71

How to Use Carbon Tax Revenues Well:
Implications from Taiwan
Yi-Hua WU1,3* and Feng-Fan LIAO2
1

National Tsing Hua University
No. 101, Section 2, Kuang-Fu Road, Hsinchu 30013, Taiwan (R.O.C.)
2
National Taiwan University
No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan (R.O.C.)
3
National Applied Research Laboratories
No. 106, Section 2, Heping E. Road, Da’an Dist., Taipei 106214, Taiwan (R.O.C.)*E-mail: d938901@oz.nthu.edu.tw

Abstract
Carbon pricing is one of the main policies for improving energy efficiency and achieving carbon reduction.
This study has aimed to investigate how to use carbon pricing revenues well to mitigate negative effects on theeconomy. Relying only on carbon pricing policies can result in high economic costs; in particular, theenergy-intensive industrial sectors suffer greater impacts. We propose that providing subsidies to sectors basedon their respective carbon pricing payments could mitigate the negative effects on the GDP better than the otherexamined scenarios of providing the carbon pricing revenues to the fiscal budget or providing subsidies tosectors based on their respective contributions to emission reductions. Thus, proper design of the use of carbon
pricing revenues could mitigate the negative economic effects of carbon pricing.
Key words: carbon pricing, economic impacts, supportive measures

1. Introduction
Carbon pricing is a useful policy measure forachieving carbon emission targets. When a carbon price is
imposed on carbon emissions, emitters have to bear thecorresponding costs for the use of fossil energy. In face ofcarbon prices, industries and households are motivated to
re-optimize their production and consumption decisions
according to the carbon prices. This provides incentives
for the use of clean energy, motivating industries andhouseholds to reduce their use of high-emission energy. In
the literature, numerous studies have investigated theimportance of carbon pricing for achieving emission
targets. Wu et al. (2019) investigated the effects of
Taiwan’s carbon trading system for achieving reduction
targets. Springer (2003) reviewed 25 models, proposingthat restricting the permit supply can increase carbon
price revenues. Springer and Varilek (2004) argued that amodest change of carbon price would have large effects
on consumers and investors. Zhang and Wei (2010)
summarized the operating mechanism and economic
effects of Europe’s carbon trading system. Wang et al.
(2016) proposed that a market-based abatement policy
had been more efficient than a command and control
Global Environmental Research
26/2022 071−078
printed in Japan

policy for China. Fan et al. (2016) showed that a national
carbon market could reduce regional economic disparities
and macroeconomic costs for the whole nation. Zhou et al.
(2013) suggested that China’s interprovincial emission
trading could reduce total abatement costs by 40 percent.
Zhang et al. (2017) demonstrated that integration ofemission trading schemes would optimize the allocationof emission permits, providing economic welfare gains tocountries that adopt permits. Wang et al. (2009) proposedthat emission limits similar to those of the Kyoto Protocol
would seriously impede future economic growth in China.
Wang et al. (2015) elaborated that the emission trading
system could reduce the economic costs of achieving the
Copenhagen commitment for China. Wu et al. (2016)
showed that the economic effects of the carbon trading
system in China depended on carbon allowance allocation
and renewable energy development.
The Taiwanese government intends to launch acarbon trading system in the future under the Greenhouse
Gas Reduction and Management Act. Various carbon
pricing plans have been proposed by researchers andscholars in Taiwan, including propositions for the use ofcarbon pricing revenues. Yang (2014), for instance,
proposed that carbon pricing revenues could be used to©2022 AIRIES72

