"ENVIRONMENTAL EFFICIENCY OF GREEN TRANSPORTATION UNDER THE BELT AND ROAD INITIATIVE: AN EMPIRICAL STUDY"

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"ENVIRONMENTAL EFFICIENCY OF GREEN TRANSPORTATION UNDER THE

BELT AND ROAD INITIATIVE: AN EMPIRICAL STUDY"

Bukhara State University

Faculty of Economics and Tourism 1st year student

Sanoyeva Ferangiz Yoshi kizi

Abstract:

This article studies the impact of green transport systems on environmental quality in

the context of economic infrastructure development within the framework of the ‘One Belt, One

Road’ initiative. The need to implement economic infrastructure projects in an environmentally

friendly manner is urgent today. The study uses a semi-experimental approach. The results show

that green transport, although a stabilizing factor for environmental quality, leads to an increase

in CO₂ emissions. This situation is explained on the basis of the theory of development

compatibility. In conclusion, the study highlights the following as a positive conclusion: the

development of green transport in developing countries reduces the increase in carbon emissions.

Keywords:

Green economy, sustainable development, ecological innovations, efficient use of

resources, air pollution, carbon footprint, renewable energy, environmental safety, bioeconomy,

economic efficiency.

Introduction.

The Belt and Road Initiative (BRI) is an innovative framework aimed at

promoting intercontinental economic cooperation, connectivity, and infrastructure development.

It requires in-depth research, considering its comprehensive impact on environmentally friendly

transport networks and sustainable environmental practices. It is essential to assess the potential

impact of the BRI on clean transport and the environment, including both positive and negative

impacts. The BRI will have significant impacts on land use, resource consumption, pollution

levels, and transport infrastructure.

1

. This study aims to explore the complex relationship

between green transport and environmental sustainability in BRI countries, and to provide useful

analysis for policymakers, urban planners, researchers, and stakeholders supporting sustainable

development. Green transport modes include electric vehicles (EVs), public transport systems,

bicycle and pedestrian infrastructure, renewable fuels and alternative energy sources, smart

transport systems, sustainable infrastructure, and carpooling services. Manufacturers are actively

engaged in EV research, battery technologies, charging infrastructure, and related services.

Public transport networks can enhance public use through smart mobility solutions, infrastructure

upgrades, and investment in new vehicles. Innovative transport systems optimize traffic flow,

reduce congestion, and minimize energy consumption.

2

. Sustainable infrastructure plays a key

1

[1] X. Chen, A. Guo, Z. Miao, P. Zhu, Assessing the performance of the transport sector within the global supply

chain context: decomposition of energy and environmental productivity, Appl. Energy 358 (2024) 122615,
https://doi.org/ 10.1016/J.APENERGY.2023.122615. [2] Q. Lu, K. Fang, R. Heijungs, K. Feng, J. Li, Q. Wen, Y. Li, X.
Huang, Imbalance and drivers of carbon emissions embodied in trade along the Belt and Road Initiative, Appl.
Energy 280 (2020) 115934, https://doi.org/10.1016/J. APENERGY.2020.115934. [3] C. Nedopil Wang, China Belt
and Road Initiative (BRI) Investment Report 2021, 2022.

2

[4] Z. Lv, W. Shang, Impacts of intelligent transportation systems on energy conservation and emission reduction

of transport systems: a comprehensive review, Green Technol. Sustain. 1 (1) (2023) 100002,
https://doi.org/10.1016/J. GRETS.2022.100002. [5] S. Singh, S.P. Upadhyay, S. Powar, Developing an integrated
social, economic, environmental, and technical analysis model for sustainable development usinghybrid multi-

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role in green transportation through practices such as the use of recycled materials and

consideration of environmental impacts at the design stage, especially in the construction of

environmentally friendly roads and bridges. In addition, car-sharing services (ridesharing and

carpooling) reduce the total number of vehicles, reduce emissions, and alleviate traffic

congestion. Environmental degradation and climate change have a wide-ranging impact on the

world, including BRI countries. Despite the fact that more than 200 countries that signed the

Paris Agreement in 2015, including BRI countries, aimed to keep global temperature increase

below 2 °C, in 2017, carbon dioxide (CO2) emissions from BRI countries amounted to 11.76

billion tons, equivalent to 35.61% of global emissions. That is, their gross domestic product

(GDP) is still 17% The BRI countries, which have not yet reached the 2020 target, produce

almost a third of the world's carbon dioxide emissions. Reconciling economic growth potential

with low-carbon emissions has become a major challenge for BRI countries. According to "A

review on global fuel economy standards, labels and technologies in the transportation sector,

