scholarly journals Assessment on Agricultural Drought Vulnerability and Spatial Heterogeneity Study in China

2021 ◽  
Vol 18 (9) ◽  
pp. 4449
Author(s):  
Hongpeng Guo ◽  
Jia Chen ◽  
Chulin Pan
Keyword(s):  
Influencing Factors ◽  
Agricultural Drought ◽  
Contribution Rate ◽  
Drought Vulnerability ◽  
Entropy Weight ◽  
Scoring Method ◽  
Resilience Index ◽  
Entropy Weight Method ◽  
Forest Coverage ◽  
Northwest Region

Reducing drought vulnerability is a basis to achieve sustainable development in agriculture. The study focuses on agricultural drought vulnerability in China by selecting 12 indicators from two aspects: drought sensitivity and resilience to drought. In this study, the degree of agricultural drought vulnerability in China has been evaluated by entropy weight method and weighted comprehensive scoring method. The influencing factors have also been analyzed by a contribution model. The results show that: (1) From 1978 to 2018, agricultural drought vulnerability showed a decreasing trend in China with more less vulnerable to mildly vulnerable cities, and less highly vulnerable cities. At the same time, there is a trend where highly vulnerable cities have been converted to mildly vulnerable cities, whereas mildly vulnerable cities have been converted to less vulnerable cities. (2) This paper analyzes the influencing factors of agricultural drought vulnerability by dividing China into six geographic regions. It reveals that the contribution rate of resilience index is over 50% in the central, southern, and eastern parts of China, where agricultural drought vulnerability is relatively low. However, the contribution rate of sensitivity is 75% in the Southwest and Northwest region, where the agricultural drought vulnerability is relatively high. Among influencing factors, the multiple-crop index, the proportion of the rural population and the forest coverage rate have higher contribution rate. This study carries reference significance for understanding the vulnerability of agricultural drought in China and it provides measures for drought prevention and mitigation.

Identifying influence patterns of regional agricultural drought vulnerability using a two-phased grey rough combined model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huifang Sun ◽  
Liping Fang ◽  
Yaoguo Dang ◽  
Wenxin Mao
Keyword(s):  
Influencing Factors ◽  
Temporal Dynamics ◽  
Decision Rules ◽  
Agricultural Drought ◽  
Drought Vulnerability ◽  
Entropy Weight ◽  
Combined Model ◽  
Content Type ◽  
Base Points ◽  
Double Base

PurposeA core challenge of assessing regional agricultural drought vulnerability (RADV) is to reveal what vulnerability factors, under which kinds of synergistic combinations and at what strengths, will lead to higher vulnerability: namely, the influence patterns of RADV.Design/methodology/approachA two-phased grey rough combined model is proposed to identify influence patterns of RADV from a new perspective of learning and mining historical cases. The grey entropy weight clustering with double base points is proposed to assess degrees of RADV. The simplest decision rules that reflect the complex synergistic relationships between RADV and its influencing factors are extracted using the rough set approach.FindingsThe results exemplified by China's Henan Province in the years 2008–2016 show higher degrees of RADV in the north and west regions of the province, in comparison with the south and east. In the patterns with higher RADV, the higher proportion of agricultural population appears in all decision rules as a core feature. A smaller quantity of water resources per unit of cultivated land area and a lower adaptive capacity, involving levels of irrigation technology and economic development, present a significant synergistic influence relationship that distinguishes the features of higher vulnerability from those of the lower.Originality/valueThe proposed grey rough combined model not only evaluates temporal dynamics and spatial differences of RADV but also extracts the decision rules between RADV and its influencing factors. The identified influence patterns inspire managerial implications for preventing and reducing agricultural drought through its historical evolution and formation mechanism.

scholarly journals The Spatial Correlations of Health Resource Agglomeration Capacities and Their Influencing Factors: Evidence from China

