Sección General

Revista de economía mundial 69, 2025, 119-144

ISSN: 1576-0162

DOI: http://dx.doi.org/10.33776/rem.v0i69.8326

DimenSionS of SuStainability: aSSeSSinG the impact of technoloGy

intenSity anD Global Value chainS in eaSt anD SoutheaSt aSia

Dimensiones De la sostenibiliDaD: evaluanDo el impacto

De la intensiDaD tecnológica y las caDenas De

valor globales en el este y suDeste asiático

Hugo Campos-Romero

hugo.campos.romero@usc.es

Department of Applied Economics, Faculty of Economics and Business Stud-

ies, ICEDE Research Group, Universidade de Santiago de Compostela

Óscar Rodil-Marzábal

oscar.rodil@usc.es

Department of Applied Economics, Faculty of Economics and Business

Studies, ICEDE Research Group, Universidade de Santiago de Compostela

Recibido: junio 2024; aceptado: noviembre 2024

abStract

This paper explores the Environmental Kuznets Curve hypothesis within

East and Southeast Asia. The analysis, drawing on OECD and World Bank data,

investigates how economic growth correlates with environmental indicators

amid intense global trade and integration into global value chains. The ﬁndings

indicate that while GDP growth and increased energy consumption initially

raise emissions, higher integration in global value chains and high-tech sectors

can mitigate these effects. The study emphasizes the role of technological

innovation and energy efﬁciency, advocating for policies that foster economic

growth alongside environmental sustainability. The results contribute to

understanding how to align economic objectives with ecological sustainability

in a highly interconnected global economy.

Keywords: Technology intensity; Emissions; Sustainability; Global value

chains; East and Southeast Asia.

reSumen

Este trabajo explora la hipótesis de la curva medioambiental de Kuznets en

el este y sudeste asiático. El análisis, basado en datos de la OCDE y el Banco

Mundial, investiga cómo se correlaciona el crecimiento económico con los

indicadores medioambientales en un contexto de intenso comercio mundial e

integración en las cadenas de valor mundiales. Las conclusiones indican que, si bien

el crecimiento del PIB y el aumento del consumo de energía elevan inicialmente

las emisiones, una mayor integración en las cadenas de valor mundiales y en los

sectores de alta tecnología puede mitigar estos efectos. El estudio destaca el papel

de la innovación tecnológica y la eﬁciencia energética, abogando por políticas que

fomenten el crecimiento económico junto con la sostenibilidad medioambiental.

Los resultados contribuyen a entender cómo alinear los objetivos económicos con

la sostenibilidad ecológica en una economía mundial altamente interconectada.

Palabras clave: Intensidad tecnológica; Emisiones; Sostenibilidad; Cadenas

globales de valor; Este y sudeste asiático.

JEL Classiﬁcation/ Clasiﬁcación JEL: N5, Q27, Q53.

Revista de economía mundial 69, 2025, 119-144

1. introDuction

Some East and Southeast Asian countries, such as China or South Korea,

have emerged as important contributors to the global economy. Initially due

to their lower production costs and, in some cases, bolstered by their learning-

by-doing and investment capacities, these Asian nations cover the entire

spectrum of the production chain, from extraction and manufacturing tasks

to high-value-added generation activities in both industry and services. This

achievement stems from the developmental trajectories that have marked their

integration into the global value chains (GVCs). However, several observations

are pertinent. Firstly, not all economies in the region have adopted identical

patterns of productive specialization. Secondly, the rate of economic growth

and convergence towards the income and welfare standards of Western

economies varies markedly across different cases. And thirdly, this rapid

growth has resulted in a substantial reliance on fossil fuels, positioning the

region as one of the leading carbon emitters globally (International Monetary

Fund, 2023).

In this context, it is of great interest to analyze the role of these countries’

productive specialization in high and medium-high technology sectors in the

gradual reduction of their carbon emissions. This is particularly relevant when

considering exports of domestic value-added (DVA) and through the lens of

the Environmental Kuznets Curve (EKC) hypothesis. There is an important gap

in the literature since it has primarily focused on the EKC in its traditional

conception and, more recently, has begun to incorporate various institutional

variables into the analysis. However, the inﬂuence of GVC participation and

technology, especially from a foreign trade perspective, has received little

attention. Regarding the role of technology, it is generally observed that there

exists an inverse relationship between the value-added and the volume of

emissions, a trend that is particularly true in extractive and industrial activities

(Campos Romero & Rodil Marzábal, 2021).

Therefore, the objective of this paper is to examine the impact of

participation in GVCs and exports of high and medium-high technology on

emission reduction among East and Southeast Asian countries. This analysis

is conducted through the lens of the EKC, incorporating a dual perspective on

emissions from both the supply side (consumption-based emissions) and the

demand side (demand-based emissions). Furthermore, this study distinguishes

between countries based on their income levels to provide a nuanced

122 Hugo Campos-Romero · Óscar Rodil-Marzábal

understanding of the interplay between economic development, technological

advancement, and environmental sustainability.

To achieve this objective, data have been collected for a total of 13 East and

Southeast Asian countries: Brunei Darussalam, Cambodia, China, Hong Kong,

Indonesia, Japan, Korea, Laos, Malaysia, Philippines, Singapore, Thailand,

and Viet Nam. Information on trade and participation in GVCs was obtained

from the OECD Trade in Value Added database (TiVA, 2023 edition). Emissions

information from both perspectives has been collected from the Carbon

Dioxide Emissions embodied in International Trade database (TECO2, 2021

edition). Additional variables have been obtained from the World Development

Indicators (The World Bank database).

The ﬁndings reveal that higher GDP levels correspond to changes in

emission trends, but caution is advised due to the sensitivity of EKC models

to variable, country, and timeframe changes. The research notes that while

developed Asian countries may see increased emissions with greater GVC

integration, there is a critical need for policy measures aimed at promoting

less emission-intensive sectors within a globally agreed framework. Proposals

include setting minimum standards for energy and water efﬁciency, emissions

measurement, and environmental impact mitigation at the ﬁrm level.

