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Happiness and Environmental Degradation: A Global Analysis

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This study contributes to the happiness-environment nexus by introducing the novel measures of environmental degradation such as species protection, marine protected areas and water quality unlike previous literature that mainly emphasized the importance of carbon dioxide emissions (CO2) in influencing happiness levels. The study also holds the distinction of using for the first time Environmental Performance Index (EPI) data for environmental degradation indicators. The empirical analysis is based on Ordinary Least Squares (OLS), Pooled OLS, Two Stage Least Squares (2SLS) and Generalized Method of Moments (GMM) techniques. The results suggest that CO2 emissions have strong negative impact on happiness whereas species protection and marine protected areas increase the level of happiness across the selected sample. Furthermore, economic affluence is improving the life satisfaction levels. This analysis emphasizes the need of environmental policies that aim at reducing harmful gasses such as CO2 and nitrous oxide emissions and promoting the factors like protection of bio diversities to ensure healthy functioning of environment and human society. Finally, findings of the study are shown to be robust to different specification, alternative estimation methods, and additional control variables.
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Pakistan Journal of Commerce and Social Sciences
2017, Vol. 11 (3), 753-772
Pak J Commer Soc Sci
Happiness and Environmental Degradation:
A Global Analysis
Muhammad Tariq Majeed
Department of Economics, Quaid-i-Azam University, Islamabad, Pakistan
Email: tariq@qau.edu.pk
Shaista Mumtaz
Department of Economics, Quaid-i-Azam University, Islamabad, Pakistan
Email: shaistamumtaz.mumtaz@gmail.com
Abstract
This study contributes to the happiness-environment nexus by introducing the novel
measures of environmental degradation such as species protection, marine protected areas
and water quality unlike previous literature that mainly emphasized the importance of
carbon dioxide emissions (CO2) in influencing happiness levels. The study also holds the
distinction of using for the first time Environmental Performance Index (EPI) data for
environmental degradation indicators. The empirical analysis is based on Ordinary Least
Squares (OLS), Pooled OLS, Two Stage Least Squares (2SLS) and Generalized Method of
Moments (GMM) techniques. The results suggest that CO2 emissions have strong negative
impact on happiness whereas species protection and marine protected areas increase the
level of happiness across the selected sample. Furthermore, economic affluence is
improving the life satisfaction levels. This analysis emphasizes the need of environmental
policies that aim at reducing harmful gasses such as CO2 and nitrous oxide emissions and
promoting the factors like protection of bio diversities to ensure healthy functioning of
environment and human society. Finally, findings of the study are shown to be robust to
different specification, alternative estimation methods, and additional control variables.
Keywords: happiness, environmental degradation, CO2 emissions, marine protected areas,
species protection, biodiversity, water quality, nitrous oxide emissions.
1. Introduction
Happiness research has made significant contributions to economic literature. It has gained
interest mainly because of the rising discontent among policymakers, environmentalists
and nationals as the economic growth has not been much successful in achieving the
promised benefits. The literature of happiness dates back to the times of ancient Greek, by
the works of Aristippus’s Hedonic view and Aristotle who define happiness as a central
purpose and goal of human life (Tiwari and Mutascu, 2015).
The empirical investigation into happiness research started with the pioneer studies of
Easterlin (1974), Scitovsky (1976) and Hirsch (1976). These studies mainly modeled
income as key determinant of happiness. However, soon it was realized that income had
little impact on the quality of life and happiness. The literature on happiness asserted that
Happiness and Environmental Degradation
754
income alone cannot guarantee enhanced human well-being (Easterlin, 1974; Tukker et al.,
2008). To attain the larger benefits there is need to build such economic models that can
ensure higher levels of happiness.
The idea that income alone cannot ensure larger happiness led to the investigation of other
variables such as health, socio-economic conditions and environmental quality that can
potentially affect well-being. Since environmental quality influences human psychology,
it has inherent relationship with happiness (Kellert and Wilson, 1983). Person surrounded
by green view and beautiful scenery is more likely to be happier than person living in lower
quality and grimy environment. Areas with greener environments manifest higher life
expectancy and well-being of their residents. In a case study of Pennsylvania over the
period 1972-1982, Ulrich (1984) finds out that the recovery rate of patients who stayed in
rooms with trees outside was much higher than those who were in rooms with brick wall.
They also required much less medications as they received healthier effects from nature.
Similarly, a research report shows that the beautiful sight improves workers efficiency and
helps to mitigate negative health conditions (California Energy Commission, 2003). This
highlights the importance of natural environment in securing happiness at large.
In happiness-environment nexus the most important concern is of environmental
degradation. Many studies have consensus that environmental degradation is a serious
threat to human happiness and health (McMichael, 2003; Ferrer-i-Carbonell and Gowday,
2007; Tiwari, 2011; Li et al., 2014). The World Health Organization (2013) report says
that almost 7 million deaths have been reported due to air pollution. Along with air
pollution, water quality and lack of sanitation are major environmental risks that cause
many infectious diseases. According to United Nations Office for Disaster Risk Reduction
Report (2004) environmental degradation is an outcome of decline in environmental
quality resulting from ambient effluence, inappropriate use of land and natural calamities.
The emergence of industrial revolution and rapid economic growth has deteriorated the
quality of environment to an alarming extent culminating major concerns globally. All
these developments transforming earth into “new state” that appears to be less welcoming
to humans. So to preserve the amenity of environment and thereby human happiness,
environmental degradation has been incorporated as the core subject of happiness research.
A substantial work has been produced on the relationship between happiness and
environmental degradation. The studies mostly consider greenhouse gases especially CO2
emissions as an indicator of environmental degradation. However, these studies ignore the
other dimensions of environmental degradation such as species protection, marine
protected areas and water quality. As these dimensions are the important constituents of
environment and also affect the happiness. Therefore, the present study attempts to
incorporate these dimensions along with CO2 emissions as main indicator of environmental
degradation and fulfills some of the research gaps of previous literature. Previous studies
are either country specific or covering small samples or using simple econometric
techniques. For instance, Tiwari (2011) covers a panel of only 21 countries, Lenzen and
Cummins (2013) merely integrate the two surveys to trace the impact of CO2 emissions on
subjective well-being only in case of Australia and Welsch (2006) simply uses OLS to
estimate the relationship of happiness with environment for ten European countries.
However, this research is not specific to some countries’ analysis like studies discussed
rather include the entire world scenario. Secondly, we have added the new dimensions of
environment such as species protection, marine protected areas and water quality. Finally,
Majeed & Mumtaz
755
this study addressed the issue of endogeneity which is altogether ignored in the previous
literature.
The broader objectives of the study include how different proxies of environmental
degradation affect happiness level, whether the individual effect of environmental variables
on happiness is consistent with overall impact and whether the conventional role of income
in maintaining happiness holds or not. This research paper emphasizes the importance of
non-income factors such as biodiversity, gasses and water quality in determining happiness
levels which have been long ignored in theoretical and empirical analysis. The main
implication of this research is that environmental degradation exerts powerful impact on
happiness as compared to economic and demographic dimensions. It creates the awareness
about importance of environment and its protection. The research suggests that policies
aimed at reducing environmental hazards can lead to a happy and healthy society.
The remaining study is arranged as follows. Section 2 includes the review of literature
pertaining happiness and environmental degradation relationship. Section 3 explains the
model and variables used in the study. Section 4 presents data description and sources of
variables. In Section 5 regressions results are interpreted and Section 6 concludes the
analysis.
2. Literature Review
The theories of happiness holds the different views about what determines and matters for
happiness. Some theories predict that happiness depends on the absolute quality of life
while others argue that it depends on relative quality of life or personal feelings of a person.
For instance, livability theory and objective list theory emphasis the absolute quality of life
while the comparison theory, hedonic theory, utilitarianism and desire theory support the
contrary view that is happiness depends more on how one feels about his life or relative
life quality regardless of the fulfillment of needs (Hagerty, 1999; Seligman and Royzman,
2003).
