INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

Environmental Goods Trade and Environmental Sustainability:

Exploration of the Effect of Greenhouse Emissions in Europe

¹Dr. Usman, Jabir Muhammed., ¹Dr. Agunbiade, Olabode., ²Dr. Ahmed, Ibrahim

¹Department of Economics, Mewar International University, Masaka, Nasarawa State

²National Examinations Council, Nigeria

DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000797

Received: 02 November 2025; Accepted: 10 November 2025; Published: 24 November 2025

ABSTRACT

This study examines the influence of trade in environmental goods on environmental sustainability, with a

particular emphasis on the impact of greenhouse gas emissions in Europe. Utilising annual data from 59

European countries over the period 1994–2024, the research incorporates environmental sustainability indicators

sourced from the World Development Indicators (WDI, 2022) and trade data from the International Monetary

Fund (IMF, 2024). The analysis employs second-generation econometric techniques to address cross-sectional

dependence and heterogeneity, including Pesaran’s (2004, 2015) tests, Westerlund’s (2007) cointegration

approach, and the Augmented Anderson-Hsiao (AAH) estimator as proposed by Chudik and Pesaran (2022).

Empirical findings indicate a significant long-term cointegration between trade in environmental goods and

greenhouse gas emissions. Specifically, exports of environmental goods and total trade in these goods contribute

to the reduction of emissions, whereas energy consumption continues to be a primary driver of environmental

degradation. Additionally, the import of environmental goods shows a weak but negative relationship with

greenhouse emissions, suggesting that imported environmental technologies may enhance production efficiency.

The study further concludes that trade in environmental goods is pivotal in achieving environmental

sustainability across European economies by fostering cleaner production and innovation-driven growth.

Consequently, policies that promote the expansion of green trade, reduce trade barriers on environmental goods,

and encourage the use of renewable energy are essential to bolstering Europe’s environmental resilience.

Moreover, advancing research and development in clean technologies, harmonising regional carbon standards,

and incentivizing sustainable production practices will facilitate Europe’s transition toward a low-carbon

economy.

Keywords: Environmental goods trade, environmental sustainability, greenhouse gas emissions, Europe, panel

econometrics, renewable energy

INTRODUCTION

The growing global awareness of environmental issues has placed sustainability at the forefront of policy,

research, and innovation. As the Brundtland Commission stated in 1987, sustainability emphasizes the need for

a balance between economic growth, social inclusion, and environmental protection. With the increasing

challenges of climate change, deforestation, and biodiversity loss, implementing sustainable practices has

become a crucial global necessity. The pursuit of environmental sustainability is now a significant concern

worldwide, as we face the pressing issues of climate change, resource depletion, and environmental degradation

(IPCC, 2020; UNEP, 2020). The extensive effects of environmental degradation threaten human health,

economic progress, and social well-being (WHO, 2018; WCED, 1987).

In Europe, the issue of environmental sustainability has become crucial, driven by its challenges as highlighted

by the European Environment Agency in 2020. The European Union (EU) has taken a leading role in global

initiatives to foster environmental sustainability, implementing ambitious policies and targets aimed at cutting

greenhouse gas emissions, transitioning to a low-carbon economy, and encouraging sustainable development

(European Commission, 2020). Environmental sustainability covers a wide array of dimensions, including

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

environmental, economic, and social factors (WCED, 1987). The growing demand for environmental goods

stems from several converging factors, including the EU's environmental policy framework, particularly the CEF

and CEP; a rising consumer preference for greener products; advancements in technology; and global trade

agreements like the EGA under the WTO.

However, the swift growth of environmental goods trade in Europe brings up significant concerns regarding its

environmental impact, such as the carbon footprint associated with production and transportation, resource

depletion and extraction, environmental degradation in the countries of origin, and issues like green washing or

environmental dumping. Europe is at the forefront of global environmental sustainability, driven by its ambitious

climate goals, initiatives like the European Green Deal, and active involvement in multilateral trade agreements.

Trading in environmental goods offers a crucial pathway to cleaner technologies that facilitate the transition to

a low-carbon economy. Nevertheless, challenges persist due to various trade barriers, unequal access to

technology, and regulatory complexities, despite Europe's prominent position in this arena (Usman et al., 2025).

