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CCUS Technology and Carbon Emissions: Evidence from the United States

Article in Energies · April 2024

DOI: 10.3390/en17071748

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Article
CCUS Technology and Carbon Emissions: Evidence from the
United States
Min Thura Mon 1 , Roengchai Tansuchat 1,2, * and Woraphon Yamaka 1,2

1 Faculty of Economics, Chiang Mai University, Chiang Mai 50200, Thailand;

minthuramon_m@cmu.ac.th (M.T.M.); woraphon.yamaka@cmu.ac.th (W.Y.)
2 Centre of Excellence in Econometrics, Faculty of Economics, Chiang Mai University,
Chiang Mai 50200, Thailand
* Correspondence: roengchai.tan@cmu.ac.th

Abstract: Carbon Capture, Utilization, and Storage (CCUS) represents a vital technology for ad-
dressing pressing global challenges such as climate change and carbon emissions. This research
aims to explore the relationship between the CCUS capability and carbon emissions in the United
States considering thirteen predictors of CCUS and carbon emissions. Incorporating these predictors,
we aim to offer policymakers insights to enhance CCUS capabilities and reduce carbon emissions.
We utilize diverse econometric techniques: OLS, Lasso, Ridge, Elastic Net, Generalized Method
of Moments, and Seemingly Unrelated Regression. Elastic Net outperforms the other models in
explaining CCUS, while OLS is effective for carbon emissions. We observe positive impacts of the
number of projects and foreign direct investment on the CCUS capacity, but limited influence from
the CCUS technology level. However, the relationship between the CCUS capacity and carbon
emissions remains limited. Our study highlights the importance of incentivizing projects to increase
CCUS capabilities and recognizes the critical role of legal and regulatory frameworks in facilitating
effective CCUS implementation in the US. Moreover, we emphasize that achieving decarbonization
goals necessitates the development of affordable green alternatives. It is essential to view CCUS as a
complementary, rather than a sole, solution for emission reduction as we work towards achieving
net-zero emission targets.

Keywords: CCUS; CCUS capacity; carbon emissions; macroeconomy; energy consumption

Citation: Mon, M.T.; Tansuchat, R.;
Yamaka, W. CCUS Technology and
Carbon Emissions: Evidence from the
United States. Energies 2024, 17, 1748. 1. Introduction
https://doi.org/10.3390/en17071748
Despite ongoing efforts to address climate change, the global consumption of fossil
Academic Editors: Rizwan Nasir and fuels, including coal, oil, and natural gas, continues to rise, and consequently, the emissions
Humbul Suleman of greenhouse gases, primarily carbon dioxide (CO2 ), remain alarmingly high [1]. Across
the globe, countries have established ambitious objectives for carbon neutrality and net-
Received: 20 March 2024
zero emissions aimed at mitigating the effects of climate change. In the endeavor to reach
Revised: 3 April 2024
net-zero objectives, one highly effective approach involves the deployment of Carbon
Accepted: 4 April 2024
Published: 5 April 2024
Capture, Utilization, and Storage technologies, commonly referred to as CCUS. CCUS
represents a comprehensive suite of technologies that holds great potential for making
substantial contributions to global energy and climate targets in diverse ways. It is widely
recognized as a pivotal clean technology, a viewpoint shared by experts, including officials
Copyright: © 2024 by the authors. at IEA [2].
Licensee MDPI, Basel, Switzerland. The CCUS capacity refers to the collective capability of CCUS technologies and projects
This article is an open access article to capture and store CO2 emissions. It includes the total capacity for active and commercial
distributed under the terms and projects across various CCUS facilities while excluding suspended and decommissioned
conditions of the Creative Commons projects as well as the transport capacity of each project [3]. This capacity is measured in
Attribution (CC BY) license (https:// terms of the amount of CO2 that can be effectively captured and stored, typically expressed
creativecommons.org/licenses/by/ in metric tons (or million metric tons) of CO2 per year. The CCUS capacity is a crucial metric
4.0/).

Energies 2024, 17, 1748. https://doi.org/10.3390/en17071748 https://www.mdpi.com/journal/energies

Energies 2024, 17, x FOR PEER REVIEW 2 of 18

Energies 2024, 17, 1748 2 of 17

capacity is a crucial metric in efforts to reduce CO2 emissions and combat climate change,
as it represents
in efforts to reduce theCO
potential for and
2 emissions mitigating
combat greenhouse gasasemissions
climate change, from
it represents theindustrial
potential
processes and power
for mitigating generation.
greenhouse gas emissions from industrial processes and power generation.
Within the realm of CCUS, significant
significant sources of CO22 emissions
emissions originate
originate from
from power
power
generation facilities, whether they are are fueled
fueled by
by biomass
biomass oror fossil
fossil resources.
resources. Furthermore,
Furthermore,
CCUS encompasses the potential for the direct capture of CO22 from the atmosphere. Once
captured, the compressed CO22 can be be transported
transported through a variety of of channels,
channels, including
including
pipelines, ships,
ships, railways,
railways, orortrucks,
trucks,and
andsubsequently
subsequentlyemployed
employedfor forvarious
variousapplications
applications if
it is
if it not immediately
is not immediatelyusedused
uponupon
capture. Additionally,
capture. CCUS offers
Additionally, CCUSthe option
offers theofoption
injectingof
CO2 intoCO
injecting deep geological
2 into formations,
deep geological such assuch
formations, saline reservoirs
as saline or depleted
reservoirs oil and
or depleted gas
oil and
reservoirs,
gas providing
reservoirs, a secure
providing andand
a secure enduring
enduringrepository forfor
repository thethe
stored COCO
stored 2 [4]. Figure
2 [4]. Figure1
1provides
providesa aschematic
schematicrepresentation
representationofofthe
theCCUS
CCUSvalue
value chain.
chain. Currently,
Currently, CCUS facilities
operating worldwide possess the capacity to abate more
worldwide possess the capacity to abate more than 40 than 40 million
millionmetric tons
metric tons(Mt) of
(Mt)
CO
of CO equivalent annually.
2 2 equivalent annually.

Figure
Figure 1.
1. Schematic
Schematic view
view of
of the
the full
full CCUS
CCUS value
value chain.
chain.

While CCUS project deployment is becoming increasingly diverse across various

While
significant concentration of global CCUS projects is evident
regions, a significant evident in
in the
the United
United States,
States,
as
Energies 2024, 17, x FOR PEER REVIEW depicted in Figure 2. Among
Among nations,
nations, the
the United
United States
States has
has assumed
assumed a leadership
3 of 18
position in CCUS,
CCUS, with
withaafull-chain
full-chaincapacity
capacityofof21.89
21.89Mt
Mtofof
COCO2 and
2 and1616
active
activeprojects
projectsas as
of
2022. This accounts for approximately 50.9% of the global capacity in that year
of 2022. This accounts for approximately 50.9% of the global capacity in that year [5]. [5].
While historical disparities between CCUS deployment and expectations have been
notable, there has been a significant surge in momentum in recent years, as evident in
Figure 2. Across the entire global CCUS value chain, there are currently more than 500
projects in various stages of development. As of early 2022, CCUS project developers have
set ambitious targets for approximately 50 new CCUS facilities to become operational by
2030. If this target is met, it could result in the annual capture of approximately 125 Mt of
CO2. However, even with this level of progress, the overall capacity of CCUS would still
fall significantly short of the one-third of 1.2 Bt of CO2 per annum that the 2050 Scenario
of Net Zero Emissions calls for [5].

(a)Capacity
Figure 2.2.(a)
Figure Capacitycomparison
comparison of of CCUS
CCUS facilities
facilities in in
thethe
USUS andand globally
globally overover 50 years
50 years and and
(b)
(b) CCUS
CCUS projects
projects in terms
in terms ofcapability
of its its capability by each
by each country
country [3]. [3].