Livability theory implies that happiness depends on the extent to which material and non-
material needs of person are satisfied. It applies to a society where living conditions
supplement person’s needs and desires (Veenhoven et. al, 1993). This theory basically
gives the concept of happiness that is based on fulfillment of human wants. Similarly,
objective list theory contains the list of number of factors that are considered necessary to
lead a happy and healthy life. This enlists the ingredients of happiness that includes
success, health, better life opportunities, comforts, money, education and affection
(Seligman and Royzman, 2003). Thus, these are some of the thresholds used to evaluate
one’s happiness.
In contrast, the commonly held belief that happiness depends on relative quality of life
finds a support in following theories. Comparison theory suggests that happiness is
determined by comparing present life with past and with other people’s experiences
(Hagerty, 1999). People determine their levels of happiness by constantly comparing and
making judgments about their life relative to others’ experiences. Hedonic theory underlies
comparison theory where people derive life satisfaction levels by comparing their pain over
pleasures (Seligman and Royzman, 2003). The theory of utilitarianism also finds its roots
in Hedonism. In earlier works of happiness Jeremy Bentham (1822) gives the concept of
human well-being based on utilitarianism. Utilitarian holds the view that every person’s
satisfaction is composed of total balance of pleasures over sufferings and this view should
Happiness and Environmental Degradation
756
be the ultimate consideration of government. Similarly, desire theory says happiness is
achieved when one gets what he/she aspires for.
Relating to our study, there are theories which relates happiness with environment. One of
the earlier works by Wilson (1983) gives the concept of ‘biophilia hypothesis’ which
establishes the relationship between happiness and environment. The biophilia hypothesis
asserts that person who interacts more with natural environment receives positive mental
and physical well-being. The reason behind this attraction is the mankind history where
humans spent centuries living in natural environments (Kellert and Wilson, 1993). The
study by Kent et al. (2017) endorses the biophilia hypothesis. It shows that green and
beautiful community contributes to the happy society furthermore, environmental
characteristics such as urban planning plays an important role in life satisfaction levels of
people. Similarly, Ecopsychology Theory says that a detachment from nature not only
leads to poor environment but also increases unhappiness and poor health (Conn, 1998).
With the evolution of happiness research many theories have been put forward to explain
its importance, factors and dimensions. Initially studies focused more on income as major
determinant of happiness. Easterlin (1974) was the first economist to empirically test
whether income contributes to greater happiness. Easterlin contradicts this notion of
positive impact of economic development on happiness in his famous theory “Easterlin
Paradox” (1995). He showed that higher levels of happiness were associated with higher
incomes within the country but not across the countries for nineteen countries of Latin
America, Asia and Africa over the period of 1946-1970. Easterlin argues that increase in
income only increases happiness up to a certain point. Blanchflower and Oswald (2005)
also find that Australia being less happy despite being one of the highest in the rank of
Human Development Index (HDI). Findings of these studies are similar to the notion of
happiness given by comparison theory which asserts that happiness depends on the
perceptions of people about their lives rather than actual conditions in life. However,
studies by Veenhoven (1991) and Gardner and Oswald (2001) reject the Easterlin argument
that income does not affect happiness and believe that money does buy happiness. Different
Studies including livability theory lend support to the belief that rich countries are happier
than poor ones (Gerdtham and Johannesson, 1997; Lelkes, 2006). This implies the
importance of economic factors such as income, employment in shaping better life
satisfaction levels with one’s own life.
The analysis in past few decades has extended to include other socio-economic and
demographic variables as important determinants of happiness. Morawetz et al. (1977)
showed that income inequality lowers the life satisfaction levels. Unemployment has also
resulted in higher levels of psychological stress by lowering the better aspects of living
(Clark and Oswald, 1994; Winkelmann and Winkelmann, 1998; Di Tella et al., 2001).
Diener et al. (2000) conclude that married people reported higher joy than people who are
not married which in turn reported greater subjective well-being than previously married
individual. While no significant impact of economic development on happiness levels in
different countries is found (Blanchflower and Oswald 2004).
Apart from economic factors environment has also very important place in happiness
literature. The study of Ulrich (1984), carried in Pennsylvania between 1972 and 1981,
shows that the recovery rate of patients who stayed in rooms with trees outside was much
higher than those who were in rooms with brick wall. They also required much less
medications as they received healthier effects from nature. Considerable amount of
Majeed & Mumtaz
757
theoretical and empirical literature shows the negative impact of environmental
degradation on human happiness. Welsch (2006) shows that Nitrogen dioxide, LEAD and
Particles, proxies of air pollution, have harmful effect on person’s well-being in ten
European countries over the period of 1990-1997. Likewise, Ferrer-i-Carbonell and Gowdy
(2007) find a negative impact of environmental degradation on happiness and a positive
association between caring for species protection and well-being.
Brereton et al. (2006) indicate that people living close to big transport points have low
levels of satisfaction due to noise while those living near to coast have higher happiness
levels. Tiwari (2011) shows that decrease in happiness follows the increase in
environmental degradation. These empirical findings also find their support in theories of
Ecopsychology and Biophilia Hypothesis that say healthy environment leads to positive
physical and mental satisfaction levels. Contrary, few studies such as Gu et. al ( 2017) find
that pollution positively affects happiness of high income section while negatively affects
those lies in middle and lower income section. While Tiwari and Mutascu (2015) show no
significant impact of environmental degradation and GDP on happiness.
Previous literature mostly considered the pollutants commonly known such as greenhouse
gases especially CO2. The studies using carbon dioxide emissions are generally narrow in
scope, countries specific and cover shorter time span (Lele, 2013; Tiwari, 2011). This study
adds to the existing literature by incorporating the broader view of environmental
degradation and happiness. The analysis covers previous literature gaps by looking into the
general and segregated impact of different environmental indicators on life satisfaction.
This study empirically examines the effect of different and new dimensions of
environmental degradation on happiness across the globe. To attain the unbiased and robust
estimates we have tackled the issue of endogeneity which is ignored in earlier studies.
3. Methodology
The pioneering work in happiness research is attributed to Easterlin (1974) in his famous
theory “Easterlin Paradox”. Easterlin (1974, 1995) was one of the social scientist who
studied data on self-reported level of happiness in United States. The author considered
happiness as a function of economic affluence and suggest that income does not entirely
guarantee happiness. This paper incorporates happiness as a function of environmental
degradation including income as a cause of happiness. To empirically examine the
relationship between happiness and environmental degradation the following model is
constructed which is consistent with Tiwari (2011).
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝐸𝐷𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝑃𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡+ µ𝑖𝑡 (1)
ED represents environmental degradation measured through the CO2, species protection,
marine protected areas, water quality and nitrous oxide. As measures of socio-economic
and demographic variables, GDP (YPP), urban population (UP) and age (A) are included
in econometric analysis to understand what influences happiness.
Tiwari (2011) mainly uses CO2 as a measure of environmental degradation. This study also
takes CO2 along with the new indicators of environmental degradation including marine
life, species protection, water quality and nitrous oxide indicators. The analysis also
incorporates demographic and socio-economic variables.
The existing levels of greenhouse emissions are disturbing the natural pace of earth’s
temperature and warming the atmosphere at a startling rate. Carbon dioxide is the second
Happiness and Environmental Degradation
758
most rich greenhouse gas after water vapor. The rise of industrial revolution, businesses
and economic growth economies dependence on fossil fuels has increased which has
accelerated the surge of CO2. These escalating levels of CO2 are increasing the global
warming and exacerbating climate change. This climate change has direct impact on
happiness such as the study by Rehdanz and Maddison (2005) shows that higher winter
temperatures adds to happiness while higher summer temperatures decrease happiness.
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝑙𝑜𝑔𝐶𝑂2𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝐶𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡 + µ𝑖𝑡 (1.1)
The second proxy for environmental degradation is marine protected areas (MPA) as
oceans are the source of food and livelihoods for marine ecosystem. These areas also
support world tourism and recreational industries.
