REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

ISSN: 1576-0162

DOI: http://dx.doi.org/10.33776/rem.v0i68.7824

MITIGATING CARBON EMISSIONS: MARKET-BASED VS.

TECHNOLOGY SUPPORT POLICIES

MITIGACIÓN DE LAS EMISIONES DE CARBONO: POLÍTICAS BASADAS

EN EL MERCADO FRENTE A POLÍTICAS DE APOYO TECNOLÓGICO

Hale Akbulut

halepehlivan@hacettepe.edu.tr

Hacettepe University

Recibido: junio 2023; aceptado: junio 2024

ABSTRACT

Various policy instruments are being used to reduce carbon emissions,

which are the root cause of climate change. This study aims to empirically test

the impact of market-based policies and technological support programs on

carbon emissions and to reveal which policy is more effective in mitigation. For

this purpose, the dataset of 19 OECD countries for the period 1994-2019 was

used. The results of the study conﬁrm that the policy coefﬁcients vary across the

values of the dependent variable. Accordingly, market-based policy stringency is

effective in reducing emissions, and the effect increases at high carbon values.

No signiﬁcant effects were found for technology support programs.

Keywords: Carbon taxes, quantile regression, regulation.

RESUMEN

Se están utilizando varios instrumentos políticos para reducir las emisiones

de carbono, que son la causa fundamental de cambio climático. Este estudio

tiene como objetivo probar empíricamente el impacto de las políticas basadas en

el mercado y los programas de apoyo tecnológico en las emisiones de carbono,

y revelar qué política es más efectiva en la mitigación. Para ello, se utilizó el

conjunto de datos de 19 países de la OCDE para el período 1994-2019. Los

resultados del análisis cuantílico del panel conﬁrman que los coeﬁcientes de

política varían según los valores de la variable dependiente. En consecuencia, la

rigurosidad de las políticas basadas en el mercado es efectiva para reducir las

emisiones, y el efecto aumenta con valores altos de carbono. No se encontraron

efectos signiﬁcativos para los programas de apoyo tecnológico.

Palabras clave: Impuestos al carbono, regresión cuantílica, regulación.

JEL Classiﬁcation/ Clasiﬁcación JEL: H23, H50, Q54, Q58.

REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

1. INTRODUCTION

Climate change is now perceived as one of the most important global

problems. Depending on climate change, many problems such as temperature

rise, drought, ﬂoods and devastating weather events occur, which not only

negatively affect biotic populations, but also harm the economy (Dell et al.,

2014; Burke et al., 2015; Batten et al., 2018).

One of the most important steps taken in recent years to combat climate

change is the Paris Agreement which aims to limit the global temperature

increase to less than 2 degrees Celsius in the long term compared to the

pre-industrial era. Adopted in 2015, the treaty marks the ﬁrst time that all

countries worldwide have committed to reducing greenhouse gas emissions.

In this context, exploring the impact of different variables on emissions has

attracted the attention of researchers, and the current literature addresses this

issue in different dimensions.

Preliminary studies addressing the economic determinants of emissions

focused mostly on the GDP or economic growth variable, based on the

environmental Kuznets curve hypothesis. However, no consensus was reached

regarding the validity of the hypothesis. For example, Aslanidis and Iranzo

(2009), Saboori and Sulaiman (2013) and Yasin et al. (2021) reached empirical

ﬁndings supporting the hypothesis, while Cole et al. (1997) found that it was

valid only for local pollutants in selected OECD countries. Perman and Stern

(2003) argued that quite restrictive assumptions were required for a long-run

environmental Kuznets Curve relationship to exist.

Subsequent studies addressing the determinants of emissions focused on

different economic and political variables. For example, the impact of foreign

direct investments has attracted the attention of many researchers (e.g.

Levinson and Taylor, 2004; Blanco et al., 2013; Bae et al., 2017; Mert and

Caglar, 2020; Opoku et al., 2021; Akbulut and Yereli, 2023). Trade volume

(e.g. Liddle, 2018; Khan et al., 2020; Wang and Zhang, 2021) and ﬁnancial

development (e.g. Abbasi and Riaz, 2016; Jiang and Ma, 2019; Vo and Zaman,

2020; Paramati et al., 2021; Li et al., 2022; He et al., 2022; Akbulut, 2023,

2024) were the other most tested economic variables. In recent years, the

effects of different policy instruments such as taxes and standards have been

tested frequently (e.g. Hashmi and Alam, 2019; Ahmed, 2020; Wang et al.,

2020; Akbulut, 2022). Unfortunately, there is no consensus on whether the

policies are successful in mitigating emissions.

120 Hale Akbulut

The objective of this study is to contribute to the literature by simultaneously

testing the impacts of different mitigation tools on carbon emissions, which

account for the largest share of total emissions. To this end, we used the

Environmental Policy Stringency (EPS) index, a comprehensive index of the

OECD, and is widely used in the literature (De Angelis et al., 2019; Wang et al.,

2020; Sohag et al., 2021, Akbulut and Yereli, 2023). The index was developed

by Botta and Kozluk (2014) and recently modiﬁed by Kruse et al. (2022). It

measures the stringency of three different types of policy instruments: market-

based, nonmarket-based, and technology support policies. Market-based

policies include trading schemes and taxes. Nonmarket-based policies include

emission limits. Technology support policies include upstream (R&D support)

and downstream policies (feed-in tariffs, auctions). Of the policy instruments

measured in the EPS, the market-based instruments and technology support

programs directly target carbon emissions, so, the impact of these two

instruments is used as the baseline in this study.

Some previous studies have used the EPS index in their empirical analyses,

but most of these studies have considered the index as a whole and have not

disaggregated the effects of different policy instruments. The studies by De

Angelis et al. (2019), Wang and Shao (2019), and Akbulut and Yereli (2023) are

among the few studies that empirically examine whether the effects of more

stringent environmental policies differ by the type of regulatory instrument

used. However, these studies made a dual distinction between market-based

and nonmarket-based policies, and ignored the effects of technology support

programs which can play an important role in reducing carbon emissions by

supporting R&D spending on low-carbon production and renewable energy.

Thus, to the best of our knowledge, this study is the ﬁrst to empirically test the

impact of the stringency of technology support spending on carbon emissions.

In sum, this study is expected to contribute to the literature in several ways.

First, by using panel data, we beneﬁt from a larger number of observations.

Second, the use of updated policy index scores obtained with the new

methodology allows us to make more reliable policy recommendations. Third,

the use of panel quantile regression analysis as a methodology allows for

the observation of the effects of independent variables for different values

of the dependent variable. Third, the study allows for a comparison of their

direct effectiveness in reducing carbon emissions by testing market-based

instruments and technology support instruments in the same context. Finally,

the empirical results are tested for robustness by including different variables

in the analyses.

The remainder of this study is divided into four sections. Section 2 provides

a literature review, section 3 discusses data and methodology, section 4

presents the results of the empirical analyses, and section 5 presents the main

ﬁndings of the study with policy recommendations.

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2. LITERATURE REVIEW

Numerous studies empirically analyze the determinants of environmental

impacts. It is noteworthy that these studies are often based on the IPAT and/or

STIRPAT models (Ehrlich and Holdren, 1971; Dietz and Rosa, 1997; Liddle and

Lung, 2010; He et al., 2017; Liddle, 2015; Shuai et al., 2018; Hashmi and Alam,

2019, Khan et al., 2021; Akbulut and Yereli, 2023, Zhang et al., 2023; Wang

and Taghvaee, 2023; Akbulut, 2024). These models analyze anthropogenic

environmental impacts using population, wealth, and technology indicators.

