随着经济发展与城镇化快速推进,中国居民膳食模式发生显著转变,并产生了严重的资源环境后果。为识别城乡居民食物消费碳-水足迹的关键驱动因素,缓解水资源消耗,助力双碳目标实现,该研究测算并对比分析了中国31(30)省市2000—2020年城乡...随着经济发展与城镇化快速推进,中国居民膳食模式发生显著转变,并产生了严重的资源环境后果。为识别城乡居民食物消费碳-水足迹的关键驱动因素,缓解水资源消耗,助力双碳目标实现,该研究测算并对比分析了中国31(30)省市2000—2020年城乡居民人均食物消费碳-水足迹,通过对数平均迪式指数算法(logarithmic mean index method,LMDI)对碳-水足迹进行了驱动因素分解。结果显示:2000—2020年中国城镇人均食物消费碳足迹(水足迹)增加了29.63%(32.94%),农村碳足迹(水足迹)增加了4.59%(7.91%)。从空间演变来看,城镇人均食物消费碳-水足迹较高的地区由沿海省份逐渐扩散至内陆,而农村呈南北高-中间低的分布格局。从驱动因素来看,经济水平是城乡居民食物消费碳-水足迹增加的主要动因,且消费水平表现为抑制作用;人口城镇化驱动城镇居民食物消费碳-水足迹增加,而在农村起到抑制作用。该研究从促进食物消费结构转型,多渠道拓宽食物来源等方面提出建议,旨在促进中国城乡居民食物可持续消费。展开更多
Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China t...Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model.In addition,given the undesirable outputs of agricultural production,we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure(GB-US-SBM)model,which combines global benchmark technology,undesirable output,super efficiency,and slacks-based measure.The results indicated that there was an upward trend in CLUE in China from 2000 to 2020,with an increase rate of 2.62%.The temporal evolution of CLUE in China could be classified into three distinct stages:a period of fluctuating decrease(2000-2007),a phase of gradual increase(2008-2014),and a period of rapid growth(2015-2020).The major grain-producing areas(MPAs)had a lower CLUE than their counterparts,namely,non-major grain-production areas(non-MPAs).The spatial agglomeration effect followed a northeast-southwest strip distribution;and the movement path of barycentre revealed a"P"shape,with Luoyang City,Henan Province,as the centre.In terms of influencing factors of CLUE,investment in science and technology played the most vital role in improving CLUE,while irrigation index had the most negative effect.It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs.Therefore,relevant departments should formulate policies to enhance the level of science and technology,improve irrigation condition,and promote sustainable utilization of cultivated land.展开更多
Achenbach行为量表是Achenbach等人基于Achenbach实证评估系统(Achenbach System of Empirically Basedsessment,ASEBA)开发出的用于评估不同年龄段人群行为问题的著名工具。着重介绍目前运用较广的儿童行为量表(Child Behavior Checkli...Achenbach行为量表是Achenbach等人基于Achenbach实证评估系统(Achenbach System of Empirically Basedsessment,ASEBA)开发出的用于评估不同年龄段人群行为问题的著名工具。着重介绍目前运用较广的儿童行为量表(Child Behavior Checklist,CBCL)和青少年自评量表(Youth Self-Report,YSR)的版本发展演变及其在诸如自闭症谱系障碍、注意缺陷及多动障碍等人群中的应用情况,以期推动该量表在国内的有序使用,并为其本土化修订提供参考。展开更多
文摘随着经济发展与城镇化快速推进,中国居民膳食模式发生显著转变,并产生了严重的资源环境后果。为识别城乡居民食物消费碳-水足迹的关键驱动因素,缓解水资源消耗,助力双碳目标实现,该研究测算并对比分析了中国31(30)省市2000—2020年城乡居民人均食物消费碳-水足迹,通过对数平均迪式指数算法(logarithmic mean index method,LMDI)对碳-水足迹进行了驱动因素分解。结果显示:2000—2020年中国城镇人均食物消费碳足迹(水足迹)增加了29.63%(32.94%),农村碳足迹(水足迹)增加了4.59%(7.91%)。从空间演变来看,城镇人均食物消费碳-水足迹较高的地区由沿海省份逐渐扩散至内陆,而农村呈南北高-中间低的分布格局。从驱动因素来看,经济水平是城乡居民食物消费碳-水足迹增加的主要动因,且消费水平表现为抑制作用;人口城镇化驱动城镇居民食物消费碳-水足迹增加,而在农村起到抑制作用。该研究从促进食物消费结构转型,多渠道拓宽食物来源等方面提出建议,旨在促进中国城乡居民食物可持续消费。
基金supported by the National Natural Science Foundation of China(72373117)the Chinese Universities Scientific Fund(Z1010422003)+1 种基金the Major Project of the Key Research Base of Humanities and Social Sciences of the Ministry of Education(22JJD790052)the Qinchuangyuan Project of Shaanxi Province(QCYRCXM-2022-145).
文摘Improving cultivated land use eco-efficiency(CLUE)can effectively promote agricultural sustainability,particularly in developing countries where CLUE is generally low.This study used provincial-level data from China to evaluate the spatiotemporal evolution of CLUE from 2000 to 2020 and identified the influencing factors of CLUE by using a panel Tobit model.In addition,given the undesirable outputs of agricultural production,we incorporated carbon emissions and nonpoint source pollution into the global benchmark-undesirable output-super efficiency-slacks-based measure(GB-US-SBM)model,which combines global benchmark technology,undesirable output,super efficiency,and slacks-based measure.The results indicated that there was an upward trend in CLUE in China from 2000 to 2020,with an increase rate of 2.62%.The temporal evolution of CLUE in China could be classified into three distinct stages:a period of fluctuating decrease(2000-2007),a phase of gradual increase(2008-2014),and a period of rapid growth(2015-2020).The major grain-producing areas(MPAs)had a lower CLUE than their counterparts,namely,non-major grain-production areas(non-MPAs).The spatial agglomeration effect followed a northeast-southwest strip distribution;and the movement path of barycentre revealed a"P"shape,with Luoyang City,Henan Province,as the centre.In terms of influencing factors of CLUE,investment in science and technology played the most vital role in improving CLUE,while irrigation index had the most negative effect.It should be noted that these two influencing factors had different impacts on MPAs and non-MPAs.Therefore,relevant departments should formulate policies to enhance the level of science and technology,improve irrigation condition,and promote sustainable utilization of cultivated land.
文摘Achenbach行为量表是Achenbach等人基于Achenbach实证评估系统(Achenbach System of Empirically Basedsessment,ASEBA)开发出的用于评估不同年龄段人群行为问题的著名工具。着重介绍目前运用较广的儿童行为量表(Child Behavior Checklist,CBCL)和青少年自评量表(Youth Self-Report,YSR)的版本发展演变及其在诸如自闭症谱系障碍、注意缺陷及多动障碍等人群中的应用情况,以期推动该量表在国内的有序使用,并为其本土化修订提供参考。