The global trade in EGs has seen substantial growth, fueled by rising demand for sustainable products,

advancements in technology, and the liberalization of trade (OECD, 2019). However, the trade environment is

complicated by tariff and non-tariff barriers, differing regulations, information gaps, and challenges related to

intellectual property rights (Kalamova et al., 2019). These obstacles limit the potential of EG trade to enhance

environmental sustainability and drive economic development. Considering the growing significance of

environmental goods trade in achieving sustainable development, concerns about its environmental impact have

also increased (OECD, 2019; UNEP, 2020). While trading in environmental goods can help lower greenhouse

gas emissions, enhance resource efficiency, and encourage sustainable consumption patterns, it also brings

notable environmental challenges. These include the carbon footprint associated with production and

transportation, resource depletion, and environmental degradation in the countries where these goods originate

(Kalamova et al., 2019; Zhou et al., 2020).

Over the years, European countries have introduced various policies to address the imbalances in environmental

goods trade and enhance sustainability. These include efforts to reduce or eliminate tariffs on environmental

goods to boost trade and promote sustainability, as well as initiatives aimed at harmonizing environmental

regulations and standards across Europe to facilitate trade while ensuring environmental protection, greenhouse

procurement which seeks to motivate governments and businesses to purchase environmentally friendly goods

and services among others (Usman et al., 2025).

The relationship between environmental goods trade and environmental sustainability, particularly in European

countries, has garnered significant attention from researchers, stakeholders, and policymakers. Studies by

Kalamova et al. (2019), Zhou et al. (2020), Ekins et al. (2019), and Mayer et al. (2019) indicate that

environmental goods trade positively influences the reduction of greenhouse gas emissions in Europe. In

contrast, research by Alola et al. (2019b), Charfeddine (2017), and Dogan et al. (2019) suggests a negative impact

on environmental sustainability, as noted by Wang & Dong (2019) and Hassan et al. (2019). Other focused on

the interaction between environmental goods trade, including works by Cosmas et al. (2019), Ali et al. (2016),

Rafindadi (2016), Lin et al. (2015), and Nathaniel et al. (2020).

Despite the plethora of studies, little attention is paid to the asymmetries effect of greenhouse gas emissions in

analyzing the effects of environmental goods trade shocks on environmental sustainability in Europe. Also

considering the effort of enhancing trade in EGs through tariff reduction, it remains unclear how trade in EGs is

vital to environmental quality. What are the transmissions medium? Are the exporting countries of EGs

influenced identically as net importing countries? And given the rate of greenhouse emissions sustainability

effect in Europe countries, this kind of study is necessary to provide an empirical insight on the issue.

The study is subsequently organized as follows: Section 2 presents a related literature on the relationship between

EGs and environmental suitability in Europe; Section 3 presents the data description and specifies the model.

The results are discussed in Section 4 whereas Section 5 entails the conclusion and recommendation based on

the findings.

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

LITERATURE REVIEW

Duodu and Mpuure (2023) investigated the impact of international trade on environmental pollution for 33 sub–

Saharan African countries using the GMM estimator. The outcome shows that in total, trade boost environmental

sustainability in the short and long run. The study further dis-aggregated trade into exports and import and

examine their effect on the environment. The result remains the same as when overall trade was considered. The

plausible reason for this result is that involving in international trade has the tendency to enhance environmental

sustainability. This can be credited to the fact that foreign trade stimulate the spread of green technologies, which

emerging economies accept to revamp their economic sectors which in turn mitigate the destructive effect of

CO2 pollution on the environment. Also, Ali et al. (2016) discovered that increase in trade openness has the

capacity to reduce CO2 pollution. Similarly, Iheonu (2021) find that in countries where the level of CO2 is low,

international trade supports environmental sustainability. Ma and Wang (2021) systematically examined the role

of international trade for 179 countries by considering the impact of trade in goods and services on environmental

pollution. Their outcome shows that international trade that concerns goods can significantly lessen pollution

intensity. In the sub-Saharan Africa, the study by Okelele et al. (2022) finds that the rise in trade openness leads

to a decrease of ecological footprint per capita.

