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International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023292

International Journal of Energy Economics and 
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2023, 13(3), 292-305.

The Role of Environmental Conditions and Purchasing Power 
Parity in Determining Quality of Life among Big Asian Cities

Marc Audi1,2,3, Amjad Ali2,1,4*

1Abu Dhabi School of Management (ADSM), UAE, 2The European School of Leadership and Management (ESLM), Belgium, 
3University Paris 1 Pantheon Sorbonne, France,  4Lahore School of Accountcy and Finance, University of Lahore, Pakistan. 
*Email: chanamjadali@yahoo.com

Received: 13 September 2022 Accepted: 12 March 2023 DOI: https://doi.org/10.32479/ijeep.13583

ABSTRACT

This article has examined the role of environmental conditions and purchasing power parity in deciding the quality of life among big Asian cities. The 
study has constructed an index for quality of life with the help of housing, crime rates, death rate, average life expectancy, environmental degradation, 
and level of education. Quality of life has been selected as the dependent variable and the level of pollution, availability of health care facilities, local 
purchasing power, availability of groceries, level of democracy, cost of living, restaurants, level of traffic, and level of rents are selected explanatory 
variables. For empirical analysis, this study uses data for the years 2017, 2018, and 2019. The estimated results show that pollution has a negative and 
significant impact on the quality of life in the case of Asian cities. Local purchasing power has a positive and significant relationship with the quality 
of life in the cities of Asia. Groceries and democracy are very important parts of the daily life of human beings but they have insignificant impacts on 
the quality of life in Asian cities. Restaurants have a positive and significant impact on quality of life. This study finds that level of traffic and the level 
of rent have a negative and significant impact on the quality of life in the case of Asian cities. The overall results conclude that selected indicators 
play a significant role in determining the quality of life in Asian cities.

Keywords: Quality of Life, Environmental Conditions, Purchasing Power Parity 
JEL Classifications: E31, J17, R11

1. INTRODUCTION

The most recent, Sustainable Development Goals (SDGs) put 
special attention on the quality of life among the nations. SDGs 
encourage national governments, international institutions, and 
civil societies to help in achieving the targeted quality of life. 
Following, the famous slogan of UNO “health and education 
for all”, the World Bank and the UNO publish different reports 
and articles related to the ranking of the quality of life among 
the countries and cities. The basis of every field of science is a 
better quality of life, but traditional societies forget this target of 
the human being (Kant, 1785). After the emergence of the UNO, 
quality of life gets special importance in the socioeconomic 
policies of each nation (McGillivray, 2007).

This general understanding of the quality of life explains that 
an individual or social group should work for the fulfillment 
of physiological needs. There are some main indicators for the 
measurement of quality of life i.e. rate of infant mortality, the mean 
life expectancy, sanitary facilities, percent of people having safe 
drinking water, and mean daily calorie supply per person (Diener 
and Diener, 1995). The quality of life has also some subjective 
as well as some objective traits. The best traditional tools for 
measuring the quality of life at the macro level are the gross 
domestic product and at the micro level per capita income. Tobin 
and Nordhaus (1972), the United Nations Development Program 
(1990), and Anand and Sen (1992) mention that traditional tools 
for measuring the quality of life are unable to explain the actual 
socioeconomic conditions of the individual household. Tobin 

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Audi and Ali: The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities

International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023 293

and Nordhaus (1972) point out that net economic welfare is the 
best criterion for the measurement of quality of life because it is 
based on the aggregate consumption of the nation. International 
Labor Organization (1976) has developed physical quality of life 
index. In this study, we have constructed an index of quality of 
life (availability of housing, crime rates, death rate, average life 
expectancy, environmental degradation, and level of education) 
with the help of principle components analysis.

The idea of environmental conditions related to the quality of life 
has a wider scope, and this depends on the quality of mediums (e.g. 
soil, water, and air). The quality of the environment is considered 
very important to the quality of human life because human health 
is greatly impacted by the existing physical conditions of the 
environment (Coan and Holman, 2008, Kahn, 2002). Extreme 
environmental conditions (earthquakes, cyclones, floods, droughts, 
and volcanic eruptions) have seriously damaged the physical and 
mental health of human beings (Ahmad and Yamano, 2011). It has 
been observed empirically that environmental conditions strongly 
impact the quality of life (Diener and Suh, 1997, UNECE, 2009). 
The quality of the environment has intrinsic value to the human 
being (Brajša-Žganec et al., 2011). The preserving environment is 
one of the most important factors in ensuring and the preservation 
of well-being over time (Liere and Dunlap, 1980). Thus, the 
policies related to the environment have a critical role to deal with 
global health priorities, as well as improving environmentally 
responsible behavior and lives (Felix and Garcia-Vega, 2012).

In the frictionless world, the prices of the same goods should 
be the same across different locations. The law of the one price 
is considered an important determinant of quality of life. If the 
prices differ in two locations, excess demand and supply in the 
cheap and expensive locations would ultimately equalize them. 
The cross-location and international prices infer that the price 
dispersion changes little over time, but the international price 
declines over time and has a deep-rooted impact on quality of 
life (Reif et al., 2021). In recent years, price convergence among 
the regions has fascinated policymakers to check its impact on 
different socioeconomic factors. As this refers to the convergence 
of quality of life among the regions and nations at the same 
time. The differences between the prices in the two countries are 
temporary if the prices are measured with the same exchange rate 
(Pires-Júnior et al., 2018).

The role of wages and rents in allocating workers’ locations, also 
decide the quality of life within that locations. If the amenity of 
production and the wage gradient is unclear while the rent gradient 
is positive, this would produce an unclear level of quality of life. 
Hence, the housing market and the non-traded good price level are 
also unclear (Roback, 1982). The long-run behavior of the cost of 
a given consumption basket at each point in time to document the 
behavior of absolute price levels (Devereux and Griffith, 2003). 
Average relative price variability has a significant impact on the 
quality of life in the same region (Chmelarova and Nath, 2008). 
Price level convergence is referred to as the overall consumer 
price index as well as the index without housing prices (Dreger 
and Kosfeld, 2010). The overwhelming evidence in support of 
price level convergence over time in major cities in the US (Huang 

et al., 2011). Border plays an important role in the variations of 
prices of similar goods and services in different cities, but the 
variations in the prices are higher in the cities located in different 
countries than in the cities in the same country (Engel and Rogers, 
1994). Based on the law of one price, purchasing power parity 
states that exchange rates do not affect relative goods, relative 
inflation will be proportional to the exchange rates, and there 
is weak evidence of purchasing power parity and quality of life 
(Serletis and Gogas, 2003). This study has examined the role of 
environmental conditions and purchasing power parity on quality 
of life in the case of selected Asian cities.

