scholarly journals Identifying the Driving Factors of Food Nitrogen Footprint in China, 2000–2018: Econometric Analysis of Provincial Spatial Panel Data by the STIRPAT Model

2021 ◽  
Vol 13 (11) ◽  
pp. 6147
Author(s):  
Chun Liu ◽  
Gui-Hua Nie
Keyword(s):  
Panel Data ◽  
Spatial Data ◽  
Driving Forces ◽  
Early Stage ◽  
Spillover Effect ◽  
Spatial Data Analysis ◽  
Ekc Hypothesis ◽  
Stirpat Model ◽  
Spatial Panel Data ◽  
Per Capita

This paper studies the EKC hypothesis and STIRPAT model. Based on the panel data of carbon emission intensity and other influencing factors of 30 provinces in China from 2000 to 2018, the spatial effect of per capita food nitrogen footprint (FNF) and the effect of different socio-economic factors in China were studied by using exploratory spatial data analysis and fixed effect spatial Durbin model for the first time. The results show that: (1) there is a spatial agglomeration effect and a positive spatial dependence relationship in China’s provincial per capita FNF (FNFP), which verifies that the relationship between China’s FNF and economy is in the early stage of EKC hypothesis curve. (2) The driving forces of China’s FNF were explored, including Engel’s coefficient of urban households (ECU), population density (PDEN), urbanization, nitrogen use efficiency (NUE) and technology. (3) The results show that there is a significant spatial spillover effect of FNFP. The ECU and NUE can reduce the regional FNFP, and can slow down the FNFP of surrounding provinces. (4) Policy makers need to formulate food nitrogen emission reduction policies from the food demand side, food consumption side and regional level.

FINANCIAL DEVELOPMENT AND CARBON EMISSIONS IN CHINESE PROVINCES: A SPATIAL PANEL DATA ANALYSIS

2018 ◽  
Vol 63 (02) ◽  
pp. 447-464 ◽  
Author(s):  
LING XIONG ◽  
SHAOZHOU QI
Keyword(s):  
Panel Data ◽  
Financial Development ◽  
Carbon Emissions ◽  
Panel Data Analysis ◽  
Spillover Effects ◽  
Stirpat Model ◽  
Spatial Panel ◽  
Green Finance ◽  
Spatial Panel Data ◽  
Per Capita

Using the panel data of 30 provinces in China between 1997 and 2011, we employed the extended STIRPAT model and spatial panel econometrics methods to investigate the relationship between financial development and carbon emissions and test the influence of financial development as well as other factors on provincial carbon emissions per capita among Chinese provinces. The estimation results show that: (i) spatial spillover effects play a role in provincial carbon emissions in China; and (ii) the sum of technical effect and structure effect of financial development surpass its’ sum of direct effect and wealth effect in China, which suggests that financial development reduces carbon emissions per capita. China should pay more attention to the integration of green finance policy and environmental regulation, and establish appropriate mechanisms to strengthen inter-provincial interaction and coordinated development.

scholarly journals Spatial-Temporal Characteristics in Grain Production and Its Influencing Factors in the Huang-Huai-Hai Plain from 1995 to 2018

2020 ◽  
Vol 17 (24) ◽  
pp. 9193
Author(s):  
Chunshan Zhou ◽  
Rongrong Zhang ◽  
Xiaoju Ning ◽  
Zhicheng Zheng
Keyword(s):  
Influencing Factors ◽  
Spatial Data ◽  
Spatial Data Analysis ◽  
Grain Production ◽  
Temporal Characteristics ◽  
Spatial Panel ◽  
Geographically Weighted Regression Model ◽  
Northern Areas ◽  
Spatial Panel Data ◽  
The Impact

