Evolution Of Spatial Patterns Research Articles

Assessing spatiotemporal variation in dry-wet abrupt alternation using an improved process-based approach

Study regionThe Pearl River Basin (PRB), located in the south of China, has a tropical to subtropical climate. Study focusIn this study, we present an improved process-based approach to assess the intensity of the Dry-Wet Abrupt Alternation (DWAA) events, considering both the transition gap (alternation) and the transition rate (urgency) between dry and wet states. Using daily precipitation data from 1961 to 2020 across 75 meteorological stations in the PRB, we analyze the temporal and spatial evolution patterns of DWAA events. New hydrological insights for the regionThe findings reveal that: (1) DWAA events in the western region generally occur less frequently but with higher intensity. (2) Dry-to-wet (D-W) events have an urgency intensity of 1.35, almost twice that of wet-to-dry (W-D) events at 0.69. However, the alternation intensity of W-D is only slightly higher by 0.17 compared to D-W. (3) The alternation and urgency changes in D-W events are asynchronous, with alternation intensity initially decreasing from 9.7 to 9.27, then increasing to 9.58, while urgency decreases continuously from 1.66 to 1.07. (4) The alternation and urgency changes in W-D events are synchronous. The alternation intensity initially decreases from 9.7 to 9.06, then rises to 10.1, while the urgency intensity decreases from 0.68 to 0.58 before subsequently climbing to 0.8.

Nonlinear Response of Summertime Synoptic-Scale Disturbance Intensity over the Tropical Western North Pacific to ENSO Amplitude

Abstract El Niño–Southern Oscillation (ENSO) exhibits nonlinearity in its amplitude and impacts. This study investigates the dependence of summertime synoptic-scale disturbance (SSD) intensity over the tropical western North Pacific (TWNP) on the ENSO amplitude. A tendency of nonlinearity exists in the observed response of the TWNP SSD intensity to the amplitude of tropical central-eastern Pacific (CEP) sea surface temperature (SST) anomalies in boreal summer. Numerical experiments are conducted with an atmospheric general circulation model with linearly varying tropical CEP SST anomalies imposed to illustrate the nonlinearity exclusively induced by changes in the ENSO amplitude. A linear increase in the amplitude of El Niño–like SST anomalies results in a nonlinear enhancement of SSD intensity over the TWNP, manifested as the increase in SSD intensity at a rate larger than expected by linear response with an eastward shift. This is attributed to the nonlinear intensification of anomalous ascent over the TWNP induced by tropical convection response to positive tropical CEP SST anomalies and the nonlinear effect of anomalous convection on the synoptic-scale activity. In contrast, as La Niña–like SST anomalies increase linearly, the SSD intensity over the TWNP decreases at a rate slower than expected from a linear response and even reaches saturation with little longitudinal shift. Due to the thermodynamic control on the occurrence of deep convection in the tropics, enhanced negative SST anomalies do not induce additional changes in anomalous descent over the tropical CEP. Thus, the TWNP SSD intensity no longer decreases with further increase in tropical CEP cold SST anomalies. Significance Statement Synoptic-scale disturbances (SSDs) over the tropical western North Pacific (TWNP) play an important role in weather and climate over East and Southeast Asia. Impacts of those SSDs are contingent on their intensity. El Niño–Southern Oscillation (ENSO) has substantial impacts on weather and climate worldwide, including the SSDs over the TWNP. ENSO displays a diversity in amplitude, spatial pattern, and temporal evolution. The present study investigates the response of the TWNP SSD intensity to varying ENSO amplitude during boreal summer and reveals a distinctive nonlinear response of the TWNP SSD intensity to the amplitude of El Niño and La Niña, which has important implication for understanding the impacts of ENSO on climate over the TWNP and Asia.

