How New Urbanization Affects Tourism Eco-Efficiency in China: An Analysis Considering the Undesired Outputs

Zhang, Fengtai; Yang, Xingyu; Wu, Jianfeng; Ma, Dalai; Xiao, Yuedong; Gong, Guofang; Zhang, Junyi

doi:10.3390/su141710820

Cited by 10 publications

(4 citation statements)

References 67 publications

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“…The proportion of urban population in the total population was positively correlated with tourism eco-efficiency, which is consistent with the research results of Zhang et al [13]. The city with the strongest positive influence was Cangzhou, and the surrounding cities of Cangzhou, Baoding and Xingtai, also had a strong influence.…”

Section: Analysis Of the Influencing Factors Of Tourism Eco-efficiencysupporting

confidence: 90%

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Regional Differences in Tourism Eco-Efficiency in the Beijing–Tianjin–Hebei Region: Based on Data from 13 Cities

Zhang

2023

Sustainability

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In order to balance the economic development and ecological impact of tourism, it is essential to study tourism eco-efficiency in the context of sustainable development. This study analyzed regional tourism eco-efficiency based on the panel data of the 13 cities of the Beijing–Tianjin–Hebei region using the super-SBM DEA model. Then, we analyzed the driving factors, compared regional differences, and investigated influencing factors of tourism eco-efficiency by applying the global Malmquist–Luenberger (GML) index, Theil index, and geographically and temporally weighted regression (GTWR) models. The results demonstrate the following: (1) The overall tourism eco-efficiency in the Beijing–Tianjin–Hebei region between 2010 and 2019 was low, but it had an increasing trend. (2) The advancement of technological progress factors was mostly responsible for the increase in tourist eco-efficiency. (3) The results for tourism eco-efficiency were significantly polarizing, but the gap among the 13 cities is gradually narrowing. Regional differences are the main contributors to differences in tourism eco-efficiency. (4) Per capita GDP, the proportion of tertiary industry in GDP, the number of patents granted, and the proportion of urban population in the total population were the main factors affecting tourism eco-efficiency. This study could serve as a model for similar countries and regions seeking to enhance tourism eco-efficiency and achieve the Sustainable Development Goals.

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Section: Analysis Of the Influencing Factors Of Tourism Eco-efficiencysupporting

confidence: 90%

“…Since then, research on tourism eco-efficiency began to expand and cover various perspectives [12]. Scholars mostly focus on measuring tourism eco-efficiency [13,14] and its influencing mechanisms [15,16].…”

Section: Introductionmentioning

confidence: 99%

Regional Differences in Tourism Eco-Efficiency in the Beijing–Tianjin–Hebei Region: Based on Data from 13 Cities

Zhang

2023

Sustainability

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“…For example, at the firm (industry) level, Fan et al (2022) measured the EE of real estate development using the super-SBM model and showed that the overall efficiency of 108 cities in the Yangtze River Economic Belt improved from 2006 to 2020. Zhang, Yang, Wu, et al (2022) used a similar approach to measure the EE of China's tourism industry and showed that it is generally in a stage of dynamic equilibrium. To assess the EE of the mining industry in 30 Chinese provinces, Zuo et al (2022) used a two-stage super-SBM model, and they found that the EE of the mining industry was higher in Western China than in other regions.…”

Section: Eco-efficiencymentioning

confidence: 99%

Using eco‐efficiency and eco‐wellbeing performance as indicators for urban sustainable development: A two‐stage network analysis

Zhang,

Cai,

Jiao

et al. 2023

Sustainable Development

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Sustainable development is one of the most significant issues of concern around the world today. Sustainable development goals cover natural resources, the environment, and human well‐being. It follows that eco‐efficiency (EE) and eco‐well‐being performance (EWP) are valid tools for measuring the level of different stages of sustainable development. This study uses a two‐stage super‐SBM model to gauge the EE and EWP of 281 cities in China from 2011 to 2019. The results show the following: (1) The national EE average is low, with M‐shaped “rising‐declining‐rising‐declining” fluctuation. The national EWP average is low, but there is a clear upward trend. (2) From a regional perspective, the best EE is in the eastern region, followed by the central and western regions, and the worst is in the northeastern region. The best EWP is in the western region, followed by the eastern and northeastern regions, and the worst is in the central region. (3) Among the 281 sample cities, approximately 12.8% are in the high EE‐high EWP development mode, 25.6% are in the high EE‐low EWP development mode, 18.2% are in the low EE‐high EWP development mode, and 43.4% are in the low EE‐low EWP development mode. Collectively, the three cities with the strongest sustainability are Shenzhen, Beijing, and Haikou, and the three weakest are Baise, Jinchang, and Baiyin. In addition, cities with larger populations tend to exhibit greater sustainability. These results provide the necessary reference for Chinese cities and cities worldwide in similar situations to formulate policies to improve sustainability.

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“…The increase of urbanization rate has significant heterogeneity for regions, and the urbanization rate is higher in the eastern region and central region at the present stage. Further increasing the level of urbanization rate will raise the level of production of carbon emissions, so the transformation of new urbanization should be accelerated, and new urbanization should play an important role in revitalizing the economy, while being a key step in the coordinated development of economic and ecological environments [77]. Therefore, new-type urbanization should be strengthened, especially in the eastern region and central region, to eliminate the high pollution and carbon emissions brought by traditional urbanization construction.…”

Section: Policy Suggestionsmentioning

confidence: 99%

Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption

Wang

2022

IJERPH

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Urban production energy consumption produces a large amount of carbon emissions, which is an important source of global warming. This study measures the quantity and intensity of carbon emissions in 30 provinces of China based on urban production energy consumption from 2005–2019, and uses the Dagum Gini coefficient, kernel density estimation, carbon emission classification and spatial econometric model to analyze the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions from China and regional production energy consumption. It was found that the growth rate of carbon emission quantity and carbon emission intensity of production energy consumption decreased year by year in each province during the study period. The imbalance of carbon emission was strong, with different degrees of increase and decrease, and there were big differences between eastern and western regions. The classification of carbon emissions differed among provinces and there was heterogeneity among regions. The quantity and intensity of carbon emissions of production energy consumption qwre affected by multiple factors, such as industrial structure. This study provides an in-depth comparison of the spatial and temporal distribution and driving factors of quantity and intensity of carbon emissions of production energy consumption across the country and regions, and provides targeted policies for carbon emission reduction across the country and regions, so as to help achieve China’s “double carbon” target quickly and effectively.

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How New Urbanization Affects Tourism Eco-Efficiency in China: An Analysis Considering the Undesired Outputs

Cited by 10 publications

References 67 publications

Regional Differences in Tourism Eco-Efficiency in the Beijing–Tianjin–Hebei Region: Based on Data from 13 Cities

Regional Differences in Tourism Eco-Efficiency in the Beijing–Tianjin–Hebei Region: Based on Data from 13 Cities

Using eco‐efficiency and eco‐wellbeing performance as indicators for urban sustainable development: A two‐stage network analysis

Spatial and Temporal Distribution and the Driving Factors of Carbon Emissions from Urban Production Energy Consumption

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