Curbing GHG Emissions Using an Output-Based Permit
Trading System
A Dynamic General Equilibrium Analysis*
Yazid Dissou** & Véronique Robichaud***
Economic Studies and Policy Analysis Division
Economic and Fiscal Policy Branch
Department of Finance, Ottawa, Canada
October 2003
Abstract
This paper analyses in an intertemporal general equilibrium setting the
economic impact of using an output-based emissions trading scheme to
reduce GHG emissions in Canada. Although major economic instruments
tend to be preferred for the control of GHG emissions because of their
cost-effectiveness, some of these instruments have undesirable
distributional consequences. The output-based emissions trading system in
which firms are provided free emissions allowances according to their
output seems to be attractive. This system could partially address the
concerns over the uneven sectoral distribution of the economic burden of
GHG abatement policies. Simulation results suggest that the economic
costs of carbon abatement policies using this type of policy is relatively
modest.
JEL classification numbers: C68, D58, D90, H23, Q43, Q48
Key words: Tradable permits, output-based permit allocation, emissions control, Kyoto,
Dynamic general equilibrium.
*
We would like to thank, without implicating, Paul-Henri Lapointe, Benoît Robidoux, Jeremy Rudin, Tim Sargent,
participants to various seminars held at the Department of Finance of Canada, as well as anonymous readers in
several federal departments for their comments and suggestions. The opinions expressed in this paper are strictly
those of the authors and should not be attributed in any case neither to the Department of Finance of Canada nor
the Government of Canada.
** Email: dissou.yazid@fin.gc.ca
***
This study was carried out when Véronique Robichaud was working for the Department of Finance of Canada.
1 - Introduction
This paper assesses the potential economic implication of curbing greenhouse gas (GHG)
emissions in Canada using the policy instruments suggested in the Reference Policy Package on
climate change. This GHG abatement policy, which differs from -- (but is close to) -- the official
Canadian plan on climate change, features a variety of instruments to reduce GHG emissions to
achieve Kyoto objectives in Canada1. Several studies, using various policy instruments, have been
carried out recently in Canada on the potential economic costs of GHG emissions abatement
policies. Ab Iorwerth et al. (2000), Bagnoli (2001), Government of Canada (2002a), Loulou and
Kanudia (1999) and Wigle (2001) are a few examples among many others. GHG abatement
policy instruments can be classified into two categories: (a) market instruments, such as a tax on
carbon and tradable permits; and (b) regulatory instruments, such as emissions performance
standards.
Most economists tend to prefer market instruments as opposed to regulatory instruments
because the former make it possible to achieve the specified targets at the lowest economic cost.
However, using market instruments could lead to a substantial increase in production cost in
some industries2 and affect their competitiveness. The greatest challenge lies thus in the design of
an effective emissions control policy that minimizes the economic costs and the negative impacts
on the industries that are potentially hardest hit.
Recent studies, including Bernard, Fischer and Vielle (2001), Fischer (2001), Goulder (2001)
and Sterner and Höglund (2000), have analysed various assistance options for industries
potentially affected by GHG abatement policies. One of these options involves changing the
permit allocation rules to minimize the distributional impacts of the GHG mitigation burden that
is unfavourable to these industries.
In a new environment characterized by emissions scarcity, the latter acquire a market value as
soon as the abatement policy relies on a market policy instrument. The distribution of free
permits to polluting firms would help them mitigate the negative impact of their GHG abatement
efforts. Referring to Fischer (2001), gratis allocation of permits based on current output could be
a particularly attractive form of assistance to industries that are potentially affected. In contrast to
freely allocated permits based on past emissions (grand-fathering), which translates into a wealth
transfer to firms, the output-based allocation of free permits has some interesting impacts on
firm’s output decision and on resource allocative efficiency.
1
The reference policy package is the penultimate version of the official climate change plan for Canada (see
Government of Canada, 2002).
2 Mainly, energy-intensive industries and energy industries.
These properties led the Government of Canada to suggest in the Reference Policy Package
the use of a permit trading system with an output-based gratis allocation of permits. In addition
to the permit trading system, the Reference Policy Package also suggests using other policy
instruments, such as technological standards.
The objective of this study is to analyse in an intertemporal general equilibrium framework
the potential economic impact of using the Reference Policy Package to achieve Kyoto targets in
Canada. To our knowledge, the only existing and exhaustive analysis of the Reference Policy
Package used another approach that combined two models, the macro-econometric model of the
Canadian economy referred to as “TIM” (The Informetrica Model) and the technological model
called “Energy 2020 (E2020)”.3 Besides, we were unable to identify any other study on the
impacts of a permit system with an output-based allocation in an intertemporal general
equilibrium framework.
We develop a dynamic general equilibrium model with rational expectations to analyse the
potential aggregate and sectoral impacts, as well as the transitional dynamics of the economy
arising from implementing the Reference Policy Package in Canada. Recent studies, like Bye
(2002) and Kirchgässner, Müller and Savioz (1998), have highlighted the importance of the
functioning of the labour market on the assessment of the economic impact of environmental
policies. In this paper, we specify the labour market functioning to consider alternatively wage
flexibility, and wage rigidity that could lead to rationing. Furthermore, we analyse the economic
impact of the abatement policy using different international permit prices and various closure
rules for the government account. The development of the present model has built upon recent
contributions to the literature on dynamic general equilibrium and on environmental policies,
such as Bovenberg and Goulder (2000), Fischer (2001) and Goulder and al. (1999), among others.
The remainder of the paper is as follows. The next section presents an intuitive description of
the model, while the third section discusses data and the model’s calibration. In the fourth
section, we present the Reference Policy Package and discuss the simulation results. The last
section concludes.
2 – The Model
2.1
– Overview
We present a multi-sector forward-looking dynamic general equilibrium model of the
Canadian economy. The model is used to track changes in the equilibrium paths of prices,
3
These two models were developed by private sector companies. See Government of Canada (2002b).
2
quantities and incomes after a shock. It is a neo-classical growth model in which the steady state
growth rate of the economy is solely determined by the population growth rate augmented by
Harrod-neutral technological progress. Household labour supply is endogenous and two
assumptions pertaining to the functioning of the labour market are considered: wage flexibility
and wage rigidity.
Canada is considered a small-open economy producing tradable and non-tradable goods
that takes world prices and interest rates as given. The economy is disaggregated into 15
industries, producing 19 products so as to take into account differences in energy intensity.4 The
model considers six energy products (electricity,5 coal, natural gas, diesel, gasoline, and other
refined petroleum products) that are used to satisfy firms and households energy needs.
The model is real in the sense that only relative prices affect real variables. The numéraire is
the ‘nominal’ exchange rate, or the conversion factor between local and foreign exchange units.6
To disentangle the dynamics resulting from the exogenous growth of the population from the
dynamics induced by policy shocks, all real variables are expressed in labour efficiency units. The
remainder of the paper provides only an intuitive description of the model; readers interested in
mathematical details can obtain from the authors upon request the list of equations, variables and
parameters.
2.2 – Households
Consider an economy populated by a finite number of infinitely-lived households. The
representative household makes consumption, savings, and labour supply decisions. It has an
unlimited access to the world capital market, where it can lend or borrow at a constant real
interest rate r*. It is liable for the country’s foreign debt and its portfolio consists of foreign
assets, government bonds and shares in domestic firms.
The representative household derives its current income from wages paid by firms,
returns on financial assets and net transfers received from both the government and the rest of
the world. The representative household pays income tax as well as consumption taxes on goods
and services. It maximizes an intertemporal utility function subject to a sequence of budget
constraints and an intertemporal solvency constraint. The intertemporal utility function, which is
4
See Table 10 for the lists of industries and products.
For data availability reasons, the energy-producing industry cannot be disaggregated to distinguish different
production modes, such as hydroelectricity and thermal electricity.
6 In other words, the model’s numéraire is the average price of foreign-produced goods.
5
3
additively separable, features a constant rate of time preference and an instantaneous logarithmic
utility function that is weakly separable and defined over aggregate consumption and leisure. The
latter two arguments are combined using a Cobb-Douglas (C-D) functional form.
By solving its optimization problem, the representative household determines the optimal
paths for consumption expenditures and labour supply. Three first-order conditions of this
standard optimization problem can be derived. The first is the consumption Euler equation,
which specifies the trade-off between consumption in two consecutive periods. This trade-off
depends on the ratio of real interest rate (in terms of consumption7) and the discount factor.
More precisely, an anticipated rise in the real interest rate relative to the rate of time preference
induces households to substitute current consumption for future consumption.
The second first-order condition of the optimization problem is the usual trade-off
between labour supply and leisure. The household supplies labour until the marginal substitution
rate between consumption and leisure is equal to the ratio between the opportunity cost of leisure
and the aggregate price of consumption.8 The third first-order condition pertains to the
accumulation of financial wealth, which is the sum of the value of domestic firms, government
bonds and net foreign assets. Through an algebraic manipulation of the first and third first-order
equations, it can be shown that the representative household aggregate consumption depends on
total wealth, which is the sum of financial wealth and human wealth. The latter depends on the
discounted sum of current and future period flows (net of taxes) of labour income and net
transfers from the government and the rest of the world.
