Spatial–Temporal Evolution Characteristics and Influencing Factors of Agricultural Water Use Efficiency in Northwest China—Based on a Super-DEA Model and a Spatial Panel Econometric Model

Lu, Weinan; Liu, Wenxin; Hou, Mengyang; Deng, Yuanjie; Deng, Yue; Zhou, Boyang; Zhao, Kai

doi:10.3390/w13050632

Cited by 23 publications

(16 citation statements)

References 25 publications

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“…Therefore, we use Exploratory Spatial Data Analysis (ESDA) to detect spatial correlation among the variables. ESDA is used to analyze spatial characteristics through global and local spatial autocorrelation measurements [ 42 , 89 ].…”

Section: Methodsmentioning

confidence: 99%

The contribution of tourism mobility to tourism economic growth in China

Liu

Yue²,

Yu³

et al. 2022

PLoS ONE

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Mobility is the key factor in promoting tourism economic growth (TEG), and the transportation infrastructure has essential functions for maintaining an orderly flow of tourists. Based on the theory of fluid mechanics, we put forward the indicator of tourism mobility (TM). This study is the first to measure the level of TM in China and analyze the spatiotemporal evolution characteristics of TM. Applying the Exploratory Spatial Data Analysis method, we analyze the global and local spatial correlation characteristics of TM. Moreover, we further estimate the contribution of TM to TEG by econometric models and the LMDI method. The results show that (1) the TM in China has maintained rapid growth for a long time. However, there are differences in the rate of growth in different regions. The TM in each region only showed a significant positive spatial correlation in 2016–2018. The space-time pattern is constantly changing over time. The local spatial autocorrelation results of TM are stable, and various agglomeration states are stably distributed in some provinces. (2) The regression results of the traditional panel data model and spatial panel data model both show that TM has a significant positive effect on TEG. Moreover, TM has a negative spatial spillover effect on neighboring regions. (3) The result from the decomposition of LMDI shows that the overall contribution of TM to TEG is 15.76%. This shows that improving TM is a crucial way to promote the economic growth of tourism.

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Section: Methodsmentioning

confidence: 99%

The contribution of tourism mobility to tourism economic growth in China

Liu

Yue²,

Yu³

et al. 2022

PLoS ONE

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“…Wang, et al [24] used a spatial econometric model to analyze whether there was a spatial correlation between AWUE in several provinces in China, and to determine the main factors that affect AWUE. Lu, et al [29] used exploratory spatial data analysis (ESDA) to find that AWUE exhibited an obvious spatial autocorrelation in Northwest China and showed high-high and low-low agglomeration characteristics. Although these studies investigated the spatial correlations between agricultural water resources, most of the basic data were attribute data, and had nothing to do with inter-regional correlation effects.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Spatial Network Structure of Agricultural Water Use Efficiency in China: A Social Network Perspective

2022

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The exploration of the spatial network structure of agricultural water use efficiency (AWUE) and its influencing factors for promoting water saving and improving water use efficiency in regional agricultural production is of great importance. In this paper, the modified gravity model and social network analysis methods were used to study the spatial correlation characteristics and influencing factors of AWUE in China between the years 2008 and 2019. It was found that (1) the overall trend of AWUE in China has been fluctuating and declining, and there are obvious differences in AWUE in each region; (2) the spatial network structure of AWUE in China is complex and relatively stable, with close interprovincial connections and obvious spatial spillover effects; (3) Shanghai, Beijing, Jiangsu, and Zhejiang are at the center of the network; and (4) the differences between geographical adjacency, technological development level, farmers’ income, and natural resource endowment have significant effects on the development of the AWUE network. These results provide a theoretical basis for the government to improve AWUE and promote collaborative regional development.

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“…The northwest region is one of the seven natural geographic regions of China, located in the center of Eurasia [35] (Figure 1). The region is characterized by arid and semi-arid climates with scarce precipitation and ecological fragility; thus, it is also known as the northwest arid region [36].…”

Section: Study Areamentioning

confidence: 99%

Spatiotemporal Evolution of Water Resource Utilization and Economic Development in the Arid Region of China: A “Matching-Constraint” Perspective

et al. 2022

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Water resources are the most important guarantees for sustainable socioeconomic development in arid regions. However, serious water scarcity puts great pressure on the sustainable development of the northwest arid region of China. Based on the “matching-constraint” perspective, this study used the Gini coefficient, imbalance index, and growth drag model of water resources to quantify the spatiotemporal evolution of water resource utilization (WRU) and economic development (ED) in the northwest arid region from 2009 to 2019. The results showed the following: (1) The matching degree of WRU and ED in Gansu and Xinjiang from 2009 to 2019 was poorer than that in Ningxia, Shaanxi, and Qinghai. Cities with the high matching type of WRU and ED were dominant, with a proportion of 60.78%. (2) During the study period, the growth drag of water resources showed an “N-shaped” change of “rising–declining–rising” and a spatial pattern of “decreasing from inland to coastal” in the northwest arid region. The average growth drag coefficients for the five northwestern provinces were as follows: Xinjiang (2.22%), Gansu (1.61%), Ningxia (1.41%), Qinghai (1.01%), and Shaanxi (0.84%). The total percentage of low and medium constraint type cities was 74.51%. (3) Based on the urban “matching-constraint” types, the WRU status was divided into four zone types: Zones I and IV had relatively well-allocated water resources; however, zone IV had more significant water resource constraints, with the growth drag coefficient ranging from 1.10% to 2.30%. An imbalance between WRU and ED existed in Zones II and III; moreover, the water resource constraints of these two zones were also significant, with growth drag coefficients ranging from 0.12% to 1.47% and 1.03% to 2.90%, respectively. Additionally, we explored the driving mechanisms of WRU and sustainable ED in the northwest arid region. Policy recommendations are proposed for the optimal use of water resources, capital, and labor for different types of cities.

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Spatial–Temporal Evolution Characteristics and Influencing Factors of Agricultural Water Use Efficiency in Northwest China—Based on a Super-DEA Model and a Spatial Panel Econometric Model

Cited by 23 publications

References 25 publications

The contribution of tourism mobility to tourism economic growth in China

The contribution of tourism mobility to tourism economic growth in China

Exploring the Spatial Network Structure of Agricultural Water Use Efficiency in China: A Social Network Perspective

Spatiotemporal Evolution of Water Resource Utilization and Economic Development in the Arid Region of China: A “Matching-Constraint” Perspective

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