1. Effects of an Energy Policy in the Industrial Sector
The Australian Market Case
Selma Dogic, Meihua Huang, Mianfeng Liu, Syed N. Miah, Qinpeng Wang,
Michael S. Williams, and Yang Yang
Department of Economics, Georgia Institute of Technology
December 02, 2014
Abstract
This paper aims to depict the market for electricity in the industrial sector of Australia
and to show the impacts that a government intervention would have. Specifically, this
paper investigates the effectiveness of the Australia Clean Energy Act of 2011 by
examining the industry response in regard to the demand of electricity and the output
of goods.
Keywords: Energy market, energy intensive industries, industrial energy demand
JEL Codes: D00, D4, Q4

2. 1 Introduction
This paper aims to depict the market for energy in the industrial sector of Australia and to
show the impact that a government intervention would have. Specifically, this paper
investigates the effectiveness of Australia’s Clean Energy Act of 2011 by examining the tax
policy, as well as its repeal, and understanding industry’s response in regards to its
demand of energy and its output of goods. The results from this research will better inform
the role that governments should or should not play in markets and it will give greater
insight into the US’s Clean Air Act, a policy similar to the Australian Clean Energy Act. In
particular, the Environmental Protection Agency (EPA), following President Obama’s
directive, will set flexible carbon pollution standards, regulations or guidelines, as
appropriate, for new power plants, modified and reconstructed power plants, and existing
power plants under Section 111 of the Clean Air Act (EPA, 2014). This research will cast
new light on the costs and benefits of the performance standards and regulations
underway in the US.
1.1 Policy History and Framework
In 2011 the Australian government proposed a Clean Energy Plan that aimed to reduce
carbon emissions, encouraging energy efficiency, and increasing the use of clean energy.
The Plan was designed to reduce greenhouse gas emissions by 5% by 2020 (compared to
2000 levels) and by 80% by 2050 (Parliament, A., 2011). However, these aggressive targets
do not strictly refer to domestic reductions. It is estimated that Australia will have to rely
1

3. on purchasing permits from other countries to meet this overall target. To be more specific,
eventually 55% of Australian tax dollars are paying firms from the rest of the world to
reduce their emissions (Robson, 2013). Related legislation in the Clean Energy Act of 2011
introduced a carbon pricing mechanism in two phases.
The first phase implemented permits on emissions with no cap; in essence these permits
acted much like a carbon tax that put a price on Australia’s carbon pollution. This was
applied to Australia’s largest carbon emitters (also called liable entities). Under the carbon
pricing mechanism, an entity was liable if it was responsible for one or more facilities that
emitted a covered scope of one emission of 25,000 tons of carbon dioxide equivalent
(CO2e) or more in an eligible financial year. For each of these financial years, liable entities
had to surrender one eligible emissions unit for every ton of CO2e that they produced.
Entities would have the opportunity to acquire eligible emissions units by purchasing
carbon units at a fixed price ($23 per unit in 2012-2013, $24.15 in 2013-2014) from the
Clean Energy Regulator or through the creation of eligible Australian Carbon Credit Units
(ACCUs) through the Carbon Farming Initiative with emission avoidance activities. The
emission avoidance activities included storing carbon in living biomass, dead organic
matter or soil. As a context of the fixed price mechanism, the EU price as of August 9th,
2013 was €4.48, or $6.50, which is roughly 27% of the carbon tax in Australia in 2013.
2