Research on the temporal and spatial pattern evolution characteristics and decoupling effect of electricity power consumption in China

Exploring the evolution of spatial and temporal patterns of electricity power consumption in China is of great significance and value for the promotion of Carbon Peaking and Carbon Neutrality Strategy and the scientific allocation of electricity resources. Therefore, this paper takes China as an example and innovatively applies nighttime light data from a combination of two remote sensing satellites. And using spatial analysis methods to analyze the evolution of spatial and temporal patterns of electricity power consumption over a longer study time series. Meanwhile, this paper also uses the Tapio decoupling model to study the decoupling effect of electricity power consumption and economic growth in China. The results of the study show that, during the period 2000–2019, China's electricity power consumption was in an unbalanced state, with the eastern coastal region dominating. It also formed a gradient distribution from the coastal area to the inland area. In terms of spatial connection, it shows an unbalanced distribution pattern of “low in the west and high in the east”. In addition, the spatial linkage of electricity power consumption among provinces is more than 93 % of weak linkage, and gradually increases to 97 %, indicating that the spatial linkage of electricity power consumption among regions is gradually weakening. The decoupling state of electricity power consumption in China is characterized by two types of decoupling: Weak decoupling and Expansive coupling. The decoupling status has experienced a significant change from 84 % of provinces being Expansive coupling to 100 % of provinces being Weak decoupling.

Analysis of the spatial–temporal evolution and driving factors of carbon emission efficiency in the Yangtze River economic Belt

As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial–temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial–temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor’s effecting intensity and impacting direction, as well as the dual-factors’ interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006–2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of “downstream areas > midstream areas > upstream areas”, “high in the east and low in the west”, “hot in the east and cold in the west”, while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.

The Spatial Pattern of Polluting Enterprises and the Effects of Local Regulation in the Guanzhong Plain Urban Agglomeration

In the context of actively undertaking the transfer of domestic and foreign industries in the central and western regions of China, local regulatory behaviors influence the spatial distribution of polluting enterprises (PEs). This study examined the Guanzhong Plain urban agglomeration (GPUA), the largest urban agglomeration in the northwest region of China and one of the main regions that undertakes industrial transfer, using kernel density estimation and geographically and temporally weighted regression to explore the spatial pattern characteristics and evolution of PEs and reveal the effects of local regulatory behaviors, including environmental regulation (ER) and local protection (LP). The results indicate that (1) The distribution of the PEs tended towards energy and mineral resources and agglomerated along the development axes, aligning with the strategic positioning of the major function-oriented zones. Agglomerated areas gradually concentrate in key development zones. Major agricultural production zones exit high-density areas, and those adjacent to high-density areas often become secondary agglomeration core areas. Key ecological functional zones do not form high-density areas. (2) Both ER and LP have a positive impact on the distribution of PEs, and the dominant influence shifts from ER to LP. Counties with strict ER have increased the tendency of PEs to exhibit a certain layout due to better pollution treatment facilities and more mature pollution control technologies. The “pollution haven effect” has not yet formed within the GPUA. (3) The role of LP was more prominent in key development zones and major agricultural production zones, whereas the role of ER was more evident in key ecological functional zones. (4) RE and LP have mutually reinforcing effects on the distribution of PEs; the “innovation compensation effect” gradually manifests, but an increase in ER leads to a decrease in regional industrial clustering. This study provides a reference value for understanding the impact of government regulation on the distribution of PEs in underdeveloped areas.

Spatial and Temporal Assessment of Baseflow Based on Monthly Water Balance Modeling and Baseflow Separation

Baseflow is the part of streamflow that is mainly replenished by groundwater. The protection of the biological environment and the growth of its water resources greatly depend on the spatial and temporal evolution of baseflow. Therefore, the Baizhiao (BZA) and Shaduan (SD) catchments of the Jiaojiang River Basin (JRB) in the Zhejiang province of China were selected as study areas. The ABCD model and Eckhardt method were used to calculate baseflow and baseflow index (BFI). The temporal and spatial evolution patterns of baseflow were analyzed through statistical analysis and the Mann–Kendall test. The results showed that the ABCD model performs well in simulating overall hydrological processes on the monthly streamflow at BAZ and SD stations with NSE (Nash–Sutcliffe Efficiency) values of 0.82 and 0.83 and Pbias (Percentage Bias) values of 9.2% and 8.61%, respectively. The spatial–temporal distribution of the BFI indicates the higher baseflow contribution in upstream areas compared to downstream areas at both stations. The baseflow and BFI had significant upward trends at the BZA and SD stations in the dry season, while their trends were not uniform during the wet period. These findings are essential guidance for water resource management in the JRB regions.

