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Zhang Jiayue
UID: 3035022837
ECON0109 Topics in Macroeconomics
Dr. Yuen Chi-Wah
Business Cycle Effects on Greenhouse Gas Emission
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Executive Summary
This paper investigates the relationship between business cycle indicators GDP growth
rate and unemployment rate and greenhouse gas (GHG) emission growth. Using GDP growth
and unemployment rate as proxies of business cycles and panel data of 30 countries from 1991 to
2011, the paper reveals that an increase in GDP growth has a positive influence on GHG
emission growth throughout the period studied, whereas unemployment rate has opposite
influence on GHG emission growth before and after financial crisis in 2008. The research also
finds nonlinearity in the relationship between GDP growth and GHG growth.
The research question whether business cycle affects greenhouse gas emission
differently before and after financial crisis in 2008 is divided into three consecutive questions in
this paper. First of all, I examine whether business cycles factors GDP growth and
unemployment rate in this paper - have any impacts on GHGs emission. Secondly, given positive
answer in the first question, I consider whether the influence is merely through energy-
consuming production activities by adding production-based CO2 into regressions. If business
cycle factors remain significant, the relationship between economy and GHGs emission may be
more complicated. For example, environmental factors may enter peoples utility function so as
to influence consumption preferences. Thirdly, I consider the effect of financial crisis in 2008.
By dividing observations into pre- and post-crisis, I study whether the influence of
macroeconomic factors is statistically different in booming and declining phases.
The data are collected from OECD and IMF databases, covering 29 OECD countries
together with Russia. I adopt panel data regression considering both entity-fixed effects and
time-fixed effects to rule out omitted variable bias related with certain countries or time period.
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Results from the regression indicate strong influence of economy on emission. Firstly,
within the scope of data collected, GDP growth rate has significant influence on emission growth
rate, whereas unemployment rate seems to have little effect. Also, nonlinearity is found in GDP
growth rates influence on emission growth. Secondly, introducing production-based CO2 does
not change the relationship fundamentally. It implies that macroeconomic factors affect emission
through more complex channels than carbon-emitting energy consumption. Thirdly, the business
cycle effects are strong in changing economys influence on emission growth rate. In the
expansionary period before crisis, better employment condition corresponds with lower emission
growth. Yet in recession, improvement in job market may be related with higher emission
growth rate. On the other hand, same percentage of GDP growth improvement in recession
comes with higher emission growth than in expansionary phase. It implies that mitigating
emission is less costly after the financial crisis, which is proved by more international
agreements on emission reduction in recent years.
In conclusion, business cycles do affect GHG emission in OECD countries from 1991 to
2011 as shown in this paper. Before financial crisis in 2008 (expansion), certain increase in GDP
growth rate corresponds with smaller increase in emission, compared with the co-movement
after crisis (recession). Similarly, in expansionary phase, increase in unemployment rate
corresponds with increase in GHG emission growth whereas in contraction phase, increase in
unemployment rate results in emission growth decreasing. It implies that economic costs for
climate change mitigation are less during recession than during expansion.
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Introduction
This paper studies the relationship between business cycle and greenhouse gases (GHGs,
hereafter) emission empirically, using data from 30 countries from 1991 to 2011. The fact that
climate change has been addressed in various international economic summits and domestic
policies with increasing importance provides a unique opportunity to investigate the
anthropogenic GHG emission problem from macroeconomic aspect. GHGs, including carbon
dioxide (CO2), methane (CH4), nitrous oxide (N2O) and other radiative gases, absorb and emit
radiation which fundamentally causes global warming (Clarke et al., 2007). Although it is
theoretically obvious that the production activities affect carbon emission by burning fossil
resources, the relationship between aggregate economy and GHG emission beyond energy
consumption has remained unexplored in existing academic literature.
As both economy and climate undergoes remarkable changes after the financial crisis in
2008, there emerges large amount of research on relationship between economic development
and environmental sustainability with policy implications (Kahn & Kotchen, 2010). One aspect
is to assess the economic costs and benefits of GHG reduction, where many scholars in 1990s
estimated marginal costs of GHG emission and reduction in dollar amount (Ekin, 1996;
Nordhaus, 1991; Tol, 1999). Galeotti, Lanza & Pauli (2006) has reassured the existence of
environmental Kuznets curve for CO2 among OECD countries, which is an inverted U-shaped
curve with income per capita on the horizontal axis and CO2 emission level on the vertical axis.
Another stream analyses panel or time-series data to measure relationship between GDP per
capita and carbon emission per capita (Holtz-Eakin & Selden, 1995; Kaygusuz, 2009). However,
the existing literature regards environmental factors as solely dependent variables of one single
economic indicator may ignore important complex relationships between emission and aggregate
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economy. With the significant economic downturn in 2008 shifting greater attention to economic
recovery from climate change mitigation as suggested by Kahn & Kotchen (2010), it remains to
be discovered whether business cycles influence GHGs emission differently before and after
crisis.
