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中国地理科学（英文版）
中国地理科学（英文版）

朱颜明

双月刊

1002-0063

egeoscien@neigae.ac.cn

0431-85542243

130012

长春市高新路3195号

中国地理科学（英文版）/Journal Chinese Geographical ScienceCSCDCSTPCD北大核心SCI
查看更多>>本刊是综合性英文地理学术刊物，主要报道自然地理学及其分支学科，人文地理学及其分支学科，遥感及地理信息系统，地图学，当今国际上关注的，如人口、资源、环境等重大问题。本刊为Current Geographical Publications和Elsevier的Geo Abstracts来源期刊；美国国会图书馆馆藏期刊；《中国期刊评价数据库》和《中国科学引文数据库》来源期刊；《中国期刊网》和《中国学术期刊（光盘版）》全文收录期刊。国内外公开发行。
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    Sustainable Intensification and Large-scale Operation of Cultivated Land Use at the Farmers'Scale:A Case Study of Shandong Province,China

    LI LiLYU XiaoZHANG AnluNIU Shandong...
    149-167页
    查看更多>>摘要：Sustainable intensification of cultivated land use(SICLU)and large-scale operations(LSO)are widely acknowledged strategies for enhancing agricultural performance.However,the existing literature has faced challenges in precisely defining SICLU and constructing comprehensive indicators,which has hindered the exploration of factors influencing LSO within the SICLU framework.To address this gap,we integrated self-efficacy theory into the design of an index framework for evaluating SICLU.We subsequently em-ployed econometric models to analyze the significant factors that impact LSO.Our findings reveal that SICLU can be divided into four key dimensions:intensive management,efficient output,resource conservation,and ecological environment optimization.Furthermore,it is crucial to incorporate belief-based cognitive factors into the index system,as farmers'understanding of fertilizer and pesticide ap-plication significantly influences their willingness to engage in LSO.Moreover,we identify grain market turnover as the most influen-tial factor in promoting LSO,with single-factor contribution rates reaching 70.9％for cultivated land transfer willingness and 62.5％for the total planting areas.Interestingly,unlike irrigation and agricultural machinery inputs,increased labor inputs correspond to larger planting areas for farmers.This trend may be attributed to reduced labor availability because of rural labor migration,whereas the reduc-tion in irrigation and agricultural input is contingent on innovations in production practices and the transfer of cultivated land manage-ment rights.Importantly,SICLU dynamically influences LSO,with each index related to SICLU having an optimal range that fosters LSO.These insights offer valuable guidance for policymakers,emphasizing farmers as their central focus,with the adjustment of input and output factors as a means to achieve LSO as the ultimate goal.In conclusion,we propose research avenues for further enriching the SICLU framework to ensure that it aligns with the specific characteristics of regional agricultural development.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Spatio-temporal Evaluation of Multi-scale Cultivated Land System Re-silience in Black Soil Region from 2000 to 2019:A Case Study of Liaon-ing Province,Northeast China

    WANG YueJIANG YutingZHU Guoxu
    168-180页
    查看更多>>摘要：It is of great significance to systematically analyze the cultivated land system resilience(CLSR)for the black soil protection and national food security.The CLSR is impacted by planting structure adjustment and cultivated land quality decline,posing major hid-den dangers to food security.It is urgent to evaluate the CLSR at multiple spatio-temporal scales.This study took Liaoning Province in the black soil region of Northeast China as an example.Based on the resilience theory,this study constructed the CLSR evaluation sys-tem from the input-feedback perspective at the provincial-scale and the city-scale,and used the rank-sum ratio comprehensive evalu-ation method(RSR)to analyze the key influencing factors of CLSR in Liaoning Province and its 14 cities from 2000 to 2019.The res-ults showed that:1)the time series changes of CLSR at the provincial-scale and the city-scale in Liaoning Province were similar,both showing an increasing trend.2)The CLSR in Liaoning Province presented a spatial pattern of'high in the west and low in the east'at the city-scale.3)There were seven and six main influencing factors of CLSR at the provincial-scale and the city-scale,respectively.In addition to the net income per capita of rural households,other influencing factors of CLSR were different at the provincial-scale and the city-scale.The feedback factors were dominant at the provincial-scale,and the input factors and feedback factors were dominant at the city-scale.The results could provide a reference for the utilization of black soil and draw on the experience of regional agricultural planning and adjustment.
      原文链接:
    • NETL
    • NSTL
    • 万方数据

    Carbon Emission Effects Driven by Evolution of Chinese Dietary Struc-ture from 1987 to 2020

    ZHU YuanyuanZHANG YanZHU Xiaohua
    181-194页
    查看更多>>摘要：Exploring carbon emission effects based on the evolution of residents'dietary structure to achieve the carbon neutrality goal and mitigate climate change is an important task.This study took China as the research object(data excluding Hong Kong,Macao and Taiwan)and used the carbon emission coefficient method to quantitatively measure the food carbon emissions from 1987-2020,then analyzed the carbon emission effects under the evolution of dietary structure.The results showed that during the study period,the Chinese dietary structure gradually changed to a high-carbon consumption pattern.The dietary structure of urban residents developed to a balanced one,while that of rural residents developed to a high-quality one.During the study period,the per capita food carbon emis-sions and total food consumption of Chinese showed an increasing trend.The per capita food carbon emissions of residents in urban and rural showed an overall upward trend.The total food carbon emissions in urban increased significantly,while that in rural increased first and then decreased.The influence of beef and mutton on carbon emissions is the highest in dietary structure.Compared with the bal-anced dietary pattern,the food carbon emissions of Chinese residents had not yet reached the peak,but were evolving to a high-carbon consumption pattern.
      原文链接:
    • NETL
    • NSTL
    • 万方数据