The spatial pattern and association network of green finance development: Empirical evidence from China

AbstractGreen finance is a market‐driven approach to achieve the “double carbon” goal. However, the existing research predominantly focuses on the connotations and empowerment aspects of green finance. Notable regional disparities exist in China's financial markets and resource endowments, resulting in uneven levels of green finance development among various areas, significantly impeding the overall green transformation of the economy and society. In light of this, our paper explores the spatial patterns and association network of green finance development in China. The research findings indicate that: (1) The development of green finance in China exhibits an “east high, west low” spatial distribution pattern, with significant spatial clustering observed among neighboring provinces. The eastern region displays a “H‐H” clustering, while the western region shows an “L‐L” clustering. (2) The spatial correlation distribution of provincial green finance in China demonstrates an eastward concentration and westward dispersion trend, with strong correlation areas gradually expanding. Notably, Jiangsu, Zhejiang, Shanghai, and the Beijing‐Tianjin‐Hebei urban agglomerations lead nationally in attractiveness. (3) The external radiation capability of provincial green finance is progressively improving, with provinces such as Beijing, Hebei, Hubei, and Sichuan likely to occupy strategic structural positions, possessing greater development advantages. This study not only expands the theoretical scope of existing green finance research but also provides a practical reference for coordinating and achieving the high‐quality development of regional green finance from a practical perspective.

Trends and Factors Influencing the Evolution of Spatial Patterns of Cropland toward Large-Scale Agricultural Production in China

Considering the essential expansion of agricultural production, current research primarily focuses on static factors, such as the distribution of fine-grained arable land, omitting an in-depth analysis of its developmental dynamics and key drivers. Addressing this knowledge gap is crucial for enhancing the scalability of agricultural production. This research utilizes landscape ecology techniques, correlation analysis, random forest algorithms, and structural equation modeling to explore spatial pattern trends of arable land in the Beijing–Tianjin–Hebei region. Its objective is to clarify how the expansion of agricultural production scale affects food production through changes in arable land patterns and to determine the impact of socio-economic factors on these configurations. The results show that: (1) the landscape pattern of arable land is transitioning to a more fragmented arrangement with complex contours, (2) grain yield per unit area correlates positively with the landscape pattern index in Beijing, negatively in Hebei, and exhibits no significant correlation in Tianjin, and (3) land ownership plays a crucial role in land fragmentation, alterations in land morphology, and influences other socio-economic variables. Analyzing the spatial pattern of arable land in conjunction with socio-economic factors is essential for developing holistic land management approaches, improving resource efficiency, minimizing external inputs, and mitigating food security challenges.

Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables.

Spatial evolution pattern of tourism flow in China: case study of the May Day Holiday based on Baidu migration data

ABSTRACT Tourism flows are an elementary part of mobility and an important topic in tourism research. Utilising Baidu migration data, we model tourism flows during China’s May Day holiday from 2019 to 2023. Employing hotspot analysis and the differential index for tourism flow methods, we scrutinise the spatial and temporal dynamics of tourism flows in China before, during, and after the COVID-19 pandemic. The findings are as follows: (1) Regarding temporal variations, the COVID-19 pandemic has profoundly impacted China’s tourism industry, albeit with a slow recovery currently underway. (2) Spatially, tourism flows primarily concentrated within four major city clusters, with emerging tourism destinations like Zibo gradually gaining prominence. (3) Analysing changes in tourism flow during holidays reveals that cities can be categorised into four major types and six minor types, with a predominant trend of continuous decrease in tourism flow. These findings shed light on the intricate dynamics of tourism flows in China, offering valuable insights for stakeholders in the tourism sector.