Despite the recognized potential of CCUS technologies in reducing greenhouse gas

emissions, their widespread implementation remains limited. Several factors contribute
to the limited implementation of CCUS projects. This includes high costs associated with
CCUS technologies acting as a deterrent, compounded by insufficient financial incentives
[6] and supportive government policies [7], complex regulatory requirements, technical
challenges, and public perception concerns further hindering adoption [7,8]. Additionally,
Energies 2024, 17, 1748 3 of 17

While historical disparities between CCUS deployment and expectations have been no-
table, there has been a significant surge in momentum in recent years, as evident in Figure 2.
Across the entire global CCUS value chain, there are currently more than 500 projects in
various stages of development. As of early 2022, CCUS project developers have set am-
bitious targets for approximately 50 new CCUS facilities to become operational by 2030.
If this target is met, it could result in the annual capture of approximately 125 Mt of CO2 .
However, even with this level of progress, the overall capacity of CCUS would still fall
significantly short of the one-third of 1.2 Bt of CO2 per annum that the 2050 Scenario of Net
Zero Emissions calls for [5].
Despite the recognized potential of CCUS technologies in reducing greenhouse gas
emissions, their widespread implementation remains limited. Several factors contribute
to the limited implementation of CCUS projects. This includes high costs associated with
CCUS technologies acting as a deterrent, compounded by insufficient financial incentives [6]
and supportive government policies [7], complex regulatory requirements, technical chal-
lenges, and public perception concerns further hindering adoption [7,8]. Additionally, the
availability of alternative emission reduction methods and limited awareness about CCUS
benefits contribute to the issue [9].
From the above, the relationship between CCUS adoption and emissions remains inad-
equately understood, leaving a gap in our comprehension of its effectiveness. While the en-
gineering, scientific, and technological aspects of CCUS have been extensively explored [2],
they may not offer a comprehensive view, particularly from an economic standpoint. This
study emphasizes the urgent need to delve deeper into the barriers hindering CCUS deploy-
ment and how macroeconomic factors, energy consumption, and energy prices influence
the feasibility of CCUS projects and their impact on carbon emissions in the United States—
a nation recognized for its leadership in certain CCUS technology and implementation
domains. Moreover, despite existing research focusing on the techno-economic analysis of
CCUS projects [10–12] and general equilibrium models [13], empirical econometric research
on CCUS capacity remains notably lacking [14]. Therefore, this study employs various
econometric modeling techniques, including Ordinary Least Squares (OLS), Lasso, Ridge,
Elastic Net, Generalized Method of Moments, and Seemingly Unrelated Regression (SUR),
to uncover crucial insights into the link between CCUS capacity and carbon emissions and
their associated factors, aiming to strengthen the robustness of the results and contribute
significantly to the existing body of knowledge.
This paper’s structure consists of five sections. Section 1 introduces the research
topic, while Section 2 offers a comprehensive literature review encompassing CCUS, car-
bon emissions, energy consumption, economic activity, and their interconnectedness. In
Section 3, the methodology is outlined, encompassing the use of econometric methods,
data description, and empirical model. Moving to Section 4, this part of the paper presents
and discusses the analytical results. Lastly, Section 5 encapsulates this study’s conclusion,
summarizing key findings and implications.

2. Literature Review
Numerous scholars have diligently identified various factors influencing carbon emis-
sions, such as finance, policy, and urbanization [15,16]. However, many studies have over-
looked the impact of CCUS, including its capacity, technology, and development [17–20].
Few studies, especially quantitative ones, have explored the relationship between CCUS
technology and carbon emissions [14,21] and the full-chain version of CCUS is more com-
mon than individual segments such as capture, storage, and utilization, as in Table 1.
Alsarhan et al. [22] stated that CCUS technology can remove carbon dioxide from the
atmosphere, essentially acting as a “carbon sink”. This process would result in a reduction
in carbon emissions present in the air. However, some researchers have asserted that
the success of CCUS in reducing carbon emissions may be hindered by several factors.
These include high failure rates, a lack of financial support and market incentives, and an
inadequate regulatory framework [22–24]. After accounting for technological regularity
Energies 2024, 17, 1748 4 of 17

and economic benefits, Wang et al. [25] concluded that uncertainty remains regarding
CCUS technology’s ability to effectively reduce carbon emissions. Furthermore, the high
cost of utilizing CCUS technology may hinder the emission reduction of CO2 [26]. It is
essential to recognize that the costs, benefits, and overall maturity of CCUS technology can
vary significantly at different stages of development because it is developed following the
technological phases [27]. As a result, the effect of CCUS technology on reducing emissions
may change depending on the stage of development. For instance, the initial stages of
CCUS innovation may have a more detrimental effect on carbon emission efficiency than in
later stages due to elevated costs and energy consumption for technological implementation.
Zhang et al. [14] discovered that China’s digital economy serves as a favorable moderator,
enhancing the impact of increased carbon emission efficiency through CCUS technology.

Table 1. Global CCUS capacity across its value chain [3].

Capture Utilization Storage Full Chain

Year
(Mt CO2 /Year) (Mt CO2 /Year) (Mt CO2 /Year) (Mt CO2 /Year)
2018 0 0.838 0 36.8769
2019 0.3 0.838 0 42.9769
2020 1.78 0.838 1.12 42.8769
2021 1.78 0.838 1.12 41.8309
2022 1.78 0.963 1.12 43.0109
Note: Transport capacity is included under full chain. The data cover CCUS projects under operation status.

For instance, a higher GDP per capita may stimulate investment in CCUS projects
and research [23]. Similarly, improved employment rates resulting from CCUS initiatives
can spur investment in CCUS projects [28]. Additionally, factors such as trade volume
and foreign direct investment can influence a nation’s industrial activity and emissions,
thereby shaping the prioritization of carbon reduction technologies, including CCUS [29].
Moreover, the relationship between energy factors, such as consumption and prices, and
CCUS adoption is complex. Energy consumption, particularly from fossil fuels, directly
contributes to carbon emissions [30], making it a critical consideration for CCUS implemen-
tation. Higher fossil fuel prices may provide incentives for CCUS investment, while lower
prices could pose obstacles to adoption [31]. Furthermore, the consumption of renewable
energy may impact the urgency of CCUS implementation, particularly for industries reliant
on fossil fuels [32].
Considering the factors related to carbon emissions, researchers have dedicated their
efforts to uncovering the relationship between macroeconomics, energy consumption,
climate change, and emissions [33–35]. However, depending on the country and region,
the impact of economic activity and energy consumption on carbon emissions can yield
inconsistent effects and conflicting outcomes [36]. Some studies suggest that as a nation
undergoes economic development and experiences a rise in energy consumption, its emis-
sions tend to increase [37,38]. Conversely, other research indicates that increased economic
activity and energy usage can lead to emission reductions. For instance, Sterpu et al. [39]
examined the relationship between economic growth, non-renewable energy use, green-
house gas emissions, and renewable energy use in European nations. They found that
while an increased utilization of renewable energy results in a decrease in greenhouse gas
emissions, heightened energy consumption leads to an increase in emissions. Moreover,
according to Maneejuk et al. [40], economic development that surpasses a certain threshold
point can lead to declining CO2 emissions.
Additionally, there is a positive correlation between economic growth and CO2 emis-
sions in developed Middle Eastern and North African nations, while emerging nations
exhibit a negative correlation [41]. Conversely, the findings of Anwar et al. [42] suggested
that increasing economic expansion raises carbon dioxide emissions in G7 countries, sub-
sequently exacerbating environmental pollution. However, they also found that higher
levels of renewable energy consumption, institutional quality enhancement, and tech-
Energies 2024, 17, 1748 5 of 17

nological innovation hindered carbon dioxide emissions. Furthermore, Sanli et al. [43]
demonstrated that in the long run, both macroeconomic stability and instability positively
influence carbon emissions, with asymmetric economic complexity shocks exacerbating
environmental pollution. Their study suggested that a sophisticated, complex production
structure may have less detrimental effects on the environment compared to conventional
production methods.
Nonetheless, Zhu et al. [44] suggested that an increase in government focus on en-
vironmental protection may moderate corporate efforts to reduce carbon emissions and
enhance environmental subsidy provisions. This observation is echoed by Cao et al. [45],
who highlighted that coordinated actions between local and central governments on carbon
reduction are achieved through attention to environmental protection regulations. Further-
more, Liu et al. [46] found that businesses in capital-intensive, technologically advanced,
and highly polluting industries demonstrate a more pronounced performance in carbon
reduction. Additionally, attention to carbon reduction and financial market stress is noted
to be closely associated with volatility spillover [47].
This study aims to fill existing research gaps by investigating the relationship between
CCUS technology and carbon emissions in the US, while also exploring various predictors
related to macroeconomics and energy fields. Its contributions are two-fold. Firstly, it
enriches the literature on low-carbon transformation by offering practical insights for
the US in developing low-carbon technology. While previous studies have highlighted
the effectiveness of CCUS technology in reducing carbon emissions, few have focused
specifically on the US context. This study addresses this gap by providing valuable insights
tailored to the US. Secondly, it uncovers key factors that enhance CCUS capacity and reduce
carbon emissions. By integrating various economic and energy factors into a comprehensive
research framework and analyzing their impacts on both CCUS technology and carbon
emissions, this study offers actionable solutions for policymakers to implement more
effective measures toward carbon emission reduction and carbon neutrality.