The results point to the need of promoting high-value technological

activities in developing Asian countries as a strategic approach to reduce

emissions, suggesting that industrial upgrading could shift from emission-

intensive to environmentally friendly practices. This requires policies supporting

industrial promotion and robust R&D systems to build the necessary human

capital. International cooperation is emphasized as crucial for facilitating

these transitions. This cooperation can take a variety of forms, such as joint

development of innovation projects, business partnerships that facilitate

knowledge sharing, and training through supranational capacity programs.

The paper is structured into four main sections, beyond this Introduction.

Section 2 reviews the most relevant literature, identifying the primary analytical

methods, highlighting the research gap concerning the inﬂuence of GVCs and

technological intensity, and identifying the main hypotheses. Section 3 details

the data and methodology employed in this study. Section 4 discusses the

study’s main ﬁndings. Lastly, Section 5 presents the paper’s key conclusions,

offers policy recommendations, and outlines several avenues for future

research.

2. literature reView

The relationship between economic growth and environmental impact has

been studied extensively explored through the EKC hypothesis, which posits a

non-linear relationship between GDP per capita and, typically, carbon emissions

per capita (Grossman & Krueger, 1991, 1993, 1995). This relationship has

often been examined in search of an inverted U-shaped curve between growth

and emissions (Hussain etal., 2021; Polloni-Silva etal., 2021; Rodil-Marzábal

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ReVista De economía munDial 69, 2025, 119-144

& Campos-Romero, 2021; Suki etal., 2020; Wang etal., 2023). However, this

concept has faced criticism for assuming that, beyond a certain level of per

capita income, countries would transition to a pathway of emissions reduction

(Hasanov etal., 2021; Sinha etal., 2019). As a result, many studies, especially

more recent ones, consider the possibility that this relationship could be

wave-shaped (N-shaped or inverse N-shape), suggesting that a country on a

decreasing emissions path at one time might see this trend reverse in the future

(Balsalobre-Lorente etal., 2023; Mohammed etal., 2024; Wang etal., 2024).

Despite the extensive development of this literature, consensus on

the validation of the EKC hypothesis has not been universally achieved. As

highlighted by several scholars, the choice of data, time period, geographical

area, and estimation method signiﬁcantly impacts the outcomes (Özcan &

Öztürk, 2019). An additional issue to consider is the error some studies make

in afﬁrming the EKC hypothesis without identifying speciﬁc turning points that

indicate the per capita income levels at which a country shifts from an increasing

to a decreasing emissions trajectory, and vice versa. In this context, an initial

analysis involves examining the signs of the estimation coefﬁcients, which

provide insights into the curve’s shape. Determining whether the minimum and

maximum values of these curves are realistic and identiﬁable is crucial before

conﬁrming the existence of this curve.

Even though there are general inconsistencies when testing this

relationship, most studies focusing on developing countries verify the EKC

(Esmaeili etal., 2023; Gyamﬁ etal., 2021; Hussain etal., 2022; S. Li etal.,

2020; Shahbaz et al., 2020, 2020). However, the foreign trade dimension

is often overlooked, despite the fact that there are numerous studies using

different hypotheses and methodologies that show a direct link between

international trade and environmental impacts. (Das etal., 2023; Saﬁ etal.,

2023; Sorroche-del-Rey etal., 2023; Zhao etal., 2023). Table 1 provides a

synthesis of the literature review, outlining the principal estimation variables,

geographical scope, models, and results concerning the validation of the EKC

and other analyses related to the environmental impacts that are relevant for

this research. It is observed that, in broad terms, the referenced studies largely

overlook the trade dimension. Accordingly, the examination of the impacts

that participation in GVCs has on the environment, and its interplay with the

EKC, represents an area minimally addressed in existing research, despite

the increasing signiﬁcance of these countries’ involvement in both forward

(via exports of DVA) and backward (through imports of foreign value-added)

participation in GVCs. Similar to the broader interest in international trade

within developing countries, the literature acknowledges the relevance of this

perspective for discerning nations’ environmental accountability, the inﬂuence

of tariffs amidst signiﬁcant production chain fragmentation, and, more broadly,

the environmental ramiﬁcations of GVC participation GVCs (Ali et al., 2024;

M. Li etal., 2023; Meng et al., 2023; Yang & Yan, 2023). Yet, the speciﬁc

implications of GVCs concerning the EKC hypothesis remain underexplored.

124 Hugo Campos-Romero · Óscar Rodil-Marzábal

table 1. literature reView Summary

Authorship Variables Sample

Countries Period Method Results

Ali et al.

(2024)

GVA upstream and downs-

tream participation, CO2

per capita, Internet users,

mobile phone subscription,

digitalization index, urban

population, GDP per capita,

FDI inﬂow, renewable energy

consumption, and industry

value-added

112

developing

countries

1990-

2018 Panel data estimation

GVC participation

leads to higher

emissions

Arshad

Ansari et

al. (2020)

Economic growth, urbaniza-

tion, energy consumption,

and globalization

Asian coun-

tries

1991-

2017

Panel cointegration, pooled

mean group, dynamic ordi-

nary least square, and

differenced panel generali-

zed methods of moments

Mixed results

Assamoi et

al. (2020)

CO2 emissions per capita,

participation in GVCs, GDP

per capita, energy use per

capita, trade openness, and

population density

Asian coun-

tries

1995-

2014

Fully modiﬁed ordinary

least square (FMOLS) and

Dynamic ordinary least

square (DOLS)

GVC participa-

tion leads to

lower emissions

Balsalobre-

Lorente et

al. (2023)

Economic complexity,

globalization, and renewable

energy consumption

Central

and East

Europe

1993-

2017 FMOLS, DOLS Verify pollution

haven hypothesis

Bhattachar-

jee and

Chowdhury

(2024)

CO2, Ch4, and N2O emis-

sions per capita; GDP per

capita, urban population,

trade openness, and institu-

tional quality

South Asia 1971-

2018 OLS panel data Verify EKC

Esmaeili et

al. (2023)

CO2 emissions per capita,

economic complexity index,

FDI, renewable energy

consumption

N-11

countries

1995-

2019 Panel quantile regression Verify EKC

Gyamﬁ et

al. (2021)

GDP, CO2 emissions per

capita, renewable, and non-

renewable energy

Emerging 7 1995-

2018 PMG-ARDL Mixed results

Gyamﬁ et

al. (2023)

CO2 emissions per capita,

GDP, renewable energy con-

sumption, information and

communication technology

imports and exports, and

Human Development Index

Bangla-

desh, India,

Nepal,

Pakistan,

and Sri

Lanka

1990-

2016

Pedroni cointegration test,

Kao residual cointegration

test, and Dumitrescu and

Harlin causality test

Verify EKC

Hanif et al.