As a result, any shock that alters household income path or the stream of aggregate
consumption price can affect current household aggregate consumption expenditures. Given the
importance of domestic products in household consumption basket, GHG abatement policies
that have a significant impact on relative prices could affect household current consumption.
Based on the optimal path for aggregate consumption (or consumption expenditures), the
representative household allocates in each period these expenditures among the available
commodities. A C-D function is used as the aggregator function to specify the relation between
aggregate consumption and the quantities of various commodities consumed by the
representative household. With this specification, the share of each commodity in consumption
That is, the international interest rate adjusted for change in aggregate consumption price between two successive
periods.
8 In the case with wage rigidity, we introduce a gap between the opportunity cost of leisure and the wage rate paid by
firms.
7
4
expenditures is fixed in each period. Finally, the aggregate consumption price mentioned earlier is
obtained by solving the dual problem in the representative household intra-period optimization
program.
2.3 – Output
2.3.1 – Technological specification
The representative firm in each industry combines labour, capital, energy and intermediate
inputs to produce a composite good that can be sold locally or exported. It has access to a
constant returns to scale technology and faces capital installation costs. It operates in a
competitive environment in the good markets, as well as in factor markets.
Given the importance of energy in reducing GHG emissions, the specification of firm
technology accounts for different substitution possibilities between, not only various sources of
energy, but also between energy and capital. In particular, a weakly separable production function,
represented by nested CES (Constant Elasticity of Substitution) functions, is used to represent
technology. Figure 1 presents a schematic description of firm technology.
Output is a CES function of the composite value-added-energy input and of the aggregate
of intermediate inputs. Labour is combined with the capital-energy aggregate input using a CES
function to produce the composite value-added-energy input. The capital-energy aggregate input
is another CES function of capital and the aggregate of energy inputs. The latter is a CES
function of electricity and the composite of fossil energy products, which is another CES
function of various “stationary” fossil energy inputs, carbon, natural gas and other refined
petroleum products.9
The aggregate of intermediate inputs is a CES function of the composite input of
non-energy intermediate inputs and the composite input of “mobile” fossil fuel inputs. The latter
is a CES function of diesel and gasoline, while the composite of intermediate inputs is a Leontief
function of the other material inputs used by the firm.
The model differentiates between “mobile” fossil energy products (used for transportation, such as diesel and
gasoline) and “non-mobile” or “stationary” fossil energy products, such as coal, natural gas and other refined
petroleum products.
9
5
Figure 1: Schematic representation of firm technology
Output
(CES)
Capital-Labour-Energy
(CES)
Capital-Energy
(CES)
Physical
capital
Materials - "Mobile" energy
(CES)
Labour
Energy
(CES)
Electricity
Liquid petroleum products
"Mobile" energy
(CES)
Various intermediate inputs
Gasoline
Diesel
"Stationary" fossil fuels
(CES)
Refined petroleum products
(C-D)
Other petroleum products
Materials
(Leontief)
Gas aggregate
(Léontief)
Natural gas
Coal
Pipelines
6
2.3.2 –Emissions control and modelling of firm behaviour
According to the Reference Policy Package, two types of instruments that could affect firms’
behaviour are worth mentioning. These are the output-based allocation of emissions in a permit
trading system and the so-called ‘targeted measures’.
(a) – Output-based allocation of emissions in a permit trading system (OPTS)
Description of the system
The permit trading system with an output-based allocation of emissions allowances would be
introduced to control GHG emissions in the group of industries identified as “Large Final
Emitters” (LFEs).10 Under this system, all LFEs must hold a permit to emit any GHG unit that
falls into the category of admissible emissions in the permit trading system.11 The permits can be
traded in the domestic and international markets. To reduce the negative impacts of increasing
production cost associated with the scarcity of emissions rights, the package provides for gratis
allocation of permits for each LFE. The free permits are offered to firms according to their
current period outputs and to their assigned emissions intensity targets.
An industry’s emissions intensity is the ratio between its emissions12 and its real output. Note
that the emissions intensity target assigned to an industry is a predetermined value that is not
necessarily equal to the one observed in the reference situation. Furthermore, it is different from
the emissions intensity actually achieved13 by the industry, which is only observed ex-post.
The gratis allocation of permits to LFEs allows them to benefit from the scarcity rent derived
from holding emissions rights. They will be able to offset (at least partially) the negative effects of
abatement efforts. However, in contrast to the grand-fathering system in which permit
assignment is based on past emissions, this system has an impact on firms’ behaviour.
The larger is the firm output, the larger the number of free permits received. The increase in
the number of free permits received reduces the net purchase of permits. Firms will therefore
have a strong incentive not to reduce their output in order to receive more free permits. They are
rather induced to reduce their emissions by lowering their emissions intensity. The latter
reduction can only be achieved through investment in physical capital and/or through
substitution among fossil fuels.
This group includes the electricity, oil and natural gas and high energy-intensive industries (see list in Table 10).
The Reference Policy Package also suggests that LFEs use the so-called ‘targeted measures’ to abate some types of
emissions.
12 Eligible emissions in the OPTS.
13 The emissions intensity actually achieved is calculated from the total effective GHG emissions (eligible under the
OPTS) and the output for the current period.
10
11
7
It is worth mentioning that the number of free permits received by LFEs does not necessarily
cover all their needs of emissions rights. Some firms may be short of permits, while other may
have permits in excess. The possibility of permit trading allows firms that are short of permits to
acquire them from firms with surplus or in the international permit market.14
Modelling firm’s abatement decisions in the OPTS
The purchase of tradable permits is an alternative to physical emissions abatement efforts. In
these conditions, the permit price becomes the true signal that determines the extent of the firm’s
abatement efforts: the lower the permit price, the smaller the abatement effort made by the firm.
The rational firm pursues its abatement efforts up to the point where the marginal cost of
abatement is equal to the cost of the permit.
Moreover, given the impact of the permit allocation system on firm behaviour, the value of
free permits can be viewed as an output incentive. Authors, like Fischer (2001) and Goulder and
al. (1999), have adopted the same approach to model the impact of output-based allocation of
free permits on firms’ behaviour. The OPTS can be modelled as a standard permit trading system
in which the permit price affects both factor utilization decisions and the effective output price
received by the producer. The firm’s static optimization problem in an OPTS can be stated as
follows:15
Max Π j = ( Pj + β j P )Y j − ∑ ( wi + Peij ) xij
i −1
s.c.
Y j = f ( xij )
i = 1,..., n
where Π j , Y j Pj , and β j represent, respectively, profit, output, output price and emissions
intensity target assigned to firm j . xij , wi , eij and P represent, respectively, the quantity and the
price of input i used by firm j , 16 the emissions factor of input i used by firm j 17, and the unit
price of the tradable permit. β j P represents the production incentive associated with the
assigned emissions intensity target; it is expressed per unit of output. Finally, f is a linear
homogeneous production function.
The Reference Policy Package allows LFEs to purchase emissions credits from offsets, which are substitutes for
international permits. Offsets are emissions credits generated by abatement activities in industries that are not part of
the LFE group; they are partially funded by the government.
15 For the sake of clarity, process emissions are ignored for the moment. They are nevertheless taken into account in
the numerical version of the model.
16 These inputs include for example various fossil energy inputs and physical capital.
17 The emissions factor of a non-polluting input is zero.
14
8
The first-order condition of this optimization problem is the standard equation of
equalization of the marginal product of the factor to its acquisition cost:
( Pj + β j P) f ' ( xij ) = wi + Peij
for i = 1,...., n
where function f ' represents the physical marginal productivity of input i . The permit price, P,
affects firm’s behaviour in two ways. First, it penalizes the use of the most polluting inputs, and
encourages firms to substitute among fossil fuels or to substitute energy for capital. Second, the
higher the permit price, the higher the production incentive received by the firm and the lower
the negative impact of the permit price on the firm’s output. It follows that the negative impact
of GHG abatement cost expenditures on firm’s output is reduced. Firms have less incentive to
reduce their output than to reduce their emissions intensity. However, at a given permit price,
total LFE emissions could be higher in comparison to a permit trading system without output
incentives. The purchase of a larger number of international permits would thus be required.
Moreover, since emissions intensity targets are assigned ex ante to LFEs, the total number of
free permits offered by the government could exceed LFEs' share in total emissions rights
granted to Canada under the Kyoto Protocol. Thus, either other sectors of the economy will have
to make further abatement or the government will have to purchase more permits to meet the
Kyoto target.
The Reference Policy Package rules out this possibility by using a scaling-back factor for
emissions intensities, α , so that the number of free permits distributed to LFEs equals their
share of the national Kyoto target. This correction factor is an endogenous variable indicating the
uniform percentage change (reduction or increase) of assigned emissions intensity targets to
achieve the above-mentioned equality. In modelling LFEs' behaviour, the assigned emissions
intensity target becomes αβ j , rather than β j .
