery of the high value area were home to the area of medium values, while the southern, central, and northern regions were home to the low value areas; carbon emissions were positively impacted by population, economics, land area, and energy, and they were negatively impacted by science, technology, and environmental factors. This study could provide technical support for the long-term time-series monitoring and remote sensing inversion of the carbon emissions from energy consumption in large-scale, complex urban agglomerations.

Analysis of spatial-temporal evolution and investment model of industrial cross-regional investment network-taking the Pearl River Delta urban agglomeration as an example

Cross-regional investment behavior plays a pivotal role in enterprise development and regional economic growth. Nonetheless, existing investment network analyses often adopt a broad perspective, most studies tend to invest in network models and focus on a particular industry, and pay less attention to investment models in different industries. Therefore, this paper will combine the dual perspectives of space and network to observe the spatial and temporal evolution characteristics of enterprise investment and its investment model. Additionally, it delves into the unique features of investment networks within various industries at the district and county levels. The results show that enterprise investment agglomeration exhibits a development pattern characterized by points, bands, and surfaces, with a notable distance attenuation trend in investment distances. The overall structure of the industry investment network showcases characteristics of being multi-centered and multi-nodal. The investment models across different industries can be categorized into four distinct types. The research conclusion is of great significance to optimize multi-scale intercity investment, realize industrial gradient transfer and promote the coordinated development of industry.

Research on the correlation network of carbon emissions and economic between Chinese urban agglomerations

Urban agglomerations are currently at the center of China's efforts to reduce carbon emission and sustainable economic development. It is critical to investigate the carbon emissions and economic correlation between urban agglomerations in order to assist their carbon emission reduction and economic coordinated development. This paper is based on an environmentally extended multi-regional input-output model, and studies the embodied carbon emissions and economic two-dimensional correlation network between major urban agglomerations in China from 2012 to 2017. The research results indicate that the production-based carbon emissions of the production and supply of electricity and heat industry in the Yangtze River Delta urban agglomeration were higher than the consumption-based carbon emissions, with a significant gap. However, the construction and service industries were the opposite. The Hohhot-Baotou-Ordos-Yulin urban agglomeration experienced carbon emission losses and economic benefits. The Yangtze River Delta urban agglomeration achieved a win-win situation for carbon emissions and economy. The Pearl River Delta urban agglomeration benefited from carbon emissions but suffered economic losses. The Central Plains urban agglomeration faced a loss-loss situation for carbon emissions and economy. Major urban agglomerations in China should implement differentiated emission reduction measures based on their own characteristics. For example, the Hohhot-Baotou-Ordos-Yulin urban agglomeration should utilize natural resources to develop green industries and high value added industries.

Impacts of forest cover change on local temperature in Yangtze River Delta and Pearl River Delta urban agglomerations of China

The continuous economic and ecological construction in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) has caused frequent temporal and spatial changes in local forests, thus affecting the regional climate. Yet few studies have addressed the temperature feedback through biophysical mechanisms due to forest change in two urban agglomerations of China. We compared MODIS and Landsat-based land cover data to detect a more accurate forest cover change. We then used the moving window strategy and spatiotemporal pattern change analysis method to quantify and compare the actual impact of forest cover change on temperature and the differences in driving factors (e.g., evapotranspiration (ET), albedo, and precipitation) from 2010 to 2020. The results showed that Landsat-based land cover data performed well. The conversion from forest to cropland was dominated in YRD and PRD, followed by the conversion of cropland to forest, with a small proportion of forest converting to impervious surface. The afforested areas in the two regions showed a diurnal cooling effect (-0.18 ± 0.07 °C and -0.10 ± 0.13 °C, respectively), which was greater than the air temperature. Forest converting to impervious surfaces led to stronger warming (0.39 ± 0.37 °C in YRD) than that of cropland (0.05 ± 0.03 °C in YRD and 0.07 ± 0.06 °C in PRD). The daytime LST variations can be explained by ET and inconsistent albedo effects. Seasonally, the cooling effects induced by afforestation predominated during the growing season (spring and summer), accompanied by the relatively high ET. This study shows that rational afforestation and control of deforestation are helpful to achieve sustainable forest management in urban agglomerations and to regulate climate warming.