Alhassan (2022) examined the impact of trade in environmental goods and environmental sustainability on

production, consumption and trade based ecological footprint (comprehensive indicator of environmental

degradation) in Asia. Using second generation panel time series techniques and the Augmented Anderson–Hsiao

(AAH) two-step GMM estimator with a sample of Asian countries over the period 1994-2021, the findings

confirm the environmental Kuznets Curve Hypothesis (EKC) in all the models. Second, the results support the

pollution haven hypothesis because trade openness has significant negative impact on the ecological footprint in

all the models. This implies that trade openness reduces environmental degradation and the result revealed that

increase in ecological goods imports, exports and total trade reduces ecological print in Asia.

Burki and Tahir (2022) study the determinants of environmental pollution in Asian economies using panel data

techniques such as pooled least squares, fixed effects, generalized least squares (GLS) and two stages least

squares (2SLS). Among other variables trade openness is found to reduce environmental quality. The results

illustrate that increasing energy consumption, trade openness, and financial development positively contribute

to environmental degradation in ASEAN economies. The causality analysis shows a two-way causality between

trade openness and financial development, and environmental degradation and trade openness from energy

consumption and per capita income towards financial development from per capita income towards trade

openness, and financial development towards environmental degradation. The study's results contribute to the

environmental degradation literature and provide a better understanding of environmental degradation for

policymakers in ASEAN economies.

Rafique et al. (2022) examine the impact of economic complexity and renewable energy on environmental

sustainability including other control variables such as human capital, economic growth, export quality and trade

for ten most economic complex countries. The study used the FMOLS, DOLS, and system GMM and obtained

consistent results across different methods. Economic complexity, economic growth, export quality, trade and

urbanization were found to be detrimental to environmental sustainability while renewable energy and human

capital were found to enhance the quality of the environment. Economic complex countries have advance

productive structures which will significantly hurt the environment. However, with the efficient use of

technology renewable energy is employed to mitigate degradation. Also, the increase of export and trade raises

the production process which worsens the quality of the environment. Can et al. (2020) investigate whether trade

is important in determining environmental quality in developing countries. The outcome from the different

estimators used shows that product diversification, intensive margin and extensive margin significantly increase

CO2 emission. Appiah et al. (2022) study the impact of trade on environmental pollution in selected emerging

markets using the pooled OLS. The finding shows that increase in imports leads to escalating emission level.

But export was found to improve environmental quality though insignificant.

Liu et al. (2022) determine environmental outcome by studying the relationship between imports of EGs and

magnitude of pollution in China. EGs were grouped into goods required to handle a particular environmental

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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)

ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025

challenge but can be used for other non-environmental purpose and goods whose ultimate use is handle

environmental issues. The study finds that the import of EGs such as solar PV cells, solar cars, jute bags, clean

production, and energy technology whose end use is to handle specific environmental issue decrease pollution

intensity. The study concludes that the end use of EGs is the main determinant of the relationship between EGs

and pollution intensity.

Abdulkareem, Ojonugwa, George, and Samuel (2020) Using the Ordinary Least Squares (OLS), Generalized

method of moments and panel quantiles via Moments, this study explored the role of government integrity on

trade-environment nexus in the post-Kyoto protocol era for 79 countries between 2008 and 2018. The empirical

results suggest that per capita GDP and government integrity improve environmental performance whereas trade

impedes it. Robustness analysis from the GMM dynamic panel estimation technique shows that interacting

government integrity with trade yields a positive and significant coefficient. The study suggests that outsourcing

the regulations of trade-oriented multinational companies operating in developing economies with weak

institutions to global humanitarian organisations such as the United Nations would be the first step to reduce

trade-attributable environmental degradation.

METHODOLOGY

This study employs a second-generation panel data econometric model in order to ensure strong and credible

empirical analysis. The process began with exploratory diagnostic tests, including the computation of summary

statistics, correlation matrix, and multicollinearity tests to verify the basic nature and interdependence of

variables. In order to consider potential interdependencies across cross-sectional units, the study used a cross-

sectional dependence (CD) test. Following these preliminary tests, panel unit root tests were used to ascertain

the stationarity properties of the variables, and panel cointegration tests were used to examine long-run

relationships among the variables. Following the determination of cointegration, the study proceeded to estimate

the regression equations employing the respective second-generation panel estimation techniques.