Renew", the transport sector is responsible for 15% of greenhouse gas emissions and 23% of

carbon dioxide emissions. In addition, the transport sector's contribution to carbon emissions

increased by 71% from 1990 to 2016, accounting for a quarter of global emissions. In particular,

the movement of goods accounts for 42% of CO2 emissions and is projected to reach 60% by

2050. Green transport can be a solution to reduce these emissions and support environmentally

sound practices within the BRI. Low- or zero-emission vehicles, such as By introducing EVs and

hydrogen-powered vehicles, BRI countries can reduce the negative environmental impact of

traditional transportation. This study examines how green transportation impacts the BRI

economies on their environmental footprints (resources used and waste generated), particularly

in terms of energy consumption and greenhouse gas emissions. Countries in transition to market

economies, often with high pollution and energy consumption, are also included in the BRI

economies. Green transportation can stimulate economic growth, reduce greenhouse gas

emissions, and support sustainable development. It also reduces air pollution, which is beneficial

to both the environment and human health. Research J.A.F. Machado, J.M.C. The Quantile

Moments method developed by Santos Silva was used to identify cross-sectional differences and

long-term changes between selected countries. The main reason why the development of green

transport systems has not achieved significant reductions in carbon emissions is the large

differences in the development stages of the BRI countries. In order to fully understand the

impact of green transport on environmental quality, it is important to deeply assess the

development stages of countries. Although green transport has the potential to improve

environmental quality, it has now become a task of "reducing CO2 emissions completely" rather

than "reducing CO2 emissions completely". This means that the introduction of green transport

systems has stopped the increase in emissions, that is, carbon emissions would have increased

even more strongly without these systems. In short, green transport systems have played an

important role in curbing the growth of CO2 emissions. This study also examines the role of

various control variables – such as innovation, domestic investment, institutional quality and

urbanization – in shaping environmental sustainability outcomes. Innovation is seen as an

important factor in promoting environmentally friendly technologies and practices, thereby

contributing to the reduction of ecological footprints. Empirical studies by show that countries

with high levels of innovation have lower carbon dioxide emissions, creating a more sustainable

environment. A key example in this context is innovation in renewable energy technologies;

studies by show the positive impact of innovation on promoting a sustainable environment by

reducing carbon dioxide emissions. In addition, domestic investment is an important driver in

promoting a sustainable environment and reducing negative environmental impacts. G.E. Halkos,

E.A. Paizanos, The channels of the effect of government expenditure on the environment:

evidence using dynamic panel data, found a positive relationship between the level of domestic

criteria decision making methods, Appl. Energy 308 (2022) 118235,
https://doi.org/10.1016/J.APENERGY.2021.118235.

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investment and improved environmental quality, as reflected in reduced carbon emissions and

lower air pollution. In support of these results, empirical studies by [31,32] have found that

increased domestic investment in environmentally friendly infrastructure, such as public

transport networks and renewable energy projects, can significantly reduce carbon emissions.

Institutional quality – that is, governance structures, policy frameworks and enforcement

mechanisms – is a key determinant of environmental quality. Strong institutional quality is

positively associated with environmental sustainability, and studies by S. Dasgupta, B. Laplante,

H. Wang, D. Wheeler, have shown that countries with stricter regulations and stronger

institutions have lower pollution levels and better environmental outcomes. Another study by S.

Dasgupta, B. Laplante, H. Wang, D. Wheeler, examined the relationships between institutional

quality, economic development, and environmental sustainability, and found that countries with

strong institutional foundations perform better on environmental indicators. Urbanization, on the

other hand, has been shown by extensive empirical studies to have negative impacts on

ecosystem functions and services, including habitat degradation, fragmentation, and biodiversity

loss. For example, a study by Seto, K.C., Davis, S.J., Mitchell, R.B., Stokes, E.C., Unruh,

examined the ecological impact of global urban footprints, noting that urban areas have high

levels of energy consumption, carbon emissions, and resource exploitation, leading to serious

environmental constraints. Also X. Zhang, T. Feng, S. Zhao, G. Yang, Q. Zhang, G. Qin, L. Liu,

X. Long, W. Sun, C. Gao, G. Li

3

A study conducted by the Institute of Environmental Policy

and Social Policy of the University of Hong Kong (ICP) has shown that there is a negative

relationship between the increase in urban population and environmental quality. This study

makes an important contribution to the in-depth study of how Green Transport affects

environmental sustainability in BRI economies. In addition, the study introduces a new Green