Y.H. WU and F.F. LIAO

improve energy efficiency, enhancing the effectiveness ofcarbon reduction. Shaw et al. (2020) showed the design offour carbon tax recycling schemes under the principle ofunchanged government total tax revenue, proposing thatthe implementation of a carbon tax could have significant
energy efficiency and carbon reduction effects. In addition,
there are numerous papers in the literature investigating
the issue of recycling carbon pricing revenues. For
example, Zhao et al. (2022) proposed that carbon pricing
increased income inequality. Imposing the same tax return
rate on all income groups would widen the income gap
while distributing higher tax returns to lower income
groups would narrow the income gap. Pradhan and Ghosh(2022) suggested that coal tax revenues could be usefulfor promoting research and development in new energy
technologies in India. Mayer et al. (2021) argued thatcarbon pricing in Austria increased public goods provision,
improving the welfare of low-income households.
Carattini et al. (2019) studied how to use carbon pricing
revenue to maximize public support.
This study has aimed to investigate how to use
carbon pricing revenues well to mitigate negative
economic effects. The funding that a government raises
through the carbon pricing may be provided to the fiscal
budget or used to promote renewable energy development
or to improve energy efficiency. Each of theaforementioned measures could have various economic
effects. Therefore, supportive measures need to be
designed to reduce the negative impacts on the economy
and industrial sectors.
This study has adopted a computable generalequilibrium (CGE) model to investigate the effects ofcarbon pricing on Taiwan’s economy. Three measures forsupporting carbon tax revenues were examined. In thefirst, the carbon pricing revenue is used as an income
source for the fiscal budget. In the second, thegovernment subsidizes sectoral industries according totheir respective contributions to emission reductions. In
the third, the government subsidizes sectoral industries
according to their respective carbon pricing payments. We
ran simulations to determine which of these threemeasures would result in the least economic disruption. It
must be noted that Taiwan has a small open economy.
Based on this economic structure, we have constructed a
CGE model for Taiwan that includes industry, households,
government departments and foreign import and export
trade, using a small open economy framework. Since theenergy sector is a key component in determining futurecarbon emission pathways, this model also specifies a
detailed design for the power supply sector, which is oneof the main sources of carbon emissions in Taiwan.
The contributions of this study are as follows: First,
we have compared the three scenarios for the use ofcarbon tax revenues, in which (1) The carbon pricing
revenue is used as an income source for the fiscal budget;(2) The government subsidizes sectoral industries

according to their respective contributions to emission
reductions; and (3) The government subsidizes sectoral
industries according to their respective carbon pricing
payments. We have found out that the third scenario–
subsidizing industrial sectors according to their respective
carbon pricing payments–results in the lowest losses ingross domestic production (GDP). This indicates that tominimize GDP losses, the industrial sectors that pay
higher carbon prices could get more support from thegovernment. Thus, this refund policy for carbon pricing
revenues could have less of a negative effect on theeconomy.
Second, we have simulated the effects of carbon
pricing on sectoral output values. The sectoral industries
that pay higher carbon prices will suffer from higher
output value losses. Moreover, industries that produce
upstream products or services may suffer higher losses of
output value. Thus, correlation coefficients between
carbon pricing payments and output value losses are
relatively high when we consider upstream industrial
sectors.
The structure of this report is organized as follows:
The second section describes the scenarios and model
designs, the third section presents the simulation results
and the last section summarizes the conclusions.

2. Scenarios and Model Designs
This section describes how the business as usual(BAU) scenario, emission reduction targets, carbon
pricing revenue support measures and model designs for
the CGE model were set up.
2.1 Scenarios
Figure 1 shows the BAU scenario of CO2 emissions
from fuel combustion and emission targets. No policy
measures have been implemented in the BAU scenario,
and a high share of electricity is generated by fossil fuel.
Thus, CO2 emissions continue to rise. In 2035, fuelcombustion emissions approach 380 million tonnes. The
official emission target for fuel combustion emissions
declines to about 190 million tonnes of CO2 in 2035. This
study analyzes the impact of the carbon pricing

Fig. 1 CO2 emissions in the BAU scenario and the reduction target.

How to Use Carbon Tax Revenues Well: Implications from Taiwan

mechanism on the industrial sectors and economy. We
will elaborate the effects of carbon pricing on theachievement of the emission target.
In 2019 the fuel combustion emissions of theindustrial sectors constitute the majority, accounting for
49% of total emissions. The other main sources of carbon
emissions are the energy sector for personal use (15%),transportation (14%), residential (11%), the service sector(10%), and agriculture (1%). Thus, the industrial sector isthe main source of emissions in Taiwan.
Table 1 elaborates the three scenarios for the use ofcarbon pricing revenues. The first scenario specifies the
carbon tax system. In it, the carbon pricing revenues aretransferred as an income source to the fiscal budget. The
fiscal income could be used for government spending,government saving and/or government investment inforeign countries. The CGE model determines therecipient of government income while balancing the fiscal
budget.
In the second scenario, the carbon price revenue isrecycled back to sectoral industries according to theirrespective carbon-reduction contributions. To distributethe carbon tax revenue, the CGE model simulates theeffect of carbon pricing on sectoral emission reductions.
By calculating the carbon emission reduction of eachsector, we can evaluate the sectoral share of contribution
to emission reduction. Finally, each industrial sector

