scholarly journals How to Maintain a Sustainable Environment? A Spatial Evolution of Urban Atmospheric Pollution and Impact Factors in China

2019 ◽  
Vol 11 (16) ◽  
pp. 4376
Author(s):  
Mingze Li ◽  
Yuan Huang ◽  
Mingdan Han
Keyword(s):  
Industrial Structure ◽  
Negative Relationship ◽  
Urban Pollution ◽  
Atmospheric Pollutants ◽  
Impact Factors ◽  
Spatial Evolution ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Panel Data ◽  
Per Capita

Urban pollution has significantly contributed to the spread of diseases and global warming. The analysis of spatial distribution characteristics of atmospheric pollutants is crucial for making sustainable industrial policy, and environmentally friendly urban planning. In this paper, GeoDa software is used to analyze how sulfur dioxide (SO2), nitrogen oxides (NOx), and smoke dust (DUS) are spatially distributed in various provinces of China. Then, global spatial correlation test and cluster analysis are carried out to obtain the spatial evolution characteristics of three pollutants. Afterward, the spatial panel data model is applied to explore the factors that affect the spatial evolution of SO2, NOx and smoke dust (DUS) nationwide. MATLAB is used to estimate the Spatial Lag Model (SLM) and the Spatial Error Model (SEM) of the three pollutants, respectively. According to our analysis, SEM is more applicable for SO2 and NOx, whereas SLM is optimal for smoke dust (DUS). The results show that foreign direct investment (FDI), industrial structure, and urbanization aggravate environmental pollution, while per capita gross domestic products (per capita GDP) has a negative relationship with the cluster of pollutants. The study concludes by informing public policy makers on environment friendly policies for a more sustainable development.

Analysis of Impact Factors of China's Carbon Dioxide Emissions

2012 ◽  
Vol 616-618 ◽  
pp. 1111-1114
Author(s):  
Xiao Yu Ma ◽  
Qiang Yi Li ◽  
Adili Tuergong
Keyword(s):  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Positive Impact ◽  
Dynamic Panel Data ◽  
Impact Factors ◽  
Dynamic Panel ◽  
Consumption Structure ◽  
Current Period ◽  
Per Capita ◽  
The Impact

This paper estimates the quantity of CO2 emissions in 30 provinces of China covering the year from 1999 to 2010, combining static and dynamic panel data model.Meanwhile, we use instruments to control the endogeny of the two models, analyzing the impact factors of China's CO2 emissions comprehensively and objectively. The result shows that a inverted U-shaped relationship is found between per capita GDP and CO2 emissions per capita .And it means that the Environmental Kuznets Hypothesis is verified in China.And energy consumption structure, industrial structure and urbanization have a positive impact on China's CO2 emissions. The CO2 emissions of last period have a crucial impact on the emissions of current 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.

scholarly journals Examining GDP Growth and Its Volatility: An Episodic Approach

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 890
Author(s):  
Jakub Bartak ◽  
Łukasz Jabłoński ◽  
Agnieszka Jastrzębska
Keyword(s):  
Economic Growth ◽  
Genetic Algorithm ◽  
Time Series ◽  
Human Capital ◽  
Growth Rates ◽  
Structural Breaks ◽  
Negative Relationship ◽  
Gdp Per Capita ◽  
Gdp Growth ◽  
Per Capita

In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes characterized by different means and volatility of growth rates. We further investigate whether a volatile economy is likely to grow more slowly and analyze the determinants of high/low growth with high/low volatility patterns. The main results indicate a negative relationship between volatility and growth. Moreover, the results suggest that international trade simultaneously promotes growth and increases volatility, human capital promotes growth and stability, and financial development reduces volatility and negatively correlates with growth.

scholarly journals A Comparative Evaluation of a Mesoscale Model and Atmospheric Global Circulation Model for Air Quality Simulation: A Multiscale, Multisite Evaluation