Transport Index using heterogeneity testing techniques to analyze the relationship between

ecological footprints and green transport. The results of the study suggest that green transport

should be promoted in BRI economies to ensure environmental safety, use resources efficiently,

reduce traffic congestion, improve air quality, and restore healthy lifestyles. As

recommendations, the study suggests that BRI countries develop laws to encourage the use of

environmentally friendly vehicles by freight carriers and passengers. In addition, governments

should actively support the transition from conventional vehicles to environmentally friendly

transport systems. The following chapters of the study follow the following order: first, the

theoretical framework explains how environmentally friendly vehicles can help to build a

sustainable ecosystem and reduce carbon emissions. The literature review section provides an

analytical overview of previously published works, while the methodology section describes the

data sources and model used in the study. This is followed by empirical results and their

discussion, followed by research conclusions and recommendations for further research.

Theoretical framework: As Papadis and Tsatsaronis (2020) point out, ensuring environmental

sustainability and reducing carbon dioxide (CO₂) emissions are among the most pressing global

challenges in the context of climate change. One of the ways to solve these problems is to use

green transport systems that include environmentally friendly vehicles. The theoretical

framework of this study is the “leapfrogging development theory”. This theory explores the

potential for achieving environmental sustainability and significantly reducing carbon emissions

through green transport systems. The study examines a broad range of technological advances,

behavioral changes, policy reforms, and their combined effects. According to the “leapfrogging

theory,” developing or underdeveloped countries can achieve development by directly adopting

advanced technologies and modern practices, without going through the complex and

environmentally harmful stages of industrial development. This theory is particularly important

in the context of the green economy, which argues that lagging countries can avoid the

3

X. Zhang, T. Feng, S. Zhao, G. Yang, Q. Zhang, G. Qin, L. Liu, X. Long, W. Sun, C. Gao, G. Li, Elucidating the impacts

of rapid urban expansion on air quality in the Yangtze River Delta, China, Sci. Total Environ. 799 (2021) 149426,
https://doi. org/10.1016/J.SCITOTENV.2021.149426

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environmental problems faced by developed countries by adopting advanced technologies. Thus,

these countries can achieve economic growth while also improving environmental quality by

introducing innovations such as digital infrastructure, renewable energy sources, and efficient

industrial practices. They can embark on a more sustainable development path by bypassing

energy- and resource-intensive, polluting phases. The study also examines the impact of foreign

direct investment (FDI) on pollutant emissions in countries that joined the “One Belt, One Road”

initiative before and after 2013. The study analyzes FDI investments in six areas: green transport,

technological progress, behavioral changes, infrastructure development, integrated planning, and

policy incentives. Previous studies have focused on developed countries, where the role of green

innovation in reducing emissions has been observed in the transition from stage B to stage C.

However, this study finds that FDI flows are being redirected from rich countries to less

developed countries within the framework of the “One Belt, One Road” initiative. As a result,

green transport systems are developed in these countries, and a specific change in pollution

levels is observed - a transition from stage A through B to stage C. This shows that there are

significant differences in the impact of green transport on environmental quality compared to

previous studies. The effective implementation of green transport systems requires technological

innovations that serve to reduce environmental impacts and reduce carbon emissions. In

particular, development is required in electric vehicle technology - battery efficiency, charging

stations and energy storage systems. Electric vehicles are considered an environmentally

acceptable alternative to traditional internal combustion engines due to their zero exhaust

emissions and reduced carbon footprint. At the same time, the use of biofuels and hydrogen fuels

is also promising for sustainable transport. In order to reduce carbon emissions, it is necessary to

encourage the transition from private cars to public transport. As part of this process, it is

envisaged to switch to the use of ecological means of transport, such as carpooling, cycling, and

walking instead of cars. Public transport systems equipped with smart technologies play an

important role in reducing traffic congestion and reducing CO₂ emissions. At the same time,

walking and cycling also serve to strengthen public health. Infrastructure investments that

promote environmental sustainability are important for the implementation of green transport.