(b) Targeted measures
Targeted measures involve a series of incentive measures provided to firms to reduce their
GHG emissions. These measures include, among other initiatives, the use of emissions standards
and financial incentives paid to firm to assist them in reducing their GHG emissions. The level of
GHG abatement that each industry must achieve through targeted measures is exogenous in the
model. These levels have been determined by the model TIM-E2020.
9
The modelling of targeted measures is not an easy task in an economic optimization model.18
We assume in the present analysis that targeted measures translate effectively into a reduction in
firms’ uses of energy and, consequently, they contribute to reducing GHG emissions. However, a
significant difference between targeted measures and a permit trading system is the lack of direct
financial penalties associated with abatement efforts.19 We model targeted measures, on the one
hand, by imposing a penalty for the use of energy and, on the other hand, by returning the
proceeds from these penalties to firms as output subsidies.
The penalty is imposed on the use of the aggregate energy input rather than on each energy
input individually. The penalty captures the incentives to reduce the demand for energy. The
output incentive attempts to counterbalance the negative effect of the penalties so as to mimic
the absence of direct financial penalties experienced by firms that undertake targeted measures.
The rate of the penalty on the aggregate energy price, in a given industry, is determined
endogenously such that total proceeds equals the projected government spending for targeted
measures. Similarly, the level of the production incentive is endogenously determined in the
model.
Moreover, we assume that the fall in energy use under targeted measures translates into a
change in the emissions factors of energy inputs so that the reduction in GHG emissions are
generated in accordance with the forecasts announced by the Reference Policy Package. Finally,
the impact of spending on targeted measures on government account is captured in the model
through goods and services expenditures in an amount equal to those projected.
(c) Intertemporal optimization problem
The manager’s objective is to maximize firm’s value, which is equal to the discounted sum
of net cash flows, subject to capital accumulation constraint in the presence of adjustment costs.
We assume that the representative firm in each industry has reached a level of maturity that
enables it to finance investment expenditures through retained earnings. In other words,
dividends paid to households are net of investment expenditures. Firms pay a tax on profits. In
the short run, physical capital is immobile among sectors because of capital installation costs. At
the beginning of each period, the capital stock is predetermined by the previous period’s
investment decision; its reallocation among sectors is achieved only in the long run through
For example, one targeted measure suggests strengthening enforcement of speed limits by motorists. The resultant
fuel savings will translate into emissions reductions in the transportation sector.
19 We abstract from indirect penalties incurred by firms that must comply with new energy efficiency standards.
18
10
accumulation. Following Hayashi (1982), we consider a convex adjustment cost function, which
is linear homogeneous in both of its arguments, i.e., investment and capital stock.
In maximizing firm’s value, managers determine the optimum paths for investment,
labour, energy inputs and other intermediate inputs. Apart from investment decisions, the
first-order conditions of the firm’s intertemporal optimization problem are the standard ones
encountered in static optimization problems. Firms use the production factor up to the point
where its marginal product equals its price. The optimum level of investment is determined so as
to equalize the marginal cost of investment to the shadow price of capital, i.e., the marginal
benefit (evaluated in terms of change in firms’ value) of changing the capital stock by a unit. The
firm’s marginal cost of investment includes not only the purchase price of capital goods, but also
the additional capital installation costs that the firm must incur. The marginal benefit of the
investment takes into account the marginal impact of investment on profits of the current and
future periods. Thus, this marginal benefit is the discounted sum of present and future marginal
gain of physical capital. This marginal gain is the sum of the marginal product and the gain
associated with the reduction in installation costs linked to the increase in the capital stock.
It appears that firms’ investment decisions can be affected through two main channels:
the purchase price of capital goods and the marginal product of capital that depends mostly on
the producer price received by the firm. On the one hand, an increase in the purchase price of
capital goods has a negative impact on investment demand. On the other hand, an increase in the
producer price (net of taxes and incentives) has a positive impact on investment.
(d) Labour market functioning
As mentioned earlier, we consider two alternative specifications for the labour market
functioning: flexible wages and rigid wages. When wages are flexible, the wage rate paid by firms
is identical to the opportunity cost of leisure used by the representative household in its labour
supply decisions. This wage rate adjusts in each period to clear the market.
In contrast, in the presence of wage rigidity, the wage rate paid by firms is not the
identical to household opportunity cost of leisure. As the level of employment in the economy is
always determined by firms, the latter are always on their labour demand curve. This model
assumes that the rigidity observed is the outcome of the wage setting that is characterized by a
bargaining process between firms and unions. Several recent studies on the wage setting process
in several industrialized countries, including Canada, such as Dalen and al. (2003), Bowitz and
Cappelen (2001), Budd (1996) and Gu and Kuhn (1998), tend to support the idea of wage setting
through bargaining. We assume that unions derive a premium through their bargaining power
11
that introduces a gap between the wage paid by firms and households’ opportunity cost of leisure.
We follow Gali (1996) by introducing a margin λt (with λt ≥ 1 ) between the wage paid by firms
and the representative household’s opportunity cost of leisure. This gap can lead to rationing,
which could translate to involuntary unemployment. In addition, with rigidity, we assume that the
wage rate paid by firms is linear homogeneous in households’ labour income tax rate. In other
words, any increase in the tax rate is entirely absorbed by the wage rate paid by firms as a result of
the wage setting process.
2.4 – The government
The government’s behaviour is simple. It collects taxes on goods and services, and on
household and corporate incomes. It consumes goods and services (fixed in real per efficiency
units), enacts transfers to households and finances expenditures related to targeted measures and
the development of offsets.
Offsets are emissions reduction activities20 that generate domestic emissions credits, which
are fully substitutable (in Canada) with international permits. These emissions credits can be
purchased by firms involved in the OPTS. According to projections used in the Reference Policy
Package, 20 MT of offsets credits could be developed. Since these credits are substitutes for
foreign permits, the government plans to pay the difference between the average cost (exogenous
in the model) of developing the offsets and the international price of tradable permits. Note that
the model does not take into account the resource costs required to develop offsets. They are
considered as given in the model and the proceeds of their sale is transferred to households.
The government finances the excess of its expenditures over its revenue by issuing bonds that
generate the same yield as foreign assets.21 It is subject to an intertemporal budget constraint that
rules out debt explosion. This objective is achieved by requiring the ratio of government debt to
GDP to be equal in each period to the one observed in the reference situation. The adjusting
variable could either be a lump-sum component of government expenditures or the labour
income tax rate.
2.5 – Other components of demand and Canada’s relations
with the rest of the world
20
21
These activities include, for example, renovation of household waste disposal sites to reduce GHG emissions.
Because of the assumption of perfect mobility of capital with the rest of the world.
12
Total domestic demand for each commodity is the sum of demands by households, by
firms (for intermediate uses and investment) and by the government. We assume that firms’ total
investment demand22 is a Leontief composite of several commodities. The demand for each
commodity entering this composite is a fixed share (in volume) of firms’ total investment
demand. It follows that average price of the capital goods is a weighted sum of the prices of the
commodities that form the composite.
We model Canada’s trade with the rest of world by adopting the traditional assumption of
commodity differentiation, both on the demand and on the supply sides. Total domestic demand
for each product is a CES composite of the locally produced good and the aggregate of imports.
The latter is another CES function of the commodity imported from the United States and
imports from the rest of the world. An expenditure minimization rule allows the determination of
the optimal composition of the composites. In particular, the ratio between demands from two
competing origins (local and foreign) of the commodity depends on their price ratio. An increase
in the relative price of the locally produced good is detrimental to local producers.
On the supply side, we assume that output produced by each representative firm is a CET
(Constant Elasticity of Transformation) composite of domestic sales and aggregate exports.
Another CET function is used to aggregate exports to the United States with exports to the rest
of the world. A revenue-maximizing rule allows the determination of the optimal composition of
supply in each market. In this condition, the ratio between sales on the domestic market and
exports depends on the ratio of the prices obtained in these two markets. A decrease in the
relative price of domestic sales will favour exports.
In each period, the current account deficit adds to the level of foreign debt. The current
account deficit is the sum of the trade deficit, net government and household transfers abroad
and net purchases of international tradable permits.
2.6 – Equilibrium and steady state conditions
The absence of non-convexities in household preferences and in firm technology
guarantees the existence of a competitive intertemporal equilibrium with rational expectations for
the economy represented in this model. This equilibrium consists of a sequence of prices and
quantities and stock variables such as:
22
Including installation costs.
13
a. Households and firms satisfy the optimal conditions arising from the maximization of
their objective functions;
b. All agents respect their budget constraints;
c. Transversality conditions are met for the stocks of physical capital, for household total
wealth and for the levels of government and foreign debts;
d. A temporary equilibrium is achieved in each period in all good and factor markets.23
In this neo-classical model, the steady state of the economy is defined as a state in which
all flow and stock variables, expressed in efficient labour units, are constant. In other words, nondetrended variables increase at the exogenous growth rate of the population increased by
Harrod-neutral type technological progress. The imposed transversality conditions in addition to
the selected functional forms guarantee the existence of this steady state.
3 – Data, calibration and numerical solution strategy
Given the complexity of the model, no effort was made to find an analytical solution to
the system of highly non-linear equations that results from firms and households’ behaviours.