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
P. Goswami ◽  
J. Baruah
Keyword(s):  
High Resolution ◽  
Mesoscale Model ◽  
Large City ◽  
Circulation Model ◽  
Urban Pollution ◽  
Atmospheric Pollutants ◽  
Scale Model ◽  
Global Circulation Model ◽  
Global Circulation ◽  
Meso Scale

Concentrations of atmospheric pollutants are strongly influenced by meteorological parameters like rainfall, relative humidity and wind advection. Thus accurate specifications of the meteorological fields, and their effects on pollutants, are critical requirements for successful modelling of air pollution. In terms of their applications, pollutant concentration models can be used in different ways; in one, short term high resolution forecasts are generated to predict and manage urban pollution. Another application of dynamical pollution models is to generate outlook for a given airbasin, such as over a large city. An important question is application-specific model configuration for the meteorological simulations. While a meso-scale model provides a high-resolution configuration, a global model allows better simulation of large-sale fields through its global environment. Our objective is to comparatively evaluate a meso-scale atmospheric model (MM5) and atmospheric global circulation model (AGCM) in simulating different species of pollutants over different airbasins. In this study we consider four locations: ITO (Central Delhi), Sirifort (South Delhi), Bandra (Mumbai) and Karve Road (Pune). The results show that both the model configurations provide comparable skills in simulation of monthly and annual loads, although the skill of the meso-scale model is somewhat higher, especially at shorter time scales.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

2021 ◽  
Vol 13 (16) ◽  
pp. 9014
Author(s):  
Yongjiao Wu ◽  
Huazhu Zheng ◽  
Yu Li ◽  
Claudio O. Delang ◽  
Jiao Qian
Keyword(s):  
Carbon Emissions ◽  
Industrial Development ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Spatial Lag Model ◽  
Workforce Allocation ◽  
Lag Model ◽  
Using Data ◽  
Shape Curve ◽  
Carbon Productivity

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

scholarly journals Impact of Public Debt on the Economic Growth of Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 599-615 ◽  
Author(s):  
Naeem Akram
Keyword(s):  
Economic Growth ◽  
Public Debt ◽  
Negative Relationship ◽  
Private Investment ◽  
Crowding Out ◽  
Per Capita Gdp ◽  
Debt Overhang ◽  
Positive Effects ◽  
Domestic Debt ◽  
Per Capita

Over the years Pakistan has failed to collect enough revenues for financing of its budget. Consequently, the problem of twin deficits emerged and to finance the developmental activities government has to rely on public external and domestic debt. The positive effects of public debt relate to the fact that in resource-starved economies debt financing if done properly leads to higher growth and adds to their capacity to service and repay public debt. The negative effects work through two main channels—i.e., ―Debt Overhang‖ and ―Crowding Out‖ effects. The present study examines the consequences of public debt for economic growth and investment in Pakistan for the period 1972-2009. It develops a hybrid model that explicitly incorporates the role of public debt in growth equations. As the some variables are I (1) and other are I (0) so Autoregressive Distributed Lag(ARDL) technique has been applied to estimate the model. Study finds that public external debt has negative relationship with per capita GDP and investment confirming the existence of ―Debt Overhang effect‖. However, due to insignificant relationships of debt servicing with investment and per capita GDP, the existence of the crowding out hypothesis could not be confirmed. Similarly, domestic debt has a negative relationship with investment and per capita GDP. In other words, it seems to have crowded out private investment. JEL classification: H63, O43, E22, C22 Keywords: Public Debt, Economic Growth, Investment, ARDL

Study on the Relationship between the Economics and Environment of Puyang City Based on Environmental Kuznets Curve

2013 ◽  
Vol 807-809 ◽  
pp. 773-782
Author(s):  
Qing Song Li ◽  
Kai Kang ◽  
Jia Wei Zhu ◽  
Qing Xiang Meng ◽  
Su Jun Deng
Keyword(s):  
Environmental Kuznets Curve ◽  
Industrial Structure ◽  
Kuznets Curve ◽  
Environmental Index ◽  
Per Capita Gdp ◽  
Sulfur Dioxide Emissions ◽  
Per Capita ◽  
Spss Software ◽  
Set Up ◽  
The Relationship