Spatial and Temporal Evolution Patterns of Droughts in China over the Past 61 Years Based on the Standardized Precipitation Evapotranspiration Index

Based on the data of 2254 daily meteorological stations in China from 1961 to 2021, this study calculated the standardized precipitation evapotranspiration index (SPEI) of the national multi-time scale by using the FAO Penman–Monteith model to quantify the changes in dry and wet conditions. The Mann–Kendall mutation test, wavelet analysis, and other methods were used to study the spatial pattern and temporal evolution of drought. The results showed: (1) In the past 61 years, there were obvious spatial and temporal differences in drought in China, and the interannual variation in drought severity in SPEI-1, SPEI-3, and SPEI-12 gradually decreased at a rate of 0.005/10a, 0.021/10a, and 0.092/10a, respectively. (2) The time point of dry and wet mutation was 1989 according to the MK mutagenicity test. (3) Wavelet analysis showed that the drought cycle on the annual scale and the seasonal scale was consistent, and the main period was about 30 years. (4) In the past 61 years, the drought intensity of different degrees in China has shown a weakening trend, and the drought intensity reached the highest value in 61 years in 1978, at 1836.42. In 2020, the drought intensity was the lowest, at 261.55. (5) The proportion of drought stations has shown a decreasing trend. The proportion of drought-free stations has fluctuated greatly, ranging from 42.12% to 89.25%, with 2020 being the highest. This study provides a scientific basis for further research on the causes and coping strategies of drought and is of great significance for strengthening China’s drought monitoring, early warning ,and adaptation capabilities.

Study of the Development Potential of Geospatial-Based PSEL (Waste Processing into Electrical Energy) Areas

Since 1989, most of the waste processing in DKI Jakarta Province has ended at the Bantar Gebang TPST, causing the available capacity to accommodate waste to approach the maximum limit. One alternative processing that has a high level of efficiency in reducing the volume of waste is the PSEL (Waste to Electrical Energy Management) facility. Planning PSEL facilities in urban areas requires consideration from all aspects. While the space utilization in DKI Jakarta is quite dense and there are no practical guidelines in determining the location of PSEL facilities, it is necessary to conduct a comprehensive study, namely a study of the potential development of PSEL areas. The purpose of this study is to obtain the location and spatial pattern of the potential development of the PSEL area and obtain the characteristics of the area with the 'Recommended' category as the location of PSEL facility development. This study uses geospatial analysis with the quantitative Weighted Overlay method, where geospatial modeling is carried out by inputting the weight of each criterion (aspects that have an influence on determining the location of PSEL facilities) obtained from the results of the Fuzzy Analytic Hierarchy Process (FAHP) analysis assisted by a questionnaire tool. The results of the study obtained 2 (two) areas that have High Category status with a total area of 1.52 ha located in Cilincing District and Kelapa Gading District, North Jakarta with the condition that the first area occupies an improper designation zone (Industrial and Warehousing Zone) while the second area has a radius distance of less than 500 m to the nearest settlement and tourist location.

The confrontation and symbiosis of green and development: Coupling coordination analysis between carbon emissions and spatial development in urban agglomerations of China

Chinese cities face challenges of uneven development, high carbon emission intensity, and low efficiency in urbanization processes. Achieving harmonization and equilibrium between urban development and carbon emissions is crucial for sustainable development. This paper analyzes the development trajectory from 2013 to 2021 of four urban agglomerations in China: Northeast China (NEC), Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). It identifies and calculates the urban spatial development patterns and carbon emissions, comparing the spatial-temporal variations in carbon emissions under different urban development patterns. The study also explores the coupling coordination relationship between these two aspects at various stages. The findings suggest that urban spatial development and carbon emissions are often uncoordinated in shrinking cities. In terms of coordination within urban agglomerations, BTH and NEC show suboptimal development, characterized by overall lag and polarization; YRD exhibits the best development, being consistently stable and balanced; PRD is generally improving, though with some cities lagging. Planning departments should revise urban and environmental policies to optimize spatial structures and infrastructure, aligning with regional development and promoting synergy in urban agglomerations. This research provides theoretical and empirical support for China to formulate relevant policies aimed at green, coordinated, and sustainable urban development.

