Business Cycle Effects on Greenhouse Gas Emission

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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.

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! !! ! !!!"#!"#$%!!!! ! !!!!"!"#$%!!!!!! !!!!"#$%&'#"!(!!!

! !!!!! !"#$%&'!"##$!"#$"%&'( ! !!"!!" !"#$!"##$!"#$"%&'( ! !!!!

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:

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! !! ! !!!"#!"#$%! ! !!!"#!"#$%!!! !!!"#$!"#$%&'(

! !!!!!!"#$$#%&!"#$%!!"#$ ! !!!!" !"#$%&'!"##$!"#$"%&'(

! !!"!!! !"#$!"##$!"#$"%&'( ! !

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.

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! !! ! !!!"#!"#$%! ! !!!"#!"! ! !"#!"#$%! ! !!!"#!"#$%!!

! !!!"#$%&'($#") ! !!!"#$#$ !!"#$%&'($#") ! !!!!!!"#$$#%&!"#$%!!"#$

! !!!!" !"#$%&'!"##$!"#$"%&'( ! !!"!!" !"#$!"##$!"#$"%!"# ! !

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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References

Ansuategi, A., & Escapa, M. (2002). Economic growth and greenhouse gas emissions.

Ecological Economics, 40(1), 23-37.

Carraro, C., Galeotti, M., & Gallo, M. (1996). Environmental taxation and unemployment: some

evidence on the double dividend hypothesis in Europe.Journal of Public Economics,

62(1), 141-181.

Clarke, L., Edmonds, J., Jacoby, H., Pitcher, H., Reilly, J., & Richels, R. (2007). Scenarios of

greenhouse gas emissions and atmospheric concentrations. US Department of Energy

Publications, 6.

Ekin, P. (1996). The secondary benefits of CO2 abatement: How much emission reduction do

they justify?.Ecological Economics, 16(1), 13-24.

Galeotti, M., Lanza, A., & Pauli, F. (2006). Reassessing the environmental Kuznets curve for

CO< sub> 2 emissions: A robustness exercise.Ecological economics, 57(1), 152-

163.

Hansen, S. (2014). The China-US climate change agreement is a step forward for green power

relations. The Guardian. Retrieved from

He, J., & Liu, B. (2004). Analysis of carbon emission intensity as the main index for greenhouse

gas emission mitigation commitments. Qinghua Daxue Xuebao/Journal of Tsinghua

University(China), 44(6), 740-743.

Kahn, M. E., & Kotchen, M. J. (2010).Environmental concern and the business cycle: The

chilling effect of recession(No. w16241). National Bureau of Economic Research.


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Kaygusuz, K. (2009). Energy and environmental issues relating to greenhouse gas emissions for

sustainable development in Turkey.Renewable and Sustainable Energy Reviews, 13(1),

253-270.

Nordhaus, W. D. (1991). To slow or not to slow: the economics of the greenhouse effect. The

economic journal, 920-937.

Tol, R. S. (1999). The marginal costs of greenhouse gas emissions. The energy journal, 61-81.

Holtz-Eakin, D., & Selden, T. M. (1995). Stoking the fires? CO2 emissions and economic

growth.Journal of public economics, 57(1), 85-101.

Business Cycle Effects on Greenhouse Gas Emission

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