Instead, we opted for a numerical solution. The model was calibrated on a horizon of 150 periods
(years) beginning in 2003, using data projected from the social accounting matrix (SAM) and the
sectoral emissions table built for the year 2010.24 Since the observed total labour supply, including
technological progress, has been standardized to one, all variables expressed per efficient unit are
constant over the entire simulation horizon in the reference situation, where the assumption of
the steady state was adopted. In addition, for simplicity, we assume that involuntary
unemployment is zero in the reference situation.25 The projected 2010 SAM was built using data
from the 2000 national accounts, the detailed structure of the 1996 input-output table and an
average GDP growth rate of 2.3% between 2000 and 2010. The value of 2.3% was selected as the
growth rate of the population including technological progress. This value is the implicit growth
rate of GDP compatible with forecasts of GHG emissions in 2010 in the AMG report (1999).26
Table 1 presents the structure of the projected SAM for 2010 for the Canadian economy.
The detailed sectoral emissions table by fuel type was built using sectoral emissions data
produced by Statistics Canada and emissions forecasts contained in the AMG (1999). It was
possible, using the SAM data and the emissions table, to calculate emissions factors for different
With wage rigidity, disequilibrium can be observed in the labour market.
2010 is the median year for the first commitment period 2008-2012.
25 Including the real value of the involuntary unemployment rate will only affect the calibrated value of total labour
supply in the reference situation.
26 The growth rate takes into account energy efficiency as well as higher levels of emissions in some industries
(energy) so as to reflect higher sectoral anticipated growth.
23
24
14
fossil fuels and the emissions intensities by industry. The emissions intensity targets assigned to
LFEs under the permit system were calculated using the percentage deviations from the reference
situation obtained from the Tradable Permits Working Group (see Table 2).27
Table 3 presents the values used for various behavioural parameters. These values, which
were borrowed from previous studies on Canada, such as Ab Iorwerth et al. (2000) and Wigle
(2001), are not very different from the values used in many other general equilibrium models of
Canada or the United States. Parameter, λt , which captures the premium of wage rate paid to
workers over the opportunity cost of leisure, has been set at a very conservative value of 0.8%.
Calibration of the model involves using the SAM, parameters borrowed from other
studies, the first-order conditions, and the steady state conditions to recover the other parameters
in the behavioural functions and the values of non-observed variables in the model in order to
reproduce the reference situation. To this end, we used the calibration procedures frequently used
in static and dynamic general equilibrium models. Dissou (2002), Keuschnigg and Kohler (1994),
and Mansur and Whalley (1984) provide details on the calibration methods of these models.
The numerical solution of a general equilibrium model with an infinite horizon requires
truncating the simulation horizon in order to have a finite number of periods. The general rule is
to select a sufficiently large number of periods to allow the economy to achieve a new steady state
after a shock, while imposing terminal conditions associated with the steady state so as to
minimize errors due to the truncation. The selected 150-year simulation horizon is large enough
for this model. The model was solved numerically by treating it as a “two-point boundary
problem” in which the initial conditions are set for the state variables and the terminal conditions
are imposed on the jumping variables. We used the “Extended Path” method suggested by
Gagnon (1990) to solve the non-linear system of equations that contains difference equations.
4 - Simulations
This section discusses the Reference Policy Package and the simulations performed to
analyse the potential impacts of a GHG abatement policy in Canada.
4.1 – Presentation of the Reference Policy Package
The GHG abatement plan, referred to as the Reference Policy Package, was unveiled in
September 2002 by the Analysis and Modelling Group (AMG). It can be viewed as a variation of
The Tradable Permit Working Group analysed various implementation options for the permit trading system in
Canada. This group was created as part of the work of the Analysis and Modelling Group (AMG). The AMG
includes analysts from Canada’s federal, provincial and territorial governments. Its mission includes among others
reflection on analytical priorities associated with climate change.
27
15
Option 4 presented in the Discussion Paper on Climate Change released by the Government of
Canada in May 2002.28 The Reference Policy Package combines two major instruments – the
permit trading system and the targeted measures. It also suggests that the government purchases
international permits.
As mentioned earlier, the Reference Policy Package analysed in this paper, while different
from the climate change plan released in November 2002, is similar. Therefore, the qualitative
lessons learned from analysing the Reference Policy Package will provide an indication of the
potential impacts of the Climate Change Plan for Canada.
Under the Kyoto Protocol, Canada must reduce its GHG emissions to 6% below the
1990 level, or to 571 MT. This corresponds to a gap of 240 MT in 2010 compared to the
anticipated level, if no specific measures were taken to alter the emissions path. Table 4 presents
the emissions abatement targets assigned to each instrument to eliminate the 240 MT gap. Under
the Reference Policy Package, Canada could claim emissions credits that would reduce the gap to
140 MT. First, if we assume that Canada obtains 70 MT credits for the export of cleaner energy,
the emissions gap falls to 170 MT. Second, the development of carbon sinks (such as forest),
which by definition are natural CO2 absorbers, will generate 30 MT of emissions credits. This
brings the gap to 140 MT.
LFEs are required to reduce their emissions by 48 MT using the output-based permit
trading system (OPTS), as described above. Targeted measures will make it possible to obtain a
further 82 MT reduction in GHG. These measures contain initiatives contributing to the
reduction of GHG, which will be carried out throughout the economy, both by LFEs and by
other industries. Table 5 presents the sectoral shares of emissions covered by the permit system.
Finally, the Reference Policy Package provides for the purchase of 10 MT of international
permits to close the 240 MT gap in 2010. It should be noted that by asking LFEs to reduce their
emissions by 48 MT using the OPTS, the number of free permits they will receive has implicitly
been set. These industries will have to buy additional international permits, should their
abatement efforts prove inadequate to meet their emissions targets. Total permit purchases by the
government and by the LFEs will be met through the 20 MT of domestic offsets credits and, if
necessary, by additional purchases on the international market.
4.2 - Description of the simulations
28
See Government of Canada (2002a).
16
We run three simulations to analyse the potential impact of the GHG abatement policy in
Canada by using two main policy instruments in the Package, the permit trading system and the
targeted measures. The three simulations adopt the “Kyoto forever” assumption in which the
total emissions target set by the Kyoto Protocol remains constant at the same level, i.e., 571 MT
of CO2 eq.
In the first simulation that has two variants (scenarios) we analyse the impacts of the
GHG mitigation policy using a permit price of CDN$10 per tonne of CO2 eq. and financing its
fiscal incidences by a change in the government’s fiscal balance (debt financing). Assumptions
about permit prices and the financing methods of the fiscal incidence are drawn from TIME2020.29 The fiscal incidence accounts for both new spending on targeted measures and permit
purchase, and changes in fiscal revenue as a result of change in GDP. The two variants in this
first simulation differ by the assumptions made on the functioning of the labour market. The first
variant considers wage rigidity, while the second examines the case of wage flexibility.
The permit price at $10, expressed in 2000 constant Canadian dollars, remains constant
over the entire simulation horizon beginning in 2008.30 In reality, the debt financing method of
the fiscal impact of the abatement policy is not sustainable in the long run, if the government
were to respect its intertemporal solvency constraint. We maintained the debt financing method
only until 2012, which is the last year of the first commitment period. As of 2013, the
government is required to bring its debt-to-GDP ratio to the value in the reference situation by
changing its lump-sum transfers to households.
In the two remaining simulations, we analyse the impacts of an increase in the price of
permits and of the use of an alternative closure rule for the government account. For the reasons
of brevity, these two simulations are performed with the assumption of wage rigidity only, even
though this choice does not presume any particular preference for this option over the other
relative to the functioning of the labour market.
The second simulation analyses the impact of an increase in the price of permits to $50,
with debt financing, and with wage rigidity. The third simulation examines the impact of an
alternative closure rule for the government account with permits at $10, and with wage rigidity. In
this latter simulation, rather than absorbing the fiscal incidence through a change in debt level,
29
See Government of Canada (2002b).
This assumption is slightly different from that in TIM-E2020 model in which the real price of the permit increases
by 5% per year.
30
17
the government levies a new tax on household labour income to keep its budget balance fixed
each year.31 The model determines endogenously the level of labour income tax that varies in
each period over the entire horizon of the simulation.
4.3 – Discussion of simulation results
Unless otherwise mentioned, all the results presented in this paper are expressed as a
percentage deviation from the reference situation.
4.3.1 – Permit at $10, with debt financing, and with/without wage rigidity
Compared to the reference situation, the main changes in this simulation result from: (a) the
need to hold permits for emissions covered by the OPTS; and (b) the implementation of targeted
measures to reduce other types of emissions. Firms’ abatement efforts, as well as the increase in
production cost associated with the purchase of permits, affect economic activity.
(a) Aggregate impacts
Tables 6 and 7 present the aggregate impacts of the GHG abatement policy in 2010 with and
without wage rigidity.