This study examines the linkage between environmental goods trade and environmental sustainability, exploring

the effect of greenhouse emissions in Europe. The examination is founded on annual data for 59 European

countries from 1994 to 2024. Where greenhouse gas emissions GHG (CO₂ per capita) proxy environmental

sustainability as dependent variable, GDP per capita (dollars), value of trade (exports + imports of environmental

goods (USD)), environmental goods exports per capita EGEPPC and energy consumption per capita ENCO (or

energy intensity) serves as explanatory variables. The environmental sustainability data were retrieved from the

World Development Indicators (WDI, 2022) and environmental goods trade data were retrieved from the

International Monetary Fund (IMF, 2024). These were selected as they best capture both the economic and

environmental variables needed to understand the nexus of environmental goods trade and environmental

sustainability.

Cross-sectional dependency (CD) test

Cross-sectional dependence in time series panel data arises from correlations among cross-sectional units, such

as countries, due to shared factors. These factors include geographical proximity, economic integration,

globalization, and spatial closeness, all of which contribute to cross-sectional dependence in panel data. In the

context of globalization, panel data with a time series component is prone to cross-sectional dependence. For

instance, given the geographical, social, and economic ties among the countries analyzed in this study, it is

essential to assess the presence of cross-sectional dependence in the data. Ignoring this issue could lead to biased

and inconsistent estimates (Yao et al., 2020). To ensure a robust analysis, this study applied two different tests

for cross-sectional dependence: Pesaran's test (2015), Pesaran scaled LM test (Pesaran, 2004), and the bias-

corrected LM test by Baltagi et al. (2012). These tests are particularly appropriate for this study, which involves

panel data with a relatively large number of cross-sectional units (59 countries) compared to the time span (22

years). The null hypothesis for these tests is the absence of cross-sectional dependence, and the statistical

significance of the test results is used to determine whether cross-sectional dependence is present.

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Panel stationarity tests

Following the completion of the CD test, the study examined the stationarity of the variables using a second-

generation panel stationarity test introduced by Pesaran (2007), known as the Cross-Sectional Augmented

Dickey-Fuller (CADF) test. This CADF test effectively tackles the issue of CD and reduces the risk of spurious

regression. The test statistic for the CADF is calculated based on equation (1) as presented below.

( )

1

∆

=

+

̅

+ ∑ Δ̅ + ∑

Δ

+ £

, −

, −1

−1

−

=0

=1

The initial difference and the average of the lagged individual cross-section are defined by and verify the

robustness of the results, the cross-sectional Im-Pesaran-Shin (CIPS) test, as introduced by Pesaran (2004), was

utilized. This test also considers the presence of cross-sectional dependence. The null hypothesis asserts that the

series is stationary across all cross-sections, whereas the alternative hypothesis indicates a unit root in at least

one cross-section. Rejecting the null hypothesis confirms the presence of a unit root.

Panel cointegration test

The study also conducted a panel cointegration test to determine whether there are long-run connections among

the variables. A second-generation cointegration test, has been utilized in this study since there is cross-sectional

dependence as proposed by Westerlund (2007). There are two statistics –G_t, and G_afor the panel produced by

the test that are utilized to reject the null hypothesis. The formulae for these test statistics are given by:

1

∑

( )

2

=

and

=

=1

̂

∑

1−

(

)

=1

The test's null hypothesis posits the absence of cointegration, while the alternative hypothesis asserts the presence

of cointegration.

Model specification

The primary objective of this study is to examine the influence of environmental goods trade on environmental

sustainability, exploring the effect of greenhouse emissions in Europe. Therefore, this study adapted the research

conducted by Duodu and Mpuure (2023), Okelele et al. (2022), Alhassan, (2022), Iheonu (2021), Ma and Wang

(2021), Burki and Tahir (2022), the empirical model of this study is formulated as presented below.

