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The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area's inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH. [ABSTRACT FROM AUTHOR]

Taking 13 cities in Beijing, Tianjin and Hebei as the research objects, this paper constructed the urban smart development index system from three aspects: smart space construction, smart element allocation and smart welfare performance, measured the urban smart development level in Beijing, Tianjin and Hebei from 2008 to 2018, and used ArcGIS software to realize the temporal and spatial measurement performance of smart development level. The obstacle degree model was used to reveal the criterion-level factors that hinder the smart development of Beijing, Tianjin and Hebei cities. The main conclusions are as follows: (1) The smart development level in Beijing, Tianjin and Hebei is low, the research period shows a weak fluctuation and downward trend, and there is room for improvement. (2) From the spatial difference of smart development level, the double core trend with Beijing and Tianjin as the high level is obvious, showing a downward trend from the center to the surrounding. (3) From the overall barrier performance of study area, smart welfare performance accounts for the largest proportion, smart space construction takes the second place, and smart factor allocation is the lowest. (4) The obstacles to smart development in different cities vary. The obstacles in Beijing and Tianjin are mainly focused on smart welfare performance. As the main core area to undertake Beijing's noncapital functions, smart space construction and smart element allocation are the obstacles in most cities. [ABSTRACT FROM AUTHOR]

Promoting sustainable development in manufacturing is a paramount goal, with a focus on advancing green innovation. This study constructs a system for evaluating green innovation efficiency and employs the Super-EBM model, incorporating unexpected output, to assess the efficiency of green innovation in 13 cities across the Beijing-Tianjin-Hebei region from 2011 to 2020. The study further conducts dynamic analysis using the Malmquist-Luenberger index. Results reveal that, statically, the overall green innovation efficiency in the manufacturing industry of the Beijing-Tianjin-Hebei region is inefficient. There exists a considerable gap in green innovation efficiency among Beijing, Tianjin, and Hebei, with Beijing and Tianjin demonstrating superior performance compared to Hebei. Substantial variations exist in the green innovation efficiency of manufacturing across different cities in the Beijing-Tianjin-Hebei region. Only Beijing, Qinhuangdao, and Baoding achieve DEA-effective green innovation efficiency in the manufacturing industry, while the other cities do not. Dynamically, the green innovation efficiency of the manufacturing industry in the Beijing-Tianjin-Hebei region is on the rise. There is a varying degree of improvement in green innovation efficiency in Beijing, Tianjin, and Hebei, with Hebei showing the highest improvement, Tianjin ranking second, and Beijing having the least improvement. With the exception of Langfang and Hengshui, the green innovation efficiency in the manufacturing industry is improving in most cities in the Beijing-Tianjin-Hebei region, with Hebei witnessing the most significant improvement. This study aims to integrate "environmental pollution" into the evaluation index system for green innovation efficiency. It assesses green innovation efficiency in the manufacturing industry of the Beijing- Tianjin-Hebei region, considering both static and dynamic perspectives. This clarification offers insights into the level of green innovation, contributing valuable information for the advancement of high-quality development in the regional manufacturing industry. [ABSTRACT FROM AUTHOR]

• Coordination among subsystems is key to improving urban resilience. • There is a large gap between the UR of Hebei Province and of Beijing-Tianjin. • The CCD of the urban resilience subsystem is trending upward but growing slowly. • The degree of spatial correlation of subsystems CCD in BTHUA is weak. Building resilient cities is a novel concept for dealing with risk crises and achieving sustainable urban development in modern times. This paper utilized data from the Beijing-Tianjin-Hebei urban agglomeration (BTHUA) spanning from 2009 to 2019, along with the CRITIC-Entropy weight method, coupling coordination degree (CCD) model and spatial correlation analysis to measure urban resilience and investigate subsystem coupling coordination relationships while considering spatial clustering characteristics. The results indicate that: (1) Spatially, there is a significant gap between the UR index of cities in Hebei Province and that of Beijing-Tianjin, with a "Beijing-Tianjin dual-core driven" trend. Regarding temporal evolution, the growth trend of the UR index is consistent with that of the subsystem resilience index. (2) In general, the CCD of the urban resilience subsystem is slowly trending upwards, with the overall CCD level of BTHUA ranging from primary coordination to moderate coordination. (3) The degree of spatial correlation of subsystem CCD in BTHUA is weak, and more cities exhibit heterogeneous agglomeration. These findings provide valuable inspiration and reference for resilient city construction and coordinated regional development to a certain extent. [ABSTRACT FROM AUTHOR]