obtains a carbon pricing subsidy according to its
carbon-reduction contributions.
In the third scenario, each industrial sector receives a
subsidy according to its respective-carbon-pricing
payments. The CGE model simulates the carbon pricing
payments of each sectoral industry. This way, we canevaluate the share of carbon pricing payments from eachsectoral industry. Finally, the subsidy is distributed
according to carbon pricing payment share.
2.2 Model Designs
The CGE model can be used to simulate the complete
range of interaction and feedback between representative
agents in an economic system. Such a model frameworkhas been widely used to assess the economic andenvironmental impacts of various climate policies.
Böhringer and Löschel (2005) and Fujimori et al. (2014)
applied the CGE framework to global issues. Zhang(1996) and Wang et al. (2009) performed CGE studies ona national level.
We collected economic data from multiple sources.
The 2016 input–output (IO) table was obtained from the
Directorate-General of Budget, Accounting and Statistics,
Executive Yuan in Taiwan. Official population, GDP
growth and sectoral future trend projections were obtained
from the National Development Council in Taiwan.
Figure 2 illustrates the nesting structure of the CGE

Table 1 Supportive measures for the use of carbon pricing revenues.

Scenario
Carbon pricing revenues are used
as an income source for the fiscal
budget
The government subsidizes sectoral
industries according to theirrespective contribution to emission
reductions
The government subsidizes sectoral
industries according to theirrespective payments for carbon
pricing

Description
Carbon pricing revenues are transferred as an income source to the fiscal budget.
The CGE specifications determine how such revenues are used endogenously whilebalancing the fiscal budget.
(1) We simulate emission reductions of each sector following the launch of carbon
pricing.
(2) We calculate the contribution of each sector to total emission reduction.
(3) The carbon pricing revenue is returned according to the share each sector
contributed.
(1) We simulate the carbon pricing payments of each industrial sector.
(2) We calculate the payments of each sector to the total carbon pricing revenues.
(3) The carbon pricing revenues are returned according to the share paid by eachsector.

Fig. 2

73

Nesting structure of the CGE model.

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Y.H. WU and F.F. LIAO

model used in this study. Detailed specifications as wellas industrial classifications are available in an appendix
upon request. The market structure follows the
specifications of the Asia-Pacific Integrated Model/
Computable General Equilibrium (AIM/CGE). AIM/CGE
was developed by the National Institute forEnvironmental Studies (NIES), with an emphasis on
specifying energy substitution. Natural gas and fuel oil aremainly used for combustion, providing heat energy for
production. Therefore, there is a substitution relationshipbetween fuel oil and natural gas. Moreover, coal also
generates heat energy. In practice, it is easier to maintaintemperatures using combustion of fuel oil and natural gas,compared with that of coal. Fuel oil and natural gas are
combined to produce non-coal goods, which are then
combined with coal to form non-electric composite goods.
Electricity can be used for power provision and heatgeneration, and is more widely applied in industries thanother fossil energy. Non-electric energy is combined withelectricity to produce energy composite goods.
As an input, efficient capital stock can generate thesame output using less energy. Capital stock can combinewith energy, generating capital-energy composite goods.
Moreover, capital stock and labor are key components forindustrial added value. Each industry combines labor andcapital-energy composite goods as capital-labor-energy
composite goods. Non-energy intermediate goods are
combined with capital-labor-energy composite goods,
producing gross outputs. Figure 2 indicates 33 production
sectors, including agriculture, industry and services. The
electricity sector has unique specifications, different from
AIM/CGE.
We specified our CGE model as a small open
economy. Thus the gross outputs are sold either as export
goods to foreign countries or as domestic goods
domestically. Moreover, the domestic goods are combinedwith import goods to produce the domestic supply.
Producers and consumers in the domestic market purchasecommodities–including for consumption, or as
government expenditure, investment and intermediate
goods. The intermediate goods are adopted by firms as
production inputs.
In this study we have designed a specific production
structure for the electricity sector to facilitate subsequent
analysis of the impact of Taiwan’s future electricity policy.
Since the electricity sector is Taiwan’s main source of fuelcombustion emissions, it is necessary to elaborate its
structure. Taiwan’s power generation is disassembled into
fossil fuel power, renewable power, nuclear power
generation and the transmission and distribution sectors.
Figure 3 depicts the nesting structure of theelectricity sector. Fossil fuel power generation is Taiwan’s
main source of power generation, consisting of fuel oil,coal and natural gas as the main inputs. The
aforementioned energy is combined to formulate fuelenergy. Moreover, electricity is also one production input,

Fig. 3

Nesting structure of electricity sector in the CGE model.

