Shandong Peninsula Urban Agglomeration Research Articles

Multistage charging facility planning on the expressway coordinated with the power structure transformation

AbstractThis study presents a novel multistage expressway fast charging station (EFCS) planning problem coordinated with the dynamic regional power structure (PS) transformation. Under the prerequisite of the EFCS network's sustainable operation, network accessibility, and orderly construction, a three‐step planning method oriented to the enhancement of energy saving and emission reduction (ESER) benefits and rationalization of facility utilization is developed: (i) EV‐expanded network, (ii) multiagent‐based dynamic traffic assignment (MA‐DTA), and (iii) deployment refinement. Embedding the MA‐DTA and customized refinement strategy into the iterative planning structure enables the integration of operations and planning of the EFCS network. A numerical experiment and an empirical study in the Shandong Peninsula urban agglomeration are conducted. It demonstrates that the method can find a high‐quality solution within acceptable computation time and is applicable to realistic large‐scale EFCS planning. The planning method can effectively play the role of economy and facility in inducing EV users' charging demands, thus enhancing the overall ESER benefits. The integration of operation and planning is proven effective in reasonably matching the supply and demand of facility service and charging loads in a full‐time period. Further, the multistage EFCS planning schemes during 2025–2045 are explored, and some insightful policy implications are revealed.

Accessibility and Equity Analysis of Highway-Railway Traffic Network Based on Real-Time Route Planning Data: A Case Study of Shandong Peninsula Urban Agglomeration, China

As a bond of geospatial and socioeconomic ties, transport supports the development of regions. With the rapid development of the economy, the coordinated development of regional transport has become a concern. To narrow the gap in transport development between cities and achieve sustainable development in regional transport, this study took the Shandong Peninsula urban agglomeration as the research object and using real-time path planning data assessed accessibility of the traffic network under two travel scenarios: cars and car-connected train. Based on the results of accessibility calculations, the Gini coefficient and Theil index were used to measure the equity of regional traffic networks. The results showed that (1) overall accessibility under the two travel scenarios showed a pattern of gradual decay from the center to the periphery; (2) both the accessibility and equity of the highway network were better than those of the railway network; (3) differences in regional development were the main sources of overall regional transport inequity. The research results provide a useful reference for the planning and construction of urban agglomeration traffic networks.

Spatial coordination and industrial pollution of urban agglomerations: Evidence from the Yellow River Basin in China

AbstractPopulation, economy, and other social factors of urban agglomerations in river basins bear the inescapable responsibility for water environmental pollution. This article utilizes data from 53 cities in three regional‐level urban agglomerations in the Yellow River Basin (YRB) from 2006 to 2015 as research samples to analyse the impact of the coupling and coordination of physical space expansion (PSE) and social space structure (SSS) of urban agglomerations on industrial pollution. Multi‐source remote sensing data is used to quantify the physical space expansion of urban agglomerations, while population and economic data are used to measure the social space structure. We analyse the coupling coordination degree and spatiotemporal evolution of the PSE and SSS of urban agglomerations by applying a coupling coordination degree model. Finally, the grey incidence model is used to rank the influencing factors of pollution. The research findings are summarized as follows: (1) Three regional‐level urban agglomerations in the YRB show excessive expansion, with the Shandong Peninsula Urban Agglomeration (SPUA) being the most severe. (2) SPUA and Central Plains Urban Agglomeration (CPUA) exhibit the polycentric SSS, while the Guanzhong Plain Urban Agglomeration (GPUA) shows the monocentric SSS. (3) PSE and SSS of the three urban agglomerations are slightly uncoordinated, but show a trend of easing. The uncoordinated level between SPE and economic distribution in CPUA ranks the highest. (4) SSS exerts the greatest impact on pollution, while the pollution impact of PSE ranks lower. The pollution impact of the coupling coordination degree between SPE and SSS falls between the two. The research results provide a reference for sustainable urban development and planning in countries around the world.

Decomposition and decoupling analysis of carbon emissions in the Yellow River Basin: evidence from urban agglomerations.