Retreat from flood zones: Simulating land use changes in response to compound flood risk in coastal communities

Coastal communities are increasingly vulnerable due to sea level rise and population growth. Managed retreat is commonly recognized as a strategy that yields multifaced benefits in community adaptation. However, limited studies have explored the cumulative effects of sea level rise, population migration, and managed retreat on the community resilience. This study presents a parcel-level land use change model to analysis land-based flood mitigation strategies in Galveston County, Texas. The developed model integrates a Gradient Boosting Decision Tree with a flood risk model and diverse datasets. Our model results reveal the spatial patterns of urban development in Galveston under different relocation policies and the compounding impacts of sea level rise and population growth. Our findings illustrate that elevating the first floors of buildings can significantly mitigate flood risks and associated relocation costs. The private adaptation measure, together with government-led buyout policies, could foster a shift toward more resilient urban development and yield a more affordable relocation strategy. Our findings emphasize the need for a multidisciplinary approach in building resilient coastal communities, particularly in the face of escalating climate risks in local communities.

El Niño‐La Niña Asymmetries in the Changes of ENSO Complexities and Dynamics Since 1990

AbstractIn around 1990, significant shifts occurred in the spatial pattern and temporal evolution of the El Niño‐Southern Oscillation (ENSO), with these shifts showing asymmetry between El Niño and La Niña phases. El Niño transitioned from the Eastern Pacific (EP) to the Central Pacific (CP) type, while La Niña's multi‐year (MY) events increased. These changes correlated with shifts in ENSO dynamics. Before 1990, El Niño was influenced by the Tropical Pacific (TP) ENSO dynamic, shifting to the Subtropical Pacific (SP) ENSO dynamic afterward, altering its spatial pattern. La Niña was influenced by the SP ENSO dynamic both before and after 1990 and has maintained the CP type. The strengthened SP ENSO dynamic since 1990, accompanied by enhanced precipitation efficiency during La Niña, make it easier for La Niña to transition into MY events. In contrast, there is no observed increase in precipitation efficiency during El Niño.

Analisis Perkembangan Kota Bukittinggi dan Sekitarnya Tahun 2005-2022 Menggunakan Metode Spatial Metric

The aim of this research is to: 1) Understand the physical development of Bukittinggi City and its surroundings in the period 2005-2015 and 2022, 2) Understand the spatial pattern of development of Bukittinggi City and its surroundings. This type of research is quantitative, using secondary data, namely SPOT images for the period 2005, 2015 and 2022. The analysis technique used is manual digitization to produce maps of built-up areas and metric spatial analysis to determine city development patterns using 5 methods, namely: PD , NP, LPI, LSI, and MPS. The overlay method is used to obtain patterns of development of built-up land regarding slopes, roads, distribution of facilities and population development. This research found: The physical development of the City of Bukittinggi and its surroundings in the period 2005-2022 shows a lot of development and improvement, which can be seen by the shrinking of non-built-up areas in each time period tending towards Urban Sprawl marked by an increase in built-up areas which spread towards the outskirts of the city. due to population growth. The development of built-up areas in the city of Bukittinggi and its surroundings tends to grow in areas with flat and gentle slopes and tends to gather on main and central roads, so it can be concluded that the development of the city of Bukittinggi and its surroundings is still concentric.

Spatiotemporal analysis of urban development patterns and COVID-19 in Mashhad, Iran

This study evaluates the impact of city spatial development patterns on the spread of airborne pandemics, focusing on Mashhad, Iran. Using ArcGIS spatial statistics, 91 spatial indices were examined between March 5, 2020, and October 2, 2021, during five distinct waves of the virus, revealing that private vehicle ownership and limited use of public transportation did not significantly reduce COVID-19 infection rates in Mashhad. Instead, factors such as proximity to transportation hubs, healthcare facility polarization, and retail shop distribution had a significant impact. Surprisingly, demographic vulnerability and conventional population-related factors had relatively minor effects. The study also highlights the influence of building concentration with high floor area ratios (FAR) and the impact of local authorities' optimism in reducing social distancing measures, especially in travel and traffic control, on the increased spread of the virus during recent waves.