4. Entities could also purchase carbon units and eligible ACCUs in the secondary market from
others who held such units (the use of international permits to meet the requirement was
not allowed during the fixed price period); or with industry assistance from the Jobs and
Competitiveness Program. The Jobs and Competitiveness Program was aimed at providing
assistance to entities that were conducting emissions-intensive and trade-exposed
activities (EITE) such that they faced high carbon costs but were unable to pass these costs
to global markets. The most EITE activities, such as aluminum production, steel
manufacturing, pulp and paper manufacturing, glass making, cement production and
petroleum refining, would receive assistance to cover 94.5% of industry average carbon
costs in the first year of the carbon price, with less EITE activities to receive assistance to
cover 66% of industry average carbon costs (Clean Energy Regulator, 2013). Eventually, if
a liable entity did not surrender any or enough units, it would be liable for a “unit shortfall
charge” at 130% of the price for the relevant financial year multiplied by the number of
shortfall units. The carbon pricing mechanism covered a range of large businesses and
industrial facilities (around 370 businesses) which were responsible for approximately
60% of Australia’s carbon emissions. Generally, smaller businesses or households would
not be affected.
Under the initial policy design, the second phase was to be implemented in 2015 with a
flexible price or cap and trade scheme. However, the carbon pricing mechanism had been
abolished in July of 2014 with the repeal of the Clean Energy Act of 2011.
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5. 1.2 Costs and Benefits of the Policy
Expected pros and cons of the policy before implementation are summarized as follows:
● The carbon tax will incentivize the industry to reduce emissions.
It was expected that emissions in the electricity sector would decrease right after
the introduction of the carbon tax. According to Australia’s national greenhouse
accounts published by the Australian Department of the Environment, emissions
from the electricity sector have been falling since 2008/2009, when electricity
emissions fell by nearly 12% mainly due to the reduction of demand and rise of
retail electricity price. With the introduction of the carbon tax, the price of an
electricity input (such as black coal) is expected to rise. From a standard producer
theory, in the long run, the electricity producer may switch to alternative inputs
with lower emissions such as hydrogen engines or solar power, and the tax will
reduce consumer demand as electricity becomes less affordable. However, the effect
on overall emissions is not as strong as indicated by Robson (2013). In his paper,
Robson wrote that the carbon tax will lead to the reduction of domestic emissions
levels below projected “business as usual” cases, but not the absolute level, which is
not expected to fall until 2043. The figure below depicts Australia’s energy
consumption by fuel type between the years of 2010 and 2013. Between the years of
2011/2012 and 2012/2013 Australia’s consumption of renewable energy grew by
11.5% while its consumption of coal decreased by almost 6%.
4

6. Figure 1. Australian Energy Consumption by Fuel Type (BREE, 2014)
● The carbon tax will help raise revenue for the government.
The revenue raised could be used to reduce income tax (by increasing the tax-free
threshold). It could also increase pensions and welfare payments to cover expected
price increases; the new tax revenue could be used to introduce compensation for
some affected industries and repair the damage caused by environmental pollution.
● The carbon tax will lead to a socially efficient outcome by removing negative
externalities.
Carbon pollution is a negative externality, which imposes a cost for the whole
society instead of the consumer alone. It is postulated that the emissions intensive
industries will create excessive carbon pollutions as negative externalities.
5

7. Therefore, the tax is a means to internalize the externality such that those who
cause environmental cost are made to pay the full social cost of their actions. With
the tax introduced as the external cost, demand will fall and the new equilibrium
will be socially efficient.
● The optimal level of carbon tax cannot be known in advance without several rounds of
changes, making it politically vulnerable.
Robson (2013) points out that internationally, the cap and trade scheme is more
popular. Although the carbon tax policy with perfect information can lead to the
same outcome as a cap and trade scheme, in reality, the perfect information
assumption is shaky. A carbon tax is preferred in circumstances where the marginal
benefit curve is relatively flat and the marginal cost curve is relatively steep, which
may not be the case, argued by Robson (2013).
● The carbon tax will have economic and fiscal effects on Australian economy.
The carbon tax may have macroeconomic outcomes such as GDP losses and
unemployment. A number of researchers are supporting this argument. For
instance, McKibbin et al (2010) evaluated the costs of commitments under the
Copenhagen for Australia: the GDP loss in 2020 is estimated to be -6.3%. In
addition, Siriwardana et al (2011) projects a 0.75% rise in the consumer price index
and a -0.68% decrease in GDP as affected by the carbon tax policy. Siriwardana et al
(2011) also estimated that the price of electricity will rise by about 26% in the short
run and by 43% above business as usual by the Australian government model.
6

