Regional differences in the industrial water use efficiency of China: The spatial spillover effect and relevant factors

Chen, Yanbin; Yin, Guanwen; Liu, Kai

doi:10.1016/j.resconrec.2020.105239

Cited by 76 publications

(28 citation statements)

References 61 publications

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“…Therefore, we selected a timeseries Tobit regression model to identify the driving factors of tourism sector eco-efficiency in Gansu. The model expression is as follows [ 62 ]:

where t denotes the year, zz t is an independent variable,

is a regression coefficient, and

represents a disturbance term.…”

Section: Methodsmentioning

confidence: 99%

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Xia

Dong

et al. 2022

IJERPH

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Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

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“…Therefore, we selected a timeseries Tobit regression model to identify the driving factors of tourism sector eco-efficiency in Gansu. The model expression is as follows [ 62 ]:

where t denotes the year, zz t is an independent variable,

is a regression coefficient, and

represents a disturbance term.…”

Section: Methodsmentioning

confidence: 99%

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Xia

Dong

et al. 2022

IJERPH

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“…Hot spot analysis is a judgment method of local spatial autocorrelation, which can directly reflect the agglomeration of high and low values in a certain area and the degree of agglomeration (Chen et al 2021). In this study, the * OrdGi Getis  index reflecting local spatial autocorrelation was used to measure the local spatial agglomeration of IEE in the Yellow River Basin.…”

Section: 2methodologymentioning

confidence: 99%

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China’s economic transformation: spatial-temporal characteristics and influencing factors

Song

Yin

et al. 2021

Environ Sci Pollut Res

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At present, China's economic development has entered a "new normal." Exploring Industrial ecological efficiency (IEE) in the background of economic transformation is of great significance to promote China's industrial transformation and upgrading and achieving high-quality economic development. Based on the Super-Efficiency DEA model, this study evaluated the IEE of cities in the Yellow River Basin from 2008 to 2017. Exploratory spatial data analysis methods were used to explore the spatial-temporal evolutionary characteristics, and a panel regression model was established to explore the influencing factors of IEE. The research results showed that: The IEE in the Yellow River Basin exhibited an elongated S-shaped evolutionary trend from 2008 to 2017, and the mean IEE of cities presented a trend whereby Yellow River Basin's regions could be ranked in the following order: lower reaches > middle reaches > upper reaches. There was significant spatial autocorrelation of the IEE in the Yellow River Basin, and the hot and cold spots showed an obvious "spatial clubs" phenomenon. The results of panel regression show that the influence factors of IEE in the Yellow River Basin showed spatial heterogeneity in their effect.

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“…To eliminate the influence of other factors on the explained variables, in accordance with previous studies [ 30 , 64 ], we selected five indicators as control variables: industrial structure, economic development level, opening-up level, science and technology investment, and industrial agglomeration.…”

Section: Methodsmentioning

confidence: 99%

Environmental Regulation, Financial Pressure and Industrial Ecological Efficiency of Resource-Based Cities in China: Spatiotemporal Characteristics and Impact Mechanism

Hou

Yin

Chen

2022

IJERPH

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A resource-based city is a type of city characterized by the exploitation and processing of natural resources as the leading industry in the region. Such cities provide essential resources for China’s economic development and support long-term rapid economic growth. However, resource-based cities (RBCs) face challenges, including resource depletion, economic recession, environmental pollution, and ecological damage, to which not enough attention has been paid. In the context of China’s increased focus on environmental protection and the economy, improving industrial ecological efficiency of RBCs has become ever more important. In the present study, the Super-SBM model was used to measure the industrial ecological efficiency of 114 RBCs in China from 2003 to 2016. The results show that during the study period, the industrial ecological efficiency of RBCs in China improved significantly, particularly in the central and western regions. The results from a Tobit model show that appropriate environmental regulation and financial pressure have a positive impact on the industrial ecological efficiency of RBCs. However, when faced with the dual pressures of environmental regulation and financial difficulty, improvement in industrial ecological efficiency was inhibited. The impact of environmental regulation and financial pressure on industrial ecological efficiency of cities in different regions and development stages and with different resource types shows heterogeneity. In accordance with the study findings, differentiated measures and suggestions are proposed to improve the industrial ecological efficiency of RBCs.

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Regional differences in the industrial water use efficiency of China: The spatial spillover effect and relevant factors

Cited by 76 publications

References 61 publications

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Eco-Efficiency and Its Drivers in Tourism Sectors with Respect to Carbon Emissions from the Supply Chain: An Integrated EEIO and DEA Approach

Industrial ecological efficiency of cities in the Yellow River Basin in the background of China’s economic transformation: spatial-temporal characteristics and influencing factors

Environmental Regulation, Financial Pressure and Industrial Ecological Efficiency of Resource-Based Cities in China: Spatiotemporal Characteristics and Impact Mechanism

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