scholarly journals Spatial Heterogeneity of the Impact Factors on Gray Water Footprint Intensity in China

2020 ◽  
Vol 12 (3) ◽  
pp. 865
Author(s):  
Lingling Zhang ◽  
Rui Zhang ◽  
Zongzhi Wang ◽  
Fan Yang
Keyword(s):  
Spatial Heterogeneity ◽  
Pollution Control ◽  
Total Population ◽  
Water Footprint ◽  
Factors Influencing ◽  
Gray Water ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Impact ◽  
Capita Output

The gray water footprint intensity represents the amount of freshwater resources that need dilution of pollutants per unit of economic output, which indicates the relationship among water pollution, water resources and economy. In this paper, the gray water footprint of 31 provinces (autonomous regions) in China was estimated based on different water bodies. The spatial pattern and spatial agglomeration characteristics of gray water footprint in China from 2000 to 2014 were explored from the perspective of spatial autocorrelation. By extending the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact of the total population, urbanization rate, per capita output value, the proportion of the tertiary industry, environmental pollution control intensity and R&D investment intensity on the degree of gray water footprint intensity are explored, with ridge regression analysis to solve the problem of collinearity affecting factors. Meanwhile, the Geographically Weighted Regression (GWR) model is used to detect the spatial heterogeneity and spatio-temporal variation characteristics of the factors influencing gray water footprint intensity among regions. The study found that from 2000 to 2014, the gray water footprint of 31 provinces and cities in China was unstable; the domestic gray water footprint accounted for the largest proportion; the agricultural gray water footprint was mainly derived from nitrogen fertilizer, and the industrial and domestic gray water footprint was mainly derived from ammonia nitrogen. Water pollution varies from east to west. The total intensity of gray water footprint shows a downward trend, which is related to economic development and improvement of technological level. There is a positive correlation between the urbanization rate and the intensity of the gray water footprint. The total population, the per capita output value, the proportion of the tertiary industry, the intensity of environmental pollution control, the intensity of R&D input and the intensity of the gray water footprint are negatively correlated, and the influencing factors boast obvious spatial heterogeneity. The purpose is to reveal the key factors influencing gray water footprint intensity to ensure the sustainable development of economy, resources and environment through the formulation of regional differences in regulation and control policies.

scholarly journals Spatial Characteristics of Life Expectancy and Geographical Detection of Its Influencing Factors in China

2020 ◽  
Vol 17 (3) ◽  
pp. 906 ◽  
Author(s):  
Yafei Wu ◽  
Ke Hu ◽  
Yaofeng Han ◽  
Qilin Sheng ◽  
Ya Fang
Keyword(s):  
College Students ◽  
Environmental Factors ◽  
Life Expectancy ◽  
Influencing Factors ◽  
Health Improvement ◽  
Spatial Characteristics ◽  
Two Factors ◽  
Per Capita ◽  
Urbanization Rate ◽  
The Impact

Life expectancy (LE) is a comprehensive and important index for measuring population health. Research on LE and its influencing factors is helpful for health improvement. Previous studies have neither considered the spatial stratified heterogeneity of LE nor explored the interactions between its influencing factors. Our study was based on the latest available LE and social and environmental factors data of 31 provinces in 2010 in China. Descriptive and spatial autocorrelation analyses were performed to explore the spatial characteristics of LE. Furthermore, the Geographical Detector (GeoDetector) technique was used to reveal the impact of social and environmental factors and their interactions on LE as well as their optimal range for the maximum LE level. The results show that there existed obvious spatial stratified heterogeneity of LE, and LE mainly presented two clustering types (high–high and low–low) with positive autocorrelation. The results of GeoDetector showed that the number of college students per 100,000 persons (NOCS) could mainly explained the spatial stratified heterogeneity of LE (Power of Determinant (PD) = 0.89, p < 0.001). With the discretization of social and environmental factors, we found that LE reached the highest level with birth rate, total dependency ratio, number of residents per household and water resource per capita at their minimum range; conversely, LE reached the highest level with consumption level, GDP per capita, number of college students per 100,000 persons, medical care expenditure and urbanization rate at their maximum range. In addition, the interaction of any two factors on LE was stronger than the effect of a single factor. Our study suggests that there existed obvious spatial stratified heterogeneity of LE in China, which could mainly be explained by NOCS.

scholarly journals Exploring Spatial Heterogeneity and Factors Influencing Construction and Demolition Waste in China