A comprehensive understanding of carbon emission reduction and decoupling in urban agglomerations of the Yellow River Basin (YRB) has significant theoretical and practical value for formulating precise carbon reduction policies and achieving ecological conservation and high-quality development in the region. This study utilized a generalized Divisia index decomposition model to identify the primary driving factors behind carbon emission changes in urban agglomerations of the YRB. Based on this, a model measuring decoupling efforts was constructed to systematically investigate the decoupling relationship between carbon emissions. The research findings indicate that technological progress and output scale are two primary drivers of carbon emission increases in the YRB and its urban agglomerations, whereas technological carbon intensity, output carbon intensity, and energy carbon intensity play key roles in reducing carbon emissions. Except for a few years, the YRB and Jiziwan metropolitan area (JWMA) did not exhibit decoupling effects on carbon emissions. The Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA) showed strong decoupling effects from 2016 to 2019. The Guanzhong Plain Urban Agglomeration (GPUA) demonstrated a strong decoupling effect from 2013 to 2019 (except from 2016 to 2017). The Lanxi Urban Agglomeration (LXUA) exhibited a strong decoupling effect from 2014 to 2019. Technological carbon intensity plays a decisive role in the transition from non-decoupling to decoupling. Therefore, the government must increase investments in green and low-carbon technologies and strictly implement carbon reduction measures. Thus, the YRB and its urban agglomerations have considerable potential for carbon emission reduction and strong decoupling effects.

Spatiotemporal Pattern of Carbon Compensation Potential and Network Association in Urban Agglomerations in the Yellow River Basin

The Yellow River Basin is an important energy base and economic belt in China, but its water resources are scarce, its ecology is fragile, and the task of achieving the goal of carbon peak and carbon neutrality is arduous. Carbon compensation potential can also be used to study the path to achieving carbon neutrality, which can clarify the potential of one region’s carbon sink surplus to be compensated to the other areas. Still, there needs to be more research on the carbon compensation potential of the Yellow River Basin. Therefore, this study calculated the carbon compensation potential using the β convergence test and parameter comparison method. With the help of spatial measurement tools such as GIS, GeoDa, Stata, and social network analysis methods, the spatiotemporal pattern and network structure of the carbon compensation potential in the Yellow River Basin were studied from the perspective of urban agglomeration. The results demonstrate the following: (1) The overall carbon compensation rate of the YRB showed a downward trend from 2005 to 2019, falling by 0.94, and the specific pattern was “high in the northwest and low in the southeast”. The spatial distribution is roughly spread along the east–west axis, and the distribution axis and the center of gravity keep shifting to the northwest. It also showed a weak divergence and a bifurcation trend. (2) The carbon compensation rate in the YRB passed the spatial correlation and β convergence tests, demonstrating the existence of spatial correlation and a “catch-up effect” among cities. (3) The overall distribution pattern of the carbon compensation potential in the YRB is a “low in the west and high in the east” pattern, and its value increased by 8.86% during the sampled period. (4) The network correlation of carbon compensation potential in the YRB has been significantly enhanced, with the downstream region being more connected than the upstream region. (5) The Shandong Peninsula Urban Agglomeration has the largest network center, followed by the Central Plains Urban Agglomeration, and the Ningxia along the Yellow River Urban Agglomeration has the fewest linked conduction paths. According to the research results, accurate and efficient planning and development suggestions are proposed for urban agglomeration in the Yellow River Basin.

Evolution and coupled matching of ecosystem service supply and demand at different spatial scales in the Shandong Peninsula urban agglomeration, China