8. Finally, the government expects the relative reduction in real wages compared to
baseline to be much steeper than the overall reduction in GDP, which will cause job
losses in certain sectors.
● The carbon tax will likely result in the shift of production to nations without the tax or
where the tax is low.
This defeats the purpose of cutting carbon emissions as a global joint effort.
1.3 Impact
The true economic consequences of the legislation can be examined by observing its impact
on specific segments of the economy. Initial projections suggested that the legislation
would have a modest overall effect on most aggregate economic variables. However,
belying the negligible projected impacts on real GDP and inflation were significant actual or
realized effects that were detrimental to specific industries and groups within the economy.
The majority of the 75,000 businesses liable to pay the tax did not meet the requirements
of the Jobs and Competitiveness Program. That is, they were not EITE entities and,
therefore, did not receive assistance to help absorb the higher carbon costs. Among such
firms, industry surveys that included the service, manufacturing and construction
industries suggest that firms in less competitive markets had a greater tendency to pass on
higher energy input costs to consumers (smaller businesses and households). In contrast,
firms in more competitive markets were more likely to incur the higher costs and accept
lower profits. In some cases - particularly in industries reliant on energy such as food
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9. processing, plastics and chemicals, metal manufacturing and oil refining - there has been a
marked reduction in planned investment and even some relocation of production facilities
offshore. While higher energy bills (namely electricity and gas) were the chief cost-related
difficulties faced by firms, the tax also added to rising packaging, transport and other
expenses that involve the use of non-renewable energy. This coincided with the largest
price increase for electricity and gas in Australia since the early 1980s (Ai Group, 2013).
The Clean Energy Act was designed to raise producers' costs, leading to higher output
prices faced by businesses and consumers who would then be compensated by the
government. Half of all businesses surveyed six months after the implementation of the tax
reported higher input costs (to which electricity costs were a major contributor). From the
policymakers’ perspective, the problem was that in many sectors the anticipated increase
in output prices did not occur broadly. As mentioned above, there has been a clear
relationship between competition and price changes in response to the policy: the more
elastic the demand for output in a given market (with respect to output price), the less
likely firms in that market were to pass on higher input costs to the consumer. For
example, while the overwhelming majority of firms in the highly competitive food
manufacturing industry reported input price increases, only a tenth of those firms raised
their prices. Meanwhile firms enjoying low demand elasticities such as producers of
pharmaceutical products and transport equipment were able to raise prices proportionally.
Trade-exposed industries, which by definition face regional and global competition, were
8

10. also found to have difficulties in passing through higher input costs. Service firms, which
make up the largest component of Australia's GDP composition and are less trade-exposed,
appear to have had greater price pass-through ability, accordingly.
What emerges from considering the micro-level effects of the tax is that there were groups
that benefited from the policy and those that were adversely affected. Firms with pricing
power or full compensation for the cost of the permits had a means of capturing additional
profits. Similarly, businesses that had low carbon output relative to competitors had a way
to undercut competitors' prices. Corporate services firms such as accounting and
consulting businesses also received some demand-side assistance from the tax because of
businesses seeking advice on absorbing high energy costs. In contrast, power sector firms
(excluding renewable energy producers) were directly harmed by the tax. Liable entities
that did not receive full compensation - especially those in more competitive markets -
faced higher carbon costs as well. Finally, consumers also bore some of the cost burden,
mostly in the form of higher electricity prices.
9

11. Figure 2. Electricity spot prices: weekly volume weighted average spot prices (AER, 2014)
While the sector-specific effects are somewhat clear, the broader level trends in the
economy have yielded less information about the policy's impacts. The policy's
implementation has coincided with unfavorable movements in key economic variables. For
example, unemployment began to steadily increase after the July 2012 implementation of
the tax despite falling for 11 of the 12 previous quarters. Total job losses also began a
steady rise. In the year following the tax's introduction, GDP growth declined from 3.6% to
10

12. 2.4% (OECD, 2014). But a precise relationship between macroeconomic variable
movements and the policy cannot be established yet. The aforementioned events do not
necessarily imply a causal relationship with the tax. In fact, the only events that can be
directly traced to the policy are increased electricity prices and a mild contribution to
inflation.
Even employment movements that were sharper than the average across all industries
such as the 29% decline in mining employment from August 2013 to one year onward are
better explained by other factors such as falling commodity prices (Bruce Einhom, 2014).
This could be interpreted as meaning that the policy did not harm the economy. But as
evidenced by its impact on competitive firm profits and consumer welfare, there were clear
detrimental effects on certain actors within the economy. Moreover, this can alternatively
be interpreted as evidence that the policy may not have adequately disincentivized carbon
emission: If the effect of the carbon tax were strong enough to induce firms to change their
production processes, then rises in the unemployment rate would be better explained by
the increase in energy costs (assuming that firms would reduce employment in the
short-run to respond to rising energy costs before implementing other cost-reduction
measures such as switching to alternative energy sources). It is also of interest to consider
that the electricity price increases that firms faced after July 2012 were driven by
additional factors as well such as rising network costs.
11