For example, separate pedestrian and bicycle lanes, convenient public transport systems that are

interconnected encourage people to use environmentally friendly means of transport. It is also

necessary to establish charging networks for electric vehicles and connect these systems to

renewable energy sources. Green transport planning must consider the balance between land use,

urban sprawl and transport efficiency. Through the cooperation of city planners, legislators,

transport managers and other stakeholders, transport networks are being established that serve to

reduce CO₂ emissions. Intelligent transport systems, on the other hand, create opportunities for

real-time data collection, optimal routing and traffic management, reducing emissions and

increasing overall transport efficiency. Strict legislation and incentive mechanisms are needed to

popularize green transport, reduce CO₂ emissions and protect the environment. Strong pollution

control regulations, fuel efficiency standards and the promotion of renewable energy sources will

accelerate the transition to green transport. Tax incentives, subsidies and other financial support

should be provided to promote the use of electric vehicles and the development of charging

infrastructure. In addition, mixed-use land use, transport-oriented urban development, and

compact building policies reduce the number of private cars and promote the use of sustainable

transport.

3. Materials and Methods

3.1. Empirical Model. This study analyzes the relationship between environmental quality

(expressed in ecological footprint and CO2 emissions) and green transportation. This relationship

is studied in the context of renewable energy, institutional quality, innovation (measured by

patents), and urbanization factors, using the example of Belt and Road countries. The study

evaluates the impact of green transportation on BRI (Belt and Road Initiative) policies over the

period 1990–2021. To determine the impact of BRI, 2013 was selected as the main treatment

year, and then the analysis continued in 2014 and 2015. "Green transportation" was taken as the

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main independent variable. This concept includes investment and infrastructure development for

electric and low-carbon vehicles (trams, trains, buses and cars) (see Table 1). In accordance with

the research objectives, the following model is built in the form of a panel model:

lnEFit = αit + βlnGTPit + ξlnIQit + γlnINOit + θlnURBit + ϑlnREit + εit (1)Where:

i is the country in the panel,

t is the time period (1990–2021),

α is a constant value,

β, ξ, γ, θ, ϑ are coefficients for green transport, institutional quality, innovation, urbanization and

renewable energy,

εit is an error term.

3.2. Data and sources. The study used annual panel data covering the period 1990–2021. The

impact of green transport on the ecological footprint and CO2 emissions is analyzed using the

example of the Belt and Road countries. Data are taken from the following sources: the United

Nations Conference on Trade and Development (UNCTAD), World Development Indicators

(WDI), and the Global Footprint Network.

3.3. Econometric methodology. The study used difference-in-differences (DID) and DIDID

(Difference-in-Difference-in-Difference) methods. These methods are effective in assessing the

impact of policies in situations where there are variations over time and space, as well as

experimental and control groups. The DIDID model increases the reliability of the results by

introducing an additional dimension (e.g., time, treatment, region, or demographic group) to

address the endogeneity problem. The DIDID model allows for a clear isolation of the program

impact in the BRI participating countries.

The model is expressed as follows:

lnEFij = β0 + β1(Treati

⋅

Postt) + ηiControlsit + γt + λi + ϵit (2)Where:

lnEFij is the environmental quality (dependent variable),

Treati is an indicator of BRI membership (Treat = 1 – member; Treat = 0 – non-member),

Postt is the time when the policy came into effect (Post = 1 if time > September 2013, 0

otherwise),

Controlsit is the control variables (renewable energy, institutional quality, urbanization, patents),

γt, λi are time and country fixed effects.

The coefficient β1 represents the difference between BRI members and non-members after 2013.

In addition to this model, the following DIDID model was used: lnEFijk = β0 +

β1(Treati

⋅

Postt

⋅

lnGTPk) + ηiControlsit + γt + λi + ϵit (3) Here β1 represents the impact of

green transport development on environmental quality after 2013. For example, if β1 = 0.5, this

means that when green transport increases by one unit, environmental quality for BRI members

improves by 0.5 units. To ensure robustness, sub-sample regression analyses were also

conducted on the DID and DIDID models: Sub lnEFijk = β0 + β1(Treati

⋅

Postt

⋅

lnGTPk) +

ηiControlsit + γt + λi + ϵit (4) Here, "Sub" is the regression on selected subgroups.

3.4. Stability testing. Several stability tests were conducted to confirm the reliability of the model

results. Each of them checks the robustness of the main regression results through separate

methodological approaches:1. Parallel trend testing: An important condition of the DID

methodology is that the experimental and control groups have the same (or very similar) trends

before the policy is implemented. To check this, changes in the ecological footprint and CO2

emissions in the years before the policy is implemented are compared through graphical analysis

and formal tests.2. Placebo tests: Artificial (fake) treatment years are selected for the period

before the start of the BRI policy and their effects on environmental indicators are tested. If a

significant effect is not observed in these years, this indicates the presence of a real policy

effect.3. Neighborhood comparison: Differences between BRI members and their geographically

and economically similar neighboring countries that are not part of the BRI are studied. This

helps to determine whether the BRI impact is a policy outcome and not a result of other external

factors. 4. Other measures of variables: Regressions are rerun by replacing variables such as

green transport, innovation, and institutional quality with alternative (i.e., based on a different

