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The integration of green finance
and logistics: implications for
green industry transformation
Ling Dai
1
, Decai Tang
2
*, Eugene Ray Atsi
3
, Meiling Zhao
4
,
Hui Zhang
4
, Yuehong Kong
5
and Liang Zhang
6
1
Business School, Hohai University, Nanjing, China,
2
School of Industry and Urban Construction,
Hengxing University, Qingdao, China,
3
School of Management Science and Engineering, Nanjing
University of Information Science and Technology, Nanjing, China,
4
Jiangsu Trendy Information
Technology Co., Ltd., Nanjing, China,
5
School of Finance and Logistics Management, Nanjing University
of Railway Technology, Nanjing, China,
6
School of Business and Law, Sanjiang University, Nanjing, China
Green finance(GF), as a critical policy tool for supporting green development, plays
a pivotal role in directing capital flows toward low-carbon and environmentally
friendly industries. This provides essential support for the green transformation (GT)
of the logistics industry (LI). However, limited research exists on the regional GT of
the LI from the perspective of GF and LI integration. To address this gap, this study
develops anevaluation index system for the integration of GF and the LI, as well as a
separate system for assessing the GT of the LI. Using panel data from 30 Chinese
provinces spanning 2010 to 2022, the study employs the Super-SBM-Undesirable
model and the coupling coordination degree model for evaluation and analysis.
Additionally, a threshold effect model is applied to examine the nonlinear
relationship between GF-LI integration and the GT of the LI, alongside a
heterogeneity analysis to explore regional disparities. The findings reveal three
key insights: (1) In recent years, the integration of GF and the LI in China has
significantly improved, advancing from a mildly imbalanced stage to moderate and
high levels of coordination. However, significant regional disparities in the level of
integration persist. (2) The integration of GF and the LI has a significant positive
effect on the GT of the regional LI, characterized by an S-shaped relationship. (3)
The impact of GF-LI integration on the GT of the LI exhibits notable regional
heterogeneity. Compared with existing research results, this study expands the
application scope of GF, focusing on the GT of the LI, a fundamental service
industry, and further verifies the universality of GF in promoting low-carbon devel-
opment in different industries. In addition, current research on the GT of the LI
mainly focuses on technological innovation, environmental regulation, and supply
chain optimization, while there is less attention paid to how GFcan promote the GT
of the LI through industrial integration.
KEYWORDS
green transformation of the logistics industry, green finance, industrial integration,
S-shaped relationship, logistics
1 Introduction
With the intensification of global climate change and the advancement of sustainable
development goals, the green economy has become a central issue in economic
transformation worldwide. The logistics industry (LI), as a vital pillar of economic
activity, has significantly contributed to economic growth through its rapid expansion.
OPEN ACCESS
EDITED BY
Ștefan Cristian Gherghina,
Bucharest Academy of Economic Studies,
Romania
REVIEWED BY
Iulia Lupu,
“Victor Slăvescu”Centre for Financial and
Monetary Research, Romania
Oana Popovici,
Bucharest Academy of Economic Studies,
Romania
Karambir Singh Dhayal,
Birla Institute of Technology and Science, India
*CORRESPONDENCE
Decai Tang,
tangdecai2003@163.com
RECEIVED 07 January 2025
ACCEPTED 25 April 2025
PUBLISHED 12 May 2025
CITATION
Dai L, Tang D, Atsi ER, Zhao M, Zhang H, Kong Y
and Zhang L (2025) The integration of green
finance and logistics: implications for green
industry transformation.
Front. Environ. Sci. 13:1556580.
doi: 10.3389/fenvs.2025.1556580
COPYRIGHT
© 2025 Dai, Tang, Atsi, Zhao, Zhang, Kong and
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Frontiers in Environmental Science frontiersin.org01
TYPE Original Research
PUBLISHED 12 May 2025
DOI 10.3389/fenvs.2025.1556580
However, it has also led to excessive resource consumption and
environmental degradation (Piecyk and Björklund, 2015). The
China Green Logistics Development Report (2023) states that
carbon emissions from China’s LI account for approximately 9%
of the nation’s total carbon emissions (Ju et al., 2023). With the
growing demand for logistics, accelerating the green transformation
(GT) of the LI has become an essential requirement for addressing
environmental challenges, enhancing corporate competitiveness,
and promoting sustainable economic development. Balancing the
improvement of logistics efficiency with achieving GT has become
an urgent issue that needs to be addressed. Green finance (GF), as a
significant policy tool supporting green development, can provide
robust support for the GT of the LI by directing capital flows toward
low-carbon and environmentally friendly industries (Dhayal et al.,
2025a). However, as a significant contributor to carbon emissions,
the LI’sfinancial support mechanisms for GT remain insufficiently
explored. Characterized by long value chains, multiple operational
links, and a wide array of participating entities, the logistics sector
follows a transformation trajectory that fundamentally differs from
those of manufacturing and energy industries. This divergence
underscores the urgent need for dedicated research into the
coupling mechanisms and effects between GF and the LI.
In recent years, the integration of GF and the GT of the LI has
garnered growing academic interest. On one hand, existing studies
have extensively examined the role of GF in advancing the green
development of traditional sectors such as industry and agriculture
(Afzal et al., 2022). On the other hand, research on the measurement
methods and influencing factors of the GT efficiency of the LI has
also become increasingly prominent (Ferrari et al., 2018). Despite
these advancements, current research still exhibits several key
shortcomings: (1) A lack of quantitative analysis on the
integration of GF and the LI. Existing studies often examined the
impact of GF from a single-dimensional perspective, lacking
systematic measurement of the integration level of the two
industries. (2) Insufficient understanding of the nonlinear
relationship between GF and the GT of the LI. A few studies
have explored the marginal effects of GF but have not yet
revealed potential threshold effects between integration levels and
the GT of the LI. (3) A lack of regional heterogeneity analysis. The
GT of the LI may exhibit significant regional characteristics due to
differences in economic development levels, policy environments,
and resource endowments, which have been insufficiently addressed
in existing studies. Moreover, although GF has yielded notable
results in advancing the GT of other industries, policy support
targeting the logistics sector remains inadequate. There is a notable
absence of dedicated financial subsidies, tax incentives, and tailored
green finance policies. Additionally, the lack of coordination
between GF initiatives and the GT of the LI has hindered the
development of a systematic and synergistic policy framework.
Existing policies often adopt a one-size-fits-all approach,
overlooking regional economic disparities and failing to provide
differentiated support across diverse local contexts. Consequently,
there is an urgent need for more refined and flexible policy measures
specifically designed for the logistics sector to foster deeper
integration between GF and the LI.
To bridge the aforementioned research gap, this study utilizes
panel data from 30 Chinese provinces spanning the years
2010–2022 and applies a series of econometric models to
systematically examine the mechanisms and effects of the
coupling and coordinated development between GF and the LI
on the GT of the logistics sector. First, an indicator system is
constructed based on the conceptual frameworks of GF
development, LI development, GT of logistics, and the coupling
coordination between the two sectors. Second, the Super-SBM-
Undesirable model is employed to evaluate the efficiency of GT
in the LI. Third, the coupling coordination degree model is used to
assess the integration level between GF and the logistics sector.
Fourth, a threshold effect model is adopted to analyze the nonlinear
relationship between their coordinated development and the GT of
logistics, followed by a heterogeneity analysis to explore regional
disparities. Finally, targeted policy recommendations are proposed
to accelerate the GT of the LI, aiming to address existing policy gaps.
The key innovations of this study are as follows: (1) It employs
the Super-SBM-Undesirable model to evaluate and analyze the
current state of GT in China’s LI. (2) It introduces and quantifies
the coupling coordination level between GF and the logistics sector,
addressing a critical gap in research on the integrated development
of these two fields. (3) This study creatively constructs an S-shaped
theoretical model to examine the impact of green finance-logistics
integration on GT efficiency, and empirically validates the existence
of this nonlinear relationship, offering a fresh perspective for
advancing GF theory. (4) It incorporates regional heterogeneity
analysis to reveal differences in policy implementation paths across
regions, thereby providing a theoretical foundation for targeted
policy-making and coordinated regional development.
