scholarly journals Spatial analysis of logistics ecological efficiency and its influencing factors in China: based on super-SBM-undesirable and spatial Dubin models

2021 ◽  
Author(s):  
Dongling Bai ◽  
Qianli Dong ◽  
Syed Abdul Rehman Khan ◽  
Yan Chen ◽  
Dongfang Wang ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Influencing Factors ◽  
Ecological Efficiency

scholarly journals Urban Ecological Efficiency and Its Influencing Factors—A Case Study in Henan Province, China

2019 ◽  
Vol 11 (18) ◽  
pp. 5048 ◽  
Author(s):  
Tao Liu ◽  
Jixia Li ◽  
Juan Chen ◽  
Shaolei Yang
Keyword(s):  
Economic Development ◽  
Influencing Factors ◽  
Efficiency Analysis ◽  
Henan Province ◽  
Measure Data ◽  
Ecological Efficiency ◽  
Ecological Civilization ◽  
Growth Trend ◽  
Static And Dynamic Analysis ◽  
Significant Difference

The urban ecological civilization construction relates to welfare of the people and the national future. It is an important field of the high-quality economic development to improve the urban ecological efficiency level. The purpose of this research is to provide a new perspective and method for the quantitative study of the urban sustainable development, and also to provide some decision-making references for the improvement of the urban ecological efficiency in Henan province. This paper uses the slacks-based measure-data envelopment analysis (SBM-DEA) model containing the undesirable output and the Malmquist index model to fully evaluate the urban ecological efficiency level in Henan province during the period of 2005–2016, via both the static and dynamic analysis. Based on this, the bootstrap regression model is applied in analyzing the influencing factors of the urban ecological efficiency. The research shows three findings. First, according to the static efficiency analysis, the urban ecological efficiency in Henan province is low as a whole and has a big promotion space. Moreover, there is a significant difference in the urban ecological efficiency level among the five regions because of the different geographical locations and social and economic development situations of the cities. Second, according to the dynamic efficiency analysis, in the last 12 years, the urban ecological efficiency in Henan province has shown an overall growth trend, and the technological progress has played a major role in promoting the urban ecological efficiency in Henan province. Third, according to the influencing factor analysis, the governmental financial support hinders the improvement of the urban ecological efficiency in Henan province, while the level of opening to the outside world, the urban population density, and the urban greening level promote it.

scholarly journals Spatial analysis of logistics ecological efficiency and its influencing factors in China: based on Super-SBM-Undesirable and spatial Dubin models

2021 ◽  
Author(s):  
Dongling Bai ◽  
Qianli Dong ◽  
Syed Abdul Rehman Khan ◽  
Yan Chen ◽  
Dongfang Wang ◽  
...  
Keyword(s):  
Environmental Regulation ◽  
Influencing Factors ◽  
Energy Intensity ◽  
Spatial Information ◽  
Spillover Effect ◽  
Positive Influence ◽  
Energy Structure ◽  
Level Energy ◽  
Ecological Efficiency ◽  
Logistics Industry

Abstract Improving the logistics ecological efficiency (LEE) has become a significant part of ensuring a sustainable development and tackling environmental pollution. Previous studies in the logistics industry seldom considered air pollutants and the association of spatial information. Therefore, innovatively considering SO2, NOx, and PM, this study adopted the Super-SBM-Undesirable model to calculate the LEE of 30 provinces in China from 2004 to 2017, and thereafter, developed information-based matrix to explore its influencing factors by using the spatial Dubin model. The results indicated that: (1) The overall LEE during the study period was low, presenting a U-shaped trend of an initial decrease and subsequent rise, and significant regional differences with the decreasing gradient pattern of the “Eastern-Central-Western.” (2) A spatial directionality distributed from the northeast to southwest, and a significant spatial autocorrelation were observed. (3) The industrial structure had the greatest positive influence on the local LEE, followed by the urbanization level, technology innovation level, environmental regulation, while the energy intensity was identified as the main inhibiting factor, followed by the economic level, energy structure and opening level. (4) The LEE had a significant positive spillover effect; the energy intensity and environmental regulation positively affected the LEE in neighboring areas, while the opening level had negative impacts. In addition, policy recommendations for enhancing the LEE were made.

scholarly journals An Integrated Measurement of the Efficiency of China’s Industrial Circular Economy and Associated Influencing Factors

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1610
Author(s):  
Chenyu Lu ◽  
Yang Zhang ◽  
Hengji Li ◽  
Zilong Zhang ◽  
Wei Cheng ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Influencing Factors ◽  
Circular Economy ◽  
Negative Impact ◽  
Positive Impact ◽  
Eastern Region ◽  
Industrial Transformation ◽  
Starting Point ◽  
Autonomous Regions ◽  
The Impact