(2020)

Renewable and non-renewa-

ble energy consumption,

Human Capital Index, tech-

nology innovation, exchange

rate, and oil prices

16 develo-

ped and 14

developing

countries

1990-

2018

Feasible Generalized Least

Square panel estimation Verify EKC

Hussain et

al. (2022)

Ecological footprint, GDP,

energy consumption, and

population density

Pakistan 1981-

2016

Autoregressive distributive

lag model Reject EKC

Li et al.

(2020)

GDP, CO2 emissions per

capita, population density,

industrialization level, and

urbanization rate

China 2000-

2017 Spatial models Verify EKC

Mao and

He (2017)

SO2 emissions per indus-

trial output, product upgra-

ding, process upgrading,

functional upgrading, and

inter-sectorial upgrading

China

(prefectu-

ral-level

cities)

2003-

2011 Panel data estimation

Environmental

improvements

depend on

productive mix

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Authorship Variables Sample

Countries Period Method Results

Polloni-

Silva et al.

(2021)

GDP per capita, CO2

emissions per capita, foreign

direct investment, industrial

sector, service sector,

residential electricity, and

population density

São Paulo

(Brazil)

2010-

2016 Fixed effect panel data Mixed results

Ponce &

Manlangit

(2023)

CO2 emissions per capita,

GDP per capita, energy

consumption per capita

ASEAN

countries

1960-

2021

Panel root and cointegra-

tion tests Verify EKC

Saﬁ et al.

(2023)

Consumption and produc-

tion-based CO2 emissions,

GDP per capita, exports

and imports to GDP, energy

productivity, renewable

energy consumption, and

eco-innovation

BRICS

countries

1990-

2020

Slope heterogeneity and

cross-sectional dependen-

cy analysis

Economic growth

and imports

tend to increase

emissions, inno-

vation, exports,

and energy

productivity tend

to reduce them

Shahbaz et

al. (2020)

GDP, and energy consump-

tion China 1980-

2018 Nonparametric panel test Verify EKC

Shahzad et

al. (2023)

GDP, coal consumption, FDI

inﬂow, total population, and

renewable energy

South and

East Asian

countries

1990-

2020

Augmented mean group,

and common correlated

mean group panel data

analysis. Slope heteroge-

neity and cross-sectional

dependency analysis

Verify EKC for

coal consump-

tion

Shouwu et

al. (2024)

GDP, urbanization, envi-

ronmental technology, and

clean energy use

North

African

countries

1990-

2019 FM-OLS, D-OLS, and DSUR Verify EKC

Suki et al.

(2020)

GDP, ecological footprint,

and economic, social, politi-

cal, and overall globalization

Malaysia 1970-

2018

Quantile autoregressive

distributed lag Verify EKC

Wang et al.

(2024)

Merchandise trade

openness, regional GDP

per capita, regional CO2

emissions per capita,

regional ecological footprint

per capita, industrial

value-added to GDP, net

FDI inﬂow to GDP, and KOF

globalization index

147 coun-

tries

1995-

2018 Panel data analysis Verify EKC

Wang et al.

(2023)

GDP, CO2, human capital

index, renewable energy

consumption, total natural

resources rents, and trade

openness

208 coun-

tries

1990-

2018

Generalized method of mo-

ments and fully modiﬁed

ordinary

least squares

Verify EKC

Yin & Chen

(2015)

CO2 emissions per capita,

GDP per capita, energy

consumption per capita,

environmental regulations,

regional R&D expenditure

to regional GDP, population,

energy consumption to GDP,

regional coal consumption

to non-renewable energy

consumption, trade open-

ness, and FDI to GDP

China

(regional

level)

1999-

2011 Generalized least squares Verify EKC

Zhao et al.

(2023) Greenhouse gas emissions World 1995-

2015 Input-output

International

trade increases

emissions in agri-

culture exports

Source: Authors.

126 Hugo Campos-Romero · Óscar Rodil-Marzábal

In the Asian context, a number of recent studies have examined this

hypothesis from different perspectives. For example, Bhattacharjee and

Chowdhury (2024) employ a balanced annual panel data for ﬁve South

Asian countries revealing an EKC pattern depending on the greenhouse gas

and estimation technique considered. Their study includes a foreign trade

variable, but without further considerations regarding the type of trade or

GVC participation. In addition, Shahzad and Aruga (2023) test the EKC in

South and East Asian countries for coal consumption and support a non-

linear relationship. Though this study ﬁnds interesting results, they also

lack in considering foreign trade effects. Ponce and Manlangit (2023) also

explore he relationship between CO2 emissions, economic growth, and

energy consumption in the ASEAN, supporting the EKC hypothesis but not

considering foreign trade variables. In contrast, Arshad Ansari etal. (2020)

incorporates a variable representing the globalization level of Asian countries,

which has been found to improve ecological and material footprints. Regarding

the EKC, the ﬁndings show mixed results when using ecological footprint,

with the EKC being supported for Central and East Asian countries but not

for West, South, and Southeast Asian countries. When using the material

footprint indicator, results support the EKC hypothesis except for Central

Asia. The study conducted by Gyamﬁ etal. (2023) is not only one of the few

that incorporates foreign trade as an explanatory variable when analyzing the

EKC in Asian countries, but it also considers high value-added exports and

imports from the information and communication technologies (ICTs) sector.

They found that ICT imports, renewable energy use, and human development

signiﬁcantly decrease CO2 levels, while ICT exports and urbanization increase

carbon emissions in the long run. Additionally, their results support the EKC

hypothesis for South Asian countries. Thus, we can derive a ﬁrst analysis

hypothesis:

h1. there iS a non-linear relationShip between GDp anD emiSSionS in the

eaSt anD SoutheaSt aSian economieS.

