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Journal of Traffic and Logistics Engineering Vol. 3, No. 1, June 2015
Economic Consequences of Climate Change
Impacts on Transportation: The Case of Atlantic
Canada
Yuri V. Yevdokimov
Department of Civil Engineering, University of New Brunswick, Fredericton, Canada
Email: [email protected]
Mikhail Zhukov and Oleg Zaytsev
Department of Economics, University of New Brunswick, Fredericton, Canada
Email: [email protected], [email protected]@unb.ca
Canada. They operate on 55,000 km of provincially
maintained roads and highways, including nearly 8,300
bridges. This also includes 4,700 km of National
Highway System highways and intermodal linkages.
These highways, connected by key ferry services, border
crossings, and the Confederation Bridge, connect all
communities within each province, and via the National
Highway System, to points throughout the region, Canada,
and the United States. Urban transit also plays an
important role for travelers in municipalities in Atlantic
Canada, moving over 24 million passengers a year [3].
As a matter of fact, regional transportation network
(RTN) is a part of the so-called Atlantic Gateway and
Trade Corridor, which is made up of Ref.[4]“a system of
major ports, international airports, key border crossings,
and road and rail connections between Atlantic Canada
and North America’s major markets”. It is a Ref. [4]
“strategic, integrated, and globally competitive
transportation system for international commerce to and
from North America”. More than $45 billion worth of
goods involved in international trade moves through the
Atlantic Canada Gateway and Trade Corridor annually.
Based on the above facts, it is obvious that climate
change impacts imposed on the RTN can bring
significant economic damage if this problem is not
addressed in a timely manner. This paper presents our
approach to the evaluation of economic consequences of
the climate change impacts on the road RTN in Atlantic
Canada.
Abstract—Climate change impacts such as an increase in
mean temperature, change in precipitation patterns and sea
level rise are affecting regional road transportation network
in Atlantic Canada. These impacts cause direct and indirect
economic consequences for the network and regional
economy. Currently a dynamic general equilibrium model
(GEM) is being constructed. In this paper, basic principles
of the GEM are discussed and the model’s architecture is
presented. Climate change impacts are regarded as
productivity shocks, and their dynamics is analyzed on the
basis of advanced time series techniques. Further these
impacts will be imposed on a dynamic regional economic
system expressed via our Dynamic GEM to trace their
economic consequences.

Index
Terms—climate
change
impacts,
regional
transportation network, economic consequences, dynamic
general equilibrium model, statistical analysis, climate
variables
I.
INTRODUCTION
According to the latest 5th Intergovernmental Panel on
Climate Change (IPCC) Assessment Report, the main
climatic drivers in North America are temperature
warming and drying trends, extreme temperature events,
extreme precipitation events, damaging cyclones and
rising sea level [1]. In this regard, Atlantic Canada as a
part of North America represents a high risk area with
respect to the climate change. Atlantic Canada is
comprised of four provinces - New Brunswick, Nova
Scotia, Prince Edward Island, and Newfoundland and
Labrador. Over 2.3 million people live in urban areas and
small communities along the coast with a span of more
than 40,000 kilometers of predominantly rocky coastline
[2]. Climate change impacts here include rising sea levels
and temperatures, change in precipitation patterns and
more frequent extreme weather events such as
thunderstorms, snowfalls, hurricanes, tornados and others.
There are over 1.3 million registered passenger
vehicles and over 40,000 registered trucks in Atlantic
II.
It is necessary to distinguish between climate change
impacts and their economic consequences. According to
the literature review, there are four major physical or
biological climate change impacts relevant to the RTN
under study:
1). Sea level rise.
2). Change in precipitation patterns.
3). Increase in temperature.
4). Increase in frequency of extreme weather events.
Manuscript received November 19, 2014; revised April 1, 2015.
©2015 Journal of Traffic and Logistics Engineering
doi: 10.12720/jtle.3.1.62-66
CLIMATE CHANGE IMPACTS VERSUS ECONOMIC
CONSEQUENCES OF THESE IMPACTS
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Journal of Traffic and Logistics Engineering Vol. 3, No. 1, June 2015
transport accidents. These accidents are associated with
all users of a transportation network – producers of
transportation and consumers of transportation – and as
such should be also incorporated into our framework.