Wage rigidity
In the presence of wage rigidity, real GDP at market price and real GDP at factor cost
decline, respectively, by 0.69% and 0.67% in 2010. Total level of employment falls by 0.54% and
the real exchange rate depreciates by 0.15%. GHG emissions reductions attributable to both
targeted measures and the permit trading system amount to 120 MT. LFEs purchase ten Mt of
permits.
While the impact on GDP at market price in 2010 is relatively small, various components of
the final demand are nevertheless diversely affected. Real household consumption and real
investment fall more, both by 1.16%, while real exports and imports fall only 0.08% and 0.63%
respectively. The more pronounced negative impact on household consumption and investment
compared to other GDP components is not surprising. Since households and firms have
forward-looking behaviour, consumption and investment do not depend solely on contemporary
variables; they are also affected by the state of the economy in future periods.
Given the assumption of “Kyoto forever”, the emissions gap increases over time. This
growing gap puts upward pressure on the purchase of international permits and has a negative
31
Compared to the value in the reference situation.
18
impact on the current account balance. It follows that foreign debt increases as well. Since
households are liable for this debt, their financial wealth deteriorates, thereby justifying a
relatively sharp decline in their aggregate consumption in comparison to GDP. The intertemporal
equilibrium of the balance of payments requires an increase in net exports, which is achieved
through real exchange rate depreciation.
A key feature of the Reference Policy Package is the importance given to firms’ abatement
efforts. The market instrument suggested in the Reference Policy Package (tradable permits) does
not affect directly household behaviour. Referring to Table 7, the tradable permit system enables
LFEs to reduce their emissions by 38 MT, compared to the 48 MT set out in the Reference
Policy Package. Targeted measures reduce emissions by 82 MT as planned. In addition to
abatement arising from targeted measures, households reduce their emissions by 0.4 MT.
Taking into account the 30 MT of emissions credits for carbon sinks, total GHG abatement
amounts to 151 MT. This represents 89% of the emissions gap of 170 MT in 2010.32 To achieve
their 48 MT reduction target, LFEs purchase ten MT of permits. The remainder of the emissions
gap is covered by the government, which purchases nine MT of emissions credits. The 19 MT of
permits purchased by LFEs and the government are fully covered by the 20 MT of emissions
credits generated by the offsets. It follows that Canada would be in a position to sell one MT of
emissions credits on the international market in 2010. Unfortunately, over the long term, with the
increase in the emissions gap, Canada becomes a net purchaser of permits on the international
market.
Flexible wages
The characteristics of the aggregate results obtained without wage rigidity are generally the
same as those obtained in the presence of rigidity. The main difference lies in the size of the
change in GDP and its components. In this case, the decline in GDP is only 0.06% in 2010
compared with 0.69% previously. As in the case with wage rigidity, real consumption is more
affected than GDP; real net exports are relatively less affected for the same reasons explained
earlier.
Because of the full flexibility of wages, labour was reallocated throughout the economy. The
least affected sectors by the GHG abatement policy were able to take advantage of the resources
freed up by others, thereby reducing the aggregate negative impact on GDP and its various
components.
The emissions gap is normally 240 MT; 170 MT accounts for the 70 MT credits for cleaner energy exports that
Canada expected to receive.
32
19
These aggregate results, which summarize only the impact of the policy on the Canadian
economy as a whole, do not reflect the nature of the adjustments observed in various industries.
We discuss below the sectoral impacts that generated these results by providing intuitive
explanations and highlighting the main transmission mechanisms at play.
(b) Sectoral impacts
The policy instruments proposed in the Reference Policy Package to reduce GHG emissions
primarily affect firms’ behaviour. We will therefore focus our discussions on adjustments in the
production sector, and then briefly highlight how shocks are transmitted to the rest of the
economy.
Tables 8 and 9 present the sectoral impacts of the policy on output, value added,
employment, investment and derived demand for energy inputs. Figures presented in these tables
suggest that industries are variously affected. In general, energy-producing industries are the
hardest hit, followed by highly energy-intensive and non-energy producing industries. The least
affected industries are the other low energy-intensive and non-energy producing industries. For
example, in the presence wage rigidity, although value added falls in coal, oil and natural gas,
electricity and cement industries, by, respectively, 4.5%, 2.1%, 2.4% and 1.0% in 2010, it declines
only by 0.1%, in the agriculture sector. There is even an increase in sectoral GDP in the ‘other
manufacturing’ industry in the simulation with wage rigidity. This latter industry benefits from the
demand recomposition as a result of the loss of interest of economic agents in polluting products.
In addition, the depreciation in the real exchange rate favours this industry’s exports.
The decline in output observed in some industries is the consequence of firms’ abatement
efforts undertaken through the permit trading system and targeted measures. Table 10 shows the
sectoral changes in total GHG emissions and GHG emissions covered by the OPTS.33 It is
interesting to note that GHG abatement efforts are similar, with and without wage rigidity. Crude
oil & natural gas industry, along with iron and steel, pipelines and electricity generation industries,
are the ones that achieve the greatest abatement efforts. Compared to the reference situation,
these industries reduce emissions admissible in the permit system by between 11% and 26%. The
other LFEs achieve lower abatements for this type of emissions, generally below 10%.
Firms can reduce their GHG emissions through three channels: (a) by lowering output; (b) by
substituting among fossil energy inputs, and/or (c) by investing in capital. The last two channels
help firms reduce emissions intensity, while the first contributes to the reduction of their activity
level. In all industries, emissions abatement came about mostly by reducing emissions intensity,
33
The model assumes that targeted measures are effective in generating the desired emissions reductions.
20
rather than by reducing output. As shown in Table 10, the contribution of the decrease in
emissions intensity to the total reduction in emissions admissible in the trading system is greater
than the contribution of reduced level of output. For example, in the simulation with wage
rigidity, the decrease in total emissions34 is 26.2% in the crude oil and natural gas industry, while
the contribution of the decrease in emissions intensities is 24.5%.
The dominance of the contribution of the decrease in emissions intensity is not surprising
given the nature of the permit system put in place. As explained in the section describing the
model, the permit system encourages firms not to reduce their output in order to receive a larger
number of free permits. Since free permits are attributed based on the current period output and
the assigned emissions intensity target, firms have a greater incentive to use the emissions
intensity reduction channel than to use the other channel.
Besides, the requirement to hold costly permits increases the cost of using fossil fuel inputs,
which generate GHG. Changes in relative prices induce firms to make substitution among inputs
to minimize production cost. In the case of fossil fuel inputs, firms substitute the least polluting
input for the most polluting one.
Referring to figures in Table 9 for both variants, the decline in demand for coal is larger than
that of the other fossil fuel products in many industries. At another level of the technological
structure, firms substitute fossil energy for electricity, which has become relatively less expensive,
although its production cost has also increased. In some industries, such as paper and cement
industries, an increase in the demand for electricity is observed, while the demand for fossil
energy decreases. In other industries, such as crude oil and natural gas, a smaller decrease in the
demand for electricity in comparison to that of fossil energy is observed.
Note that GHG abatement efforts through targeted measures result in an increase (although
slight) in production cost, even though the government paid for expenditures on targeted
measures. The rationale behind this increase is relatively simple. We assume that firms are
rational, i.e., they always choose the least costly combination of inputs. At a given vector of input
and output prices, no other combination of inputs could be cheaper than the one chosen initially.
It follows that with targeted measures paid by the government, the firm’s profit with the new
combination of factors could only be lower or at best equal to the previous level. In other words,
if the combination of factors initially used was the one generating the maximum profit, the new
combination imposed by the new regulation cannot be more profitable. In these circumstances,
implementing targeted measures will result in an increase (although slight) in the production cost.
34
Eligible in the OPTS.
21
The increase in the production cost that results from the permit trading system and the
implementation of targeted measures induces an upward shift of the supply curve. Still, a
downward movement generated by the production incentive, linked to the permit allocation
system, offsets this upward shift. As explained earlier, the permit allocation system encourages
firms not to reduce their output. The importance of this incentive varies from one industry to
another and depends on the permit price and on the assigned emissions intensity target.
In the presence of wage rigidity, the production incentive is not large enough in most
industries to neutralize the negative impact of the increase in production cost on the demand for
their products35. Output decreases slightly in all industries, except in the steel industry and ‘other
manufacturing’ industry, where supply increases by 0.12% in 2010. In these two industries, the
impact of the output incentive on supply outweighs that of the increase in production cost that
has been contained, thanks to a significant change in emissions intensity.
Indeed, because these two industries were able to reduce significantly their emissions
intensity, they lessened the impact of the permit price on production cost. In addition, these two
industries are the ones that benefited the most from the depreciation in the real exchange rate
required to maintain the intertemporal equilibrium of the balance of payments. However, we
must point out that the increased output experienced by the steel industry did not translate into
an increase in value added, which decreased by 0.96%.
In the presence of wage rigidity, the reallocation of labour among industries is imperfect. The
downward-rigidity of the wage rate prevents the labour market to clear. The employment level
falls in most industries and increases only slightly in a limited few.
In contrast, in the second variant with flexible wages, the perfect reallocation of labour
among industries benefits other industries, such as the pulp and paper, chemical products and
electricity generation industries. They were able to take advantage of the decline in real wages to
increase their output, which had fallen under wage rigidity. In particular, electricity generation
industry benefited from the increase in domestic demand for its product following the expansion
of many other industries.