Population impacts on carbon emissions can occur in two different ways.

If population growth facilitates energy use, pollution will increase. However,

if population growth facilitates intensive energy use and increases efﬁciency,

pollution will decrease (Hao et al., 2018). Satterthwaite (2009) also argues

that consumption rather than population growth has an impact on climate

change. Empirical studies have also reached different conclusions. Although

some studies argue that population has a positive impact on emissions (He

et al., 2017; Hashmi and Alam, 2019), there are also studies with opposite

results (Begum et al., 2015, Ahmad et al., 2019). While Alam et al. (2016)

concluded that there is a signiﬁcant relationship between population growth

and emissions for India and Brazil, they found non-signiﬁcant results for China

and Indonesia. In other words, the results may differ depending on the sample

of countries considered.

Generally, per capita income or growth rates have been used as indicators

of well-being. The idea that there is an inverted U-shaped relationship between

wealth and environmental degradation was ﬁrst put forward by Grossman and

Krueger (1995). This pattern was adopted as EKC in later studies (Panayotou,

1993; Grossman and Krueger, 1995, Stern et al., 1996). Some previous

studies conﬁrm the existence of EKC for samples of individual countries: a)

China (Yin et al., 2015; Bese et al., 2022), b) Italy (Bento and Moutinho, 2016),

c) Malaysia (Lau et al., 2014), d) Russia (Sohag et al., 2021), e) Thailand and

Singapore (Saboori and Sulaiman, 2013), f) Turkiye (Gokmenoglu and Taspinar,

2016). Some others conﬁrm EKC for a group of countries: a) less developed

countries (Yasin et al., 2021), b) OECD countries (Galeotti et al., 2006), c)

some ASEAN countries (Heidari et al., 2015).

Some studies do not conﬁrm the EKC hypothesis. Ozcan (2013) studied the

relationship between environmental degradation and per capita income in 12

Middle Eastern countries. However, an inverted U relationship was conﬁrmed

for only 3 of the countries. In addition, positive evidence of a U-shaped

relationship was found for 5 Middle Eastern countries. In addition, using the

STIRPAT model for China, He et al. (2017) showed that carbon emissions

increase with income. The results of Hashmi and Alam (2019) showed that

GDP is the driving force for the increase in carbon emissions in the context of

OECD countries. Similarly, Demiral et al. (2021) found a positive relationship

between income and carbon emissions in their study of 15 major emitting

122 Hale Akbulut

countries. Moreover, the direction of the relationship was parallel for different

income levels.

Some studies examined whether the environmental Kuznets curve is

N-shaped instead of U-shaped. While Allard et al. (2018) supported the

existence of an N-shaped EKC in some countries with different income groups,

Awan and Azam (2022) conﬁrmed the N-shaped EKC for G-20 economies.

It is useful to emphasize that various variables are used as indicators of

technology, but energy consumption is the most commonly used among them.

In particular, the increase in fossil fuel-based energy consumption is expected

to increase the amount of emissions. Studies in the literature generally conﬁrm

this expectation (Alam et al., 2016, Ahmad et al., 2019). In contrast, Allard et

al. (2018) used patent applications as a proxy for technological development

in their study analyzing the determinants of emissions for 74 countries using

a panel quantile regression. They concluded that technology has a positive

impact on emissions in low- and middle-income countries and found that the

impact was inconclusive in different quantiles of high-income countries. Their

explanation for the ﬁnding that technology increases emissions was that they

used all patents, not just patents related to clean technologies. In a more recent

study, Demiral et al. (2021) found that energy productivity reduces emissions

in high and middle-income countries, while the mitigating effect is higher in

middle-income countries.

In addition to the variables in the IPAT and/or STIRPAT models, policy

instruments to reduce emissions must be included in the analysis, consistent

with the purpose of this study. The literature has examined the effects of

different policy instruments using different samples and methodologies. In the

case of China, Hao et al. (2018) used the ratio of the production value of

comprehensive recycling of three wastes to GDP as a policy indicator. Using

city-level panel data, they concluded that current regulations have not reduced

pollution. The results of Hashmi and Alam (2019) suggest a negative effect of

environmental tax revenues on emissions in OECD countries. Moreover, they

found that a 1% increase in green patents reduces emissions by 0.017%.

Therefore, market-based instruments such as carbon pricing and patents have

been suggested as effective policy options.

Aydin and Esen (2018) examined the role of various taxes, including

environmental, energy, and transport taxes, on emissions in EU countries

using a dynamic panel threshold regression model. They concluded that

the threshold effect was signiﬁcant for all tax types except transport taxes.

Accordingly, the impact of taxes exceeding certain thresholds changed from

insigniﬁcantly positive to signiﬁcantly negative. The study by Neves et al.

(2020) is another study that looks at the impact of tax revenues on emissions.

According to their results, environmental taxes seem to reduce emissions in the

long run in 17 EU countries.

A signiﬁcant number of subsequent studies have used the OECD’s

environmental stringency index (De Angelis et al., 2019; Demiral et al., 2021;

Ahmed and Ahmed, 2018; Ahmed, 2020; Wang and Shao, 2019; Wang et

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al., 2020; Sohag et al., 2021; Wolde-Rufael and Weldemeskel, 2020, 2021)

because it is an internationally comparable and comprehensive measure. Some

of these studies argue that stringent policies are effective tools for mitigation

(Ahmed and Ahmed, 2018; De Angelis et al., 2019; Ahmed, 2020; Wang et

al., 2020), while other studies argue that stringent policies are ineffective, and

in some cases even have emission-increasing effects (Demiral et al., 2021).

The study by Sohag et al. (2021), draws attention by dividing the sample into

two regimes according to gross regional production in the case of Russia. The

results of the dynamic panel regression analysis showed that the EPS had a

positive effect on emissions in the lower regime and a negative effect in the

upper regime. Thus, the effects of the policy differed by income level. Another

notable study comes from De Angelis et al. (2019). In this study, the effects

of market-based and non-market-based policies were analyzed separately.

Using a ﬁxed-effects model, they found negative coefﬁcients for both policy

variables. In another study that differentiates market-based and non-market-

based policies, Akbulut and Yereli (2023) revealed that non-market-based

policy instruments have threshold effects that can support the pollution halo

hypothesis. However, these studies were based on the old methodology of

the environmental policy stringency index and ignored technology support

programs. Based on the above discussions, it is noteworthy that the effects of

the environmental policy stringency index have not been tested with the ﬁgures

obtained from the current methodology, including the spending instrument.

Finally, some studies have suggested that the EPS has nonlinear effects.

For example, Wolde-Rufael and Weldemeskel (2020, 2021) pointed out the

existence of an inverted-U relationship in a panel of developing countries.

Therefore, it is also important to consider the possible nonlinear effects. In this

context, this study is expected to contribute to the literature by considering

the expenditure dimension of the current environmental policy stringency

index and potential non-linear relationships.