Sea level rise can lead to flooding of transportation
infrastructure and eventually to soil erosion in the area.
Change in precipitation patterns can affect infrastructure
and vehicles as well as their maintenance. It can also
change driving conditions in the area. Increase in
temperature can affect transportation infrastructure and
vehicles operation. Increase in frequency of extreme
weather events can affect transportation infrastructure, its
maintenance as well as driving conditions. As a matter of
fact, all these impacts combined affect RTN directly in
terms of transportation infrastructure, associated services
and operations and indirectly through the associated with
RTN economic activities.
Accordingly economic consequence or what is further
called economic effects of these climate change impacts
on the RTN can be divided into direct and indirect. Direct
economic effects are the ones associated with production
of transportation services. In an economic sense, those
are impacts on inputs of production and services
produced by these inputs of production. In this regard, by
inputs of production first of all we mean capital assets
and labor. Climate change impacts directly affect
transportation infrastructure and vehicles (capital assets)
and labor involved in production of transportation
services. As well climate change impacts affect
transportation services produced by transportation capital
(infrastructure and vehicles) and transportation labor.
From an economic standpoint direct effects are associated
with supply side of transportation.
There are two types of direct economic effects on
transportation infrastructure and vehicles: (i) direct
damage, and (ii) increased maintenance costs. In turn,
transportation labor is affected via negative health effects.
It is also possible to identify two types of direct effects
on transportation services: (i) temporary or permanent
disruption of transportation services, and (ii) deterioration
of the quality of transportation services.
Indirect economic effects of climate change impacts
are associated with consumers of transportation services
such as households and economic sectors other than
transportation. Households mostly consume passenger
transportation services while economic sectors mostly
consume freight transportation. Decrease in production of
transportation services due to climate change impacts
may lead to a loss of labor income and profits in sectors
other than transportation. In addition, interruption of
transportation services due to climate change impacts can
lead to increased travel time of all users of a
transportation network. Finally, since these consumers of
transportation are affected by climate change impacts
directly as well, they may change their consumption and
production patterns which eventually may lead to a
change in their demand for transportation services. From
an economic standpoint, indirect effects are associated
with demand side of transportation.
Special consideration should be given to the link
between climate change impacts and transport accidents.
Literature on transport accidents suggests that an increase
in precipitations, temperature and frequency of extreme
weather events lead to an increase in the number of
©2015 Journal of Traffic and Logistics Engineering
III.
MODELING CLIMATE CHANGE IMPACTS ON
TRANSPORTATION
Our analysis of the existing literature on economic
evaluation of climate change impacts in various segments
of an economic system, led us to the conclusion that
dynamic general equilibrium model (GEM) is the best
framework for our analysis. Ref. [5] is a seminal work in
this regard followed by more recent studies [6], [7] and
[8]. These studies helped us formulate basic principles for
our model as follows:
1). Both micro- and macroeconomic aspects of the
RTN should be incorporated in our model
2). Modified Computable General Equilibrium
Model (CGEM) is our fundamental tool since it
allows us to incorporate micro- and
macroeconomic dynamics as well as dynamics
of climate change impacts
3). Modeling has to address the following two goals:
(i) design of the appropriate architecture to
include microeconomic and macroeconomic
dynamics affecting our RTN; (ii) modeling of
the dynamics of climate change variables.
Once both goals presented above are achieved,
dynamics of the climate change variables will be imposed
on the dynamics of the basic CGEM to trace economic
consequences of the climate change impacts.
In this regard, the following architecture of our CGEM
was constructed: Transportation module is the
centerpiece of the CGEM. It consists of a system of
dynamic equations for price and volume of regional
transportation. These equations were obtained on the
basis of Vector Autoregression estimation in statistical
software package STATA using historical data. Within
the module, this system produces equilibrium price of
regional transportation for the RTN as a whole based on
major economic determinants such as regional traffic,
value added at regional hubs, overall price level, regional
GDP, oil price, population and some other.