Calculation of Urban Green Competitiveness and Analysis of Spatial and Temporal Evolution Characteristics in China

It is particularly important to analyze the influencing factors of urban green competitiveness and the spatial distribution characteristics under the constraint of carbon emissions. The research ideas of this paper: firstly, this paper selects the carbon emission intensity and urban green competitiveness data in 2010, 2013, 2015, 2018 and 2020 for panel data regression; secondly, this paper applies a variety of methods to carry out the robustness test, and the results show that the regression model is better, and analyzes the development of urban green competitiveness for the heterogeneity of large cities and small cities; subsequently, the use of inverse geographic matrix to analyze the spatial correlation between the global Moran index and local Moran index for urban green competitiveness, and to analyze the spatial and temporal pattern evolution of urban green competitiveness. The conclusions of the study show that, from the viewpoint of influencing factors, carbon emission intensity presents a significant negative effect on the development of urban green competitiveness, and has a greater impact on the green competitiveness of large cities than that of small cities. From the perspective of spatial correlation, urban green competitiveness presents positive spatial correlation and shows a growing trend over time. Finally, this paper puts forward relevant policy recommendations based on the findings of the study.

Mechanisms for realizing the ecological products value: Green finance intervention and support

The essence of ecological product value is the rational development and utilization of natural ecological and environmental resources, which are converted into social attributes or economic value. An accurate assessment of the value of ecological products is a prerequisite for realizing their economic value. However, few studies have measured the interaction mechanism between green finance and ecological products value. This study measured the ecological products value at the city level in China from 2010 to 2021, based on the dynamic equivalent moderator model. A spatial panel data model was used to analyze the spatial pattern and dynamic evolution of ecological products value and identify their driving factors. The ecological products value in China showed an increasing trend from 2010 to 2021, and there were obvious spatial correlations between regions. Green finance significantly contributes to the realization of ecological products value, and robust estimation results can still be obtained after controlling for endogeneity. Meanwhile, the role of green finance has a spatial spillover effect. This study provides a new research perspective for correctly assessing ecological products value and studying the role of green finance in realizing ecological products value.

Reducing the “digital divide” to reap the “digital dividend”: spatial differences and convergence of the digital economy in cities of China

Bridging the “digital divide” to benefit from the “digital dividend” reflects the inclusive development of the digital economy, which affects the sustainable development of the overall economy and society. Based on the different spatial perspectives of certain regions, city clusters, and cities, this study presents the “digital divide” and “digital dividend” for the development of China's digital economy. A combined entropy method was used to measure the development of the digital economy in cities of China from 2011 to 2019. The Dagum Gini coefficient and its decomposition were used to explore the spatial differences of the digital economy and their sources, while the coefficient of variation and the spatial panel model were used to examine the convergence of the digital economy. Our findings suggest that the development level of the digital economy in Chinese cities has continuously improved. Second, the spatial differences of the digital economy in the four regions and nine city clusters of China that were investigated are decreasing, indicating that the “digital divide” is narrowing and presenting a significant “digital dividend.” The total differences were mainly owing to those between the regions (among the city clusters). Third, the development of the digital economy in the four regions and nine city clusters demonstrated characteristics of σ, β, and club convergences. Finally, the evolution path of the spatial differentiation of the digital economy in cities of China is presented, and the “digital dividend” also demonstrates different stage characteristics. This study analyzed the spatial differences in the digital economy of cities in China and clarified the convergence and influence mechanisms of its development at different spatial scales. The findings provide a basis for the evolution of spatial and temporal patterns of the digital economy of cities in China and presents policy implications for promoting the regionally coordinated and sustainable development of the digital economy. Suggestions from the national and regional levels are more necessary to further narrow the “digital divide,” create a “digital dividend,” and digital drives sustainable economic development.