combined with fuel energy as energy composite goods.
The new capital for electricity generation is more efficient,
generating the same output using less energy. Capitalstocks and the energy composite are combined ascapital-energy composite goods. The labor supply andcapital-energy composite goods are combined to generate
capital-energy-labor composite goods. Finally, thecomposite goods and the other non-energy intermediate
inputs are used to generate fossil energy power.
The electricity generated by renewables does not
involve the use of coal, fuel oil no natural gas; however,
the renewables still need electricity for power generation.
Capital and electricity are combined to generate
capital-energy composite goods for renewables. Moreover,such composite goods are combined with the labor supply
to generate capital-energy-labor composite goods. In thelast nest, the other non-energy intermediate goods and theaforementioned inputs are combined for the production ofrenewable electricity. Nuclear power generation does not
involve the use of fossil energy; thus, its production nestis the same as that of renewable energy.
Electricity consists of fossil fuel power, renewableenergy power, and nuclear power. The electricity
transmission and distribution sector is essential for thetransmission of the electricity to households andindustries. This sector consists of capital and labor. These
two inputs are combined with other intermediate goods
for the transmission and distribution of electricity.
Electricity is combined with the transmission anddistribution sector, providing the final electricity supply tothe market.

3. Simulation Results
This section presents the results of simulating the useof carbon pricing to achieve carbon reduction targets. We
demonstrate the impacts on the industrial sectors and GDP,
investigating the effects of supportive measures forcarbon pricing revenues.

How to Use Carbon Tax Revenues Well: Implications from Taiwan

3.1 Carbon Pricing to Achieve Targets
As shown in Fig. 1, a reduction of approximately 40
million tonnes was needed in 2021 and around 180
million tonnes will be needed in 2035 to achieve Taiwan’s
emission target.
Figure 4 shows the simulated carbon prices forachieving these emission reduction targets. The
government collects carbon pricing revenue as income for
its fiscal budget. To achieve Taiwan’s carbon reduction
target in 2025, a carbon price of around 150 U.S. dollar

Carbon prices necessary for achieving emission targets.

Fig. 5

Impact of carbon prices on the GDP.
Billions USD

per tonne (USD/tonne) will be needed. Moreover, thecarbon price has to be set to around 440 USD/tonne forthe large emission reduction required in 2035. Therefore,a high carbon price is necessary for achieving emission
targets without launching other government measures.
3.2 Impacts of Carbon Pricing on the GDP
Figure 5 presents the impacts of carbon pricing on
the GDP for achieving emission reduction targets.
Assuming other conditions remain unchanged, the launchof carbon pricing will decrease the GDP by about 5% in2025 compared with BAU, and this pricing system will
reduce the GDP by 11% in 2035. Therefore, carbon
pricing alone has substantial negative effects on the GDP.
3.3 Impacts of Carbon Pricing on the Industrial
Sectors
Figure 6 shows the effects of carbon pricing on
sectoral output values. Carbon pricing payments are
depicted on the horizontal axis and losses to industrial
output value, on the vertical axis. The supportive measureuses the carbon pricing revenues as fiscal budget income.
We have divided the industrial sectors into two groups toinvestigate the effects of carbon pricing on output values.
The first group is represented by solid red circles and thesecond group, by blue circles. The numbers in parentheses
denote output value share of the 𝑗 th sector to theaggregate output value of all sectors. The first group hasan output value share higher than 4.3% while the second
group’s is lower than that threshold value. A higher value
indicates that the sector in question is more closely
connected to other sectors. The launch of carbon pricing
would not only have negative effects on production in this
sector but also reduce its demand for production inputs.
Therefore, interconnection of the production chain

Carbon pricing payments

Output values

Fig. 4

Note: Carbon pricing payments are depicted on the horizontal axis and losses of industrial output value, on thevertical axis. We have divided the industrial sectors into two groups to investigate the effects of carbon pricing
on output values. The numbers in parentheses denote the output-value share of the 𝑗th sector to the aggregate
output value of all sectors. The supportive measure uses the carbon pricing revenues as fiscal budget income.
Fig. 675

Impacts of carbon pricing on output values.