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝑀𝑃𝐴2𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝐶𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡 + µ𝑖𝑡 (1.2)
The third proxy for environmental degradation is species protection (SP). The
biodiversities and habitats are vital to sustain planet biological and physical cycles.
However, some of these factors are in declines which have further repercussions. The
disappearance of biodiversity and extinction of species are environment disasters that
inflict damage to human societies. Ferrer-i-Carbonell and Gowday (2007) explore that
ozone depletion and biodiversity loss have negative impact on well-being.
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝑆𝑃
2𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝐶𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡+ µ𝑖𝑡 (1.3)
The fourth proxy is unsafe water quality which is named as water quality (WQ). Access to
safe water is crucial for promoting human health, socioeconomic development and
individual well-being. Better access to clean drinking water is one of the goals of
Millennium Development Goals (Environmental Performance Report, 2016).
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝑊𝑄2𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝐶𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡 + µ𝑖𝑡 (1.4)
Finally, apart from CO2, another important greenhouse gas known as nitrous oxide (NO) is
included to check its impact on happiness.
𝐻𝑖𝑡 = 𝑎0+ 𝑎1𝑁𝑂2𝑖𝑡 + 𝑎2𝑙𝑜𝑔𝑌𝑃𝐶𝑖𝑡 + 𝑎3𝑈𝑃𝑖𝑡 + 𝑎4 𝐴2𝑖𝑡 + µ𝑖𝑡 (1.5)
4. Data and Variables Description
The analysis covers 99 countries dataset used in the empirical model for the period of 1980-
2015. Happiness is the focused dependent variable of study. CO2, species protection,
marine protected areas, water quality and nitrous oxide are main independent variables
while GDP, urban population and age are used as control variables. Table A1 in appendix
shows the brief description, construction and sources of the variables used in the analysis.
Table A2 shows the summary statistics of data that gives a detailed review of data. It
provides the maximum values and information on mean and standard deviation of
happiness and environmental degradation indicators. Happiness levels show variation
across countries. The people of Andorra are the happiest with 7.52 (out of 10) value as
against the general belief that people of developed countries are the happiest one while
Croatians are less happy with value 2.78 (out of 10). Andorra is surrounded with beautiful
mountains and climate where people are more connected to nature and its natural beauty
attracts huge number of tourists. In the case of CO2 emissions Chad with -3.517 is the
country that has lowest CO2 emissions while Qatar has highest CO2 emissions standing at
3.87.
Majeed & Mumtaz
759
Table 1: Summary Statistics of Cross-Sectional Data and Variables
Variable
Observations
Mean
Std. Dev.
Min
Max
Independent Variable
Happiness
99
4.139199
0.886266
2.7875
7.52
Dependent Variables
CO2
197
0.564095
1.621848
-3.51749
3.878691
Species protection
202
10.37874
5.46788
0
17
Marine prot. areas
203
5.826601
10.91998
0
75.13333
Water quality
179
0.612
0.348495
0
0.996179
Nitrous oxide
199
7.391938
7.174069
0
50.3368
Control Variables
GDP
190
8.849981
1.210175
6.30724
11.65673
Urban population
208
54.16002
24.68545
7.879416
100
Age
192
6.707162
4.402217
1.045787
17.63407
In marine protection Ecuador is at the top with highest value at 73.13 while Hong Kong,
Eritrea, Iraq, Kazakhstan, Sao Tome and Principe, San Marino, Montenegro, Uzbekistan,
Zambia and Zimbabwe doing poor performance in saving these areas as their value stood
at 0. The countries including American Samoa, Bermuda, Hong Kong, Guamand Monaco
are doing better in protecting species while Marshall Islands, San Marino, Nauru, Sao
Tome and Principe, Macao the situation of species protection is worse. The countries
including Australia, Austria American Samoa, Aruba, Brunei Darussalam, Canada,
Cyprus, Finland, France, Italy UK, US have better water quality due to their advanced
technologies and development in water and sanitation facilities. In the case of nitrous oxide
people of Andorra, Greenland, Marshall Islands, Nauru, Palau and Tuvalu are happier due
to less emissions while Equatorial Guinea have maximum nitrous oxide emissions. In
economic development Qatar shows the best performance with highest GDP and Congo
Dem Repuplic lags behind with lowest GDP. The countries of Bermuda, Gibraltar,
Monaco, Nauru, Singapore and Sint Maarten (Dutch part) are found to be most urbanized.
5. Results
The cross sectional and panel data estimation techniques are used to examine the impact of
environmental degradation on happiness.
5.1. Cross Sectional Results
In estimation we followed the strategy of using different environmental proxies with fixed
control variables in every regression. Table 2 shows the OLS results of CO2 emissions,
species protection, marine protected areas and water quality indicators for 99 cross sections
averaged over 1980-2015. Column 1 presents estimated coefficients of happiness and
carbon dioxide emissions along with control variables. The coefficient of CO2 indicates
that with 1 percent increase in carbon dioxide emissions decreases happiness up to 0.46
units. The negative sign shows that with high carbon dioxide emissions people will have
low life satisfaction levels. Mainly because increasing levels of CO2 are escalating the
global warming and exacerbating climate change. Climate change warming earths’
temperature and disturbing the ecological balance conferring overall negative impact on
psychological well-being of people. The findings are consistent with the theoretical
argument that is growing carbon dioxide emissions are increasing the levels of
environmental pollution leading to many respiratory and other diseases (Tiwari, 2011). The
studies of Tiwari, (2011), Lele (2013) and Lenzen and Cummins (2013) support our result.
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760
Column 2 shows the positive relationship between happiness and species protection. The
results show that with one percent increase in protection of species the level of happiness
increases by 0.06 units. This result finds its support in Ecosystem Services Approach which
advocates the protection of species and thereby contributing to human societies’ welfare
(Gascon et. al, 2015). The study by Ferree-i-Carbonell and Gowday (2007) also highlights
the positive psychological advantages individuals receive from protecting the species.
Because species are the vital part of environment and their losses endanger the environment
as well as market economies. For instance, extinction of pollinators is threatening the
agricultural production across the globe (Hsu et al., 2014).
Table 2: OLS Regression of Happiness and Environmental Degradation
(1)
(2)
(3)
(4)
(5)
Variables
Dependent Variable: Happiness
GDP
0.929***
0.279**
0.342**
0.320**
0.368**
(0.203)
(0.137)
(0.145)
(0.159)
(0.144)
Urban
Population
-0.00969*
-0.00462
-0.00798
-0.00888
-0.00894
(0.00570)
(0.00569)
(0.00601)
(0.00621)
(0.00600)
Age
-
0.0944***
-0.118***
-0.106***
-0.121***
-0.102***
(0.0212)
(0.0213)
(0.0225)
(0.0355)
(0.0223)
Co2
-0.465***
(0.123)
Species
Protection
0.0650***
(0.0170)
Marine Prot.
Areas
0.00720
(0.00594)
Water Quality
-0.369
(0.552)
Nitrous Oxide
-0.0867*
(0.0462)
Constant
-2.530
2.092**
2.338**
2.946**
2.953***
(1.563)
(0.932)
(1.000)
(1.467)
(1.067)
Observations
96
97
97
95
96
R-squared
0.321
0.321
0.225
0.218
0.242
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Columns 3-4 depict that marine protected areas have positive while water quality (unsafe)
have negative impact on happiness but insignificant. This insignificance is may be justified
on the basis of lack of data on marine protected and water quality variables. Like CO2
emissions, nitrous oxide emissions also have detrimental and significant impact on
happiness as 1 percent increase in nitrous oxide emissions brings 0.08 units decline in
happiness. The results of control variables are consistent with previous studies’ findings.
In all four specifications, GDP is positive and highly significant in explaining happiness.