The depreciation in the real exchange rate, which makes imports less competitive, is
beneficial to Canadian firms; it helps reduce the negative impact of GHG abatement efforts. In
the two variants, when total domestic demand for a product declines, imports fall more sharply
than their local counterparts in many industries (Table 11).
Without this production incentive, the cost increase resulting from abatement efforts produces in a sharper decline
in firm supply. In unreported simulations, the negative impact on firms is even greater in the case where free permits
would be allocated to firms without any reference to their current output. The decline in GDP would have been
0.92% in 2010 compared to 0.69% in the present case.
35
22
In the two variants of the simulation, the negative impact on the purchase prices of
commodities is not significant36. In particular, the impact is relatively small for energy products in
2010 (Table 12). The purchase prices of energy products decrease despite increase in their
production cost because of the more pronounced decline in their demand.37 Only the prices of
gasoline and diesel increase about 1.5% in the two variants. These latter two products are used
more for transportation activities whose price elasticities are low.
(c) Regional impacts
This section discusses the potential impact of the reference policy package on regional GDP
at factor cost. It is noteworthy to mention that the regional results presented here do not come
directly from the model, which does not have any regional dimension. We follow the
methodology suggested in Peluso (2000) to compute the potential impacts of the policy on GDP
at the factor cost in each region of Canada using (a) the impacts on sectoral GDP provided by
the national model, and (b) the fixed shares of the regional distribution of Canadian sectoral
GDP. Besides, the national model distinguishes only a single aggregate electricity generation
industry. Adjustments were made in the computation of the regional GDP impacts to reflect the
geographic distribution of various modes of electricity production (from hydroelectric power,
coal, natural gas, etc.).38
Table 14 presents the regional impacts of the GHG emissions abatement policy on GDP for
various regions of Canada. As could be expected, the most affected regions are those with the
highest shares of energy-producing and energy-intensive industries. Alberta is slightly harder hit
than the others, with a decline of 1.22% in GDP at factor cost in 2010, followed by Saskatchewan
in the scenario with wage rigidity. Quebec is the least affected province; its GDP falls only by
0.24%. The low impact on GDP in Quebec is explained by the mode of electricity generation in
this province. The abundance of hydroelectricity reduces the negative impact of abatement
efforts on the cost of energy in this region.
It should be noted that, while certain provinces are more affected than others are, the burden
of abatement efforts is generally distributed throughout the country. The system of allocating free
permits, which encourages firms to reduce their emissions intensities rather than their output, is
largely responsible for the regional distribution of GDP impacts obtained.
36
Given the product differentiation assumption on the demand side, the purchase price is an average of the prices of
the imported and the locally produced commodity.
37 This is a change in relative prices that does not account for inflation.
38
See Peluso (2000) for further information on the method of calculating regional impacts.
23
4.3.2 – Other simulations
To provide a better understanding of the results of the model, we carried out two other
simulations relating to (a) the price of permits and (b) to an alternative closure rule of the
government account. The main components of the GHG abatement policy remain the same as in
the two variants of the simulation discussed earlier. The transmission mechanisms in these new
simulations are almost identical to those illustrated above. The discussions will focus on
differences from the previous simulation in order to avoid repetition.
(a) Simulation 2: impact of a higher permit price with debt financing and with wage
rigidity
The only difference between this simulation and the variant with rigidity analysed above is the
permit price, which is now $50 per tonne of CO2 eq. The fiscal incidence of the policy is still
financed by an increase in government debt until the end of the first commitment period (2012).
The $50 price was chosen to illustrate the impacts of a higher international permit price39
An increase in the price of tradable permits has two opposite effects. On the one hand, a
higher permit price increases the penalty on emissions. On the other hand, the increase in the
permit price raises the production incentive provided to firms through the permit allocation
system. Raising the penalty on the use of fossil fuels increases production cost and has a negative
impact on output. However, increasing the production incentive has a positive impact on output
because firms have higher incentives not to reduce their output. Instead, they are induced to
reduce their emissions intensities. However, increased efforts to reduce emissions intensities are
not beneficial to fossil energy producers, who experience a sharper decline in demand for their
products.
Thus, with a higher permit price, the positive impact of the production incentive may
outweigh, in the short term, the negative impact of the increase in abatement costs. Output of
some LFEs could then be less affected in comparison with a lower permit price. In contrast,
energy producers experience a sharper decline in their output, because of a larger decrease in the
demand for their products.
Table 13 presents the aggregate impacts in 2010 with international permits at $50. The
negative impact on GDP is less than that with permits at $10. The result that is due to a
composition effect can easily be understood in light of explanations provided above and the
sectoral impacts that we do not present for space reasons. The lower negative impact on GDP of
According to a group of international experts mandated by the AMG, the upper bound of the international permit
prices is CDN$50 per tonne of CO2 eq. See Government of Canada (2002a).
39
24
a $50, compared to $10, is due to two non-energy producing industries (paper and ‘other
manufacturing’). These two industries were able to take greater advantage of the higher
production incentive. They experience an increase in their sectoral GDP, while all the other LFEs
suffer from a decrease, in comparison with the simulation with the permit price at $10.
Unfortunately, because of the growing nature of the emissions gap,40 which reduces the
relative size of the production incentive, the increase in some LFE output in 2010 is only
temporary; it disappears in the longer term. This is shown in 2030, for example, where the decline
in the GDP is more important at $50 that the one observed at $10 (Table 13).
Finally, note that, while GDP is less affected in 2010 with the $50 permit than with the $10
permit, this is not the case for either consumption or investment (see Table 13). At $50,
household aggregate consumption is more affected than in the case with the permit price at $10,
because of the more pronounced negative impact on permanent income. The same result is
observed with the total real investment of firms, which have forward-looking behaviour.
(b) Simulation 3: permit price at $10 with financing by a new tax and with wage rigidity
The main difference between this simulation and the earlier one with permit price at $10 with
wage rigidity pertains to the closure rule of government account. Unlike previous simulations, in
which changes in lump sum transfers to households is used to achieve the government’s
intertemporal budget balance, a new tax on household labour income is considered in this
simulation. In addition, this tax is collected in all periods to keep the government balance
constant at its reference situation value. As mentioned earlier, due to the wage setting, the new
tax is fully absorbed by the wage rate paid by firms.
In contrast to previous simulations, the new tax introduces further distortion to resource
allocation. Table 13 presents aggregate impacts in 2010. Real GDP at market price falls 0.90%
compared to 0.69% in the first variant of Simulation 1. Similarly, household aggregate
consumption is also more significantly affected in 2010.
Since the level of employment is determined by firms, the new tax on labour income has a
direct impact on labour demand and, as a consequence, on production cost. Sectoral output is
more affected and the labour income paid to households falls more sharply. As the emissions gap
increases over time, the distortion introduced by the labour income tax is more important.
Household permanent income falls further; this leads to a larger decline in aggregate
consumption accompanied with larger depreciation of the real exchange rate. Finally, net exports
40
Given the “Kyoto forever” assumption, which maintains constant emissions targets, the growth in emissions in
the reference situation results in an increase in the emissions gap over time.
25
are less affected in comparison with the simulation using changes in lump-sum transfers to
households.
5 - Conclusions
We have presented an intertemporal and multi-sector general equilibrium model in order to
analyse the potential impact of implementing the Reference Policy Package to reduce GHG
emissions in Canada. We have analysed the short- and long-run potential impacts on various
aggregate and sectoral variables. An especially attractive feature of this package is the use of an
output-based permit trading system.
Simulation results suggest that, while the real GDP impact of the Reference Policy Package is
modest, in particular without wage rigidity, the negative impact on household aggregate
consumption is slightly more significant. An increase in the number of international permits
purchased as a result from a growing emissions gap over time, is not favourable to households,
which experience a drop in their wealth. The analysis of the Reference Policy Package revealed
that the suggested permit system, with an output-based allocation of free permits, reduces
significantly the impact of the abatement policy on Canadian firms’ output and competitiveness.
The system provides strong incentives to firms not to reduce output as a means of reducing
emissions. Rather, it encourages them to reduce their emissions intensities. To some extent, this
system contributes to reducing the sectoral and regional disparities of the national burden of
GHG abatement.
We conclude by pointing out that the Reference Policy Package that we analysed only
provides a limited indication of the potential impacts of GHG emissions abatement policy in
Canada. The analysis does not include several factors that could affect the size of the economic
impact. For example, this study took for granted the 70 MT credits for cleaner energy exports, as
suggested in the Reference Policy Package. Besides, it does not consider co-benefits associated
with GHG abatement and technological advances that could reduce emissions.
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Loulou, Richard and Amit Kanudia (1999): “The Kyoto Protocol, Inter-provincial Cooperation,
and Energy Trading: A Systems Analysis with Integrated MARKAL Models’’ Energy
Studies Review, 9, 1, pp. 1-23.