This paper aims to understand mobility patterns in mega-city regions from perspectives of accessibility and air-rail intermodality. Travellers' preferences for intercity mode and its access and egress modes were simultaneously collected in the context of the Beijing-Tianjin-Hebei mega-city region. The accessibility of transport hubs and destinations was measured to enable interpreting mobility behaviour in three dimensions, i.e., travel mode decisions, departure transport hub decisions, and transport service decisions. The calibrated behavioural model highlights a series of determinants regarding level-of-service attributes, travel characteristics, accessibility, socio-demographics, and their interaction. Based on the behavioural insights, twenty domestic cities were selected as destinations for analysing the dynamics of mobility patterns in terms of the competition within the multi-airport system and air-rail cooperation potential in the long-haul market. The elasticity of shifts in transport demand between the primary and secondary airports in the Beijing multi-airport system was assessed under various pricing strategies, highlighting the vital role of the most accessible airport in air traveller decision-making. Moreover, the results underline the effect of air-rail intermodal service upgrades, e.g., offering baggage handling systems and flexible tickets, on stimulating air-rail intermodal mobility in the mega-city region. • Behavioural determinants of intermodal travellers. • Joint decisions on transport modes, hubs, and services. • Effect of accessibility on competitive relationships in multi-airport systems. • Market share of air-rail intermodality in Beijing-Tianjin-Hebei mega-city region. [ABSTRACT FROM AUTHOR]

Revealing the characteristics of the spatial and temporal evolution of regional tourism-related ecological security and exploring its driving factors are of great theoretical and practical value to promoting the coordinated and sustainable development of the tourism economy and the ecological environment. Taking the Beijing–Tianjin–Hebei region as a case study, this study constructed a tourism-related ecological security evaluation index system based on the DPSIR theoretical framework. The tourism-related ecological security index was measured from 2011 to 2022 and its spatiotemporal characteristics and dynamic evolutionary process were analyzed. Finally, the panel quantile regression model was used to analyze its driving factors. The results show that: (1) in time, the average value of tourism-related ecological security fluctuates and rises, and the differences between the units show a convergent trend, which is mainly manifested in the catching-up effect from low-value cities to higher-value cities; (2) in space, Beijing and its southeastern cities have a high level of tourism-related ecological security, and although the study area is mainly at the less secure level, it is developing continuously and progressively; (3) in terms of the dynamic evolution characteristics, the type transfer of tourism-related ecological security has certain "path dependence" and "self-locking" effects, often occurring between neighboring levels, and the type transfer under the influence of different neighborhoods has significant differences; (4) in terms of driving factors, environmental pollution has a significant inhibitory effect on the level of tourism-related ecological security, and the level of economic development has the largest positive marginal effect on tourism-related ecological security, while other influencing factors such as the level of tourism development have certain differences in terms of their positive role in the promotion of the level of tourism-related ecological security. This study can provide a reference for decisionmakers to promote ecological protection and high-quality tourism development in the Beijing–Tianjin–Hebei region. [ABSTRACT FROM AUTHOR]

High-quality economic development is an innovation-driven economy, and collaborative innovation is key to maximizing its effects. In terms of the influence of cooperative innovation of urban agglomerations on high-quality economic development, urban agglomerations are of considerable relevance to the coordinated development of China's regional economy. This research established an evaluation system of high-quality economic development indicators for the Beijing–Tianjin–Hebei urban agglomeration based on panel data of 13 cities from 2003 to 2020, and then estimated the level of high-quality development of each city's economy. The spatial Durbin model was used in this article to examine the effects of collaborative innovation on the high-quality development of the economy. The findings indicated that, although high-quality economic development was increasing across the board in the Beijing–Tianjin–Hebei urban agglomeration, it varied greatly between the individual cities. Beijing and Tianjin had much higher levels of high-quality economic development than the other cities in Hebei, and there was some variation within the Hebei cities as well. The high-quality economic development of the Beijing–Tianjin–Hebei urban agglomeration exhibited no spatial correlation characteristics under the weight of geographical distance. However, there was an aggregation effect on the differential relationship of economic development, which was also significant under the dual influence of economic geography. The collaborative innovation of Beijing–Tianjin–Hebei urban agglomeration could promote the high-quality economic development of both the inner and surrounding cities, and could also improve the high-quality economy development level of the overall urban agglomeration. [ABSTRACT FROM AUTHOR]

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas. [ABSTRACT FROM AUTHOR]

Under the "Digital China" strategy and "Carbon Peaking and Carbon Neutrality" goal, it is significant to explore the carbon reduction effect from the digital economy development in a multi-dimensional way. Based on the panel data of 13 cities in the Beijing-Tianjin-Hebei (BTH) region from 2011 to 2019, this study uses mechanism test model, threshold effect model, and spatial Durbin model which empirically test the influence mechanism and spatial spillover effect of digital economy development on regional CEI. The research found that (1) the digital economy development in the BTH region can reduce regional CEI, and it passes the endogenous test; (2) the digital economy indexes of 13 cities in the BTH region have significantly increased with time evolution, but there is obvious spatial unevenness; the CEI of each city except Tianjin decreases significantly with time evolution, and Tianjin shows a trend of decreasing and then increasing; (3) digital economy has a positive spatial correlation, showing the characteristics of "H–H" and "L-L" clustering. Furthermore, the digital economy has a spatial spillover effect on the CEI of neighboring cities; (4) the digital economy development can promote the industrial structure rationalization and upgrade, improves the urban green innovation quantity and quality, then reduces the regional CEI through them; and (5) the impact strength of digital economy on CEI varies at different threshold intervals of the mechanism variable. [ABSTRACT FROM AUTHOR]