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761
As one percent increase in GDP leads to 0.92, 0.27, 0.34 and 0.32 units increment in
happiness (columns1-4). This result clearly negates the Easterlin Paradox and joins the
studies of Tiwari (2011), Inglehart (1995) and Oswald (1997) who believe that income
increases the likelihood of happiness. The theoretical argument behind this is as income
increases it improves the prospects of life, purchasing power and quality of living. Urban
population in first specification lowers happiness, as with one percent increase in urban
population happiness decrease by 0.009, units. Ecopsychology theory says that
disengagement from greenery and nature reduces person happiness as we see urbanization
removes the green spaces therefore it lowers the well-being human receives from natural
environments (white et al., 2013). On demographic side age is incorporated that appears to
have negative relationship with happiness. The coefficient shows that a one year increase
in age leads to 0.09, 0.11, 0.10 and 0.12 units decrease in happiness, respectively. The
negative sign of age refers that people in their 60s will have lower levels of happiness
among total population. Lele (2013) gives the justification and say the increase in
percentage of these people put more pressure on social security program and tax burden
(Lele, 2013).
There are studies that show happiness also affects environment. For instance, Frey and
Stutzer (2002) argue that person with greater happiness shows more care for environmental
protection. The study by Duroy (2005) shows the positive impact of happiness on
environmental knowledge and positive environmental behaviors. According to Tiwari and
Mutascu (2015) there exits reverse causality between happiness and environmental
degradation where environmental degradation affects happiness and happiness in turn
affects environment. So the literature also suggests the reverse causality between happiness
and environmental quality. As a result there is possibility of endogeneity in our model. To
tackle the above issue, this study incorporates the instrumental variable technique Two
Stage Least Square Method (2SLS) on cross sectional data.
The 2SLS method gives efficient and reasonable results even in the presence of
endogeneity. Table 3 is based on the 2SLS regression results. In all specifications different
proxies of environmental degradation except water quality significantly affects happiness.
Carbon dioxide and nitrous oxide emissions again show negative association with
happiness while species protection and marine protected areas increase the level of
happiness. For control variables we get the similar results obtained in Table 2. There exists
positive relationship between GDP and happiness implying that income increases the
psychological wellbeing of individual by improving different aspects of life (Winkelmann
and Winkelmann, 1998; Gerdtham and Johannesson, 2001). The coefficient indicates a one
percent increase in GDP raises happiness by 0.93, 0.27, 0.32 and 0.31 respectively.
Whereas urbanization here is having insignificant impact on happiness and age negatively
affects happiness.
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762
Table 3: 2SLS Regression of Happiness and Environmental Degradation
(1)
(2)
(3)
(4)
(5)
Variables
Dependent Variable: Happiness
GDP
0.936***
0.277**
0.325**
0.318**
0.373***
(0.245)
(0.135)
(0.144)
(0.155)
(0.140)
Urban
Population
-0.00899
-0.00426
-0.00762
-0.00828
-0.00859
(0.00556)
(0.00560)
(0.00594)
(0.00608)
(0.00588)
Age
-0.0956***
-0.119***
-0.108***
-0.123***
-0.102***
(0.0207)
(0.0210)
(0.0222)
(0.0352)
(0.0218)
CO2
-0.468***
(0.169)
Species
Protection
0.0673***
(0.0191)
Marine Prot.
Areas
0.0119*
(0.00721)
Water Quality
-0.401
(0.550)
Nitrous Oxide
-0.137**
(0.0681)
Constant
-2.605
2.073**
2.471**
2.969**
3.337***
(1.952)
(0.912)
(0.994)
(1.442)
(1.126)
Sargan Test
(P = 0.4666)
(P =
0.2073)
(P =
0.0541)
(P = 0.0848)
(P =
0.0518))
Basmann
Test
(P = 0.4798)
(P =
0.2182)
(P =
0.0572)
(P = 0.0901)
(P =
0.0547)
Observations
95
96
95
94
95
R-Squared
0.327
0.317
0.220
0.220
0.242
Standard Errors In Parentheses
*** P<0.01, ** P<0.05, * P<0.1
5.2. Panel Data Results
Panel data has more degree of freedom and sample variability. It has the capability to
incorporate complexity of human behavior as compared to cross sectional data. Panel data
gives more accurate predictions for outcomes by pooling the data (Hsiao, 2007).
Considering the following we turn to panel data analysis for validity and accuracy of our
results. The findings of pooled panel, Fixed and Random Effects models are consistent
with the findings of OLS and 2SLS as shown in Table 4 and Table 5.
Majeed & Mumtaz
763
Table 4: Pooled OLS Regression of Happiness and Environmental Degradation
(1)
(2)
(3)
(4)
(5)
Variables
Dependent Variable: Happiness
GDP
1.157***
0.497***
0.843***
0.700***
0.694***
(0.115)
(0.0958)
(0.285)
(0.218)
(0.0936)
Urban
Population
-
0.0156**
*
-0.0145***
-0.0355***
-0.0193**
-0.0202***
(0.00419)
(0.00420)
(0.0123)
(0.00949)
(0.00421)
Age
-0.0329**
-0.0652***
-0.0187
-0.000195
-0.0556***
(0.0147)
(0.0144)
(0.0438)
(0.0508)
(0.0146)
CO2
-0.572***
(0.0770)
Species
Protection
0.0675***
(0.0113)
Marine Prot.
Areas
0.0263**
(0.0129)
Water Quality
0.798
(0.776)
Nitrous Oxide
-0.0866***
(0.0309)
Constant
-4.348***
0.366
-0.0525
-1.004
0.403
(0.839)
(0.610)
(1.857)
(1.951)
(0.650)
Observations
1,018
1,066
159
251
1,053
R-Squared
0.093
0.077
0.100
0.040
0.054
Standard Errors In Parentheses
*** P<0.01, ** P<0.05, * P<0.1
Happiness and Environmental Degradation
764
Table 5: FEM and REM Regression of Happiness and Environmental Degradation
FEM
REM
FEM
REM
FEM
REM
FEM
REM
FEM
REM
Variables
Dependent Variable: Happiness
GDP
2.647
***
1.475
***
1.400
***
0.608
***
3.429
***
0.843
***
1.618
*
0.700
***
1.839
***
0.847
***
(0.373)
(0.148)
(0.323)
(0.127)
(0.821)
(0.28)
(0.876)
(0.218)
(0.322)
(0.125)
Urban
-0.007
-0.01
***
-0.046
-0.0187
***
0.137
-0.035
***
-0.051
-0.0193
**
-0.025
-0.026
***
Population
(0.035)
(0.005)
(0.035)
(0.0057)
(0.092)
(0.012)
(0.090)
(0.009)
(0.035)
(0.005)
Age
0.196
**
-0.029
0.337
***
-0.0661
***
0.986
***
-0.018
0.478
**
-0.000
0.369
***
-0.051
**
(0.094)
(0.019)
(0.080)
(0.0203)
(0.168)
(0.043)
(0.185)
(0.050)
(0.084)
(0.020)
CO2
-1.64
***
-0.76
***
(0.347)
(0.099)
Species
0.140
***
0.089
***
Protection
(0.032)
(0.014)
Marine
Prot.
0.061
**
0.026
**
Areas
(0.028)
(0.012)
Water
5.822
0.798
Quality
(10.06)
(0.776)
Nitrous
-0.476
-0.101
**
Oxide
(0.320)
(0.0449)
Constant
-19.1
***
-6.78
***
-10.31
***
-0.547
-43.06
***
-0.0525
-13.84
-1.004
-9.962
***
-0.432
(2.445)
(1.083)
(2.292)
(0.812)
(5.396)
(1.857)
(9.767)
(1.951)
(3.361)
(0.896)
Observati
ons
1,018
1,018
1,066
1,066
159
159
251
251
1,053
1,053
R-
Squared
0.156
0.14
0.151
0.10
0.753
0.46
0.117
0.07
0.138
0.086
No Of Id
95
95
97
97
95
95
95
95
96
96
Standard Errors In Parentheses
*** P<0.01, ** P<0.05, * P<0.1
There is possibility of reverse causality between happiness and environmental degradation.