2. LITERATURE REVIEW

There are extensive theoretical and empirical studies that 
investigate the link between environmental conditions and 
quality of life, purchasing power, and quality of life. Here, the 
most relevant studies have been selected for the literature review. 
Quality of life refers to reasonable and good living conditions 
(Cella, 1992; Felce and Perry, 1995; Sarvimäki and Stenbock‐Hult, 
2000; Kelley-Gillespie, 2009; Lagadec et al., 2018; Ohta et al., 
2021; Solberg et al., 2022). Good and reasonable living conditions 
are comprised of material sources and subjective perceptions e.g. 
feelings and emotions. Ventegodt et al., (2003) mentioned that 
quality of life has its integrated attributes. This study provides three 
notions of quality of life i.e., objective quality of life, existential 
quality of life, and subjective quality of life.

Environmental conditions being the objective indicators i.e. 
the concentrations and emissions of various pollutants should 
be combined here with indicators based on people’s subjective 
perceptions of the quality of the environment where they live. 
As in the case of other subjective data, indicators of satisfaction 
with environmental quality may be affected by cultural biases and 
other limits that could affect cross-country comparisons, so these 
indicators are excluded from the assessment of environmental 
indicators of quality of life (Liao, 2009). Local environmental 
conditions have a direct influence on the quality of life in general 
and human health in specific. Better quality of the environment 
is a big foundation for human satisfaction and improved mental 
health, and it permits individuals to recover from the stresses of 
everyday life and perform physical activities. Sufficient availability 
and access to physical resources e.g., green spaces, forests, and 
rivers contribute to the quality of life. Economies rely not only 
on healthy and productive workers but also on natural resources 
like water, timber, fisheries, plants, and crops (Zheng, 2010). 
The consumption of environmental services and amenities has 
a direct impact on quality of life, and conversely, the quality of 
these services is affected by human behavior. Environmentally 
responsible behavior through activities such as saving energy, 
using renewable resources and sustainable consumption is the 
main driver of the quality of environmental services provided 
(Osbaldiston and Sheldon, 2003, Thogersen, 2006).

Following the theories related to the quality of life, the wider 
spectrum incorporates the subjective and objective realm of 
living (Berleant, 1997; Ya-Wen and Hsiao, 2006; Ventegodt et al., 
2003). The process considers diverse dimensions ranging from 



Audi and Ali: The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities

International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023294

economic status to personal security (Durand, 2015; Balestra 
and Tonkin, 2018). There has been quite a bit of recent scholarly 
effort aiming to understand the relationship between economic 
status and quality of life (Cella, 1994; Smith, 1999; Tang, 2007; 
Kautonen et al., 2017). Purchasing power is one of the practical 
indicators representing economic condition (Guesalaga and 
Marshall, 2008; Rogoff, 1996). Frenkel (1981) studies the trend of 
purchasing power parities among the US dollar and France Franc 
for May 1979–July 1979. The study finds that during the 1970 s 
currency rate of exchange between the USA dollar and the France 
Franc has fallen. The study concludes that the currency rate of 
exchange and level of price cannot be separated from each other, 
so any variation in the rate of exchange will impact the domestic 
price level and lifestyle of the masses. Taylor and Brown (1988) 
investigates the long-run trend of purchasing power parity with 
the help of Co-integration techniques among five major exchange 
currencies from 1973 to 1985. Seasonally unadjusted monthly 
based data has been used in this study. The outcomes of this study 
are against the traditional hypothesis of purchasing power parity, 
there is no integration has been found among the major exchange 
currency during the selected period. This study concludes that 
proportionality exists among the relative prices ad exchange rates 
over the long run.

Apte et al. (1994) examine the relative purchasing power parity 
monthly. The data have been used in this study post-1972. 
The authors argue that martingale behavior deviates from the 
purchasing power parity. This study uses all information implicit 
in all cross-rates too. Secondly, an instrumental variable that is 
specifically designed to cope with lead-and-lag effects in non-
traded versus traded-goods inflation. Our estimates indicate 
that most lead-and-lag effects seem to occur within a 6-month 
window. Engel and Rogers (2001) explore the failures of the law 
of one price across European cities. The focus of this study is to 
explore the local currency pricing and floating exchange rates 
for the international failures of the law of one price. This study 
is paying more attention to the local currency instead of previous 
studies. This study derives an expression for the variance of the 
relative prices across locations in terms of nominal exchange rate 
and transportation costs. It is concluded that the border effects 
are coming from the local currency pricing with the fluctuating 
nominal exchange rate. The border effect has a positive and 
significant effect on real exchange variations.

Silvester et al. (2004) investigate the purchasing power in the 
panel of cities. This study uses a long-time span of panel data 
and the short-run deviations measured by the half-lives, thus, 
the results show non-rejection of the purchasing power parity. In 
highly integrated economies, real factors may be due to a slow rate 
of convergence to a common price index. Coakley et al. (2005) 
investigate the first test of purchasing power parity and the theory 
of general relativity. The study finds that inflation differentials 
reflect, on average one-for-one in long-run nominal exchange 
rate reduction that general relative purchasing power parity holds.