Mansur, A. H. and J. Whalley (1984): “Numerical Specification of Applied General Equilibrium
Models: Estimation, Calibration, and Data” in Applied General Equilibrium Analysis, 1984,
pp. 69-127, Cambridge, New York and Sydney: Cambridge University Press.
Peluso, T. (2000): Obtaining Provincial Results from National Estimates of the Economic Impact
of Greenhouse Gas-Reduction Policies: A Progress Report, mimeo, ESPAD, Finance of
Canada.
27
Sterner, T. and L. Höglund (2000): “Output-Based Refunding of Emission Payments: Theory,
Distribution of Costs, and International Experience”, Resources for the Future Discussion
Paper 00/29.
Wigle, R. (200l): "Sectoral Impact of Kyoto Compliance," Industry Canada Research Publications
Program, WP 34.
28
Table 1: Characteristics of the projected social accounting matrix for Canada for 2010
Industries
Agriculture
Mining
Coal
Crude Oil and Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Refineries
Gas Pipelines
Electricity
Transport Industry
Services
Non-competitive Imports
Total
Household
GDP at
factor cost consumption
(shares %)
(shares %)
Exports
(shares %)
Imports Exports as %
(shares %)
of output
Domestic
Domestic Imports as %
total goods as %
sales as %
of total
domestic
of output
domestic
demand
demand
2.7
1.4
0.1
3.6
2.6
0.1
0.5
0.5
1.7
13.4
0.1
0.9
2.6
3.1
66.6
0.0
1.4
0.0
0.0
0.3
1.1
0.0
0.0
0.0
1.4
19.8
1.4
0.6
2.3
2.2
69.2
0.2
3.2
2.4
0.4
6.5
7.1
0.1
1.1
4.4
3.8
52.8
1.4
0.7
0.4
3.0
12.8
0.0
1.3
1.0
0.3
2.3
2.9
0.0
1.5
2.1
6.4
64.7
1.0
0.2
0.1
2.2
12.5
1.5
19.9
41.3
68.8
59.9
49.6
32.2
29.4
76.9
37.1
55.0
20.0
25.1
5.0
20.4
4.9
-
80.1
58.7
31.2
40.1
50.4
67.8
70.6
23.1
62.9
45.0
80.0
74.9
95.0
79.6
95.1
-
8.5
20.8
58.3
32.5
26.5
8.7
33.5
59.2
47.4
57.4
14.5
7.2
0.6
14.4
4.3
100.0
91.5
79.2
41.7
67.5
73.5
91.3
66.5
40.8
52.6
42.6
85.5
92.8
99.4
85.6
95.7
0.0
100.0
100.0
100.0
100.0
-
-
-
-
Source: Statistics Canada, data from various sources and authors' calculations
-: not relevant
29
Table 2: Assigned emissions intensity targets to LIE as % change
from emissions intensities in the reference situation*
Large Industrial Emitters
Percentage change
from reference
situation
Mining
Crude Oil and Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Refineries
Gas Pipelines
Electricity
Average Large industrial Emitters (LIE)
-6.8
-4.5
-10.8
-3.5
-7.7
-5.7
-2.7
-3.6
-8.3
-7.5
7.2
7.0
Source: Domestic Emissions Trading Working Group (DETWG) and authors'
calculations
* The target values are the working hypotheses of the DETWG
Tableau 3: Values of some literature-searched behavioural parameters
Parameters
Substitution elasticity between value added-energy
and intermediate inputs
Substitution elasticity between labour & capitalenergy
Substitution elasticity between capital & energy
Substitution elasticity between electricity & fossil
energy
Substitution elasticity between stationary fossil fuels
Substitution elasticity between other intermediate
inputs & mobile fossil fuels
Substitution elasticity among mobile fossil fuels
Capital adjustment cost parameter
Rate of capital depreciation
Population growth rate including Harrod-neutral
technological progress( %)
Substitution elasticity between imports and domestic
goods
Substitution elasticity between exports and domestic
goods
Substitution elasticity among same industry
products*
World interest rate (%)
Values
0.2-0.7
1.0
0.25-0.8
0.5-0.7
0.4-0.7
0.2-0.8
1.0
3.0
0.06
2.3
0.75-2.5
2.0
2.0
6
Sources: Various studies
* For multi-products industries
30
Table 4: Emissions reduction targets in 2010 (Mt of CO2 eq.)
Sectors and instruments
Reference Policy Package*
Decomposition
(Mt)
Large Final Emitters (LFE)
permit trading
targeted measures
Other economic agents
permit trading
targeted measures
Government purchase of permits
Emissions credits
sinks
cleaner energy exports
Reduction
targets (Mt)
94
48
46
37
0
37
10
100
30
70
Total
240
Source: Analysis Modelling Group (AMG)
* This distribution of emissions reduction targets is in conformity with the earlier versions of the
reference policy package released by the AMG. Still, it is slightly different from the one presented in
Government of Canada (2002a).
Table 5: Some characteristics of sectoral emissions in 2010
Shares in total
industrial
emissions (%) *
Agriculture
Mining
Coal
Crude Oil and Natural Gas
Refineries
Gas Pipelines
Electricity
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Transport industry***
Services
14
1
1
15
5
5
19
2
2
3
3
5
4
10
13
Shares of
industrial
emissions
covered by the
permit trading
system**
0
100
0
69
100
71
81
100
88
100
98
64
15
0
0
Source: Statistics Canada, Natural Resources Canada, Domestic Emissions Trading Working Group
and authors' calculations
* Industrial emissions do not include those related to transportation activities, i.e., from mobile sources,
like gasoline, diesel.
31
Table 6: Aggregate impacts in 2010 from simulations with permits at $10, with debt financing and
with/without wage rigidity
Percentage deviation from reference situation
GDP at market prices
GDP at factor cost
Employment
Household aggregate real consumption
Total real investment
Total real exports
Total real imports
Real exchange rate*
Rigid wage
Flexible wage
-0.69
-0.67
-0.54
-1.16
-1.16
-0.08
-0.63
0.15
-0.06
-0.06
0.14
-0.28
-0.19
0.43
0.09
0.14
Source: Simulation results
* A positive number means depreciation
Table: 7 Impacts on emissions in 2010 from simulations with permits at $10, with debt financing
and with/without wage rigidity
Emissions gap in Mt of CO2 eq.*
Domestic abatement as % of the gap (without offsets)
Domestic abatement in Mt CO2 eq. (without offsets)
Abatement due to permit trading (Mt)
Abatement due to targeted measures (Mt)
Other abatement by households (Mt)
Abatement due to sinks
Total permit purchase by LIEs in Mt (incl. offsets)
Total permit purchase by the government in Mt (incl. offsets)
Total permit purchase
Offsets
International permits
Rigid wage
Flexible wage
170
89
151
38
82
0.4
30
10
9
19
20
-1
170
88
149
37
82
0
30
11
10
21
20
1
Source: Simulation results
* The gap is net of 70 Mt of credits for cleaner energy exports
32
Table 8: Sectoral impacts on output, employment, and investment in 2010 from simulations with permits at $10, with
debt financing and with/without wage rigidity
(Percentage deviation from the reference situation)
Output
Industries
Agriculture
Mining
Coal
Crude Oil and Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Refineries
Gas Pipelines
Electricity
Transport Industry
Services
Rigid wage
-0.05
-0.41
-4.71
-1.81
-0.16
-0.96
0.12
-0.47
-0.38
0.12
-2.32
-2.25
-0.38
-0.49
-0.69
Value added
Flexible Rigid wage
wage
0.55
-0.01
-4.37
-1.36
0.32
-0.39
0.67
-0.26
0.11
0.73
-1.71
-1.70
0.27
0.15
-0.06
-0.04
-0.41
-4.47
-2.10
-0.31
-1.01
-0.96
-0.80
-0.49
0.12
-3.51
-2.84
-2.43
-0.43
-0.70
Employment
Flexible Rigid wage
wage
0.57
-0.01
-4.12
-1.65
0.18
