Risk Assessment of Geological Hazards of Qinling-Daba Mountain Area in Shaanxi Province Based on FAHP and GIS

This paper aims to identify effective mechanisms for government poverty alleviation measures based on the livelihood sustainability of farm households in Southern Shaanxi province, China. The paper utilizes data from 414 farm households, collected through field observations and in-depth interviews in 24 rural communes in Qinba Mountain Area of Shaanxi province, China. Using theoretical research methods and employing the sustainable livelihood approach (SLA) framework, this paper analyzes poverty alleviation measures as well as the impact of varied capital availability on sustainable livelihood. The study shows that developing local industries and governmental financial support improve the sustainable livelihood of farmers and eradicate absolute poverty. The findings of this study further indicate that there is a positive correlation between poverty alleviation measures and natural and social capital for sustainable livelihood. The paper provides empirical and quantitative evidence on alleviation of poverty, and the findings will help improve the sustainability of livelihood capability of farming households. This study suggests impactful approaches to stabilizing mechanisms for poverty alleviation in rural areas over the longer term.

Download Full-text

Sustainable development early warning and financing risk management of resource-based industrial clusters using optimization algorithms

Journal of Enterprise Information Management ◽

10.1108/jeim-03-2021-0152 ◽

2022 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yawen Wang ◽

Weixian Xue

Keyword(s):

Risk Assessment ◽

Early Warning ◽

Industrial Cluster ◽

Industrial Clusters ◽

Shaanxi Province ◽

Support Vector ◽

Content Type ◽

Financing Risk ◽

Yulin City ◽

Warning Model

PurposeThe purpose is to analyze and discuss the sustainable development (SD) and financing risk assessment (FRA) of resource-based industrial clusters under the Internet of Things (IoT) economy and promote the application of Machine Learning methods and intelligent optimization algorithms in FRA.Design/methodology/approachThis study used the Support Vector Machine (SVM) algorithm that is analyzed together with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm. First, Yulin City in Shaanxi Province is selected for case analysis. Then, resource-based industrial clusters are studied, and an SD early-warning model is implemented. Then, the financing Risk Assessment Index System is established from the perspective of construction-operation-transfer. Finally, the risk assessment results of Support Vector Regression (SVR) and ACO-based SVR (ACO-SVR) are analyzed.FindingsThe results show that the overall sustainability of resource-based industrial clusters and IoT industrial clusters is good in the Yulin City of Shaanxi Province, and the early warning model of GA-based SVR (GA-SVR) has been achieved good results. Yulin City shows an excellent SD momentum in the resource-based industrial cluster, but there are still some risks. Therefore, it is necessary to promote the industrial structure of SD and improve the stability of the resource-based industrial cluster for Yulin City.Originality/valueThe results can provide a direction for the research on the early warning and evaluation of the SD-oriented resource-based industrial clusters and the IoT industrial clusters, promoting the application of SVM technology in the engineering field.

Download Full-text

Geological hazards, vulnerability, and risk assessment using GIS: model for Glenwood Springs, Colorado

International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts ◽

10.1016/0148-9062(95)90222-q ◽

1995 ◽

Vol 32 (3) ◽

pp. 132A

Keyword(s):

Risk Assessment ◽

Geological Hazards ◽

Gis Model

Download Full-text

A Quantitative Assessment of Comprehensive Ecological Risk for a Loess Erosion Gully: A Case Study of Dujiashi Gully, Northern Shaanxi Province, China

Sustainability ◽

10.3390/su10093239 ◽

2018 ◽

Vol 10 (9) ◽

pp. 3239 ◽

Cited By ~ 5

Author(s):

Di Liu ◽

Xiaoying Liang ◽

Hai Chen ◽

Hang Zhang ◽

Nanzhao Mao

Keyword(s):

Risk Assessment ◽

Sustainable Development ◽

Risk Management ◽

Ecological Risk ◽

Risk Model ◽

Well Being ◽

Risk Identification ◽

Assessment System ◽

Shaanxi Province ◽

Dominant Factor

As a tool that can effectively support ecosystem management, ecological risk assessment is closely related to the sustainable development of ecosystems and human well-being and has become an active area of research in ecology, geography and other disciplines. Taking Dujiashi Gully for the study of gully loess erosion, a comprehensive risk assessment system for identifying risk probability, sensitivity and impairment was established. The spatial distribution of comprehensive ecological risk was analyzed, the ecological risk management categories were simultaneously delineated based on the risk dominant factor and the risk management strategies were formulated in loess regions. The results were as follows: (1) the spatial differences in comprehensive ecological risk were significantly different in the research area. The regions with extremely high and high risk were mainly located in gully areas and secondary erosion gullies, which are in 28.02% of study area. The extremely low-risk areas covered 1/3 of the study area and were mainly distributed to the northwest and south of the study area, where hills are widely spaced. (2) The combined analysis of ecological risk and terrain found that the elevation decreased first and then rose but the comprehensive ecological risk increased first and then decreased from north to south. Comprehensive ecological risk and terrain generally showed an inverse relationship. (3) The study area was divided into four types of risk management categories. Risk monitoring zones, habitat recovery zones, monitoring and recovery zones and natural regulation zones encompass 14.84%, 12.44%, 26.47% and 46.25% of the study area, respectively. According to four types of risk management categories, different risk reduction measures were designed to improve regional sustainable development capacity. Risk identification and risk management categories based on comprehensive ecological risk model can design a sustainable development path for social ecosystem and local farmers and provide a method for sustainable development for similar gully landscapes.

Download Full-text

Urban Sustainability Evaluation Based on the DPSIR Dynamic Model: A Case Study in Shaanxi Province, China

Sustainability ◽

10.3390/su12187460 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7460

Author(s):

Shidong Liu ◽

Peiyi Ding ◽

Binrui Xue ◽

Hongbing Zhu ◽

Jun Gao

Keyword(s):

Sustainable Development ◽

Economic Development ◽

Academic Research ◽

Urban Sustainability ◽

Urban Agglomeration ◽

Shaanxi Province ◽

Mountain Area ◽

The Sustainable Development ◽

Urban Sustainable Development ◽

Conceptual Study

The sustainability of urban cities has been the focus of significant academic research in recent years and is emphasized in Goal 11 of the Sustainable Development Goals (SDGs). In this study, we adopted the Drive-Pressure-State-Impact-Response model (DPSIR) to promote a conceptual study of sustainable development index (SDI) to compare the different urban sustainable development status and try to find the factors that affect the urban sustainable development. The framework of indicators we used is mainly based on Goal 11 of the SDGs’ targets and indicators. We chose six cities in the Shaanxi Province of China and studied them from 2008 to 2018. The results show that: (1) the sustainable development of urban cities is greatly influenced by China’s national economic development plans and urban development strategies; (2) the economic growth and management level of authorities can significantly promote urban sustainability; (3) the urban sustainability of the six cities in Shaanxi Province showed a significant imbalance and this imbalance affected the overall development of the region; (4) compared with Guanzhong urban agglomeration, Shannan urban agglomeration is subject to the policy needs of environmental protection in the Qinling mountain area and its economic development is restricted; therefore, its urban sustainability is relatively low. Theoretical contributions are presented to assist in addressing these challenges and to support policies and initiatives that move these cities in China towards achieving SDG 11.

Download Full-text