{"type": "FeatureCollection", "features": [{"id": "10.1016/j.jclepro.2020.125466", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:15:55Z", "type": "Journal Article", "created": "2020-12-16", "title": "Spatial differentiation characteristics and driving factors of agricultural eco-efficiency in Chinese provinces from the perspective of ecosystem services", "description": "Farmland ecosystem service is an important output of agricultural production, but it has been incompletely reflected in current studies on eco-efficiency. In this study, the value of improved farmland ecosystem services is used as one of the expected outputs. The data envelopment method is used to evaluate the agricultural eco-efficiency (AEE) of 31 provincial administrative regions in China from 2006 to 2018. The spatial autocorrelation method is used to explore the characteristics of AEE in China. Geographical detector model (Geodetector) is adopted to detect the driving factors of AEE spatial differentiation in China. China\u2019s AEE trend from 2006 to 2018 was downward with the efficiency value decreasing from 1.023 to 0.995. China\u2019s AEE level has improved with an average of 1.004. The spatial distribution pattern represented in space is in the following order: eastern region > western region > northeast region > central region. The AEE gap among provinces in the western region is the largest, and that in the northeast region is the smallest. China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. Water resource input, pesticide input and labor input are the significant control factors of AEE spatial differentiation in the eastern, central, and western regions of China.", "keywords": ["Economics and Econometrics", "China", "Environmental Engineering", "Economics", "Discrete Choice Models in Economics and Health Care", "Social Sciences", "Mathematical analysis", "01 natural sciences", "Environmental science", "Data envelopment analysis", "Life Cycle Assessment and Environmental Impact Analysis", "11. Sustainability", "FOS: Mathematics", "Ecosystem services", "Spatial distribution", "Biology", "Ecosystem Services", "Ecosystem", "0105 earth and related environmental sciences", "Agricultural economics", "2. 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Such high dimensional observations can be naturally encoded as tensors, exhibiting significant redundancies across dimensions. This property is exploited by tensor decomposition techniques that are being increasingly used for compactly encoding large multidimensional arrays. While efficient, these methods are incapable of utilizing prior information present in training data. In this paper, a novel tensor decomposition learning method is proposed for the compression of high dimensional signals. Specifically, instead of extracting independent bases for each example, our method learns an appropriate basis for each dimension from a set of training samples by solving a constrained optimization problem. As such, each sample is quantized and encoded into a reduced-size core tensor of coefficients that corresponds to the multilinear combination of the learned basis matrices. Furthermore, the proposed method employs a symbol encoding dictionary for binarizing the decomposition outputs. 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These changes create abiotic stresses, including desiccation and nutrient limitation, which act on microbes. However, our understanding of the responses of microbial communities to co-occurring climate change drivers is limited. We surveyed soil bacterial and fungal diversity and composition after a 1-year warming and altered precipitation manipulation in the Tibetan plateau alpine grassland. In isolation, warming and decreased precipitation treatments each had no significant effects on soil bacterial community structure; however, in combination of both treatments altered bacterial community structure (p = 0.03). The main effect of altered precipitation specifically impacted the relative abundances of Bacteroidetes and Gammaproteobacteria compared to the control, while the main effect of warming impacted the relative abundance of Betaproteobacteria. In contrast, the fungal community had no significant response to the treatments after 1-year. Using structural equation modeling (SEM), we found bacterial community composition was positively related to soil moisture. Our results indicate that short-term climate change could cause changes in soil bacterial community through taxonomic shifts. Our work provides new insights into immediate soil microbial responses to short-term stressors acting on an ecosystem that is particularly sensitive to global climate change.", "keywords": ["Abiotic component", "Microbial population biology", "Climate Change", "Soil Science", "Precipitation", "soil microbial community structure", "Microbiology", "Mathematical analysis", "Environmental science", "Agricultural and Biological Sciences", "Meteorology", "11. Sustainability", "FOS: Mathematics", "Genetics", "Climate change", "alpine grassland", "Biology", "Ecosystem", "2. 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China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. 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China\u2019s AEE spatial correlation distribution presents random distribution characteristics. During the research period, the lowehigh (LH) efficiency response area has centered on Yunnan Province. The lowelow (LL) level concentration area has centered on Inner Mongolia autonomous region and Liaoning Province. The highelow (HL) level diffusion effect agglomeration area has centered on Heilongjiang Province. Energy input, water resource input, and carbon emission are the core drivers of AEE spatial differentiation in China. 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