As noted, existing literature has not extensively explored the impact of

GVCs in relation to the EKC, also in the case of Asian economies. However,

analyzing this relationship within these countries is especially pertinent due

to the potential positive environmental effects that participation in GVCs may

offer Asian economies (Assamoi etal., 2020), and because of the intense

level of engagement these countries have in global trade (Campos-Romero &

Rodil-Marzábal, 2024), both as exporters of parts and components and as

exporters of ﬁnal goods. The increasing technological sophistication of exports

from some East and Southeast Asian economies is also signiﬁcant, though

such proﬁles vary among them since different income levels coexist within the

same region. Additionally, the lower environmental impact associated with

higher value-added tasks has been recognized, whether these tasks are more

service-oriented than manufacturing, or because they utilize more advanced

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and generally less polluting technologies (Campos Romero & Rodil Marzábal,

2021). In this context, integration into GVCs may entail patterns of economic

and environmental upgrading, resulting in emission reductions via changes

in the structure of production (Gerefﬁ & Fernandez-Stark, 2016; Hofstetter

et al., 2021; Khattak & Pinto, 2018; Mao & He, 2017). This observation

leads us to propose the following hypothesis:

h2. participation in GVcS leaDS to lower emiSSionS in eaSt anD SoutheaSt

aSian countrieS.

The technological component emerges as a crucial element in

interpreting the EKC. Technology has traditionally been integrated into the

EKC literature through the analysis of environmental impacts divided into

scale, composition, and technical or technology effects, as delineated by

Copeland and Taylor (1994, 2001, 2004). The scale effect pertains to the

volume of production and is generally suggested that this factor contributes

to increased emissions, absent changes in the production structure. The

composition effect precisely relates to the sectoral structure of an economy,

with its impacts being ambiguous, contingent on shifts towards economic

structures that are either more or less specialized in relatively polluting

sectors. The technology effect concerns the adoption of new technologies

which, being deemed more efﬁcient in terms of energy and material use,

typically result in lower emissions. It is vital to note that the technology

effect does not exclusively refer to the adoption of clean technologies or a

productive shift towards less polluting sectors but to the broader impacts of

technological upgrading processes across any sector. However, there exists

a notable gap in the literature regarding the analysis of the role played

by changes in the production structure towards the development of more

advanced technologies. Particularly in the context of Asian countries, known

for their signiﬁcant export activity, it is essential to also consider these shifts

in industrial composition within their foreign trade structures.

Many studies incorporating the technology factor, beyond examining the

inﬂuence of the three aforementioned effects, focus on the impact of adopting

clean technologies on emissions. Research such as that by Smulders etal.

(2011) or Shouwu etal. (2024) suggests that through innovation policies and

the promotion of clean technologies, pollution can be mitigated, leading to

cleaner production models. Similarly, other studies highlight the signiﬁcant

role of technological innovation in potentially advancing the tipping points

posited by the EKC, thereby enabling earlier reductions in environmental

impacts (Hanif etal., 2020; Yin etal., 2015). It is posited that, among Asian

economies, the shift towards productive structures with higher technological

and innovative content should manifest within their export structure. Based

on these considerations, we propose the following hypothesis for analysis:

128 Hugo Campos-Romero · Óscar Rodil-Marzábal

h3. hiGh- anD meDium-hiGh-tech exportS haVe a moDeratinG effect on emiSSionS.

The subsequent section outlines the primary data sources and the

methodological framework employed to examine the three hypotheses

previously introduced.

3. Data anD methoDoloGy

This paper aims to examine the impact of value-added exports in high and

medium-high technology across a selection of 13 Asian countries1, spanning

from 1995 to 2018. It considers the countries’ income levels and their

emissions from both producer and consumer perspectives. Thus, it accounts

for the environmental damage attributable to production activities, regardless

of the location of the ﬁnal consumption; as well as the environmental impacts

stemming from domestic consumption, irrespective of the goods and services’

geographical origin.

To attain this goal, data are compiled from various sources. For variables

related to foreign trade and GVC participation, information is sourced from the

database Trade in Value Added (TiVA, OECD, 2023 edition). Emissions data from

both perspectives are obtained from Carbon dioxide emissions embodied in

international trade (TECO2, OECD, 2021 edition). Additional variables of interest,

such as renewable energy consumption, are gathered from The World Bank.

The use of export variables expressed in terms of value-added is considered

particularly meaningful and relevant in this analysis, as opposed to traditional

trade indicators expressed in gross terms. Traditional trade indicators introduce

double counting of intermediate goods and services, which does not allow them

to be calculated correctly and also does not represent the true contribution

of value-added in each country. Due to the growing importance of trade in

intermediate goods and the ability to correctly attribute the value generated

to its origin, international trade indicators obtained by measuring trade in

value-added are the most appropriate for this study. (Koopman etal., 2010,

2014). Furthermore, this approach facilitates the computation of the GVCs

participation index, which is deﬁned as the aggregate of forward and backward

participations. The forward participation in GVCs pertains to DVA exports

that are subsequently re-exported by third countries, whereas the backward

share encompasses the value added by third countries that is exported by the

reference economy. Collectively, these indicators, expressed as a percentage

of a country’s gross exports, serve as a gauge of its integration within GVCs.

Although the data are directly sourced from TiVA database, the

methodological process enabling the extraction of trade variables in terms

of value-added, utilizing the multiregional input-output methodology, is

delineated next. For countries and sectors, the basic input-output entities

1 Brunei, Cambodia, China, Hong Kong, Indonesia, Japan, South Korea, Laos, Malaysia, Philippines,

Singapore, Thailand, and Vietnam.