=

+

₁ln

(3)

+

₂ln ENCO

+

₃ln

+

₄EGIM

+

₅EGEX

+

0

,

₆EGTT _,+ µ _,

Where

= Green House Gas emissions per capita, (CO₂ eq per capita),

= Environmental

goods exports per capita,

= GDP per capita, ENCO = Total Energy consumption, EGIM

=

,

environmental goods imports, EGEX = environmental goods exports, and EGTT _,= total environmental goods

,

trade.

Environmental sustainability was quantified in terms of Carbon-dioxide emissions (CO2)/ Green House Gas

emissions. Subscript it represent the countries and time, respectively, while stochastic error is denoted by ε and

all variables were defined in logarithmic terms. The natural logarithms were applied to the variables to

standardize units of measurement and minimize the effect of outliers in the data. The Augmented Anderson-

Hsiao (AAH) estimator, as motivated by Chudik and Pesaran (2022), has been employed in this study to estimate

the regression model. It is ideal for panel data where the number of units (n) is large enough compared to the

time dimension (T) so that it is efficient for dynamic panel data estimation with short T and also AAH imposes

fewer restrictions, and the estimates are still consistent if errors are correlated, than other estimators.

Furthermore, the first difference GMM and system GMM estimators are not asymptotically efficient due to the

averaging of moment conditions over T, among others. Conversely, the AAH is effective in such a case. Because

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of the character of our data, therefore, the AAH estimator is quite appropriate. We accordingly applied the AAH

estimator to estimate all the regression models within this study.

RESULTS AND DISCUSSION

Table 1 Descriptive Statistics

Variable

Lnghg

Lnegeppc

Lngdppc

Enco

Obs

Mean

Std. Dev.

8.315

Min

Max

1259 6.127

0.056

48.237

1256 10756.367

1247 72.08

14695.053

23.795

218.684

29.027

59.322

2.077

73493.266

125.551

21420.629

183756

351476

535232

902

908

906

908

2764.91

3653.264

21375.568

28798.357

49294.198

Egim

8054.854

8530.949

16585.802

Egex

0.000276

3.188

egtt

Descriptive Statistics

The dataset covers several European countries over multiple years, with 892–1,259 observations depending on

variable availability. The descriptive statistics summarize the principal variables employed in analyzing the

effect of environmental goods trade on environmental sustainability within Europe. The result indicate

significant variation in per capita greenhouse gas emissions (lnGHG) with a mean value of 6.13 and high

standard deviation of 8.32, which implies significant differences in emission intensity among European nations.

Other nations have very low per capita emissions, which reflect cleaner production practices, while others emit

much more per capita as a result of energy- intensive industries. There are also large differences in environmental

goods exports per capita (lnEGEPC) and GDP per capita (lnGDPPC), implying that richer countries are doing

more in terms of trading environmental goods. The average GDP per capita of 72.08, with a standard deviation

of 23.80, reflects income diversity in the region, which could influence both trade capacity and environmental

results.

Similarly, the data reflect extensive ranges of total energy consumption and environmental goods trade flows.

The average total energy consumption (ENCO) of 2,764.91 and extreme country variability indicate variations

in industrialization and energy dependence. Environmental goods imports (EGIM), exports (EGEX), and total

environmental goods trade (EGTT) have wide ranges, indicating that while some European economies are

strongly involved in environmental trade, others are less involved. The high standard deviations in the trade

variables highlight the unbalanced nature of environmental technology exchange within the region. Generally,

the descriptive results suggest that European countries more deeply engaged in trading environmental goods

through greater green technology exports and imports would be able to achieve better environmental

performance as well as lower greenhouse gas emissions, a relationship that is to be tested in the following

econometric specification.

Table 2. Pairwise correlations

Variables

(1)lnGH (2)lnEGEP (3)lnGDPP (4)lnENC (5)lnEGI

(6)lnEGE (7)lnEGT

G

C

O

M

X

T

1.000

0.745

0.812

(1)lnGHG

-0.325

-0.298

-0.275

-0.315

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-0.325

0.745

1.000

0.781

1.000

0.688

0.736

0.564

0.478

0.537

0.462

0.589

0.495

(2)lnEGEP

C

(3)lnGDPP

C

0.812

0.688

0.564

0.537

0.589

0.736

0.478

0.462

0.495

1.000

0.352

0.338

0.361

0.352

1.000

0.864

0.925

0.338

0.864

1.000

0.903

0.361

0.925

0.903

1.000

(4)lnENCO

(5)lnEGIM

(6)lnEGEX

(7)lnEGTT

-0.298

-0.275

-0.315

The correlation analysis is conducted to examine the presence of multicollinearity in the data which suggests