At each hub, the above price of transportation is taken
as an input to define the value added produced by the hub
based on this price as well as geographical and industrial
characteristics. Each hub is further disaggregated with
respect to major industrial consumers of transportation in
that sub-region. It means that initially the value added
and traffic at each hub were estimated via panel Vector
Autoregression based on historical data using major
economic determinants and major industrial consumers of
transportation. For example, the largest consumers of
transportation at Fredericton hub were identified as
follows:
 Manufacturing
 Forestry and logging
 Retail trade
 Wholesale trade
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Journal of Traffic and Logistics Engineering Vol. 3, No. 1, June 2015
Further, all sectors’ values added are then summed up
at each hub and sent back to the transportation module
along with the hub’s traffic volume to determine next
period equilibrium price of transportation for the RTN.
This process repeats itself to produce time paths of our
major economic and transportation variables associated
with the RTN over time given current and expected
micro- and macroeconomic conditions.
In line with the CGEM framework, eventually we
receive a time path which is a set of short-run equilibria
represented by the transportation module and five
regional hubs. Microeconomic forces are internal in this
model since they are based on the supply/demand
dynamics of the regional industries/sectors while
macroeconomic forces are based on external dynamics
given exogenously. In other words, given initial
conditions, our model replicates evolution of the RTN,
driven by internal microeconomic and external
macroeconomic forces, with all elements estimated on the
basis of statistical analysis and historical data outside the
model. Statistical analysis of the model’s elements was
done via comprehensive time series analysis on the basis
of Vector Autoregression (VAR), panel VAR and
cointegration using methodology described in detail in [9].
The designed model was tested with the help of
exogenously imposed shocks. For example, our previous
analysis of climate change impacts on the RTN showed
that approximately 1.5% of the traffic is lost due to these
impacts. Therefore, a 1.5% negative quantity shock was
imposed on the Halifax hub – one of the five regional
hubs in our RTN. As a result, this negative shock led to
the loss of $503.1 million in value added over first 15
years at this hub with spillover effects to the entire RTN.
For example, this spillover impact on Fredericton hub
was estimated as $22.29 million over first 15 years. These
values give us some benchmarks for the investments into
climate change mitigation measures.
In parallel, dynamics of climate variables in the region
has been analyzed. Fundamental climate variables in our
study are: temperature, precipitation and sea level. Some
statistical work on dynamics of these variables in Canada
is presented in [10] and [11]. Our analysis of climate
change dynamics is based on stationary linear models
widely used in time series econometrics: autoregressive
moving-average (ARMA) and generalized autoregressive
TABLE I.
conditional heteroskedastic (GARCH) models with
ARMA errors.
By the time of this paper, we have completed time
series analysis of the regional temperature using monthly
time series data for the 1871-2012 period. According to
our analysis, obtained linear trend is upward sloping,
which suggests that the mean monthly temperature in
Atlantic Canada will be increasing during 1872-2101
period. For example, we predict that cumulative increase
in mean monthly temperature at Saint John hub will
amount to +3.25°C by January 2101 compared to the
initial temperature level observed in January 1872, or to
+1.43°C compared to the initial level of January 2000.
We also found statistical evidence that the underlying
data generating process of mean monthly temperatures
had started to change in the mid-1950s. Eventually, we
found the best-fit models to describe dynamics of
temperature at various regional hubs of our RTN such as
Northern New Brunswick or Edmundson-Bathurst area,
Moncton, Saint John, Fredericton and Halifax. Currently
we are analyzing dynamics of precipitation and sea level
rise in Atlantic Canada using the same methodology.
With respect to precipitation, common knowledge is
that climate in Atlantic Canada is getting wetter.
However, there is a substantial variety in the tendencies
over the areas. We use the following time series: rainfall,
snowfall, and total precipitation, which is summation of
the two. The data is taken from the second generation
Adjusted Precipitation for Canada (APC2) dataset.
This is the data extracted from National Climate Data
Archive, and corrected to take into account equipment
measurement errors, and aggregated over closely-related
stations to receive longer time series. Data is monthly,
constructed from daily values of rainfall, snowfall, and
total precipitation in millimeters.