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are as follows: is deﬁned as the intermediate transactions matrix,

as the ﬁnal demand matrix and is the total output vector, obtained as

the sum of matrices and . From them, the matrices of technical production

coefﬁcients ( ) and the inverse Leontief matrix ( ) can be

obtained.

is the matrix of technical coefﬁcients of production, is a

diagonal matrix containing the elements of the total output matrix on the main

diagonal, the other elements being null; is the Leontief inverse matrix

and is the identity matrix. The value-added coefﬁcients, , which

reﬂect the proportion of value created by sector in country is obtained as

follows:

(1)

With being a vector of ones used to perform sums by columns. When

performing sums by rows, we use a vector would be preferred. To deﬁne

export ﬂows in terms of value-added we shall consider any three countries, ,

and ; and any three sectors , and ; such that but intra-

sector trade is possible, so . Therefore, we deﬁne country DVA

exports for total of columns as follows:

(2)

The ﬁrst sum in equation (2) represents the so-called traditional trade, i.e.

value generated totally by the country of origin exported and also exported

by it as a ﬁnal good. The second sum represents simple GVC trade, i.e.,

value generated by the country of origin, exported as an intermediate good

and consumed as a ﬁnal good by the direct importer. Finally, the third sum

represent complex GVC i.e. value generated by the country of origin, exported

as an intermediate good, re-exported by the direct importer and ultimately

consumed in a third country. Value-added domestic exports are composed by

the addition of these three elements.

Regarding the econometric study, we propose the following two panel data

models incorporating ﬁxed effects to analyze the EKC in East and Southeast

Asian countries considering both consumptions and production-based

emissions.

(3)

This model is replicated by treating emissions from both the production and

consumption perspectives as dependent variables. These, along with the

explanatory variables, are compiled along with the descriptive statistics in

Table 2.

130 Hugo Campos-Romero · Óscar Rodil-Marzábal

table 2. DeScriptiVe StatiSticS. n. of obSerVationS: 312. perioD: 1995-2018

Mean Std. Deviation Minimum Maximum Deﬁnition Source

CO2CPC 6.85 6.62 0.19 21.73

Consumption based

emissions, tones per

capita

TECO2

(OECD,

2021

edition)

CO2PPC 7.03 7.41 0.04 31.28

Production based

emissions, tones per

capita

GDPPC 13736.84 15982.66 243.99 66836.52 GDP per capita, US

dollars

TiVA

(OECD,

2023

edition

GDPPC2 4.43×10e8 7.55×10e8 59529.39 4.46×10e9 Square of GDPPC

GDPPC3 1.75×10e13 3.86×10e13 1.45×10e7 2.98×10e14 Cube of GDPPC

BP 25.68 11.31 5.40 48.00

Foreign value-

added share in gross

exports (%)

FP 17.70 6.20 8.40 41.70

DVA embodied in

foreign gross exports

(%)

TP 43.37 9.01 24.80 65.80

Total participation

in GVCs (sum of BP

and FP)

DVAHMHT 41.65 28.41 0.00 89.10

High and medium-

high technology DVA

exported as a share

of total DVA industry

exports (%)

GDPG 4.94 3.62 -13.13 14.52 Interannual GDP

growth (%)

The World

Bank

GDPPCG 3.61 3.56 -14.48 13.64 Interannual GDP per

capita growth (%)

REC 23.82 25.97 0.00 86.62

Renewable energy

consumption as a

share of total energy

consumption (%)

Source: Authors from TECO2, TiVA, and The World Bank.

4. reSultS anD DiScuSSion

In this section, we present the main ﬁndings of the paper, beginning

with a series of descriptive statistics followed by the results of the proposed

econometric models. Initially, we provide data on the contribution of

industrial sectors to the GDP of the analyzed economies, categorized by their

technological intensity (see Table 3). The results indicate markedly different

patterns based on several factors: primarily, the level of regional development

and, secondarily, the region’s specialization in services.

In the ﬁrst scenario, economies such as Brunei, Cambodia, Indonesia, and

Laos exhibit a minimal presence of high-tech production within their economic

structures, with a dominant share of medium-low or low-tech activities. Vietnam

is an exception, which, from 1995 to 2018, increased its industrial participation

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across all levels of technological intensity, notably in high-tech sectors. In the

second scenario, it is important to note that all countries demonstrate a higher

proportion of service activities relative to GDP. In certain cases, the share of

industrial sectors is less than 10%, as seen in Laos (8%) and Hong Kong (1%).

Finally, it is worth noting the share of the high and medium-high technology

sectors in Korea and Singapore, followed by Vietnam, China, Japan, Thailand,

and Malaysia.

table 3. Share of technoloGical Value-aDDeD to total GDp per technoloGy intenSity

High technology Medium-high

technology

Medium-low

technology Low-technology

1995 2018 1995 2018 1995 2018 1995 2018

Brunei 0.08 0.03 0.26 0.49 7.80 10.23 0.74 0.54

Cambodia 0.10 0.14 0.35 0.51 2.42 2.36 15.64 19.45

China 3.51 3.56 8.66 8.35 9.72 9.50 9.14 7.02

Hong Kong 0.23 0.04 0.61 0.12 1.03 0.13 6.13 0.75

Indonesia 1.18 0.89 4.77 4.91 8.22 4.94 11.07 10.26

Japan 3.37 2.42 8.31 8.66 7.59 6.70 5.22 3.76

Korea 4.74 8.60 8.53 9.46 8.19 7.51 5.43 3.20

Laos 0.15 0.18 0.23 0.43 2.55 2.28 8.21 5.96

Malaysia 7.17 5.06 3.96 4.47 6.11 6.55 4.26 3.77

Philippines 3.74 3.53 3.39 3.36 4.73 3.33 11.86 11.56

Singapore 9.09 10.61 9.30 7.14 2.41 2.49 1.66 1.17

Thailand 3.99 4.09 5.83 6.66 7.84 6.68 9.76 8.46

Vietnam 1.63 7.76 4.66 5.36 7.62 9.79 12.04 13.95

Source: Authors from TiVA.

Turning to foreign trade and focusing on industrial sectors, Table 4 shows the

content of exported DVA by technological intensity, along with the proportion

of domestic carbon emissions in gross exports, relative to total industrial

activity. From the perspective of exported DVA, all developed economies

exhibit a greater share of medium-high and high technology-intensive exports.

In the case of developing economies, the distribution is heterogeneous, with

this situation occurring only in Brunei, China, Malaysia and the Philippines.

However, from an emissions perspective, most of them are concentrated

in medium-low and low technology-intensive exports. This is particularly

remarkable considering the lower importance in USD of these exports in the

case of countries such as Japan, Korea and Singapore. This trend may be

attributed to the fact that activities in higher technological intensity sectors

typically have lower emissions intensity, as they are less detrimental to the

environment. In contrast, lower intensity activities often require more energy

and material consumption, resulting in a higher volume of emissions.