The paper is structured as following. In part I, relationships about variables of interests,
namely GDP, unemployment and emission are discussed and three consecutive hypotheses are
introduced. In part II, I discuss data collected for this research. In part III, regression results and
discussion are introduced. Part VI discusses possible modifications to existing models and part V
discusses limitations of the paper. Part VI presents conclusion of the paper.
I. GDP, Unemployment and Emission
By examining the influence of GDP growth and unemployment rate on GHGs emission,
the paper intends to explore the relationship of macroeconomic factors and environmental factors.
As suggested by He & Liu (2004), for developed countries, GHGs emission cannot be simply
indexed by carbon emission. Therefore, models below include production-based CO2 emission
to proxy carbon emission in the regressors. The intention is to identify whether the influence
from economic factors on GHGs emission will remain significant after considering the channel
through carbon-intense production. Comparison with models without CO2 variables is also
available for illustration of robustness.
The research question whether business cycle affects greenhouse gas emission
differently before and after financial crisis in 2008 is further divided into three consecutive
questions for clearer illustration. First of all, I examine whether macroeconomic factors that
determine business cycles GDP growth and unemployment rate in this paper - have any
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impacts on GHGs emission. If there exists no significant relationship between them, the major
research question becomes redundant. Secondly, given positive answer in the first question, I
consider whether the influence is merely through energy-consuming production activities. If
adding production-based CO2 emission makes the variable of interest not significant, then GHGs
can be viewed as a by-product of production. It implies that mitigation of climate change has to
sacrifice economic growth. Otherwise, the relationship between economy and GHGs emission
may be more complicated. For example, environmental factors may enter peoples utility
function so as to influence consumption preferences. Thirdly, I consider the effect of financial
crisis in 2008. By dividing observations into pre- and post-crisis, I study whether the influence of
macroeconomic factors is statistically different in booming and declining phases.
The hypotheses corresponding to above analysis are explained as below. All models
include variable of interests as discussed below, year dummy variables and country dummy
variables to control for time-fixed effects and entity-fixed effects.
Hypothesis 1: Macroeconomic factors have no relationship with GHGs emission. (H1)
Rejection of H1 requires significant coefficients of GDP growth rate and unemployment
rate in the regression where GHGs emission growth rate is the dependent variable. As Galeotti,
Lanza & Pauli (2006) has reassured the existence of environmental Kuznets curve for CO2
emission, a nonlinear relationship between GDP growth rate and GHGs emission growth rate is
highly likely. Therefore the model also includes quadratic term of GDP growth rate to examine
the robustness of this nonlinear relationship.
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The regression models for testing H1 are as below, where i indexes countries and t
indexes years.
!"!#!"#$$#%&!"#$%!!!!
! !! ! !!!"#!"#$%!!!! ! !!!!"!"#$%!!!!!! !!!!"#$%&'#"!(!!!
! !!!!! !"#$%&'!"##$!"#$"%&'( ! !!"!!" !"#$!"##$!"#$"%&'( ! !!!!
Hypothesis 2: Macroeconomic factors affect GHGs emission merely through
production-based CO2 channel. (H2)
If H2 is valid, including production-based CO2 into the regression would cause GDP and
unemployment variables to be insignificant. This hypothesis checks if the relationship between
macroeconomic factors and GHGs emission is merely through production channel. Rejection of
H2 implies complex relationship between aggregate economy and GHGs emission, which cannot
be simplified by mere CO2 emission, as suggested by He & Liu (2004). It is possible that more
complex relationship involves peoples expectation of consequences and costs of climate change,
which may alter utility functions and ultimately changes consumption behaviors.
Regression models used to test this hypothesis are:
!"!#!"#$$#%&!"#$%!
! !! ! !!!"#!"#$%! ! !!!"#!"#$%!!! !!!"#$!"#$%&'(
! !!!!!!"#$$#%&!"#$%!!"#$ ! !!!!" !"#$%&'!"##$!"#$"%&'(
! !!"!!! !"#$!"##$!"#$"%&'( ! !
Hypothesis 3: Crisis does not change the relationship between macroeconomic factors
and GHGs emission. (H3)
Although previous literature studies the financial crisis effects on peoples concern about
climate change, the real impact on emission before and after the economic downturn is yet to be
studied (Kahn & Kotchen, 2010). This hypothesis assumes the relationship between
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macroeconomic factors and GHGs emission is consistent throughout business cycles. It is
suggested by Kahn & Kotchen (2010) that financial crisis has changed peoples attitudes towards
climate change, shifting more attention to unemployment from tackling global warming. If the
hypothesis is rejected, it is possibly because financial crisis in 2008 has changed representatives
perception of climate change, which in turn affects emission related economic activities.