2 Literature review
First, the GT of the LI has become a key global concern, with its
core objective being the realization of low-carbon, efficient, and
sustainable logistics services (Karagülle, 2012). The study
systematically reviews the relevant literature on the GT of the LI
from three aspects: evaluation of GT, analysis of influencing factors,
and regional differences, to analyze the research progress on this
topic. (1) A scientific evaluation of the GT of the LI represents the
foundation for studying its mechanisms and policies. Many scholars
use methods such as life cycle analysis, analytic hierarchy process,
and data envelopment analysis models to evaluate the GT of the LI,
as shown in Table 1 (Dekker et al., 2012;Chan et al., 2006;
Markovits-Somogyi and Bokor, 2014). For example, Bai applied
the super-SBM model to measure the green efficiency of the LI in
major Chinese cities (Bai et al., 2022a). It was found that the
efficiency of coastal cities in the east was generally higher than
that of inland cities. In addition, some studies combine dynamic
change analysis methods, such as the Malmquist index method, to
capture the temporal evolution characteristics of green efficiency
(Rusli et al., 2022). For instance, Long’s analysis using the Malmquist
index found that the green efficiency of the LI shows a slow upward
trend globally (Long et al., 2020). Among these, Asian countries are
experiencing faster growth rates than European and American
countries. (2) The analysis of the influencing factors of the GT of
the LI is key to revealing its development path and optimization
strategies. The GT of the LI is influenced by various factors,
including external conditions such as policy, technology, and
industrial structure, as well as endogenous factors such as
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Dai et al. 10.3389/fenvs.2025.1556580
corporate behavior and regional resource endowments, as shown in
Table 2 (Xu and Xu, 2022;Dhayal et al., 2025b;Liu and Li, 2019;Yan
et al., 2022;Xu et al., 2022). Existing studies indicate that stringent
environmental regulations can significantly accelerate the
application of green technologies in logistics companies.
Specifically, European countries have successfully improved the
energy efficiency of logistics transportation by implementing
strict carbon emission standards (Peters et al., 1998). In terms of
industrial structure upgrading, Chen and other scholars point out
that regions with a high proportion of the secondary industry often
experience higher carbon emission intensity in logistics, making GT
more challenging (Chen and Zhang, 2022). Similarly, the
improvement in urbanization levels has had a significant impact
on the centralization and efficiency of logistics demand. Some
studies suggest that during the rapid urbanization process in
Southeast Asia, the modernization of logistics facilities has not
advanced synchronously, leading to a lag in GT (Nguyen, 2021).
(3) The study of regional differences in the GT of the LI is also an
important basis for formulating region-specific policies and
achieving coordinated development. The GT of the LI exhibits
significant regional differences worldwide. For example, the green
efficiency of the LI is generally higher in OECD countries, especially
in Nordic countries. Due to their well-established green policy
support and advanced technological reserves, these countries are
at the forefront of green logistics globally (Çakır, 2017). In contrast,
African countries face severe constraints on the development of
green logistics due to weak infrastructure, lack of funding, and low
technological levels (Buvik and Takele, 2019). This disparity exists
not only between developing and developed countries, but also
between different countries or provinces within the same region.
For instance, Quan and other scholars point out that the eastern
regions of China, with their developed economy and advanced
technology, exhibit higher efficiency in green logistics, while the
central and western regions have relatively lower efficiency due to
inadequate infrastructure and uneven policy enforcement (Quan
et al., 2020). Although the aforementioned studies have made
significant progress in calculating green logistics efficiency and
analyzing influencing factors, existing literature mainly focuses
on research related to a single industry or a single-dimensional
factor. This depicts the lack of a systematic study of the GT of the LI
from the perspective of industrial integration.
As an important means of promoting green economy, research
on the integration of GF with other industries has gradually attracted
academic attention. Relevant studies primarily focus on the
mechanisms of GF, the theory and practice of industrial
integration, and the measurement and impact assessment of
integration levels. (1) The mechanism of GF serves as a key
driving force for the GT of industries. GF significantly promotes
the development of green industries by optimizing resource
allocation, incentive mechanisms, and innovative capital supply,
as shown in Table 3 (Khan et al., 2022). Numerous studies have
shown that green financial instruments, such as green credit and
green bonds, play a crucial role in supporting the research and
application of green technologies (Gilchrist et al., 2021). For
example, research by Madaleno and colleagues found that the
implementation of green credit policies significantly reduced the
credit costs of high-polluting industries and increased the
investment willingness of clean technology enterprises (Madaleno
TABLE 1 Common evaluation methods for GT of the LI.
Evaluation method Method evaluation
Life Cycle Assessment (LCA) This method evaluates environmental impact from a full life cycle perspective, offering strong systematization. However, it requires
complex processes and extensive data collection
Analytic Hierarchy Process (AHP) Capable of breaking down complex problems into hierarchies and determining weights based on expert judgment. It has a clear
structure but involves high subjectivity and relies heavily on expert experience
Grey Relational Analysis (GRA) Effective in analyzing system relationships under incomplete information; it is simple to compute but offers weak causal logic and
relatively coarse conclusions
Entropy Weight Method Objectively assigns weights based on the amount of information in the data, reducing human interference. However, it is susceptible to
extreme values and lacks a mechanism for subjective adjustment
Data Envelopment Analysis (DEA) Suitable for evaluating efficiency with multiple inputs and outputs without requiring a preset functional form. Nevertheless, it is
sensitive to outliers and the results are less interpretable
TABLE 2 The main factors affecting the GT of the LI.
Influencing factor Key findings
Policy Government green policies serve as a crucial external force in guiding logistics enterprises toward transformation and elevating the overall
green level of the industry
Technology Technological innovation significantly enhances the logistics sector’s ability to reduce carbo n emissions and improve efficiency, acting as a
key driving force for GT
Industrial Structure Industrial upgrading creates development opportunities and synergistic momentum for green logistics
Corporate Behavior The green awareness and governance capacity of logistics enterprises directly affect the extent of their GT
Resource Endowment Regional resources and foundational conditions constrain the transformation pathways and development speed of green logistics
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et al., 2022). Germany’s green bond market has effectively promoted
the financing of clean energy projects, providing financial support
for the low-carbon transition of traditional energy enterprises
(Schäfer, 2018). In addition, GF has demonstrated remarkable
effectiveness in optimizing corporate capital structures and
improved the accessibility of green project finance (Li et al.,
2023). (2) The theory and practice of industrial integration are
key pathways to achieving the coordinated development of financial
resources and the real economy. The concept of integrating GF with
other industries originates from sustainable development theory,
emphasizing the synergy between financial resources and the real
economy (Yin and Xu, 2022a). Theoretical studies suggest that GF
can promote the transformation of traditional industries towards
green development models through funding for technological
innovation and policy guidance (Jiakui et al., 2023). In practice,
GF has driven significant transformation in industries such as
energy and manufacturing (Dhayal et al., 2024). For example,
green credit has supported technological upgrades in high-
energy-consuming enterprises within China’s manufacturing
industry and accelerated industrial upgrade in the renewable
energy field (Wu et al., 2022). However, in the LI which is a
critical foundational industry, the involvement of GF remains in
its infancy, with existing studies primarily focusing on policy
analysis and case studies. For instance, Zhu’s case study
emphasizes the role of GF policies in advancing the development
of new energy vehicle fleets within logistics enterprises. However,
systematic and cross-regional studies on this topic remain scarce
(Zhu et al., 2023a). (3) Measuring the integration level of GF with
other industries is both a challenge and a focus in current research.
Some scholars have proposed coupling coordination models based
on indicator systems to measure the integration depth of GF with
various industries (Tomal, 2021). For example, the indicator system
developed by Dong et al. has been widely applied to assess the
integration level of GF with green urbanization (Dong et al., 2021).