The study set up the model of per capita GDP and the environmental index based on the Environmental Kuznets Curve (EKC) with the support of SPSS software and the 2003-2011 economics and environment data of Puyang City. And the result shows that the environmental Kuznets Curve (EKC) of industrial wastewater discharge and industrial sulfur dioxide emissions both display inverted U-shape; and just across the turning point, the discharge present downward trend with the increasing of per capita GDP; while the EKC of industrial fumes emissions display positive U-shape, and its emission present upward trend first and then downward with the increasing of per capita GDP. It shows that the environmental problems of Puyang City has partly improved, but has not been fully restrained. The main reasons are unreasonable industrial structure, single dominated industy and relatively low investment on environmental improvement.

Irrigation Water Valuation Using Spatial Hedonic Models in GIS Environment

2010 ◽  
Vol 1 (4) ◽  
pp. 1-13 ◽  
Author(s):  
Zisis Mallios
Keyword(s):  
Irrigation Water ◽  
Regression Models ◽  
Spatial Regression ◽  
Software Tool ◽  
Hedonic Pricing ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weights Matrix ◽  
Lag Model ◽  
Spatial Regression Models

Hedonic pricing is an indirect valuation method that applies to heterogeneous goods investigating the relationship between the prices of tradable goods and their attributes. It can be used to measure the value of irrigation water through the estimation of the model that describes the relation between the market value of the land parcels and its characteristics. Because many of the land parcels included in a hedonic pricing model are spatial in nature, the conventional regression analysis fails to incorporate all the available information. Spatial regression models can achieve more efficient estimates because they are designed to deal with the spatial dependence of the data. In this paper, the authors present the results of an application of the hedonic pricing method on irrigation water valuation obtained using a software tool that is developed for the ArcGIS environment. This tool incorporates, in the GIS application, the estimation of two different spatial regression models, the spatial lag model and the spatial error model. It also has the option for different specifications of the spatial weights matrix, giving the researcher the opportunity to examine how it affects the overall performance of the model.

scholarly journals Macrolevel Traffic Crash Analysis: A Spatial Econometric Model Approach

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Shaohua Wang ◽  
Yanyan Chen ◽  
Jianling Huang ◽  
Ning Chen ◽  
Yao Lu
Keyword(s):  
Population Density ◽  
Spillover Effects ◽  
Positive Influence ◽  
Least Square ◽  
Spatial Autoregression ◽  
Road Density ◽  
Traffic Crashes ◽  
Spatial Error ◽  
Spatial Lag Model ◽  
Spatial Weight

This study presents a spatial approach for the macrolevel traffic crashes analysis based on point-of-interest (POI) data and other related data from an open source. The spatial autoregression is explored by Moran’s I Index with three spatial weight features (i.e., (a) Rook, (b) Queen, and (c) Euclidean distance). The traditional Ordinary Least Square (OLS) model, the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM) were developed to describe the spatial correlations among 2,114 Traffic Analysis Zones (TAZs) of Tianjin, one of the four municipalities in China. Results of the models indicated that the SDM with the Rook spatial weight feature is found to be the optimal spatial model to characterize the relationship of various variables and crashes. The results show that population density, consumption density, intersection density, and road density have significantly positive influence on traffic crashes, whereas company density, hotel density, and residential density have significant but negative effects in the local TAZ. The spillover effects coefficient of population density and road density are positive, indicating that the increase of these variables in the surrounding TAZs will lead to the increase of crashes in the target zone. The impacts of company density and hotel density are just the opposite. In general, the research findings can help transportation planners and managers better understand the general characteristics of traffic crashes and improve the situation of traffic security.

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