Material metabolism and associated environmental impacts in Pearl River Delta urban agglomeration

AbstractRapid urbanization has resulted in significant bulk materials use, raising concerns over associated environmental impacts and sustainability challenges. However, a significant gap remains in the city‐level analysis of bulk materials production, use, and associated environmental impacts in China. This study calculated the stocks and flows of 13 bulk materials and their associated greenhouse gas (GHG) emissions across nine cities in the Pearl River Delta urban agglomeration (PRDUA) of China during 2000–2020. Results showed that total and per‐capita material stocks within the PRDUA experienced a continuous increase, with an average annual growth rate of 0.5 Gt/year and 4.4 t/cap/year, respectively. Both material stocks and flows exhibited similar spatial distribution patterns that gradually decreased from the center to the perimeter. As stocks continuously increase, GHG emissions from material production were rising annually, reaching 187.2 Mt CO2e in 2020. While recycling end‐of‐life materials contributes to reducing GHG emissions, the current limited mass of recycling curtails its broader impacts. This situation highlights a significant untapped potential within the city to meet decarbonization goals. To maximize the carbon reduction benefits, it is essential to enhance recycling efforts. Moreover, it is crucial that recycling strategies are specifically tailored to suit the timing, location, and types of materials involved.

Spatial Distribution Characteristics and Driving Factors of Little Giant Enterprises in China’s Megacity Clusters Based on Random Forest and MGWR

As a representative of potential “hidden champions”, a concept originating in Germany, specialized and innovative Little Giant Enterprises (LGEs) have become exemplary models for small and medium-sized enterprises (SMEs) in China. These enterprises are regarded as crucial support for realizing the strategy of building a strong manufacturing country and addressing the weaknesses in key industrial areas. This paper begins by examining urban agglomerations, which serve as the main spatial carriers for industrial restructuring and high-quality development in manufacturing. Based on data from LGEs in the Yangtze River Delta (YRD) and Pearl River Delta (PRD) urban agglomerations from 2019 to 2023, the study employs the Random Forest (RF) and Multi-scale Geographically Weighted Regression (MGWR) methods to conduct a comparative analysis of their spatial patterns and influencing factors. The results are as follows: (1) LGEs exhibit spatial clustering in both the YRD and PRD regions. Enterprises in the YRD form a “one-axis-three-core” pattern within a distance of 65 km, while enterprises in the PRD present a “single-axis” pattern within a distance of 30 km, with overall high clustering intensity. (2) The YRD is dominated by traditional manufacturing and supplemented by high-tech services. In contrast, the PRD has a balanced development of high-tech manufacturing and services. Enterprises in different industries are generally characterized by a “multi-point clustering” characteristic, of which the YRD displays a multi-patch distribution and the PRD a point–pole distribution. (3) Factors such as industrial structure, industrial platforms, and logistics levels significantly affect enterprise clustering and exhibit scale effects differences between the two urban clusters. Factors such as industrial platforms, logistics levels, and dependence on foreign trade show positive impacts, while government fiscal expenditure shows a negative impact. Natural geographical location factors exhibit opposite effects in the two regions but are not the primary determinants of enterprise distribution. Each region should leverage its own strengths, improve urban coordination and communication mechanisms within the urban cluster, strengthen the coordination and linkage of the manufacturing industry chain upstream and downstream, and promote high-tech industries, thereby enhancing economic resilience and regional competitiveness.