In order to assess the crisis effect on emissions, a dummy variable Crisisis created
with value 1 if the data point is collected from 2008-2011, or value 0 if collected from 1991-
2007. Products of dummy variable and variables of primary focus - namely GDP growth rate and
unemployment rate - are also included to examine differences before and after financial crisis.
The models used to assess H3 are as follows.
!"!#!"#$$#%&!"#$%!
! !! ! !!!"#!"#$%! ! !!!"#!"! ! !"#!"#$%! ! !!!"#!"#$%!!
! !!!"#$%&'($#") ! !!!"#$#$ !!"#$%&'($#") ! !!!!!!"#$$#%&!"#$%!!"#$
! !!!!" !"#$%&'!"##$!"#$"%&'( ! !!"!!" !"#$!"##$!"#$"%!"# ! !
II. Data
This paper uses GHGs emission data compiled from OECD with 29 OECD countries and
Russia, most of which are developed countries in Europe and America. GDP growth rate and
unemployment rate are obtained from OECD and IMF databases. Besides the major variables of
concern, data of production-based CO2 emission and coastline-to-land-area ratio are obtained
from OECD in order to control for omitted variable bias.
Some countries are excluded from the sample because of missing data. For example,
China, India, Chile, Israel, etc. are excluded from the model because emission data are not
publicly available. Similarly, unemployment rate and R&D percentage are not available in Czech
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Republic and Estonia, which causes imbalanced panel. So these countries are dropped as well.
As a result, 30 countries remained in the panel, covering 21 years period. The panel is strongly
balanced with 629 data points (one data point missing).
Variables and data summary are as shown in table below. Data sources are OECD
statistics, IMF database and Wikipedia (for coastline/land ratio only).
Table 1 Summary of variables
Variable Description Obs Mean Std. Dev. Min Max
unem Unemployment rate (%) 629 7.145285 3.564377 0 22.1
gdp_g GDP growth rate (%) 630 2.385702 3.23026 -14.5314 11.27189
gdp2 Quadratic term of gdp_g 630
emis_g GHG emission growth rate (%) 629 0.282289 4.204569 -18.6133 16.76401
co2_g Production-based CO2 emission
growth rate (%)
630 0.641057 5.028807 -19.7408 22.60063
cl_ratio Coastline/land ratio 630 23.46167 35.8738 0 172.4
crisis 1 if year > 2007
0 if year < 2008
630
cri_x Product of crisis and x variable, x
= gdp_g, emis_g, unem, or gdp2
630
The scatter plot of GDP growth rate and GHGs emission growth rate demonstrates
nonlinear relationship, which corresponds to existing literature. Graph below demonstrates linear
(green) and nonlinear (red) fitted values of emission growth rate to GDP growth rate. As can be
observed from the graph, quadratic term of GDP growth rate should be included to avoid omitted
variable bias.
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Figure 1 Scatter plot and fitted lines of GDP growth rate and GHG emission growth rate
III. Result and Discussion
The regression results as shown in the table below reveal that GDP growth rate and
unemployment rate are effective in explaining GHGs emissions, with or without production-
based CO2 emission included. Also, factors denoting crisis influence is also significant,
implying substantial paradigm changes before and after financial crisis.
Table 2 Regression results
(1) (2) (3) (4) (5) (6)
VARIABLES emis_g emis_g emis_g emis_g emis_g emis_g
unem 0.0336 0.0746 0.0561 0.0702* 0.0725* 0.0739*
(0.0722) (0.0750) (0.0385) (0.0402) (0.0398) (0.0398)
cri_unem -0.310*** -0.128** -0.109* -0.102*(0.112) (0.0610) (0.0587) (0.0586)
gdp_g 0.598*** 0.596*** 0.197*** 0.198*** 0.195*** 0.191***
(0.0579) (0.0634) (0.0326) (0.0358) (0.0328) (0.0327)cri_gdp -0.0613 -0.0181
(0.155) (0.0878)
gdp2 -0.0104 -0.0152* -0.0119*** -0.0140*** -0.0134*** -0.0114**(0.00841) (0.00884) (0.00449) (0.00474) (0.00467) (0.00449)
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cri_gdp2 0.0476** 0.0197* 0.0172(0.0216) (0.0117) (0.0116)
Constant 0.596 0.415 -1.100** -1.167*** -1.166*** -1.214***(0.815) (0.825) (0.437) (0.445) (0.442) (0.441)
Observations 628 628 628 628 628 628
R-squared 0.401 0.413 0.830 0.832 0.831 0.831Adj. R-squared 0.3471 0.3568 0.8142 0.8152 0.8153 0.8149
Years 1991-2011 1991-2011 1991-2011 1991-2011 1991-2011 1991-2011
Country effects? Yes Yes Yes Yes Yes Yes
Time effects? Yes Yes Yes Yes Yes Yes
Clustered
standard errors?