Ecosystem services are the environmental basis of human living and sustainable development, and the coupled matching of ecosystem service supply and demand (ESSD) plays an important role in socioeconomic and eco-environmental development. As one of the major regions supplying ecological products in China, the Shandong Peninsula urban agglomeration (SPUA) has unique geographical advantages and a strategic position. Based on high-resolution land-use and regional socioeconomic development data in SPUA, this study used value quantity assessment and an ecosystem service demand model to quantify ecosystem service supply (ESS) and ecosystem service demand (ESD) at both the city and county scale in 2000, 2010 and 2020. Meanwhile, the CA-Markov model was used to predict the potential land-use, the Moran index and Getis-Ord Gi* index were used to analyze spatial clusters of ESS and ESD, and a coupling coordination degree model and matching index were used to analyze the relationship between ESS and ESD. The results show that: (1) The most prominent land-use change in SPUA from 2000 to 2020 was the conversion of cropland to other land-use types; the process of land-use change was more intense from 2010 to 2020 than from 2000 to 2010; and the center of city activities might expand by 2030, which is related to the regional development plan. (2) The ESS was related to the regional natural conditions. Coastal regions or regions with abundant natural resources in SPUA had higher ESS, and the southwest region had lower ESS in SPUA. The ESS presented high–high clusters, low–low clusters and hot spots, and the cluster phenomena gradually diminished from 2000 to 2020. (3) The ESD was related to the regional location and economic development, with the southwest region of SPUA having a higher ESD than the northeast. The ESD presented low–low clusters, and Jinan and Qingdao had hot spots, which are the most developed cities in SPUA. (4) The study scale had impacts on coupled matching of ESSD. Coupled matching of ESSD was found to be unbalanced at the county scale from 2000 to 2020, but tended to be balanced at the city scale, which was related to resource management at different spatial scales. This study reveals the evolution and coupled matching of ESSD at different scales, which has important implications for the sustainable development of regional urban agglomerations.

Automatic extraction of built-up areas in Chinese urban agglomerations based on the deep learning method using NTL data

Rapidly and accurately extracting built-up areas is an essential prerequisite of urbanization research. There have been many studies on the extraction of built-up areas using remote sensing technologies. So far, few studies have been conducted to evaluate the applicability of the deep learning method to extract built-up areas under the condition that only nighttime light (NTL) data are used. This study proposed a deep learning method to extract the built-up areas using NTL data, and applied the method to analyze the spatial and temporal changes of the built-up areas in Chinese two urban agglomerations from 2000 to 2020. The results show that the U-Net deep learning method can be used to extract built-up areas efficiently under the condition that only NTL data are used. The proposed method was able to improve the accuracy of built-up area extraction significantly compared to the existing method. For the extraction of built-up areas in large regions with long time series, the proposed method can facilitate the work and improve the processing efficiency. The gravity centre of the built-up areas in the Central Plains Urban Agglomeration migrated south-eastward, and the gravity centre of the built-up areas in the Shandong Peninsula Urban Agglomeration migrated south-westward, with these gravity centres gradually approaching the geometric centres of the corresponding urban agglomerations. The built-up areas in the Central Plains and Shandong Peninsula Urban Agglomerations grew rapidly, increasing by 4.14 times and 3.73 times from 2000 to 2020, respectively. The built-up areas in the Central Plains Urban Agglomeration expanded faster, while the urban development degree of the Shandong Peninsula Urban Agglomeration was higher. The urban distributions and development modes of these two urban agglomerations were quite different. The Central Plains Urban Agglomeration tended to further agglomerate, while the Shandong Peninsula Urban Agglomeration tended to disperse.

Spatio-temporal evolution of resources and environmental carrying capacity and its influencing factors: A case study of Shandong Peninsula urban agglomeration

Promoting ecological conservation and high-quality development in the Yellow River basin is an important objective in China's 14th Five-Year Plan. Understanding the spatio-temporal evolution of and factors affecting the resources and environmental carrying capacity (RECC) of the urban agglomerations is critical for boosting high-quality green-oriented development. We first combined the Driver-Pressure-State-Impact-Response (DPSIR) framework and the improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to evaluate the RECC of Shandong Peninsula urban agglomeration in 2000, 2010 and 2020; we then used trend analysis and spatial autocorrelation analysis to understand the spatio-temporal evolution and distribution pattern of RECC. Furthermore, we employed Geodetector to detect the influencing factors and classified the urban agglomeration into six zones based on the weighted Voronoi diagram of RECC as well as specific conditions of the study area. The results show that the RECC of Shandong Peninsula urban agglomeration increased consistently over time, from 0.3887 in 2000 to 0.4952 in 2010 and 0.6097 in 2020, respectively. Geographically, RECC decreased gradually from the northeast coast to the southwest inland. Globally, only in 2010 the RECC presented a significant spatial positive correlation, and that in the other years were not significant. The high-high cluster was mainly located in Weifang, while the low-low cluster in Jining. Furthermore, our study reveals three key factors—advancement of industrial structure, resident consumption level, and water consumption per ten thousand yuan of industrial added value—that affected the distribution of RECC. Other factors, including the interactions between residents’ consumption level and environmental regulation, residents’ consumption level and advancement of industrial structure, as well as between the proportion of R&D expenditure in GDP and resident consumption level also played important roles resulting in the variation of RECC among different cities within the urban agglomeration. Accordingly, we proposed suggestions for achieving high-quality development for different zones.