13. As initially expected, producers of alternative energies were among the beneficiaries of the
policy. But a rapid shift in the sector’s outlook between enforcement and abandonment of
the policy illustrate an important point. Total investment in the nation's renewable energy
sector doubled in the first full year of the carbon tax (Giles Parkinson, 2014). Post-repeal,
there was a steep drop off in such financing. Australia is now on track to record its lowest
level of asset financing for large-scale renewables since 2002 in the current year. Each of
the three largest firms investing in Australia's renewable energy sector have cited
uncertainty about the current government's RET as the primary reason for the decline in
investment. This highlights an important point to be observed: regardless of the pros and
cons of environmental regulation, policy consistency is needed to fully determine whether
it can be successful or not.
The prospect and realization of higher energy prices, reduced investment and international
competitiveness as well as spillover price increases in other energy-reliant markets
produced a strong political opposition to the carbon tax. The legislation was also construed
as favorable to firms with monopoly power. As a result, it was anticompetitive, according to
this view. In addition, there was a perceived lack of balance with respect to responsibility
and liability. While ten percent of the economy accounts for ninety percent of carbon
pollution, the resulting higher energy prices and cost pressures affected a much larger
proportion of the economy than the offending ten percent. Such a view does not, however,
give consideration to the benefits gained from the carbon tax; for example, increased future
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14. living standards. These factors all contributed to the political developments that lead to
repeal.
1.4 State of the Australian Economy
If the Australian experience with environmentally-focused government intervention is seen
as an experiment upon which other industrialized countries can base their policies, then
the unique characteristics of the Australian economy should be accounted for before any
generalizations can be made about the impact of such legislation. Since 1983, Australia has
seen the floating of its currency to make exchange rates more flexible, transformation of
the financial system through deregulation and substantial reduction of trade barriers such
as import quotas and tariffs, among many other economically liberal changes to its
economy. These reforms have made Australia the third freest economy in the world (The
Heritage Foundation, 2014).
Australia is one of the few industrialized countries that weathered the recent global
recession without any significant slump in its economic performance. GDP growth rates
progressively increased from 1.7% at the height of the global recession in mid-2009 to
3.7% in 2012 (The World Bank, n.d.). One of the key drivers of economic growth has been
strong investment in the resource sector in response to what has been called "a mining
boom". Over the last decade, investment in this segment of the economy has quadrupled
while the capital stock has grown by 300% (The Commonwealth of Australia, 2013). This
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15. pouring of resources into resources has largely been the result of China's unprecedented
growth over the same time period. In particular, there has been great demand for iron ore
and coal, which are Australia's top export products. Iron ore is a key input in making the
steel used for public infrastructure and housing projects while coal supplies the majority of
China's energy consumption. With the country becoming Australia's biggest trade partner,
investment in mining drove about half of GDP growth in 2011 (The Economist, 2012). In
short, China's development has helped propel Australia's recent prosperity.
In part for the same reason, the most recent growth projections for Australia have been
dimmer than usual. China's growth rate is at the slowest pace since the global recession
five years ago (The World Bank, n.d). The uncertainty surrounding China's economy, falling
commodity prices and the increasing cost of doing business - partly because of regulatory
burden (Productivity Commission, 2014) - has led to a decline in investment in the
Australian resource sector. With mining and related sectors accounting for 19% of Gross
Domestic Product (Tim Colebatch, 2012), investment in these sectors is an important part
of the growth picture. Therefore, the reduced investment is expected to dampen economic
growth in coming years and, indeed, there has already been a noticeable slow down (The
Commonwealth of Australia, 2013; Hannam, 2014).
14

16. Figure 3. Australian GDP growth (Reserve bank of Australia, 2014)
At the same time, new drivers of growth are needed to strengthen growth prospects
moving forward and lower the risk of eventually falling into a recession. The Government
of Australia now expects the economy to transition from having its growth be based on
resource sectors to growth based on non-resource sector output (The Commonwealth of
Australia, 2013). However, this transition will be slow as non-resource based growth has
15

17. been subdued. Even with resource sectors moving from the investment phase to the
production phase and the exports coming out of those sectors expected to make a larger
contribution to growth, production outside of resource sectors will need to expand to fill
the void created by falling investment. Exports may also be constrained by a strong
currency.
While Australia's national debt is among the lowest in the developed world, its year-to-year
budget position has diminished in recent years, with deficits expected every year up to
2024 without a policy change (The Commonwealth of Australia, 2013). Part of this
deterioration is due to a weaker economic outlook and the resulting reduced public
revenue. Future growth is expected to be led by growth outside of the mining and natural
resource sectors and such growth has appeared to be sluggish. As a result, real GDP is
forecast to grow at 2.5 % in 2014-15, compared to the 3 % estimate for 2013. According to
the ruling Liberal government's estimates, the other reason for the weakening of the
budget position is that expenditures are needed to address unresolved fiscal matters from
the outgoing Labor party's administration of government. These include a grant to the
Reserve Bank of Australia to help withstand shocks ($8.8 Billion) , providing additional
funding for a policy relating to offshore processing of illegal maritime arrivals ($1.2 billion),
restoring outlays for a fair funding agreement for Australian schools ($1.2 Billion) and
following up on a backlog of announced but unlegislated tax and pension measures ($2.9
billion) (The Commonwealth of Australia, 2013).
16