2020 ◽  
Vol 17 (22) ◽  
pp. 8705
Author(s):  
Qingbin Guo ◽  
Kang Luo ◽  
Ruodi Hu
Keyword(s):  
Influencing Factors ◽  
Spillover Effect ◽  
Health Resources ◽  
Eastern Region ◽  
Spatial Network ◽  
Health Resource ◽  
Spatial Correlations ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Positive Correlations

We measured the health resource agglomeration capacities of 31 Chinese provinces (or municipalities) in 2004–2018 based on the entropy weight method. Using a modified spatial gravity model, we constructed and analyzed the spatial correlation network of these health resource agglomeration capacities and their influencing factors through social network analysis. We found that: (i) China’s health resource agglomeration capacity had a gradual strengthening trend, with capacity weakening from east to west (strongest in the eastern region, second strongest in the central region, and weakest in the western region). (ii) The spatial network of such capacities became more densely connected, and the network density and level (efficiency) showed an upward (downward) trend. (iii) In terms of centrality, the high-ranking provinces (or municipalities) were Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, Shandong, Hunan, Hubei, Fujian, Anhui, Jiangxi, and Tianjin, while the low-ranking were Tibet, Qinghai, Gansu, Ningxia, Inner Mongolia, Heilongjiang, Yunnan, Guizhou, Xinjiang, Hainan, Shaanxi, and Shanxi. (iv) Block 1 (eight provinces or municipalities), including Beijing, Tianjin, and Hebei, had a “net spillover” effect in the spatial network of health resource agglomeration capacities; Block 2, (seven provinces or municipalities), including Shanghai, Jiangsu, and Zhejiang, had a “bidirectional spillover” effect in the spatial network; Block 3 (seven provinces or municipalities), including Anhui, Hubei, and Hunan, had a “mediator” effect in the network; and Block 4, (nine provinces or municipalities), including Sichuan, Guizhou, and Tibet, had a “net beneficial” effect in the network. (v) The economic development, urbanization wage, and financial health expenditure levels, and population size had significant positive correlations with the spatial network of health resource agglomeration capacities. Policy recommendations to enhance the radiating role of health resources in core provinces (or municipalities), rationally allocate health resources, and transform ideas to support public health resource services were provided.

scholarly journals Identifying Influencing Factors of Regional Agricultural Drought Vulnerability Based on PSR-TGRC Method

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Dongxing Zhang ◽  
Wenkai Cao ◽  
Bing Qi
Keyword(s):  
Influencing Factors ◽  
Agricultural Drought ◽  
Analysis Method ◽  
Drought Vulnerability ◽  
Relative Variation ◽  
Factors Affecting ◽  
Relational Analysis ◽  
Theoretical Support ◽  
Number Of Factors ◽  
Evaluation Problem

Regional agricultural drought vulnerability (RADV) is a complex nonlinear problem caused by the interaction of multiple factors, and an objective and systematic method is proposed by this paper to identify its influencing factors, which plays an important role in preventing and regulating the risks of regional agricultural drought. Firstly, to provide a reference for the evaluation problem in selecting the number of factors, the influencing factors affecting RADV are revealed by using the method of phase space reconstruction (PSR). Secondly, to rank the importance of influencing factors, a grey trend relational analysis (TGRA) method is proposed, considering the dynamic development relationship between the RADV index and the influencing factors and integrating the absolute and relative variation of sequences in each corresponding period. Finally, to reduce the collinearity between the influencing factors, a grey trend relational clustering (TGRC) analysis method is proposed. According to the above steps, the process of identifying factors based on PSR-TGRC method is formed. Taking Henan Province as an example, 14 main influencing factors and their effects on RADV are identified from all 42 factors, and the identification results which are consistent with the actual drought relief work show the rationality and practicality of PSR-TGRC method and provide theoretical support for formulating strategies of regional agricultural disaster prevention and mitigation.

scholarly journals An Optimization Route Selection Method of Urban Oversize Cargo Transportation

2021 ◽  
Vol 11 (5) ◽  
pp. 2213
Author(s):  
Da Huang ◽  
Mei Han
Keyword(s):  
Influencing Factors ◽  
Cloud Model ◽  
Route Selection ◽  
Topsis Method ◽  
Entropy Weight ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Cargo Transportation ◽  
Transportation Routes ◽  
Transportation Route

