Conference PaperPDF Available

The Relationship between Energy and Human Development

Authors:
Hassan Naseri at Ministry of energy,Iran,Tehran
Hassan Naseri
  • Ministry of energy,Iran,Tehran
Download full-text PDF
Read full-text
Download citation
Content uploaded by Hassan Naseri
Author content
All content in this area was uploaded by Hassan Naseri on Apr 30, 2018
Content may be subject to copyright.
Statistique, Développement et Droits de l‘Homme
Session C-Pa 6e
The Relationship between Energy and Human
Development
Hassan NASERI
The Relationship between Energy and Human Development
Hassan NASERI
Human Resources Office, Ministry of Energy
81 North Felestin st. - 8th Floor
14154 Teheran, Iran
T. + 98 21 89 09 137 F. + 98 21 88 98 619
amir2214@moe.or.ir
ABSTRACT
The Relationship between Energy and Human Development
The Human Development Index (HDI) is calculated by some factors for instance real GDP
per capita, life expectancy at birth, adult literacy rate, combined first, second and third level gross
enrolment ratio, etc. It seems that we can optimize this formula by determination of factors in own
Montreux, 4. – 8. 9. 2000
Statistique, Développement et Droits de l‘Homme
statistical standardization form and also we can determine the coefficients of each factors by
principal component methods .
It can be proved that consumption per capita has exponential relation with real GDP per
capita and we can say that this relation exist for some economical and cultural development indices
and so it must be one important factor for calculating HDI.
We use real data for more than 120 countries for showing this subject.
RESUME
Relations entre énergie et développement humain
On calcule l'indice de développement humain (HDI) d'après un certain nombre de facteurs,
dont le PIB brut pro capite, l'espérance de vie à la naissance, le taux d'alphabétisation des adultes
et le taux brut de fréquentation scolaire aux premier, second et troisième niveau. Il semble qu'on
peut l'optimiser à partir de facteurs employés dans les modèles de normalisation statistique et
déterminer les divers coefficients correspondants d'après un certain nombre d'éléments principaux.
On peut prouver que la consommation pro capite présente une relation exponentielle avec le
PIB réel, et que tel est également le cas pour certains indices de développement économique et
culturel, ce qui en démontre l'importance pour le calcul du HDI.
Nous avons utilisé les données effectives pour plus de 120 pays afin de prouver cette
affirmation.
1. Introduction
Calculating of development indices is equivalence with explanation of quality by quantity and
this is very important, first we must find the best variables which have the most weights of
development subject. Second we must find the value of these weights. These two steps are not done
distinctly.
According to the Human Development Report Office (HDRO) in 1995, the value of HDI is
calculated by arithmetic mean of four ratios:
ie : (Xi–Min(x))/(Max(x)–Min(x))
Xi=Quantity for ith Country i=1,2,3,...
Min(x)=Minimum of x1, x2, x3,...
Max(x)=Maximum of x1, x2, x3,...
Quantities:
1) real GDP per capita (GDP)
2) Life expectancy at birth (LEB)
3) Adult literacy rate (ALR)
4) Combined first second and third level gross enrolment ratio (FST)
In the other view , if we compare the rank of HDI which is calculated by the above formula
with the rank of real GDP per capita for each country, we have:
Montreux, 4. – 8. 9. 2000
2
R A N K _ G D P
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
100
110
120
130
140
150
160
170
180
R A N K _ H D I
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0
Statistique, Développement et Droits de l‘Homme
By regression analysis:
Dependent Variable: RANK_HDI
R-square : 0.9170
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 3.709189 2.21610502 1.674 0.0960
RANK_GDP 1 0.957609 0.02196515 43.597 0.0001
As we see , rank of gdp per capita has a good linear relation with rank of HDI such that we
can estimate HDI rank with good errors by the following linear function:
Rank_HDI=(0.9576)*Rank_gdp
Also it has been proven that there is an exponential relation between energy consumption per
capita and gdp per capita. If for example we want to analyze the relation between rank of electricity
consumption per capita with rank of HDI we have :
By regression analysis:
Montreux, 4. – 8. 9. 2000
3
R A N K _ E
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
100
110
120
130
140
150
160
170
180
R A N K _ H D I
0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 0 1 4 0 1 5 0 1 6 0 1 7 0 1 8 0
Statistique, Développement et Droits de l‘Homme
Dependent Variable: RANK_HDI
R-square : 0.7497
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 11.736297 3.84852397 3.050 0.0027
RANK_E 1 0.865871 0.03814504 22.699 0.0001
It seems that the influential of gdp component on HDI not only is more than electricity
consumption but also we can say that it is the principal component.