The Huang-Huai-Hai Plain is the major crop-producing region in China. Based on the climate and socio-economic data from 1995 to 2018, we analyzed the spatial–temporal characteristics in grain production and its influencing factors by using exploratory spatial data analysis, a gravity center model, a spatial panel data model, and a geographically weighted regression model. The results indicated the following: (1) The grain production of eastern and southern areas was higher, while that of western and northern areas was lower; (2) The grain production center in the Huang-Huai-Hai Plain shifted from the southeast to northwest in Tai’an, and was distributed stably at the border between Jining and Tai’an; (3) The global spatial autocorrelation experienced a changing process of “decline–growth–decline”, and the area of hot and cold spots was gradually reduced and stabilized, which indicated that the polarization of grain production in local areas gradually weakened and the spatial difference gradually decreased in the Huang-Huai-Hai Plain; (4) The impact of socio-economic factors has been continuously enhanced while the role of climate factors in grain production has been gradually weakened. The ratio of the effective irrigated area, the amount of fertilizer applied per unit sown area, and the average per capita annual income of rural residents were conducive to the increase in grain production in the Huang-Huai-Hai Plain; however, the effect of the annual precipitation on grain production has become weaker. More importantly, the association between the three factors and grain production was found to be spatially heterogeneous at the local geographic level.

Analysis on Spatial Difference of the Rural Resident’s per Capita Net Income in Qinhuangdao City Based on ESDA

2014 ◽  
Vol 955-959 ◽  
pp. 3893-3898
Author(s):  
Yu Hong Wu
Keyword(s):  
Spatial Data ◽  
Rural Economy ◽  
Spatial Association ◽  
Spatial Data Analysis ◽  
Moran’S I ◽  
Net Income ◽  
Gis Technology ◽  
Moran's I ◽  
Per Capita ◽  
Global Moran’S I

Based on the exploratory spatial data analysis (ESDA) and GIS technology, the spatial differences of the rural economic development level of Qinhuangdao city was investigated by adopting the rural resident’s per capita net income data at town level in Qinhuangdao city from 2007 to 2011. The results of global Moran’s I value for rural resident’s per capita net income at town level showed that there existed significant positive spatial autocorrelation and significant spatial aggregation in the spatial distribution of rural resident’s per capita net income. However, the global Moran’s I value showed a decreasing trend during 2007 to 2011, indicating an enlarged spatial disparity of rural economy at the town level. The results of the Moran scatter plots and LISA cluster maps of 2007 and 2011 showed that most of towns were characterized by positive local spatial association , ie. They were located in the HH or the LL quadrant. The significant HH towns were mostly to be found in the south of Qinhuangdao city, Haigang district, Changli county, Lulong county. The significant LL towns were mostly to be found in the Qinglong county, north of Qinhuangdao city.

scholarly journals Understanding the Driving Patterns of the Carbon Emissions in Transport Sector in China: A Panel Data Analysis and Zoning Effect

2018 ◽  
Author(s):  
Danting Lin ◽  
Rongzu Qiu ◽  
Xisheng Hu ◽  
Jiankai Wang ◽  
Lanyi Zhang ◽  
...  
Keyword(s):  
Panel Data ◽  
Carbon Emissions ◽  
Inner Mongolia ◽  
Carbon Emission ◽  
Driving Forces ◽  
Panel Data Analysis ◽  
Transport Sector ◽  
Significant Variable ◽  
Provincial Level ◽  
Per Capita

China’s transportation industry has made rapid progress, which has led to a mass of carbon emissions. However, it is still unclear how the carbon emission from transport sector is punctuated by shifts in underlying drivers. This paper aims to examine the process of China’s carbon emissions from transport sector as well as its major driving forces during the period of 2000 to 2015 at the provincial level. We firstly estimate the carbon emissions from transport sector at the provincial level based on the fuel and electricity consumption using a top-down method. We find that the carbon emission per capita is steadily increasing across the nation, especially in the provinces of Chongqing and Inner Mongolia. However, the carbon emission intensity is decreasing in most provinces of China, except in Yunnan, Qinghai, Chongqing, Zhejiang, Heilongjiang, Jilin, Inner Mongolia, Henan and Anhui. We then quantify the effect of socio-economic factors and their regional variations on the carbon emissions using panel data model. The results show that the development of secondary industry is the most significant variable in both the entire nation level and the regional level, while the effects of the other variables vary across regions. Among these factors, population density is the main motivator of the increasing carbon emissions per capita from transport sector for both the whole nation and the western region, whereas the consumption level per capita of residents and the development of tertiary industry are the primary drivers of per capita carbon emissions for the eastern and central region.

