Likamata et al-2026-Mineral Economics

ORIGINAL PAPER
Mineral Economics
https://doi.org/10.1007/s13563-026-00683-2
Simplice A. Asongu
[email protected]
Blaise P. Likamata
[email protected]
Dieudonné Mignamissi
[email protected]
1 University of Yaoundé II, Soa, Cameroon
2 University of South Africa, Pretoria, South Africa
3 Korea University, Seoul, Republic of Korea
Abstract
This paper examines the impact of natural resource exploitation, a determinant of embodied carbon that remains underex-
plored beyond single-country contexts, on carbon transfer in international trade, using a structural gravity model estimated
with the Pseudo-Poisson Maximum Likelihood High Dimension Fixed Eects (PPMLHDFE) estimator and embodied
carbon data from the Enriched Open-Source Multi-Region Input-Output Database (EORA), covering 106 exporting and
114 importing countries over the period 1999–2021. The results show that natural resource exploitation signicantly
increases embodied carbon in exported goods, underscoring its role as a key driver of carbon transfer in international
trade. This eect varies depending on the natural resource type and the income level of countries, with stronger impacts in
middle- and high-income economies compared to low-income economies. This work contributes to the literature by pro-
viding a more direct and comprehensive empirical analysis of how natural resource exploitation shapes embodied carbon
in international trade, and by oering policy-relevant evidence on carbon reduction strategies while supporting economic
development and the energy transition.
Keywords Natural resources · Carbon transfer · Trade
JEL Classification Q20 · Q54 · Q56 · F10 · O31
Received: 12 May 2026 / Accepted: 20 July 2026
© The Author(s) 2026
Exploitation of natural resources and carbon transfer in trade
Blaise P.Likamata1· Simplice A.Asongu2,3· DieudonnéMignamissi1
carbon emissions, the main driver of climate change (Des-
roziers et al. 2025).
Economic development is closely linked to carbon emis-
sions. To protect the environment, the Paris Agreement on
climate change set targets for reducing greenhouse gas emis-
sions while pursuing the goal of sustainable growth. In this
context, states have committed through their Nationally
Determined Contributions (NDCs), which dene national
commitments to reduce emissions and promote the energy
transition. To this end, economies like Argentina have pledged
not to exceed 359 Mt of emissions by 2030 (Saaleld 2022).
The European Union, for its part, has set a target of achieving
a 55% reduction by 2030. Despite this commitment, Lee et al.
(2023) show that the eorts implemented are still insucient
to meet the objectives of the Paris Agreement.
However, the exploitation of natural resources could
be a factor hindering the achievement of these objectives.
The extraction of oil, coal, and minerals, as well as defor-
estation, generates carbon emissions and other greenhouse
gases (Danish et al. 2019). These resources are then trans-
formed or used to produce manufactured goods for interna-
tional trade, thus contributing to carbon transfer.
Introduction
The exploitation of natural resources plays a fundamental
role in economic activity, but it is also central to environ-
mental concerns. The strengthening of trade integration
through the emergence of value chains contributes to the
intensication of trade, thereby bringing economies closer
together and multiplying trade ows (Zhong et al. 2022).
This fragmentation of production improves the competi-
tiveness of economies but is accompanied by an increase in

1 3
B. P. Likamata et al.
Several authors have investigated the factors determining
carbon emissions embodied in trade. The literature identies
energy intensity ( Hastuti et al. 2021); industrial structure
(Zhu et al. 2018); sectoral specialization (Su and Ang 2014);
and dependence on natural resources (Desroziers et al. 2025)
as the main factors inuencing the level of carbon embodied
in exports. Among these determinants, dependence on natu-
ral resources stands out as a particularly signicant factor;
highly extractive economies tend to focus on carbon-inten-
sive industries, which amplies carbon leakage. However,
this specic mechanism remains largely unexplored at the
international level. Indeed, existing research is limited to
single-country contexts, such as China (Bai et al. 2025; Tan
et al. 2025; Wang and Fujita, 2023), leaving a lack of under-
standing of the regional and global mechanisms linking
resource extraction to embodied carbon.
Furthermore, importing countries indirectly consume the
emissions generated by the production of goods in export-
ing countries. This phenomenon is called embodied carbon.
Analyzing the relationship between natural resource
exploitation and carbon transfer is crucial for understanding
the underlying environmental mechanisms. This study con-
tributes primarly by examining how natural resources aect
carbon transfer across a broad set of countries, addressing a
gap in the literature highlited above.