A graph-factor-based random forest model for assessing and predicting carbon emission patterns - Pearl River Delta urban agglomeration

As China actively fulfills its emissions reduction obligations to meet the Paris climate goals, it is crucial to explore carbon reduction policies at the city level, given the important role and potential of cities in China's emissions reduction efforts. In this study, a comprehensive assessment and prediction of carbon emission patterns in the Pearl River Delta urban agglomeration was conducted by developing an integrated model that incorporates graph representation learning, factorial analysis, and random forest methods. The main findings are (1) there is significant heterogeneity in carbon emissions across industries and across time and space; (2) carbon clusters can more accurately characterize the flow of carbon emissions than carbon supply chains; (3) the nonmetallic manufacture, electricity supply and transportation industries play a decisive role in carbon emission reduction; and (4) the prediction results show that the carbon emissions of the Pearl River Delta urban agglomeration will reach 349.2 million tons in 2021, and will then show a declining trend year by year, dropping to a projected 179.8 million tons in 2035. Based on the above findings, it is recommended that the monitoring of key carbon emission clusters should be strengthened, and the monitoring data should be utilized to establish a cross-city and cross-industry cooperation mechanism for emission reduction. In addition, a clear set of action plans and strategies should be formulated and implemented to ensure that the carbon emission targets for 2035 are realized.

The power of patent transfer: The impact of green technology acquisition on non-residential CO2 emissions under the intervention of government actions

Green technology acquisition solves the mismatch of innovation elements through transactions and realizes the rapid and reasonable allocation of green patents among enterprises with different technology levels. However, whether green technology acquisition can effectively reduce carbon emissions has not yet received the attention it deserves. Based on data of 153 Chinese cities from 2011 to 2019, this paper explores the impact direction and mechanism of green technology acquisition on non-residential CO2 emissions, and analyzes the moderating role of government actions in this process. The study finds that: (1) Green technology acquisition in China has formed a multi-core spatial distribution pattern based on urban agglomerations, among which the Yangtze River Delta, Beijing-Tianjin-Hebei and Pearl River Delta urban agglomerations constitute the main agglomeration areas for green technology acquisition. (2) Green technology acquisition can significantly reduce non-residential CO2 emissions, which is mainly achieved through the cleaner production effect and R&D innovation effect. (3) In terms of government actions, low-carbon advocacy can effectively moderate the mediating role of the cleaner production effect between green technology acquisition and non-residential CO2 emissions, and environmental incentives and environmental penalties can effectively moderate the mediating role of the R&D innovation effect between green technology acquisition and non-residential CO2 emissions, but the moderating paths are different. (4) There is a threshold effect of intellectual property protection in the impact of green technology acquisition on non-residential CO2 emissions. Only when the degree of intellectual property protection exceeds a certain threshold, green technology acquisition can effectively reduce non-residential CO2 emissions. This paper not only explains the key value of green technology for low-carbon development from the perspective of knowledge flow, but also provides a theoretical reference for the rational matching of government actions under the carbon neutrality target.

Identifying the Spatial Range of the Pearl River Delta Urban Agglomeration by Fusing Nighttime Light Data with Weibo Sign-In Data

Accurately identifying the spatial range of urban agglomerations holds significant practical importance for the precise allocation of various elements and coordinated development within urban agglomerations. However, current research predominantly focuses on the physical spaces of urban agglomerations, overlooking their sphere of influence. This study begins with the spatial interactions of population elements within urban agglomerations and fuses Weibo sign-in data with NTL data to identify the spatial range of urban agglomerations. It further compares and validates the results before and after the fusion of data. The results reveal that the accuracy of identifying the spatial range of urban agglomerations with the fusion of NTL data and Weibo sign-in data has improved by 7%, with a Kappa increase of 0.1766 compared to using NTL data alone, which indicates that fusing social media data can significantly enhance the accuracy of identifying the spatial range of urban agglomerations. This study proposes a novel approach for identifying the spatial range of urban agglomerations through the fusion of NTL data and social media data from a data fusion perspective. On one hand, it supplements the application of data fusion in the study of urban agglomeration spaces; on the other hand, it accurately identifies the spatial range of urban agglomerations, which holds great practical value for the sustainable development of urban agglomerations.

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.

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.

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.