However, specific measurement studies focused on the LI remain
relatively scarce, with most existing literature limited to qualitative
analysis. Yuan proposed the impact pathways of GF on the
transformation of cold chain logistics but failed to evaluate its
effects within a quantitative framework (Yuan et al., 2024).
Moreover, existing studies have rarely considered the impact of
regional heterogeneity on integration levels, limiting the broader
applicability of their findings (Zhao et al., 2023). In summary,
current research has made significant progress in the
mechanisms of GF, the theory and practice of industrial
integration, and the measurement of integration levels. However,
research on the integration of GF with the LI remains inadequate.
In recent years, scholars have extensively employed the DEA
method in studies evaluating industrial GT, particularly those
accounting for undesirable outputs. DEA, a non-parametric
technique based on linear programming, is widely utilized to
assess the relative efficiency of multiple decision-making units
(DMUs) under environments characterized by multiple inputs
and outputs (Bowlin, 1998). However, traditional DEA models
such as the CCR and BCC models exhibit limited capacity in
handling undesirable outputs, rendering them inadequate in
accurately capturing the efficiency variations inherent in the GT
process (Mardani et al., 2017). To address this limitation, Tone
introduced the non-radial, non-angular Slacks-Based Measure
(SBM) model, which effectively considers the slacks in both
inputs and outputs (Chang et al., 2014). Building upon the SBM
framework, Andersen further developed the Super SBM model,
enabling the ranking of DMUs even when multiple units achieve
an efficiency score of one (Lee, 2021). In the context of green
economic transformation, where undesirable outputs such as CO
2
emissions and energy consumption are prevalent, the model has
been further refined into the Super SBM Undesirable model
(Cecchini et al., 2018). This variant allows the incorporation of
negative output variables and has been widely applied in research on
energy efficiency, industrial GT, and ecological efficiency. For
instance, Meng employed the Super SBM Undesirable model to
measure the green efficiency of China’s manufacturing sector, while
Guo utilized it to evaluate the impact of green credit on the efficiency
of green technological innovation (Meng and Qu, 2022;Guo et al.,
2022). The model offers two distinct advantages: first, its capacity to
handle undesirable outputs aligns well with the measurement
demands of GT; second, it enhances the discriminatory power in
efficiency ranking, making it suitable for cross-sectional and
longitudinal analyses across multiple regions and years.
Therefore, this study adopts the Super SBM Undesirable model
as the evaluation tool for assessing the GT efficiency of the LI,
representing both a continuation of existing green efficiency
assessment methodologies and a contextually appropriate
extension in light of the logistics sector’s GT.
Although the aforementioned studies provide valuable
references for the integration of GF and the GT of the LI, several
shortcomings remain. First, research on the integration of GF with
the LI is insufficient. Current studies primarily focus on the
application of GF in traditional high-emission industries such as
TABLE 3 The main factors affecting the GT of the LI.
Influencing factor Key findings
Policy Government green policies serve as a crucial external force in guiding logistics enterprises toward transformation and elevating the overall
green level of the industry
Technology Technological innovation significantly enhances the logistics sector’s ability to reduce carbo n emissions and improve efficiency, acting as a
key driving force for GT
Industrial Structure Industrial upgrading creates development opportunities and synergistic momentum for green logistics
Corporate Behavior The green awareness and governance capacity of logistics enterprises directly affect the extent of their GT
Resource Endowment Regional resources and foundational conditions constrain the transformation pathways and development speed of green logistics
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energy and manufacturing, with limited attention to the LI, which
features a complex supply chain and significant emissions. As a
result, the specific pathways through which GF impacts the GT of
the LI have not been fully elucidated. Second, the exploration of
nonlinear relationships is inadequate. Existing literature often
assumes a linear relationship between GF and industrial green
development, overlooking the potential for phase-specific
variations. Specifically, during different stages of integration
between GF and the LI, the marginal effects on the GT of the LI
may vary significantly, yet this issue remains under-researched.
Third, research on regional heterogeneity is not sufficiently in-
depth. Although some studies have noted the impact of regional
differences on the GT of the LI, they have not thoroughly examined
the disparities in the integration of GF and the LI across regions.
To address the aforementioned shortcomings, we employ the
Super-SBM-Undesirable model to evaluate the GT of China’s LI.
Secondly, we use a coupling coordination degree model to measure
the integration level of GF and the LI, and apply a threshold effect
model to examine its nonlinear impact on the GT of the LI. Finally,
we conduct a regional heterogeneity analysis to explore the regional
characteristics exhibited by the GT of the LI due to regional
differences.
3 Theoretical analysis and research
hypothesis
The integration of GF and the LI may not exhibit a linear impact
on the GT of the LI but rather demonstrates distinct phased
characteristics as the level of integration deepens. As shown
in Figure 1.
(1) Early Stage: Low level integration with limited promotion
effect. In the initial stage of integrating GF with the LI,
numerous constraints limit the effectiveness of GF in
driving the GT of the LI. First, the insufficient supply of
financial resources serves as a critical bottleneck. Green
financial products, such as green credit and green bonds,
are still in their developmental phase, and financial
institutions tend to adopt conservative risk assessment
practices. As a result, the scale of funding remains
inadequate to meet the substantial capital demands
required for the GT of the LI. This limitation stifles the
potential of GF to support comprehensive green initiatives
within the industry (Zhu et al., 2023b). Second, insufficient
information coordination further weakens the environmental
benefits of GF. Due to information asymmetry between
financial institutions and logistics enterprises, the matching
efficiency of green financial resources is low. This mismatch
results in financial products being poorly designed or
misallocated, leaving some resources either underutilized or
idle (Bui et al., 2024). Third, technological barriers present a
significant challenge during this phase. The development and
adoption of green technologies often require substantial
financial and technical thresholds. However, GF exhibits
limited capability to intervene in these high-threshold
projects, slowing progress in critical areas such as energy-
saving technologies and new energy vehicles (Wang et al.,
2024). Moreover, this initial phase also encounters issues such
as incomplete policies and insufficient market incentives
(Barut et al., 2023). Local governments may lack adequate
fiscal subsidies and tax incentives to encourage green
initiatives. Additionally, the market demand for green
logistics remains underdeveloped, providing little
motivation for enterprises to adopt sustainable practices.
This lack of external incentives compounds the internal
challenges faced by logistics enterprises, slowing the overall
progress of GT. To overcome these limitations, policy
FIGURE 1
Theoretical analysis framework.
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Dai et al. 10.3389/fenvs.2025.1556580
guidance and mechanism innovation are needed to gradually
deepen the integration of these two industries, laying a solid
foundation for subsequent development.
(2) Mid-Term Stage: Moderate integration enhances the promoting
effect. In the mid-term phase of GF and LI integration, their
synergistic effects become increasingly evident. This phase is
characterized by notable advancements in collaborative
innovation, economies of scale,andstrengthenedpolicy
support. The collaboration between GF and logistics enterprises
deepens, enabling logistics companies to access greater amounts of
low-cost funding (Du et al., 2022). These financial resources are
channeled into acquiring new energy equipment and optimizing
operational models, driving the adoption and innovation of green
technologies within the industry. As green financial products, such
as green supply chain finance, mature, the scale of funding
progressively expands. This growth supports critical initiatives,
including the optimization of logistics networks and the
construction of green warehousing, which significantly enhance
the overall efficiency of the LI’sGT(Judijanto et al., 2024). These
advancements not only improve operational processes but also
reduce the environmental footprint of logistics enterprises,
contributing to broader sustainability goals. Government
intervention plays a crucial role in amplifying the impact of GF
during this phase. Through fiscal support and tax incentives,
governments reduce the cost burden of GT for enterprises,
encouraging greater investment in sustainable practices. These
measures stimulate enthusiasm among enterprises to adopt green
solutions and foster deeper integration between GF and the LI. The
combined effects of private sector collaboration, innovative
financial products, and government support collectively
promote sustainable development within the LI, marking a
pivotal step toward achieving long-term environmental and
economic goals (Zhu et al., 2020).