132 Hugo Campos-Romero · Óscar Rodil-Marzábal

table 4. Share of exporteD DVa anD DomeStic co2 emiSSionS to total inDuStry,

2018

High and medium-high technology Medium-low and low technology

DVA content of

gross exports

Domestic CO2 emissions

embodied in gross exports

DVA content of

gross exports

Domestic CO2 emissions

embodied in gross exports

Brunei 54.60 24.71 45.40 75.29

Cambodia 0.89 1.38 99.11 98.62

China 55.60 52.10 44.40 47.90

Hong Kong 29.35 8.66 70.65 91.34

Indonesia 27.27 26.32 72.73 73.68

Japan 81.09 57.72 18.91 42.28

Korea 80.72 55.08 19.28 44.92

Laos 9.67 3.87 90.33 96.13

Malaysia 52.83 43.89 47.17 56.11

Philippines 61.96 51.80 38.04 48.20

Singapore 84.88 51.75 15.12 48.25

Thailand 46.31 40.27 53.69 59.73

Vietnam 32.68 17.93 67.32 82.07

Source: Authors from TiVA and TECO2.

Before presenting the results of the proposed models, it is important to

highlight the value of incorporating both consumer and producer perspectives

in the analysis of carbon emissions. Traditionally, the producer perspective

has been used to assign environmental responsibilities, yet this approach is

biased and disproportionately affects countries that have integrated more

manufacturing-related tasks into the global economic structure, particularly

for products of medium to low technological intensity. On the other hand,

analyzing emissions solely from the consumer perspective also presents a

skewed view, favoring countries with low consumption and high production

levels. Thus, it is advantageous to consider both perspectives simultaneously.

Accordingly, Figure 1 illustrates the per capita tons of carbon emitted

by the countries studied in 1995 and 2018 from both the consumer and

producer viewpoints. Most of the countries display a balance between the two

perspectives, with a variance of approximately 1 ton of CO2 per capita. However,

the cases of Singapore and Hong Kong are notable exceptions: Singapore

exhibits a higher volume of emissions from the producer perspective, signifying

its role as a signiﬁcant supplier of inputs; Hong Kong, in contrast, shows a

predominance of emissions from the consumption perspective.

Finally, Table 5 presents the results of the proposed models, categorizing

the countries into three groups: the complete set, the developing economies

(Brunei, Cambodia, China, Indonesia, Laos, Malaysia, Philippines, Thailand,

and Vietnam), and the developed economies (Hong Kong, Japan, Korea,

and Singapore). Additionally, within each group, emissions are distinguished

between the consumer and producer perspectives.

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Starting with an evaluation of the EKC, several observations emerge. First,

across most models, there is a positive linear relationship between GDP per

capita and emissions from both perspectives. The exception is observed in

developed countries from the producer perspective, where an increase in GDP

correlates with reduced emissions. The latter result can be explained by the

progressive offshoring of various manufacturing activities and the growth of

services in the economic structure of these countries. However, as the level of

consumption per capita in the developed economies has not been reduced, an

increase in disposable income implies higher emissions from the consumer’s

perspective.

fiGure 1. co2 emiSSionS baSeD on conSumption anD proDuction, 1995 anD 2018

Source: Authors from TECO2

134 Hugo Campos-Romero · Óscar Rodil-Marzábal

Regarding the existence of Kuznets curves, the signiﬁcance of the quadratic

(parabolic shape) and cubic (N or inverse N shape) elements of GDP per capita is

analyzed (GDPPC, GDPPC2, and GDPPC3). The production-based model among

developing countries and the consumption-based model among developed

countries do not exhibit this relationship. In contrast, for other models, these

elements are signiﬁcant, typically revealing an inverse U-shaped curve when

considering the quadratic model, except for emissions from the perspective of

the producer in developed economies, where a U-shaped relationship is found.

The cubic models suggest the possibility of either an N-shaped or inverse

N-shaped relationship. However, to conﬁrm these relationships, it is essential

to evaluate the inﬂection points in all cases. Table 6 provides a summary of the

EKC analysis results for each model, which generally support H1 and are in line

with the ﬁndings obtained by Ponce and Manlangit (2023) and Shahzad and

Aruga (2023), among others.

These results reinforce the interest in analyzing the existence of

environmental Kuznets curves as a function of the level of economic

development of countries, as important differences have been found. For

developing countries, although no relationship was found from production-

based emissions, the inverted U-shaped relationship and N-shape show that

increases in per capita income can lead to a temporary reduction in emissions

from a consumption perspective, probably due to changes in consumption

habits. The results show that reaching a certain level of per capita income is

not a sufﬁcient condition to guarantee an emission reduction path in the long

run. The same is true for production-based emissions in developed countries,

where technical and technological progress seems to have reached a limit in

terms of reducing emissions.

table 5. eStimation reSultS. DepenDent VariableS: co2 from conSumption anD proDuction

perSpectiVeS (toneS per capita)

All countries Developing countries Developed countries

CO2

consumption

CO2

production

CO2

consumption

CO2

production

CO2

consumption

CO2

production

GDPPC 4.15E-4

(5.56E-5) ***

0.00035

(0.00006) ***

0.00063

(0.00006) ***

0.00043

(0.00006) ***

0.00047

(0.00018) ***

-0.00043

(0.00019) **

GDPPC2 -8.7E-9

(1.83E-9) ***

-4.8E-9

(1.87E-9) ***

-2.67E-8

(3.3E-9) ***

-4.3E-9

(3.04E-9)

-7.28E-9

(5.01E-9)

1.65E-8

(5.37E-9) ***

GDPPC3 6.28E-14

(1.91E-14) ***

3.8E-14

(1.95E-14) **

3.39E-13

(5. 05E-14) ***

-2.45E-15

(4.66E-14)

3.29E-14

(4.47E-14)

-1.41E-13

(4.79E-14) ***

GDPPCG 0.00178

(0.01879)

0.00804

(0.01921)

-0.0182

(0.0193)

-0.01501

(0.01779)

0.00816

(0.03657)

0.02394

(0.03922)

TP -0.03078

(0.0137) **

0.02282

(0.014008) *

0.00568

(0.01459)