Happier people pay more attention to preserve environment and environment in turn affects
psychological well-being of individuals (Frey and Stutzer, 2002). To the best of our
knowledge the issue of endogeneity between happiness and environmental degradation has
Majeed & Mumtaz
765
not been considered seriously and addressed in the previous literature. So we tackle the
issue of endogeneity by using Arrelano Bond Model. We have used foreign direct
investment as external instrument along with own lags as internal instruments. The
instruments used are valid for CO2 emissions and species protection. Foreign direct
investment is the major determinant of CO2 emissions. Though FDI increases investment
and economic growth in a country but it also imposes cost on environment in the form of
increased pollution and increased CO2 emissions (Peng et al., 2015).The foreign direct
investment also affects species protection by bringing the cleaner investment and improves
the environmental performance including biodiversities’ protection (Mabey and McNally,
1999).
Table 6: System GMM Regression of Happiness and Environmental Degradation
(1)
(2)
Variables
Dependent Variable: Happiness
Happiness
0.348***
0.665***
(0.0771)
(0.00895)
GDP
0.761***
0.0752***
(0.195)
(0.00774)
Urban Population
-0.000736
-0.00599***
(0.00622)
(0.000259)
Age
0.0364
-0.00431*
(0.0239)
(0.00224)
CO2
-0.697***
(0.177)
Species Protection
0.0342***
(0.000535)
Constant
-3.830***
0.595***
(1.358)
(0.0485)
AR(1)
Pr > Z = 0.005
Pr > Z = 0.002
AR(2)
Pr > Z = 0.980
Pr > Z = 0.393
Observations
525
549
Number Of Id
92
94
Standard Errors In Parentheses
*** P<0.01, ** P<0.05, * P<0.1
System GMM results shows the clear association between happiness and environmental
degradation proxies where CO2 having negative and species protection positive
relationship with happiness (Tiwari, 2001; Ferrer-i-Carbonell and Gowdy 2007; Welsch,
2006).
The overall results based on the empirical findings of cross-sectional and panel data show
that environmental degradation is bad for psychological well-being of individual following
the effects of carbon dioxide emissions and water quality (unsafe) on happiness and
environmental quality. Furthermore, good environment quality increases happiness. For
instance, species protection and marine protection improve the states of happiness while
Happiness and Environmental Degradation
766
CO2 emissions have negative impact on happiness. The empirical estimates are robust and
sound based on the results of diagnostic tests.
5.3. Sensitivity Analysis
We have conducted the sensitivity analysis to check the robustness of our findings. In
sensitivity analysis, three additional control variables unemployment, life expectancy and
inflation have been introduced. Table 7 shows the estimation results of independent
variables after adding sensitivity variables. The impact of CO2 emissions on happiness
remains same, significant and negative across all sensitivity variables. Similarly, the
positive impact of species protection on environmental degradation remains intact.
However, the inclusion of these additional control variables does alter the results for marine
protected areas and water quality which becomes insignificant. The nitrous oxide still
remains negative and significant. Overall results of sensitivity analysis suggest that the
variables of study are robust.
Table 7: Sensitivity Analysis of Variables
Sensitivity Variables
Unemployment
Life Expectancy
Inflation
Variables
Dependent Variable: Happiness
CO2 Emissions
-0.378**
-0.412**
-0.498***
(0.187)
(0.163)
(0.170)
R-squared
0.3467
0.3517
0.3431
Species
protection
0.0591***
0.0803***
0.0680***
(0.0193)
(0.0191)
(0.0190)
R-squared
0.3483
0.3670
0.3211
Marine prot.
areas
0.0101
0.0111
0.0124*
(0.00698)
(0.00713)
(0.00718)
R-squared
0.2800
0.2374
0.2234
Water quality
-0.307
-0.380
-0.512
(0.530)
(0.544)
(0.557)
R-squared
0.2821
0.2385
0.2280
Nitrous oxide
-0.170**
-0.145**
-0.141**
(0.0661)
(0.0683)
(0.0679)
R-squared
0.326
0.248
0.246
Robust standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
6. Conclusion
Literature on happiness adds to new findings and knowledge to existing views. One of the
most significant is the large impact of non-financial variables on happiness. This does not
imply the triviality of economic factors such as income in determining happiness rather it
suggests the more relevance of non-financial factors to happiness. In particular
environment has innate relationship with psychological well-being (Wilson, 1984).
Given this importance of happiness and environment link, this study used cross sectional
and panel datasets to explore the relation between happiness and different environment
Majeed & Mumtaz
767
indicators. The study carries the analysis of 99 countries and covers the time period of
1980-2015. Estimation techniques of cross-sectional and panel data are used to estimate
empirical results. We have also conducted the sensitivity analysis by including three
additional variables where results were found to be insensitive. All the measures of
environmental degradation have significant impact on happiness based on cross-sectional
and panel data methods findings. The magnitude of greenhouse gases coefficient implies
the strong negative impact of carbon dioxide (CO2) and nitrous oxide emissions on
happiness. Increasing levels of greenhouse gases especially CO2 proves to be damaging to
environment and human happiness. Secondly, protection of biodiversities and marine life
can ensure larger benefits for human via tourism and broaden economic activity suggested
by empirical results.
In the light of above findings it is argued that governments and policy makers should
formulate strict environmental laws that root out the causes of environmental deterioration
and to create awareness about protecting the environment for present and future
generations’ greater happiness.
6.1. Limitations of Study
The study has some limitations. Due to limited resources we cannot conduct primary
research using questionnaires and surveys that can give more accurate picture of what
really matters for individuals’ happiness. This research did not consider some other
indicators of environmental degradation such as noise pollution (SPL/dB), land
deforestation, soil erosion and forest degradation in future. Furthermore, we have seen the
overall impact of environmental degradation on happiness instead of separately looking
into developed and developing world.
6.2. Contribution of Study
Previous studies mostly take common indicators of environmental degradation such as CO2
emissions, nitrous oxide, sulfur and others. Secondly, mostly the analysis is restricted to
some countries and for shorter time span. Thirdly, much of the work ignores the issue of
simultaneity between happiness and environmental degradation. By considering the gaps
in literature, this paper intends to do better on them. First, we use broad and new measures
of environmental degradation. Secondly, the analysis is not restricted to some country
specific cases rather include broad spectrum of countries to obtain bigger picture of
environmental degradation implications. Thirdly, this is the first study to utilize new index
of Environmental Performance Index for environmental data that required lot of effort and
time. Finally, we highlight and tackle the issue of endogeneity that may exist between
happiness and environmental degradation unlike previous literature.
6.3. Theoretical Implications
Theories of happiness postulate different standards on which happiness depends. Some
theories such as livability theory and objective list theory suggest absolute standards while
others such as comparison theory and hedonism suggest relative standards. However, the
empirical findings of this study mostly support logics of livability and objective list theories
which predict that satisfaction of needs is the ultimate cause of happiness. As the empirical
findings clearly proves clean environment and income as important human needs and their
gratification does have impact on happiness.
Happiness and Environmental Degradation
768
6.4. Directions for Future Research
Since we have worked with secondary data, the same work can be done with primary data
using Logit and Probit models to extract more accurate picture about people’s happiness
and environmental degradation. This research can be extended to incorporate new
indicators of environmental degradation such as noise pollution (SPL/dB), land
deforestation, soil erosion and forest degradation in future. The study invites the decision
makers to revise their welfare and environmental policies and focus more on the strategies
that promote good environmental quality and people’s happiness.
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Appendix
Table A1: Data and Variables Description
Variables
Definition
Construction
Sources of Data
Happiness
Happiness is mental state
characterized by positive
feelings ranging from
satisfaction to delight.