2021 ◽  
Author(s):  
Zhenshuang Wang ◽  
Zhongsheng Zhang ◽  
Jiangkuang Liu
Keyword(s):  
Spatial Heterogeneity ◽  
Spatial Autocorrelation ◽  
Treatment Strategies ◽  
Construction And Demolition Waste ◽  
Growth Trend ◽  
Demolition Waste ◽  
Positive Role ◽  
Factors Influencing ◽  
Per Capita ◽  
Temporal And Spatial

Abstract Natural disasters, new urbanization and urban renewal activities generated a large amount of construction and demolition waste (C&DW), and managing C&DW has become an urgent problem to be solved in the construction of “Zero-waste cities”. Based on the calculation of C&DW generation in China from 2005 to 2019, this study analyzed the temporal and spatial characteristics of C&DW generation in China, and empirically explored the factors influencing factors C&DW of China using spatial autocorrelation and geographically weighted regression. The results showed that: (1) C&DW generation in China increased every year, and the overall distribution was characterized as “high in the east and low in the west”, with distinct regional differences. The provinces with the highest per capita C&DW generation were Zhejiang, Jiangsu, Beijing, Shanghai, and Fujian. The generation intensity of C&DW in China and all its provinces showed a decreasing trend every year. The regions with rapid growth of C&DW generation in China were concentrated in the eastern coastal areas, with distinct differences between the east and west, and there was significant spatial heterogeneity in the growth trend. (2) There is a significant spatial autocorrelation in C&DW generation in China. Overall, the hot spots for C&DW generation were distributed in Jiangsu, Zhejiang, and Shandong provinces, and the spatial agglomeration effect of C&DW generation in provinces was evident. (3) Factors such as population size, per capita Gross Domestic Product and the scale of the construction industry played a positive role in promoting C&DW generation in each province, whereas labor efficiency played a negative role inhibiting C&DW generation; there was significant temporal and spatial heterogeneity. Finally, differentiation and cross-regional joint treatment strategies according to regional conditions were proposed to achieve precise measures of C&DW reduction management.

scholarly journals Information Technology and the World Growth Resurgence

2007 ◽  
Vol 8 (2) ◽  
pp. 125-145 ◽  
Author(s):  
Dale W. Jorgenson ◽  
Khuong Vu
Keyword(s):  
Economic Growth ◽  
Information Technology ◽  
World Economy ◽  
It Investment ◽  
The World ◽  
World Economic ◽  
Per Capita ◽  
The Impact ◽  
Capita Output ◽  
World Output

Abstract This paper analyzes the impact of investment in information technology (IT) on the recent resurgence of world economic growth. We describe the growth of the world economy, seven regions, and 14 major economies during the period 1989-2004. We allocate the growth of world output between input growth and productivity and find, surprisingly, that input growth greatly predominates! Moreover, differences in per capita output levels are explained by differences in per capita input, rather than variations in productivity. The contributions of IT investment have increased in all regions, but especially in industrialized economies and Developing Asia.

scholarly journals Identifying the Determinants of Urbanization in Prefecture-Level Cities in China: A Quantitative Analysis Based on Spatial Production Theory

2019 ◽  
Vol 11 (4) ◽  
pp. 1204 ◽  
Author(s):  
Zhen Yang ◽  
Jun Lei ◽  
Jian-Gang Li
Keyword(s):  
Real Estate ◽  
Real Estate Investment ◽  
Rural Income ◽  
Coastal Cities ◽  
Factors Influencing ◽  
Income Ratio ◽  
Urban Rural ◽  
Per Capita ◽  
Fiscal Expenditure ◽  
The Impact

China’s urbanization has attracted considerable academic attention. However, more work is required to understand and examine the driving mechanisms of urbanization, especially within the context of the transition from socialism. We used a regression model and a geographical detector technique to determine the direction and strength of factors influencing urbanization in prefecture-level cities in China. We found that China’s urbanization development model has two characteristics: administrative hierarchy and spatial agglomeration. Based on our conceptual framework, we identified the positive effect exerted by the real estate investment and per capita fiscal expenditure on urbanization, as well as the negative impact exerted by the urban–rural income ratio. The results revealed that China’s urbanization is a hybrid process: the forces from power and capital contribute to high levels of urbanization. When comparing the strength of the factors influencing urbanization between coastal and inland cities, we found that real estate investment and per capita fiscal expenditure in coastal cities have a greater impact on urbanization than in inland cities. In contrast, the impact of the urban–rural income ratio on urbanization is larger in inland cities than in coastal cities. A close examination on the factors influencing China’s rapid urbanization is the key to further advancing our understanding of the dynamic mechanisms of China’s urbanization. Our findings can help Chinese policy makers formulate more detailed urbanization strategies with consideration of regional characteristics.

