Urban agglomerations are not only the core areas leading economic growth but also the fronts facing severe resource and environmental challenges. This paper aimed to increase our understanding of urban eco-efficiency and its influencing factors and thus provide the scientific basis for green development. We developed a model that incorporates super-efficiency, slacks-based-measure, and global-frontier technology to calculate the total-factor eco-efficiency (TFEE) and used a spatial panel Tobit model to take into account spatial spillover effects. An empirical study was conducted utilizing a prefecture-level dataset in the Yangtze River Delta Urban Agglomeration (YRDUA) from 2003 to 2016. The main findings reveal that significant spatial differences exist in TFEE in the YRDUA: high-TFEE cities were majorly located in the coastal areas, while low-TFEE cities were mostly situated inland. Overall, TFEE shows a trend of “decline first and then rise with fluctuation”; the disparity between inland and coastal regions has expanded. Further regression analysis suggests that industrial structure, environmental regulation, and innovation were positively related to TFEE, while foreign direct investment was not conducive to the growth in TFEE. The relationship between population intensity and urban eco-efficiency is an inverted U-shaped curve. Finally, several specific policy implications were raised based on the results.

This paper investigated the factors driving the changes in industrial wastewater emission intensity (IWEI) across provinces in China. To do this, we proposed a Super-efficiency Slacks-based Measure-Global Malmquist Index (SSBM-GMI) to decompose the change in IWEI into the effects from efficiency change (ECE), technological change (TCE), capital–wastewater substitution (KWE) and labor–wastewater substitution (LWE). The method was applied to conduct an empirical study using Chinese provincial data from 2003–2015. The main findings include the following: firstly, TCE was the dominant driving force behind the reduction in IWEI with an average annual contribution of −6.4% at the national level, followed by KWE (−5.3%), LWE (−1.8%) and ECE (1.2%). Secondly, significant differences exist in the driving factors behind the reduction in IWEI across regions. The reduction in IWEIs in the Northeast area and the Great Northwest area was mainly driven by productivity growth, while the reduction in IWEIs in the other areas was mainly driven by factor substitution. Thirdly, the shortage of KWE and LWE has impeded IWEI reduction in the Great Northwest area, the Middle Reaches of the Yellow River, the Northeast area and the North area. Finally, some particular policy implications were also recommended for reducing industrial wastewater emission in China.

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Quinoa is widely cultivated for its nutritional value and its high tolerance to environmental problems. Our study was conducted at two planting densities (LD, 10 plants/ m 2 ; HD, 65 plants/m 2 ) on ameliorated coastal mudflats in Jiangsu Province, China.The microbial composition in soil was determined by high-throughput sequencing technology, and root metabolites were determined by a liquid chromatograph-mass spectrometer (LC-MS). The results showed soil salinity and organic matter were higher at the HD than LD treatment and compared with the non-rhizosphere (bulk) soil, the salinity of the rhizosphere soil was higher. Quinoa grown at HD was taller, with thicker stalks and lower yields per plant, but higher yield per unit area. Amplicon sequencing showed that Proteobacteria, Bacteroidota and Acidobacteria accounted for the absolute majority. Regarding the rhizosphere soil, the Shannon index was higher in the HD than LD, and Proteobacteria and Bacteroidota were more abundant in the HD treatment. Fifty-one differential metabolites were identified by metabolomic assays, belonging to 14 annotated metabolic pathways. S-adenosylmethionine was the most abundant and up-regulated metabolite (fold change >1.67), and was more abundant in the roots from the LD than in HD treatment. Docosahexaenoic acid was more abundant in the HD than LD treatment, and was a down-regulated metabolite.In conclusion, planting density was an important factor affecting quinoa yield; compared with unplanted soil, planting quinoa at low density increased the content of the important metabolite S-adenosylmethionine in the root system of quinoa, and high-density cultivation of quinoa increased soil salinity and microbial abundance and diversity.