Reif and Rumler (2014) examine the price dispersion in the Euro 
area. For this purpose, the disaggregated price data set has been 
taken from the AC Nielsen to the euro system. The study concludes 

that the price dispersion moves together closely in locations 
that are closer to each other, but the price dispersion across the 
cross-country price dispersion is by an order of magnitude larger 
than within-country price dispersion even after controlling for 
product heterogeneity. Tax rates, income level, and consumption 
intensities could be explained as a large part of the cross-country 
price difference. It also indicates that price dispersion in the Euro 
area has declined since the start of the monetary union. Beko 
and Kavkler (2019) reexamining the purchasing power parity 
for small central and Eastern European economies. The results 
show that even after considering the nonlinear reversion of real 
exchange rates of choosing central and Eastern economies, there 
is no confirmed validity of purchasing power parity. The non-
stationarity hypothesis of the real exchange rate is rejected in 
the case of 3 countries out of 10. This study gives the direction 
for further research to point out the determinants that cause the 
violation of the purchasing power parity concepts in the Central 
Eastern European economies.

3. THE MODEL

Quality of life has been the prime objective of all human activities 
(Shin and Johnson, 1978; Elizur and Shye, 1990; McKee-Ryan 
et al., 2005). Simply, quality of life presents the overall situation 
of peoples’ lives in the country (McGillivray, 2007). Somehow, 
this general understanding of the quality of life is unable to explain 
an individual or any particular social group’s living standard. 
Literature (Headey and Wearing, 1989; Costanza et al., 2007; 
Renwick et al., 2017; Mouratidis, 2021) has highlighted that there 
are some objective as well as some subjective traits attached to the 
quality of life. Thus, for this purpose, the selection of key indicators 
and their assigned weight matter a lot. For a couple of years, there 
are some measures have been used for judging the quality of life 
(Veenhoven, 1993). The best traditional tools for measuring the 
quality of life at the macro level are the gross domestic product 
and at the micro level per capita income. Nordhaus and Tobin 
(1973), the United Nations Development Program (1990), and 
Anand and Sen (1992) mention that traditional tools for measuring 
the quality of life are unable to explain the actual socioeconomic 
conditions of the individual household. Quality of life directly 
explains the participation of an individual in the socioeconomic 
activities of the community (Coleman and Rainwater, 1978; 
Rainwater, 1990; Sen 1992). Sen (1992) further points out that 
aggregate household consumption is not quality of life, whereas 
it is the ability to consume and the capability to participate in 
socio-economic activities that are called real quality of life. 
United Nations Development Program (1990) has developed a 
comprehensive index for measuring the quality of life with the help 
of health, education, and income. Following the methodologies 
of Duesenberry (1949), Scitovsky (1976), Layard (1980), Frank 
(1985), Schor (1998), Cooper et al. (2001), Ravallion and Lokshin 
(2001), Ferrer-I-Carbonell (2002), Ali (2015), Ali and Rehman 
(2015), Ali and Bibi (2017), and Ali (2018) we have constructed 
an index of quality of life in the case of selected Asian cities. The 
functional form of the model becomes as:

 QLi=f (PIi, HCIi, PPIi, GIi, DMi, CLIi, RIIi, TIi, RIi) (1)



Audi and Ali: The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities

International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023 295

QL= Quality of life index has been constructed with the help 
of availability of housing, crime rates, death rate, average life 
expectancy, environmental degradation, and level of education 
by using Principle Component Analysis (PCA).
PI = level of pollution (CO2 emissions)
HCI = availability of health care facilities (number of hospitals, 
doctors, nurses, hospitals beds, lady health workers, etc.)
PPI = local purchasing power (Local Purchasing Power shows 
relative purchasing power in buying goods and services in a 
market)
GI = grocery index comprised of availability of necessary grocery 
items
DM= democracy (measured with a dummy variable, 1 for 
democracy and 0 otherwise)
CLI= cost of living (the prices of food, shelter, transportation, 
energy, clothing, healthcare, childcare, etc.)
RII = availability of restaurants
TI= availability of transportation
RI= housing rent

To examine the elasticity of the explanatory variables to the 
dependent variable, the econometric model of the study becomes 
as:

QLi=A+β1PIi+β2HCIi+β3PPIi+β4GIi+β5DMi+β6CLIi+β7RIIi+β8T
Ii+β9RIi+Ui (2)

All the variables explained above expect A and U,
A=constant intercept
U=error term i.e. white noise having zero mean and constant 
variance

In this study, cross-sectional data from the years 2017, 2018, 
and 2019 have been used. The data of selected Asian cities have 
been taken from the World Bank and some national databases of 
each country. 42 cities of Asian countries have been collected for 
selected: Dubai (UAE), Tokyo (Japan), Singapore (Singapore), 
Bangalore (India), Pune (India), Istanbul (Turkey), Kuala Lumpur 
(Malaysia), Chennai (India), Delhi (India), Mumbai (India), 
Bangkok (Thailand), Tehran (Iran), Kolkata (India), Shanghai 
(China), Doha (Qatar), Hong Kong (Hong Kong), Hyderabad 
(India), Ankara (Turkey), Beirut (Lebanon), Jakarta (Indonesia), 
Tbilisi (Georgia), Manila (Philippines), Amman (Jordan), 
Riyadh (Saudi Arabia), Bursa (Turkey), Taipei (Taiwan), Seoul 
(South Korea), Tel Aviv-Yafo (Israel), Izmir (Turkey) Almaty 
(Kazakhstan), Yerevan (Armenia), Beijing (China), Karachi 
(Pakistan), Ho Chi Minh City (Vietnam), Colombo (Sri Lanka), 
Mangalore (India), Muscat (Oman), Gurgaon (India), Baku 
(Azerbaijan), Lahore (Pakistan), Ahmedabad (India) and Hanoi 
(Vietnam).

4. RESULTS AND DISCUSSION

This section consists of a discussion and estimated results that how 
selected indicators can impact the quality of life in the selected 
cities of Asia. Quality of life is taken as an explained variable, 
whereas traffic, the cost of living, rent, grocery, restaurant, local 
purchasing power parity, pollution, health care, and democracy 

have been used as independent variables. Appendix Table A 
presents the descriptive statistic of the selected variables, the 
results of the descriptive show the mean, standard deviation, 
minimum, maximum, Skewness, Kurtosis, and Jarque-Bera 
values of explained as well as explanatory variables. Based on 
the estimated results, all of our selected variables have reasonable 
intertemporal properties to estimate the regression analysis. The 
estimated results of correlation have been given in appendix 
Table  B. The results show that most selected variables have a 
significant relationship with each other, but there is no higher 
correlation which creates the issue of multicollinearity among 
our decided regressors.