scholarly journals Smart and IoT-Based Coupling and Coordination Development for Financial and Logistics Industries

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ji-you Li ◽  
Qi-qing Zhou ◽  
Pan-pan Yin
Keyword(s):  
Spatial Data ◽  
Driving Forces ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Spatial Data Analysis ◽  
Theil Index ◽  
Eastern Region ◽  
Coordinated Development ◽  
The Republic ◽  
Total Difference

Based on the panel data, collected through various Internet of Things (IoT) devices, of 31 various provinces and cities in the Republic of China from 2004 to 2019, due to the analysis of mechanism and the significance of coupled and coordinated development, methods like fuzzy comprehensive evaluation, entropy, coupling, and coordination degree model, exploratory spatial data analysis, and Theil index are widely used to analytically evaluate the dynamic coupling development of China’s financial and logistics industries. The analysis of the collected data shows that demand promotion, technological progress, corporate decision-making, and policy stimulus are the driving forces for the coordinated development. In addition, the coordinated development of both industries can achieve a win-win situation. Moreover, during the sample period, the level of coupled and coordinated development has made considerable progress, achieving a transition from moderate to slightly unbalanced level, but overall, it is still at a low level. The level of coupled and coordinated development is showing east-central-west, that is, a three-level declining trend. Guangdong is the province with the highest level, and Qinghai and Ningxia are the provinces with the lowest levels of coupled and coordinated development. The general evolution trend of the total difference in the levels of coupled and coordinated development is declining in fluctuation, and the differences in the eastern region and within the zones are the main reasons for the total difference.

scholarly journals Convergence of electricity consumption in Turkey: Spatial panel data analysis

2020 ◽  
Vol 67 (2) ◽  
pp. 241-256 ◽  
Author(s):  
Gülsüm Akarsu ◽  
Burcu Berke
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Empirical Studies ◽  
Electricity Consumption ◽  
Spatial Panel ◽  
Spatial Panel Data ◽  
Per Capita ◽  
Conditional Beta ◽  
Beta Convergence

The issue of convergence has been discussed in many theoretical and empirical studies. Because per capita electricity consumption is considered as an indication of economic development, this study aims to determine the presence of ?absolute and conditional beta (?) convergence? of per capita total electricity consumption across the provinces of Turkey between 1986 and 2013. This work is the first investigation of electricity consumption convergence in Turkey. Based on the annual balanced panel data and the spatial panel data model, our findings indicate absolute ? convergence of per capita electricity consumption across the provinces of Turkey. We conclude that regional policies are successful in reducing regional disparities in per capita electricity consumption among the provinces of Turkey. However, other indicators of economic development should be examined to determine the overall convergence.

scholarly journals The spatiality and driving forces of population ageing in China

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0243559
Author(s):  
Lianxia Wu ◽  
Zuyu Huang ◽  
Zehan Pan
Keyword(s):  
Economic Development ◽  
Spatial Data ◽  
Census Data ◽  
Local Economic Development ◽  
Driving Forces ◽  
Population Ageing ◽  
Spatial Data Analysis ◽  
Western China ◽  
Practical Significance ◽  
Driving Mechanism

Studying the spatial characteristics of China’s ageing and its influencing factors is of great practical significance because China has the largest elderly population in the world. Using 2000 and 2010 census data, this study explores the degree, pace, and pattern of population ageing and its driving mechanism using exploratory spatial data analysis and the geographically weighed regression model. Between 2000 and 2010, population ageing increased rapidly countrywide; yet, spatial differences between eastern and western China narrowed. The degree of provincial population ageing and its spatiality were determined by natural population growth, migration, and local economic development. Life expectancy and mortality were the primary long-term factors, and GDP per capita was the prime contributor in the early days of economic development; the migration rate was the dominant influence after 2010. China’s overall spatial differentiation of population ageing shifted from a north–south to an east–west division.

Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

2020 ◽  
Vol 05 (1&2) ◽  
pp. 1-13
Author(s):  
Mohamed R Abonazel ◽  
Keyword(s):  
Panel Data ◽  
Personal Income ◽  
Spatial Weights ◽  
Spatial Panel Data Models ◽  
Spatial Weights Matrix ◽  
Spatial Panel Data ◽  
Spatial Autoregressive ◽  
Per Capita ◽  
Capita Personal Income ◽  
Inverse Distance

Over the last decades, the Per Capita Personal Income (PCPI) variable was a common measure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by using spatial panel data models for 48 U.S. states during the period from 2009 to 2017. We utilize the three following models: spatial autoregressive (SAR) model, Spatial Error (SEM) Model, and Spatial Autoregressive Combined (SAC) model, with individual (or spatial) fixe deffects according to three different known methods for constructing spatial weights matrices: binary contiguity, inverse distance, and Gaussian transformation spatial weights matrix. Additionally, we pay attention for direct and indirect effects estimates of the explanatory variables for SAR, SEM, and SAC models. The second objective of this paper is to show how to select the appropriate model to fit our data. The results indicate that the three used spatial weights matrices provide the same result based on goodness of fit criteria, and the SAC model is the most appropriate model among the models presented. However, the SAC model with spatial weights matrix based on inverse distance is better compared to other used models. Also, the results indicate that percentage of individuals with graduate or professional degree, real Gross Domestic Product (GDP) per capita,and number of nonfarm jobs have a positive impact on the PCPI, while the percentage of individuals without degree or bachelor’s degree have a negative impact on the PCPI.

scholarly journals Evaluating the EKC Hypothesis for the BCIM-EC Member Countries under the Belt and Road Initiative

2020 ◽  
Vol 12 (4) ◽  
pp. 1478 ◽  
Author(s):  
Arifur Rahman ◽  
S. M. Woahid Murad ◽  
Fayyaz Ahmad ◽  
Xiaowen Wang
Keyword(s):  
Panel Data ◽  
Carbon Emission ◽  
Structural Breaks ◽  
Energy Use ◽  
Trade Openness ◽  
Gdp Per Capita ◽  
Ekc Hypothesis ◽  
Long Run ◽  
Short Run ◽  
Per Capita

This paper attempts to examine the environmental Kuznets curve (EKC) hypothesis for the BCIM-EC (Bangladesh–China–India–Myanmar economic corridor) member countries under the Belt and Road Initiative (BRI) of China. Both time series and panel data are covered, with respect to carbon dioxide (CO2) emissions, GDP per capita, energy use, and trade openness. For panel data analysis, GDP per capita and energy consumption have positive effects on CO2, while the effect of the quadratic term of GDP per capita is negative in the short-run. However, the short-run effects do not remain valid in the long-run, except for energy use. Therefore, the EKC hypothesis is only a short-run phenomenon in the case of the panel data framework. However, based on the Autoregressive Distributed Lag (ARDL) approach with and without structural breaks, the EKC hypothesis exists in India and China, while the EKC hypothesis holds in Bangladesh and Myanmar with regard to disregarding breaks within the short-run. The long-run estimates support the EKC hypothesis of considering and disregarding structural breaks for Bangladesh, China, and India. The findings of the Dumitrescu and Hurlin panel noncausality tests show that there is a unidirectional causality that runs from GDP per capita to carbon emission, squared GDP to carbon emission, and carbon emission to trade openness. Therefore, the BCIM-EC under the BRI should not only focus on connectivity and massive infrastructural development for securing consecutive economic growth among themselves, but also undertake a long-range policy to cope with environmental degradation and to ensure sustainable green infrastructure.

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