The data was taken from meteorological stations close
to five hubs of our RTN. In this regard, we have
individual station data for Fredericton, Saint John,
Halifax, and Moncton. The data for the Northern New
Brunswick and Miramichi is given by three stations
located in Edmundson, Bathurst, and Miramichi. Number
of observations varies from 1,080 for Edmundston to
1,692 for Halifax. Since there are some missing
observations, time series approximation procedure was
applied to fill the gaps. The following table summarizes
our data:
METEOROLOGICAL STATIONS
Station ID
Province
From /To
Number of observations
missing, snow
missing, rain
8101500
8104900
8202250
8103200
810AL00
8100503
8101000
NB
NB
NS
NB
NB
NB
NB
1874 /2009
1871 /2011
1872 /2011
1898 /2011
1916 /2005
1884 /2012
1873 /2004
1632
1692
1680
1368
1080
1548
1584
13
1
27
16
29
252
2
16
1
22
16
30
251
2
Since global sea level rise is projected to accelerate
over the next century because of continued thermal
expansion and melting of land ice, Atlantic Canada will
©2015 Journal of Traffic and Logistics Engineering
be also a subject to the rising sea level. In this regard,
several sources of the sea level data were analyzed. The
data set provided by Canadian Hydrographic Service was
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Journal of Traffic and Logistics Engineering Vol. 3, No. 1, June 2015
respectfully. Shediac station at Moncton hub is
characterized by a lot of missing observations. Rimouski
station was implemented only in 1984; it contains 349
observations. Since the missing observations are present
in the data, some time series procedures to fill the gaps
are required. The information about stations is
summarized in the following table:
chosen as the main source since it provided the most
consistent data set. The monthly mean sea level data for
three tide-gauge stations were obtained from the Atlantic
Zone Monitoring Program (AZMP) websites. The
stations were chosen in accordance with transportation
hubs of our RTN. Sea level is measured by tide-gauges
relatively to land. Stations at Halifax and Saint John hubs
contain a long data set with 1,128 and 962 observations
TABLE II. SEA LEVEL STATIONS
Station ID
Station name
Province
From/to
Obs
Missed, obs
00490
HALIFAX
NB
1920/2014
1128
26
00065
SAINT JOHN
NB
1920/2014
962
79
02985
RIMOUSKI
NB
1984/2014
349
16
1326
SHEDIAC BAY
NB
1972/2011
480
198
http://www2.gnb.ca/content/dam/gnb/Departments/trans/pdf/en/P
ublications/2008-2018AtlanticCanadaTransportationStrategy.pdf
[4] Canada’s Atlantic Gateway (2010). Atlantic Gateway and Trade
Corridor Strategy: Connecting Canada with the World. [Online].
Available:
http://www.atlanticgateway.gc.ca/media/documents/en/brochure.p
df
[5] W. D. Nordhaus and Z. Yang, “A regional dynamic generalequilibrium model of alternative climate-change strategies,” The
American Economic Review, vol. 4, no. 86, pp. 741-765, 1996.
[6] A. Rezai, L. Taylor, and R. Mechler, “Ecological macroeconomics:
An application to climate change,” Ecological Economics, vol. 85,
pp. 69–76, 2013
[7] W. S. Jaglom, “Assessment of projected temperature impacts from
climate change on the U.S. electric power sector using the
integrated planning models,” Energy Policy, vol. 73, pp. 524-539,
2014
[8] V. J. Nannen, Van den Bergh, and A. E. Eiben, “Impact of
environmental dynamics on economic evolution: A stylized agentbased policy analysis,” Technological Forecasting and Social
Change, vol. 80, pp. 329-350, 2013
[9] W. Enders, Applied Econometric Time Series, 3rd edition, John
Wiley & Sons, Inc., 2010.
[10] E. Mekis and L. A. Vincent, “An overview of the second
generation adjusted daily precipitation dataset for trend analysis in
Canada,” Atmosphere-Ocean, vol. 49, no. 2, pp. 163-177, 2011.
[11] L. A. Vincent, X. L.Wang, E. J. Milewska, H. Wan, F. Yang, and
V. Swail, “A second generation of homogenized Canadian
monthly surface air temperature for climate trend analysis,”
Journal of Geophysical Research, vol. 117, pp. 1804-1807, 2012
Once
consistent
datasets
are
constructed,
comprehensive time series analysis will be applied to
capture dynamics of these climate variables similar to the
temperature to impose that dynamics on previously
developed CGEM.