3. DATA AND METHODOLOGY

Usual regressions refer to the mean, but as Cade and Noon (2003)

suggest, in the case of ecological processes, there may be stronger and

useful predictive relationships with other parts of the distribution of the

response variable. Because quantile regressions are based on the median or

other points in the conditional distribution of the dependent variable, more

robust results to outliers can be obtained (Hubler, 2007). Hubler (2007) also

draws attention to the marginal and the change in the level of emissions over

economic development. This difference justiﬁes the use of quantile regression

in estimating emissions.

The technique of quantile regression was ﬁrst introduced by Koenker and

Basset (1978), and given , the conditional quantile of is given as follows:

(1)

124 Hale Akbulut

where i is the country, t is the year, Qyit (τ|xit) is the τ – th quantile of the

dependent variable, xit τ is the vector of explanatory variables for quantile τ,

and βτ symbolizes the slopes of explanatory variables for quantile τ.

This paper tests the relationship between carbon emissions (crb) and two

important policy instruments: the market-based EPS index (mp) and technology

support programs index (ts). For this purpose, the IPAT model proposed by

Ehrlich and Holdren (1971) was used as a basis. The model explains the human

impact on the environment, by using the variables of afﬂuence, population and

technology. In some later studies (York et al., 2003; Fan et al., 2006; Hashmi

et al., 2019), a stochastic structure was added to the model (STIRPAT), and

the effects of each variable on the environment were empirically tested. In the

model discussed here, based on the aforementioned models, the indicators

of afﬂuence (gdp), population (popcit) and technology (fs) were used, and in

addition, policy variables (mp and ts) were included in the model. The variables

crb, gdp, popcit, and fs were used in natural logarithmic form. The explanations

and sources of the variables are listed in Table 1. Two models were developed

for the analysis as follows:

Model 1:

(2)

Model 2:

(3)

where the subscripts i (i=1, 2, …, N) and t (t=1, 2, …, T) denote the cross-

section (country) and time period (year), respectively. is the error term such

that eit~iid(0,σ2).

TABLE 1. DATA DESCRIPTION AND SOURCES

Variable Deﬁnition Source

crb CO2 emissions (metric tons per capita)

World Bank

https://data.worldbank.org/indicator/EN.ATM.

CO2E.PC

popcit Population in the largest city (% of urban

population)

World Bank

https://data.worldbank.org/indicator/EN.URB.

LCTY.UR.ZS

gdp GDP per capita (constant 2015 USD)

World Bank

https://databank.worldbank.org/source/sustai-

nable-development-goals-(sdgs)/Series/NY.GDP.

PCAP.KD

fs Fossil fuels per capita (kWh)

Our World in Data

https://ourworldindata.org/grapher/fossil-fuels-

per-capita

mp EPS index

(market-based policies)

OECD

https://stats.oecd.org/Index.

aspx?DataSetCode=EPS (Continue)

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Variable Deﬁnition Source

ts EPS index

(technology support policies)

OECD

https://stats.oecd.org/Index.

aspx?DataSetCode=EPS

tr Trade volume (% of GDP)

World Bank

https://data.worldbank.org/indicator/NE.TRD.

GNFS.ZS

fdi Foreign direct investment inﬂows (% of GDP)

World Bank

https://data.worldbank.org/indicator/BX.KLT.DINV.

WD.GD.ZS

Due to data availability, the sample includes 19 OECD countries (Austria,

Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Japan,

Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkiye, United

Kingdom, and the United States) with annual data from 1994 to 20191.

To examine the impact of environmental policies, we used the EPS index.

This index, ﬁrst constructed by Botta and Kozluk (2014), is based on a dual

distinction between market-based and non-market-based policies. While

market-based policies consisted of taxes, trading schemes, and feed-in tariffs,

non-market-based policies included standards and R&D subsidies. Later, the

calculation method of the index was developed by Kruse et al. (2022), so the

update of the dataset was based on a threefold distinction: market-based

policies, nonmarket-based policies, and technology-support policies. Among

these subcomponents, market-based and technology-support programs

in particular are closely related to carbon emissions. While market-based

instruments have subcomponents such as carbon taxes and carbon trading

permits, technology-support programs include low-carbon R&D spending and

support for solar and wind energy. The indices take values between zero and

six, and the higher their value, the more stringent the environmental policy.

4. EMPIRICAL ANALYSIS

The empirical analysis starts with the cross-sectional dependency test.

With the information obtained from this test result, the appropriate unit root

test was determined. Then, the variables were tested for stationarity and

became stationary to eliminate the spurious regression problem. Afterward,

a cointegration test was performed to test whether there was a long-term

relationship between the variables. Upon determining the long-term relationship

between the variables, the model was tested ﬁrst with the linear ﬁxed effects

method, and then with the non-linear panel quantile method. In the last part of

the analysis, robustness analyses were carried out using additional variables.

When the variables of the countries in the sample exhibit cross-sectional

dependence, the ﬁrst-generation unit root tests lose their reliability. In such a

case, it is necessary to apply second-generation unit root tests to check the

stationarity of the variables. Therefore, the analysis starts with three different

1 The data set can be shared with interested readers upon request.

126 Hale Akbulut

tests for cross-sectional dependency: Breusch-Pagan (1980) Lagrange

Multiplier (LM) test, the Pesaran, Ullah, and Yamagata (2008) bias-adjusted

LM test, and the Pesaran (2004) Cross-Sectional Dependence (CD) test. The

results of the tests are shown in Table 2.

TABLE 2. THE RESULTS OF CROSS-SECTIONAL DEPENDENCE TESTS

LM LM adj. LM CD

crb 2642

(0.0000)

361

(0.0000)

38.96

(0.0000)

popcit 2713

(0.0000)

371.4

(0.0000)

-0.8875

(0.3748)

gdp 3354

(0.0000)

465.9

(0.0000)

56.27

(0.0000)

fs 2675

(0.0000)

365.8

(0.0000)

40.71

(0.0000)

mp 1706

(0.0000)

223

(0.0000)

29.87

(0.0000)

ts 1902

(0.0000)

252

(0.0000)

38.95

(0.0000)

tr 2794

(0.0000)

383.4

(0.0000)

49.56

(0.0000)

fdi 425.8

(0.0000)

34.31

(0.0000)

11.83

(0.0000)

Note: Figures in parentheses are p-values.

As can be seen in Table 2, the null hypothesis that there is no covariance

between the cross-sectional residuals was rejected in all tests. Therefore, the

Cross-Sectionally Augmented Dickey-Fuller (CADF) test developed by Pesaran

(2007), one of the second-generation unit root tests, was used to control for

stationarity. The test results are summarized in Table 3. Since the variables crb,

fs, mp, and ts are stationary at a 5% signiﬁcance level, they are used at their

levels. For the other variables, their ﬁrst differences were used.

TABLE 3. PESARAN (2007) CADF SECOND GENERATION UNIT ROOT TEST RESULTS

Z(t-bar) p-value

Without trend With trend Without trend With trend

crb level -0.838 -3.079*** 0.201 0.001

1st diff. -11.070*** -8.653*** 0.000 0.000

popcit level 2.795 -4.490*** 0.997 0.000

1st diff. -3.704*** -2.402*** 0.000 0.008

gdp level -0.968 -0.691 0.166 0.245

1st diff. -4.516*** -2.980*** 0.000 0.001

fs level -1.510* -3.479*** 0.066 0.000

1st diff. -11.595*** -9.695*** 0.000 0.000

mp level -2.051** -0.107 0.020 0.458

1st diff. -9.997*** -8.665*** 0.000 0.000

ts level -6.641*** -4.139*** 0.000 0.000

(Continue)

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Z(t-bar) p-value

Without trend With trend Without trend With trend

1st diff. -9.450*** -7.376*** 0.000 0.000

tr level 0.007* 2.554 0.503 0.995

1st diff. -5.803*** -4.392*** 0.000 0.000

fdi level -0.970 0.577 0.166 0.718

1st diff. -11.20*** -8.667*** 0.000 0.000

Note: ***, **, and * denote statistical signiﬁcance at 1%, 5%, and 10% levels, respectively.