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statistical source or calculation method) indicators. If the results do not change, this confirms

their robustness. 5. Sensitivity to public disclosures: Some countries may have a strong influence

on the results. The results are retested by removing such countries from the model. If the main

coefficients remain significant, the model is considered robust. The results of these tests show

that investment in green transport infrastructure under the BRI significantly improves

environmental quality. In particular, the ecological footprint is reduced and CO2 emissions are

reduced. The results are consistent across models, groups, and methodologies.

4. Empirical results and discussion

Table 2

Descriptive statistics

Variable

Average

value

Distribution

rate

Smallest(min) The

largest

(max.)

General

2.3829

1.0321

-0.4971

4.0716

InEF

Among

1.647

20.296

27.592

Countries

0.821

19.464

24.876

InGPT

Within

16.345

1.674

13.074

21.078

General

1.678

13.701

20.979

Among

0.195

14.182

17.271

Countries

− 0.733

1.043

− 8.849

1.693

InINO

Within

0.925

− 3.145

1.442

General

0.498

− 6.437

1.277

Among

4.384

0.314

3.040

5.053

InURB

Countries

0.271

3.402

4.806

Within

0.163

3.442

4.957

General

1.9657

0.8245

0.549

3.576

InCO2

Among

0.7947

0.733

3.217

Countries

0.3310

0.789

3.1383

InRE

Within

14.871

1.734

11.174

20.242

General

1.716

12.177

20.008

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This section presents the results of the main econometric analysis conducted under different

economic scenarios in the countries of the "One Belt, One Road" (BRI). First, we predict our

model representing the impact of green transport on the ecological footprint and carbon dioxide

emissions over time. In the final step, we check whether our findings are consistent with the

results of the main analysis.

Table 3

Correlation matrix

Table 4

Base model (years after 2013)

Table 5

Base model (years after 2014)

Variable

1

2

3

4

5

6

InEF

1,000

(0.000)

InGPT

0.648

1.000

(0.000)

InINO

0,358

0.067

1.000

(0.000)

(0.007)

InURB

0.093

-0.304

0.128

1.000

(0.000)

(0.000)

(0.000)

InCO2

0,698

0.968

0.056

-0.292

1.000

(0.000)

(0.000)

(0.023)

(0.000)

InRE

0.445

-0.357

0.402

0.487

-0.331

1.000

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

O’zgaruvchilar

InEF

InCO2

InEF

InCO2

Treat#post

-0.0938

0.0172

0.0553

0.122

(-1.009)

(0.186)

(1.301)

(3.278)

RE

1.026

1.006

(77.96)

(81.09)

InQI

-0.0881

-0.154

(-1.427)

(-2.640)

InURB

-0.0327

0.241

(-0.665)

(5.253)

InGPT

0.943

0.780

(47.75)

(31.09)

InINO

-0.0840

0.0462

(-6.229)

(3.684)

Doimiy qiymat

23.35

23.93

-0547

(53.61)

(59.66)

(-1.435)

(4.260)

Kuzatuvlar soni

2042

2042

2042

2042

Mamlakatlar

soni

66

66

66

66

Variables

InEF

InCO2

InEF

InCO2

Treat#post

-0.0867

0.0185

0.0622

0.125

(-0.851)

(0.181)

(1.437)

(3.184)

RE

1.023

1.005

(79.43)

(82.79)

InQI

-0.0867

-0.154

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Table 2 presents descriptive statistics. The standard deviations of the “within” and “between”

measures of the variables indicate the variability of the variables over time and across BRI

countries. As can be seen from Table 2, most variables have significant standard deviations in

the “between” and “within” differences. Table 3 presents the results of the correlation matrix.

Green transport is strongly correlated with the ecological footprint and CO₂. Table 4 presents the

regression results of the main model, with environmental quality (lnEF) and carbon dioxide

emissions (lnCO₂) as dependent variables, respectively. The models in the first and second

columns do not include control variables, while the models in the third and fourth columns

include control variables. The results in the first and second columns of Table 4 indicate that

control variables are necessary because environmental quality is influenced by many factors.

