Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Fo...Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO<sub>2</sub> e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.展开更多
Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes fa...Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes factors that contribute to crash occurrence based on two national datasets in the United States (CISS and NASS-CDS) for the years 2017-2022 and 2010-2015, respectively. Three taxonomies were applied to enhance understanding of the various crash contributing factors. These taxonomies were developed based on previous research and practice and involved different groupings of human factors, vehicle factors, and roadway and environmental factors. Statistics for grouping the different types of factors and statistics for specific factors are provided. The results indicate that human factors are present in over 95% of crashes, roadway and environmental factors are present in over 45% of crashes, and vehicle factors are present in less than 2% of crashes. Regarding factors related to human error and vehicle maintenance, speeding is involved in over 25% of crashes, distraction is involved in over 20% of crashes, alcohol and drugs are involved in over 9% of crashes, and vehicle maintenance is involved in approximately 0.45% of crashes. Approximately 4.4% of crashes involve a driver who “looked but did not see.” Weather is involved in over 13% of crashes. Conclusions: The findings indicate that, consistent with previous research, human factors or human error are present in around 95% of crashes. Infrastructure and environmental factors contribute to about 45% of crashes. Vehicle factors contribute to only 1.67% - 1.71% of crashes. The results from this study could potentially be used to inform future safety management and improvement activities, including policy-making, regulation development, safe systems and systemic safety approaches to safety management, and other engineering, education, emergency response, enforcement, evaluation, and encouragement activities. The findings could also be used in the development of future Driver Assistance Technologies (DAT) systems and in enhancing existing technologies.展开更多
We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an ...We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger(OLTC) and transmission lines. The system power factor(PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method.Capacitor-only control strategy is a common photovoltaic(PV)regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.展开更多
Taking the relevant data of 27 provinces in China during 2013 and 2017 as samples,this paper firstly measured the agricultural total factor productivity( TFP) using Malmquist index method. Then,it built the panel data...Taking the relevant data of 27 provinces in China during 2013 and 2017 as samples,this paper firstly measured the agricultural total factor productivity( TFP) using Malmquist index method. Then,it built the panel data model,and empirically tested the impacts of agricultural TFP on the income gap between urban and rural residents. The results show that the improvement in agricultural TFP can promote the narrowing of the income gap between urban and rural residents,and the factors such as urbanization level and industrial structure also have significant impacts on the income gap between urban and rural residents. On the basis of these,it came up with recommendations,including increasing agricultural human capital investment and establishing agricultural production research institutions.展开更多
Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when ...Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.展开更多
文摘Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO<sub>2</sub> e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.
文摘Understanding crash contributing factors is essential in safety management and improvement. These factors drive investment decisions, policies, regulations, and other safety-related initiatives. This paper analyzes factors that contribute to crash occurrence based on two national datasets in the United States (CISS and NASS-CDS) for the years 2017-2022 and 2010-2015, respectively. Three taxonomies were applied to enhance understanding of the various crash contributing factors. These taxonomies were developed based on previous research and practice and involved different groupings of human factors, vehicle factors, and roadway and environmental factors. Statistics for grouping the different types of factors and statistics for specific factors are provided. The results indicate that human factors are present in over 95% of crashes, roadway and environmental factors are present in over 45% of crashes, and vehicle factors are present in less than 2% of crashes. Regarding factors related to human error and vehicle maintenance, speeding is involved in over 25% of crashes, distraction is involved in over 20% of crashes, alcohol and drugs are involved in over 9% of crashes, and vehicle maintenance is involved in approximately 0.45% of crashes. Approximately 4.4% of crashes involve a driver who “looked but did not see.” Weather is involved in over 13% of crashes. Conclusions: The findings indicate that, consistent with previous research, human factors or human error are present in around 95% of crashes. Infrastructure and environmental factors contribute to about 45% of crashes. Vehicle factors contribute to only 1.67% - 1.71% of crashes. The results from this study could potentially be used to inform future safety management and improvement activities, including policy-making, regulation development, safe systems and systemic safety approaches to safety management, and other engineering, education, emergency response, enforcement, evaluation, and encouragement activities. The findings could also be used in the development of future Driver Assistance Technologies (DAT) systems and in enhancing existing technologies.
文摘We present an electrical grid optimization method for economical benefit. After simplifying an IEEE feeder diagram, we build a compact smart grid system including a photovoltaic-inverter system, a shunt capacitor, an on-load tapchanger(OLTC) and transmission lines. The system power factor(PF) regulation and reactive power dispatching are indispensable to improve power quality. Our control method uses predictive weather and load data to decide engaging or tripping the shunt capacitor, or reactive power injection by the photovoltaic-inverter system, ultimately to keep the system PF in a good range. From the perspective of economics, the economical model is considered as a decision maker in our predictive data control method.Capacitor-only control strategy is a common photovoltaic(PV)regulation method, which is treated as a baseline case. Simulations with GridLAB-D on profiled loads and residential loads have been carried out. The comparison results with baseline control strategy and our predictive data control method show the appreciable economical benefit of our method.
文摘Taking the relevant data of 27 provinces in China during 2013 and 2017 as samples,this paper firstly measured the agricultural total factor productivity( TFP) using Malmquist index method. Then,it built the panel data model,and empirically tested the impacts of agricultural TFP on the income gap between urban and rural residents. The results show that the improvement in agricultural TFP can promote the narrowing of the income gap between urban and rural residents,and the factors such as urbanization level and industrial structure also have significant impacts on the income gap between urban and rural residents. On the basis of these,it came up with recommendations,including increasing agricultural human capital investment and establishing agricultural production research institutions.
基金supported by the National Nature Science Foundation of China(Grant No.71401052)the National Social Science Foundation of China(Grant No.17BGL156)the Key Project of the National Social Science Foundation of China(Grant No.14AZD024)
文摘Identification of security risk factors for small reservoirs is the basis for implementation of early warning systems.The manner of identification of the factors for small reservoirs is of practical significance when data are incomplete.The existing grey relational models have some disadvantages in measuring the correlation between categorical data sequences.To this end,this paper introduces a new grey relational model to analyze heterogeneous data.In this study,a set of security risk factors for small reservoirs was first constructed based on theoretical analysis,and heterogeneous data of these factors were recorded as sequences.The sequences were regarded as random variables,and the information entropy and conditional entropy between sequences were measured to analyze the relational degree between risk factors.Then,a new grey relational analysis model for heterogeneous data was constructed,and a comprehensive security risk factor identification method was developed.A case study of small reservoirs in Guangxi Zhuang Autonomous Region in China shows that the model constructed in this study is applicable to security risk factor identification for small reservoirs with heterogeneous and sparse data.