-0.40
-0.39
-0.57
0.01
0.73
-2.84
-2.29
-1.77
0.21
-0.07
0.00
-0.30
-5.07
-1.45
-0.04
1.09
1.09
-0.21
-0.20
0.12
-0.33
-1.30
1.63
-0.46
-0.71
Real investment
Flexible Rigid wage Flexible wage
wage
0.69
0.19
-4.64
-0.90
0.55
1.73
1.75
0.08
0.40
0.81
0.41
-0.63
2.38
0.28
-0.03
-0.21
-1.33
-9.54
-3.20
-0.63
-2.96
-0.52
-2.60
-0.84
-0.01
-1.48
-2.67
-3.73
-0.82
-1.05
0.85
-0.45
-8.72
-2.27
0.34
-1.97
0.59
-1.80
0.13
1.09
-0.49
-1.69
-2.73
0.17
-0.22
Source: Simulation results
33
Table 9: Sectoral impacts on energy demand in 2010 from simulations with permits at $10, with debt financing and with/without wage rigidity
(Percentage deviation from the reference situation)
Total energy demand
Industries
Agriculture
Mining
Coal
Crude Oil and Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Refineries
Gas Pipelines
Electricity
Transport Industry
Services
Rigid wage Flexible wage
-0.25
-1.34
-4.16
-17.05
-1.04
-9.32
-2.98
-1.49
-1.77
0.05
-4.92
-22.28
-9.76
-0.61
-0.89
0.26
-0.98
-3.87
-16.67
-0.61
-8.86
-2.49
-1.32
-1.32
0.58
-4.32
-21.85
-9.26
-0.07
-0.40
Demand for electricity
Rigid wage Flexible wage
0.53
-0.37
-4.17
-2.66
0.97
1.64
1.57
0.11
-0.08
0.42
-1.16
-3.19
0.00
0.02
0.11
1.04
0.00
-3.87
-2.23
1.41
2.16
2.09
0.29
0.41
0.96
-0.54
-2.66
0.00
0.57
0.61
Demand for fossil energy
Rigid wage Flexible wage
-1.72
-3.21
-4.16
-32.36
-6.62
-20.51
-5.32
-6.18
-2.33
-0.62
-5.28
-32.67
-9.76
-1.12
-3.16
-1.23
-2.87
-3.88
-32.04
-6.23
-20.10
-4.83
-6.01
-1.89
-0.12
-4.68
-32.30
-9.26
-0.58
-2.69
Demand for coal
Rigid wage Flexible wage
0.00
-0.59
-4.22
0.00
-17.95
-26.93
3.60
-6.16
0.00
-4.85
-27.23
0.00
-12.96
0.00
-2.97
0.00
-0.22
-3.91
0.00
-17.58
-26.54
4.14
-5.98
0.00
-4.32
-26.76
0.00
-12.46
0.00
-2.46
Demand for natural gas
Rigid wage Flexible wage
-1.73
-2.83
-4.15
-34.41
-7.53
-12.27
-3.19
-7.17
-2.51
-0.70
-2.99
-32.94
2.76
-1.14
-3.19
-1.25
-2.49
-3.87
-34.09
-7.16
-11.84
-2.72
-7.03
-2.08
-0.20
-2.40
-32.57
3.31
-0.62
-2.72
Demand for refined
petroleum products
Rigid wage Flexible wage
-1.69
-4.47
-4.21
-29.19
-0.06
-26.96
-30.73
-3.58
-0.82
0.10
-6.71
-11.61
3.71
-1.01
-2.96
-1.17
-4.11
-3.91
-28.85
0.39
-26.57
-30.35
-3.38
-0.33
0.65
-6.11
-11.11
4.31
-0.44
-2.44
Source: Simulation results
34
Table 10: Decomposition of changes in sectoral emissions covered by the permit trading system in 2010 from simulations with permits
at $10, with debt financing and with/without wage rigidity
(Percentage deviation from the reference situation)
Large Final Emitters
Mining
Crude Oil and Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Refineries
Gas Pipelines
Electricity
Total percentage change in emissions
covered by the permit trading system
Contribution of change in emissions
intensity
Contribution of change in output
Rigid wage
Flexible wage
Rigid wage
Flexible wage
Rigid wage
Flexible wage
-4.06
-26.21
-6.90
-8.45
-22.21
-2.21
-1.04
-0.58
-5.52
-24.10
-11.45
-3.70
-25.86
-6.49
-7.93
-21.80
-2.02
-0.57
-0.04
-4.91
-23.68
-10.94
-3.65
-23.58
-6.21
-7.60
-19.98
-1.99
-0.94
-0.52
-4.97
-21.68
-10.30
-3.69
-24.52
-6.81
-7.54
-22.47
-1.76
-0.68
-0.78
-3.22
-22.00
-11.21
-0.41
-2.63
-0.69
-0.85
-2.23
-0.22
-0.10
-0.06
-0.55
-2.42
-1.15
-0.01
-1.34
0.32
-0.39
0.67
-0.26
0.11
0.74
-1.69
-1.68
0.27
Source: Simulation results
35
Table 11: Sectoral impacts on supply, demand and international trade in 2010 from simulations with permits at $10, with debt financing and with/without wage rigidity
(Percentage deviation from the reference situation)
Total supply
Products
Agriculture
Mining
Coal
Crude Oil
Natural Gas
Pulp and paper
Cement
Iron and Steel
Non-ferrous Smelting
Chemicals
Other Manufacturing products
Gasoline
Diesel
Liquid petroleum products
Other refined petroleum products
Pipelines
Electricity
Transport
Services
Total exports
Rigid wage Flexible wage
-0.05
-0.41
-4.71
-1.52
-2.49
-0.16
-0.96
0.12
-0.47
-0.38
0.12
-1.95
-2.23
-3.26
-3.74
-2.25
-0.38
-0.49
-0.69
0.55
-0.01
-4.37
-1.08
-2.02
0.32
-0.39
0.67
-0.26
0.11
0.73
-1.27
-1.65
-2.73
-3.21
-1.70
0.27
0.15
-0.06
Domestic sales
Rigid wage
Flexible wage
0.16
-0.24
-3.51
-1.29
-1.29
0.02
-2.53
0.22
-0.53
-0.32
0.29
-2.24
-2.24
-2.24
-2.24
-1.89
0.44
-0.24
-0.43
0.71
0.11
-3.20
-0.92
-0.92
0.42
-2.03
0.68
-0.36
0.08
0.85
-1.73
-1.73
-1.73
-1.73
-1.48
1.00
0.35
0.22
Total domestic demand
Rigid wage Flexible wage
-0.11
-0.53
-7.40
-1.92
-3.87
-0.34
-0.22
0.08
-0.30
-0.41
-0.09
-1.91
-2.23
-4.10
-6.42
-2.37
-0.42
-0.55
-0.70
0.51
-0.10
-6.96
-1.35
-3.28
0.22
0.38
0.66
0.05
0.13
0.57
-1.20
-1.63
-3.55
-5.86
-1.78
0.23
0.10
-0.07
Rigid wage Flexible wage
-0.12
-0.58
-8.94
-2.07
-3.87
-0.45
-0.01
0.04
-0.15
-0.46
-0.32
-1.87
-2.27
-4.50
-9.73
-2.41
-0.43
-0.57
-0.71
0.51
-0.14
-8.45
-1.45
-3.16
0.16
0.59
0.63
0.28
0.15
0.40
-1.14
-1.64
-3.91
-9.12
-1.80
0.22
0.09
-0.08
Total imports
Rigid wage Flexible wage
-0.27
-0.82
-9.10
-2.28
0.00
-0.75
2.15
-0.03
-0.07
-0.50
-0.48
-1.58
-2.23
-5.93
-10.42
-2.86
-1.28
-0.67
-0.81
0.38
-0.31
-8.61
-1.60
0.00
-0.01
2.85
0.65
0.47
0.17
0.28
-0.66
-1.53
-5.33
-9.82
-2.08
-0.54
0.01
-0.19
Source: Simulation results
36
Table 12: Impacts on purchase prices of selected energy products
in 2010 from simulations with permits at $10, with debt financing
(Percentage deviation from the reference situation)
Energy products
Natural gas
Gasoline
Diesel
Liquid petroleum products
Other refined petroleum products
Electricity
Rigid wage lexible wage
-1.31
-1.20
1.47
1.55
1.48
1.51
-0.87
-0.84
-0.55
-0.54
-0.43
-0.38
Source: Simulation results
Tableau 13: Impacts on some aggregate variables from various simulations in 2010 and 2030
(Percentage deviation from the reference situation)
Permit price
10$
50$
10$
Financing of the fiscal incidence
Debt
Debt
Tax on labour income
Rigid wage
Rigid wage
Labour market closure rule
Rigid wage
Flexible wage
Years
2010
2030
2010
2030
2010
2030
2010
2030
GDP at market prices
GDP at factor cost
Employment
Household aggregate real consumption
Total real investment
Total real exports
Total real imports
-0.69
-0.67
-0.54
-1.16
-1.13
-0.08
-0.63
-0.85
-0.88
-0.64
-1.17
-1.12
-0.70
-0.85
-0.06
-0.06
0.14
-0.28
-0.19
0.43
0.09
-0.11
-0.16
0.08
-0.27
-0.31
0.20
-0.05
-0.55
-0.65
-0.18
-1.66
-1.23
0.96
-0.76
-1.01
-1.21
-0.48
-1.66
-1.96
-0.46
-0.76
-0.90
-0.85
-0.76
-1.84
-1.49
0.26
-0.96
-1.57
-1.59
-1.36
-1.99
-2.25
-1.54
-1.64
Source: Simulation results
37
Table 14: Impacts on regional GDP at factor cost in 2010 in simulations with
permits at $10, with debt financing and with/without wage rigidity
(Percentage deviation from the reference situation)
Labour market closure rule
Rigid wage
Flexible wage
Newfoundland
Prince-Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Colombia and Territories
-0.83
-0.67
-0.87
-0.79
-0.24
-0.69
-0.62
-0.92
-1.22
-0.65
-0.22
-0.04
-0.24
-0.17
0.37
-0.08
0.00
-0.33
-0.64
-0.04
Canada
-0.67
-0.06
Source: Simulation results
38