(3) Late Stage: High-level integration leads to diminishing marginal
effects. In the high-level integration phase of GF and the LI,
although the promotion effect has reached a considerable scale, the
marginal returns gradually decrease. This primarily manifests in
bottleneck effects, resource misallocation risks, and industrial
overcapacity. Long development cycles for technology and
declining investment returns create bottlenecks in the GT
process (Bai and Lin, 2023). Excessive reliance on GF may
concentrate funds in certain areas, such as the procurement of
new energy vehicles, while neglecting other critical aspects, leading
to resource misallocation (Liu et al., 2020). Moreover, intensified
competition due to the homogenization of green technologies
undermines the overall transformation effect and may even result
in industrial overcapacity (Wang and Wang, 2021). Thus, both
enterprises and governments should focus on optimizing resource
allocation, fostering technological innovation, and promoting
industrial upgrades, avoiding over-reliance on a single model to
ensure the sustainability and efficiency of the GT.
In conclusion, this study proposes the following research
hypothesis: The integration of GF and the LI exerts an S-shaped
curve impact on GT. Specifically, in the initial stage, the integration
of the two industries has a limited effect on promoting the GT of the
LI. As the level of integration increases, improvements in financial
support, technological innovation, and the policy environment
gradually enhance the transformation effect, leading to a phase of
high efficacy. However, as integration deepens further, issues such as
resource misallocation, technological homogenization, and market
saturation cause diminishing marginal returns. This ultimately
reaches a saturation or decline and forms a typical S-shaped pattern.
4 Research design
4.1 Evaluation of GT in the LI
The Super-SBM-Undesirable model is suitable for evaluating the
GT of China’s LI, primarily because it simultaneously considers both
positive and negative outputs, offering a comprehensive reflection of
the multifaceted impacts in the GT process (Shah et al., 2022). In this
process, the LI not only focuses on improving economic efficiency
and resource utilization but also addresses negative issues such as
energy consumption and pollutant emissions. The model effectively
handles resource misallocation and nonlinear relationships in GT,
identifying bottlenecks and resource distribution issues that arise
during the transformation. Additionally, the Super-SBM model’s
super-efficiency analysis capability plays a critical role in identifying
regions with exceptional performance, offering valuable lessons that
can be applied to other areas and contributing to the overall green
GT of the LI (Xiao et al., 2022). The model’sflexibility allows it to
accommodate logistics enterprises of varying scales and types,
making it a versatile tool for performance evaluation. By
providing a comprehensive assessment of the LI’s GT, the model
supports governments in optimizing resource allocation,
formulating targeted policies, and making informed decisions to
promote sustainable development.
Building on prior research, this study constructs the Super-
SBM-Undesirable model by integrating the SBM model—renowned
for its ability to address undesirable outputs, as proposed by
Tone—with the super-efficiency evaluation model developed by
Andersen, as outlined in Equation (1) (Tone et al., 2020;
Andersen and Petersen, 1993). This approach enhances the
model’s analytical precision and applicability, making it an
effective tool for examining the intricate dynamics of GF and LI
integration.
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⎩
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In Equation 1,ρrepresents the level of GT in the regional LI,
which is the objective function value of the model, reflecting the
overall efficiency. x denotes the values of input indicators, while yg
and ybrepresent the values of desirable and undesirable output
indicators, respectively. The slack variables s−,s
g+, and sb−
correspond to input excess, insufficient desirable output, and
excess undesirable output, respectively. λrepresents the linear
combination weights used to construct the virtual decision-
making unit. k refers to the k-th evaluated province, m is the
number of input indicators, and q1and q2represent the numbers
of desirable and undesirable output indicators, respectively. The
definitions of these symbols provide a scientific basis for the
comprehensive evaluation of the GT of the LI.
To comprehensively evaluate the current state of GT in China’s
LI, this study constructs a three-tiered indicator system-comprising
inputs, desirable outputs, and undesirable outputs-based on the
Super SBM Undesirable model, as shown in Table 4. The selection of
indicators is informed by the relevant research of scholars such as
Bai, while also fully considering data availability, representativeness,
and alignment with the conceptual connotation of GT (Bai et al.,
2022b). At the input level, labor, energy, and capital are identified as
key variables, representing the dependence on human resources, the
efficiency of energy utilization, and the extent of investment in green
infrastructure, respectively. At the desirable output level, the value-
added of the LI and freight turnover are employed to capture
economic performance and service efficiency. At the undesirable
output level, carbon emissions are introduced to reflect the
environmental constraints of GT. This indicator system provides
a comprehensive framework for depicting the efficiency
characteristics and ecological performance of the logistics sector’s
green transition.
4.2 Evaluation of industry integration
The coupling coordination degree model is a mathematical tool
designed to evaluate the interactions and coordinated development
levels between systems. It is particularly effective in analyzing the
degree of coupling among multiple subsystems and determining
whether these subsystems can develop synergistically. Widely
applied across diverse fields such as economics, ecology, and
sociology (Xing et al., 2019), this model provides valuable
insights into system dynamics. Accordingly, this study adopts the
coupling coordination degree model to assess the coordinated
development relationship between GF and the LI, as outlined in
the following steps.
Step 1: Build a system indicator system. This study establishes an
evaluation indicator system for the two industries based
on the Coupling Coordination Degree Model. Drawing on
extensive domestic and international research, and
aligning with the principles of green development and
the practical needs of the logistics sector’s GT, the
indicator selection adheres to the principles of scientific
validity, systematic structure, representativeness, and data
accessibility. A total of 18 secondary indicators are
carefully selected from two dimensions—GF and the
LI—as detailed in Table 5 (Rahman et al., 2024). The
GF dimension encompasses five primary indicators: green
credit, green bonds, green insurance, green investment,
and carbon finance. These are designed to
comprehensively evaluate the capacity of financial
resource allocation to guide green and low-carbon
industries, to impose constraints on high-carbon
sectors, and to support environmental governance and
carbon reduction initiatives. The LI dimension, on the
other hand, comprises indicators from three perspectives:
input levels, output performance, and growth potential.
These reflect the foundational investments in human,
energy, and capital resources, operational outcomes,
and prospects for future development, thereby
illustrating both the path and achievements of green-
oriented progress. The overall framework emphasizes
the design of indicators that balance coordination and
differentiation. It accounts for the dual role of GF in
providing positive incentives for green industries and
exerting regulatory pressure on carbon-intensive
sectors, while highlighting the LI’s objectives of
reducing emissions and improving efficiency through
GT. By integrating both positively and negatively
oriented indicators, the system aims to fully capture the
coupling characteristics and coordination mechanisms
TABLE 4 Evaluation index system for GT of the LI.
Goal Layer Dimension
Layer
Variable name Variable name
Input Labor LI Labor Input Urban employment in the transportation, warehousing, and postal industries
Energy LI Energy Consumption Energy consumption in transportation, warehousing, and postal industries * energy conversion
standard coal coefficient
Capital LI Public Budget
Expenditure
Public budget expenditure in transportation
LI Capital Stock Fixed asset investment in transportation, warehousing, and postal industries
Output Desirable Output LI Added Value Added value in transportation, warehousing, and postal industries
Cargo Turnover Total cargo turnover in railways, highways, waterways, shipping, and oil pipelines
Undesirable Output LI Carbon Emissions Energy consumption in transportation, warehousing, and postal industries * carbon emission
coefficients
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between GF and the LI, thereby ensuring that the
measurement of coupling coordination is scientifically
rigorous, systematically structured, and empirically
insightful.
Step 2: Comprehensive evaluation of subsystems. Using the
previously established evaluation indicator system, the
entropy method is employed to comprehensively assess
each subsystem, yielding a comprehensive evaluation
value. The calculation process is detailed in Equation 2
(Porta et al., 1999). In this formula: Xirepresents the raw
data of the subsystem, Tidenotes the comprehensive score
of the subsystem i, Wij is the weight of the j-th indicator
for subsystem i.