1(very happy) to 4 (not at all
happy)
0(least happy) to 10 (most happy)
World Values
Survey(2014)
World Database of
Happiness (2016)
Carbon Dioxide
(CO2) Emissions
Carbon dioxide is one of
the greenhouse gases and
released into atmosphere
through human activities
like fossil fuel burning.
(Metric tons per capita)
World Development
indicators (2016)
Marine
Protected Areas
Marine protected areas are
areas of intertidal or
subtidal terrain--and
overlying water and
associated flora and fauna
and historical and cultural
features.
(% of territorial waters)
World Development
indicators (2016)
Species
Protection
The average area of species
- bird, mammals, and
amphibians - distributions
in a country under
protection.
The average area of species - bird,
mammals, and amphibians -
distributions in a country under
protection(Percentage)
Hsu et al. (2014)
Water Quality
Exposure to unsafe water
quality and population
lacking access to drinking
water.
min(0 ) max (0.9999745)
Hsu et al. (2014)
GDP per capita
GDP is the “sum of gross
value added by all resident
producers in the
economy”.
(Current US$)
World Development
indicators (2016)
Urban
Population
Urban population refers to
people living in urban
areas.
(% of actual population)
World Development
indicators (2016)
Gini Index
Gini measures the income
distribution of nations’
residents.
(World Bank estimate)
World Development
indicators (2016)
Income Scale
Income scale of respondent
1 (lowest income) to 10(highest
income)
World Development
indicators (2016)
Age
Population ages 65 and
above as a percentage of
the total population.
(% of total population)
World Development
indicators (2016)
Happiness and Environmental Degradation
772
Table A2: List of Countries (in the study)
No
Country
No
Country
No
Country
1
Albania
34
Georgia
67
Niger
2
Algeria
35
Germany
68
Norway
3
Andorra
36
Ghana
69
Pakistan
4
Argentina
37
Greece
70
Paraguay
5
Armenia
38
Guatemala
71
Peru
6
Australia
39
Honduras
72
Philippines
7
Austria
40
Hong Kong
73
Poland
8
Azerbaijan
41
Hungary
74
Portugal
9
Bangladesh
42
Iceland
75
Puerto Rico
10
Belarus
43
India
76
Romania
11
Belgium
44
Indonesia
77
Russian
Federation
12
Bolivia
45
Iran, Islamic Rep.
78
Rwanda
13
Bosnia &
Herzegovina
46
Iraq
79
Saudi Arabia
14
Brazil
47
Ireland
80
Serbia
15
Bulgaria
48
Israel
81
Singapore
16
Burkina Faso
49
Italy
82
Slovak Republic
17
Canada
50
Japan
83
Slovenia
18
Chile
51
Jordan
84
South Africa
19
China
52
Kyrgyz Republic
85
Spain
20
Colombia
53
Latvia
86
Sweden
21
Costa Rica
54
Lithuania
87
Switzerland
22
Croatia
55
Luxembourg
88
Tanzania
23
Cyprus
56
Macedonia, FYR
89
Thailand
24
Czech Republic
57
Malaysia
90
Trinidad and
Tobago
25
Denmark
58
Mali
91
Turkey
26
Dominican Rep.
59
Malta
92
Uganda
27
Ecuador
60
Mexico
93
Ukraine
28
Egypt, Arab Rep.
61
Moldova
94
United Kingdom
29
El Salvador
62
Montenegro
95
United States
30
Estonia
63
Morocco
96
Uruguay
31
Ethiopia
64
Netherlands
97
Venezuela, RB
32
Finland
65
New Zealand
98
Zambia
33
France
66
Nicaragua
99
Zimbabwe
... This unfortunate incident has become a disturbing problem facing the global community. Fossil fuel consumption has been identified as the primary source of greenhouse gas emissions [1,2]. In 2012, the carbon dioxide generated by GHGs and released into the atmosphere was 47,599 million tonnes (MtCO2), increasing to about three times in the past six decades. ...
... Kos Island presently supplied oil-based generated electricity through an undersea cable to Tilos Island [301]. [1]. ...
Preprint
Full-text available
Climate change has become a global nightmare, and the awareness of the causes of carbon emissions have resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon growth plan or emission reduction strategy. The world is transitioning towards a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. The study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All suggested that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.
... The exponential growth of textile industry has led to the widespread use of dyes across various sectors, but this has also resulted in the discharge of untreated dyeing effluent or wastewater causing pollution. The discharge of effluent containing complex mixture of chemicals and dyes severely affects water bodies making the water unsuitable for human use and disrupts aquatic ecosystem [7,8]. ...
... In order to optimize the process variables such as dye concentration (100-500 mg/L), biosorbent concentration (100-900 mg/L), pH (3)(4)(5)(6)(7)(8)(9)(10)(11)(12), temperature (25-45 °C), and incubation time (24-120 h) for maximum RB19 decolorization, batch adsorption experiment was performed with the aid of response surface methodology. After each incubation period, the solutions were filtered and measured using a UV-vis spectrophotometer at 590 nm, to determine the percent decolorization [46]. ...
Article
Full-text available
In the present investigation, an attempt was made to study the impact of the green algae Rhizoclonium hieroglyphicum for the removal of reactive blue 19 (RB19) dye from an aqueous solution and to assess the reuse potential in environmental field. Optimization of the process variables, namely, dye concentration (100–500 mg/L), biosorbent concentration (100–900 mg/L), pH (3–12), and incubation time (24–120 h) for RB19 removal was performed using response surface methodology (RSM) under room temperature. The dye adsorption mechanism was assessed through the application of isotherm, kinetics, and thermodynamic models. The interaction between the adsorbate and biosorbent was confirmed through analytical methods. The recycling and regeneration efficiency of the dye adsorbed alga was evaluated using different eluents. The nature of the treated and untreated dye solutions was assessed in terms of microbial toxicity and phytotoxicity. The dye desorbed alga was further subjected to composting and the macronutrients were analyzed. To support the results of in vitro investigation, in silico docking was performed. Results through RSM revealed that maximum decolorization (88%) was recorded in the aqueous solution amended with 300 mg/L dye and 500 mg/L biosorbent at pH 8 on 72 h at 25 °C. The empirical data exhibited strong conformity with Freundlich isotherm and the pseudo-second-order kinetic models. Thermodynamic investigations disclosed that the adsorption process was characterized as endothermic, spontaneous, and favorable. Recycling studies revealed maximum dye recovery (84%) when 0.1 M HCl was used as an eluent with 79% regeneration efficiency. UV–visible, FT-IR, and SEM analyses elucidate the interaction between the biosorbent and adsorbate corroborating the decolorization process facilitated by R. hieroglyphicum. Microbial and phytotoxicity study substantiated the non-toxic nature of the treated dye. The values obtained in the physicochemical analysis of algal compost can help to improve the soil fertility and plant growth. The result of the docking analysis demonstrated a significant binding energy between RB19 and the R. hieroglyphicum protein (heat shock protein 70) affirming a robust interaction. The present study is the first report on the removal of reactive blue 19 utilizing R. hieroglyphicum which can bolster their resilience and can promote the wellbeing of the freshwater environment grappling with pollution issues.
... Human activities have led to environmental degradation, ultimately affecting the quality of the environment due to the depletion of natural resources, species expansion, changes in air and ecological scarcity [30,62]. In other words, natural resource use can lead to problems such as the degradation of ecological environments and landscapes, global warming and desertification [63]. ...
Article
Full-text available
This study investigated the impact of natural resources, urbanization, biological capacity, and economic growth (EG) on the ecological footprint (EFP) in Turkey between 1970 and 2018. The Autoregressive Distributed Lag (ARDL) method was used to investigate the short- and long-term effects. The findings indicate that EG and biological capacity increase the EFP in both the short and long term. In addition to these results, the long-term results show that the Environmental Kuznets Curve (EKC) hypothesis is valid for Turkey and that urbanization has a negative impact on the EFP. The Vector Error Correction Model (VECM) applied to determine the relationship between the variables reveals that, in the short term, unilateral causalities exist from EG to the EFP, from urbanization to economic growth, and from biological capacity to EG. The long-term causality results show a bidirectional causality relationship between the EFP, urbanization and biological capacity. In light of these findings, important policy recommendations are provided for policymakers in Turkey to achieve sustainable growth and improve environmental quality.