0.00081

(0.01345)

-0.0444

(0.02943)

0.09865

(0.03156) ***

DVAHMHT -0.01584

(0.0068) **

-0.02726

(0.00695) ***

-0.01106

(0.00663) *

-0.02699

(0.00611) ***

-0.03096

(0.02925)

0.06476

(0.03136) **

REC -0.03295

(0.0101) ***

-0.03308

(0.01033) ***

-0.0065

(0.00925)

-0.03063

(0.00852) ***

-0.38250

(0.20385) *

-0.3751

(0.21859) *

Constant 6.67574

(0.72965)

4.5745

(0.746)

2.4586

(0.8172)

4.12807

(0.75325)

10.3864

(1.83671)

6.3579

(1.96953)

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All countries Developing countries Developed countries

CO2

consumption

CO2

production

CO2

consumption

CO2

production

CO2

consumption

CO2

production

R2 within 0.33 0.61 0.48 0.65 0.35 0.7

R2 bet-

ween 0.93 0.73 0.83 0.99 0.5 0.84

R2 overall 0.9 0.71 0.74 0.96 0.47 0.75

rho 0.95 0.96 0.97 0.95 0.83 0.95

Note 1: The table shows the coefﬁcients and the standard errors in brackets.

Note 2: ***, **, and * represent signiﬁcance at 1%, 5%, and 10% respectively.

Source: Authors from TECO2, TiVA, and The World Bank.

As for the other variables in the estimation, economic growth (GDPPCG), as

measured through GDP per capita yearly change rate, is not signiﬁcant; thus,

increases in this variable do not have a relevant impact on carbon emissions in

either direction. However, participation in GVCs (TP) shows signiﬁcant effects

in certain scenarios. When all countries are considered collectively, GVC

participation positively impacts emissions from the producer perspective and

negatively from the consumption perspective. This variable is also signiﬁcant

from the producer perspective for developed countries. These outcomes reﬂect

the diverse integration patterns of the countries under consideration. From

the consumption perspective, the reduction in emissions associated with GVC

participation largely results from the importation of products that require less

energy and material consumption. Conversely, from the producer perspective,

increased emissions are linked to the manner of integration into GVCs,

where increased forward participation (e.g., through value-added exports)

necessitates higher levels of production, material, and energy consumption,

thus leading to increased emissions. In this context, H2 is partially conﬁrmed,

as participation in GVCs can lead to a reduction in emissions depending on the

mode of integration and the emissions perspective adopted.

table 6. eKc eStimation reSultS Summary

Group Emissions

perspective Parabolic Inﬂection point

(USD per capita) Cubic Inﬂection points

(USD per capita)

All countries CO2 consumption Inverted U-shape 23,856 No relation -

CO2 production Inverted U-shape 36,552 No relation -

Developing

countries CO2 consumption Inverted U-shape 11,726 N-shape 17,679 and

34,828

CO2 production No relation - No relation -

Developed

countries CO2 consumption No relation - No relation -

CO2 production U-shape 13,027 Inverted N-shape 16,529 and

61,484

Source: Authors.

136 Hugo Campos-Romero · Óscar Rodil-Marzábal

From the technological perspective, which considers the impact of foreign

trade on emissions, exports of high and medium-high technology goods play

a key role (DVAHMHT). These exports generally have a signiﬁcant downward

impact on emissions, except for emissions from the producer perspective

among developed countries. In this case, the substantial role of high-tech

activities within the industrial GDP means that any further increases could

lead to a greater impact on emissions. However, in developing economies, an

increased specialization in high-tech activities may lead to a reduction in less

technology-intensive tasks, which, as previously discussed, tend to cause more

environmental damage. In this context, H3 is substantiated for developing

economies in East and Southeast Asia, suggesting that an increase in their

exports of high and medium-high technology goods would result in a reduction

in emissions. Lastly, renewable energy consumption as a percentage of total

energy consumed (REC), included as a control variable, yields the anticipated

outcome. It shows a signiﬁcant and negative effect on emissions across all

scenarios, underscoring the positive environmental impact of integrating

renewable energy sources into energy consumption patterns.

5. concluSionS

In the context of climate emergency, it is critical to examine the factors that

can contribute to emission reductions and more efﬁcient resource use. East

and Southeast Asian countries have emerged as signiﬁcant global suppliers

to both neighbored economies and Western countries. Additionally, domestic

consumption plays a crucial role in driving GDP in some of these countries.

Accordingly, this paper aims to analyze the impact of high and medium-high

technology exports on reducing emissions through the lens of the EKC. It also

considers both consumer and producer perspectives on carbon emissions.

The ﬁndings indicate a non-linear relationship between emissions and GDP

per capita, although year-on-year changes in GDP do not signiﬁcantly affect

this relationship. A parabolic or wave-like relationship has been established

in instances where inﬂection points exist, thus identifying the levels of GDP

per capita at which trend changes may occur. Caution is advised when

interpreting these levels due to the EKC estimation models’ high sensitivity

to changes in dependent and independent variables, countries, and the time

frames considered. On this basis, the results show that the share of GVCs and

the DVA of high and medium-high technology goods in reducing emissions are

particularly signiﬁcant.

The effects of GVCs largely depend on the way that Asian countries are

integrated and the development level. Notably, developed Asian countries

tend to exhibit higher emissions from the production perspective when their

participation in GVCs increases. In response, policy measures should be

devised to guide productive specialization in foreign trade towards sectors

that are less intensive in emissions. However, these policies must be crafted

within a global consensus framework, considering their potential impact on

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the productive structures of developing economies. Speciﬁc proposals could

include establishing minimum standards for efﬁcient energy and water use,

measuring emissions, and designing and implementing plans to mitigate

environmental impact at the ﬁrm level.

Precisely, this study underscores the importance of incentivizing activities

with higher technological content and value-added in developing countries as

a key strategy for reducing emissions. Industrial upgrading in the developing

economies of East and Southeast Asia could replace emission-intensive

activities with more environmentally friendly alternatives. Consequently, there

is a need for industrial promotion policies complemented by plans to develop

robust R&D systems. Over the medium to long term, these initiatives are

expected to produce a sufﬁcient supply of human capital with the required

knowledge to develop such activities. International cooperation, particularly

from more developed countries, is essential to support and accelerate the

economic transition in developing regions. From the consumer perspective, it

is also of great importance to adopt consumer awareness policies, especially

considering the gradual improvement in per capita income levels in some Asian

economies and the effects this may have on personal consumption (Menezes

& Rodil-Marzábal, 2012).