Ti
n
j1
XiWij (2)
Step 3: Calculate the coupling degree. The coupling degree reflects
the interaction between multiple subsystems. The calculation
formula is shown in Equation 3 below.nisthenumberof
subsystems. The coupling degree C ranges from 0 to 1. The
closer the value is to 1, the higher the degree of coupling
between the systems, indicating a stronger interaction and
coordination between the subsystems.
Cn
i1Ti
1/n
n
i1Ti
(3)
Step 4: Calculate the coupling coordination degree. The coupling
coordination degree, based on the coupling degree, takes
into account the overall development level of the systems.
The calculation formula is shown in Equation 4 below.
Tavg represents the average comprehensive score of all
subsystems,
D
C·Tavg
(4)
Step 5: Coupling coordination level division. According to the
degree of coupling coordination D, the coupling
coordination state can be divided into different levels,
as shown in Table 6.
4.3 Variable settings
Based on a systematic review of existing research and
considering the specific characteristics of GT in China’s LI, the
study has developed a research indicator framework. In this
framework, the GT of the LI serves as the dependent variable,
while the coupling coordination degree between GF and the LI is
the core independent variable. To ensure the scientific validity of the
model and the reliability of the results, we also incorporate a
comprehensive set of control variables, including economic level,
urbanization level, technological level, and environmental
regulation (Lean et al., 2014;Bretzke, 2013;Moldabekova et al.,
2021;Ngo, 2021). Specifically, the economic level, reflected by
TABLE 5 The evaluation index system for the integration of GF and the LI.
Industry Primary indicator Secondary indicator Attribute
Green Finance Green Credit Proportion of interest expenses in high-energy-consuming industries Negative
Borrowing scale of environmental protection listed companies Positive
Green Bonds Proportion of market value of environmental protection enterprises Positive
Proportion of market value of high-energy-consuming industries Negative
Green Insurance Proportion of agricultural insurance income Positive
Agricultural insurance claim rate Positive
Green Investment Proportion of investment in environmental pollution control Positive
Proportion of local fiscal environmental expenditure Positive
Carbon Finance Carbon emissions per unit of GDP Negative
Logistics Industry Input Level Number of employees in the LI Positive
Total energy consumption of the LI Positive
Fixed asset investment in the LI Positive
Output Level Added value of the LI Positive
Freight volume Positive
Freight turnover volume Positive
Growth Potential Business growth rate Positive
Employment growth rate Positive
Energy consumption per unit of freight Positive
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regional GDP per capita, serves as a fundamental supporting factor
for the GT of the LI. The urbanization level, measured by the
proportion of the urban population to the total population, captures
the potential impact of urbanization on adjusting logistics demand
structures and promoting green development. Technological level, a
key driver of GT, is assessed through the number of patents granted
per unit of GDP, representing the role of technological innovation in
advancing green technologies. Environmental regulation, a core
aspect of policy constraints and incentives, is represented by the
proportion of industrial pollution control investment to industrial
value-added, highlighting the guiding role of environmental policies
in fostering green production and operations within enterprises.
The selection of the aforementioned variables creates a
comprehensive and scientifically grounded indicator system,
encompassing economic, social, technological, and policy
dimensions. This system not only provides a holistic depiction of
the integration between GF and the LI but also elucidates the
mechanisms by which this integration drives the GT of the LI.
By offering a robust theoretical framework and data foundation, this
indicator system lays the groundwork for empirical analysis. The
detailed indicator system and variable design, including variable
definitions, calculation methods, and abbreviations, are presented
in Table 7.
4.4 Model construction
To explore the impact mechanism of the integration of GF and
the LI on the GT of the LI, an verify the potential nonlinear
relationship, we adopted a threshold effect model. The threshold
effect model is a method used to test and quantify the nonlinear
relationship between variables. It works by setting one or more
thresholds to divide the sample space, analyzing whether the
relationship between the dependent and independent variables
differs under different threshold conditions (Lee et al., 2011).
This model can effectively reveal the nonlinear characteristics of
the relationship between variables, making it particularly suitable for
testing situations where critical points or turning points exist in
certain economic or social phenomena. Drawing on the research of
Hansen and other scholars, we use the coupling coordination degree
of the two industries as the threshold variable and construct the
threshold effect model, as shown in Equation 5.
GTLIit α0+α1ILit ·IIL≤τ1
()
+α2ILit ·Iτ1<IL ≤τ2
()
+α3ILit
·IIL〉τ2
()
+α4ELit +α5ULit +α6TLit +α7ERit +ηi+λt
+εit
(5)
In Equation 5, i represents the province, and t represents the
year. α0denotes the intercept term, ηirepresents the unobservable
individual effect, λtdenotes the time fixed effect, and εit is the
random error term. The variables GTLI,IL,EL,UL,TL, and ER
have the same meaning as in Table 7. Additionally, I(·) denotes the
indicator function, and τ1and τ2are the threshold values that divide
the integration level into three stages.
4.5 Data interpretation
We select panel data from 30 provinces in China (excluding
Hong Kong SAR, Macao SAR, Taiwan Province, and Tibet
Autonomous Region) from 2010 to 2022 as the research sample.
The data sources primarily include the China Statistical Yearbook,
China Energy Statistical Yearbook,China Logistics Statistical
Yearbook, and the CNRDS database. For missing values in the
dataset, we applied linear interpolation for reasonable imputation
TABLE 6 Classification table of coupling coordination level.
Level Coupling coordination degree (D) Type
10.0≤D<0.2 Extremely uncoordinated
20.2≤D<0.4 Low coordination
30.4≤D<0.6 Moderate coordination
40.6≤D<0.8 Highly coordinated
50.8≤D≤1.0 Extremely high coordination
TABLE 7 Index system of measurement model.
Variable Calculation method Symbol
Interpreted variable GT of the LI As described in Section 4.1 GTLI
Core explanatory variable Integration Level of Green finance and the LI As described in Section 4.2 IL
Control variables Economic Level GDP/Population size EL
Urbanization Level Urban population/Total population UL
Technological Innovation Number of patent applications authorized/GDP TI
Environmental Regulation Proportion of industrial pollution control investment to industrial value-added ER
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to ensure data integrity and coherence. After obtaining and
organizing the data, we conducted a detailed descriptive
statistical analysis of the main variables in the selected sample.
This step aims to reveal the basic distribution characteristics of each
variable, including its mean, minimum value, maximum value, and
standard deviation. The specific statistical results are shown
in Table 8.
5 Results
5.1 Analysis of the integration of the two
industries
Based on the coupling coordination degree model and the
sample data, the evaluation results of the integration level of GF
and the LI in China are shown in Figure 2.
From 2010 to 2022, the integration between GF and the LI in
China exhibited a clear upward trend, evolving from mild imbalance
to moderate and even high coordination. This progress can be
attributed to the systematic enhancement of GF policies, the
modernization of the LI, and the implementation of regional
coordinated development strategies. Since 2010, the Chinese
government has introduced key policies, such as the Green Credit
Guidelines and the Green Finance Development Plan (2016–2020),
which have provided substantial financial support for green
industries. As a result, the LI, a major beneficiary of GF, has
made significant strides in greening its operations. These
advancements include the accelerated adoption of new energy
vehicles and the promotion of smart logistics technologies,
leading to increasingly evident synergies between GF and the LI.
However, despite the notable national improvements in coupling
and coordination, regional disparities persist. The gap between the
eastern regions and the central and western regions remains
unbridged, highlighting the need for more targeted efforts to
address these imbalances.
Regionally, the eastern developed provinces have taken the lead
in entering the high coordination stage, while the central and
western provinces continue to improve, though significant
regional disparities persist. These differences are closely related to
factors such as regional economic development levels, policy
support intensity, logistics infrastructure construction, and the
distribution of GF resources. (1) Eastern Region: The eastern
region has consistently led the national integration level, with the
coupling coordination degree rapidly improving from a mild
coordination stage in 2010 to a high coordination stage by 2022.