3. Methodology
3.1. Estimation Methods
3.1.1. Ordinary Least Squares (OLS)
The relationship between a dependent variable and one or more independent variables
is analyzed through the multiple linear regression model [48,49]. The general form of the
linear regression model is
y = Xβ + ε (1)
where yt is the T × 1 vector representing the dependent variable, and X is T × K, a matrix
of independent variables. β is the K × 1 vector of unknown parameters, and ε is the
T × 1 vector representing random disturbance that follows a normal distribution with a
mean of 0 and variance σ2 , denoted as ε ∼ iidN (0, σ2 ). OLS is the most commonly used
method for estimating the unknown parameters in a linear regression model [48,49]. Then,
OLS
the OLS estimator is β̂ = argmin∥y − xβ∥, where ||. || is the standard L2 norm in the
β
T-dimensional Euclidean space.

3.1.2. Lasso Regression

The least absolute shrinkage and selection operator (Lasso) was developed by Tibshi-
rani [50] for regression analysis where the number of predictors is larger than observations,
i.e., p > T. Lasso performs not only the selection of variables but also parameter estimation
to improve the prediction precision and interpretability of the generated statistical model.
Energies 2024, 17, 1748 6 of 17

lasso lasso lasso lasso

Let β̂ = ( β̂1 , . . . , β̂k ), and the Lasso estimate β̂ is defined as the solution to the
following optimization problem:
(  2 )
lasso T K
β̂ = argmin ∑ yt − ∑ βlasso
k xkt
βlasso t =1 k =1
(2)
K
subject to ∑ βlasso
k ≤c
k =1

where c ≥ 0 is the tuning parameter and for all t, the Lasso function can also be expressed
in compacted form as

lasso 2
β̂ = argmin(y − Xβlasso ) + λ βlasso (3)
1
βlasso

where λ is a non-negative regularization parameter and the exact relationship between λ

and the number of non-zero coefficients, denoted as M, is data-dependent.

3.1.3. Ridge Regression

Ridge regression, presented by Hoerl and Kennard [51] is a technique for calculat-
ing the coefficients of multiple-regression models in cases where predictors are highly
correlated, i.e., multicollinearity issue. The Ridge regression is formulated as
(  2 )
ridge T K ridge
β̂ = argmin ∑ yt − ∑ βk xkt
ridge t =1 k =1
β
p
(4)
ridge 2
 
subject to ∑ βj ≤c
j =1

The Ridge function can be stated alternatively as

ridge 2
β̂ = argmin(y − Xβridge )2 + λ βridge (5)
2
βridge

3.1.4. Elastic Net Regression

Elastic Net is a regularization regression method that combines penalties from both the
Lasso and Ridge methods [52]. It is a suitable method for cases involving many predictors
and multicollinearity. The Elastic Net regression can be defined as
(  2 )
elastic T K
β̂ = argmin ∑ yt − ∑ βelastic
k xkt
βelastic t =1 k =1 (6)
2
elastic elastic
subject to (1 − α) β +α β ≤c
1 2

2 K  2 K
where βelastic = ∑ βelastic
k , βelastic = ∑ βelastic
k , and α is the weight parameter.
2 k =1 1 k =1
elastic
The Elastic Net estimator β̂ can be revised as

elastic 2
β̂ = argmin(y − Xβelastic )2 + λ1 βelastic + λ2 βelastic (7)
1 2
βelastic

Elastic Net regression stands out as a robust regularization technique, offering solu-
tions to several drawbacks encountered in Lasso and Ridge regression approaches. Its
versatility makes it particularly valuable in datasets characterized by high dimensionality
Energies 2024, 17, 1748 7 of 17

and multicollinearity. Elastic Net effectively combines variable selection and shrinkage,
making it a powerful tool for predictive modeling in complex data scenarios.

3.2. Data
This section outlines the variables utilized in this study, categorized into three groups.
The first group encompasses CCUS and carbon emission-related variables, including CCUS
capacity (CCUSCap), number of CCUS projects (Proj), CCUS technology level (Techlv),
government CCUS policy (Govpol), and carbon emissions (CO2E). The second group
comprises economic indicators, such as GDP per capita (Gdppc), unemployment rate
(Unemp), trade volume (Trad), foreign direct investment (FDI), and industrial production
and capacity utilization (Indprod). The third group consists of energy factors, including
prices of coal (CoalP), oil (OilP), and natural gas (NgP), as well as energy consumption of
fossil fuels (ECFF) and renewable energy (ECRE).
Table 2 presents the variables along with their respective data sources and literature
references. The focus of this research is on the United States CCUS capacity and carbon
emissions, along with their associated predictors, spanning from 1972 to 2022, encompass-
ing yearly data and a total of 51 observations. The United States was chosen due to its
extensive CCUS experience, with over 50 years of involvement and possession of nearly
half of the world’s CCUS capacity [3].

Table 2. List of variables, data sources, and related literature.

Name Description Source Authors

Carbon Capture, Utilization, and Storage capacity
CCUSCap International Energy Agency
(Mt CO2 /yr)
Carbon Capture, Utilization, and Storage projects (active
Proj International Energy Agency
project/yr)
Carbon Capture, Utilization, and Storage technology
Techlv European Patent Office [14,53]
level (new patent publication/yr)
Govpol Government policy on CCUS (active policy/yr) Climate Policy Database [54,55]
Carbon dioxide emission from fuel combustion
CO2E Our World in Data [14,56–60]
(Bt CO2 /yr)
Gdppc Gross domestic product (GDP) per capita (current USD) World Bank [14,56–60]
Unemp Unemployment (%) World Bank [61,62]
Trad Trade (% of GDP) World Bank [63–65]
Foreign direct investment (net inflows, balance of
FDI World Bank [14,36,56,65,66]
payment, millions current USD)
Indprod Industrial production and capacity utilization (index) Federal Reserve Board [67,68]
CoalP Coal price (Australia market, in USD, real price) World Bank [36,69,70]
Crude oil price (average spot price of Brent, Dubai, and
OilP World Bank [36,70]
West Texas Intermediate, equally weighed, real price)
NgP Natural gas price (US Henry hub, real price) World Bank [70,71]
Primary energy consumption from fossil fuels U.S. Energy Information
ECFF [36,71]
(quadrillion Btu) Administration
Primary energy consumption from renewable energy U.S. Energy Information
ECRE [36,71]
(quadrillion Btu) Administration

Table 3 provides detailed statistics for each parameter. To maintain consistency in units
and mitigate the influence of long-term stochastic trends or unit roots, the time series data
are transformed into growth rates. Unit root and multicollinearity tests are also conducted
using the variance inflation factor (VIF).
This study examines the asymmetric distribution of variables by scrutinizing their
skewness. From the data showcased in Table 3, it becomes evident that certain variables,
including CO2E, Gdppc, Trad, FDI, Indprod, NgP, ECFF, and ECRE, exhibit negative
skewness. This negative skewness suggests a downward trend in economic, energy, and
carbon-related factors over time. Essentially, it indicates that the majority of observations
have lower values, signifying a decline in these factors. Conversely, variables such as
Energies 2024, 17, 1748 8 of 17

CCUSCap, Proj, Techlv, Govpol, Unemp, CoalP, and OilP portray a positive trend in
associated indicators. These variables suggest an upward trajectory in their respective
factors. Furthermore, the outcomes of Jarque–Bera normality tests reveal that, apart from
Techlv, Gdppc, FDI, and NgP, the data do not adhere to a normal distribution. Moreover,
nearly all variables exhibit kurtosis values surpassing 3, indicating the presence of excess
kurtosis. This implies that the distributions have heavier tails than a normal distribution,
highlighting potential outliers or extreme values.

Table 3. Summary of descriptive statistics.