In the next step, the presence of cointegration between variables was

conﬁrmed by Pedroni’s (1999) panel cointegration test. This test offers

signiﬁcant advantages in that it allows for heterogeneity in both the intercepts

and trend coefﬁcients in the structural model across cross-sections, and the

coefﬁcient across the cross-sections in equation 4:

(4)

where e it refers to the estimated residual of the Model 1 and 2.

The test provides four within-dimension and three between-dimension

statistics. In the case of within-dimension, the null and alternative hypotheses

are H0:ρ=1 for all i and H1:ρ1=ρ < 1, respectively. In the case of between-

dimension, the null and alternative hypotheses are H0:ρi=1for all i and H1:ρ1<1

< 1for at least one i. The test results are shown in Table 4.

TABLE 4. PEDRONI PANEL COINTEGRATION RESULTS

Model 1 Model 2

Within-dimension Between-dimension Within-dimension Between-dimension

Modiﬁed variance

ratio

-2.7966***

(0.0026)

- -3.2794***

(0.0005)

Modiﬁed PP t-stat 0.9186

(0.1791)

2.4979***

(0.0062)

1.8921**

(0.0292)

2.8311***

(0.0023)

PP t-stat -3.0529***

(0.001)

-1.8874**

(0.0296)

-1.3337*

(0.0911)

-0.8517

(0.1972)

ADF t-stat -3.7250***

(0.0001)

-2.8809***

(0.0020)

-0.3921

(0.3475)

-1.4067*

(0.0798)

Note: ***, **, and * denote statistical signiﬁcance at 1%, 5%, and 10% levels, respectively.

Based on statistical signiﬁcance at the 5% level, three out of four of the

within-dimensions models and all of the between-dimensions models conﬁrm

the presence of cointegration among the reference variables of Model 1.

Two out of four of the within-dimensions models and one out of three of the

between-dimensions models conﬁrm the presence of cointegration among the

reference variables of Model 2. Therefore, more detailed regression analyses

can be performed.

128 Hale Akbulut

In the next step, ﬁxed effects regression was performed. The estimator

used was the methodology of Driscoll-Kraay (1998), which provides robust

results in the case of heteroskedasticity, autocorrelation, and cross-sectional

dependence. According to the results, fossil fuel consumption is an important

variable affecting emissions in both models. A 1% increase in fs increases the

crb by about 1-1.6%.

While gdp is signiﬁcant at the 10% level in Model 1, it is signiﬁcant at the

1% level in Model 2. In both models, gdp has a positive effect on emissions.

This can be interpreted to mean that the countries in the rising part of the

EKC dominate among the countries in the sample. The population variable was

found to be non-signiﬁcant in both models.

TABLE 5. ROBUST FIXED EFFECTS REGRESSION ESTIMATES

Model 1 Model 2

c-9.0081***

(0.2823)

-9.4333***

(0.3356)

popcit 0.0477

(0.3449)

0.1501

(0.3736)

gdp 0.1358*

(0.0685)

0.1629***

(0.0546)

fs 1.0619***

(0.0273)

1.1003***

(0.0316)

mp -0.0152**

(0.0067)

ts 0.0015

(0.0035)

R20.9382 0.9368

F-prob. 0.0000 0.0000

Note: Driscoll-Kraay standard errors in parentheses.

***, **, and * denote statistically signiﬁcance at 1%, 5%, and 10% levels, respectively.

Among the policy variables, it is noteworthy that only the market-based policies

are signiﬁcant at the 5% level. Accordingly, increasing mp has a decreasing effect

on crb. ts is not statistically signiﬁcant at the 5% level, but it may also have an

impact on emissions at different quantiles. Therefore, the panel quantile regression

analysis (QREG) discussed in the next step focused on both mp and ts.

TABLE 6. QREG ESTIMATES FOR MODEL 1

Quantile 0.25 0.5 0.75

coefﬁcient std. error coefﬁcient std. error coefﬁcient std. error

popcit 0.1620 0.4631 0.0436 0.3483 -0.0791 0.4858

gdp 0.1021 0.0799 0.1370** 0.0601 0.1732** 0.0838

fs 1.0882*** 0.0231 1.0610*** 0.0175 1.0327*** 0.0242

mp -0.0124* 0.0066 -0.0153*** 0.0049 -0.0184*** 0.0069

Note: ***, **, and * denote statistical signiﬁcance at 1%, 5%, and 10% levels, respectively.

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REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

Table 6 presents the QREG results of model 1 for the 25th, 50th, and 75th

percentiles. The empirical results generally show that the effects of the variables

on crb are heterogeneous. Accordingly, the coefﬁcient of gdp is positive but not

signiﬁcant at the 25th quantile at the 5% signiﬁcance level. However, at the 50th

and 75th quantiles, the coefﬁcients are signiﬁcant. Moreover, the value of the

coefﬁcient increases with the higher quantile. Therefore, the impact of gdp on

carbon emissions is higher in countries with high emissions. Some countries in

the sample (Denmark, Finland, Germany, Ireland, the United Kingdom, and the

United States) have both high emission levels and high GDP per capita. These

countries may be in the rising part of the EKC, so the increase in gdp increases

the amount of emissions. This result is consistent with the ﬁndings of Allard et al.

(2018) and Awan and Azam (2022), which support the N-shaped EKC.

Another important result of the analysis relates to the variable fs. Accordingly,

the coefﬁcients of fs are statistically signiﬁcant in all three percentiles, but their

magnitudes differ. The impact of fs on crb is lower in high-emission countries.

These countries may place more emphasis on mitigation and implement more

stringent policies.

As shown in Table 6, the mp also has a heterogeneous effect. Although

the coefﬁcient of mp at the 25th percentile is not statistically signiﬁcant,

the coefﬁcients at the 50th and 75th percentiles are signiﬁcant. mp reduce

pollution by increasing the cost of pollution. Policies such as carbon taxes or

marketable pollution permits, implemented at high emission levels, impose

higher costs. For this reason, producers are more sensitive to rising costs

and take initiatives to reduce their emissions. Therefore, policy effectiveness

increases at high emission levels. In addition, high-emission countries tend

to have more stringent policies as a whole, which increases the impact of

market-based policies on mitigation. Therefore, the joint use of different policy

instruments also increases the effectiveness of the policy. Figure 1 shows the

coefﬁcient shifts during the reference period.

The next step was to test the impact of ts with QREG. Table 7 presents the

results of Model 2 for the 25th, 50th, and 75th percentiles. The effects of gdp

and fs on crb are consistent with Model 1. Accordingly, increases in gdp at the

0.5 and 0.75 quantiles have an increasing effect on emissions, and this effect

is ampliﬁed at high emission levels. An increase in fs also leads to an increase

in crb, but this effect appears to be attenuated at higher emission levels.