This progress is largely attributed to a robust economic foundation,
substantial policy support, abundant GF resources, and advanced
technological innovation. For example, Shanghai has facilitated the
adoption of new energy logistics vehicles and park renovations
through green bonds, achieving a coupling coordination degree
of 0.85 in 2022—the highest in the nation. (2) Central Region:
Provinces like Henan and Hubei have made significant progress in
recent years. The coupling coordination degree has improved from
the mild imbalance stage (around 0.3–0.4) to the moderate
coordination stage (around 0.6–0.7), but there are still inter-
provincial imbalances. Taking Henan as an example, leveraging
national logistics hub policies and GF support, the coupling
coordination degree reached 0.68 in 2022, positioning it among
the leading provinces in the central region. (3) Western Region: The
western region, despite its late start and weak foundation, has shown
notable progress, with a relatively low overall coupling coordination
degree but rapid growth. In 2010, the region was in the mild
imbalance stage (around 0.2–0.3) but gradually advanced to the
preliminary coordination stage by 2022. Certain areas, such as
Sichuan and Shaanxi, are beginning to close the gap. Benefiting
from the Belt and Road initiative, the western region has made
significant strides in improving logistics infrastructure and
attracting GF inflows. For instance, Sichuan supported the
adoption of new energy vehicles and the development of a green
logistics network, achieving a coupling coordination degree of
TABLE 8 Descriptive statistics of the main variables.
Variable Mean Std. Dev Maximum Minimum
GTLI 0.643 0.587 1.311 0.054
IL 0.502 0.367 0.854 0.202
EL 0.597 0.288 1.923 0.203
UL 0.611 0.124 0.899 0.354
TI 0.193 0.142 0.699 0.023
ER 0.834 0.675 3.605 0.238
FIGURE 2
The spatial distribution of the integration level between GF and the LI.
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0.65 in 2022. While all regions have demonstrated continuous
improvement in integration levels, the gap between the eastern
region and the central and western regions remains substantial.
This underscores the need for enhanced regional coordination to
promote balanced national integration and development.
5.2 Empirical analysis of the integration of
the two industries on the GT of the LI
Using the integration level of GF and the LI as the threshold
variable, the study employed Stata 15.0 to conduct single-threshold,
double-threshold, and triple-threshold significance tests to
determine the number of thresholds (Seo and Shin, 2016). The
test results are presented in Tables 9,10.
As shown in Table 9, the F-values for the single-threshold and
double-threshold tests are significant at the 1% level, passing the
threshold significance tests. However, the F-value for the triple-
threshold test is not significant, indicating the absence of a triple-
threshold effect. This suggests a double-threshold effect between the
integration of GF and the LI’s GT. Therefore, this study adopts a
double-threshold effect model to analyze the impact of industrial
integration on the GT of the LI. According to Table 10, the double-
threshold values are 0.389 and 0.641, respectively. Based on these
thresholds, the level of industrial integration is divided into three
intervals: low integration level [0, 0.389], medium integration level
[0.389, 0.641], and high integration level [0.641, 1]. Finally, the
regression results of the threshold effect model are presented
in Table 11.
As shown in Table 11, the impact of the integration level
between GF and the LI on the GT of the LI exhibits nonlinear
characteristics. In the low-level integration phase (IL ≤0.389), the
regression coefficient is 0.412, but it is not significant. This indicates
that the integration of the two industries has not yet exerted a
noticeable positive effect on GTLI during this phase. In the medium-
level integration phase (0.389 <IL ≤0.641), the regression
coefficient significantly increases to 1.403, demonstrating that the
promotion effect of industrial integration on GTLI reaches its peak
with a strong and significant driving force. In the high-level
integration phase (IL >0.641), the regression coefficient drops to
0.218. Although still positive, the marginal effect weakens
significantly. Despite the insignificance of the coefficient in the
low-level integration phase, the overall model analysis and
theoretical framework suggest that the impact of GF and the LI
integration on GTLI follows an S-shaped pattern. Based on these
regression results, we further analyzed the specific impact
mechanisms within each phase.
(1) Low-level integration Phase: In the low-level integration
phase (IL ≤0.389), the promoting effect of GF on the GT
of the LI is weak and insignificant. This phase is characterized
by the limited dissemination of green financial products and
services, making it difficult for logistics enterprises to secure
sufficient funding to support GT efforts. Moreover,
enterprises exhibit low awareness of green development,
and the application rate of green technologies and
equipment is insufficient, resulting in a weak foundation
for GT. Additionally, the infrastructure for green logistics,
such as new energy vehicles and green warehousing, remains
underdeveloped, further hindering the overall green
development of the LI. For instance, in underdeveloped
provinces in central and western regions like Tibet and
Qinghai, the green financial system is still immature, with
weak green credit policies and limited policy support. As a
result, investments in green logistics equipment by enterprises
are minimal, leading to negligible progress in GT. These
regions are characterized by small-scale logistics
enterprises, outdated technologies, and insufficient
infrastructure, exacerbating the challenges of achieving
green development. (2) Medium-level integration Phase: In
the medium-level integration phase (0.389 <IL ≤0.641), the
integration of GF with the LI significantly promotes GT,
exhibiting a robust facilitation effect. During this phase,
GF policies are progressively implemented, with financial
institutions providing substantial support for green
logistics projects. This drives enterprises to invest in green
technologies and infrastructure development. Additionally,
the application of advanced technologies and economies of
scale improve the efficiency of green development in the LI.
Within the LI, competition and demonstration effects are
gradually emerging, encouraging more enterprises to actively
engage in GT. Economically developed provinces along the
TABLE 9 Significance test of threshold effect.
Threshold variable Threshold Fstat Prob
IL Single 206.34 0.000
Double 79.39 0.000
Triple 36.17 0.268
TABLE 10 Estimation of threshold value.
Threshold value Estimated value Lower Upper
τ10.389 0.356 0.422
τ20.641 0.587 0.695
TABLE 11 Regression results of threshold effect model.
Variable Coefficient
IL(IL ≤0.389) 0.412
IL(0.389 <IL ≤0.641) 1.403***
IL(IL >0.641) 0.218**
EL −1.314***
UL −0.931**
TI 1.768***
ER 1.213**
R-squared 0.884
Prob >FF = 0.000
***p<0.01, **p <0.05.
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eastern coast, such as Jiangsu and Zhejiang, serve as prime
examples. These regions have well-established GF policies,
with financial institutions supporting the development of new
energy logistics vehicles and green warehousing through
green loans and subsidies. For instance, many areas in
Jiangsu have introduced large-scale fleets of new energy
logistics vehicles and solar-powered warehousing facilities,
realizing significant green development benefits. Policy
incentives in these regions have also attracted more
enterprises to participate in GT initiatives. (3) High-level
Integration Phase: In the high-level integration phase
(integration level >0.641), the promoting effect of GF on
the GT of the LI begins to weaken. While GF continues to
drive green initiatives, its marginal effectiveness declines, and
issues related to over-integration arise. Redundant resource
allocation and technological bottlenecks lead to reduced
efficiency, while the costs of further advancing GT increase
sharply. In some cases, insufficient regulatory coordination
results in a mismatch between financial support and the actual
needs of enterprises, diminishing the effectiveness of
transformation efforts. For example, Guangdong and
Shanghai, as regions with high integration levels between
GF and the LI, have successfully achieved widespread
adoption of new energy logistics vehicles and green
warehousing facilities. However, inefficiencies such as
underutilized charging stations and surplus vehicles have
started to emerge. Additionally, enterprises in these regions
are approaching technological bottlenecks, where further
advancements require significantly higher marginal costs.
As a result, the pace of GT improvements has
noticeably slowed.