76

Y.H. WU and F.F. LIAO

amplifies the negative effects of a carbon tax on the large
industrial sector.
The correlation coefficient between carbon pricepayments and output values regarding the first group is−0.84. This group includes industrial sectors such as othermanufacturing,semiconductors,
otherelectronic
component manufacturing, wholesale and retail, power,
and construction. Regarding the second group, thecorrelation coefficient is −0.95. This group consists ofbasic chemicals, cement, passenger and cargo transport,
steel products, and iron and crude steel. The respective
correlation coefficients of these two groups indicate thatthe higher the carbon price paid by the industrial sector,
the larger the negative effects on its output value.
Industrial sectors in the first group tend to produce
upstream products or services, such as power andsemiconductors These sectors have higher energy
intensity compared with those in the second group.
Moreover, these sectors have large market shares,
enlarging the negative effects of carbon pricing. Thus, thelaunch of carbon pricing could exacerbate the negative
effects on sectors in the first group compared with thosein the second group, given the same level of carbon
emissions. We need to distinguish between these twogroups in order clearly to identify the negative
relationship between carbon pricing payments and output
value losses.
3.4 Uses of Carbon Pricing Revenues
Figure 7 shows the effects on the GDP of the uses ofcarbon pricing revenues. In the first scenario using thecarbon pricing revenues as an income source for the fiscal
budget, the carbon prices have the largest negative effecton the GDP. Compared with the BAU scenario, thenegative effects on the GDP reach 5% in 2025, 9% in
2030, and 11% in 2035. In the second scenario, using thecarbon price revenues for subsidies according tocontribution to emission reductions, the negative effects
on the GDP are −3% in 2025, −5% in 2030, and −8% in
2035. In the third scenario, subsidizing industrial sectors
according to carbon pricing payments, the negative effects
on the GDP are −2% in 2025, −5% in 2030, and −6% in
2035. Thus, the largest negative effects on the GDP occur
in the scenario of using carbon pricing revenues as anincome source for the fiscal budget–the CGE model
determines how a government uses such revenues
endogenously. Moreover, providing subsidies according tosectoral carbon price payments can effectively mitigate
the negative effects of carbon pricing.
Figure 8 shows the carbon price necessary forachieving the emission target for each of the three
supportive measures. The negative effects on the GDP are
the largest in the first scenario of using carbon tax
revenues as income for the fiscal budget. The reduced
economic activity results not only in lower energy use but
also in lower carbon emissions. Therefore, the carbon

price necessary for achieving the emission target is thelowest among three scenarios. In the third scenario, wheresubsidies are provided for sectoral carbon pricing
payments, the government needs to impose the highest
carbon prices in the face of the smallest negative effects
on the GDP. In the second scenario where subsidies areprovided for sectoral contributions to emission reductions,both the carbon prices and their negative effects on the
GDP range between those of the two aforementioned
scenarios. Thus, the supportive measures for carbon
pricing revenues affect both the carbon pricing and its
negative effects on the GDP.
Table 2 shows the sectoral effects of carbon pricing
in the case of each of the three supportive measures.
Regarding the scenario of treating revenue as income forthe fiscal budget, the first column corresponds to its
sectoral effects as shown in Fig. 6. The second column
corresponds to subsidies regarding emission reduction
while the third column corresponds to subsidies regarding
carbon pricing payments. The carbon pricing policy has
the largest effects on the “other manufacturing” sector.
The recycling of carbon pricing revenue according toemission reduction or carbon pricing payments mitigates
the negative effects of carbon pricing on that sector. The
former recycling scheme mitigates the negative effects toa large extent compared with the latter one. The
semiconductors sector ranks as the second hardest
affected sector in the three scenarios. The sasic chemicals
sector ranks in third place (−27 billion USD) in thesecond scenario, while the electronics sector is number
three (−26 billion USD) in the case of the third scenario.

Fig. 7 Effects of supportive measures for carbon pricing on
the GDP.

Fig. 8 Carbon prices necessary for achieving the emission target
in the case of each alternative supportive measure.

How to Use Carbon Tax Revenues Well: Implications from Taiwan

77

Table 2 Sectoral effects with respect to the three supportive measures.

(1) Income for the fiscal budget
Other manufacturing
Semiconductors
Other electronics
Wholesale and retail
Basic chemicals
Construction
Power

−61
−48
−34
−28
−23
−21
−20

Machines and equipment
Crude steel
Other services
Financial and insurance
services
CementSteel products and ironChemical products
Residential services

−13
−12
−10
−10
−6
−5
−4
−4

Unit: Billion USD(2) Subsidy with regard to emission
reduction
Other manufacturing−40
Semiconductors−35
Basic chemicals−27
Other electronics
−25
Power
−22
Wholesale and retail−21
Construction−11
Financial and insurance−10
services
Crude steel−9
Other services−8(3) Subsidy with regard to carbon
pricing payments
Other manufacturing−42
Semiconductors−37
Other electronics−26
Basic chemicals
−22
Power
−22
Wholesale and retail−22
Construction−12
Financial and insurance−9
services
Crude steel−9
Other services−8