The Administrative Center or Economic Center: Which Dominates the Regional Green Development Pattern? A Case Study of Shandong Peninsula Urban Agglomeration, China

Central cities play a critical role as beneficial areas for regional development and lead the high-quality development of an entire region and surrounding cities. As social and economic activities expand, the regional development pattern becomes more complex. This is typically manifested in the separation of administrative and economic centers. In this context, there is high research value in identifying the roles of different types of central cities in the regional green development pattern. This paper analyzes the urban linkage pattern and evolving characteristics of green development in the Shandong Peninsula, China, from 2016 to 2020. The study adopts the methods of Bootstrap Data Development Analysis, Modified Gravity Model, and Social Network Analysis. The results show the following. (1) Both the administrative center and economic center have positive driving effects on the spatial connection of surrounding cities in the urban agglomeration. There is a multi-point, multi-core, and multi-path spatial correlation in the Shandong Peninsula urban agglomeration. (2) The joint strength of the administrative center is higher compared to the economic center, and most of the green development partnerships are centered on the provincial capital. (3) The gravitational force of cities close to the administrative center is generally higher than seen with the economic center cities, but there is a gradual emergence of a substantial gravitational effect of the economic center.

Assessment and driving factor of housing vacancies in Shandong Peninsula urban agglomeration based on multi-source remote sensing data

As the urbanization rate in the world has increased rapidly, the housing vacancy problem has become serious and attracting more attention. Calculating and analyzing vacant housing can help reduce the wasteful use of resources. This paper measures the housing vacancy rate and housing vacancy stock in the Shandong Peninsula urban agglomeration using night-time lighting and land use data. The results show that the average housing vacancy rate in the Shandong Peninsula urban agglomeration rose rapidly from 14.68% in 2000 to 29.71% in 2015 before declining slowly to 29.49% in 2020. Since urban population growth is lower than the housing construction rate, the average annual growth of housing vacancy stock between 2000 and 2020 exceeds 3 million square meters in megacities and is around 1–2 million square meters in large and medium-sized cities. The vacant housing has caused considerable waste of housing resources. The driving factors of the housing vacancy were further analyzed using the LMDI decomposition method. Results indicate that the economic development level is the most significant driving factor of the vacant housing stock. In addition, the value effect of unit floor areas is the major driving factor inhibiting the growth of vacant housing stock, while the decline of unit floor area value is conducive to the reduction of this stock.

Analysis of the Coordination of Built-Up Area Expansion and Population Growth in the Shandong Peninsula Urban Agglomeration

Exploring the coordinated relationship between built-up area expansion and population growth in the urbanization process is important for the planning and sustainable development of cities. The article took Shandong Peninsula urban agglomeration as the study area, which was divided into the Provincial Capital Economic Circle, Jiaodong Economic Circle, and Lunan Economic Circle according to the plan named the Implementation Plan for Implementing the Opinions of the CPC Central Committee and the State Council on Establishing a New Mechanism for More Effective Regional Co-ordinated Development. The people–land coordination relationship in the region was analyzed from the perspective of spatio-temporal integration through indices such as fractal dimension, spatial autocorrelation, population sprawl, and city land expansion and population growth coordination index (CPI) The results showed that (1) the relationship between built-up area expansion and population growth in the Shandong Peninsula urban agglomeration was uncoordinated. The three economic circles mainly changed from rapid population growth to significant expansion of built-up areas. (2) The development patterns of the three economic circles were different. The Provincial Capital and Jiaodong showed a pattern with Jinan and Qingdao as the core, driving the periphery area. Lunan showed a multi-point scattered development; there was no central city, but Linyi City was developing faster. (3) Jiaodong had the biggest changes in terms of population and built-up area. This study provides a scientific reference for the urban planning and sustainable development of the Shandong Peninsula urban agglomeration.