IV.
CONCLUSION
It is an established fact that regional transportation
networks all over the world are vulnerable to the climate
change impacts such as increasing temperatures,
changing precipitation patterns and rising sea level.
However, according to our literature review, there is no
comprehensive model to trace economic consequences of
these climate change impacts over time. This study is the
first attempt to produce such a model. The model is based
on dynamic general equilibrium framework associated
with Atlantic Canada regional road transportation
network
that
includes
microeconomic
and
macroeconomic aspects. Climate change impacts in this
framework are regarded as productivity shocks imposed
on the regional transportation network. This approach
allows us to trace economic consequences of the climate
change impacts on regional transportation network as
consequences of the productivity shocks imposed on a
dynamic economic system. In this regard, basic principles
of the model’s architecture are presented; first version of
the model is designed and tested. In parallel, dynamics of
climate variables such as temperature, precipitations and
sea level is being analyzed and some first results of this
analysis are presented. In the future, dynamics of the
climate variables will be imposed on the designed
dynamic model of the regional transportation network.
Yuri Yevdokimov is a Professor at the University of New Brunswick
(Fredericton, Canada). Due to his degrees in economics and engineering,
he holds joint appointment with two Departments – Economics and
Civil Engineering. Dr. Yevdokimov teaches courses in undergraduate
and graduate programs in both Departments, and transportation
economics is among them. His primary research interest is in economic
evaluation of climate change impacts on regional transportation. Dr.
Yevdokimov has participated in three projects dedicated to this topic
over last four years and, as a result, published several articles, papers
and chapters in monographs both nationally and internationally. In
general, Dr. Yevdokimov has more than 20 publications. One
monograph, two textbooks, twelve refereed journal articles, and nine
chapters in books are among these publications.
REFERENCES
[1]
[2]
[3]
Intergovernmental Panel on Climate Change. (2013). 5th IPCC.
Available: Assessment Report (AR5). [Online]. Available:
http://www.ipcc.ch/report/ar5/wg1/
Environment Canada. (2012). Celebrating our coastlines from
rivers
to
oceans.
[Online].
Available:
http://www.ec.gc.ca/envirozine/default.asp?lang=en&n=82B538B
3-1
New Brunswick Department of Transportation (2008). The
Atlantic Canada Transportation Strategy 2008-2018. [Online].
Available:
©2015 Journal of Traffic and Logistics Engineering
Oleg Zaytsev is a Ukrainian born economist who in 2010 received a
joint MA degree in economics with minor in financial economics from
Kyiv School of Economics (Ukraine) and University of Houston (USA).
In 2014, Oleg received another MA degree in economics from the
University of New Brunswick (Fredericton, Canada). During his studies
at that University, as a research assistant he participated in the Project
“Modeling economic consequences of the climate change impacts on
ground transportation in Atlantic Canada” financed by provincial
government. Specifically, Oleg was estimating evolutionary dynamics
65
Journal of Traffic and Logistics Engineering Vol. 3, No. 1, June 2015
from the University of New Brunswick (Fredericton, Canada). During
his studies at that University, Mikhail has participated in the Project
“Modeling economic consequences of the climate change impacts on
ground transportation in Atlantic Canada” financed by provincial
government. Specifically, he was designing a dynamic general
equilibrium model of the regional economy in Atlantic Canada. In 2013,
he presented his research in this area at the 5th Annual Conference of
Acadia Economic Society in Atlantic Canada. Currently Mikhail is a
Ph.D. student at McGill University (Montreal, Canada).
of monthly temperature in Atlantic Canada on the basis of
comprehensive time series analysis. The main focus of his research was
on ARMA and (G)ARCH class of models. In general, Oleg’s
professional interests lie in macroeconomics, economics of climate
change, time series econometrics and statistics.
Mikhail Zhukov’s first Master’s degree was in applied mathematics
received from Moscow Institute of Physics and Technology (Russia) in
2009. Later in 2014, he graduated with a Master’s degree in economics
©2015 Journal of Traffic and Logistics Engineering
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