为科学评估“双碳”目标下中国氢燃料电池产业的碳排放绩效,突破现有研究对国外数据库依赖性强、缺乏空间异质性考量的局限,研究基于GIS-LCA平台构建了整合地理信息系统与生命周期评价的质子交换膜氢燃料电池(PEMFC)本土化碳排放评价模...为科学评估“双碳”目标下中国氢燃料电池产业的碳排放绩效,突破现有研究对国外数据库依赖性强、缺乏空间异质性考量的局限,研究基于GIS-LCA平台构建了整合地理信息系统与生命周期评价的质子交换膜氢燃料电池(PEMFC)本土化碳排放评价模型,系统核算了PEMFC从原材料生产、运输、组装、使用到报废回收5个阶段的碳排放量,并对比了氢源分别来自于煤气化制氢、甲烷重整制氢、工业副产品提纯制氢及绿电—电解水制氢4种不同制氢路径下的PEMFC全生命周期碳足迹。以额定功率80 kW为功能单位进行计算,结果表明:该燃料电池系统在基础阶段(除使用阶段外的其余4个阶段)的碳排放为1 198.57 kg CO_(2)-eq,其中原材料生产阶段贡献达66.26%。铂的生产是此阶段最大的碳排放源,其碳排放量占基础阶段碳排放的30.73%。当考虑不同氢源的间接碳排放时,使用阶段成为总碳排放的主导环节,燃料电池用氢来源于煤气化制氢方式时的PEMFC全生命周期碳排放最高,为28 698 kg CO_(2)-eq,其次为甲烷重整制氢方式,全生命周期碳排放为14 948 kg CO_(2)-eq,当氢气来源于绿电—电解水制氢方式时,全生命周期碳排放最低,为4 848 kg CO_(2)-eq,仅占煤气化制氢方式下总碳排放的16.9%。展开更多
Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to ac...Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.展开更多
Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations i...Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha^(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.展开更多
本文应用生命周期评价法(LCA)对裙带菜栽培加工产业进行了全周期的碳足迹分析,明确了各环节中碳排放源的种类和数量。结果表明:裙带菜浮筏栽培加工阶段的碳排放总量为3.95×10^(5) kg CO_(2)e/百亩,高于百亩裙带菜栽培阶段形成的碳...本文应用生命周期评价法(LCA)对裙带菜栽培加工产业进行了全周期的碳足迹分析,明确了各环节中碳排放源的种类和数量。结果表明:裙带菜浮筏栽培加工阶段的碳排放总量为3.95×10^(5) kg CO_(2)e/百亩,高于百亩裙带菜栽培阶段形成的碳汇量,从全产业的尺度来看,裙带菜栽培加工产业尚不是一个碳汇产业。在裙带菜产业链中,首先为加工阶段的碳排放量最大,主要来自包装的大量使用;其次为存储阶段的碳排放,主要来自制冷设备的电耗;最后为栽培阶段的碳排放,主要来自柴油消耗。为了提升裙带菜产业的碳汇能力,建议通过改变能源形式、提高材料的使用寿命、选择低碳替代品等途径来降低裙带菜产业的碳排放量。展开更多
文摘为科学评估“双碳”目标下中国氢燃料电池产业的碳排放绩效,突破现有研究对国外数据库依赖性强、缺乏空间异质性考量的局限,研究基于GIS-LCA平台构建了整合地理信息系统与生命周期评价的质子交换膜氢燃料电池(PEMFC)本土化碳排放评价模型,系统核算了PEMFC从原材料生产、运输、组装、使用到报废回收5个阶段的碳排放量,并对比了氢源分别来自于煤气化制氢、甲烷重整制氢、工业副产品提纯制氢及绿电—电解水制氢4种不同制氢路径下的PEMFC全生命周期碳足迹。以额定功率80 kW为功能单位进行计算,结果表明:该燃料电池系统在基础阶段(除使用阶段外的其余4个阶段)的碳排放为1 198.57 kg CO_(2)-eq,其中原材料生产阶段贡献达66.26%。铂的生产是此阶段最大的碳排放源,其碳排放量占基础阶段碳排放的30.73%。当考虑不同氢源的间接碳排放时,使用阶段成为总碳排放的主导环节,燃料电池用氢来源于煤气化制氢方式时的PEMFC全生命周期碳排放最高,为28 698 kg CO_(2)-eq,其次为甲烷重整制氢方式,全生命周期碳排放为14 948 kg CO_(2)-eq,当氢气来源于绿电—电解水制氢方式时,全生命周期碳排放最低,为4 848 kg CO_(2)-eq,仅占煤气化制氢方式下总碳排放的16.9%。
基金supported by the National Natural Science Foundation of China(42101382 and 41901342)the Shandong Provincial Natural Science Foundation(ZR2020QD016)the National Key Research and Development Program of China(2016YFD0300101).
文摘Accurate estimation of regional winter wheat yields is essential for understanding the food production status and ensuring national food security.However,using the existing remote sensing-based crop yield models to accurately reproduce the inter-annual and spatial variations in winter wheat yields remains challenging due to the limited ability to acquire irrigation information in water-limited regions.Thus,we proposed a new approach to approximating irrigations of winter wheat over the North China Plain(NCP),where irrigation occurs extensively during the winter wheat growing season.This approach used irrigation pattern parameters(IPPs)to define the irrigation frequency and timing.Then,they were incorporated into a newly-developed process-based and remote sensing-driven crop yield model for winter wheat(PRYM–Wheat),to improve the regional estimates of winter wheat over the NCP.The IPPs were determined using statistical yield data of reference years(2010–2015)over the NCP.Our findings showed that PRYM–Wheat with the optimal IPPs could improve the regional estimate of winter wheat yield,with an increase and decrease in the correlation coefficient(R)and root mean square error(RMSE)of 0.15(about 37%)and 0.90 t ha–1(about 41%),respectively.The data in validation years(2001–2009 and 2016–2019)were used to validate PRYM–Wheat.In addition,our findings also showed R(RMSE)of 0.80(0.62 t ha–1)on a site level,0.61(0.91 t ha–1)for Hebei Province on a county level,0.73(0.97 t ha–1)for Henan Province on a county level,and 0.55(0.75 t ha–1)for Shandong Province on a city level.Overall,PRYM–Wheat can offer a stable and robust approach to estimating regional winter wheat yield across multiple years,providing a scientific basis for ensuring regional food security.
基金supported by the National Key Research and Development Program of China(2016YFD0300101,and 2016YFD0300110)the National Natural Science Foundation of China(41871253 and 31671585)+1 种基金the“Taishan Scholar”Project of Shandong Province,Chinathe Key Basic Research Project of Shandong Natural Science Foundation,China(ZR2017ZB0422)。
文摘Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha^(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.
文摘本文应用生命周期评价法(LCA)对裙带菜栽培加工产业进行了全周期的碳足迹分析,明确了各环节中碳排放源的种类和数量。结果表明:裙带菜浮筏栽培加工阶段的碳排放总量为3.95×10^(5) kg CO_(2)e/百亩,高于百亩裙带菜栽培阶段形成的碳汇量,从全产业的尺度来看,裙带菜栽培加工产业尚不是一个碳汇产业。在裙带菜产业链中,首先为加工阶段的碳排放量最大,主要来自包装的大量使用;其次为存储阶段的碳排放,主要来自制冷设备的电耗;最后为栽培阶段的碳排放,主要来自柴油消耗。为了提升裙带菜产业的碳汇能力,建议通过改变能源形式、提高材料的使用寿命、选择低碳替代品等途径来降低裙带菜产业的碳排放量。
