The Spatial Pattern and Spillover Effect of the Eco-Efficiency of Regional Tourism from the Perspective of Green Development: An Empirical Study in China

Abstract

Scientifically analyzing the spatial pattern and spillover effect of the eco-efficiency of regional tourism embodies the green development theory. In addition, it is also of important significance for realizing the sustainable development of regional tourism and promoting regional ecological civilization. This study incorporates energy consumption and CO2 emissions of tourism into the efficiency evaluation index system. On this basis, the slacks-based measure–data envelopment analysis (SBM-DEA) with undesirable output, the spatial autocorrelation (SAC) model and the spatial Durbin model (SDM) are used to study the spatial pattern and spillover effect of the eco-efficiency of provincial tourism in China from 2008 to 2017. Results show that the following: (1) The average eco-efficiency of national tourism is 0.534, which is at the medium development level as a whole. Among the decomposed efficiencies of eco-efficiency, the scale efficiency drives the optimal development of eco-efficiency in tourism. (2) The eco-efficiency of tourism shows a spatial differentiation pattern on the regional scale as follows: it is the highest in the central region, moderate in the western region, and lowest in the eastern region. (3) The degree of clustering of the eco-efficiency of tourism first increases and then decreases. The SAC-based cluster pattern is dominated by a low-low (LL) cluster, followed successively by a high-high (HH) cluster and a low-high (LH) outlier, while a high-low (HL) outlier is the least significant (4). Among the influencing factors, the technical level shows spatial spillover effects on both the eco-efficiency and pure technical efficiency of tourism; the economic development level and traffic accessibility mainly have spatial spillover effects on the pure technical efficiency and scale efficiency of tourism; the industrial structure and environmental regulation separately have a spatial spillover effect only on the pure technical efficiency and the scale efficiency of tourism.

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

“…Furthermore, some scholars discussed influencing factors of tourism eco-efficiency. The main influencing factors of regional tourism eco-efficiency are technical level, economic development level, traffic accessibility, industrial structure, urbanization and environmental regulation factors (Chen et al, 2020;Li et al, 2022). The same views are held by other scholars, such as Liu et al (2021), who studied the determinants of tourism ecoefficiency of 30 provinces in China, pointed out that the level of economic development, traffic conditions, the professional level of tourism, and openness degree can significantly promote tourism efficiency.…”

Spatial and temporal distribution characteristics and influencing factors of tourism eco-efficiency in the Yellow River Basin based on the geographical and temporal weighted regression model

“…The spatial distributions of cold and hot spots are mutually validated with the local spatial interrelation ( Fig 7 ). The spatial agglomeration characteristics of tourism eco-efficiency in the Yellow River Basin are obvious, and they are influenced by the spatial spillover effect of tourism eco-efficiency [ 36 , 61 ]. The agglomeration areas in the middle reaches are distributed in Shanxi Province and those in the upper reaches are distributed in Ningxia, Inner Mongolia, Gansu, and Qinghai provinces.…”

Factors Influencing the Spatial Spillovers of the Interprovincial Tourism Economy Based on Three-dimensional Distance: Evidence From China

“…The correlation and spillover effects between different cities (Bo et al, 2017), regions (Alola et al, 2019), and countries (Škrinjarić, 2019) are confirmed by scholars across different countries. The main methods used in previous studies are the gravity model (Kadiyali & Kosová, 2013), social network analysis (Kelman et al, 2016; Zuo & Yang, 2021) geographically weighted regression model (Liu et al, 2021), data envelopment analysis (S. Li et al, 2022), spatial panel measures (Eleftheriou & Sambracos, 2019), and so on.…”

“…The correlation and spillover effects between different cities (Bo et al, 2017), regions (Alola et al, 2019), and countries (S ˇkrinjaric´, 2019) are confirmed by scholars across different countries. The main methods used in previous studies are the gravity model (Kadiyali & Kosova´, 2013), social network analysis (Kelman et al, 2016;Zuo & Yang, 2021) geographically weighted regression model (Liu et al, 2021), data envelopment analysis (S. Li et al, 2022), spatial panel measures (Eleftheriou & Sambracos, 2019), and so on. Hence, there are six main contributing factors to the spatial spillover effect on the tourism economy in the existing studies, which can be summarized as follows: Song and Song (2011) argue that there is an obvious spatial spillover effect of innovation on tourism economic growth in China's provinces, which not only dominates the growth of the tourism economy itself but also has a positive tourism economic spillover effect on the neighboring regions through the spatial transmission mechanism.…”