Future research lines should explore the potential barriers that the

developing economies of East and Southeast Asia face in crafting proposals

aimed at strengthening their productive structures and fostering activities

that generate higher value. Additionally, it would be beneﬁcial to examine the

positive impacts these measures could have on social upgrading, particularly

in addressing economic, gender, and labor inequalities.

aKnowleDGmentS anD funDinG:

This research has been supported by the ICEDE research group, to which

the authors belong, Galician Competitive Research Group ED431C 2022/15

ﬁnanced by Xunta de Galicia, project “REVALEC” REFERENCE PID2022-

141162NB-I00 Financed by MCIN/AEI/10.13039/501100011033/EFRD, EU,

and project "CEBCAT", reference 101179061, ﬁnanced by the ERASMUS+

PROGRAMME, European Union EACEA. Hugo Campos-Romero acknowledges the

support received from the Xunta de Galicia 2024 postdoctoral training support

programme (ayudas de apoyo a la etapa de formación posdoctoral), co-funded by

the Consellería de Cultura, Educación, Formación Profesional y Universidades and

the Agencia Gallega de Innovación (reference number ED481B_048).

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All countries Developing countries Developed countries

CO2 consumption CO2 production CO2 consumption CO2 production CO2 consumption CO2 production

Coefﬁcient Standard

error Coefﬁcient Standard

error

Coef-

ﬁcient

Standard

error Coefﬁcient Standard

error

Coef-

ﬁcient

Standard

error

GDPPC 0.0004 0.0001*** 0.0004 0.0001*** 0.0006 0.0001*** 0.0004 0.0001*** 0.0005 0.0002** -0.0005 0.0002**

GDPPC2 -8.86E-09 1.88E-

09*** -5.25E-09 1.9E-09*** -2.63E-08 3.18E-09*** -4.72E-09 2.93E-09 -6.99E-09 5.06E-09 1.68E-08 5.24E-

09***

GDPPC3 6.61E-14 1.94E-

14*** 4.1E-14 1.97E-14** 3.32E-13 4.83E-14*** 1.98E-15 4.44E-14 3.09E-14 4.51E-14 -1.42E-

4.68E-

14***

GDPPCG 0.0009 0.0190 0.0097 0.0192 -0.0179 0.0193 -0.0142 0.0178 0.0066 0.0369 0.0254 0.0383

BP -0.0073 0.0145 0.0272 0.0146* -0.0004 0.0140 0.0116 0.0129 -0.0297 0.0365 0.1436 0.0378***

DVAHMHT -0.0199 0.0066*** -0.0255 0.0067*** -0.0100 0.0063 -0.0280 0.0058*** -0.0360 0.0293 0.0744 0.0304**

REC -0.0275 0.0102*** -0.0325 0.0103*** -0.0083 0.0089 -0.0280 0.0082*** -0.4513 0.1983** -0.3061 0.2057

Constant 5.7059 0.6695 4.6302 0.6781 2.7413 0.6451 3.7703 0.5931 9.9070 1.8189 6.5560 1.8867

R2 within 0.32 0.61 0.65 0.65 0.34 0.71

R2 be-

tween 0.94 0.73 0.99 0.99 0.56 0.93

R2 overall 0.91 0.71 0.95 0.95 0.51 0.85

rho 0.95 0.96 0.97 0.95 0.81 0.93

Source: Authors from TECO2, TiVA, and The World Bank.

Note 1: ***, **, and * represent signiﬁcance at 1%, 5%, and 10% respectively.

appenDix a

table a1. aDDitional estimation results: backwarD participation

144 Hugo Campos-Romero · Óscar Rodil-Marzábal

All countries Developing countries Developed countries

CO2 consumption CO2 production CO2 consumption CO2 production CO2 consumption CO2 production

Coefﬁcient Standard

error Coefﬁcient Standard

error

GDPPC 0.0005 0.0001*** 3.58E-04 0.000060*** 0.000617 0.000059*** 0.000442 0.000055*** 0.000449 0.000174** -0.0003473 0.000199*

GDPPC2 0.0000 0.0000*** -4.62E-09 1.9E-09** -2.67E-08 3.19E-09*** -3.51E-09 2.93E-09 -7.05E-09 4.93E-09 1.51E-08 5.65E-09***

GDPPC3 0.0000 0.0000*** 3.46E-14 1.95E-14* 3.41E-13 4.98E-14*** -1.93E-14 4.57E-14 3.25E-14 4.41E-14 -1.33E-13 5.05E-14***

GDPPCG 0.0054 0.0187 0.0087 0.0193 -0.0194 0.0194 -0.0119 0.0179 0.0099 0.0361 0.0285 0.0414

FP -0.0628 0.0220*** -0.0052 0.0227 0.0125 0.0208 -0.0242 0.0191 -0.1536 0.0725** 0.0210 0.0831

DVA-

HMHT -0.0148 0.0067** -0.0234 0.0070*** -0.0110 0.0063* -0.0250 0.0058*** -0.0202 0.0297 0.0750 0.0340**

REC -0.0269 0.0096*** -0.0384 0.0099*** -0.0076 0.0082 -0.0319 0.0076*** -0.3003 0.2089 -0.1759 0.2394

Constant 6.0041 0.5301 5.5029 0.5472 2.5614 0.5163 4.4898 0.4742 10.6656 1.8092 8.0121 2.0734

R2 within 0.33 0.61 0.48 0.00 0.37 0.66

between 0.92 0.71 0.85 0.00 0.80 0.41

R2 overall 0.89 0.70 0.75 0.00 0.71 0.41

rho 0.95 0.96 0.97 0.95 0.75 0.96

table a2. aDDitional eStimation reSultS: forwarD participation

Source: Authors from TECO2, TiVA, and The World Bank.

Note 1: ***, **, and * represent signiﬁcance at 1%, 5%, and 10% respectively.