Spatial–temporal pattern of urban ecological construction in the Yellow River basin and its optimization and promotion paths

IntroductionUrban ecological instruction is an important method of sustainable development that couples the needs of the population and the environment, thus facilitating high-quality development. This study aimed to transform existing large-scale considerations of ecological urban construction to provide a more grounded evaluation framework. We constructed an integrated framework that considers economic, environmental, social, cultural, and ecological factors and their contribution to urban ecological construction.MethodsWe used methods of subjective and objective empowerment, exploratory spatial data analysis, and the obstacle degree model to explore spatiotemporal variations in urban ecological construction.ResultsOur results showed that: (1) From 2006 to 2018, urban ecological construction in the Yellow River basin (YRB) exhibited a “polycentric” spatial differentiation pattern and a significant, gradually decreasing “center-periphery” spatial distribution trend. (2) High–high areas (with high agglomeration and high urban ecological construction) in the YRB are mainly distributed in Shandong Province and the adjacent regions, whereas low–low areas are mainly distributed in south–central Ningxia Province and southern Gansu Province. (3) Analysis of urban ecological construction characteristics across different city scales, functional types, spatial carriers, and basin locations shows that the urban ecological construction level is directly proportional to the scale of the city. The level of urban ecological construction is relatively higher in the Shandong Peninsula urban agglomeration, in the priority development area of the North China Plain, and in the downstream and right bank of the YRB. Spatial differences are mainly controlled by the net difference between regions from the upstream and midstream scales, as well as intra-regional differences between the left and right banks. (4) Marked differences can be observed in the obstacles to ecological construction in different types of cities.DiscussionTypical barriers in cities in the YRB include the total water resources per capita, energy consumption per unit of GDP, the proportion of research and development investment in fiscal expenditure, the number of books in public libraries per 10,000 people, and land use efficiency. In the future, urban ecological construction should be based on the development conditions of each individual city, and should be improved according to local conditions to achieve the construction high-quality ecological cities.

Spatial Correlations of Land Use Carbon Emissions in Shandong Peninsula Urban Agglomeration: A Perspective from City Level Using Remote Sensing Data

The spatial and temporal characteristics of land use carbon emissions are relevant to the sustainable use of land resources. Although spatial and temporal studies have been conducted on land use carbon emissions, the spatial correlation of land use carbon emissions at the city level still requires further research. Here, we estimated the distribution of carbon emissions at the city level in Shandong Peninsula urban agglomeration in spatial and temporal terms based on land use remote sensing data and fossil energy consumption data during 2000–2019. The results showed that the land use change in the 16 cities in the study area was the conversion of cropland to construction land. Carbon emissions from land use had an upward trend for all 16 cities overall during the period of 2000–2019, but the incremental carbon emissions trended downward after 2010. Among them, Jinan and Qingdao had higher carbon emissions than other cities. In addition, we also found that land use carbon emissions at the city level were characterized by stochasticity, while per capita carbon emissions displayed geospatial aggregation. Among them, Yantai displayed a spatial pattern of high–high clustering of carbon emissions, while Jining presented a spatial pattern of low–low clustering in terms of land-average carbon emissions and carbon emissions per capita during 2000–2019. The results of the study are important for guiding the achievement of urban carbon emission reduction and carbon neutrality targets at the city level.

Comparative analysis of structural characteristics of China's 18 typical urban agglomerations based on flows of various elements

Flows of different elements among cities can affect urban organizational structures, leading to specific links between and roles for cities within urban agglomerations. Previous studies have mainly focused on flows produced by specific elements in a single urban agglomeration, and they cannot reveal differences in organizational structures affected by multiple elements among different urban agglomerations. Therefore we selected 18 typical urban agglomerations in China and explored their structural characteristics under comprehensive effects of four elements: economy, resources, population and technology, with gravity model and social network analysis. Guangdong-Hong Kong-Macao Urban Agglomeration had a maximum flow intensity of 5.15, this concentration range was the largest from 0.65 to 3.75, indicating that more elements flowed internally. Shandong Peninsula Urban Agglomeration had the largest network density at 0.412, the lowest efficiency at 0.567, and the lowest hierarchy at 0.000, showing that intercity relationships within SP were the tightest among the18 urban agglomerations. Geographical proximity and topological structure of subgroups proved that connections among subgroups were not complex, and cities of different provinces lacked close interactions. Furthermore, most of the key cities with high centrality indexes were municipalities directly under the Central Government or provincial capitals; these cities played important roles in networks and were distributed into “main spillover” and “bidirectional spillover” subgroups. This paper provides a unified analytical framework aimed at urban agglomerations that accounts for flows of multiple types of elements and explores urban organizational structures, and our results could provide scientific guidance for promoting the coordinated and sustainable development of urban agglomerations.