TABLE 7. QREG ESTIMATES FOR MODEL 2

Quantile 0.25 0.5 0.75

coefﬁcient std. error coefﬁcient std. error coefﬁcient std. error

popcit 0.2574 0.4844 0.1422 0.3689 0.0262 0.5020

gdp 0.1545* 0.0881 0.1635** 0.0671 0.1725* 0.0913

fs 1.1253*** 0.0220 1.0984*** 0.0170 1.0714*** 0.0228

ts 0.0035 0.0028 0.0014 0.0022 -0.0008 0.0029

Note: ***, **, and * denote statistical signiﬁcance at 1%, 5%, and 10% levels, respectively.

130 Hale Akbulut

FIGURE 1. QREG GRAPHS FOR MODEL 1

As shown in Table 7, the effect of ts on emissions is not statistically

signiﬁcant, consistent with the ﬁxed effects model. In this case, the ts variable

does not have a signiﬁcant effect on crb even at different quantiles. Figure 2

shows the coefﬁcient shifts during the reference period.

FIGURE 2. QREG GRAPHS FOR MODEL 2

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REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

4.1. ROBUSTNESS ANALYSIS

Two robustness check analyses were conducted using alternative models

that included additional variables to test the validity of the earlier results. These

variables include FDI and trade, which are commonly used in the literature to

explain emissions. Two opposing hypotheses come to the fore in explaining the

impact of FDI on emissions. According to the Pollution Haven Hypothesis (PHAH),

proposed by Walter and Ugelow (1979) and Pethig (1976), FDI, especially

from environmentally intensive industries, tends to ﬂow to countries with low

environmental regulations, which negatively affects environmental quality

in the host country. In contrast, according to the Pollution Halo Hypothesis

(PHH), FDI contributes to improving environmental standards by transferring

clean technology and environmentally-friendly production standards to the

host country. There are numerous studies in the literature that support both

hypotheses (Bae et al., 2017; Raﬁndadi et al., 2018; Balsalobre-Lorente et al.,

2019; Hanif et al., 2019; Mert and Caglar, 2020; Neves et al., 2020). Thus, the

relationship between FDI and emissions is not clear, yet.

Moreover, the nature of the relationship between trade and emissions remains

unclear. Trade can have positive effects by setting the stage for environmental

improvements through efﬁcient resource use and economic growth. On the

other hand, free trade causes developed countries to shift their dirty industries

to developing countries, which may tend to increase pollution in developing

countries. There are numerous studies in the literature that empirically estimate

the impact of trade on emissions (Hao and Liu, 2015; Shahbaz et al., 2017;

Allard et al., 2018; Liobikiene and Butkus, 2019; Essandoh et al., 2020). In this

context, the effects of FDI and trade variables were empirically tested in separate

models. Model R1 includes FDI ﬂows (% of GDP) (fdi) and model R2 includes

trade volume of goods and services (% of GDP) (tr) as explanatory variables. The

coefﬁcient estimates obtained from the QREG analysis are presented in Table 8.

TABLE 8. QREG ESTIMATES FOR ROBUSTNESS ANALYSIS

Quantile 0.25 0.5 0.75

R1 R2 R1 R2 R1 R2

popcit 0.1516

(0.4608)

0.2079

(0.4717)

0.0474

(0.3506)

0.0776

(0.3556)

-0.0699

(0.4915)

-0.0609

(0.4956)

gdp 0.1195

(0.0823)

0.1395

(0.0873)

0.1417**

(0.0626)

0.1589**

(0.0658)

0.1668*

(0.0878)

0.1794*

(0.0917)

fs 1.0884**

(0.0229)

1.0892***

(0.0234)

1.0622***

(0.0176)

1.0616***

(0.0178)

1.0327***

(0.0244)

1.0323***

(0.0245)

mp -0.0124*

(0.0065)

-0.0119*

(0.0066)

-0.0152***

(0.0050)

-0.0151***

(0.0050)

-0.0184***

(0.0070)

-0.0185***

(0.0069)

fdi -0.0003

(0.0003)

-0.0001

(0.0002)

0.0001

(0.0003)

tr -0.0006

(0.0005)

-0.0003

(0.0004)

-0.0001

(0.0006)

Note: Standard errors in parentheses.

132 Hale Akbulut

As can be seen in Table 8, the economic variables included in the analysis

are not statistically signiﬁcant, but the inclusion of these variables did not

change our main results. Accordingly, in high-emitting countries, emissions

increase at a decreasing rate with an increase in gdp and with an increase in fs.

The effect of the mp on emissions is consistent with our earlier results, both in

magnitude and sign. Accordingly, mp has a negative effect on emissions, and

this effect increases at higher emission levels. The coefﬁcient of mp is (-0.012)

at the 25th percentile, (-0.015) at the 50th percentile, and (-0.018) at the 75th

percentile.

5. CONCLUSIONS

Different policy tools are used to reduce emissions. Among these

instruments, market-based options that increase the price of externalities and

technological support programs that encourage the use of renewable energy

are important options for reducing emissions. In this study, we compared the

impacts of these options by simultaneously testing them empirically. In doing

so, we beneﬁted from a large data set and used the panel quantile regression

method, which allowed us to better assess the distribution of ecological

variables.

The results of the study show that support policy does not have a statistically

signiﬁcant impact on emissions. The results of both the robust ﬁxed-effects

model and the panel quantile regression analysis support this ﬁnding. As

Kruse et al. (2022) suggest, the level of technology-enhancing policies has

weakened over the past decade, and this may be the reason why our results

regarding these programs are not signiﬁcant. In addition, the mitigation effects

of renewable energy support are likely to occur only in the long term, so it

would not be correct to abandon the technology support programs according

to our ﬁndings. In future studies, thanks to the expansion of the dataset, it

will be useful to consider studies that include long-term analyses to show the

beneﬁts of technology support.

Market-based policies, on the other hand, were found to be effective in

mitigation according to both the robust ﬁxed-effects and the panel quantile

regression analyses. As QREG results suggest, this effect is even larger at

high emission levels. This result can be explained by the fact that some of the

countries with relatively high emissions in the sample (Denmark, Finland, UK)

generally have more stringent environmental policies. Accordingly, the joint use

of different policy instruments may also be an important factor in increasing

policy effectiveness.

The fact that policy instruments have different effects at different emission

levels may be due to the fundamental characteristics of the instruments.

Market-based instruments impose signiﬁcant ﬁnancial burdens on the polluter

by increasing the price of the externality, and these burdens automatically

increase at high emission levels. Thus, producers’ tolerance for increasing

ﬁnancial burdens will decrease. Clearly, under these circumstances, producers

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REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

are much more willing to reduce their emissions to alleviate their ﬁnancial

burden. On the other hand, technology programs support rather than blame

polluters for producing and deploying cleaner technologies. The level of these

supports can be increased during periods of high emissions, but unlike market-

based instruments, it is not a spontaneous process. Thus, it would be more

useful to focus on the long-term effectiveness of support programs than on

their impact on reducing the ﬁnancial burden.