scholarly journals An Empirical Study of the Impact of Urbanization on Industry Water Footprint in China

2020 ◽  
Vol 12 (6) ◽  
pp. 2263
Author(s):  
Daxue Kan ◽  
Weichiao Huang
Keyword(s):  
Service Industry ◽  
Water Footprint ◽  
Generalized Method Of Moments ◽  
Virtual Water ◽  
Eastern Region ◽  
Industrial Water ◽  
Agricultural Water ◽  
Gray Water ◽  
Impact Of Urbanization ◽  
The Impact

How to advance new urbanization initiatives and reduce the water footprint of industries is one urgent issue about urbanization that needs to be resolved. Based on spatial dynamic panel data, we used the system GMM (Generalized Method of Moments) to study the impact of urbanization on the industrial water footprint. The results show that, overall, urbanization increases the industrial water footprint, industrial virtual water footprint, and industrial gray water footprint in China. There are sectoral and regional differences in the impact of urbanization. Specifically, urbanization reduces the agricultural water footprint and agricultural virtual water footprint but raises the agricultural gray water footprint. Urbanization increases the manufacturing water footprint, manufacturing virtual water footprint, and gray water footprint. Urbanization reduces the virtual water footprint of the service industry but increases the water footprint and gray water footprint in the service industry. At the regional level, urbanization increases the industrial water footprint and gray water footprint across the three major regions. In the eastern region, urbanization has little effect on increasing the industrial water footprint, and reduces the industrial virtual water footprint, whereas in the central and western regions urbanization increases the industrial virtual water footprint. In all three regions, urbanization reduces the agricultural water footprint, increases the manufacturing and service water footprints, reduces the virtual water footprints of agriculture and services, and increases the gray water footprint of agriculture, manufacturing, and services. In the eastern region, the reducing effect of urbanization is the greatest and the increasing effect of urbanization is the smallest. Additionally, in the eastern region, urbanization has reduced the virtual water footprint of manufacturing, whereas in the central and western regions urbanization has increased the virtual water footprint of manufacturing.

How to Psychologically Minimize Scratching Impulses

2014 ◽  
Vol 222 (3) ◽  
pp. 140-147 ◽  
Author(s):  
Ariane Sölle ◽  
Theresa Bartholomäus ◽  
Margitta Worm ◽  
Regine Klinger
Keyword(s):  
Cognitive Processes ◽  
Placebo Effect ◽  
Psychological Factors ◽  
Pharmacological Treatments ◽  
Physiological Basis ◽  
Psychological Mechanisms ◽  
Physical Complaints ◽  
Factors Influencing ◽  
The Impact ◽  
Placebo Model

Research in recent years, especially in the analgesic field, has intensively studied the placebo effect and its mechanisms. It has been shown that physical complaints can be efficiently reduced via learning and cognitive processes (conditioning and expectancies). However, despite evidence demonstrating a large variety of physiological similarities between pain and itch, the possible transfer of the analgesic placebo model to itch has not yet been widely discussed in research. This review therefore aims at highlighting potential transfers of placebo mechanisms to itch processes by demonstrating the therapeutic issues in pharmacological treatments for pruritus on a physiological basis and by discussing the impact of psychological mechanisms and psychological factors influencing itch sensations.

Reproduction potential-based differentiation of Russian regions

2020 ◽  
Vol 18 (7) ◽  
pp. 1304-1319
Author(s):  
M.V. Moroshkina
Keyword(s):  
Spatial Development ◽  
Uneven Development ◽  
Regional Differentiation ◽  
Russian Regions ◽  
Policy Documents ◽  
Factors Influencing ◽  
Strategic Policy ◽  
Main Factors ◽  
Per Capita ◽  
Research Period

Subject. This article examines the issues related to changes in reproduction capacity and heterogeneity of the development of Russian regions. Objectives. The article aims to assess regional differentiation and investigate the main factors influencing the uneven development of the areas. Methods. For the study, we used the methods of comparative and correlation analyses. Results. The article identifies groups of leading and lagging Russian regions and assesses the possibility of convergence of Russian regions according to the analyzed indicators, such as GRP, GRP per capita, and the output of industry. Conclusions. The results obtained can be used when preparing strategic policy documents, spatial development programmes and concepts. The observed heterogeneity suggests that the regions maintain their positions throughout the research period.