Jarque–Bera Unit Root Equation (8) Equation (9)

Variable Mean Max Min SD Skew Kurt
Test Test VIF VIF
CCUSCap 8.2057 137.7784 −12.9397 25.4278 3.5757 16.2961 484.12 *** −7.676 *** NA 3.80
Proj 6.7955 69.3147 −7.4108 16.2677 2.6041 9.6403 152.2 *** −8.514 *** 1.33 3.52
Techlv 5.1427 105.4441 −65.3927 34.5834 0.3044 3.2287 0.8683 −8.767 *** 1.24 1.20
Govpol 7.0265 109.8612 −6.0625 19.8716 3.6620 17.1874 542.03 *** −6.733 *** 1.40 1.27
CO2E 0.2875 6.5732 −11.0443 3.3981 −0.8194 4.2425 8.786 ** −7.037 *** 72.84 NA
Gdppc 5.1189 11.1254 −2.8720 3.0489 −0.0675 3.2040 0.0341 −4.842 *** 3.24 2.54
Unemp −0.9416 78.5480 −40.8576 19.8243 1.5619 7.2747 77.149 *** −5.951 *** 3.49 3.46
Trad 1.8307 22.8996 −18.7570 7.0074 −0.1415 4.4480 4.959 * −6.688 *** 7.92 9.10
FDI 12.0074 116.7361 −96.3151 49.9526 −0.1843 2.4578 0.8858 −9.024*** 2.11 2.14
Indprod 1.9482 9.1821 −12.0660 4.3036 −1.0988 4.5978 14.299 *** −6.050 *** 8.18 8.69
CoalP 7.2200 91.5619 −57.0508 29.1879 0.8164 4.0419 5.3744 * −5.345 *** 2.67 2.58
OilP 7.9432 136.3325 −63.9827 33.2699 0.9264 6.5674 38.968 *** −6.409 *** 4.08 4.95
NgP 6.9924 64.8129 −80.7442 28.1008 −0.3691 3.7934 3.2464 −6.689 *** 3.16 2.38
ECFF 0.3829 5.7313 −9.4953 3.1671 −0.7053 3.6409 4.799 * −6.799 *** 69.97 3.99
ECRE 2.4209 1.3869 −15.3743 5.1674 −0.7268 4.7471 10.15 *** −6.806 *** 1.36 1.26
Note: ***, **, and * in the Jarque–Bera and unit root tests mean p-value ≤ 0.01, ≤0.05, and ≤0.1. “NA” implies that
the variable is a dependent variable.

Additionally, none of the variables exhibit a unit root, suggesting the absence of
non-stationarity. This implies that the variables are stationary over time, without any
significant long-term trend. Concerning multicollinearity, in CCUSCap Equation (8), high
multicollinearity is solely observed between CO2E and ECFF. This indicates a strong
relationship between these two variables, potentially necessitating caution in their inter-
pretation. Conversely, in the case of CO2E Equation (9), no multicollinearity is identified,
with all variance inflation factors (VIFs) registering as less than 10. This suggests that the
variables in this equation are relatively independent of each other, reducing the risk of
multicollinearity-related issues.

3.3. Empirical Model

In this study, several economic and energy factors are included to analyze the deter-
minants of CCUS capacity and carbon emissions. Our empirical models are presented in
Equations (8) and (9), respectively. To address potential endogeneity issues in our empirical
study, we introduce lagged independent variables into our equations. Specifically, we
include lagged terms for the independent variables to mitigate any endogeneity concerns.
Equation (8) now integrates lagged independent variables alongside energy, economic,
carbon emissions, and other related factors to elucidate the relationship with CCUS ca-
pacity. Similarly, Equation (9) incorporates lagged independent variables to explore the
associations between carbon emissions and CCUS capacity, as well as energy, economic,
and carbon-related variables.
CCUSCapt = β0 + β1 Projt−1 + β2 Techlvt−1 + β3 Govpolt−1 + β4 CO2Et−1 + β5 Gdppct−1
+ β6 Unempt−1 + β7 Tradt−1 + β8 FDIt−1 + β9 Indprodt−1 + β10 CoalPt−1 (8)
+ β11 OilPt−1 + β12 NgPt−1 + β13 ECFFt−1 + β14 ECREt−1 + εit

CO2Et = β0 + β1 Projt−1 + β2 Techlvt−1 + β3 Govpolt−1 + β4 CCUSCapt−1 + β5 Gdppct−1

+ β6 Unempt−1 + β7 Tradt−1 + β8 FDIt−1 + β9 Indprodt−1 + β10 CoalPt−1 (9)
+ β11 OilPt−1 + β12 NgPt−1 + β13 ECFFt−1 + β14 ECREt−1 + εit
Energies 2024, 17, 1748 9 of 17

4. Results and Discussion

4.1. Model Selection
In this research, we have employed four econometric estimations: Ordinary Least
Squares (OLS), Lasso regression, Ridge regression, and Elastic Net models. The selection
criterion for determining the most suitable model is based on identifying the one that
exhibits the best performance. To assess this, we consider the Akaike Information Criterion
(AIC) and Bayesian Information Criterion (BIC) values, with lower values indicating
better performance.
Table 4 presents the results of the AIC and BIC values for each model. Remarkably,
the Elastic Net model outperforms the others, demonstrating the lowest AIC and BIC
values in the CCUSCap Equation (8). Conversely, OLS performs comparatively better
than other models in the case of CO2E Equation (9). Therefore, the Elastic Net model
and OLS are selected as the statistical models of choice for the CCUSCap and CO2E cases,
respectively. This selection aligns with findings from previous studies indicating that the
Elastic Net model performs better than OLS, Lasso, and Ridge regressions in the presence
of multicollinearity, as observed in the CCUSCap case. On the other hand, the lack of
multicollinearity in the CO2E case means OLS provides better performance. This is because
regularization bias may not be necessary in the absence of multicollinearity, and OLS is
capable of reducing both bias and variance, thereby exhibiting the smallest variance.

Table 4. Model selection.

CCUSCap CO2E
Model
AIC BIC AIC BIC
OLS 1303.9109 1332.8882 832.0914 861.0687
Lasso 845.0513 852.7786 1050.6142 1066.0688
Ridge 973.6168 1002.5942 1013.4992 1042.4765
Elastic Net 794.1751 801.9024 1056.1488 1073.5352

4.2. The Impact of Carbon Emission and Related Factors on CCUS Technology
Based on the findings presented in Table 5, several noteworthy insights emerge regard-
ing CCUSCap.

Table 5. Elastic Net regression results for CCUS capacity estimation.

Variable Elastic Net

Proj 6.0560
Techlv -
Govpol -
CO2E -
Gdppc -
Unemp -
Trad -
FDI 1.5704
Indprod -
CoalP -
OilP −0.3398
NgP -
ECFF -
ECRE -
Constant -
Note: “-” denotes the variable is not selected by the model. Standard errors are shown in parentheses.

Firstly, positive impacts on CCUSCap are observed in the case of Proj and FDI. Specifi-
cally, a 1% increase in Proj is associated with a substantial 6.0560% increase in CCUSCap,
highlighting the significant role of project expansion in enhancing the country’s CCUS
Energies 2024, 17, 1748 10 of 17

capabilities for carbon reduction. This underscores the importance of investment in projects
aimed at carbon capture and storage technologies as a means to address carbon emissions
effectively. Additionally, FDI exhibits a positive effect, with a 1% increase in FDI leading to
a noticeable 1.5704% increase in CCUSCap. This can be attributed to the decarbonization
efforts in hard-to-abate sectors such as cement manufacturing, chemical production, and
steelmaking, which lack alternative low-carbon technology options apart from CCUS. In
response to this challenge, these sectors are driven to invest more in CCUS technologies,
facilitated by the net inflows of FDI and the increasing number of CCUS projects. Notably,
the US government’s provision of performance-based tax credits for carbon capture projects
further incentivizes such investments [72].
Conversely, a negative effect on CCUSCap is detected in OilP. A 1% rise in oil prices is
associated with a 0.3398% reduction in CCUSCap. This finding contrasts with expectations,
as higher oil prices typically stimulate investment in CCUS technologies, particularly
due to their relevance in enhancing oil recovery processes. One plausible rationale is
that despite the presence of higher oil prices, economic uncertainty or market volatility
may be prompting companies to exercise caution and withhold long-term investments
in technologies like CCUS. This hesitance could arise from concerns regarding future
economic conditions or regulatory uncertainties, leading companies to prioritize short-term
stability over long-term sustainability initiatives. Thus, while higher oil prices typically
signal opportunities for CCUS investment, prevailing economic dynamics may be exerting
a counteracting influence, contributing to the observed negative correlation between oil
prices and CCUSCap. In terms of magnitude, among these three variables, it is evident
that Proj plays a pivotal role in increasing CCUSCap. An intriguing finding pertains to the
limited impact of Techlv. Surprisingly, the level of CCUS technology, as measured by the
count of CCUS technology-related patents, does not demonstrate any significant influence
on the CCUS capacity. This suggests that increasing technological advancements in CCUS
may not necessarily lead to a corresponding increase in the CCUS capacity, despite Techlv
promoting carbon emission efficiency.
Unexpectedly, Govpol did not drive the increase in the US’s CCUS capacity over
50 years, as earlier policy instruments implicitly and partly covered CCUS until the enact-
ment of the 45Q tax credit under the Energy Improvement and Extension Act in 2008 [9].
Although the 45Q tax credit underwent major reform in 2018, the US still lacks a legislature-
based climate strategy at the federal level [8]. Additionally, we cannot statistically deduce
the relationship between CO2E and CCUSCap, given the disparity between CO2E and
CCUS expansion over five decades. During this time, CCUS was primarily used for
enhanced oil and gas recovery instead of the carbon reduction option [73].
Within the economic indicators, only FDI shows a significant effect on the CCUS
capacity, while Gdppc, Unemp, Trad, and Indprod do not exhibit any notable impact on
the CCUS capacity. Similarly, among energy factors, only OilP demonstrates a discernible
influence on the CCUS capability, while variables such as CoalP, NgP, ECFF, and ECRE do
not show significant effects in our study on the CCUS capacity. This observation can be
attributed to various factors such as a high failure rate, insufficient financial investment,
limited market opportunities, and regulatory constraints surrounding CCUS projects,
which hinder their widespread adoption [23–25]. Despite the United States’ extensive
history of injecting CO2 into the subsurface for over half a century, its primary challenge
lies more in policy and economic realms rather than technological constraints [9]. Therefore,
the successful implementation of CCUS necessitates the establishment of robust legal
and regulatory frameworks to ensure the effective management of CCUS operations and
the safe storage of CO2 [74]. Ultimately, achieving decarbonization goals will require
ensuring that green alternatives are economically viable for businesses, encouraging their
voluntary adoption of more affordable fossil fuels [75]. Nonetheless, CCUS remains a
crucial technology for reducing emissions, and our findings underscore the importance
of promoting CCUS projects to enhance the CCUS capacity. This observation aligns with
Energies 2024, 17, 1748 11 of 17