18. The factors mentioned up to this point provide some context for evaluating the impact of
the carbon tax on the Australian economy. But the effect of the tax on macroeconomic
variables should be considered alongside more sector-specific information to properly
gauge what the true consequences of the policy are. A microeconomic accounting for the
policy's impact revealed significant factors that were detrimental to specific industries and
groups within the economy. Thus the Australian carbon tax should be viewed through the
prism of an economy with a resource sector that is expected to slow down and is in need of
non-resource drivers of growth, which signifies that the effect of the policy on individual
groups and actors in the economy are of paramount importance.
The weaker economic outlook given by government projections underscores the need to
emphasize policies that improve productivity, allow firms to be more efficient and reduce
the regulatory burden on businesses and individuals. Ending the carbon tax was expected
to reduce cost pressures on households and businesses and is consistent with meeting
these needs. Predictably, the Australian senate vote repealing the tax coincided with a
surge in business confidence (National Australia Bank, 2014). The repeal has also been
linked to above-average consumer optimism as reduced energy costs offset some of the
concerns over stagnant wages, rising unemployment and high housing prices (National
Australia Bank, 2014). Annual household savings produced by the repeal may vary
between $250 and $550 per household. Much of these savings are on electricity costs. In
addition, some firms that received full compensation from the government (essentially
17

19. getting free pollution permits) were known to pass higher costs on to consumers
regardless, keeping the difference as profit. The repeal has ended this practice.
It seems clear that the carbon tax in Australia reshuffled economic welfare from certain
groups to others. It likely affected business and consumer sentiment, which are important
for the country's economic performance. But broad economy-wide impacts have not been
attributed to the policy. Since the United States and Australian economies are structurally
similar, this might have implications for what the consequences of environmental
regulation could be in the United States. Australia serves as a reasonable test tube as long
as some of the factors mentioned above are accounted for (e.g. the mining boom, growth
spurred by China's development and the transition to non-resource drivers of growth).
Assuming an identical policy in the US, analyzing its merits might be a matter of
determining whether a rise in electricity costs for consumers and businesses is an
acceptable price for achieving environmental objectives.
2 Models
In order to investigate the Australian policy and its effect on industry, it is imperative to
understand what role energy plays in the industrial sector. Energy is an important input to
the production process; it plays a similar role to labor and capital. The industrial
consumption of energy can be altered by either using an alternative energy source, using a
more energy efficient model, or by changing the utilization of capital stock. Only the last
18

20. option can be implemented in the short run, implying that in the short run in order for a
firm to decrease its energy consumption it must decrease its production. Industrial demand
for energy follows the fundamental relationship between price and quantity, as price of
energy increases the demand for energy decreases (all else equal). Hill and Cao (2012)
showed in their paper that the short run price elasticity for electricity is .24, showing that a
1% increase in the price of electricity leads to a .24% decrease in the demand for electricity
(all else equal). The authors also showed that there is a positive relationship between gross
value added (GVA) and the demand for electricity, which is a logical deduction because as
firms produce more their demand for electricity rises.
The associated effects of a change in electricity price give a clear indication of what the
expected effects of the regulation of energy are. Supply and demand forces, including
regulations, can be measured using price. Usually new regulations of any market will make
prices rise and deregulation will make prices fall. An abrupt long-term change in the price
trendline is a good indication that a change in regulation has been implemented. Wholesale
market prices are the first-line indication of the effects of supply and demand forces, and
the prices of the wholesale electricity markets of Australia are the prices of which this
research will focus to examine the effects of regulation.
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21. 2.1 Data Collection
The following empirical model will utilize Australian government published data. Energy
consumption data were collected from the Bureau of Resources and Energy Economics
Australian Energy Statistics publication. Electricity prices are sourced from the Australian
Bureau of Statistics Producer Index Series. Output data was obtained from the Australian
Bureau of Statistics National Accounts Branch publications. The research also defines
energy intensive industries based on energy consumption data published by the Bureau of
Resources and Energy Economics. This allows for further understanding of the impact on
energy intensive sectors and the effectiveness of the Jobs and Competitiveness Program.
2.2 Empirical Models
This paper uses three different models to measure the effectiveness of the Australian policy
: time-series, simultaneous equations, and ANOVA. The ANOVA and time-series models
were used to analyze the relationship between average electricity price and the carbon tax
policy. The simultaneous models built the aggregate demand and supply and were utilized
to demonstrate how the policy affect the manufacture industry market.
2.2.1 Time-series Model
2.2.1.1 Theortical Model
This model utilizes a combined regression-time series model to investigate the relationship
between electricity price and the carbon tax policy. The form of the model is
20