CementMachines and equipment
Passenger and cargo transport
Steel products and iron
Fuel

CementMachines and equipment
Steel products and iron
Passenger and cargo transport
Fuel

The wholesale and retail sector, other electronics, andbasic chemicals sectors experience losses of 28, 25 and 22
billion USD, respectively, in each scenario.
As a consequence, recommending a best policy
which could minimize negative effects on industrial
sectors is not easy. The policy recommendation would
depend on the target at hand. For instance,
semiconductors constitute one of the most important
industrial sectors in Taiwan. When the government sets up
a target to minimize the negative effects of carbon pricing
on such a sector, the subsidy should be provided
according to sectoral emission reduction (−35 billion
USD). However, regarding the goal to minimize the lossesof the basic chemicals sector, the government should
recycle the carbon revenue according to carbon pricing
payments (−22 billion USD), instead of subsidizing withregard to emission reduction (−27 billion USD). Carbon
pricing is a single policy which affects various sectors.
We should not propose one scheme for recycling revenues
to industrial sectors that would dominate over the otherschemes, with respect to sectoral effects. Regarding thesectoral effects of carbon pricing, any revenue recycling
scheme will involve distribution issues which are beyond
the scope of our study. However, these two scenarios inwhich carbon revenues are recycled to industrial sectors
would mitigate the negative effects to a large extent
compared with using the revenue as income for the fiscal
budget. Therefore, the proper design for the use of carbon
pricing revenues will depend on the target at hand.

4. Conclusions
This study has aimed to investigate how to use
carbon pricing revenues well to mitigate the negative

−7
−6
−5
−5
−4

−6
−6
−5
−5
−4

effects on the economy. We find that relying solely on acarbon pricing policy could induce high economic costs.
For instance, relying on carbon pricing reduces the GDP
by 11% relative to the BAU scenario in 2035. Such anegative effect would be more severe than in carbon
pricing scenarios in which subsidies are provided
according to contributions to emission reductions (−8% of
GDP in 2035) or carbon pricing payments (−6% of GDP
in 2035). In particular, the energy-intensive industrial
sectors suffer from greater impacts. Regarding GDP
losses, we recommend a scheme for recycling the revenue
according to individual carbon pricing payments.
Recommending a best policy that would minimize
the negative effects on all industrial sectors is not easy.
When the government sets up a target to minimize thenegative effects of carbon pricing on semiconductors, thesubsidy should be provided according to sectoral emission
reduction. However, if the goal is minimizing losses forthe basic chemicals sector, the government should recycle
the carbon revenue according to carbon price payments.
With respect to the sectoral effects of carbon pricing, any
revenue-recycling scheme will involve distribution issues
that are beyond the scope of our study.
We can extend this study by considering other policy
measures for reducing carbon emissions. For example, thegovernment could promote renewable energy investment,with positive effects on the economy. Moreover, theprivate sectors might be willing to invest in energy
efficiency improvement in the face of high carbon prices.
The producers could achieve the same output using lessenergy through improved energy efficiency, mitigating
carbon emissions. We leave these issues for future
research.

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Y.H. WU and F.F. LIAO

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Yi-Hua WU
The research interests of Dr. Yi-Hua WU
includeenergyeconomics
andmacroeconomics. Since 2015, Dr. Wu has
cooperated with the Asia-Pacific Integrated
Model (AIM) team of National Institute forEnvironmental Studies Japan, building acomputable general equilibrium (CGE)model for Taiwan. He can conduct analyses for carbon pricing,energy efficiency, and renewable investments using a CGE model.
Dr. WU has published international journals with AIM team. Dr.
WU received his Ph.D. in Economics from National Taiwan
University. He got a master’s degree in Economics from National
Tsing Hua University and a bachelor’s degree in Public Financefrom National Taipei University.

Feng-Fan LIAO
Dr. Feng-Fan LIAO research aims to bridgeenergyefficiency
andorganization
management, offering competitive strategy todiscover factor of energy efficiency
improvement for enterprise. His backgroundis in business management, humanity andengineering. Dr. LIAO received his Ph.D. in
Business Administration from National Taiwan University. He has amaster's degree in History from National Tsing Hua University anda bachelor's degree in Materials and Mineral Resources
Engineering from National Taipei University of Technology.
(Received 13 May 2022, Accepted 16 October 2022)