Since the 1990s, the notion of a circular economy has been developing globally; countries all over the world have been considering the development of a circular economy as an important means of achieving sustainable development. As the development of an industrial circular economy can help promote the efficient recycling of resources, it is an important starting point for industrial transformation and upgrading, and represents a key factor that will lead to the development of a circular economy in China. China’s varying provinces (municipalities and autonomous regions) have successively implemented circular economy practices in the industrial field. The research object of the present study is 30 provinces, autonomous regions, and municipalities directly under the control of central government (Hong Kong, Macao, Taiwan, and Tibet were not included owing to lack of data). Through the integration of geographic information systems (GIS) technology and the spatial analysis model, data envelopment analysis (DEA) model, and Tobit regression model, a measure model and index system are constructed, in order to carry out a multi-angle comprehensive study integrating the efficiency evaluation, spatial analysis, and influencing factors analysis of China’s industrial circular economy. It is an important innovation, and an important contribution to the existing research system. The conclusions are as follows: (1) In general, the overall level of China’s industrial circular economy’s efficiency was not high, and there was still a lot of room for improvement. The integrated efficiency of the industrial circular economy in the eastern region was relatively high, followed by that in the western region, and the lowest level in the middle region. (2) The efficiency of China’s industrial circular economy displayed obvious spatial aggregation characteristics at the provincial level, including clear spatial dependence and spatial heterogeneity. High-value aggregation areas were mainly distributed in the eastern coastal areas, and low-value aggregation areas were concentrated and contiguously distributed in the middle and western inland areas. (3) The four elements of economic level, openness to the outside, government regulation, and industrialization aggregation each impose a significant positive impact on the efficiency of China’s industrial circular economy, which can promote its efficiency. The level of industrialization exerts a significant negative impact on the efficiency of the industrial circular economy, which hampers its improvement. The impact of technological innovation on the efficiency of the industrial circular economy is not statistically significant.

Building a historical flooding map through spatial analysis

2020 ◽  
Author(s):  
Priscila Barros Ramalho Alves ◽  
Iana Alexandra Alves Rufino ◽  
Slobodan Djordjévic ◽  
Akbar Javadi
Keyword(s):  
Spatial Analysis ◽  
Influencing Factors ◽  
Flow Direction ◽  
Policy Makers ◽  
Model Builder ◽  
Flood Susceptibility ◽  
Multiple Datasets ◽  
Gis Environment ◽  
Flood Susceptibility Mapping ◽  
The City

<p>Due to the increase of flooding cases around the world, there is a need for producing even more accurate flood susceptibility mapping. For this, different models, software and frameworks have been developed for many years to assist local authorities and policy-makers for forecasting hazards and mitigating flooding impacts. However, spatially model flooding in real-world systems remains considered as a difficult task. Forecasting flooding requires knowledge from past events and mapping flood locations is crucial to explain the correlation among the flooding and the influencing factors as well as model calibration and validation. In developing countries, a collection of flooding records and inventories remains challenging, either because the data is not available, or because it is not in the suitable scale and resolution. Building historical flooding map is considered a time-consuming process with multiple datasets and normally with costly field surveys. Besides, acquiring this data can be harder due to the inexistence of flooding insurance or civil protection agencies support. This work aims to contribute to this context by developing and assessing a GIS-based framework to map historical flooding cases through the use of spatial analysis. In this study, we used ArcGIS Pro software to construct a historic flooding map for Campina Grande, Brazil. The city faces recurrent flooding episodes, but there is not an available official map with flooding locations to guide decisions for mitigation. The GIS-based framework allows analysing and better understand the interactions of flooding locations and geographic features. The analysis obtained 230 flooding locations in different scales (buildings, streets and neighbourhoods) and sources for the period from 2004 to 2018. Topographic and hydrologic flood influencing factors (altitude, slope, distance to rivers and lakes, flow direction and accumulation) were selected and combined as layers in the GIS environment. Further, criteria were modelled based on spatial analysis and relations to estimate proximity areas around flood occurrence points with high probability of flooding conditions. These tools allowed to compare visual and data patterns of features and surfaces. All locations and factors were then integrated through Model Builder in order to generate a surface with flooding locations within the city. The final historical flooding map was evaluated and validated with 172 points of confirmed flood cases in the city. The GIS-based framework represents a way of analysing and producing historical inventory maps for flooding management using spatial analysis.</p>

Using Gis-based Spatial Analysis To Determine Factors Influencing the Formation of Sinkholes in Greene County, Missouri

2018 ◽  
Vol 24 (3) ◽  
pp. 251-261 ◽  
Author(s):  
Shishay T. Kidanu ◽  
Neil L. Anderson ◽  
J. David Rogers
Keyword(s):  
Spatial Analysis ◽  
Influencing Factors ◽  
Ground Surface ◽  
Ordinary Least Squares ◽  
Hazard Mapping ◽  
Infrastructure Development ◽  
Least Squares Regression ◽  
Karst Terrain ◽  
Gwr Model ◽  
Greene County

Abstract Sinkholes are inherent features of the karst terrain of Greene County, Missouri, that present hazards and engineering challenges to construction/infrastructure development. Analysis of relationships between the spatial distribution of sinkholes and possible influencing factors can help in understanding the controls involved in the formation of sinkholes. The spatial analysis outlined herein can aid in the assessment of potential sinkhole hazards. In this research, Geographic Information System–based ordinary least squares regression (OLS) and geographically weighted regression (GWR) methods were used to determine and evaluate principal factors appearing to influence the formation and distribution of karst sinkholes. From the OLS result, seven out of 12 possible influencing factors were found to exert significant control on sinkhole formation processes in the study area. These factors are overburden thickness, depth to groundwater, slope of the ground surface, distance to the nearest surface drainage line, distance to the nearest geological structure (such as faults or folds), distance to the nearest road, and distance to the nearest spring. These factors were then used as independent variables in the GWR model. The GWR model examined the spatial non-stationarity among the various factors and demonstrated better performance over OLS. GWR model coefficient estimates for each variable were mapped. These maps provide spatial insights into the influence of the variables on sinkhole densities throughout the study area. GWR spatial analysis appears to be an effective approach to understand sinkhole-influencing factors. The results could be useful to provide an objective means of parameter weighting in models of sinkhole susceptibility or hazard mapping.

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