Exploring the Characteristics and Drivers of Expansion in the Shandong Peninsula Urban Agglomeration Based on Nighttime Light Data

Exploring the Characteristics and Drivers of Expansion in the Shandong Peninsula Urban Agglomeration Based on Nighttime Light Data

Heterogeneous impacts and spillover effects of green innovation network and environmental regulation on water use efficiency: A spatiotemporal perspective from 269 cities in China

Water is a strategic resource for economic and social development, and exploring the evolutionary mechanism of water use efficiency (WUE) is key to realizing regional sustainable development. This study takes 269 cities in China from 2008 to 2019 as the research unit and uses the spatial econometric model to reveal the spatial effect of the green innovation network (GIN) and environmental regulation on WUE. The results show that WUE in China shows a fluctuating upward trend and has strong spatiotemporal heterogeneity and spatial correlation characteristics. The high-value areas of WUE are mainly concentrated in the Shandong Peninsula urban agglomeration, and the low-value areas are mainly distributed in the middle reaches of the Yangtze River. The GIN and WUE have a significant U-shaped relationship, indicating that only by breaking through " Valley of Death" and "Darwinian Sea" can the GIN exert "network effects" and "technological effects" to improve WUE. Environmental regulation has a significant positive spillover effect on WUE, which verifies the rationality of the Porter hypothesis in WUE. In addition, the impact of the GIN on WUE has strong spatiotemporal heterogeneity, so when strengthening the GIN connection, the rebound effect must be prevented from negatively affecting WUE.

A Study on the Impact of Industrial Restructuring on Carbon Dioxide Emissions and Scenario Simulation in the Yellow River Basin

Based on a detailed analysis of the impact mechanism of industrial restructuring on carbon dioxide emissions in the Yellow River Basin, this paper first calculated the carbon dioxide emission data of 57 prefecture-level cities in the Yellow River Basin from 2009 to 2019 and constructed indicators from two dimensions: the advancement and the rationalization of the industrial structure. Then, the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was used to empirically analyze the influencing factors of industrial structure adjustments on carbon dioxide emissions in the Yellow River Basin. Consequently, changing carbon dioxide emission trends in the Yellow River Basin under various scenarios were predicted. The research observed the following: (1) the eastern part of the Shandong Peninsula Urban Agglomeration and the Energy Golden Triangle have higher carbon dioxide emissions; (2) the advancement of industrial structures in the Yellow River Basin has a better emission reduction effect than the rationalization of industrial structures; (3) increased foreign investment will lead to an increase in carbon dioxide emissions in the Yellow River Basin, and a “Pollution Refuge Effect” will emerge; (4) accelerated industrial transformations and upgrades, high-quality economic development, and a moderate population growth rate are consistent with future development trends.

Key indicators of high-quality urbanization affecting eco-environmental quality in emerging urban agglomerations: Accounting for the importance variation and spatiotemporal heterogeneity

As the basis of social sustainable development, eco-environmental quality (EEQ) plays an essential role in the process of high-quality urbanization (HQU). Therefore, evaluating the response mechanism of EEQ in the context of HQU will provide effective guidance for promoting sustainable development. Based on the requirements of high-quality development and the component elements of urbanization and eco-environment, the appropriate variables were selected to construct an evaluation index system between HQU and EEQ of the Shandong Peninsula urban agglomeration (SPUA) in north China. This study employed the improved coupling coordination degree (CCD) model and Moran's I to explore the dynamic mechanism and spatiotemporal distribution of coupling coordination between HQU and EEQ of the SPUA from 2001 to 2020. Meanwhile, the random forest model and the geographically and temporally weighted regression model were used to further investigate the variation in importance and spatiotemporal heterogeneity of the HQU indicators affecting EEQ. This study found that the CCD between HQU and EEQ followed a continuous upward trend in the SPUA from 2001 to 2020, which was much higher in eastern developed cities with a spatial dependency of high-high clustering. Due to the irreplaceability of energy in residents' lives, per capita energy consumption was found to be the most important impact indicator on EEQ. Population density achieved the fastest growth in importance over the study period, and the spatial indicators exhibited greater importance in the west of the SPUA. Furthermore, significant spatiotemporal differences were identified in the effects of HQU indicators on EEQ. HQU was more conducive to an improvement of EEQ in the eastern coastal SPUA. These findings could provide guidance for policy makers to adopt scientific measures in rational urban planning and ecological management of urban agglomerations in developing countries.