It is worth noting that the study has some limitations. First, policy effects

are likely to emerge over longer periods, and as the time dimension of the data

set increases in the coming years, the opportunity to conduct a longer-term

analysis may arise. The second limitation relates to the policy variable used. The

environmental policy stringency index does not cover regulations in all sectors

around the world. For this reason, the index may not be a sufﬁcient indicator

for countries with high production in the sectors not covered. Additionally,

analyzing market-based and technology-support policy instruments by dividing

them into sub-components will allow the effects of policy instruments such as

solar and wind energy supports to be observed separately.

Finally, the results of the study should be taken as a tribute to market-

based instruments rather than a denigration of support programs. In addition,

the stringency of market-based policies should be increased, while the long-

term effects of support programs should be analyzed in future studies using

different methods and a larger data set.

REFERENCES

Abbasi, F. and Riaz, K. (2016). CO2 emissions and ﬁnancial development in an

emerging economy: An augmented VAR approach. Energy Policy, 90,102–

114.

Ahmad, N., Du, L., Tian, X., and Wang, J. (2019). Chinese growth and dilemmas:

modelling energy consumption, CO2 emissions and growth in China. Quality

and Quantity, 53, 315-338.

Ahmed, K. (2020). Environmental policy stringency related technological change

and emissions inventory in 20 OECD countries. Journal of Environmental

Management, 274, 1-7. https://doi.org/10.1016/j. jenvman.2020.111209

Ahmed, K., and Ahmed, S. (2018). A predictive analysis of CO2 emissions,

environmental policy stringency, and economic growth in China.

Environmental Science and Pollution Research, 25, 16091-16100. https://

doi.org/10.1007/s11356-018-1849-x

Akbulut, H. (2022). Effectiveness of environmental policies on carbon

emissions: A panel threshold analysis. Economics and Business Review,

8(3), 82-108. https://doi.org/10.18559/ebr.2022.3.5

Akbulut, H. (2023). Could ﬁnancial deepening be the solution to the carbon

emission problem? Evidence from CEE countries. Transylvanian Review

of Administrative Sciences, 2023(69), 5-24. http://dx.doi.org/10.24193/

tras.69E.1

134 Hale Akbulut

Akbulut, H. and Yereli, A. B. (2023). A new look at the pollution halo hypothesis:

The role of environmental policy stringency. Estudios de Economia, 50(1),

31-54

Akbulut, H. (2024). Financial development, energy consumption

and environmental quality in developing countries. Investigacion

Economica, 83(327), 9-29. https://doi.org/10.22201/

fe.01851667p.2024.327.86235

Alam, M., Murad, W., Noman, A. H., and Ozturk, I. (2016). Relationships among

carbon emissions, economic growth, energy consumption and population

growth: Testing Environmental Kuznets Curve hypothesis for Brazil, China,

India and Indonesia. Ecological Indicators, 70, 466-479.

Allard, A., Takman, J., Uddin, G. S., and Ahmed, A. (2018). The n-shaped

environmental Kuznets curve: An empirical evaluation using a panel quantile

regression approach. Environmental Science and Pollution Research, 25,

5848-5861. https://doi.org/10.1007/s11356-017-0907-0

Aslanidis, N. and Iranzo, S. (2009). Environment and development: Is there a

Kuznets curve for CO2 emissions?. Applied Economics, 41(6), 803–810.

https://doi. org/10.1080/00036840601018994

Aydin, C., and Esen, O. (2018). Reducing CO2 emissions in the EU member

states: Do environmental taxes work?. Journal of Environmental Planning

and Management, 61, 2396–2420. https://doi.org/10.1080/09640568.2

017.1395731

Awan, A. M., and Azam, M. (2022). Evaluating the impact of GDP per capita

on environmental degradation for G-20 economies: Does N-shaped

environmental Kuznets curve exist?. Environment, Development, and

Sustainability, 24, 11103-11126. https://doi.org/10.1007/s10668-021-

01899-8

Bae, J. H., Li, D. D., and Rishi, M. (2017). Determinants of CO2 emission for

Post-Soviet Union Independent Countries. Climate Policy, 17, 591-615.

https://doi.org/10.1080/14693062.2015.1124751

Balsalobre-Lorente, D., Gokmenoglu, K. K., Taspinar, N., and Cantos-Cantos, J.

M. (2019). An approach to the pollution haven and pollution halo hypotheses

in MINT countries. Environmental Science and Pollution Research, 26,

23010-23026. https://doi.org/10.1007/s11356-019-05446-x

Batten, S. (2018). Climate change and the macro-economy: A critical review

(January 12, 2018). Bank of England Working Paper No. 706, https://ssrn.

com/abstract=3104554orhttp://dx.doi.org/10.2139/ssrn.3104554

Begum, R. A., Sohag, K., Abdullah, S. M. S., and Jaafar, M. (2015). CO2

emissions, energy consumption, economic and population growth in

Malaysia. Renewable and Sustainable Energy Reviews, 41, 594-601. http://

doi.org/10.1016/j.rser.2014.07.205

Bento, J. P. C., and Moutinho,, V. (2016). CO2 emissions, non-renewable and

renewable electricity production, economic growth, and international trade

in Italy. Renewable and Sustainable Energy Reviews, 55, 142–155. https://

doi.org/10.1016/j.rser.2015.10.151

135

MITIGATING CARBON EMISSIONS: MARKET-BASED VS. TECHNOLOGY SUPPORT POLICIES

REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

Bese, E., Friday, S. S.,& Ozden, C. (2022). Coal consumption environmental

Kuznets curve (EKC) in China and Australia: Evidence from ARDL model.

Journal of Applied Business and Economics, 22(11), 25-26

Blanco, L., Gonzalez, F. &Ruiz, I. (2013). The impact of fdi on CO2 emissions in

Latin America. Oxford Development Studies, 41(1), 104-121. https://doi.or

g/10.1080/13600818.2012.732055

Botta, E., and Kozluk. T. (2014). Measuring environmental policy stringency in

OECD countries: A composite index approach. Retrieved from OECD website:

https://www.oecd-ilibrary.org/economics/measuring-environmental-policy-

stringency-in-oecd-countries_5jxrjnc45gvg- en.

Breusch, T. S., and Pagan, A. R. (1980). The Lagrange Multiplier test and

its implications to model speciﬁcation in econometrics. The Review of

Economic Studies, 47(1), 239-253.

Burke, M., Hsiang, S. M. and Miguel, E. (2015). Global non-linear effect of

temperature on economic production. Nature, 527, 235–239.

Cade B., and Noon, B. (2003). A gentle introduction to quantile regression for

ecologists. Frontiers in Ecology and the Environment, 1(8), 412–420.

Cole, M. A., Rayner, A. J. and Bates, J. M. (1997). The environmental Kuznets

curve: An empirical analysis. Environment and Development Economics, 2,

401-416.

De Angelis, E. M., Di Giacomo, M., and Vannoni, D. (2019). Climate change

and economic growth: The role of environmental policy stringency.

Sustainability, 11(2273), 1-15. https://doi.org/10.3390/su11082273

Dell, M., Jones, B. F., Olken, B. A. (2014). What do we learn from the weather?

The new climate–economy literature. Journal of Economic Literature, 52

(3), 740–798. http://www.jstor.org/ stable/24434109.

Demiral, M., Akca, E. E., and Tekin, I. (2021). Predictors of global carbon

dioxide emissions: Do stringent environmental policies differ?.

