Spatial-temporal evolution and driving factors of grey water footprint efficiency in the Yangtze River Economic Belt

Spatial network and influencing factors of green water use efficiency in the YREB: considering carbon emissions and pollution indicators

Improving green water use efficiency is based on the synergy of reducing pollutants and carbon emissions, and effectively identifying its spatial development structure is a prerequisite for realizing the green transformation of water management. This paper adopted the slacks-based measure- "energy-environment-economy" (SBM-3E) model with carbon emission and pollution indicators as unanticipated outputs to measure the green water use efficiency of the 11 provinces (cities) in the Yangtze River Economic Belt from 2000 to 2018, then analyzed the efficiency network structure evolution by using the social network analysis method, and finally examined the drivers of efficiency networks. The results show that (1) the value of green water utilization efficiency decreases from 0.78 to 0.51, and its spatial distribution is downstream > middlestream > upstream, with the values of 0.41, 0.61, and 0.86, respectively. (2) The overall spatial network efficiency is gradually interconnected, with the density increasing from 0.32 to 0.6; it has undergone a structural evolution from "flat" to "inverted tower." The core-edge structure of the main body status within the basin has been strengthened. (3) The efficiency network is significantly influenced by water resource endowment, industrial structure, and foreign investment level. The conclusions are expected to provide useful insights for designing the basin's water protection policy and greening the region's development.

Cross-regional ecological compensation under the composite index of water quality and quantity: A case study of the Yellow River Basin

The calculation of ecological compensation (EC) constitutes the core element of the EC mechanism, which is related to the practicability of the mechanism. Based on the comprehensive index of water quality and quantity, this study establishes a measurement method for EC across administrative divisions. First, the improved Nemerow pollution index (NPI) is adopted to assess the water quality of specific sections, while the water rights and GDP indicators are employed to characterize the water quantity of each section. Secondly, given the spatial relationship of each region within the basin, two compensation allocation schemes are established referring to the water intake and grey water footprint indicators. Finally, the EC amounts of each region in the Yellow River Basin from 2016 to 2021 are calculated using the proposed method. The results show that: (1) the improved NPI is significantly higher in the upstream than in the downstream, implying better water quality in the upstream region; (2) From 2016 to 2021, both Gansu and Ningxia exhibit negative EC coefficients, suggesting superior water quality in the upper section of these regions, while Henan Province demonstrates the opposite trend; (3) During the same period, Qinghai, Gansu, Shanxi, Shaanxi, and Shandong experience an EC deficit, while Ningxia, Inner Mongolia, Henan, and Yellow River Conservancy Commission exhibit an EC surplus. Using water quality and quantity indicators, this study includes the implementation of basin unification and regional environmental comprehensive management into the scope of responsibility of the basin government, which can effectively solve the drawbacks of unclear responsibilities of stakeholders and ineffective implementation of compensation in the basin EC.

Does green finance improve carbon emission efficiency? Experimental evidence from China

As a noteworthy initiative of financial supply-side reform to precisely support the green development system, can green finance (GF) help achieve the dual goals of "carbon peaking" and "carbon neutrality"? Using data from China's provincial panel between 2007 and 2019, this paper measured the green finance index by the entropy method and the carbon emission efficiency (CEE) with carbon emission as the non-desired output by the Super-SBM model. Then, the influence of GF on CEE was empirically investigated by the dynamic panel model and the spatial Durbin model. The findings show that GF can significantly improve CEE and has a positive spillover impact on CEE in provinces with close economic ties; the upgrading of the industrial structure is a key mediator in the transmission of GF to CEE; and regional heterogeneity analysis finds that GF notably improves CEE in eastern, high development levels of economic and GF regions. The research can offer some theoretical and empirical references for green finance to contribute to low-carbon economic growth.

Variation and internal-external driving forces of grey water footprint efficiency in China's Yellow River Basin

Grey water footprint (GWF) efficiency is a reflection of both water pollution and the economy. The assessment of GWF and its efficiency is conducive to improving water environment quality and achieving sustainable development. This study introduces a comprehensive approach to assessing and analyzing the GWF efficiency. Based on the measurement of the GWF efficiency, the kernel density estimation and the Dagum Gini coefficient method are introduced to investigate the spatial and temporal variation of the GWF efficiency. The Geodetector method is also innovatively used to investigate the internal and external driving forces of GWF efficiency, not only revealing the effects of individual factors, but also probing the interaction between different drivers. For demonstrating this assessment approach, nine provinces in China's Yellow River Basin from 2005 to 2020 are chosen for the study. The results show that: (1) the GWF efficiency of the basin increases from 23.92 yuan/m3 in 2005 to 164.87 yuan/m3 in 2020, showing a distribution pattern of "low in the western and high in the eastern". Agricultural GWF is the main contributor to the GWF. (2) The temporal variation of the GWF efficiency shows a rising trend, and the kernel density curve has noticeable left trailing and polarization characteristics. The spatial variation of the GWF efficiency fluctuates upwards, accompanied by a rise in the overall Gini coefficient from 0.25 to 0.28. Inter-regional variation of the GWF efficiency is the primary source of spatial variation, with an average contribution of 73.39%. (3) For internal driving forces, economic development is the main driver of the GWF efficiency, and the interaction of any two internal factors enhances the explanatory power. For external driving forces, capital stock reflects the greatest impact. The interaction combinations with the highest q statistics for upstream, midstream and downstream are capital stock and population density, technological innovation and population density, and industrial structure and population density, respectively.