Pollution is a major risk contributor to human health (McKenzie 
et al., 2012; Audi and Ali, 2018; Huang et al., 2018; Rinklebe 
et  al., 2019; Beni and Esmaeili, 2020). Different human activities 
produce different gases (sulfur oxides, nitrogen oxides, carbon 
monoxide, carbon dioxide, volatile organic compounds, particulate 
matter, chlorofluorocarbons, and ammonia) which hurt human 
health (WHO, 2002; Cohen et al., 2005; Sadalla, 2005; Mathew 
et al., 2013). Our estimated results of regression for the years 
2017, 2018, and 2019 have been given in Table 1. The outcomes 
present that pollution level has a negative and significant influence 
on the quality of life in Asia cities. The rising level of pollution 
creates more chances of different diseases i.e. respiratory, lung 
and skin cancer, and asthma, etc., (Sadalla, 2005; Pruss-Ustun 
and Corvalán, 2006). Under such an environment, there are more 
chances of children and adult respiration (WHO, 2014 and CDC, 
2010). About 5% of lung cancer has been attributed to pollution 
and it has a continuously rising trend (WHO, 2002; Cohen et  al., 
2004). Exposures to pollution accounted for approximately 2 
% of the global cardiopulmonary disease burden (Cohen et al., 
2004; WHO, 2002). Empirics show that pollution is estimated to 
be responsible for about 3% of adult cardiopulmonary disease 
mortality; about 5% of the trachea, bronchus, and lung cancer 
mortality; and about 1% of mortality in children from acute 
respiratory infection in urban areas worldwide (Cohen et al., 2005). 
Thus, specifically, the rising level of pollution reduces the quality 
of life in a nation and a city in general. Our results reveal that a 
1% rise in the pollution level, (−0.917000) %, (−0.672991) % and 
(−0.780301) % fall happened in the quality of life in Asian cities 
during the years 2017, 2018, and 2019. Literature from the last 
three decades (Van Aardenne et al., 1999; Brunekreef and Holgate, 
2002; Sadalla, 2005; Bell et al., 2008; Ali and Audi, 2016; Audi 
and Ali, 2016; Ali et al., 2021) show that pollution contributes to 
illness and death in all age groups, but the association between 
child death rate and pollution is very strong.

The availability of health care facilities will make the citizen of 
the nation more assured to get the health services they needed. 
Better quality of life is directly and indirectly attached to health 
care standards (Testa and Nackley, 1994; Ferrans et al., 2005; 
Karimi and Brazier, 2016), as by keeping the public health 
status promotive, rehabilitative, curative, and preventive, we 
may have better health outcomes (Engineer et al., 2008; De 
Mello and Pisu, 2009; Todaro and Smith, 2011; Craigwell, 
et al., 2012). Our results for the year 2017 show that there is 
a positive but insignificant link existed between health care 



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International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023296

and the quality of life in Asia. Better health care is attached to 
an educated population, skilled labor, and better medication. 
Based on the results, this reveals that Asian countries have more 
uneducated populations, unskilled labor, and less medication 
for the population. Moreover, Asian cities have poor health 
care systems i.e. medical staff is not competent and skilled, 
examining and reporting is slow, modern equipment for disease 
diagnosis is not available, the convenience of location for the 
center is not available, and huge and congested cities (Kaplan, 
2003; Amaghionyeodiwe, 2009; Allin et al., 2010; and Kim 
et  al., 2017). This creates an insignificant impact on the health 
care system for the masses of cities in Asia. So, deficiencies in 
health care are impacting badly the quality of life in the cities of 
Asia. But health care is positively and significantly influencing 
the quality of life during the years 2018 and 2019. Health care 
should be the basic priority of governments, so over time, 
Asian governments are paying much attention to the health of 
the masses.

Purchasing power is one of the practical indicators representing 
the economic condition of the masses of the country (Frenkel, 
1978; Abuaf and Jorion, 1990; Rogoff, 1996; Taylor and Taylor, 
2004), thus there is a strong relationship is existed between 
purchasing power and quality of life (Chin, 2001; Fisher, 2006; 
Tan et al., 2018). There are numerous studies (Costanza et al., 
2007; Coletto et al., 2017; Greco and Polli, 2021) that highlighted 
that security perception has an important role in determining the 
level of human well-being. The purchasing power presents the 
currency value in terms of buying goods and services relative to 
other international currencies (Balassa, 1964; Taylor and Taylor, 
2004; Haidar, 2011). The physical needs are the initial basis of the 
realization of quality of life (Hyde et al., 2003; Aivazian, 2016). 

The degree to which material needs are satisfied depends on the 
level of income (Diener and Oishi, 2000; Diener and Biswas-
Diener, 2002). Purchasing power can be the indicator representing 
the level of income (Gelb and Diofasi, 2016; Chen and Hu, 2018; 
Bronchetti et al., 2019). Empiric shows that the purchasing power 
of Asian cities is increasing and it has a significant influence on the 
quality of life. The rising purchasing power has created a positive 
economic environment. Our results indicate that if the purchasing 
power increases, then the quality of life enhances because higher 
purchasing power enables individuals to buy more goods and 
services (De Estado, 2013). The results indicate that the 1% rise 
in the local purchasing power rises by (0.265952) %, (0.478234) 
%, and (0.466222) % during 2017, 2018, and 2019 respectively. 
So, empirics show that as purchasing power increases the people 
buying capacity of people, thus it raises the standard of living in 
the cities of Asia.