Study on the Evolution of the Spatial-Temporal Pattern and the Influencing Mechanism of the Green Development Level of the Shandong Peninsula Urban Agglomeration

Improving the level of green development is an inevitable requirement for promoting the construction of an ecological civilization. In this paper, through a green development level evaluation index system, the CRITIC weight method is used to comprehensively evaluate and analyze the green development level of the Shandong Peninsula Urban Agglomeration from 2007 to 2019. On this basis, a panel data model was constructed to analyze the key influencing factors of the green development level of the Shandong Peninsula Urban Agglomeration. Studies have shown that (1) the level of green development in the Shandong Peninsula Urban Agglomeration from 2007 to 2019 has been steadily improved, but the overall level is still at a low level, with significant differences among the cities. (2) Qingdao and Jinan are the two growth poles of the green development level of Shandong Peninsula Urban Agglomeration. The dual-core leading effect is obvious, forming a spatial pattern in which the green development level of the eastern and central regions is higher than that of other regions’ green development level. (3) Weihai, Dongying, and Qingdao are high-level cities of green development, while Jinan, Yantai, Zibo, Tai’an, and Linyi are medium-level cities of green development. The green development level of other cities is relatively low, and the high-level cities of green development are mostly the Jiaodong economic circle and provincial capital economic circle. (4) Industrial structure, scientific and technological innovation, and government policies are the critical factors in promoting the green development of the Shandong Peninsula Urban Agglomeration.

Spatial Expansion and Correlation of Urban Agglomeration in the Yellow River Basin Based on Multi-Source Nighttime Light Data

The Chinese government proposed a major national strategy for ecological protection and high-quality development in the Yellow River Basin. The Framework of the Plan for Ecological Protection and High-Quality Development of the Yellow River Basin proposes building a dynamic development pattern characterized by “one axis, two regions and five poles” in the Yellow River Basin with high-quality and high-standard urban agglomerations along the Yellow River. The urban agglomeration is the economic growth pole of the Yellow River Basin and the main carrier of the population and productivity. This study integrates DMSP/OLS (Defense Meteorological Satellite Program/Operational Linescan System) and NPP/VIIRS (Suomi National Polar-Orbiting Partnership/Visible Infrared Imaging Radiometer Suite) night light remote sensing data from 2000 to 2020 and uses methods such as spatial expansion measurement, the center of gravity offset, urban primacy, and the gravity model to study the spatial expansion and correlation characteristics of five urban agglomerations. The results show that: (1) From 2000 to 2020, urban agglomeration in the Yellow River Basin continued to expand, and the area increased by 6.4 times. The total amount of nighttime lights in the city presents a spatial distribution pattern that is high in the east and low in the west. (2) The expansion centers of the five major urban agglomerations all shifted. The centers of gravity of the Shandong Peninsula urban agglomeration, the Jiziwan urban agglomeration of the Yellow River, the Guanzhong Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration all shifted westward, while the center of gravity of the Central Plains urban agglomeration shifted to the southeast. (3) Qingdao, Zhengzhou, Xi’an and Lanzhou are the primate cities of the four urban agglomerations of the Shandong Peninsula, Central Plains, Guanzhong Plain, and Lanzhou–Xining, respectively. The primate city in the Jiziwan urban agglomeration of the Yellow River was changed from Taiyuan to Yinchuan and then to Yulin. (4) The density of the gravitational network of the urban agglomeration in the Yellow River Basin and the distribution of the maximum gravitational line show the spatial differentiation characteristics of being dense in the east and sparse in the west.