expert opinions advocating for the acceleration of CCUS alongside the development of
renewable energy options [30].

4.3. The Impact of CCUS Technology and Related Factors on Carbon Emission
The estimation results are summarized in Table 6. Positive impacts on carbon emissions
are observed for several factors, including Govpol, Gdppc, NgP, and ECFF. Specifically, a
1% increase in these factors leads to a growth in carbon emissions by 0.0063%, 0.0952%,
0.0131%, and 0.9999%, respectively. These findings underscore the significant influence of
key factors driving the growth in carbon emissions in the US.
Notably, ECFF, Gdppc, and NgP emerge as pivotal contributors to the rise in carbon
emissions. These results are consistent with prior research, which has also emphasized the
positive effect of non-renewable energy consumption, energy prices, and GDP per capita
on carbon emissions [76,77].

Table 6. Results of CO2 emission estimation using OLS.

Variable OLS
CCUSCap 0.0055 (0.0050)
Proj 0.0011 (0.0075)
Techlv 0.0018 (0.0021)
Govpol 0.0063 * (0.0037)
Gdppc 0.0952 ** (0.0341)
Unemp −0.0012 (0.0061)
Trad 0.0221 (0.0281)
FDI −0.0021 (0.0019)
Indprod −0.0255 (0.0447)
CoalP −0. 0032 (0.0036)
OilP −0.0026 (0.0044)
NgP 0.0131 *** (0.0036)
ECFF 0.9999 *** (0.0412)
ECRE −0.0265 * (0.0142)
Constant −0.6058 *** (0.1716)
Note: Significance at 0.01, 0.05, and 0.1 level indicated by ***, **, and *, respectively. Standard errors are shown in
parentheses.

Remarkably, Govpol shows a positive correlation with CO2E, indicating that govern-
ment policy on CCUS does not effectively reduce carbon emissions. This finding reinforces
the notion that policy and economic factors, rather than technological ones, are the primary
barriers to emission reduction [9]. On the contrary, a negative impact on carbon emissions is
observed for ECRE. A 1% rise in ECRE leads to a reduction in carbon emissions by 0.0265%.
These results align with previous research that has investigated the impact of ECFF and
ECRE on greenhouse gas emissions [36,37]. The findings underscore that increasing the
use of renewable energy sources would contribute to a decrease in CO2 emissions.
Due to the higher p-values of CCUSCap, Proj, Techlv, Unemp, Trad, FDI, Indprod,
CoalP, and OilP, they are not assumed to have significant impacts on carbon emissions, as
indicated by our findings in Table 6. It is interesting to note that, despite being a carbon
reduction technology, CCUSCap should theoretically have a negative relationship with
CO2E. However, this expected relationship has not been observed [26]. This suggests that
CCUSCap alone may not be sufficient to effectively reduce CO2 emissions, possibly due to
its relatively small contribution—less than 0.5% of the overall US CO2 emissions, as depicted
in Figure 3. Furthermore, Techlv and Proj also exhibit non-significant relationships with
CO2E. When considering all the CCUS indicators—CCUSCap, Proj, Techlv, and Govpol—to
CO2E, the overall effectiveness of CCUS in carbon reduction is questionable. This highlights
the existing barriers to CCUS adoption in the US.
 CCUSCap alone may not be sufficient to effectively reduce CO2 emissions, possibly due
to its relatively small contribution—less than 0.5% of the overall US CO2 emissions, as
depicted in Figure 3. Furthermore, Techlv and Proj also exhibit non-significant
relationships with CO2E. When considering all the CCUS indicators—CCUSCap, Proj,
Energies 2024, 17, 1748 Techlv, and Govpol—to CO2E, the overall effectiveness of CCUS in carbon reduction is
12 of 17
questionable. This highlights the existing barriers to CCUS adoption in the US.

Figure 3. The percentage of reduced CO2 by CCUS from the US’s total carbon emission [3].
Figure 3. The percentage of reduced CO2 by CCUS from the US’s total carbon emission [3].
Overall, we observed that renewable energy can reduce carbon emissions while CCUS
Overall,
is not we observed
well-developed that renewable
enough energy emissions
to contain carbon can reduce as carbon emissions
its capacity is verywhile
limited.
CCUS is notCCUS
However, well-developed enough
has the potential to to contain
abate carbon
carbon emissions
emissions whenas itswidely
it is capacity is very
applied, and
its costs
limited. are lowerCCUS
However, than in the
has case
the of renewable
potential energy
to abate [77].
carbon emissions when it is widely
applied, and its costs are lower than in the case of renewable energy [77].
4.4. Robustness Checks
To ensure the robustness of our results from the previous section, we re-estimated
our models using the Generalized Method of Moments (GMM) estimation. The GMM
is a flexible estimation method commonly employed in econometrics, especially when
the underlying distribution of the data is unknown or challenging to specify. In the
context of our models, the GMM proves particularly valuable in addressing issues such
as endogeneity, serial correlation, or heteroscedasticity. Furthermore, we recognize that
in estimating Equations (8) and (9) separately, there may be potential interdependencies
between the equations that are overlooked. To address this concern and enhance the
robustness of our results, we employ GMM estimation along with Seemingly Unrelated
Regression (SUR) estimation. This approach allows us to simultaneously estimate both
equations, thus capturing any potential interdependencies between them and providing
more reliable results, as in Table 7.

Table 7. Robustness analysis.

SUR GMM
CCUSCap CO2E CCUSCap COE2
CCUSCap 0.0055 (0.0048) 0.0054 * (0.0029)
CO2E 5.8352 (5.3742) 5.1253 (3.8895)
Proj 1.1689 *** (0.1515) 0.0011 (0.0072) 1.1939 *** (0.2237) 0.0014 (0.0056)
Techlv 0.0064 (0.0689) 0.0018 (0.0019) 0.0065 (0.0668) 0.0022 (0.0014)
Govpol −0.0129 (0.1272) 0.0063 * (0.0035) −0.0131 (0.0589) 0.0069 ** (0.0031)
Gdppc −0.1166 (1.2637) 0.0952 ** (0.0314) −0.1192 (1.0950) 0.1077 *** (0.0262)
Unemp −0.1013 (0.2017) −0.0012 (0.0062) −0.1014 (0.1824) 0.0044 (0.0069)
Trad 2.1202 * (0.8593) 0.0221 (0.0269) 2.1392 ** (1.0700) 0.022 (0.0172)
FDI 0.0565 (0.0623) −0.0021 (0.0019) 0.0521 (0.0391) −0.0010 (0.0019)
Indprod −2.3183 (1.4221) −0.0255 (0.042) −2.3293 (1.5012) −0.0281 (0.0391)
CoalP −0.0882 (0.1198) −0.0023 (0.0036) −0.0792 (0.0846) −0.0049 (0.0030)
OilP −0.3701 ** (0.1299) −0.0032 (0.0041) −0.3934 * (0.1939) −0.0026 (0.0025)
NgP 0.0201 (0.1353) 0.0131 *** (0.0034) 0.0204 (0.1057) 0.0123 *** (0.0025)
ECFF −5.6612 (5.6514) 0.9999 *** (0.0404) −5.6384 (4.1727) 1.0195 *** (0.0377)
ECRE −0.4462 (0.4822) −0.0265 * (0.0136) −0.4463 (0.3892) −0.0270 ** (0.0115)
Constant 5.7847 (6.7422) −0.6058 *** (0.1543) 5.7840 (6.0982) −0.7034 *** (0.1453)
Note: Significance at 0.01, 0.05, and 0.1 level indicated by ***, **, and *, respectively. Standard errors are shown
in parentheses.
Energies 2024, 17, 1748 13 of 17

Based on the results obtained from the GMM and SUR estimations, it is evident that the
estimated coefficients and their significance align with the findings of our main estimations
presented in Tables 5 and 6. This consistency underscores the robustness of our results
from the previous section.
Additionally, as we have not found evidence of a relationship between CCUS tech-
nology and carbon emissions, we further validate this result by employing the Granger
causality test [78]. The results of this test are reported in Table 8. Notably, the findings
indicate that we do not observe a significant impact of the CCUS capacity on CO2 emis-
sions, and vice versa. This reaffirms the results obtained from the main model, providing
additional support for our conclusions.