The regression coefficients of the control variables EL and UL
are significantly negative. (1) The inhibitory effect of EL on the green
development of the LI is primarily reflected in its high energy
consumption characteristics, path dependence, and blind
expansion tendencies. As a key indicator of regional economic
development, an increase in per capita GDP is often
accompanied by greater demand for freight transportation and
energy consumption, thereby reinforcing the LI’s high-emission
characteristics. Furthermore, in the absence of stringent
environmental regulations, economic expansion often prioritizes
production and logistics demands over GT objectives. This tendency
reinforces a high-energy, high-emission growth trajectory, further
hindering progress toward sustainable development. (2) The
inhibitory effect of UL on the LI’s green development is more
closely related to the concentration of logistics demand. Higher
levels of urbanization indicate a significant concentration of urban
populations and economic activities, driving a sharp increase in
demand for traditional logistics services. This surge often promotes
the expansion of conventional logistics models, delaying GT due to
market pressures. Additionally, rapid urbanization is typically
accompanied by intensive construction and excessive resource
consumption, exacerbating environmental pollution. These
challenges create substantial obstacles to the GT of the LI,
making the adoption of sustainable practices more difficult.
Notably, the regression coefficients of the control variables for TI
and ER are significantly positive, highlighting their supportive role
in driving GT. (3) Technological innovation facilitates the
development and adoption of green logistics technologies, such
as energy-efficient transportation tools, smart warehousing
systems, and green supply chain management solutions. It also
enhances operational efficiency and resource allocation, enabling
logistics enterprises to reduce operational costs while minimizing
environmental impacts. Moreover, technological innovation
stimulates market competition, encouraging firms to accelerate
technological iterations and promote industry-wide green
upgrades. (4) Environmental regulation, as a critical external
policy tool, supports GT through constraints and incentives.
Strict environmental regulations compel logistics enterprises to
invest in green technologies, such as new energy vehicles and
low-emission facilities, while internalizing pollution costs, which
incentivizes optimization of logistics processes, reduction of
resource waste, and minimization of emission intensity.
Additionally, environmental regulation provides financial support
for green technology research and sustainable development
strategies through subsidies, tax incentives, and other measures.
These mechanisms collectively highlight the pivotal roles of
technological innovation and environmental regulation in driving
the GT of the LI.
5.3 Robustness test
The robustness test ensures that the model results remain
reliable and valid when faced with various potential anomalies,
thereby enhancing the credibility and interpretability of the research
(Sharma et al., 2022). In this study, we perform robustness tests by
altering the calculation method of the core explanatory variables and
adding control variables. The test results are presented in Tables 12,
13,14.
(1) We replaced the entropy method with principal component
analysis (PCA) to recalculate the integration level of GF and
the LI (core explanatory variable). Using PCA, we extracted
the principal components of each subsystem and assigned
appropriate weights to calculate the integration level. We then
reconstructed the threshold effect model with the new core
explanatory variable to test whether the threshold effect
remains significant. As shown in the column (1) of the
Robust Test in Tables 12,13,14, the model still exhibits
two significant threshold values, 0.381 and 0.647. These are
close to the threshold values of the original model. The
regression coefficients of the threshold variables are
consistent with the original model in terms of direction
and significance in each interval, confirming the robustness
of the model results.
(2) Next, we added industrial structure (the proportion of the
secondary industry, IS) as a new control variable and re-
estimated the model. The results are shown in the column (2)
of the Robust Test (2) in Tables 12,13,14. After controlling
for the interference of industrial structure, the significance of
the threshold values and the S-shaped relationship between
variables still hold. Furthermore, the direction of the
regression coefficients of the threshold variables in each
stage remains unchanged. Specifically, the regression
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coefficient in the low-level integration phase is still
insignificant, the promotion effect is the strongest in the
medium-level integration phase, and a diminishing
marginal effect is observed in the high-level integration
phase. The regression coefficient of the newly added
control variable is negative, indicating that when the
proportion of the secondary industry is high, it has a
suppressive effect on the GT of the LI. This is because the
secondary industry typically includes manufacturing and
heavy industries, which have high logistics demand but are
often associated with high energy consumption and pollution.
5.4 Heterogeneity analysis
Significant differences in factors such as economic
development, GF, industrial structure, and resource availability
across provinces in China result in varying impacts of industrial
integration on the GT of the LI (Yu et al., 2021). To account for this
heterogeneity, we divided the study sample into four major
regions—East, Central, West, and Northeast China—based on
China’s geographical division standards. The analysis results are
presented in Tables 15,16,17.
As shown in Tables 15,16,17, the Eastern and Central
provinces of China passed the double-threshold test, while the
Western and Northeastern provinces only passed the single-
threshold test: Firstly, in the Eastern provinces, the promoting
effect of industrial integration is most significant in the second
stage, driven by a well-established financial system and advanced
logistics infrastructure, such as the widespread adoption of new
energy vehicles and smart warehouses. However, in the third stage,
the marginal effect diminishes, and challenges related to resource
allocation efficiency begin to surface. Furthermore, in the Central
provinces, the threshold values are similar to those in the Eastern
provinces, with industrial integration significantly promoting GT
in the second stage. This effect is largely attributed to strong policy
support and growing acceptance of green technologies by
enterprises. However, in the third stage, limited logistics
infrastructure and technological capabilities reduce the
coefficient to 0.327, reflecting a decline in the promoting effect.
Moreover, in the Western provinces, the integration level of GF
and the LI remains generally low. However, once the initial
threshold is surpassed, GF significantly enhances the GT of the
LI. The low level of integration in this region is primarily due to
economic underdevelopment and resource constraints.
Nevertheless, national policy support and the implementation
of pilot projects have effectively accelerated the GT process.
Lastly, in the Northeastern provinces, the high proportion of
traditional industries, sluggish economic growth, and limited
application of green technologies in logistics enterprises have
significantly constrained the development of high-level
industrial integration.
In summary, the heterogeneity analysis indicates that the impact
of the integration of GF and the LI on the GT of the LI varies
significantly across regions. The Eastern and Central provinces pass
TABLE 12 Significance test of threshold effect.
Threshold variable Robust test (1) Robust test (2)
Threshold Fstat Prob Threshold Fstat Prob
IL Single 193.27 0.000 Single 186.55 0.000
Double 77.31 0.000 Double 80.27 0.000
Triple 35.14 0.283 Triple 32.92 0.318
TABLE 13 Estimation of threshold value.
Threshold value Robust test (1) Robust test (2)
Estimated value Lower Upper Estimated value Lower Upper
τ10.381 0.349 0.413 0.376 0.344 0.408
τ20.647 0.592 0.702 0.638 0.584 0.692
TABLE 14 Regression results of threshold effect model.
Variable Robust test (1) Robust test (2)
Coefficient Coefficient
IL(IL ≤τ1)0.389 0.426
IL(τ1<IL ≤τ2)1.411*** 1.363***
IL(IL >τ2) 0.232** 0.192**
EL −1.289*** −1.023***
UL −0.893** −0.768**
TI 1.604*** 1.263***
ER 1.153** 1.141**
IS —−0.345**
R-squared 0.874 0.859
Prob >FF = 0.000 F = 0.000
***p<0.01, **p <0.05.
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the double-threshold test, showing a multi-stage promoting effect of
industrial integration on the GT of the LI. Among them, the Eastern
provinces have the most significant promoting effect due to
improved policies and advanced technologies. The Central
provinces are developing steadily however, it is constrained by
resource allocation efficiency. In contrast, the Western and
Northeastern provinces only pass the single-threshold test,
primarily due to the low level of industrial integration, weak GF
systems, and insufficient infrastructure and technological
capabilities. Furthermore, in the high-integration stage, both the
Eastern and Central provinces experience diminishing marginal
effects, highlighting the need to improve resource utilization
efficiency through technological innovation and policy
optimization. Therefore, the study recommends that the Eastern
provinces prioritize resource optimization to achieve high-level
green development, the Central provinces enhance technological
innovation and financial support, and the Western and
Northeastern provinces focus on improving infrastructure and
implementing robust policy measures to accelerate the
integration of GF and the LI.