22. pricet = c + dummy +
vt vt ~ AMAR(p; q)
2.2.1.2 Estimated Model and Results
In this model, price is the average electricity price from July 2011 to June 2013. As the
carbon tax policy of Australia began July 1st, 2012 , the dummy was set to 1 after July 2012.
The dummy was used as the only explanatory variable to determine whether it caused a
change to the mean of the price.
The result is as below:
21

23. From the form, the final regression function is
2.2.1.3 Hypothesis Testing
After doing several tests, it is clear that the regression model satisfied assumptions both in
the linear regression part and the MA part. In the regression result, the p-value for the
Dummy variable is 0.0001, showing that the carbon tax had a very significant effect to the
electricity price. As t=4.94 and p¡=0.05 for the dummy variable, it is possible to conclude
that the carbon tax policy made a significant effect to the electricity price. After the policy
began, the average electricity price raised about $17.9 per megawatt hour. However, the
success of the policy is ambiguous. If the demand elasticity of electricity is big enough, it
will have a large positive effect to reduce the energy cost; but if the elasticity is
considerably small, it will only cause consumers to pay more (subsequently reducing
consumer surplus). In order to calculate elasticity, the demand function of electricity must
be determined.
22

24. Tests for the assumptions:
1. Heteroskedasticity Test: Breusch-Pagan-Godfrey:
2. Unit Root test for ut :
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25. 3. Q-statistics for vt:
2.2.2 Simultaneous Equations Model
2.2.2.1 Theoretical Models
Simultaneous equations were used to build up a model regarding the aggregate demand
and supply in order to estimate whether the policy’s effectiveness. To set up the model, the
structural equations must first be designed. Secondly, the equation’s completeness must be
ensured (the number of equations is equal to number of endogenous variables). Lastly, the
structural equation parameters (depending on characteristics of the model) need to be
estimated. These steps produce the aggregate demand and supply equations as below:
qd = a0 + a1 pd + a2x1 + e1
qs = b0 + b1 ps + b2y1 + e2
where qd = qs is the equilibrium condition
24

26. Here variable x1 is something that shifts the demand equation but does not shift the supply
equation and variable y1 is something that shifts the supply holding demand constant.
2.2.2.2 Estimated Models
In this section, manufacturing output (market supply) was used to estimate market
demand and producer price index as price sold or purchased. Moreover, variable lelep is
the variable that will shift the supply holding demand constant and variable lgdp is the
variable that will shift the demand without shifting supply curve. Clearly, the endogenous
variables are quantity and ppi, where variable quantity is on the left hand side of the
equations and variable ppi is on the right hand side of the equations. The remainder of the
variables are the exogenous ones. The simultaneous equations models are as below:
Demand : quantityd = a0 + a1 ppi + a2 lgdp + a3 quantity2 + e1
Supply : quantitys = b0 + b1 ppi + b2 lelep + e2
where
variable quantitys(quantityd ) = Manufacture Sector Supply(Demand) variable
quantity2 = Manufacture Sector Input
variable ppi = Producer Price Index
variable lelep = log of Average Electricity Price
variable lgdp = log of GDP
25

27. 2.2.2.3 Estimated Results
Normally, OLS provides best linear unbiased estimators, but under the simultaneous
equations model OLS is biased. Thus, the estimates from reg3 OLS option might be not
good. One could use reg3 in Stata by using 2SLS, yielding consistent estimates for each
just-identified or over-identified structural equation in a system. When using 2SLS,
however, the structural parameters of each equation are estimated separately. Although
the 2SLS estimator for each equation makes use of the information on all of the exogenous
and pre-determined variables for the whole system, it ignores the information contained in
the excluded endogenous variables in that equation and information contained in the error
covariances. Therefore, 2SLS is not efficient, and thus 3SLS was used instead.
Since the three stage least squares model is created by combining the 2SLS notion with SUR
Model, iteration over the estimated disturbance covariance matrix and parameter
estimates until the parameter estimates converge is preferred by most statisticians. Hence,
the best results are expected to be generated by iterated 3SLS. After running the three
stage least square regressions with different options (naive OLS, 2SLS, default 3SLS and
iterated 3SLS) in Stata, a table with the comparison of the four different models is
presented. Below are the best (iterated 3SLS) estimated demand and supply equations:
Demand : quantityd = 172.20 - 0.03 ppi + 8.18 lgdp + 0.37 quantity2 + e1
Supply : quantitys = 62.30 + 0.04 ppi + 8.71 lelep + e2
26