Without control variables, the Treat#Post parameters are −0.0938 (t = −1.009) and 0.0172 (t =

0.186), respectively, and are not statistically significant. However, when control variables are

included, the Treat#Post parameter in the third column is 0.0533 (t = 1.301), which is not

statistically significant, but the direction is consistent with the expected effect. The parameter in

the fourth column is 0.122 (t = 3.278), which is statistically significant, indicating that

environmental quality has improved in BRI member countries since 2013. To account for the

possibility of time lag in the DID analysis, the regression results for years after 2014 (Post =

Years > 2014) are presented in Table 5, confirming the results in Table 1.

Table 6

Main DIDID model (years after 2013)

(-1.404)

(-2.641)

InURB

-0.0370

0.238

(-0.755)

(5.197)

InGPT

0.941

0.777

(48.05)

(31.18)

InINO

-0.0835

0.0466

(-6.188)

(3.1717)

Constant value

23.40

23.97

-0.473

1.950

(53.76)

(59.77)

(-1.259)

(4.443)

Number

of

observations

2042

2042

2042

2042

Number

of

countries

66

66

66

66

Variables

InEFI

InCO2

InEFI

InCO2

Treat#post

InGTP

-0.0665

-0.109

0.0746

0.0486

(-1.243)

(-2.042)

(2.942)

(2.093)

RE

1.026

1.006

(78.51)

(80.60)

InQI

-0.0783

-0.154

(-1.281)

(-2.646)

InURB

-0.0227

0.250

(-0.461)

(5.382)

InINO

-0.0803

0.0472

(-5.970)

(3.754)

Constant Value

-1.969

-1.201

(-0.392)

1.838

(-1.097)

(-0.648)

(-0.604)

(2.229)

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BRI membership has had a significant positive impact on environmental quality compared to

non-member countries. This improvement is due to three main factors: 1. International

cooperation and exchange of experiences within the BRI have led to the formation of a common

understanding of green development, which has improved environmental outcomes. 2. The

integration of green innovations into the BRI has helped enterprises improve their production

technologies and reduce polluting emissions. 3. The financial support provided by the BRI has

helped modernize production technologies, thereby reducing their environmental footprint.

However, no significant results have been observed in the parameters of the LnEF model, which

is in contrast to the impact more clearly expressed in the LnCO₂ model. This difference may be

due to the statistical settings of the LnEF model and the fact that other factors are not taken into

account. The results of the DIDID (Difference-in-Difference) regression analysis are presented in

Table 6 (Post = Years > 2013) and Table 4 (Post = Years > 2014). In Table 6, the corresponding

coefficients for Treat#Post#lnGTP in models 3 and 4 are significant: 0.0746 (t = 2.942) and

0.0569 (t = 2.324). These results indicate that green transport significantly improves

environmental quality. There are two ways to reduce environmental damage and improve quality

under the BRI: 1. Countries that receive BRI investment will have financial resources to develop

green transport infrastructure, which will lead to reduced CO₂ emissions through electric vehicles,

pedestrians, and bicycle lanes. 2. BRI cooperation will enable the introduction of advanced green

transport technologies through technological spillover effects. This will reduce pollutant

emissions and contribute to a healthy ecosystem. The effectiveness of these routes depends on

the level of development of the recipient country. For less developed countries, the positive

regression parameters, for example, the values of 0.0746 (t = 2.942) and 0.0569 (t = 2.324) in

Models 3 and 4 in Table 7, indicate a technological leapfrogging process, rather than a negative

one, of green transport. In addition, to maximize the positive environmental impact of the BRI, it

is necessary to carefully plan and implement projects, take into account environmental factors,

and introduce transparent monitoring and evaluation systems. In summary, green transport

within the BRI will bring environmental benefits in two main directions: 1. Reduction of CO₂

emissions and improvement of environmental quality through the development of green transport

Observation

2042

2042

2042

2042

Number

of

Countries

66

66

66

66

Variables

InEFI

InCO2

InEFI

InCO2

Treat#post

InGTP

-0.0694

-0.103

0.0746

0.0569

(-1.199)

(-1.771)

(2.774)

(2.324)

RE

1.023

1.006

(79.90)

(82.38)

InQI

-0.0773

-0.155

(-1.263)

(-2.665)

InURB

-0.0283

0.251

(-0.576)

(5.419)

InINO

-0.0798

0.0486

(-5.929)

(3.861)

Constant value

-2.672

-1.664

-0.333

2.043

(-1.506)

(-0.906)

(-0.522)

(2.557)

Observation

2042

2042

2042

2042

Number

of

countries

66

66

66

66

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182

infrastructure at the expense of financial resources. 2. Reduction of polluting emissions and

improvement of environmental performance through the introduction of advanced technologies

at the expense of technological diffusion. The control variables (lnQI, lnURB, lnINO) play a

significant role in reducing CO₂ emissions and improving environmental quality. Although the

results for RE (renewable energy) are statistically positive, in practice this occurs through

technological leapfrogging in developing countries. Therefore, future research should focus on

refining statistical adjustments and further studying the environmental impact of BRI

membership.