Following the objective factors of quality of life (Lawton et al., 
1999; Ruggeri et al., 2001; Parra et al., 2010), the intake of calories 
is directly impacted by the availability of groceries (Schroeter 
et  al., 2008; Valin et al., 2014). The poor population of developing 
countries has a low amount of groceries, hence there is a low level 
of quality of life (Zezza and Tasciotti, 2010; Popkin et al., 2012). 
Our estimated results for the years 2017 and 2018 show that in the 
case of Asian cities, there is a positive and insignificant impact of 
groceries on the quality of life. Although, groceries play a vital 
role in our basic life. If the quality of groceries is not good, then it 
depressed the quality of life among the masses (Drewnowski and 
Evans, 2001; Lim and Taylor, 2005; Rasheed and Woods, 2013). 
But during the year 2019 groceries have a negative and significant 
impact on quality of life. This also shows that the standard of 
groceries in Asian cities is very bad which is harmful to human 

Table 1: Regression outcomes-year
Dependent variable: QL

Variables Year-2017 Year-2018 Year-2019
Coefficient Std. error Coefficient Std. error Coefficient Std. error

PI −0.917000*** 0.324959 −0.672991** 0.309342 −0.780301*** 0.147959
HCI 0.470872 0.499288 0.931557** 0.410969 0.853693*** 0.274245
PPI 0.265952* 0.135231 0.478234*** 0.163878 0.466222*** 0.091359
GI 0.652375 1.203359 0.288499 1.019981 −1.443005*** 0.469808
DM −27.02107 17.53992 −18.93161 14.01571 4.678003 6.718584
CLI −1.687793 1.814835 −1.185922 1.567984 1.713598** 0.678702
RII 1.481704* 0.813700 1.427618* 0.753392 −0.037075 0.345602
TI −0.209172** 0.076728 −0.194604*** 0.065118 −0.178981*** 0.038908
RI −0.959136*** 0.478294 −1.120078** 0.421298 −0.755148*** 0.248177
C 198.0765 58.51343 125.9796 43.36071 115.0171 24.93568
R-squared 0.645458 0.719446 0.890869
Adjusted R-squared 0.531499 0.642932 0.860176
S.E. of regression 27.58177 24.74640 12.25112
Sum squared resid 21301.12 20208.69 4802.874
Log likelihood −174.1693 −193.2967 −159.1207
F-statistic 5.663911 9.402724 29.02499
Prob (F-statistic) 0.000180 0.000001 0.000000
Mean dependent var 104.6724 104.7119 115.8748
S.D. dependent var 40.29644 41.41297 32.76304
Akaike info criterion 9.693122 9.455661 8.053368
Schwarz criterion 10.12407 9.865243 8.467099
Hannan-Quinn criter. 9.846448 9.606702 8.205016
Durbin-Watson stat 2.080583 1.781287 1.943951
***1% significant level **5% significant level *10% significant level



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International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023 297

life, thus groceries impact badly on the quality of life. The results 
illustrate that a 1% in the groceries −1.443005% fall is happening 
in the quality of life in the selected Asian cities.

Many social scientists believe that political rights have an intrinsic 
impact on the human well-being of individuals within a nation 
(Dasgupta, 1990; Diener and Suh, 2003; Tov and Diener, 2009; 
Murphy, 2014). Most of them are agreed that these are political 
and democratic rights that enable individuals to demand such 
public policies which enhance their welfare. Sen (1999) mentions 
that each society has faced a trade-off between adopting those 
institutions that preserve the essential political rights exercised 
by individuals to raise their quality of life, and demanding such 
institutions that reduce constraints to produce equal opportunities 
for all individuals to raise their wellbeing. Our estimated results 
show that democracy has an insignificant impact on the quality of 
life in Asian cities. Numerous studies (Kosack, 2003; Wong et al., 
2011; Buhaug and Urdal, 2013) show that developing countries 
have weak institutions for political and democratic rights, thus, 
our finding has an insignificant relationship between democracy 
and quality of life among Asian countries. In a democracy, people 
have the right to select the legislature, they all have equal rights 
and the government is run under the rules and regulations. In 
Asia, democracy puts an insignificant effect on the quality of life 
of the general public (Bloom et al., 2001; Chang and Chu, 2006; 
Buhaug and Urdal, 2013). Democracy is attached to an educated 
population, in Asia, mostly the population is uneducated and they 
don’t understand the true benefits of being democratic.

The cost of living is comprised of the price of food, childcare, 
healthcare, clothing, energy, etc. Quality of life is the direct 
link to the cost of living (Boskin, 1996; Bernanke and Mishkin, 
1997; Piazzesi et al., 2007). The cost of living is negatively and 
insignificantly impacting the quality of life in the selected cities 
of Asia. Empiric shows that the cost of necessities has risen trend 
in Asia and the living standard is directly attached to the basic 
requirement of living human beings i.e. food, health, and housing, 
but in the case of Asia, most population have little or insufficient 
access to necessities (Johansen, 1993; Hossain and Fischer, 1995; 
Chang et al., 2013). Moreover, in Asian population have fewer 
earnings and an insufficient amount of money, which is required 
for maintaining the standard of living (Banerjee and Duflo, 2007; 
Saxena et al., 2007). This creates an insignificant link between the 
quality of life and the cost of living in Asia.

Availability of food is part and parcel of human life in general 
and quality of life in specific (Dasgupta and Weale, 1992; Rapley, 
2003; Neulinger et al., 2020). Presently, the availability of food 
from outside the home has become an integral part of human 
life, and the demand for such food depends upon the availability 
of restaurants (McCracken and Brandt, 1987; Gustafsson et al., 
2006; Mehta and Chang, 2008). It is the availability of restaurants 
in cities, which presents the eating habits of the inhabitants, and 
the quality of food and services given in the restaurants have a 
direct influence on the quality of life (Edwards and Hartwell, 2009; 
Williams, 2009). Our results show that restaurants are positively 
and significantly impacting the quality of life in selected cities. The 
results reveal that a 1% increase in restaurants brings a 1.481704% 

and 1.427618% rise in quality of life during the years 2017 and 
2018 respectively. But restaurants have an insignificant impact 
on the quality of life during the year 2019. This insignificance of 
the relationship shows that during the year 2019, the quality of 
restaurants deteriorated which automatically impacted the quality 
of life in general.

