Spatial-temporal evolution and influencing factors of tourism eco-efficiency in China’s Beijing-Tianjin-Hebei region

Abstract

Tourism eco-efficiency is an important index to measure the sustainable development of the tourism industry. The super-SBM (slacked-based measure) model based on undesired output, geographical spatial analysis method, and panel Tobit regression model were used to explore the spatial-temporal evolution characteristics of tourism eco-efficiency and its influencing factors on 13 cities in China’s Beijing-Tianjin-Hebei region from 2010 to 2019. The results reveal that: 1) During the study period, high-efficiency cities were mainly in Beijing, Tianjin, and their surrounding areas, and the number of these cities did not change, whereas low-efficiency areas were farther away from central cities, and the number of these cities increased earlier but decreased later. 2) The tourism eco-efficiency in the Beijing-Tianjin-Hebei region exhibits significant positive spatial autocorrelation characteristics, and the driving effect of the spatial radiation of high-efficiency cities on low-efficiency cities in the urban agglomeration is more obvious. 3) The levels of economic development, tourism industry structure, urbanization, and technological progress have a greater impact on tourism ecological efficiency.

The observed features and some possible reasons of annual temperature extremes over Beijing‐Tianjin‐Hebei region for a century long‐term based on newly constructed daily observations

“…Perhaps this may produce some sample errors in assessment of the quantitative results. Second, in terms of attribution of temperature extremes, we give analyses of El Niño years and relationship with global SST in statistics, but atmospheric circulation and its interaction with SST anomalies are also the important factors reflecting climate internal variability in the previous studies (Chen & Lu, 2015; Freychet et al, 2018; Li et al, 2015; Zou et al, 2015). Moreover, recently many researches have pointed out that loss of Artic sea ice can affect the atmospheric circulation and thus climate warming over the Arctic has happened in some extent (Chripko et al, 2021; Onarheim et al, 2018; Pedersen et al, 2016; Sun et al, 2018).…”

“…Beijing‐Tianjin‐Hebei region as one of the typical urban climate group, the characteristics of the average climatology and extreme climate have a new change, with the vigorous development of our social economy and the large‐scale transformation of urban structure in recent years. Climate warming and increasing extreme events since the 1950s induced by global warming and urbanization have been documented by most studies (Li et al, 2015; Lin et al, 2016; Wang et al, 2013; Wang & Lin, 2022), and one of the most interesting characteristics for Tianjin city is that urbanization effect on average and extreme temperature warming in rural areas is more prominent than that in urban areas during the last 60 years, derived from the homogenized daily surface air temperature (Si et al, 2021b).…”

The research on the carbon footprint of road freight market based on dynamic data of heavy-duty trucks

“…Peng et al [14] through analyses heavy-duty freight vehicles carbon emissions, speed and loading relationship, calculate carbon emissions index, and found that CO2 emission index decreases when vehicle speed and loading rate increase, and the relationship between CO2 emission index freight turnover is negative correlation. Lv [15] established SLM-STIRPAT and GWR-STIRPAT model based on the spatial correlation and spatial heterogeneity, to analyze the driving factors of vehicle transport carbon emissions in Beijing-Tianjin-Hebei region. Li et al [16] developed a method through the combination of the fuel consumption method and emissions coefficients based on three types of freight vehicles to calculate carbon emissions of highway in Beijing-Tianjin-Hebei region.…”

Research on the Spatial-Time Evolution and Influence Mechanism of Logistics Network Structure in Zhengzhou Metropolitan Area

“…In recent years, the research on logistics spatial network has continued to grow and has become a research hotspot. There are mainly three methods to construct logistics network in the existing research: First, the gravity model is used to calculate the logistics interaction force between regions as the connection strength and form a hierarchical system [2][3]. This model has a strong rationality in measuring the logistics connection, and the expression of logistics quality and distance can more accurately approach the actual situation, becoming an important means to measure the logistics spatial connection.…”

“…Logistics links social production and consumption, and its network form is a complex network including transportation, warehousing, circulation and distribution, with numerous influencing factors. Based on ordinary least squares (OLS) [3], fixed effect model (FEM) [24], quadratic assignment procedure (QAP) analysis [25][26], geographical detectors [27] and other research methods [28][29], some scholars have discussed the influencing factors of logistics network from different aspects such as the level of economic development [25,27], population size [27], industrial structure [29] and openness [3,25], but they have not yet formed a unified scientific understanding of this, especially few scholars have conducted in-depth discussions on the formation mechanism of the logistics network in the metropolitan area, and less consider the spatial spillover effect.…”