2. New Method
Now note to the variation of each factors by their box plot charts in the following illustration:
The above figure shows that the variation of GDP is almost as same as electricity variation
and gdp has the highest variance with respect to ALR, LEB & FST. And it seems that this
component must be the first statistical factor for calculating of HDI, but if we use the principal
component methods for finding the best weights in calculation of HDI instead of arithmetic mean
we have :
Eigenvalues of the Correlation Matrix
Eigenvalue Difference Proportion Cumulative
PRIN1 3.07486 2.51726 0.768716 0.76872
PRIN2 0.55761 0.34583 0.139402 0.90812
PRIN3 0.211770.05601 0.052943 0.96106
PRIN4 0.15576 . 0.038939 1.00000
Eigenvectors
PRIN1 PRIN2 PRIN3 PRIN4
ALR 0.512629 -.437980 -.029671 0.737906
LEB 0.527366 0.023045 -.757983 -.383165
FST 0.519176 -.301045 0.612267 -.514740
GDP 0.435377 0.846767 0.222957 0.209099
Now we can calculate HDI by the following formula:
HDI=(0.513*S_ALR)+(0.527*S_LEB)+(0.519*S_FST)+(0.435*S_GDP)
Montreux, 4. – 8. 9. 2000
4
Statistique, Développement et Droits de l‘Homme
with S_.= Standard value of (.)
Therefore by the above formula our assumption is not true and so gdp is the final component.
If we calculate the new rank of HDI for each country by this method and then we compare the
conclusions by the following scatter plot:
As the above figure is showing us the main difference between Rank and Ranknew is in the
middle ranks. Now again, by applying regression analysis between the new ranks and electricity
consumption per capita and also GDP we have :
Dependent Variable: RANKNEW
R-square: 0.7748
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 10.481762 3.65096785 2.871 0.0046
RANK_E 1 0.880208 0.03618694 24.324 0.0001
Dependent Variable: RANKNEW
R-square: 0.7663
Parameter Estimates
Parameter Standard T for H0:
Variable DF Estimate Error Parameter=0 Prob > |T|
INTERCEP 1 10.901734 3.71863562 2.932 0.0038
RANK_GDP 1 0.875409 0.03685764 23.751 0.0001
So, electricity consumption by the above analyze have influence on HDI, equivalence or
greater than GDP.
3. Conclusion
In this paper we focus on electricity in the title of one type of energy , if we work with total
net energy consumption we can forecast for good estimation.
Montreux, 4. – 8. 9. 2000
5
R A N K N E W
0
2 0
4 0
6 0
8 0
100
120
140
160
180
R A N K
0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180
Statistique, Développement et Droits de l‘Homme
GDP is one important component which is impressionable directly by energy and so we must
include energy consumption instead of GDP for accessing to HDI index because the correlation of
energy consumption and HDI must be big.
REFERENCES
[1] Johnson Dallas E., Applied Mulivariate Methods for data Analysis, Puxbury Press (ITP), 1998
[2] Human Development Indicators (1995), Human Development Office, (1996).
Montreux, 4. – 8. 9. 2000
6
... To bring the two nuclei close enough together, they need to collide with a very high speed, which means that the temperature of the gas must be high. If a 5 gas is heated beyond a certain temperature, the electrons are separated from the atoms which they belong to [16], and together they form a gas of charged particles, in which the electrons and nuclei move independently. That state is called a plasma. ...
... ……….. (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) Γ is a parameter describing the contribution to sputter yield of the mechanism whereby ions penetrate deep into the surface of the material, collide with atoms, and are reflected back to sputter material at the surface . This mechanism is most common with light, fast incident ions. ...
... Chapter Two 37 W is another fitting parameter that is dependent on target material and given in (Table 2-2), [ 118]. and for M 1 ≤ M 2 ... (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) for M 1 ≥ M 2 M 1 and M 2 are the masses of the incident ion and target atoms, respectively .k e is the electronic stopping coefficient: ...
Calculation of Sputtering Yield of Nuclear Fusion Wall to Relevant Mono-atomic Materials Due to Light Ions by using TRIM program
Thesis
Full-text available
  • Apr 2019
Abstract : Extended calculations for sputtering yield for bombarded (4Be, 5B, 6C, 13Al, 14Si, 22Ti, 24Cr, 26Fe, 27Co, 28Ni, 29Cu, 40Zr, 42Mo, 74W, 75Re) targets by (H+, D+, T+, He+) ions plasma are accomplished using a Monte Carlo code TRIM 2013.The calculations include changing the input parameters : the energy of (H+, D+, T+, He+) ions plasma, the hitting target angle, the atomic mass of incident ion, the number of incident ions, the width of target ,atomic number of target and the surface binding energy of target . There are many mathematical models that illustrate the interaction of ions with metals, as well as several of the simulation programs which describes this interaction. In this study it has been verified the interaction of four light ions with fifteen elements that used or candidate for use in nuclear fusion reactors by these mathematical models. Furthermore, the global TRIM program also been used to simulate the process of sputtering. The sputtering yield of elements gradually increase with increasing incidence angle reaching to a maximum value and then starts to decrease.It also increase with increasing the ion energy and incident ion mass, While it decrease with increasing the atomic mass of target, The sputtering yield is the largest for the Beryllium target and is the lowest for Tungsten and Rhenium. The sputtering yield decrease with increasing the atomic number of target, A small change in surface binding energy of target (whether to increase or decrease ) leads to large and significant change in sputtering yield. The results show that sputtering yield is directly dependent upon the input parameters and any change in those parameters leads to a significant change in sputtering yield. Methodical models are applied for TRIM simulation data to fit the sputtering yield with incident angle and ion energy. The best – fit values of the parameters included in the formulae have been tabulated for all of the mono-atomic materials interact with light ions. The data of sputtering yield is fitted using to a polynomial provided by Matlab software package and (Ior ) by IGOR Pro. Comparison of sputtering yield models the Sigmund formula for sputtering yield and Three semi-empirical equations based on Sigmund’s formula are graphically plotted. Also mesh graphic showing sputtering yield vs. ion energy and incident angle, is illustrated. A comparison of TRIM simulation data with some experimental data was graphically carried out them. Moreover the influence of the surface binding energy and sputtering threshold energy is studied .