The availability of transportation is one of the important factors 
in human personal life and business activities (Popova, 2017). 
The availability of transportation in its simple meaning is to 
carry passengers and freight from one place to another (Bruzzone 
et al., 2021). When transportation is considered from the quality 
of life frame, it simply refers to carrying individuals to their 
job, education, health centers, tourist places, etc., (Myers, 1988; 
Macke et al., 2018). The development of transportation, vehicles, 
and infrastructure and using new technologies in this sector speed 
up traveling facilities. But in the case of developing countries, 
the available transportation is insufficient to facilitate a huge 
amount of population (Suzuki et al., 2015; Demissie et al., 2016). 
Thus, our results show that the availability of transportation has 
a negative and significant impact on the quality of life in the 
case of Asian cities. The role of transportation in our daily life 
is very important because when cities have bad transportation 
and people spend more time in traffic, then they bear mental 
depression and lower quality of life (Downs, 2005; Novaco 
and Gonzalez, 2009). In Asia, there are big, old, and congested 
cities that hurt the quality of life in these cities (Glaeser, 2011; 
Wen et al., 2019). Moreover, mostly the cities of Asia do have 
not proper road infrastructure which creates traffic issues if the 
roads are blocked by traffic and the vehicles get stuck on the 
road and creating depression and noise pollution for the masses 
(Forman et al., 2003; WHO, 2012; Newman and Kenworthy, 
2013; Cervero et al., 2017).

The availability of housing facilities covers both subjective and 
objective dimensions for quality of life. Housing facilities are 
directly linked with rent, thus, house rent is one of the important 
indicators of quality of life (Shapiro, 2006; McCrea et al., 2006; 
Aragonés et al., 2017). As a concern to objective dimensions, 
the fluctuations in house rent may have a significant impact on 
individual wealth and reduces his/her capacity to spend on health 
and education. Whereas, subjective dimensions of quality of life 
(i.e. mental health, etc.) are also impacted by the fluctuations 
in rent (McCrea et al., 2006). The drivers of housing rent 
potentially influence the quality of life, as house rent reflects the 
benefits derived from living in better areas in addition to possible 
wealth shocks (Glaeser and Gottlieb, 2009; Stone et al., 2015). 
Moreover, the exposure to better neighborhood conditions points 
towards a positive relationship between housing rent and quality 
of life irrespective of tenure status and could lead to a positive 
relationship with overall renters if living in better areas outweighs 
any negative wealth shocks (Byrne and Diamond, 2007; Rowley 
and Ong, 2012). But in the case of developing countries, most 
of the population has a weak shock absorption capacity, and any 
fluctuations in rent may disturb the overall quality of life (Collier 
et  al., 2010; Barma et al., 2012). Our estimated results show 
that rent has a negative and significant impact on quality of life. 
The results explain that a 1% rise in housing rent −0.959136%, 



Audi and Ali: The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities

International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023298

−1.120078%, and −0.755148% fall has occurred in the quality of 
life during the years 2017, 2018, and 2019, respectively.

4.1. Comparative Analysis of Outcomes
We also make the comparative analysis based on our estimated 
results and discussion. The calculated outcomes of all the models 
explain that level of pollution is negatively and significantly 
influencing the quality of life (Audi and Ali, 2018; Huang 
et al., 2018; Rinklebe et al., 2019). Availability of health care 
facilities positively but insignificantly influenced the quality of 
life during the year 2017 but during the years 2018 and 2019, 
availability of health care facilities positively and significantly 
influenced the quality of life in the case of selected Asian 
cities. It shows that Asian cities are improving their health care 
system the raising their living quality (Neirotti et al., 2014). 
The calculated outcomes show that local purchasing power is 
positively and significantly influencing the quality of life during 
all the selected years 2017, 2018, and 2019. This shows that 
higher quality of life is attached to higher purchasing power 
parity (Taylor and Taylor, 2004). The outcomes of the years 
2017 and 2018 show that the availability of groceries has a 
positive and insignificant impact on quality of life, but during 
the year 2019, the availability of groceries is negatively and 
significantly influencing the quality of life in Asian cities. This 
highlights that the quality and availability of groceries are 
very low, so there is an inverse link between the availability of 
groceries and the quality of life in selected Asian cities (Lucan 
and Mitra, 2012). The level of democracy negatively and 
insignificantly influenced living quality during the years 2017 
and 2018 but during 2019 there is a positive but insignificant 
impact of the level of democracy on quality of life. This shows 
that the masses of Asian cities are unable to enjoy the true 
fruits of democracy (Kurlantzick, 2013). The cost of living has 
a negative and insignificant impact on the quality of in selected 
cities during the years 2017 and 2018, but the cost of living has 
a positive and significant impact on the quality of life in 2019. 
Restaurants have a positive and significant impact on quality 
of life during the year 2017 and 2018 but the restaurants are 
negatively and insignificantly influencing the quality of life in 
Asian cities during 2019. The calculated outcomes reveal that 
level of traffic is negatively and significantly influencing the 
quality of life in Asian cities during the years 2017, 2018, and 
2019. This highlights that rising levels of traffic reduce human 
well-being (Douglas, 2012). The results show that level of 
rent put an inverse influence on living quality in Asian cities 
during the years 2017, 2018, and 2019. This shows that with 
the rising level of rents the people of selected cities are unable 
to get affordable residents for their families (Wu, 2004), thus, 
the overall quality of life is depressed in Asian cities.

4.2. Diagnostics Outcomes
The estimated outcomes of diagnostic tests have presented in 
appendices C, D, E, etc. Breusch-Godfrey’s LM method of 
autocorrelation is used for checking the autocorrelation in the 
data series. Breusch-Pagan-Godfrey has been applied the checking 
the heteroskedasticity of the data. The results of Tables C-E show 
that there is no issue of serial correlation and heteroskedasticity 
in the selected data for the years 2017, 2018, and 2019 models. 