that greenhouse gas emissions (lnGHG) are negatively correlated with environmental goods imports, exports,

and total trade, which suggests that greater participation in environmental goods trade contributes to greater

environmental sustainability in Europe. In specific, countries with greater total environmental goods trade have

lower per capita greenhouse emissions, and this suggests that environmental technology and environmental

products trade contributes to cleaner production and environmentally friendly growth. Conversely, economic

growth (lnGDPPC) and energy consumption (lnENCO) are positively correlated with GHG emissions strongly,

which reflects that higher income and higher energy consumption still drive environmental degradation in the

region. The findings clearly revealed that exporting countries of EGs are influenced identically as net importing

countries of Europe.

Moreover, environmental goods imports and exports are highly and positively correlated with each other,

reflecting the high level of European economies' integration in environmental goods markets. The modest

positive correlation of GDP per capita and environmental goods trade indicates that more affluent economies are

more engaged in the exchange of environmental products, possibly due to their greater technological ability and

environmental awareness. Generally, these findings provide preliminary evidence that environmental goods

trade is a significant factor to reduce emissions, corroborating the green trade hypothesis that green trade is a

path toward guaranteeing sustainable environmental performances in Europe.

Table 3: Results of Cross-sectional Dependence (CD) Tests

Variable Pesaran

Scaled LM

(2004) Bias-Corrected

Scaled LM

Pesaran

(2015) CD

Remark

lnGHG

240.482***

239.618***

364.427***

361.892***

117.835***

524.739***

23.756*** High cross-dependence in emissions

due to shared EU climate policy

lnEGEPC 365.279***

lnGDPPC 362.745***

lnENCO 118.967***

81.942*** Strong interlinkage from environmental

goods export patterns

80.657*** Economic growth highly synchronized

across countries

17.962*** Moderate

dependence

reflecting

regional energy market ties

lnEGIM

525.611***

126.485*** High dependence due to common trade

networks

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lnEGEX 92.374***

91.521***

17.184*** Moderate-high dependence in export

trends

lnEGTT

290.538***

289.671***

21.856*** Evidence of integrated environmental

goods trade activity

*** denote 1% level of significance

Results of cross-sectional dependence tests in Table 3 confirm very high interdependence of variables in

European countries. The Pesaran (2004), bias-corrected scaled LM, and Pesaran (2015) CD statistics are all

significant at the 1% level for greenhouse gas emissions (lnGHG), environmental goods exports per capita

(lnEGEPC), GDP per capita (lnGDPPC), energy consumption (lnENCO), environmental goods imports

(lnEGIM), environmental goods exports (lnEGEX), and total environmental goods trade (lnEGTT). These

results indicate the existence of strong cross-sectional dependence, with the implication that environmental and

economic changes in a European country have a tendency to influence others. This finding is theoretically

consistent with the structure of an integrated market for Europe, where practice, energy policy, and

environmental policy green are very integrated into shared EU platforms such as the European Green Deal and

the Emission Trading System (ETS).

The presence of high cross-sectional dependence means that the processes of environmental goods trade and

greenhouse gas emissions are not independent events but are driven by regional spillover dynamics and policy

interactions. EU member countries often share collective strategies to promote sustainable trade, cleaner

production, and carbon mitigation—making it possible that environmental developments in one country can

affect others through trade patterns and technology diffusion. Hence, the bigger CD estimates give justification

for the use of second-generation panel estimators such as the Common Correlated Effects Mean Group (CCE-

MG) or Augmented Mean Group (AMG) models that explicitly account for such cross-sectional dependencies.

These models will enable tighter estimation of the contribution of environmental goods trade towards

environmental sustainability by accounting for the influence of unobserved common causes, such as coordinated

environmental policies, technological spillovers of innovation, and regional initiatives toward energy transition.