28. From the Stata output, the p-value for the equations are less than 0.0015. Additionally, the
model offers other strong evidence: in the iterated 3SLS model, most of the coefficients are
statistically significant at the 1% level and only the coefficient on ppi in the supply equation
is slightly statistically significant, at the 10% level, and its p-value is approximately 0.086.
Therefore, the results are very convincing that the carbon tax policy forced the whole
manufacturing sector to produce less.
After replacing the mean value for variable lgdp, lelep and quantity2, Stata generated the
demand and supply curves. It is obvious to see that the manufacturing sector produced less
after the government regulation was imposed, indicating that energy intensive industries
consume a large proportion of energy for production.
27

29. 2.2.2.4 Hypothesis Testing
The basic assumptions such as nonsingularity , uncorrelated with the error term, the rank
of the exdogenous variables and asymptotically convergent will hold if the stata doesn’t
give us an error or warning. Since Stata generate the output for us, we assume these basic
assumptions are satisfied. Another two important specification tests for simultaneous
equations regression models are testing of exogeneity and testing of overidentifying
restrictions. To test the the endogenity and whether the equations overidentify, Sargan’s
(1958) statistic or Basmann’s (1960) statistic were used. Results are as below:
By assuming ppi is also an endogenous variable, two unknowns exist in these equations,
which are quantity and ppi. On the other hand, the test gives us Hansen-Sargan
overidentification statistic, which rejects the null hypothesis that the model is
overidentified. Therefore, the model meets expectations.
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30. 2.2.2.5 Interpretation and Intuition
Is the carbon tax policy on manufacture sector really effective? Because variable quantity
and ppi are measured by index numbers, it is known that the demand equation represents
the demand elasticity of manufactured products. However, −0.03 is inelastic for the
quantity demanded, signifying that however the price changes the demand for
manufactured products, the effect is not large. And the quantity produced predicted by the
models only decreases about 3%. Indeed, the model has room to improve due to the
insufficient information. However, the conclusion is logical, because society is heavily
reliant on these products (such as food and petroleum products). Clearly, further research
needs to be conducted in order to explore the efficiency of the policy and its ability to
reduce energy consumption without sacrificing social welfare.
29

31. 2.2.3 Analysis of Variance Model
This section investigates how government intervention through the implementation of a
carbon tax impacts industrial output with seasonally adjusted GVA (gross value added) as
an indicator. Quarterly GVA data from March 2010 to June 2014 were collected from the
Australian Bureau of Statistics National Accounts Branch publications. Since it was
30

32. postulated that the carbon tax policy directly impacts the electricity price while indirectly
impacting the industrial output, average quarterly wholesale electricity prices were
collected and studied jointly with the GVA data, which are shown below.
A preliminary correlation analysis indicates a strong negative correlation (correlation
coefficient -0.793) between the electricity price and manufacturing industry output, these
results are expected. Figure 3 below depicts the electricity price against manufacturing
industry output. A visual inspection reveals that the before and after electricity price and
GVA seem to be clustered around different means, suggesting a drastic change incurred by
implementing the carbon tax policy. Such a hypothesis leads to a k-means clustering
analysis testing whether n observations could be partitioned into k clusters in which each
observation belongs to the cluster with the nearest mean, serving as a prototype of the
31

33. cluster. In this case, only two categories exist: pre- and post- intervention, making k equal
to 2.
The k-means clustering analysis leads to the following observations:
32

34. 1. Pre- and post- intervention quarterly price and manufacturing GVA are clearly
clustered around distinct centroids, indicating the policy intervention incurred a
sharp change on electricity price and industry output.
2. The centroid GVA of manufacturing of metal products reduced from 4587 to
4328 million and the centroid electricity price increased from $30.1 to $55.3 per
megawatt hour with the introduction of the policy.
2.2.3.1 ANOVA Model
A similar approach is the ANOVA (analysis of variance). Considering the introduction of the
carbon policy as a treatment, the pre- and post- intervention levels of quarterly
manufacturing GVA reveal the effect of the treatment, which justifies the analysis of a
one-way layout design of experiment. The model is as follows:
yi j = hˆ + tˆ + ri j
where hˆ is the grand mean, tˆ is the treatment effect, ri j is the residual.
33