This paper uses a structural gravity model adapted to
carbon transfer, built from embodied carbon data from the
Enriched Open-Source Multi-Region Input-Output Data-
base (EORA database), for 106 exporting and 114 importing
countries over the period 1999–2021. The study exam-
ines the impact of natural resource exploitation on carbon
transfer in international trade. The main ndings show that
natural resource exploitation leads to an increase in carbon
emissions associated with exported goods. Resource-rich
countries, where extraction, production, and export are
intensive, generate higher carbon transfer. This eect varies
depending on the type of resource, with oil appearing as the
resource that contributes most to carbon transfer in trade.
Furthermore, a country’s income level also inuences the
volume of embodied carbon: the eect is relatively moder-
ate in low-income countries, while it is more pronounced in
middle- and high-income economies.
Literature review
CO2 emissions incorporated in trade
Since the pioneering contribution of Wycko and Roop
(1994) on carbon embodied in imports from six OECD
countries, the issue of carbon emissions embodied in inter-
national trade has attracted increasing interest in the litera-
ture over the years (Fig. 1).
The literature on embodied carbon emissions follows two
lines of thought (Liu and Wang 2025): the rst focuses pri-
marily on the quantication of emissions and the second on
the determinants or factors that inuence them.
In the rst area, one of the main theoretical frameworks
for analyzing carbon emissions embodied in trade is the
input-output model (Wang and Fujita 2023). The model
measures the carbon embodied in the production of goods
intended for domestic consumption and export. Two vari-
ants of the input-output model are identied in the litera-
ture: the single-region input-output (SRIO) model and the
multi-region input-output (MRIO) model.
The SRIO model focuses on carbon emissions gener-
ated within a region or country during the production of
goods for export and assumes that CO2 emission factors are
Fig. 1 CO2 ows by volume
incorporated in trade. Source:
Authors

1 3
Exploitation of natural resources and carbon transfer in trade
identical across all countries (Peters and Hertwich 2008;
Weber et al., 2008). The MRIO model, on the other hand,
provides a broader view of carbon emissions by taking into
account indirect emissions related to imports from other
countries. Also, unlike SRIO, CO2 emissions are not identi-
cal between countries in MRIO (Yao and Wang 2025; Wang
et al. 2025a, b).
Regarding the second axis, the literature identies sev-
eral determinants of trade-embedded carbon emissions. For
example, using a gravity model, Sun et al. (2024) and Zhong
et al. (2022) identied economic size, geographic location,
distance, language, and environmental policy as explana-
tory factors for trade-embedded carbon. Other studies
have analyzed the eects of consumption patterns (Hastuti,
2021), industrial structure (Ribeiro et al. 2023), popula-
tion growth (Guan et al. 2018a, b), and energy intensity
(Peng et al. 2024) on trade-embedded carbon. Liu and Gao
(2025) examine the digital channel of trade and conclude
that strengthening trade rules could contribute to reducing
export-related carbon emissions.
Regarding the determinants of natural resources, Weber
et al. (2021) show that economies dependent on rents from
natural resources such as coal tend to intensify their spe-
cialization in this sector which in turn increases emissions
embodied in trade. In a more recent study, Desroziers et al.
(2025) conrm that oil and natural gas exploitation increases
carbon transfer. Moreover, this eect is more pronounced in
developing countries. Danish et al. (2019), however, nd a
heterogeneous eect between natural resources and carbon
emissions.
The literature also highlights trade liberalization as a
determinant of the transfer of carbon emissions between
countries (Yang et al. 2025). Trade openness and the
removal of trade barriers facilitate the integration of econ-
omies into global value chains, enabling specialization in
sectors where each country has a comparative advantage
(Johnson and Noguera 2012). However, this liberalization
can also have negative environmental repercussions. While
liberalization generates economic gains, it can also transfer
carbon emissions related to the production of goods to coun-
tries with less stringent environmental standards. In this
regard, Liu and Gao (2025) emphasize that this dynamic of
carbon transfer is particularly pronounced in sectors such as
heavy manufacturing and natural resource extraction, where
developing countries, often less equipped with eco-friendly
technologies, increase their production with high carbon
intensity to meet global demand.
Furthermore, the link between monetary arrangements
and carbon emissions has been addressed in the literature.
Liu et al. (2022) proposed a theory on carbon currency,
which envisions global monetary cooperation aimed at
reducing carbon emissions. According to their approach,
this currency would incentivize countries to adopt stricter
environmental policies, as the value of the currency would
be directly linked to the climate performance of nations.
This could also promote a fairer distribution of environ-
mental responsibilities between producing and consuming
countries, reinforcing economic incentives to reduce car-
bon emissions in international trade. However, while lim-
ited studies, to our knowledge, have addressed the impact
of monetary unions on emissions and carbon transfer, we
explore this perspective in this paper.
More recently, the literature has highlighted the growing
role of articial intelligence (AI) in improving environmen-
tal performance and the transition to a low-carbon economy.