Table 4: Results of Unit root tests

Variable

CADF Test

Levels

CIPS Test

Levels

First Diff

-3.284***

-2.978***

-3.068***

-3.527***

-3.204***

-3.889***

-3.275***

5%

First Diff

-4.472***

-3.395***

-4.521***

-5.061***

-4.448***

-4.795***

-4.312***

10%

lnGHG

-2.185**

-1.082

-2.645***

-2.114

lnEGEPC

lnGDPPC

lnENCO

-2.242**

-2.101**

-1.913

-2.856***

-3.305***

-2.758**

-3.187***

-2.726**

1%

lnEGIM

lnEGEX

-2.221**

-2.183**

10%

lnEGTT

Critical Values

Level

-2.02

-2.08

-2.19

-2.52

1st Difference

-2.01

-2.08

-2.20

-2.53

*** denote 1% level of significance

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The results of the second-generation panel unit root tests (CADF and CIPS) in Table 4 show that all the variables

are non-stationary at levels but become stationary when first differenced, confirming their integration order of

one, I(1). Specifically, for both CADF and CIPS tests, the test statistics for greenhouse gas emissions (lnGHG),

GDP per capita (lnGDPPC), energy consumption (lnENCO), and total environmental goods trade (lnEGTT) are

lower than the critical values at the 5% and 1% significance levels after first differences, indicating stationarity.

Similarly, environmental goods imports (lnEGIM) and exports (lnEGEX) are stationary after first differencing,

while only some variables such as environmental goods exports per capita (lnEGEPC) are borderline stationary

at levels. These findings are consistent with the dynamics of macroeconomic and environmental data that are

typically featured by persistence in the time series due to sluggish structural and policy reforms in European

economies.

The evidence of non-stationarity at levels but stationarity at first difference indicates the existence of potential

long-run equilibrium relationships among greenhouse gas emissions, environmental goods trade, energy

consumption, and income in Europe. This validates the use of panel cointegration techniques to examine the

long-run causal effects of environmental goods trade on environmental sustainability. With the high degree of

economic and environmental interdependence established by the cross-sectional dependence tests, the findings

here corroborate that indeed there are common stochastic trends in environmental and economic variables for

European countries. Thus, the subsequent estimation using second-generation panel cointegration and error-

correction models will allow for more accurate assessment of the contribution of environmental goods trade in

mitigating greenhouse gas emissions and promoting sustainability in Europe.

Table 5: Result of Cointegration test

Full Sample

Statistic Z-value Robust P-value

Westerlund (2007) Panel Cointegration Test

Gt

-3.248

-2.874

-2.957 0.001

-1.982 0.045

Ga

Gengenbach, Urbain, and Westerlund (2015) Error-Correction-Based Test

ECT (Error Correction Term)

-0.982

-3.286 0.012

The cointegration test results of the panel presented in Table 5 confirm the long-run relationship between

greenhouse gas emissions, trade in environmental goods, energy consumption, and per capita GDP of the

European countries. The Westerlund (2007) test statistics (Gt = -3.248; Ga = -2.874) are significant at both 1%

and 5% levels, and these reject the null hypothesis of no cointegration. This means that although there is short-

run variation, the variables follow one another in the long run, indicating that fluctuations in environmental

goods trade and economic growth are strongly correlated with fluctuations in greenhouse gas emissions.

Similarly, the Gengenbach, Urbain, and Westerlund (2015) error-correction-based cointegration test also returns

a statistically significant and negative error correction term (ECT = -0.982; p = 0.012), further supporting that

there exists a long-run adjustment mechanism correcting equilibrium whenever short-run imbalances occur.

The implication of such results is that environmental goods trade plays a big role in determining Europe's course

of environmental sustainability. The robust long-run relationship indicates that rising trade in environment goods

such as green energy technologies, abatement technology, and energy-efficient appliances has a long-run impact

on lowering greenhouse gas emissions in the long run. This result is in agreement with theoretical expectation

that green trade accelerates technology diffusion, clean production processes, and sustainable economic growth.

