scholarly journals A Dynamic Spatial Panel of Subnational GHG Emissions: Environmental Effectiveness of Emissions Taxes in Spanish Regions

2020 ◽  
Vol 12 (7) ◽  
pp. 2872 ◽  
Author(s):  
Jaime Vallés-Giménez ◽  
Anabel Zárate-Marco
Keyword(s):  
Spatial Dependence ◽  
Ghg Emissions ◽  
Regional Level ◽  
Environmental Taxes ◽  
Spatial Panel ◽  
Spanish Regions ◽  
Environmental Effectiveness ◽  
Emissions Taxes ◽  
Temporal Persistence ◽  
Spatio Temporal

In this paper we study the effectiveness of environmental taxes and policies of the regional level of government to reduce GHG emissions. We use panel data for the 17 Spanish regions in the period 1999–2017, controlling the spatial nexus between regions and using a dynamic Durbin model. The results show there is spatial dependence and spatio-temporal persistence of GHG emissions at the regional level in Spain, and that in this context, the taxes and policies intended to manage emissions introduce a slight disincentive to generating them. This fact, together with some relative decoupling which seems to exist between growth and emissions at the regional level, would suggest the need for tougher measures to combat environmental pollution in order to meet EU commitments.

Convergence of regional population density in Russia over 120 years

2020 ◽  
pp. 133-158
Author(s):  
K. A. Kholodilin ◽  
Y. I. Yanzhimaeva
Keyword(s):  
Population Density ◽  
Spatial Dependence ◽  
Population Distribution ◽  
Regional Level ◽  
Capital City ◽  
County Level ◽  
Spatial Effects ◽  
Russian Regions ◽  
Regional Population ◽  
Regional Population Density

A relative uniformity of population distribution on the territory of the country is of importance from socio-economic and strategic perspectives. It is especially important in the case of Russia with its densely populated West and underpopulated East. This paper considers changes in population density in Russian regions, which occurred between 1897 and 2017. It explores whether there was convergence in population density and what factors influenced it. For this purpose, it uses the data both at county and regional levels, which are brought to common borders for comparability purposes. Further, the models of unconditional and conditional β-convergence are estimated, taking into account the spatial dependence. The paper concludes that the population density equalization took place in 1897-2017 at the county level and in 1926—1970 at the regional level. In addition, the population density increase is shown to be influenced not only by spatial effects, but also by political and geographical factors such as climate, number of GULAG camps, and the distance from the capital city.

scholarly journals Assessment of the Dependence of GHG Emissions on the Support and Taxes in the EU Countries

2021 ◽  
Vol 13 (14) ◽  
pp. 7650
Author(s):  
Astrida Miceikienė ◽  
Kristina Gesevičienė ◽  
Daiva Rimkuvienė
Keyword(s):  
Energy Consumption ◽  
Empirical Study ◽  
Ghg Emissions ◽  
Gdp Per Capita ◽  
Environmental Taxes ◽  
Environmental Support ◽  
Eu Countries ◽  
Support Measures ◽  
Per Capita ◽  
The Eu

The reduction of GHG emissions is one of the priorities of the EU countries. The majority of studies show that financial support and environmental taxes are one of the most effective measures for the mitigation of the negative consequences of climate change. The EU countries employ different environmental support measures and environmental taxes to reduce GHG emissions. There is a shortage of new studies on these measures. The aim of the present study is to compare the effectiveness of the environmental support measures of the EU countries with the effectiveness of environmental taxes in relation to the reduction of GHG emissions. This study is characterized by the broad scope of its data analysis and its systematic approach to the EU’s environmental policy measures. An empirical study was performed for the EU countries with the aim of addressing this research problem and substantiating theoretical insights. A total of 27 EU member states from 2009 to 2018 were selected as research samples. The research is based on a cause-and-effect relationship, where the factors affecting environmental pollution (environmental taxes and subsidies) are the cause, and GHG emissions are the effect. Statistical research methods were used in the empirical study: descriptive statistics, the Shapiro–Wilk test, one-way analysis of variance (ANOVA), simple regression and cluster analysis. The results show that the older member countries of the EU, which had directed the financial measures of environmental policy towards a reduction in energy consumption, managed to achieve a greater reduction in GHG emissions compared to the countries which had not applied those measures. The Central and Eastern European countries are characterized by lower environmental taxes and lower expenditure allocated to environmental protection. The countries with a higher GDP per capita have greater GHG emissions that the countries with lower GDP per capita. This is associated with greater consumption, waste, and energy consumption. The study conducted gives rise to a discussion regarding data sufficiency in the assessment and forecasting of GHG emissions and their environmental consequences.

Evaluation on Methods of Spatial Dependence Test

2013 ◽  
Vol 397-400 ◽  
pp. 675-678
Author(s):  
Tong Xian Ren ◽  
Lu Mei Yang
Keyword(s):  
Data Model ◽  
Spatial Dependence ◽  
Wald Test ◽  
Spatial Panel ◽  
Section Data ◽  
Lm Test ◽  
Post Test ◽  
Spatial Panel Data ◽  
Lr Test ◽  
Spatial Section

Methods of spatial dependence test are divided into prior-test and post-test in this paper. Prior-test main includes Moran's I test, the joint LM test, the maximum likelihood LM test, robust LM test, etc. Post-test main includes conditions LM test, LR test, Wald test etc. Research on methods of spatial dependence are evaluated in this paper. It pointed out that methods of spatial dependence test are mainly based on spatial section data model. The research on spatial dependence test of the spatial panel data model needs to be deepened.

scholarly journals Building W Matrices Using Selected Geostatistical Tools: Empirical Examination and Application

Stats ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 112-133 ◽  
Author(s):  
Elżbieta Antczak
Keyword(s):  
Spatial Data ◽  
Spatial Dependence ◽  
Regression Models ◽  
Seemingly Unrelated Regression ◽  
Spatial Data Analysis ◽  
Spatial Dependency ◽  
Empirical Examination ◽  
Spatial Panel ◽  
Spatial Weights ◽  
Unrelated Regression

This paper investigates how to determine the values (elements) of spatial weights in a spatial matrix (W) endogenously from the data. To achieve this goal, geostatistical tools (standard deviation ellipsis, semivariograms, semivariogram clouds, and surface trend models) were used. Then, in the econometric part of the analysis, the effect of applying different variants of matrices was examined. The study was conducted on a sample of 279 Polish towns from 2005–2015. Variables were related to the quantity of produced waste and economic development. Both exploratory spatial data analysis and estimations of spatial panel and seemingly unrelated regression models were performed by including particular W matrices in the study (exogenous-random as well as distance and directional matrices constructed based on data). The results indicated that (1) geostatistical tools can be effectively used to build Ws; (2) outcomes of applying different matrices did not exclude but supplemented one another, although the differences were significant; (3) the most precise picture of spatial dependence was achieved by including distance matrices; and (4) the values of the assessed parameter at the regressors did not significantly change, although there was a change in the strength of the spatial dependency.

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