4.2. Placebo tests

To strengthen the results, we repeated each DIDID (difference of differences) analysis 500 times,

in which the value of “Treat” was randomly assigned. If the regression results in Tables 6 and 7

were truly significant, then the results in these tables should be within the bounds of the 500

regression parameters. The results presented in Figures 3–5 show that Models 3 and 4 (in Table 3)

successfully passed the placebo test. The true regression parameters are within the bounds of the

500 randomly assigned regression results with “Treat”, indicating that these results are reliable.

Figure 3 plots the distribution of the 500

regression parameters using the SK kernel

method, with the true parameter indicated by

the red line.

Figure 1 Parallel trend test

6 Figures 1 and 7 confirm that models 3 and

4 in Table 7 pass the placebo test. The true

parameters are within the limits of the results

obtained for the randomly assigned “Treat”

values in 500 regressions, which indicates

the reliability of the conclusions. These

results indicate that the development of

green transportation and the country’s

participation in the BRI (One Belt, One Road) initiative are important in reducing CO2

emissions.

Figure 2 Placebo Trial (Model 3 in Table 6)

Figure 3 Placebo Trial (Model 4 in Table

6)

Figure 4 Placebo test (Model 3 in Table 7)

6- and the placebo test results for model 7 show that

the parameters in Table 7 are reliable. To further

ensure the reliability of the results, all countries were

divided into two groups using the median distance

clustering method based on carbon emissions (lnCO2)

and environmental quality (lnEF). There is a high

degree of similarity within these groups. DID and

DIDID analyses were then conducted, and the results

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are presented in Tables 8 and 9.

Figure 5 Placebo test (model 4 in Table 7)

8- and the results of Table 9 show a significant

positive trend in the cross term coefficients. This

means that green transport has an unexpected impact

on environmental quality after the inclusion of BRI.

However, this positive result should not be interpreted

as green transport worsening environmental quality.

On the contrary, it indicates that the transport

infrastructure in the host countries is developing

rapidly – that is, in a “leap” manner. The placebo test

results for Models 1, 2, 3 and 4 (in Table 9) once again confirm these conclusions.

Table 8 DID analyses

Table 9 DIDID analyses

Cluster 1

Cluster 2

Cluster 1

Cluster 2

Variables

InEF

InEF

InCO2

InCO2

Treat#post

0.0241

-0.0850

0.106

0.295

(0.700)

(-0.626)

(3.009)

(2.761)

RE

0.903

1.012

0.974

0.944

(51.20)

(45.95)

(53.37)

(51.96)

InQI

-0.159

0.0454

-0.274

-0.0642

(-2.533)

(0.419)

(-4.211)

(-0.690)

InURB

0.00785

-0.0141

0.366

0.0214

(0.145)

(-0.181)

(6.559)

(0.316)

InGTP

0.742

0.979

0.696

0.862

(33.34)

(32.73)

(25.24)

(28.41)

InINO

0.0231

-1.137

-0.0182

0.0392

(1.369)

(-6.056)

(-1.038)

(2.085)

Continuous

Indicators

3.168

-1.319

3.282

1.718

(6.877)

(-2.144)

(6.380)

(2.968)

Observations

1101

941

1101

941

Number

of

Countries

52

47

52

47

Cluster1

Cluster2

Cluster1

Cluster2

Variables

InEF

InEF

InCO2

InCO2

Treat#post

0.0681

0.311

0.0757

-0.0712

(3.228)

(2.006)

(3.477)

(-0.583)

RE

0.896

1.017

0.982

0.941

(51.03)

(45.89)

(53.21)

(51.96)

InQI

-0.131

0.0212

-0.259

-0.0819

(-2.113)

(0.190)

(-4.001)

(-0.875)

InURB

0.0361

-0.0314

0.370

0.0264

(-0.670)

(-0.395)

(6.565)

(0.391)

InINO

0.0293

-0.150

-0.0171

0.0380

(1.769)

(-6.410)

(-0.908)

(1.972)

Continuous

6.415

-2.960

5.345

-0.568

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One of the main results is that the coefficient on renewable energy consumption (RE) is -1.006 (t