... Human activities have led to environmental degradation, ultimately affecting the quality of the environment due to the depletion of natural resources, species expansion, changes in air and ecological scarcity [30,62]. In other words, natural resource use can lead to problems such as the degradation of ecological environments and landscapes, global warming and desertification [63]. ...
Article
Full-text available
This study investigated the impact of natural resources, urbanization, biological capacity, and economic growth (EG) on the ecological footprint (EFP) in Turkey between 1970 and 2018. The Autoregressive Distributed Lag (ARDL) method was used to investigate the short- and long-term effects. The findings indicate that EG and biological capacity increase the EFP in both the short and long term. In addition to these results, the long-term results show that the Environmental Kuznets Curve (EKC) hypothesis is valid for Turkey and that urbanization has a negative impact on the EFP. The Vector Error Correction Model (VECM) applied to determine the relationship between the variables reveals that, in the short term, unilateral causalities exist from EG to the EFP, from urbanization to economic growth, and from biological capacity to EG. The long-term causality results show a bidirectional causality relationship between the EFP, urbanization and biological capacity. In light of these findings, important policy recommendations are provided for policymakers in Turkey to achieve sustainable growth and improve environmental quality.
... Quality of life is defined by the Dictionary of Human Geography (Johnston 1981) as the condition of social well-being of individuals or groups, as perceived by them or as determined by 'observable indicators.' UNDP (1998), WHOQoL Group (1996), Ray and Dasgupta (2012) defined QoL as the satisfaction of basic needs such as health, education, cultural, value systems and standard of living, while Sirgy et al. (2004) defined it as a qualitative measure of well-being that incorporates consumer, health, social, economic, and physical well-being. Prior to Anand and Sen (2000) and Majeed (2019), scholars and economists measured QoL using GDP per capita (Nussbaum and Sen 1993;Majeed and Mumtaz 2017). However, Anand and Sen (2000) and Majeed (2019), WHOQoL Group (1996), Ray and Dasgupta (2012) stated that QoL is affected not only by income, but also by other major sociological, physiological, and human dimensions of an individual's life that were previously ignored. ...
... Quality of life is defined by the Dictionary of Human Geography (Johnston 1981) as the condition of social well-being of individuals or groups, as perceived by them or as determined by 'observable indicators.' UNDP (1998), WHOQoL Group (1996), Ray and Dasgupta (2012) defined QoL as the satisfaction of basic needs such as health, education, cultural, value systems and standard of living, while Sirgy et al. (2004) defined it as a qualitative measure of well-being that incorporates consumer, health, social, economic, and physical well-being. Prior to Anand and Sen (2000) and Majeed (2019), scholars and economists measured QoL using GDP per capita (Nussbaum and Sen 1993;Majeed and Mumtaz 2017). However, Anand and Sen (2000) and Majeed (2019), WHOQoL Group (1996), Ray and Dasgupta (2012) stated that QoL is affected not only by income, but also by other major sociological, physiological, and human dimensions of an individual's life that were previously ignored. ...
Article
Full-text available
Quality of life is said to be intimately interlinked with the process of urbanization and development because urbanization is regarded as a manifestation of economic development. ‘Quality of life’ refers to an individual’s or society’s overall well-being, whereas ‘basic amenities’ are things required for each person and have an evident relationship to QoL. City centers are regarded as the engines of society, so the current paper examines the quality of life with regard to levels of selected essential amenities in West Bengal’s urban centers for the year 2011. Seven key basic amenities are considered. Data were obtained from secondary sources such as the Houselisting and Housing Census, Census of West Bengal 2011, and the Primary Census Abstract, Census of West Bengal, 2011 (both in electronic formats). Coefficient of Variation, Correlation Coefficient, and One-way ANOVA techniques were used to analyze the data. According to the results of the study, there are differences in civic facilities across the whole range of urban hierarchy (size class and civic status category-wise). Additionally, a noticeable difference is found between Census Towns (CTs) and Statutory Towns (STs). The availability and persistent scarcity of fundamental services in urban areas largely depends on rapid urbanization, rapid immigration from rural areas, economic foundation, competence and development of the urban centers where the city or town is located. Additionally, more than 500 new towns incorporated in the most recent census, majority of which are small towns and considered Census Towns, report decreased accessibility to essential facilities. Therefore, instead of implementing different strategies throughout time, a considerable disparity in the provision of amenities in the state’s urban centers is a key barrier to urban holistic development. This circumstance encourages the use of the concept of co-production in the planning process.
Article
Gelişmekte olan ülkeler ekonomik refaha doğru ilerlerken, bu süreç onların ekolojik ayak izini de arttırabilmektedir. Bu nedenle, sürdürülebilir bir kalkınma için ekolojik ayak izini (EF) etkileyen faktörlerin belirlenmesi önemlidir. Bu perspektiften bakıldığında bu çalışma, Türkiye’nin 1980'den 2019’a kadarki dönemde ekonomik büyümesinin, finansal kalkınmasının ve insan sermayesinin, EF üzerindeki etkisini Genişletilmiş ARDL (Augmented ARDL) yöntemiyle araştırmaktır. Analiz sonuçlarına göre Türkiye’nin 1980'den 2019'a kadar olan dönemde finansal gelişme, ekonomik büyümenin ekolojik ayak izini artırdığı, insan sermayesinin ise ekolojik ayak izini azalttığı gözlemlenmiştir. Nedensellik sonuçlarına göre ekolojik ayak izi – ekonomik büyüme ve ekolojik ayak izi – finansal gelişme arasında çift yönlü, ekonomik büyümeden finansal gelişmeye, beşerî sermayeden finansal gelişmeye ve beşerî sermayeden ekolojik ayak izine doğru tek yönlü nedensellik ilişkisi tespit edilmiştir. Ulaşılan ekonometrik analiz sonuçlarına göre politika yapıcılara öneriler sunulmuştur.
Article
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The increase in global temperature and climate change over the past decade is the result of an increase in CO2 emissions. Several factors such as economic activity (represented by Gross Domestic Product/GDP), population, deforestation, and energy consumption are considered to have the most influence on increasing CO2 emissions. This study uses panel data analysis to analyze the relationship between GDP growth, population, deforestation, and energy consumption on increasing CO2 emissions in four ASEAN countries, namely Indonesia, Malaysia, Thailand and Vietnam, for the period of 2001-2021. The research method used is panel data regression with Fixed Effect Model (FEM). The results showed that GDP growth and energy consumption have a positive effect while population and deforestation have no influence on CO2 emissions. Further, the increase in carbon emissions in the four ASEAN countries is more due to the amount of energy consumption by the community and economic growth rather than deforestation and population growth. Abstrak Peningkatan suhu global dan perubahan iklim yang terjadi selama satu dekade terakhir merupakan hasil dari peningkatan emisi CO2. Beberapa faktor seperti aktivitas ekono-mi (diukur dengan Produk Domestik Bruto/PDB), populasi, deforestasi, dan konsumsi energi diduga berpengaruh terhadap peningkatan emisi CO2. Penelitian ini menggunak-an analisis data panel untuk menganalisis hubungan antara pertumbuhan PDB, jumlah penduduk, deforestasi, dan konsumsi energi terhadap peningkatan emisi CO2 di empat negara ASEAN (Indonesia, Malaysia, Thailand, dan Vietnam) pada tahun 2001-2021. Me-tode estimasi yang digunakan adalah regresi data panel dengan Fixed Effect Model (FEM). Hasil penelitian menunjukkan bahwa secara parsial pertumbuhan PDB dan konsumsi energi berpengaruh positif, sedangkan jumlah penduduk dan deforestasi tidak memiliki pengaruh terhadap emisi CO2. Peningkatan emisi karbon di empat negara ASEAN lebih disebabkan oleh besarnya konsumsi energi yang dihasilkan masyarat dan pertumbuhan ekonomi, bukan disebabkan oleh deforestasi dan pertumbuhan penduduk.