Environment, Development and Sustainability, 23. 18337-18361. https://

doi.org/10.1007/s10668-021-01444-7

Dietz, T., and Rosa, E. A. (1997). Effects of population and afﬂuence on CO2

emissions. Proceedings of the National Academy of Sciences, 94, 175-179.

https://doi.org/10.1073/pnas.94.1.175

Driscoll, J. C., and Kraay, A. C. (1998). Consistent covariance matrix estimation

with spatially dependent panel data. Review of Economics and Statistics,

80, 549-560.

Ehrlich, P. R., and Holdren, J. P. (1971). Impact of population growth. Science,

171, 1212-1217. https://doi.org/10.1126/science.171.3977.1212

Essandoh, O. K., Islam, M., and Kakinaka, M. (2020). Linking international trade

and foreign direct investment to CO2 emissions: Any differences between

developed and developing countries?. Science of the Total Environment,

712, 1-10. https://doi.org/10.1016/j.scitotenv.2019.136437

Fan, Y., Liu, L., Wu, G., and Wei, Y. (2006). Analyzing impact factors of CO2

emissions using the STIRPAT model. Environmental Impact Assessment

Review, 26, 377-395.

136 Hale Akbulut

Galeotti, M., Lanza, A., and Pauli, F. (2006). Reassessing the environmental

Kuznets curve for CO2 emissions: A robustness exercise. Ecological

Economics, 57, 152-163.

Gokmenoglu, K., and Taspinar, N. (2016). The relationship between CO2

emissions, energy consumption, economic growth and fdi: The case of

Turkey. The Journal of International Trade and Economic Development, 25,

706-723. https://doi.org/10.1080/09638199.2015.1119876

Grossman, G. M., and Krueger, A. B. (1995). Economic growth and the

environment. Quarterly Journal of Economics, 110, 353–377.

Hanif, I., Raza, S. M. F., Gago-de-Santos, P. and Abbas, Q. (2019). Fossil

fuels, foreign direct investment, and economic growth have triggered CO2

emissions in emerging Asian economies: Some empirical evidence. Energy,

171, 493-501.

Hao, Y., Deng, Y., Nu, Z. and Chen, H. (2018). Is environmental regulation

effective in China? Evidence from city-level panel data. Journal of Cleaner

Production, 188, 966-976. https://doi.org/10.1016/j.jclepro.2018.04.003

Hao, Y. and Liu, Y. M. (2015). Has the development of fdi and foreign trade

contributed to China’s CO2 emissions? An empirical study with provincial

panel data. Natural Hazards, 76, 1079–1091.

Hashmi, R. and Alam, K. (2019). Dynamic relationship among environmental

regulation, innovation, CO2 emissions, population, and economic growth in

OECD countries: A panel investigation. Journal of Cleaner Production, 231,

1100-1109. https://doi.org/10.1016/j.jclepro.2019.05.325

He, Z., Xu, S., Shen, W., Long, R. and Chen, H. (2017). Impact of urbanization

on energy related CO2 emission at different development levels: Regional

difference in China base on panel estimation. Journal of Cleaner Production,

140, 1719-1730. http://dx.doi.org/10.1016/j.jclepro.2016.08.155

He, Y., Gao, X. and Wang, Y. (2022). Sustainable ﬁnancial development: Does it

matter for greenhouse gas emissions?. Sustainability, 14, 5064.

Heidari, H., Turan Katircioglu, S. and Saeidpour, L. (2015). Economic growth,

CO2 emissions, and energy consumption in the ﬁve ASEAN countries.

International Journal of Electrical Power and Energy Systems, 64, 785–

791. https://doi.org/10.1016/j.ijepes.2014.07.081

Hubler, M. (2017). The inequality-emissions nexus in the context of trade and

development: A quantile regression approach. Ecological Economics, 134,

174–185.

Jiang, C. and Ma, X. (2019). The impact of ﬁnancial development on carbon

emissions: A global perspective, Sustainability,11(19), 5241.https://doi.

org/10.3390/su11195241

Khan, Z., Ali, M., Jinyu, L., Shahbaz, M. and Siqun, Y. (2020). Consumption-

based carbon emissions and trade nexus: Evidence from nine oil exporting

countries. Energy Economics, 89, 104806. https://doi.org/10.1016/j.

eneco.2020.104806

Khan, D., Ozcan, B. and Ulucak, R. (2021). An empirical investigation of

nuclear energy consumption and carbon dioxide (CO2) emission in India:

137

MITIGATING CARBON EMISSIONS: MARKET-BASED VS. TECHNOLOGY SUPPORT POLICIES

REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

bridging IPAT and EKC hypotheses. Nuclear Engineering and Technology,

53, 2056–65. https://doi.org/10.1016/J. NET.2020.12.008.

Koenker, R. and Basset, G. (1978). Regression quantiles. Econometrica, 46,

33-50.

Kruse, T., Dechezleprêtre, A., Saffar, R. and Robert, L. (2022). Measuring

environmental policy stringency in OECD countries: An update of the OECD

composite EPS indicator. Retrieved fromOECD website: https://www.

oecd-ilibrary.org/economics/measuring-environmental-policy-stringency-

in-oecd-countries_5jxrjnc45gvg-en

Lau, L. S., Choong, C. K. and Eng, Y. K. (2014). Investigation of the

environmental Kuznets curve for carbon emissions in Malaysia: Do foreign

direct investment and trade matter? Energy Policy, 68, 490–497. https://

doi.org/10.1016/j.enpol.2014.01.002.

Levinson, A. and Taylor, M. S. (2004). Unmasking the pollution haven effect.

NBER Working Paper Series, No. 10629. Cambridge.

Li, X., Ozturk, I., Majeed, M. T., Hafeez, M. and Ullah, A., ‘Considering the

asymmetric effect of ﬁnancial deepening on environmental quality in

BRICS economies: Policy options for the green economy’, 2022, Journal

of Cleaner Production, 331 (129909), p.1-9. https://doi.org/10.1016/j.

jclepro.2021.129909

Liddle, B. (2015). What are the carbon emissions elasticities for income and

population? Bridging STIRPAT and EKC via robust heterogenous panel

estimates. Global Environmental Change, 31, 62-73.

Liddle, B. (2018). Consumption-based accounting and the trade-carbon

emissions nexus. Energy Economics, 69, 71-78. https://doi.org/10.1016/j.

eneco.2017.11.004

Liddle, B. and Lung, S. (2010). Age-structure, urbanization, and climate change

in developed countries: Revisiting STIRPAT for disaggregated population and

consumption-related environmental impacts. Population and Environment,

31, 317-343. https://doi.org./10.1007/s11111-010-0101-5

Liobikiene, G. and Butkus, M. (2019). Scale, composition, and technique

effects through which the economic growth, foreign direct investment,

urbanization, and trade affect greenhouse gas emissions. Renewable

Energy, 132, 1310-1322. https://doi.org/10.1016/j.renene.2018.09.032

Mert, M. and Caglar, A. E. (2020). Testing pollution haven and pollution halo

hypotheses for Turkey: A new perspective. Environmental Science and

Pollution Research, 27, 32933-32943. https://doi.org/10.1007/ s11356-

020-09469-7

Neves, S. A., Antonio, C. M. and Patricio, M. (2020). Determinants of CO2

emissions in European Union countries: Does environmental regulation

reduce environmental pollution? Economic Analysis and Policy, 68, 114-

125.