In order to select the optimal transportation route among alternative transportation routes more accurately and objectively, the choice of urban oversize cargo transportation routes was studied by taking the optimization weight–TOPSIS combination method for specific calculations. This model, based on an entropy weight method, cloud model, and TOPSIS method, combines the superiority of the cloud model for reflecting the randomness and discreteness of subjective evaluation with the advantages of the TOPSIS method in dealing with the problem of multi-objective programming. Through selecting and classifying several the main road influencing factors of urban oversize cargo transportation, based on the data of four urban roads, the entropy weight method is used to initially determine the weights of each influencing factor, the cloud model is used to optimize weights, the TOPSIS method is used to compare and evaluate the paths, and the optimal transportation route is selected on this basis. The results showed that the optimization weight–TOPSIS method is scientific and accurate for the multi-objective planning of oversize cargo transportation route selection, and solves the problem of the impact of subjective factors in existing methods and the difficulty of processing multiple influencing factors. The Pearson consistency test results show that the Pearson correlation coefficient between the proposed method and the actual oversize cargo transportation route selection is 0.995, which is higher than the calculation results without using the combination weight.

A fire risk assessment method based on the combination of quantified safety checklist and structure entropy weight for shopping malls

2021 ◽  
pp. 1748006X2098737
Author(s):  
Quanle Zou ◽  
Tiancheng Zhang ◽  
Wei Liu
Keyword(s):  
Evaluation Method ◽  
Research Result ◽  
Fire Risk ◽  
Living Standard ◽  
Shopping Malls ◽  
Entropy Weight ◽  
Essential Components ◽  
Entropy Weight Method ◽  
Weight Method ◽  
Laws And Regulations

In recent years, various large- and medium-sized shopping malls have been essential components of each city with the speed-up of China’s urbanization process and the improvement of residents’ living standard. A method for evaluating fire risk in shopping malls based on quantified safety checklist and structure entropy weight method was proposed according to related literatures as well as laws and regulations by analyzing the characteristics of fires occurring in shopping malls in recent years. At first, the factors influencing the fire risk in shopping malls were determined by carrying out on-site survey and visiting related organizations to construct an evaluation index system for fires occurring in shopping malls; afterwards, a quantified safety checklist composed of four parts (i.e. safety grade, grade description, scoring criterion and index quantification) was established based on related laws and regulations; subsequently, index weights were determined by utilizing structure entropy weight method, thus putting forward a method for assessing fire risk in shopping malls based on quantified safety checklist and structure entropy weight method. Eventually, the applicability of the evaluation method was validated exampled by Wal-Mart. The research result provides a theoretical basis for further improvement of the theoretical system for fire risk evaluation in shopping malls, and also exerts practical and guidance significance on timeous and effective early warning as well as prevention and control of building fires.

Research on Degree Evaluation of Rural Waste Recycling of Heilongjiang Province Based on the Integrated Evaluation Method

2011 ◽  
Vol 347-353 ◽  
pp. 1735-1739
Author(s):  
Jie Shang ◽  
Yuan Yao
Keyword(s):  
Index System ◽  
Agricultural Waste ◽  
Waste Recycling ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Heilongjiang Province ◽  
Entropy Weight ◽  
Development Direction ◽  
Entropy Weight Method ◽  
Weight Method

This paper has analyzed the degree of agricultural waste recycling utilization, and problems existing in current rural calculated degree of waste recycling in Heilongjiang province, using AHP and entropy weight method integrated and construct the rural waste recycling system, and points out that the evaluation index system of agricultural waste recycling after the development direction.,This paper has analyzed the degree of agricultural waste recycling utilization, and problems existing in current rural calculated degree of waste recycling in Heilongjiang province, using AHP and entropy weight method integrated and construct the rural waste recycling system, and points out that the evaluation index system of agricultural waste recycling after the development direction.

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