Table 8. Granger causality test.

Hypothesis Wald Statistic

CO2E does not cause CCUSCap 2.5208
CCUSCap does not cause CO2E 1.7738

5. Conclusions
This study aims to explore the relationship between the Carbon Capture, Utilization,
and Storage (CCUS) capacity and carbon emissions in the US from 1972 to 2022. Employing
four distinct statistical methods and considering thirteen relevant predictors, we endeavor
to deepen our empirical understanding of this complex relationship. To enhance the scope
of our investigation, we introduce two novel variables, CCUSCap and Proj, which provide
additional insights into the dynamics at play. Notably, our analysis reveals that the Elastic
Net model proves most effective in elucidating the CCUS capacity, whereas the OLS model
is better suited for understanding carbon emissions. Our findings are robust and further
validated through GMM and SUR estimations, providing confidence in the reliability of
our results. Through this study, we contribute to the existing body of knowledge in the
field of CCUS and carbon emissions, shedding light on the key factors driving or hindering
progress in these critical areas of environmental concern.
The findings regarding the CCUS capacity equation suggest that while OilP has a
negative impact on the US’s CCUS capacity, Proj and FDI show significant positive correla-
tions. Specifically, Proj emerges as one of the most influential factors among the variables
analyzed, demonstrating a substantial positive effect on raising CCUSCap. Interestingly,
the analysis also reveals that technological advancements in CCUS may not always trans-
late into an increased CCUS capacity. The 50-year increase in the US CCUS capacity was
unexpectedly not driven by Govpol. Moreover, the widening gap between CO2E and
CCUS over five decades, coupled with CCUS’s predominant use in enhanced oil and
gas recovery rather than carbon reduction, makes it difficult to statistically determine
the relationship between CO2E and CCUSCap. Nevertheless, our results underscore the
importance of prioritizing Proj to enhance CCUSCap. This aligns with the perspectives of
CCUS experts who emphasize the necessity of accelerating both renewable energy sources
and CCUS technologies.
In the case of the carbon emissions equation, our analysis reveals that an increase in
ECRE leads to a reduction in carbon emissions in the US. Conversely, Govpol, Gdppc, NgP,
and ECFF are associated with increases in carbon emissions. CCUSCap is theoretically
expected to have a negative correlation with CO2E as a carbon reduction technology; our
study does not observe this relationship. This suggests that CCUSCap alone may not be
sufficient to effectively reduce CO2 emissions. This discrepancy could be attributed to the
significant disparity between the amount of CO2 captured and the total CO2 emissions,
where the captured CO2 accounts for less than 0.5% of the total US CO2 emissions. Consid-
ering all the CCUS indicators—CCUSCap, Proj, Techlv, and Govpol—in conjunction with
CO2E, the overall effectiveness of CCUS in reducing carbon emissions appears uncertain.
Energies 2024, 17, 1748 14 of 17

However, if CCUS becomes more widely adopted and cost-effective compared to renewable
energy sources, it could potentially play a significant role in reducing carbon emissions.
Despite more than 50 years of injecting CO2 underground in the US, the real challenge
lies in policy and economic considerations rather than technological limitations. To ensure
the effective management of CCUS operations and the safe storage of CO2 , it is imperative to
develop legal and regulatory frameworks. Decarbonization efforts will be most successful
when green alternatives become economically viable for businesses, encouraging the
voluntary adoption of fossil fuels. It is important to recognize that CCUS is not a standalone
solution but rather a complementary approach to carbon emission reduction. To achieve
net zero goals, a combination of strategies, including renewable energy adoption, energy
efficiency improvements, and behavioral changes, will be necessary.
Our study has several limitations that warrant consideration. Firstly, we solely em-
ployed the linear regression model, neglecting the potential impacts of thresholds. Future
research could explore threshold regression techniques to better capture nonlinear rela-
tionships in the data. Secondly, our focus was limited to the United States, which may
restrict the generalizability of our findings to other countries. Extending the analysis to
include countries with advanced CCUS technology could provide a more comprehensive
understanding of the factors influencing the CCUS capacity and carbon emissions on a
global scale.

Author Contributions: Conceptualization, M.T.M. and R.T.; methodology, M.T.M., R.T., and W.Y.;
software, M.T.M. and W.Y.; validation, M.T.M., R.T., and W.Y.; formal analysis, M.T.M.; investigation,
M.T.M.; resources, M.T.M.; data curation, M.T.M.; writing—original draft preparation, M.T.M. and
R.T.; writing—review and editing, M.T.M., R.T., and W.Y.; visualization, M.T.M. and R.T.; supervision,
R.T. and W.Y.; funding acquisition, R.T. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Acknowledgments: The first author would like to acknowledge the master’s degree program
in Economics in the Faculty of Economics, Chiang Mai University, under the CMU Presidential
Scholarship. This research work was partially supported by Chiang Mai University, Thailand.
Conflicts of Interest: The authors declare no conflicts of interest.

References
1. IPCC. Climate Change 2021 The Physical Science Basis Summary for Policymakers. 2021. Available online: https://www.ipcc.ch/
report/ar6/wg1/chapter/summary-for-policymakers/ (accessed on 19 September 2023).
2. Dziejarski, B.; Krzyżyńska, R.; Andersson, K. Current Status of Carbon Capture, Utilization, and Storage Technologies in the
Global Economy: A Survey of Technical Assessment. Fuel 2023, 342. [CrossRef]
3. IEA. CCUS Projects Explorer. Available online: https://www.iea.org/data-and-statistics/data-tools/ccus-projects-explorer
(accessed on 19 September 2023).
4. IEA. About CCUS; Paris, 2021. Available online: https://www.iea.org/energy-system/carbon-capture-utilisation-and-storage
(accessed on 19 September 2023).
5. IEA. Tracking Clean Energy Progress 2023, Paris, 2023. Available online: https://www.iea.org/reports/tracking-clean-energy-
progress-2023 (accessed on 19 September 2023).
6. Davoodi, S.; Al-Shargabi, M.; Wood, D.A.; Rukavishnikov, V.S.; Minaev, K.M. Review of Technological Progress in Carbon Dioxide
Capture, Storage, and Utilization. Gas Sci. Eng. 2023, 117. [CrossRef]
7. Bhavsar, A.; Hingar, D.; Ostwal, S.; Thakkar, I.; Jadeja, S.; Shah, M. The Current Scope and Stand of Carbon Capture Storage and
Utilization—A Comprehensive Review. Case Stud. Chem. Environ. Eng. 2023, 8. [CrossRef]
8. Thielges, S.; Olfe-Kräutlein, B.; Rees, A.; Jahn, J.; Sick, V.; Quitzow, R. Committed to Implementing CCU? A Comparison of the
Policy Mix in the US and the EU. Front. Clim. 2022, 4. [CrossRef]
9. McLaughlin, H.; Littlefield, A.A.; Menefee, M.; Kinzer, A.; Hull, T.; Sovacool, B.K.; Bazilian, M.D.; Kim, J.; Griffiths, S. Carbon
Capture Utilization and Storage in Review: Sociotechnical Implications for a Carbon Reliant World. Renew. Sustain. Energy Rev.
2023, 177. [CrossRef]
Energies 2024, 17, 1748 15 of 17