Studies show that AI promotes the deployment of renewable
energy, improves energy eciency and helps to increase
carbon eciency through technological innovations, the
quality of institutions and the development of clean energy
(Sun et al. 2026; Zhang et al. 2026a). Other studies highlight
that the environmental eects of AI are non-linear and vary
according to income level, human capital, the quality of gov-
ernance, the development of renewable energy and depen-
dence on natural resources (Wang et al. 2026a, b; Zhang et
al. 2026b). Finally, recent advances in cognitive automation
and large language models could also contribute to sustain-
able development by improving productivity and resource
allocation (Li et al. 2026). However, this research focuses
primarily on national carbon emissions, energy eciency
or the energy transition, and pays little attention to the inter-
national dimension of emissions – particularly the carbon
embedded in trade or to the role that natural resources may
play in the transmission of these emissions.
Research gap
Although several studies in the literature have explored the
determinants of carbon transfer, there are some shortcom-
ings. These include: (i) the predominance of studies at the
national level, with a particular focus on China (Wang and
Fujia, 2023), which limits our understanding of the global
dynamics of carbon emissions; and (ii) the lack of in-
depth studies examining the mechanisms by which natural
resources inuence carbon transfer within regional contexts.
These shortcomings underscore the need to explore the link
between natural resources and carbon transfer.
Structural gravity model with xed eects
Theoretical framework
In line with Heid et al. (2021a, b), we adopt the framework
of the structural gravity model, in which bilateral trade ows

1 3
B. P. Likamata et al.
Xijt
between an exporter i and an importer j are expressed
as:
Xij =
Y
i
Ω
i
E
j
Φ
j
τij (1)
where Yi represents the total output of the exporting country,
Ej
the total expenditure of the importing country, and
τij
the bilateral trade costs. The terms
Ωi
and
Φj
denote mul-
tilateral export and import barriers, respectively. The struc-
tural gravity model imposes the following conditions: (i) the
value of country i’s output is equal to the sum of exports to
all countries j, including domestic ows, i.e.,
Yi=∑jXij
and (ii) the total expenditure of country j corresponds to the
sum of all its imports, i.e.,
Ej=∑iXij
.
Bilateral trade costs τij consist of tari and non-tari bar-
riers. They can be expressed as follows:
τ
ij
=ηϵ1
ij ψϵ2
ij
(2)
where
ηij
denotes tari barriers such as customs duties
and
ψij
shows non-tari frictions such as geographical
distance and trade agreements. Furthermore, following the
theoretical framework of Anderson and Wincoop (2003),
the parameters
ϵ1
and
ϵ2
are related to the elasticity of
substitution between varieties, generally denoted by
σ
.
Depending on how trade ows are measured, these param-
eters can be expressed in terms of
1−σ
or
−σ
.
Introduced by Anderson and Wincoop (2003), the mul-
tilateral resistance terms capture the average diculty
encountered by a country in trading with all its trading part-
ners. Thus, bilateral trade between two countries depends
not only on trade costs but also on the trade costs they incur
in relation to the rest of the world. Multilateral resistance
is not directly observable and is generally controlled for
by including exporter-year and importer-year xed eects
(Yotov et al. 2016; Heid et al. 2021a, b).
Introduced by Anderson and Wincoop (2003), the mul-
tilateral resistance terms capture the average diculty
encountered by a country in trading with all its trading part-
ners. Thus, bilateral trade between two countries depends
not only on trade costs but also on the trade costs they incur
in relation to the rest of the world. Multilateral frictions
are not directly observable and are generally controlled for
by including xed exporter-year and importer-year eects
(Yotov et al. 2016; Heid et al. 2021a, b).
From theory to empirics
Our objective is to analyze the eect of natural resource
exploitation on the transfer of carbon embodied in exports.
Following the recommendations of Santos Silva and
Tenreyro (2006) and Correia et al. (2020), we use the
PPMLHDFE estimator, which overcomes the limitations
associated with estimating the model using OLS in its log-
linear form. These limitations include the heteroscedasticity
of bilateral trade data and the abundance of zero trade ows.
Furthermore, we introduce xed eects for exporter-time
and importer-time to capture multilateral resistance terms.