= 82.38). This means that there is a positive, statistically significant relationship between the use

of renewable energy and the increase in pollution. That is, as countries start to pay more attention

to renewable energy, the level of pollution also increases. The second important finding is the

coefficient on institutional quality (lnQI) is -0.155 (t = -2.665), which indicates a negative

relationship. Countries with lower institutional quality tend to have higher pollution levels. The

coefficient on urbanization (lnURB) is also found to be -0.251 (t = 5.419). This suggests that

there is a positive relationship between urbanization and pollution: as the level of urbanization in

a country increases, pollution also increases. The coefficient on the number of patents (lnINO) is

0.046 (t = 3.684), which also indicates a positive and significant relationship. Countries with

more patents have higher pollution levels. Analysis of the control variables shows that reliance

on renewable energy, low institutional quality, urbanization level, and a large number of patents

lead to increased pollution. That is, countries with a low level of development, low institutional

quality, and high urbanization face more pollution. The relationship between the number of

patents and pollution also suggests that economic development (patent accumulation) can lead to

increased pollution. To strengthen the results, placebo tests were conducted on the four models in

Table 9. By randomly grouping the samples, models 1, 2, 3 and 4 were re-estimated 500 times

each. The kernel density distribution of the parameters was calculated and the true parameters

are shown as red lines in Figures 8, 9, 10 and 11 (Appendix 1). The placebo test results showed

that models 1, 2 and 3 passed the test, but model 4 failed the test. This proves the positiveness of

the parameters in Table 9. The presence of a sample of developed and developing countries

allows the study to have a deep analysis and helps to better understand the complex relationship

between the control variables and pollution. These observations strengthen the theoretical

framework and interpretation of the difference-in-differences (DID) model. The DID analysis

sheds light on the additional pollution caused by economic growth and the pollution levels

associated with growth constraints.

5. Conclusions and Policy Recommendations. In our empirical study, we have identified a

paradoxical and complex phenomenon regarding the impact of green transport within the Belt

and Road Initiative (BRI) member states. Our results show that there is an unexpected negative

relationship between the introduction of green transport and environmental quality in the context

of less developed countries within the BRI. This result contradicts existing theoretical views,

which generally associate the introduction of green transport systems with reduced carbon

dioxide (CO₂) emissions and improved environmental conditions. However, our study suggests

that this unusual phenomenon, when observed within the BRI, requires an alternative theoretical

framework that takes into account the specific dynamics.

6-chart Placebo test (model 1 in Table 7)

Chart 7 Placebo test (model 2 in Table 7)

indicators

(7.713)

(-2.988)

(5.497)

(-0.614)

Observations

1101

941

1101

941

Number

of

countries

52

47

52

47

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According to our proposed theoretical view, the reason for this contradictory result lies in the

complex process of green transport adoption and economic development in the less developed

BRI member countries that is taking place simultaneously. As these countries adopt green

transport strategies, their economic landscape undergoes significant changes, resulting in

increased CO₂ emissions. This complex interplay suggests that the positive environmental

impacts of green transport are offset by the negative environmental impacts of rapid economic

growth—which opens up a unique and new development path. The concept of “leapfrogging”

(development by bypassing intermediate stages) is central to our proposed theoretical framework.

This approach explains the development path of the less developed BRI countries. They jump to

previously developed stages rather than passing through the traditional stages. This process

causes the positive environmental impact of green transport to be overshadowed by the rate of

economic growth, thus creating a situation that contradicts classical theories.

Figure 8 Placebo test (Model 3 in Table 7) Figure

10 Placebo test (Model 4 in Table 7).

Also, the wealth disparity among the BRI members plays an important role. The cooperative and

supportive nature of this initiative leads to the investment of rich member states in their poorer

partners and the transfer of advanced technologies. Thanks to this cooperation, less developed

countries quickly move towards industrialized societies, bypassing the intermediate stages. This

process is a unique path that reveals the relationship between wealth disparity, leapfrogging, and

the environmental consequences of green transport. In conclusion, our study reveals a

multifaceted contradiction in the environmental consequences of green transport in the BRI

countries. The theoretical framework we propose, based on leapfrogging development and

wealth disparity, serves to explain the observed discrepancies. This new development path is a

stark departure from the traditional paradigm and requires a deeper understanding of the

environmental implications of green transport initiatives in collaborative organizations like the

BRI. Our research broadens the scope of ecological analysis by illuminating the complex

interactions between transport systems, economic development, and wealth inequality.

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