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Abandoned cropland is a widespread issue globally, with the impacts of utilizing abandoned cropland, such as grain production and carbon emissions, raising increasing concern. However, existing studies have largely overlooked the potential spillover effects on other regions through grain flows when increasing grain production in one region by utilizing abandoned cropland. Therefore, this study aimed to comprehensively estimate the impacts of using winter fallow fields (a typical seasonal abandoned cropland) on grain production and carbon emissions, particularly its neglected spillover impact. Focusing on Zhejiang province, this study used remote sensing techniques to identify winter fallow fields in 2018 and then assessed the impact of using those winter fallow fields on grain production based on grain yield data from the FAO, as well as its local and spillover impacts on carbon emissions based on the Greenhouse Gas Emission Factor method and a transportation carbon emission model. The results indicate the following: (1) The winter fallow fields in Zhejiang cover 5,161,000 hectares, accounting for 40.8% of the total cropland, with a notable prevalence in Jiaxing, Huzhou, Jinhua, and Quzhou. (2) Using winter fallow fields would increase grain production by 1,870,000 tons. (3) At the same time, local carbon emissions would rise by 261,000 tons if using winter fallow fields, but this would be paired with a reduction of 668,000 tons of carbon emissions from other regions (that is, a spillover impact), reflecting a net reduction (−447,000 tons) in overall emissions. In conclusion, using winter fallow fields can achieve a 'win–win' effect, increasing grain production while reducing carbon emissions. This study highlights that the spillover effects of using winter fallow fields on carbon emissions significantly surpass the localized impact, underscoring a critical aspect that has been traditionally undervalued, which should be paid more attention when policymakers formulate and implement cropland use policies. This study not only contributes to the academic discourse on sustainable land management but also serves as a practical guide for policymakers seeking to optimize agricultural productivity while curtailing the carbon footprint, thereby advancing towards a more secure and environmentally responsible food system. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper introduces a spatial decision support system to assist mineral planning practise in England and Wales. [•] It creates a spatial interaction model to reproduce detailed flows of aggregates. [•] A linear model is used to estimate CO2 emissions associated with the production and transportation of aggregates in response to this baseline situation. [•] A wide range of policy scenarios are explored to reduce CO2 emissions and an indication of the cost-effectiveness of alternative policies is provided. [Copyright &y& Elsevier]

In recent years, the incidence of neurodegenerative diseases (NDs) has gradually increased over the past decades due to the rapid aging of the global population. Traditional research has had difficulty explaining the relationship between its etiology and unhealthy lifestyle and diets. Emerging evidence had proved that the pathogenesis of neurodegenerative diseases may be related to changes of the gut microbiota's composition. Metabolism of gut microbiota has insidious and far-reaching effects on neurodegenerative diseases and provides new directions for disease intervention. Here, we delineated the basic relationship between gut microbiota and neurodegenerative diseases, highlighting the metabolism of gut microbiota in neurodegenerative diseases and also focusing on treatments for NDs based on gut microbiota. Our review may provide novel insights for neurodegeneration and approach a broadly applicable basis for the clinical therapies for neurodegenerative diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions, (Copyright © 2024 Fu, Gu, Cao, Zuo, Huang, Song, Jiang and Wang.)