Measuring Effectiveness Of Economic Growth On Reducing Infant Mortality Rate In Tamilnadu

2019 ◽  
Vol 118 (4) ◽  
pp. 129-141
Author(s):  
Mr. Y. EBENEZER
Keyword(s):  
Economic Growth ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Negative Relationship ◽  
Infant Mortality Rate ◽  
Domestic Product ◽  
Per Capita ◽  
The Impact ◽  
Adf Test ◽  
Bound Test

                   This paper deals with economic growth and infant mortality rate in Tamilnadu. The objects of this paper are to test the relationship between Per capita Net State Domestic Product and infant mortality rate and also to measure the impact of Per capita Net State Domestic Product on infant mortality rate in Tamil Nadu. This analysis has employed the ADF test and ARDL approach. The result of the study shows that IMR got reduced and Per capita Net State Domestic Product increased during the study period. This analysis also revealed that there is a negative relationship between IMR and the economic growth of Tamilnadu. In addition, ARDL bound test result has concluded that per capita Net State Domestic Product of Tamilnadu has long run association with IMR.

The impact of Airbnb on a non-touristic city. A Case study of short-term rentals in Santa Cruz de Tenerife (Spain)

Erdkunde ◽  
2020 ◽  
Vol 74 (3) ◽  
pp. 191-204
Author(s):  
Marcus Hübscher ◽  
Juana Schulze ◽  
Felix zur Lage ◽  
Johannes Ringel
Keyword(s):  
Santa Cruz ◽  
Short Term ◽  
Rental Market ◽  
Rural Space ◽  
Use Of Data ◽  
Web Scraping ◽  
Per Capita ◽  
Per Se ◽  
The Impact

Short-term rentals such as Airbnb have become a persistent element of today’s urbanism around the globe. The impacts are manifold and differ depending on the context. In cities with a traditionally smaller accommodation market, the impacts might be particularly strong, as Airbnb contributes to ongoing touristification processes. Despite that, small and medium-sized cities have not been in the centre of research so far. This paper focuses on Santa Cruz de Tenerife as a medium-sized Spanish city. Although embedded in the touristic region of the Canary Islands, Santa Cruz is not a tourist city per se but still relies on touristification strategies. This paper aims to expand the knowledge of Airbnb’s spatial patterns in this type of city. The use of data collected from web scraping and geographic information systems (GIS) demonstrates that Airbnb has opened up new tourism markets outside of the centrally established tourist accommodations. It also shows that the price gap between Airbnb and the housing rental market is broadest in neighbourhoods that had not experienced tourism before Airbnb entered the market. In the centre the highest prices and the smallest units are identified, but two peripheral quarters stand out. Anaga Mountains, a natural and rural space, has the highest numbers of Airbnb listings per capita. Suroeste, a suburban quarter, shows the highest growth rates on the rental market, which implies a linkage between Airbnb and suburbanization processes.

The Quantification of Structural Reforms in OECD Countries

2018 ◽  
Author(s):  
Balázs Égert ◽  
Peter Gal
Keyword(s):  
Product Market ◽  
Physical Capital ◽  
Oecd Countries ◽  
Market Regulation ◽  
Structural Reforms ◽  
Long Term Effects ◽  
Per Capita ◽  
Product Market Regulation ◽  
The Impact

This chapter describes and discusses a new supply-side framework that quantifies the impact of structural reforms on per capita income in OECD countries. It presents the overall macroeconomic impacts of reforms by aggregating over the effects on physical capital, employment, and productivity through a production function. On the basis of reforms defined as observed changes in policies, the chapter finds that product market regulation has the largest overall single policy impact five years after the reforms. But the combined impact of all labour market policies is considerably larger than that of product market regulation. The paper also shows that policy impacts can differ at different horizons. The overall long-term effects on GDP per capita of policies transiting through capital deepening can be considerably larger than the five- to ten-year impacts. By contrast, the long-term impact of policies coming only via the employment rate channel materializes at a shorter horizon.

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