OECD.stat. (2022). Organisation for economic co-operation and development

statistics. Retrieved from https://stats.oecd.org/

138 Hale Akbulut

Opoku, E. E. O., Adams, S. and Aluko, O. A. (2021). The foreign direct

investment-environment nexus: Does emission disaggregation matter?.

Energy Reports, 7, 778-787.

Our World in Data (2022). Retrieved from https://ourworldindata.org/grapher/

fossil-fuels-per-capita

Ozcan, B. (2013). The nexus between carbon emissions, energy consumption

and economic growth in Middle East countries: A panel data analysis. Energy

Policy, 62, 1138-1147. http://dx.doi.org/10.1016/j.enpol.2013.07.016

Panayotou, T. (1993). Empirical tests and policy analysis of environmental

degradation at different stages of economic development. Retrieved from

ILO website: http://www.ilo.org/public/libdoc/ilo/1993/93B09_31_engl.pdf

Paramati, S. R., Mo, D. and Huang, R. (2021). The role of ﬁnancial deepening

and green technology on carbon emissions: Evidence from major OECD

economies. Finance Research Letters, 41 (101794), 1-6. https://doi.

org/10.1016/j.frl.2020.101794

Pedroni, P. (1999). Critical values for cointegration tests in heterogenous

panels with multiple regressors. Oxford Bulletin of Economics and Statistics,

61(special issue), 653-670.

Perman, R. and Stern, D. I. (2003). Evidence from panel unit root and

cointegration tests that the environmental Kuznets curve does not exist.

The Australian Journal of Agricultural and Resource Economics, 47(3),

325–347.

Pesaran, M. H. (2004). General diagnostic tests for cross section dependence

in panels. Retrieved from IZA website: https://docs.iza.org/dp1240.pdf

Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross

section dependence. Journal of Applied Econometrics, 22(2), 265-312.

Pesaran, M. H., Ullah, A. and Yamagata, T. (2008). A bias-adjusted LM test of

error cross- section independence. The Econometrics Journal, 11(1), 105-

127.

Pethig, R. (1976). Pollution, welfare and environmental policy in the theory

of comparative advantage. Journal of Environmental Economics and

Management, 2, 160-169.

Raﬁndadi A. A., Muye, I. M. &. Kaita, R. A. (2018). The effects of fdi and energy

consumption on environmental pollution in predominantly resource-based

economies of the GCC. Sustainable Energy Technologies and Assessments,

25, 126-137.

Saboori, B. and Sulaiman, J. (2013). CO2 emissions, energy consumption

and economic growth in Association of Southeast Asian Nations (ASEAN)

countries: A co-integration approach. Energy, 55, 813-822.

Satterthwaite, D. (2009). The implications of population growth and

urbanization for climate change, environment and urbanization. 21(2),

545-567. http://dx.doi.org/10.1177/0956247809344361

Shahbaz, M., Nasreen, S., Ahmed, K. and Hammoudeh, S. (2017). Trade

openness-carbon emissions nexus: The importance of turning points of

trade openness for country panels. Energy Economics, 61, 21-232.

139

MITIGATING CARBON EMISSIONS: MARKET-BASED VS. TECHNOLOGY SUPPORT POLICIES

REVISTA DE ECONOMÍA MUNDIAL 68, 2024,117-140

Shuai, C., Chen, X., Wu, Y., Tan, Y. Zhang, Y. and Shen, L. (2018). Identifying

the key impact factors of carbon emission in China: Results from a largely

expanded pool of potential impact factors. Journal of Cleaner Production,

175, 612-623. https://doi.org/10.1016/j.jclepro.2017.12.097

Sohag, K., Mariev, O. and Davidson, N. (2021). Revising environmental

Kuznets curve in Russian regions: Role of environmental policy stringency.

Environmental Science and Pollution Research, 28, 52873–52886. https://

doi.org/10.1007/s11356-021-14515-z

Stern, D. I., Common, M. S. and Barbier, E. B. (1996). Economic growth

and environmental degradation: The environmental Kuznets curve and

sustainable development. World Development, 24(7), 1151–1160. https://

doi.org/10.1016/0305-750X(96)00032-0

Vo, X. V. and Zaman, K. (2020). Relationship between energy demand, ﬁnancial

development and carbon emissions in a panel of 101 countries: “go the

extra mile” for sustainable development. Environmental Science and

Pollution Research, 27, 23356–23363.

Walter, I. and Ugelov, J. (1979). Environmental policies in developing countries.

Ambio, 8, 102-109.

Wang, F. and Taghvaee, V. M. (2023).Impact of technology and economic

complexity on environmental pollution and economic growth in developing

and developed countries: using IPAT and STIRPAT models. Environmental

Science and Pollution Research, 30, 73349–73360. https://doi.

org/10.1007/s11356-023-27569-y

Wang H. and Zhang, R. (2021). Effects of environmental regulation on CO2

emissions: An empirical analysis of 282 cities in China. Sustainable

Production and Consumption, 29, 259–272. https://doi.org/10.1016/j.

spc.2021.10.016.

Wang, X. and Shao, Q. (2019). Non-linear effects of heterogeneous

environmental regulations on green growth in G20 countries: Evidence

from panel threshold regression, Science of the Total Environment, 660,

1346-1354.

Wang K., Yan, M., Wang, Y. and Chang, C. P. (2020). The impact of environmental

policy stringency on air quality, Atmospheric Environment 231, 1-11.

https://doi.org/10.1016/j.atmosenv.2020.117522.

Wolde-Rufael, Y. and Weldemeskel, E. M. (2020). Environmental policy

stringency, renewable energy consumption and CO2 emissions: Panel

cointegration analysis for BRIICTS countries. International Journal of Green

Energy, 17(10), 568–582. https://doi.org/10.1080/15435075.2020.177

9073.

Wolde-Rufael, Y. and Weldemeskel, E. M. (2021). Do environmental taxes

and environmental stringency reduce CO2 emissions? Evidence from 7

emerging economies, Environmental Science and Pollution Research, 28,

22392-22408. https://doi.org/10.1007/s11356-020-11475-8

WB- WDI (2022) World Bank World Development Indicators. Retrieved from

https://databank.worldbank.org/source/world-development-indicators

140 Hale Akbulut

Yasin, I., Ahmad, N. and Chaudhary, M. A. (2021). The impact of ﬁnancial

development, political institutions, and urbanization on environmental

degradation: Evidence from 59 less-developed economies. Environment

Development and Sustainability, 23, 6698-6721. https://doi.org/10.1007/

s10668-020-00885-w

Yin, J., Zheng, M. and Chen, J. (2015). The effects of environmental regulation

and technical progress on CO2 Kuznets curve: An evidence from China.

Energy Policy, 77, 97–108 . https://doi.org/10.1016/j.enpol.2014.11.008

York, R., Rosa, E. A. and Dietz, T. (2003). STIRPAT, IPAT and ImPACT:

Analytic tools for unpacking driving forces of environmental impacts.

Ecological Economics, 46, 351-365. https://doi.org/10.1016/S0921-

8009(03)00188-5

Zhang, Z., Mu, X. and Hu, G. (2023). Analysis of inﬂuencing factors of

energy consumption in Beijing: based on the IPAT model. Environment,

Development and Sustainability, https://doi.org/10.1007/s10668-023-

03900-y.