Table 6: Long-Run Estimation Results (Dependent Variable: Greenhouse Gas/CO₂ Emissions)

Independent Variables

lnEGEPC

(1)

(2)

(3)

2.0845*** (0.0857)

2.1128*** (0.1642)

2.0256*** (0.1723)

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lnGDPPC

-0.1287*** (0.0063)

-0.1225*** (0.0098)

-0.1204*** (0.0095)

lnENCO

1.0124*** (0.0287)

0.9546*** (0.0335)

1.0078*** (0.0319)

lnEGIM

-0.00593 (0.00451)

—

lnEGEX

—

-0.01147** (0.00512)

—

lnEGTT

—

-0.00318 (0.00564)

Constant

-15.982*** (0.3684)

-15.721*** (0.6712)

-15.784*** (0.6525)

Observations

Number of Countries (cid)

614

57

614

57

614

57

*** denote 1% level of significance

The long-run estimation results presented in Table 6 reveal that environmental goods trade revealed a statistically

significant effect on greenhouse gas emissions across European countries. The coefficient of environmental

goods exports per capita (lnEGEPC) is positive and highly significant across all models, suggesting that as trade

in environmental goods expands, greenhouse gas emissions initially rise. This outcome aligns with the

transitional phase of the Environmental Kuznets Curve (EKC), where economic and trade expansion though

environmentally oriented can temporarily increase energy demand and production activities that elevate

emissions before achieving cleaner efficiency gains in the long run. The negative and significant coefficients of

GDP per capita (lnGDPPC) across all models confirm the long-run decoupling of economic growth from

environmental degradation, implying that advanced European economies have reached a stage where higher

income levels are associated with improved environmental performance.

Energy consumption (lnENCO) remains positively and strongly associated with greenhouse gas emissions,

indicating that energy demand continues to be a dominant driver of environmental pressure in Europe despite

growing reliance on renewables. Conversely, the trade-related variables environmental goods imports (lnEGIM),

exports (lnEGEX), and total environmental goods trade (lnEGTT) display negative coefficients, though only

environmental goods exports are statistically significant at the 5% level. This implies that greater participation

in environmental goods trade, particularly through exports of green technologies and energy-efficient products,

contributes to reducing carbon emissions over time. The overall findings suggest that while trade in

environmental goods initially stimulates production-related emissions, it ultimately supports environmental

sustainability through technological diffusion, cleaner production processes, and policy-driven innovation across

Europe. Therefore, promoting green trade integration alongside energy efficiency initiatives is essential for

achieving sustained reductions in greenhouse gas emissions in the region.

CONCLUSION AND POLICY RECOMMENDATIONS

The study examined the effect of environmental goods trade on environmental sustainability from the perspective

of the impact of greenhouse gas emissions for 57 European countries. The findings revealed a complex but

policy-relevant relationship between green trade, energy consumption, and the environment. Specifically, though

short-run environmental goods trade leads to higher emissions through higher production intensity and trade

intensity, its long-run effect becomes eco-friendly by virtue of technological innovation and efficiency

spillovers. The negative and statistically significant impact of GDP per capita on emissions validates the

hypothesis of the Environmental Kuznets Curve (EKC) that higher levels of income in high-income economies

of Europe are associated with cleaner production and improved environmental performance. Energy

consumption, however, remains a frequent source of greenhouse gas emissions, with the continued dependence

of many European economies on high-carbon energy sources despite advances in renewable energy transitions.

The results show that environment goods trade can be a source of drive towards environmental sustainability in

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Europe if there are accompanying stable energy and innovation policies. While efficiency and cleaner technology

adoption are promoted by economic growth and environment goods exports, policy attention is required to

address transitional emissions owing to trade expansion and industry restructuring.

Based on the findings, this study recommends that European policymakers to strengthen interlinkages between

environmental products trade and higher climate and energy frameworks to make the environment sustainable.

Curtailing trade barriers on green products, promoting the utilization of renewable energy, and fuel efficiency

are pivotal in abating emissions linked to increased trade. Furthermore, innovation development through research

and development in clean technologies, enhanced carbon pricing mechanisms, and the convergence of

environmental standards at the regional level will enhance low-carbon economy transition. Finally, support for

sustainable production and consumption trends across sectors will ensure that environmental goods trade will

make an effective contribution to long-term greenhouse gas abatement and sustainable growth in Europe.

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