Yes Yes Yes Yes Yes Yes
Crisis effect? No Yes No Yes Yes Yes
Production-
based CO2included?
No No Yes Yes Yes Yes
Robust standard errors in parentheses
*** p
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H2: Macroeconomic factors affect GHGs emission merely through production-based CO2
channel.
The information provided from regression (3) (6) demonstrates that when including
CO2 variable, macroeconomic factors remain significant. Comparing (1) with (3), and (2) with
(4), although the coefficient of GDP growth is reduced from 0.6 to 0.2, coefficients for
unemployment rate and quadratic term of GDP growth do not change much. Also, significance
of quadratic GDP growth increases after including the CO2 term, which corresponds with the
theory of environmental Kuznets curve. It indicates that production-based CO2 should be
included as a control variable. Therefore, H2 is rejected from the fact that GDP and
unemployment affects GHGs emission through channels in addition to energy-consuming
production.
H3: Crisis does not change the relationship between macroeconomic factors and GHGs
emission.
By comparing regression (2) to (1), and (4) to (3), one can reject H3 because interaction
terms of crisis & unemployment, and crisis & quadratic GDP growth rate are both significant at
least at 10% level. The only difference between these two sets of comparison is that the second
set contains production-based CO2 emission growth rate as a control variable. It can be inferred
from regression (4) that when unemployment rate increases by 1%, GHGs emission rate is
expected to increase by 0.07% before financial crisis in 2008, whereas the change would become
-0.058% after the crisis.
According to Kahn & Kotchen (2010), people became less concerned about climate
change after the financial crisis when unemployment rate surges. If the peoples concern about
climate change directly influences their consumption behaviors, then an increase in
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more substantial international agreements on carbon reduction goals are reached in recent years,
like the China-US binary binding agreement on controlling domestic emissions (Hansen, 2014).
IV. Modification
One improvement of the above model is to consider whether a country has coastlines. As
climate change directly affects sea level, which subsequently alters coastal environmental and
economical conditions, countries with longer coastlines proportionally would be more concerned
about climate change. Longer coastlines also generate higher adaptation costs for sea level rising
after climate change, such as lifting seashore drilling foundations and maintenance of other
infrastructures along coastlines. Therefore, paradigm changes after crisis regarding emission may
differ among these countries.
The data include 4 inland countries, namely Austria, Hungary, Luxembourg and
Switzerland. Applying the best-fitted model regression (5) from above analysis to coastal
countries and inland countries, the regression shows that the response towards unemployment
rate changes is stronger in inland countries than in coastal countries. Except for Hungary, scatter
plots of GDP growth rate and GHG emission growth rate from the other three countries
demonstrate little linear relationship. It indicates that the business cycle influence on emission
may arise through very different channels for coastal countries and inland countries. However, as
the number of inland countries is too small, the inference cannot be interpreted with strong
realistic implications.
V. Limitations and Future Improvements
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Although the paper finds strong support for business cycles influence on GHG emission,
there are certain limitations that may hinder the robustness. Firstly, the model interprets business
cycle using yearly data, which may ignore certain dynamics captured only by quarterly data. For
example, the 2001 Dot-com bubble crisis is followed by an eight-month contraction, which is too
short to be captured by this data set. However, as most countries do not have quarterly emission
data, it is not possible to improve research in this direction at current stage. Secondly, the model
in this paper simply studies one business cycle with global influence, whose expansion and
contraction is separated by year 2008. To obtain a more robust result, it is recommended to
identify several business cycles and study if there are consistent results. One obstacle for this
improvement is to define global business cycles that influence all countries studied. Thirdly, this
model lacks in theoretical support, which remains to be discovered by scholars in this field. One
possible direction is to develop a theoretical model incorporating emission or other
environmental factors and to use simulated data for same regressions above. If the process yields
consistent results, the robustness of findings in this paper can be strengthened.
VI. Conclusion
In conclusion, business cycles do affect GHG emission in OECD countries from 1991 to
2011 as shown in this paper. Before financial crisis in 2008 (expansion), certain increase in GDP
growth rate corresponds with smaller increase in emission, compared with the co-movement
after crisis (recession). Similarly, in expansionary phase, increase in unemployment rate
corresponds with increase in GHG emission growth whereas in contraction phase, increase in
unemployment rate results in emission growth decreasing. It implies that economic costs for
climate change mitigation are less during recession than during expansion.
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