10. Bazhenov, S.; Chuboksarov, V.; Maximov, A.; Zhdaneev, O. Technical and Economic Prospects of CCUS Projects in Russia. Sustain.
Mater. Technol. 2022, 33. [CrossRef]
11. Ren, B.; Male, F.; Duncan, I.J. Economic Analysis of CCUS: Accelerated Development for CO2 EOR and Storage in Residual Oil
Zones under the Context of 45Q Tax Credit. Appl. Energy 2022, 321. [CrossRef]
12. Rakhiemah, A.N.; Xu, Y. Economic Viability of Full-Chain CCUS-EOR in Indonesia. Resour. Conserv. Recycl. 2022, 179. [CrossRef]
13. Xiao, K.; Yu, B.; Cheng, L.; Li, F.; Fang, D. The Effects of CCUS Combined with Renewable Energy Penetration under the Carbon
Peak by an SD-CGE Model: Evidence from China. Appl. Energy 2022, 321. [CrossRef]
14. Zhang, L.; Jiang, L.; Zhang, F. CCUS Technology, Digital Economy, and Carbon Emission Efficiency: Evidence from China’s
Provincial Panel Data. Environ. Sci. Pollut. Res. 2023, 30, 86395–86411. [CrossRef]
15. Zhang, W.; Zhu, Z.; Liu, X.; Cheng, J. Can Green Finance Improve Carbon Emission Efficiency? Environ. Sci. Pollut. Res. 2022, 29,
68976–68989. [CrossRef] [PubMed]
16. Zhang, L.; Mu, R.; Hu, S.; Yu, J.; Zhang, J. Industrial Coagglomeration, Technological Innovation, and Environmental Pollution in
China: Life-Cycle Perspective of Coagglomeration. J. Clean. Prod. 2022, 362. [CrossRef]
17. Ravikumar, D.; Keoleian, G.; Miller, S. The Environmental Opportunity Cost of Using Renewable Energy for Carbon Capture and
Utilization for Methanol Production. Appl. Energy 2020, 279. [CrossRef]
18. Ramadhan, R.; Abdurrahman, M.; Bissen, R.; Maneeintr, K. Numerical Simulation of Potential Site for CO2 Sequestration in a
Depleted Oil Reservoir in Northern Thailand. Energy Rep. 2023, 9, 524–528. [CrossRef]
19. Dubois, L.; Thomas, D. Comparison of Various Configurations of the Absorption-Regeneration Process Using Different Solvents
for the Post-Combustion CO2 Capture Applied to Cement Plant Flue Gases. Int. J. Greenh. Gas Control 2018, 69, 20–35. [CrossRef]
20. Mohsin, I.; Al-Attas, T.A.; Sumon, K.Z.; Bergerson, J.; McCoy, S.; Kibria, M.G. Economic and Environmental Assessment of
Integrated Carbon Capture and Utilization. Cell Rep. Phys. Sci. 2020, 1. [CrossRef]
21. He, Y.Y.; Wei, Z.X.; Liu, G.Q.; Zhou, P. Spatial Network Analysis of Carbon Emissions from the Electricity Sector in China. J. Clean
Prod. 2020, 262. [CrossRef]
22. Alsarhan, L.M.; Alayyar, A.S.; Alqahtani, N.B.; Khdary, N.H.; Raganati, F.; Ammendola, P.; Sa, A.S.A. Circular Carbon Economy
(CCE): A Way to Invest CO2 and Protect the Environment, a Review. Sustainability 2021, 13, 11625. [CrossRef]
23. Chen, D.; Jiang, M. Assessing the Socio-Economic Effects of Carbon Capture, Utility and Storage Investment from the Perspective
of Carbon Neutrality in China. Earths Future 2022, 10. [CrossRef]
24. Li, Q.; Li, X.; Liu, G.; Li, X.; Cai, B.; Liu, L.C.; Zhang, Z.; Cao, D.; Shi, H. Application of China’s CCUS Environmental Risk
Assessment Technical Guidelines (Exposure Draft) to the Shenhua CCS Project. Energy Procedia 2017, 114, 4270–4278. [CrossRef]
25. Wang, N.; Akimoto, K.; Nemet, G.F. What Went Wrong? Learning from Three Decades of Carbon Capture, Utilization and
Sequestration (CCUS) Pilot and Demonstration Projects. Energy Policy 2021, 158. [CrossRef]
26. Carneiro, J.; Martinez, R.; Suaréz, I.; Zarhloule, Y.; Rimi, A. Injection Rates and Cost Estimates for CO2 Storage in the West
Mediterranean Region. Environ. Earth Sci. 2015, 73, 2951–2962. [CrossRef]
27. Porter, R.T.J.; Cobden, P.D.; Mahgerefteh, H. Novel Process Design and Techno-Economic Simulation of Methanol Synthesis from
Blast Furnace Gas in an Integrated Steelworks CCUS System. J. CO2 Util. 2022, 66. [CrossRef]
28. Serin, E.; Valero, A.; Martin, R.; Shah, A.; Andres, P.; Mealy, P. Seizing Sustainable Growth Opportunities from Carbon Capture,
Usage and Storage in the UK. 2021. Available online: https://www.lse.ac.uk/granthaminstitute/wp-content/uploads/2021/09/
LSE-Sustainable-Growth-from-CCUS-in-the-UK-poster-16-Nov.pdf (accessed on 19 September 2023).
29. Qalati, S.A.; Kumari, S.; Tajeddini, K.; Bajaj, N.K.; Ali, R. Innocent Devils: The Varying Impacts of Trade, Renewable Energy and
Financial Development on Environmental Damage: Nonlinearly Exploring the Disparity between Developed and Developing
Nations. J. Clean Prod. 2023, 386, 135729. [CrossRef]
30. Martins, T.; Barreto, A.C.; Souza, F.M.; Souza, A.M. Fossil Fuels Consumption and Carbon Dioxide Emissions in G7 Countries:
Empirical Evidence from ARDL Bounds Testing Approach. Environ. Pollut. 2021, 291, 118093. [CrossRef] [PubMed]
31. Lin, B.; Tan, Z. How Much Impact Will Low Oil Price and Carbon Trading Mechanism Have on the Value of Carbon Capture
Utilization and Storage (CCUS) Project? Analysis Based on Real Option Method. J. Clean Prod. 2021, 298, 126768. [CrossRef]
32. Lyons, M.; Durrant, P.; Kochhar, K. Reaching Zero with Renewables: Capturing Carbon; IRENA: Abu Dhabi, United Arab Emirates,
2021.
33. De Jong, S.P.L.; Wardenaar, T.; Horlings, E. Exploring the Promises of Transdisciplinary Research: A Quantitative Study of Two
Climate Research Programmes. Res. Policy 2016, 45, 1397–1409. [CrossRef]
34. Mowery, D.C.; Nelson, R.R.; Martin, B.R. Technology Policy and Global Warming: Why New Policy Models Are Needed (or Why
Putting New Wine in Old Bottles Won’t Work). Res. Policy 2010, 39, 1011–1023. [CrossRef]
35. Watson, J.; Byrne, R.; Ockwell, D.; Stua, M. Lessons from China: Building Technological Capabilities for Low Carbon Technology
Transfer and Development. Clim. Change 2015, 131, 387–399. [CrossRef]
36. Yamaka, W.; Phadkantha, R.; Rakpho, P. Economic and Energy Impacts on Greenhouse Gas Emissions: A Case Study of China
and the USA. Energy Rep. 2021, 7, 240–247. [CrossRef]
37. Zubair, A.O.; Abdul Samad, A.R.; Dankumo, A.M. Does Gross Domestic Income, Trade Integration, FDI Inflows, GDP, and
Capital Reduces CO2 Emissions? An Empirical Evidence from Nigeria. Curr. Res. Environ. Sustain. 2020, 2. [CrossRef]
38. Marjanović, V.; Milovančević, M.; Mladenović, I. Prediction of GDP Growth Rate Based on Carbon Dioxide (CO2 ) Emissions. J.
CO2 Util. 2016, 16, 212–217. [CrossRef]
Energies 2024, 17, 1748 16 of 17