35. The null hypothesis that there is no treatment effect is rejected at the 5x10 -5 level,
suggesting that the carbon tax policy has a significant negative impact on the output of the
metal product manufacturing industry.
3 Discussion
This research finds that the effects of Australia’s Clean Energy Act of 2011 are clearly
measurable and significant. Using several variables, we analyze the time periods directly
before and after the bill went into effect. By doing this, the immediate costs and benefits of
the bill become obvious, and we find why the bill was shortly repealed.
To the average consumer there was no benefit and only a cost. The only difference the
average consumer could measure is an increase in the price of energy. The benefits, a
healthier environment and sustained resources, would only be noticeable in the long run,
and would be more difficult to quantify.
The policy was implemented in order to reduce CO2e emissions and to encourage the use of
alternative energy sources, a seemingly sound policy considering that Australia is the
among the heaviest polluting nations in the world. In fact, according to the US Energy
Information Administration, in 2011 Australia was the second largest per capita carbon
dioxide emitter among the G20 nations. However, it is ambiguous whether or not the policy
really targeted the nation’s heaviest polluters. The Jobs and Competitiveness Program
34

36. offered assistance to entities that were emissions intensive in order to mitigate their higher
carbon costs; the program aimed to help soothe the cost disruptions in the market but
decreased the impact of the policy as the push to lower emissions and switch to alternative
energies was weakened.
It is also noteworthy that in 2013, coal was Australia’s second greatest export. Additionally,
Australia’s other major exports were emissions intensive, signifying that the country had a
comparative advantage in the production of emissions intensive goods and a relatively high
opportunity cost of producing goods with low emissions. These factors caused the
cost-burden of the tax to be relatively high to consumers. Although the industrial sector
noticed an increase in the price of energy as well as a reduction in potential output, many
firms were unable to pass along the costs. This was likely the cause of a competitive global
market, which means that rather than a tax that led to a reduction of global emissions, the
tax led to carbon leakage, thus defeating the purpose of the policy on a macro scale
(Australian Government Department of Foreign Affairs and Trade; Robson, A. 2013).
It is arguable that the timing of the bill could have been one of the causes of contention.
Shortly after the worst recession since the Great Depression, the passage of a bill that raises
energy prices at a rate more than three times that of the already-established EU rate is hard
to economically justify, and part of the reason the act was abolished. Job loss increased and
35

37. GDP growth declined during the 8 quarters that the carbon tax was in effect, something not
yet precisely attributable to the carbon tax, but certainly politically so.
4 Conclusion
The models used in this paper show that the policy had significant impacts in the industrial
sector. Tests yield that the tax policy significantly impacted electricity prices. Yet, the
effects of the rise in prices could have been offset by the demand elasticity for electricity.
The policy caused a sharp reduction in the output of metal products as the policy caused
electricity prices to rise. There is convincing evidence that the carbon tax caused the
manufacturing industry to produce less. However, due to the nature of inelasticity, the
effect of price on demand for manufactured goods is relatively small. In all, the ANOVA test
was able to suggest that the policy had a significant effect to reduce metal product
manufacturing output.
It is also noteworthy that the policy faced harsh political opposition. Though a tax seems to
be the most efficient form of an environmental regulation, and poses as a revenue to the
government, it is impossible to determine the best rate in advance. This causes the tax to
fluctuate early in its implementation and, due to its nature, the tax may appear to be
unstable to the public and unpopular to many.
36

38. Irrespective of the repeal of the tax in Australia, it serves as a tool for other nations
considering similar policies. The greatest lesson to be learned is the impact of the costs to
consumers and their perceived benefits. Nations wishing to implement environmental
regulations of this kind must seriously consider the political repercussions as well as the
economic costs. It has been shown that such a policy not only raises the prices of electricity,
but also deeply disrupts the economy without benefiting the environment at a noticeable
scale.
Australia’s brief encounter with carbon emissions policy reform did not occur without
political repercussions. A bill passed just two years earlier, then repealed due to negative
coincidal events rather than amended, brings forth questions of whether the state was
effectively governing. With that, some may find it the case that the government would have
been more effective by simply lowering the carbon tax to a level more in tune with the
proven-effective rates of the European Union. In any case, the Australia Clean Energy Act of
2011 was passed, then repealed, and there is much to be learned from the event by
countries, such as the United States, that seek to pass similar legislation.
37

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