Bilateral xed eects are also included in order to capture
all characteristics that remain constant over time between
trading partners. The specication of the base model can
therefore be written as follows:
CO
2ijt
=exp(α
1
nat_ress
it
+α
2
ECI_trade
it
+α
3
ECI_tech
it
+
α4ECI_researchit +α4WTOijt +α5RT Aijt+
α
6
CU
ijt +
µ
it +
δ
jt +
π
ij ×
ϵ
ijt)
(3)
where
CO2ijt
is the volume of carbon incorporated in
exports. When
i=j
,
CO2ijt
represents national and
international ows if
i̸=j
. The inclusion of domestic
ows allows us to estimate the eect of international bor-
ders (Anderson et al. 2018a, b) and domestic trade costs
(Anderson, 2018a). Including export-time xed eects
(
µit)
and importer-time xed eects
(
δ
jt)
in the estima-
tion helps control for multilateral resistance terms. Baldwin
and Taglioni (2006) highlight the diculty of eectively
capturing multilateral resistance and the potential for biased
results, hence the recommendation to use country-time xed
eects. Furthermore, we incorporate pairwise xed eects
into the model to capture time-invariant bilateral relation-
ships such as distance, a shared language, a common colo-
nial past, and a shared religion. This also helps avoid the
endogeneity bias of trade policy variables (Baier and Berg-
strand 2007). The variable of interest in this study is given by
nat_ressit
which measures the share of natural resources
in GDP. The model also incorporates the diversication and
sophistication of economies’ productive capacities based on
the economic complexity index
(ECIit)
. Stojkoski et al.
(2023) distinguish three dimensions of economic complex-
ity: (i) trade complexity; (ii) technology complexity and (iii)
research economic complexity.
Trade agreements are represented by the binary variables
WTOijt
and
RT Aijt
. They respectively represent the
simultaneous membership of the exporter and importer in
the World Trade Organization and a Regional Trade Agree-
ment.
CUijt
is a dummy variable that measures the eect
of a common currency on carbon transfer.
However, estimating the exporter-time xed eects in
Eq. 3 raises an identication problem. This is because the
variable of interest and the variables measuring the vari-
ous dimensions of economic complexity are specic to the
exporter and vary over time. They are collinear with the
exporter-time xed eects. Consequently, their eect can-
not be estimated directly.

1 3
Exploitation of natural resources and carbon transfer in trade
To address this issue, we adopt the strategy proposed by
Heid et al. (2021a, b). This strategy is based on the inclusion
of domestic ows
(i=j)
and international ows
(i̸=j)
,
as well as on the interaction of exporter-specic variables with
an international trade indicator variable
(INTij )
dened as
follows:
{
INTij
=1
if i ̸
=
j
INTij =0if i =j (4)
The model to be estimated becomes:
CO
2ijt
=exp(α
1
nat_ress
it
×INT
ij
+α
2
ECI_trade
it
×
INTij +α3ECI_techit ×INTij +α4ECI_researchit ×
INT
ij
+α
4
WTO
ijt
+α
5
RT A
ijt
+α
6
CU
ijt
+µ
it
+δ
jt
+π
ij
)
×
ϵ
ijt
(5)
The identication is based on a comparison between domes-
tic and international ows. The exporter-year xed eects
account for the level of exporter-specic variables, but do
not account for their interactions with the variable
INTij
.
The coecient α1 thus measures the eect of natural resource
exploitation on international carbon transfers relative to
domestic ows. Furthermore, only the interactions between
INTij
and the variables specic to the exporting country are
included in the model. Indeed, the inclusion of interactions
between
INTij
and variables specic to the importing coun-
try would lead to perfect multicollinearity with the importer-
time xed eects, rendering their coecients unidentiable.
Data
Data on carbon emissions embodied in exports
This analysis is based on a sample of 106 exporting countries
and 114 importing countries (see Table 8 in the appendix for
the countries included in the sample) whose data cover the
period 1999–2021.
The data on carbon emissions in trade are obtained from
the EORA database. It shows carbon ows embodied by
country pair, expressed in gigagrams of CO2 equivalent.
This measure has been used in recent works such as Wang
et al. (2025a, b). As shown in Fig. 1, carbon emissions
are trending according to the income level of economies.
Middle-income countries have higher levels of carbon emis-
sions and are experiencing an upward trend compared to
high- and low-income countries.,
Furthermore, a detailed analysis (Fig. 2) shows that
Kazakhstan has the largest carbon footprint in its exports,
followed by China and India.
Natural resource data
The data concerning natural resources come from the World
Bank. This variable is widely used in the literature (Alemu
2025). The analysis uses total natural resource rents as a per-
centage of GDP, as well as their main components, namely,
oil rents, coal rents, mineral rents, and forest rents. Figure 3
presents the evolution of total natural resource rents by
income group and the evolution of the dierent categories
of natural resource rents over the study period.
Other variables
The variable that measures economic complexity is an index
comprising three dimensions developed by Stojkoski et al.
(2023) and derived from the ECO (Economic Complexity
Observatory). The rst dimension concerns trade complex-
ity and measures a country’s capacity to produce and export
sophisticated goods. The second focuses on technologi-
cal complexity, reecting an economy’s ability to produce
Fig. 2 Countries with the
largest volumes of CO2 in
exports. Source: Authors