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Climate change has become a global nightmare, and the awareness of the causes of carbon emissions has resulted in rigorous studies. These studies linked the increase in global warming with booming economic growth. Since global warming has become more apparent, researchers have explored ways to decouple economic activities from carbon growth. Economic and carbon growth must be decoupled to achieve a low-carbon economy to support the carbon-growth plan or emission-reduction strategy. The world is transitioning toward a carbon-neutral and green ecosystem, so finding ways to decouple carbon emissions from economic activities is an exciting topic to explore. This study reviews current information on the importance of decoupling energy from economic growth innovative techniques that thoroughly examine the challenges and constraints of low-carbon energy systems. In order to examine the detrimental effects of carbon emissions on ecosystems and the ways in which economic expansion contributes to carbon footprints, more than three hundred research papers were gathered using several search engines, including Elsevier and Google Scholar. This review revealed that decarbonization and dematerialization had been achieved without declining global economic growth. It also provides information on energy use and economic activities leading to global carbon emissions and alternative solutions to the global challenge of climate change. The decoupling methods commonly used to determine the impact of energy decarbonization on economic growth are explored. All the results suggest that economic growth is a primary mover of global carbon emission increase and must be separated to achieve a carbon environment.
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This research examines the complex relationship between financial development and carbon emissions. This strategy emphasises distinguishing industrialised and developing nations due to their different economic circumstances. The research assumes that carbon emissions rise as economies and financial systems grow. It is vital to understand the root reasons for this scenario. The research shows that financial development has two effects on carbon emissions. Financial institutions allow businesses that may not value environmental sustainability to grow, which can increase carbon emissions. However, financial development can reduce carbon emissions by supporting green technology and practices. The research uses a thorough literature review to synthesise and interpret the vast empirical research data. Study methods, components, and timeframes from previous studies are carefully reviewed. Although findings vary, the analysis shows that countries with well-developed financial systems perform better environmentally. Their ability to promote green technologies and pollution reduction programmes gives them this advantage. The report also finds a research gap, highlighting the need for a more comprehensive study of the complicated relationship between financial development and carbon emissions. According to this statement, green financial instruments encourage environmentally sustainable projects and investments, reduce emissions, and alleviate state development inequities. The study illuminates policy effects for policymakers. This shows that an advanced financial system could enable environmentally friendly economic growth. This shows that governments and financial organisations may boost economic growth and environmental protection by enacting intelligent policies and offering incentives. The report thoroughly examines the complex relationship between financial development and carbon emissions, offering insights into balancing economic expansion with environmental protection.
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Using a sample of province-level panel data, this paper investigates the Granger causality associations among economic growth (GDP), foreign direct investment (FDI) and CO2 emissions in China. By applying the bootstrap Granger panel causality approach (Kónya, 2006), we consider both cross-sectional dependence and homogeneity of different regions in China. The empirical results support that the causality direction not only works in a single direction either from GDP to FDI (in Yunnan) or from FDI to GDP (in Beijing, Neimenggu, Jilin, Shanxi and Gansu), but it also works in both directions (in Henan). Moreover, we document that GDP is Granger-causing CO2 emissions in Neimenggu, Hubei, Guangxi and Gansu while there is bidirectional causality between these two variables in Shanxi. In the end, we identify the unidirectional causality from FDI to CO2 emissions in Beijing, Henan, Guizhou and Shanxi, and the bidirectional causality between FDI and CO2 emissions in Neimenggu.
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Crafting environmental policies that at the same time enhance, or at least not reduce people’s wellbeing, is crucial for the success of government action aimed at mitigating environmental impact. However, there does not yet exist any survey that refers to one and the same population, and that allows the identifying relationships and trade-offs between subjective wellbeing and the complete environmental impact of households. In order to circumvent the lack of comprehensive survey information, we attempt to integrate two separate survey databases, and describe the challenges associated with this integration. Our results indicate that carbon footprints are likely to increase, but wellbeing levels off with increasing income. Living together with people is likely to create a win-win situation where both climate and wellbeing benefit. Car ownership obviously creates emissions, however personal car ownership enhances subjective wellbeing, but living in an area with high car ownership decreases subjective wellbeing. Finally, gaining educational qualifications is linked with increased emissions. These results indicate that policy-making is challenged in striking a wise balance between individual convenience and the common good.
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This pioneering pilot-study examines empirically whether people living in an equal-income society are happier, less happy, or no different from people living in an unequal-income society. The two societies we examine (Israeli villages) are otherwise similar in as many ways as possible. Both contain mainly Polish immigrants, who have lived there for about the same amount of time (14-16 years per person on average). In both, the land is leased from the state, and most of the income is derived from egg and poultry production. The residents in both have the same Jewish religion, and about the same degree of religious observance (not very observant). The two settlements have comparable age structures, and similar educational experience (more than 80% have had more than nine years of schooling). Only one adult in 20 is not currently married. Families have an average of two children each, and live in simple, relatively standard 3 or 4 room houses. Both communities belong to the same right-of-centre politico-ideological movement. The main difference between the two villages is: In one, all property (except personal possessions) and production are communal -- and incomes are distributed equally. In the other, each family owns its own land rights, home, farm equipment, and livestock -- and incomes vary from family to family. We find that self-rated happiness is slightly higher in the equal-income village than in the unequal-income village. We hope that others will do more comprehensive studies of this subject.
Book
"An important, controversial account ... of the way in which man's use of poisons to control insect pests and unwanted vegetation is changing the balance of nature." Booklist.
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Subjective well-being, or a positive evaluation of one’s own life, is an important component of human health. The concept is regularly used as a policy measure for social progress, with proponents promoting the idea of the ‘happy city’. But empirical research on ways to nurture subjective well-being through urban planning remains limited. Previous relevant research has considered the impact of specific aspects of the built environment on components of subjective well-being, but a gap exists in literature on the exploration of multiple built environment effects on multiple components of subjective well-being. This study fills this gap. It presents a quantitative analysis of results from a survey of 562 households in Sydney, Australia. The relative influence of objective and perceived built environment variables are analysed with multiple elements of subjective well-being to show a more comprehensive picture of the impacts of place on subjective well-being. We found perceived evaluations of built environment characteristics were more often associated with subjective well-being, with perceptions of aesthetics and community cohesion particularly important. The paper concludes with recommendations for policy and research, including the need to incorporate perceived measures of built environment and health variables in analyses of links between built environments and health.
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China's rapid socioeconomic growth in recent years and the simultaneous increase in many forms of pollution are generating contradictory pictures of residents' well-being. This paper applies multilevel analysis to the 2013 China General Social Survey data on social development and health to understand this twofold phenomenon. Multilevel models are developed to investigate the impact of socioeconomic development and environmental degradation on self-reported health (SRH) and self-reported happiness (SRHP), differentiating among lower, middle, and higher income groups. The results of the logit multilevel analysis demonstrate that income, jobs, and education increased the likelihood of rating SRH and SRHP positively for the lower and middle groups but had little or no effect on the higher income group. Having basic health insurance had an insignificant effect on health but increased the likelihood of happiness among the lower income group. Provincial-level pollutants were associated with a higher likelihood of good health for all income groups, and community-level industrial pollutants increased the likelihood of good health for the lower and middle income groups. Measures of community-level pollution were robust predictors of the likelihood of unhappiness among the lower and middle income groups. Environmental hazards had a mediating effect on the relationship between socioeconomic development and health, and socioeconomic development strengthened the association between environmental hazards and happiness. These outcomes indicate that the complex interconnections among socioeconomic development and environmental degradation have differential effects on well-being among different income groups in China.