View
... To bring the two nuclei close enough together, they need to collide with a very high speed, which means that the temperature of the gas must be high. If a 5 gas is heated beyond a certain temperature, the electrons are separated from the atoms which they belong to [16], and together they form a gas of charged particles, in which the electrons and nuclei move independently. That state is called a plasma. ...
... ……….. (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16) Γ is a parameter describing the contribution to sputter yield of the mechanism whereby ions penetrate deep into the surface of the material, collide with atoms, and are reflected back to sputter material at the surface . This mechanism is most common with light, fast incident ions. ...
... Chapter Two 37 W is another fitting parameter that is dependent on target material and given in (Table 2-2), [ 118]. and for M 1 ≤ M 2 ... (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) for M 1 ≥ M 2 M 1 and M 2 are the masses of the incident ion and target atoms, respectively .k e is the electronic stopping coefficient: ...
الاطروحةpdf
Thesis
  • Apr 2019
Abstract : Extended calculations for sputtering yield for bombarded (4Be, 5B, 6C, 13Al, 14Si, 22Ti, 24Cr, 26Fe, 27Co, 28Ni, 29Cu, 40Zr, 42Mo, 74W, 75Re) targets by (H+, D+, T+, He+) ions plasma are accomplished using a Monte Carlo code TRIM 2013.The calculations include changing the input parameters : the energy of (H+, D+, T+, He+) ions plasma, the hitting target angle, the atomic mass of incident ion, the number of incident ions, the width of target ,atomic number of target and the surface binding energy of target . There are many mathematical models that illustrate the interaction of ions with metals, as well as several of the simulation programs which describes this interaction. In this study it has been verified the interaction of four light ions with fifteen elements that used or candidate for use in nuclear fusion reactors by these mathematical models. Furthermore, the global TRIM program also been used to simulate the process of sputtering. The sputtering yield of elements gradually increase with increasing incidence angle reaching to a maximum value and then starts to decrease.It also increase with increasing the ion energy and incident ion mass, While it decrease with increasing the atomic mass of target, The sputtering yield is the largest for the Beryllium target and is the lowest for Tungsten and Rhenium. The sputtering yield decrease with increasing the atomic number of target, A small change in surface binding energy of target (whether to increase or decrease ) leads to large and significant change in sputtering yield. The results show that sputtering yield is directly dependent upon the input parameters and any change in those parameters leads to a significant change in sputtering yield. Methodical models are applied for TRIM simulation data to fit the sputtering yield with incident angle and ion energy. The best – fit values of the parameters included in the formula have been tabulated for all of the mono-atomic materials interact with light ions. The data of sputtering yield is fitted using to a polynomial provided by Matlab software package and (Ior ) by IGOR Pro. Comparison of sputtering yield models the Sigmund formula for sputtering yield and Three semi-empirical equations based on Sigmund’s formula are graphically plotted. Also mesh graphic showing sputtering yield vs. ion energy and incident angle, is illustrated. A comparison of TRIM simulation data with some experimental data was graphically carried out them. Moreover the influence of the surface binding energy and sputtering threshold energy is studied .
View
Energy services and life satisfaction: a study on households in two Mexican communities
Presentation
Full-text available
  • Apr 2017
This study explores the validity of energy services as surrogates of human well-being, through the optic of six energy services (communications, entertainment, food preservation, illumination, temperature regulation and transport) measured in a 5-point Likert scale.
View
On the Effect of Magnetic Field upon Plasma Parameters
Article
Full-text available
  • Jan 2017
Plasma confinement is the most important issue in nuclear fusion research. Among all the confinement techniques magnetic confinement is most popular across the globe. We have successfully accomplished a plasma gun which is capable of producing moving plasma. This plasma gun is a compact and effective device. The density of plasma is of the order of 1016 m-3 for discharging potential 1kV and ambient pressure 0.2 mb. It is observed that in the presence of magnetic field 250 Gauss the plasma parameters are modified. The pulse width of pulse forming network (PFN) associated with gun is 140 μs.
View
ResearchGate has not been able to resolve any references for this publication.