Rural energy consumption in China has increased dramatically in the last decades, and has become a significant contributor of carbon emissions. Yet there is limited data on energy consumption patterns and their evolut...Rural energy consumption in China has increased dramatically in the last decades, and has become a significant contributor of carbon emissions. Yet there is limited data on energy consumption patterns and their evolution in forest rural areas of China. In order to bridge this gap, we report the findings of field surveys in forest villages in Weichang County as a case study of rural energy consumption in northern China. We found that the residential energy consumption per household is 3313 kgce yr^-1 （kilogram standard coal equivalent per year）, with energy content of 9.7×lO7 kJ yr^-1, including 1783 kgce yr^-1 from coal, 1386 kgce yr^-1 from fuel wood, 96 kgce yr^-1 from electricity, and 49 kgce yr^-1 from LPG. Per capita consumption is 909 kgce yr^-1 and its energy content is 2.7×lO7 kJ yr^-1. Due to a total energy utilization efficiency of 24.6%, all the consumed energy can only supply about 2.4×107 kJ yr^-1 of efficient energy content. Secondly, household energy consumption is partitioned into 2614 kgce yr^-1 for heating, 616 kgce yr^-1 for cooking, and 117 kgce yr^-1 for home appliances. Thirdly, the associated carbon emissions oer household are 2556 kzC yr^-1, includinz1022 kgC yr^-1 from unutilized fuel wood （90% of the total fuel wood）. The rest of emissions come from the use of electricity （212 kgC yr^-1, coal （13Ol kgC yr^-1 and LPG （21 kgC yr^-1. Fourthly, local climate, family size and household income have strong influences on rural residential energy consumption. Changes in storage and utilization practices of fuel can lead to the lO%-30% increase in the efficiency of fuel wood use, leading to reduced energy consumption by 924 kgce yr^-1 per household （27.9% reduction） and 9Ol kgC yr^-1 of carbon emissions （35-3% reduction）.展开更多
In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hy...In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.展开更多
文摘Rural energy consumption in China has increased dramatically in the last decades, and has become a significant contributor of carbon emissions. Yet there is limited data on energy consumption patterns and their evolution in forest rural areas of China. In order to bridge this gap, we report the findings of field surveys in forest villages in Weichang County as a case study of rural energy consumption in northern China. We found that the residential energy consumption per household is 3313 kgce yr^-1 （kilogram standard coal equivalent per year）, with energy content of 9.7×lO7 kJ yr^-1, including 1783 kgce yr^-1 from coal, 1386 kgce yr^-1 from fuel wood, 96 kgce yr^-1 from electricity, and 49 kgce yr^-1 from LPG. Per capita consumption is 909 kgce yr^-1 and its energy content is 2.7×lO7 kJ yr^-1. Due to a total energy utilization efficiency of 24.6%, all the consumed energy can only supply about 2.4×107 kJ yr^-1 of efficient energy content. Secondly, household energy consumption is partitioned into 2614 kgce yr^-1 for heating, 616 kgce yr^-1 for cooking, and 117 kgce yr^-1 for home appliances. Thirdly, the associated carbon emissions oer household are 2556 kzC yr^-1, includinz1022 kgC yr^-1 from unutilized fuel wood （90% of the total fuel wood）. The rest of emissions come from the use of electricity （212 kgC yr^-1, coal （13Ol kgC yr^-1 and LPG （21 kgC yr^-1. Fourthly, local climate, family size and household income have strong influences on rural residential energy consumption. Changes in storage and utilization practices of fuel can lead to the lO%-30% increase in the efficiency of fuel wood use, leading to reduced energy consumption by 924 kgce yr^-1 per household （27.9% reduction） and 9Ol kgC yr^-1 of carbon emissions （35-3% reduction）.
基金Projects(42177164,52474121)supported by the National Science Foundation of ChinaProject(PBSKL2023A12)supported by the State Key Laboratory of Precision Blasting and Hubei Key Laboratory of Blasting Engineering,China。
文摘In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.