39. Sterpu, M.; Soava, G.; Mehedintu, A. Impact of Economic Growth and Energy Consumption on Greenhouse Gas Emissions:
Testing Environmental Curves Hypotheses on EU Countries. Sustainability 2018, 10, 3327. [CrossRef]
40. Maneejuk, N.; Ratchakom, S.; Maneejuk, P.; Yamaka, W. Does the Environmental Kuznets Curve Exist? An International Study.
Sustainability 2020, 12, 9117. [CrossRef]
41. Muhammad, B. Energy Consumption, CO2 Emissions and Economic Growth in Developed, Emerging and Middle East and
North Africa Countries. Energy 2019, 179, 232–245. [CrossRef]
42. Anwar, A.; Chaudhary, A.R.; Malik, S.; Bassim, M. Modelling the Macroeconomic Determinants of Carbon Dioxide Emissions in
the G-7 Countries: The Roles of Technological Innovation and Institutional Quality Improvement. Glob. Bus. Rev. 2021, 23, e2834.
[CrossRef]
43. Sanli, D.; Gülbay Yiğiteli, N. Do Economic Complexity and Macroeconomic Stability Asymmetrically Affect Carbon Emissions in
OECD? Evidence from Nonlinear Panel ARDL Approach. Environ. Dev. Sustain. 2023. [CrossRef]
44. Zhu, K.; Du, L.; Feng, Y. Government attention on environmental protection and firms’ carbon reduction actions: Evidence from
text analysis of manufacturing enterprises. J. Clean Prod. 2023, 423. [CrossRef]
45. Cao, Y.; Ren, W.; Yue, L. Environmental regulation and carbon emissions: New mechanisms in game theory. Cities 2024, 149.
[CrossRef]
46. Liu, X.; Cifuentes-Faura, J.; Zhao, S.; Wang, L. Government environmental attention and carbon emissions governance: Firm-level
evidence from China. Econ. Anal. Policy 2023, 80, 121–142. [CrossRef]
47. Zhang, Q.; Wei, R. Carbon reduction attention and financial market stress: A network spillover analysis based on quantile VAR
modeling. J. Environ. Manag. 2024, 356, 120640. [CrossRef]
48. Hayashi, F. Econometrics; Princeton University Press: Princeton, NJ, USA, 2000.
49. Greene, W.H. Econometric Analysis, 7th ed.; Pearson: Boston, MA, USA, 2012.
50. Tibshirani, R. Regression Shrinkage and Selection via the Lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 1996, 58, 267–288. [CrossRef]
51. Hoerl, A.E.; Kennard, R.W. Ridge Regression: Biased Estimation for Nonorthogonal Problems. Technometrics 2000, 42. [CrossRef]
52. Zou, H.; Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 2005, 67.
53. Lin, S.; Chen, Z.; He, Z. Intra-City Industrial Collaborative Agglomeration, Inter-City Network Connectivity and Green Technology
Innovation. Sustainability 2021, 13, 8835. [CrossRef]
54. Nascimento, L.; Kuramochi, T.; Iacobuta, G.; den Elzen, M.; Fekete, H.; Weishaupt, M.; van Soest, H.L.; Roelfsema, M.; De
Vivero-Serrano, G.; Lui, S.; et al. Twenty Years of Climate Policy: G20 Coverage and Gaps. Clim. Policy 2022, 22, 158–174.
[CrossRef]
55. D’Orazio, P.; Thole, S. Climate-Related Financial Policy Index: A Composite Index to Compare the Engagement in Green Financial
Policymaking at the Global Level. Ecol. Indic. 2022, 141. [CrossRef]
56. Azam, A.; Rafiq, M.; Shafique, M.; Zhang, H.; Yuan, J. Analyzing the Effect of Natural Gas, Nuclear Energy and Renewable
Energy on GDP and Carbon Emissions: A Multi-Variate Panel Data Analysis. Energy 2021, 219. [CrossRef]
57. Kilinc-Ata, N.; Proskuryakova, L.N. Empirical Analysis of the Russian Power Industry’s Transition to Sustainability. Util. Policy
2023, 82. [CrossRef]
58. Neagu, O. The Link between Economic Complexity and Carbon Emissions in the European Union Countries: A Model Based on
the Environmental Kuznets Curve (EKC) Approach. Sustainability 2019, 11, 4753. [CrossRef]
59. Salahuddin, M.; Gow, J.; Ozturk, I. Is the Long-Run Relationship between Economic Growth, Electricity Consumption, Carbon
Dioxide Emissions and Financial Development in Gulf Cooperation Council Countries Robust? Renew. Sustain. Energy Rev. 2015,
51, 317–326. [CrossRef]
60. Zheng, Y.; Gao, L.; He, S. Analysis of the Mechanism of Energy Consumption for CO2 Capture in a Power System. Energy 2023,
262. [CrossRef]
61. Chen, T.; Zhang, Y.; Jiang, C.; Li, H. How Does Energy Efficiency Affect Employment? Evidence from Chinese Cities. Energy 2023,
280, 128071. [CrossRef]
62. Lee, C.C.; Zhao, Y.N. Heterogeneity Analysis of Factors Influencing CO2 Emissions: The Role of Human Capital, Urbanization,
and FDI. Renew. Sustain. Energy Rev. 2023, 185, 113644. [CrossRef]
63. Zheng, L.; Yuan, L.; Khan, Z.; Badeeb, R.A.; Zhang, L. How G-7 Countries Are Paving the Way for Net-Zero Emissions through
Energy Efficient Ecosystem? Energy Econ. 2023, 117. [CrossRef]
64. Wang, X.; Wang, X.; Ren, X.; Wen, F. Can Digital Financial Inclusion Affect CO2 Emissions of China at the Prefecture Level?
Evidence from a Spatial Econometric Approach. Energy Econ. 2022, 109. [CrossRef]
65. Rahman, M.M. The Dynamic Nexus of Energy Consumption, International Trade and Economic Growth in BRICS and ASEAN
Countries: A Panel Causality Test. Energy 2021, 229. [CrossRef]
66. Guo, Z.; Chen, S.S.; Yao, S.; Mkumbo, A.C. Does Foreign Direct Investment Affect SO2 Emissions in the Yangtze River Delta? A
Spatial Econometric Analysis. Chin. Geogr. Sci. 2021, 31, 400–412. [CrossRef]
67. Bennedsen, M.; Hillebrand, E.; Koopman, S.J. Modeling, Forecasting, and Nowcasting, U.S. CO2 Emissions Using Many
Macroeconomic Predictors. Energy Econ. 2021, 96. [CrossRef]
68. Kennedy, J.E. An Analysis of Time-Series Estimates of Capacity Utilization. J. Macroecon. 1998, 20, 169–187. [CrossRef]
69. Dabrowski,
˛ M.A.; Papież, M.; Rubaszek, M.; Śmiech, S. The Role of Economic Development for the Effect of Oil Market Shocks on
Oil-Exporting Countries. Evidence from the Interacted Panel VAR Model. Energy Econ. 2022, 110. [CrossRef]
Energies 2024, 17, 1748 17 of 17

70. Chevallier, J.; Khuong Nguyen, D.; Reboredo, J.C. A Conditional Dependence Approach to CO2 -Energy Price Relationships.
Energy Econ. 2019, 81, 812–821. [CrossRef]
71. Usman, M.; Jahanger, A.; Makhdum, M.S.A.; Balsalobre-Lorente, D.; Bashir, A. How Do Financial Development, Energy
Consumption, Natural Resources, and Globalization Affect Arctic Countries’ Economic Growth and Environmental Quality? An
Advanced Panel Data Simulation. Energy 2022, 241. [CrossRef]
72. Victor, N.; Nichols, C. CCUS Deployment under the U.S. 45Q Tax Credit and Adaptation by Other North American Governments:
MARKAL Modeling Results. Comput. Ind. Eng. 2022, 169. [CrossRef]
73. IEA. CO2 Storage Resources and Their Development—An IEA CCUS Handbook. 2022. Available online: https://www.iea.org/
reports/co2-storage-resources-and-their-development (accessed on 19 September 2023).
74. IEA. Legal and Regulatory Frameworks for CCUS An IEA CCUS Handbook. 2022. Available online: https://www.iea.org/
reports/legal-and-regulatory-frameworks-for-ccus (accessed on 19 September 2023).
75. Stock, J.H. Climate Change, Climate Policy, and Economic Growth. NBER Macroecon. Annu. 2020, 34, 399–419. [CrossRef]
76. IEA. The Relationship between Growth in GDP and CO2 Has Loosened; It Needs to Be Cut Completely. 2024. Available
online: https://www.iea.org/commentaries/the-relationship-between-growth-in-gdp-and-co2-has-loosened-it-needs-to-be-
cut-completely (accessed on 19 September 2023).
77. Global CCS Institute. Understanding Carbon Capture and Storage What the Experts Say about CCS. 2022. Available online: https:
//www.globalccsinstitute.com/wp-content/uploads/2022/03/What-The-CCS-Experts-Are-Saying-March-18-Update.pdf (ac-
cessed on 19 September 2023).
78. Dolado, J.J.; Lütkepohl, H. Making Wald Tests Work for Cointegrated VAR Systems. Econom. Rev. 1996, 15, 369–386. [CrossRef]

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