Thus, simple, ordinary least squares are the best fit for empirical 
analysis. Hansen (1996) points out that misspecification of the 
model and variables may generate biased outcomes that impact 
the predicting power estimated results. The most famous, CUSUM 
and CUSUMsq tests have been utilized for this checking the 
predicting power of the parameters. Brown et al. (1975) highlight 
that these methods provide facilities to check the gradual variations 
in the parameters. The expected value of recursive residual is zero 
leading to accepting the null hypothesis of parameter constancy is 
correct, otherwise not. This study has examined the determinants 
of quality of life in the case of selected Asian cities in 2017, 2018, 
and 2019. The plots of both CUSUM and CUSUMsq are shown 
in Figures 1-6 at a 5% level of significance. Results indicate that 
plots of both tests are within critical bounds at a 5% level of 
significance. This shows that our selected models are correctly 
specified in the case of selected Asian cities.

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0.6

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1.0

1.2

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12 14 16 18 20 22 24 26 28 30 32 34 36 38

CUSUM of Squares 5% Significance

Figure 2: Year 2017

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-8

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0

4

8

12

16

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CUSUM 5% Significance

Figure 1: Year 2017

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5

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15

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CUSUM 5% Significance

Figure 3: Year 2018



Audi and Ali: The Role of Environmental Conditions and Purchasing Power Parity in Determining Quality of Life among Big Asian Cities

International Journal of Energy Economics and Policy | Vol 13 • Issue 3 • 2023 299

5. CONCLUSIONS POLICY SUGGESTIONS

The article has investigated the influence of environmental 
conditions and purchasing power parity on the quality of life in 
selected Asian cities. Quality of life index (availability of housing, 
crime rates, death rate, average life expectancy, environmental 
degradation, and level of education) has been taken as regressand. 
Level of pollution, availability of health care facilities, local 
purchasing power, availability of groceries, level of democracy, 
cost of living, restaurants, level of traffic, and level of rents are 
selected regressors. The empirical analysis is based on 42 cities 
in Asia for the years 2017, 2018, and 2019 have been selected. 
The results show that level of pollution, level of traffic, and level 
of rent has a negative and significant impact on the quality of 
life in Asian cities. Availability of health care facilities had an 

insignificant impact on quality of life during 2017, it has a positive 
and significant impact on quality of life during the years 2018 and 
2019. The results show that local purchasing power has a positive 
and significant impact on the quality of life in the case of Asian 
cities. Availability of groceries has a positive but insignificant 
impact on quality of life during the years 2017 and 2018, but this 
relationship turns negative and significant during the year 2019. 
The level of democracy and the cost of living has an insignificant 
impact on the quality of life in Asian cities. Cost of living has a 
positive and significant impact on the quality of life of Asian cities 
during the year 2019. Restaurants have a positive and significant 
impact on quality of life but this relationship turns insignificant 
during the year 2019. The overall results conclude that selected 
independent variables play a significant role in determining the 
quality of life in Asian cities.

Based on estimated results, there are some policy suggestions 
drawn for improving the quality of life in Asian cities. Based on 
the results, it is suggested that Asian governments should take 
serious steps to reduce levels of pollution for attaining a higher 
level of quality of life. Health cares is one of the basic needs 
of the general public. So, if the health care system of selected 
cities can be improved a higher quality of life can be achieved. 
The purchasing power of the currency is directly attached to 
investment and the level of employment in the country. Thus, by 
enhancing purchasing power the overall higher quality of life in 
Asian cities can be attained. Moreover, the government provides 
charity funds and other monetary benefits to the people, which 
enhance the purchasing power of the masses. This study suggests 
that by lowering the cost of living, the level of quality of life can be 
increased. As with the higher cost of living, the people are unable to 
meet their necessities. For this purpose, governments of these cities 
provide subsidies on basic needs, so with the higher cost of living 
people can afford necessities. As restaurants have a positive and 
significant impact on quality of life, this reveals that for a higher 
quality of life the standard of restaurants can be improved. For 
the improvement of restaurants, the city government of selected 
cities should properly check the standards of restaurants. Although, 
numerous cities have separate departments for this purpose, and 
for the improvements of the restaurants these departments should 
improve their working efficiency. The quality of life environment 
is directly impacted by the quality of the environment, and the 
environment is directly impacted by the level of traffic. So, for the 
improvement in environmental quality, the level of traffic can be 
reduced. For this purpose, the governments of the selected cities 
can motivate their people to use public transport rather than private 
one, this will reduce the number of vehicles on the road and the 
level of environmental degradation comes down. Moreover, the 
fuel-efficient and environment-friendly technology of the vehicles 
should be encouraged and fuel inefficient, environmentally 
unfriendly, and outdated vehicles should be discouraged in Asian 
cities.

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Table A: Descriptive statistic
QL PI HCI PPI GI DM CLI RII TI RI

Mean (2017) 104.6724 70.30462 67.09143 78.16987 44.33133 0.743590 45.63669 33.57515 189.0997 23.21733
Mean (2018) 104.7119 71.91448 66.28445 68.44116 45.56605 0.813953 46.67628 35.23814 186.4379 21.20884
Mean (2019) 115.8748 71.53800 65.62095 62.14976 39.68762 0.785714 42.48667 33.03024 178.8988 18.91548
Med (2017) 104.4700 75.57000 67.99000 80.59000 43.02000 1.000000 43.47000 28.25000 167.6600 14.84000
Med (2018) 104.7800 75.07000 67.36000 56.89000 37.48000 1.000000 40.97000 29.18000 162.9300 12.10000
Med (2019) 112.6200 76.96000 67.17000 56.05500 34.27000 1.000000 38.02500 26.62500 172.7500 12.46500
Max (2017) 188.4800 95.35000 86.96000 184.0900 91.92000 1.000000 83.67000 89.20000 366.7900 82.57000
Max (2018) 188.4800 94.60000 85.73000 129.2000 107.0600 1.000000 93.81000 97.21000 366.7900 79.54000
Max (2019) 174.0400 93.21000 85.38000 135.9600 97.22000 1.000000 88.45000 86.44000 283.6800 76.83000
Min (2017) 31.41000 18.45000 35.88000 26.39500 25.20000 0.000000 24.06000 13.87000 85.78000 5.730000
Min (2018) 24.13000 24.90000 36.67000 27.62000 24.65000 0.000000 25.46000 16.04000 85.78000 5.700000
Min (2019) 65.11000 27.21000 36.87000 26.24000 19.21000 0.000000 21.58000 15.83000 95.94000 4.580000
S.D (2017) 40.29644 18.69303 11.68147 38.33626 17.61380 0.442359 17.50942 17.30736 67.24994 21.68495
S.D (2018) 41.41297 17.21894 11.72955 30.39780 21.20206 0.393750 19.69294 17.67874 68.44578 19.51739
S.D (2019) 32.76304 17.24454 10.01587 28.94254 18.62143 0.415300 17.89212 15.92315 55.29497 15.89434
Skew (2017) −0.015534 −0.908954 −0.497182 0.729451 0.997842 −1.115718 0.736190 1.374765 0.507416 1.597806
Skew (2018) −0.121584 −0.883579 −0.659579 0.364454 1.360427 −1.613559 1.093440 1.503164 0.658396 1.678162
Skew (2019) 0.239282 −0.905487 −0.432997 0.889151 1.491108 −1.392621 0.996888 1.307787 0.470625 1.598025
Kurtosis (2017) 2.280672 3.260371 3.527305 3.008179 3.454726 2.244828 2.544235 4.484521 2.601466 4.449991
Kurtosis (2018) 2.280334 3.129392 3.221381 1.827880 3.962438 3.603571 3.085996 4.969441 2.621423 4.822596
Kurtosis (2019) 1.926570 2.973072 3.422534 2.984470 4.678774 2.939394 3.070961 4.323392 2.191553 5.623537
J-Bera (2017) 0.820797 5.480451 2.058569 3.458749 6.807982 9.018093 3.860386 15.86605 1.931661 20.01091
J-Bera (2018) 1.033881 5.625097 3.205631 3.413437 14.92340 19.31163 8.581796 23.14242 3.363429 26.13463
J-Bera (2019) 2.417233 5.740613 1.624843 5.534543 20.49581 13.58219 6.965316 15.03704 2.694193 29.92095
Ob. (2017) 39 39 39 39 39 39 39 39 39 39
Ob. (2018) 43 43 43 43 43 43 43 43 43 43
Ob. (2019) 42 42 42 42 42 42 42 42 42 42

APPENDIX 



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Table C: Diagnostics test-year 2017
Breusch-Godfrey Serial Correlation LM Test:

F-statistic 1.692909 Prob. F (2,26) 0.2036
Obs*R-squared 4.378340 Prob. Chi-square (2) 0.1120

Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.831333 Prob. F (9,28) 0.5936

Obs*R-squared 8.012960 Prob. Chi-square (9) 0.5328
Scaled explained SS 3.348587 Prob. Chi-square (9) 0.9489

Table E: Diagnostics test-year 2019
Breusch-Godfrey Serial Correlation LM Test

F-statistic 0.008802 Prob. F (1.31)
Obs*R-squared 0.011922 Prob. Chi-square (1)

Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.605927 Prob. F (9.32)
Obs*R-squared 6.115353 Prob. Chi-square (9)
Scaled explained SS 5.305575 Prob. Chi-square (9)

Table B: Correlation matrix
Variables QL PI HCI PPI GI DM CLI RII TI
PI (2017) −0.577210***
HCI (2017) 0.43290*** −0.520***
LPPI (2017) 0.272333* −0.140107 0.117120
GI (2017) 0.154722 −0.416183 0.31510** 0.227488
DM (2017) −0.178861 0.160104 −0.069714 −0.4005** −0.682***
CLI (2017) 0.240171 −0.432*** 0.296211* 0.254556 0.9379*** −0.732***
RII (2017) 0.319105* −0.3334** 0.184361 0.199080 0.6689*** −0.609*** 0.8590***
TI (2017) −0.5282*** 0.40749** −0.244757 0.022788 −0.246801 0.167364 −0.305962 −0.30724*
RI (2017) 0.064473 −0.29217* 0.086951 0.32521** 0.82533** −0.765*** 0.8643*** 0.7346*** −0.210953
PI (2018) −0.5632***
HCI (2017) 0.49938*** −0.38020**
PPI (2018) 0.52509*** −0.4328*** 0.37890**
GI (2018) 0.233748 −0.5383*** 0.4807*** 0.4357***
DM (2018) −0.236462 0.233587 −0.158753 −0.4118*** −0.4784***
CLI (2018) 0.274144* −0.5291*** 0.4250*** 0.4580*** 0.9441*** −0.5977***
RII (2018) 0.32729** −0.3931*** 0.253219 0.4437*** 0.6545*** −0.6161*** 0.8463***
TI (2018) −0.5456*** 0.4934*** −0.23918 −0.164771 −0.2661* 0.165474 −0.2781* −0.243161
RI (2018) 0.097941 −0.3605** 0.179911 0.4748*** 0.7247*** −0.6774*** 0.8244*** 0.8017*** −0.180699
PI (2019) −0.7259***
HCI (2019) 0.4961*** −0.4283***
PPI (2019) 0.6645*** −0.4166*** 0.4231***
GI (2019) 0.28689* −0.4565*** 0.5051*** 0.3740**
DM (2019) −0.108014 0.137562 −0.16677 −0.4139*** −0.4717***
CLI (2019) 0.3775** −0.4745*** 0.4217*** 0.4652*** 0.9449*** −0.5694***
RII (2019) 0.4143*** −0.4280*** 0.19493 0.5323*** 0.6656*** −0.6088*** 0.8404***
TI (2019) −0.5274*** 0.3751** −0.02317 −0.18668 −0.202181 0.040147 −0.230312 −0.191029
RI (2019) 0.201781 −0.3691** 0.24311 0.4905*** 0.7417*** −0.6832*** 0.8203*** 0.7526*** −0.151503
***1% significant level **5% significant level *10% significant level

Table D: Diagnostics test-year 2018
Breusch-Godfrey serial correlation LM test

F-statistic 0.195141 Prob. F (2,31) 0.8237
Obs*R-squared 0.534629 Prob. Chi-square (2) 0.7654

Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.067395 Prob. F (9,33) 0.4115
Obs*R-squared 9.695267 Prob. Chi-square (9) 0.3757
Scaled explained SS 7.465962 Prob. Chi-square (9) 0.5887