6 Discussion
This study analyzes data from 30 provinces in China from
2010 to 2022, revealing the promoting effect of the integration of
GF and the LI on the GT of the LI, along with its nonlinear
relationship and regional heterogeneity. Below, we compare and
discuss the results from both horizontal and vertical dimensions.
Firstly, we find that the integration level of GF and the LI in
China has significantly improved in recent years, progressing from
mild misalignment to moderate and high coordination stages. This
finding is consistent with existing research on the integration of GF
with other industries. For instance, studies by Zhang, Yin, and Han
have explored the integration of GF with the digital economy,
economic growth, and agriculture, respectively (Zhang and Zhao,
2023;Yin and Xu, 2022b;Han et al., 2023). In contrast, this study
expands the application of GF by focusing on the GT of the LI, a
fundamental service sector. This highlights the universality of GF in
promoting low-carbon development across various industries.
Significant regional differences in integration levels were also
observed. Eastern provinces exhibit higher integration levels due
TABLE 15 Significance test of threshold effect.
Threshold variable Threshold Prob.
Eastern
provinces
Central
provinces
Western
provinces
Northeastern
provinces
IL Single 0.000 0.000 0.000 0.000
Double 0.000 0.000 0.183 0.206
Triple 0.184 0.293 0.318 0.341
TABLE 16 Estimation of threshold value.
Threshold value Estimated value
Eastern provinces Central provinces Western provinces Northeastern provinces
τ10.395 0.383 0.423 0.408
τ20.650 0.633 ——
TABLE 17 Regression results of threshold effect model.
Variable Coefficient
Eastern provinces Central provinces Western provinces Northeastern provinces
IL(IL ≤τ1)0.323 0.416 0.437*** 0.451***
IL(τ1<IL ≤τ2)1.562*** 1.383*** 1.772*** 1.698***
IL(IL >τ2) 0.256*** 0.327** ——
Controls Yes Yes Yes Yes
R-squared 0.834 0.849 0.873 0.852
Prob >FF = 0.000 F = 0.000 F = 0.000 F = 0.000
***p<0.01, **p <0.05.
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Dai et al. 10.3389/fenvs.2025.1556580
to their advanced economic development and stronger policy
implementation compared to central and western provinces.
These findings align with Fang’s research on regional disparities
in China’sGF(Fang and Shao, 2022). Additionally, Zhang pointed
out that differences in GF levels often stem from variations in
infrastructure, technological innovation capabilities, and policy
support—factors that are especially prominent in the LI (Zhang,
2023). These results underscore that GF plays a pivotal role not only
in high-carbon-emission industries but also in facilitating the GT of
service industries, further demonstrating its broad applicability in
driving sustainable development.
Secondly, using the threshold effect model, the study reveals that
the integration of GF and the LI follows an S-shaped trajectory in
influencing the GT of the LI. This progression consists of three
distinct phases: an initial inefficient phase, a rapid improvement
phase, and a subsequent stable development phase. This nonlinear
characteristic contrasts sharply with existing discussions on the
marginal effects of GF, which primarily focus on either
increasing or diminishing returns. By identifying this S-shaped
relationship, the study broadens the understanding of GF’s role
in the GT of the LI. Previous studies by Sun, Dhayal, and
Rasoulinezhad, have highlighted the importance of GF in
promoting green transformations in manufacturing, industrial,
and energy sectors (Sun et al., 2023;Dhayal et al., 2023;
Rasoulinezhad and Taghizadeh-Hesary, 2022). However, they
have not identified the phased nature of GF’s impact nor
examined its role within industrial integration. Similarly, research
on the GT of the LI has largely emphasized technological innovation,
environmental regulation, and supply chain optimization, while
giving limited attention to how GF fosters GT through industrial
integration. For example, Anser explored the role of technological
upgrades in enhancing carbon emission efficiency in logistics but
overlooked GF as a critical external support tool (Anser et al., 2020).
Likewise, Lai focused on the constraining effects of environmental
regulation on the LI’s green development but did not incorporate the
perspective of financial resources (Lai and Wong, 2012). In contrast,
the analysis of the threshold effects of GF-LI integration, reveals that
the synergistic mechanisms play across different development
stages. This approach provides a novel and comprehensive
perspective on the interplay between GF and the LI, advancing
the research on sustainable development in service industries.
Thirdly, the study also indicates a significant regional
heterogeneity in the impact of the integration of GF and the LI
on the GT of the LI. For instance, integration in the eastern region
has the most significant impact on improving GT efficiency, while
the central and western regions demonstrate greater development
potential. This finding aligns with Kumar et al., who observed that in
economically developed regions, GF more effectively fulfills its
resource allocation function, thereby accelerating the GT process
(Kumar et al., 2024). Similarly, Muganyi et al. highlighted that weak
infrastructure, low adoption rates of green technologies, and limited
financing capacity for green projects are critical barriers to the GT of
the central and western regions (Muganyi et al., 2021).
Finally, this study takes China as a case to explore the nonlinear
impact of the integration between GF and the LI on the efficiency of
the industry’s GT. Although the empirical analysis is based on data
from China, the findings are also applicable to other developing
countries with similar industrial foundations and at comparable
stages of GF development. In many such countries, the logistics
sector is likewise characterized by high energy consumption and
carbon emissions, while GF remains in its nascent or intermediate
phase, with relatively low efficiency in financial resource allocation.
As a result, the role of GF in driving the GT of the logistics sector
may also exhibit an S-shaped pattern—being limited in the early
stages, significantly effective in the mid-term, and potentially
showing diminishing returns when excessively relied upon.
Therefore, this study not only offers policy recommendations for
optimizing the integration pathway of GF and the LI in China, but
also provides valuable insights for other developing economies in
designing GF strategies and promoting industrial green transitions.
In particular, for Belt and Road countries, BRICS nations, and
similar economies, establishing effective GF guidance
mechanisms, optimizing industrial structures, and enhancing
institutional support are crucial steps toward improving green
transition efficiency and achieving sustainable development goals.
7 Conclusion and policy implications
7.1 Conclusion
This study systematically analyzes the facilitating effects and
mechanisms of the integration of GF and the LI on the GT of the LI.
It reveals that in recent years, the integration level of GF and the LI in
China has significantly improved, progressing from mild
misalignment to moderate and high coordination. However,
significant disparities exist among provinces. The eastern region,
leveraging its strong economic foundation and policy support, has
taken the lead in achieving high coordination. The central region has
made notable progress but remains uneven in development, while
the western region, despite its weak foundation, has maintained
steady growth. Secondly, the integration of GF and the LI
significantly affects the efficiency of GT in logistics, exhibiting an
S-shaped relationship. At low levels of integration, its contribution
to GT is limited. As the integration level increases, the marginal
effect on GT gradually strengthens. However, once the integration
surpasses a certain threshold, its effects tend to saturate or even
weaken. Finally, regional heterogeneity significantly influences the
integration effects. In the eastern region, advanced policies and
cutting-edge technologies enable integration to have the most
significant impact on GT. In contrast, the central region faces
challenges from resource allocation inefficiencies, the western
region struggles with lagging economic development and limited
resources, and the northeastern region is hindered by a high reliance
on traditional industries. These factors collectively constrain the
enhancement of the facilitating effects of integration in
these regions.
7.2 Policy implications
Significant disparities in economic development, industrial
structure, resource endowment, and policy environments among
provinces have led to varying conditions for GT in their LI. To foster
the integration of GF and the LI and accelerate the GT across
provinces, we propose the following targeted policy
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Dai et al. 10.3389/fenvs.2025.1556580
recommendations based on the study’sfindings and regional
characteristics.
Eastern provinces have achieved a high level of coordination
between GF and the LI; however, the diminishing marginal effects
are becoming evident, necessitating a focus on advanced resource
integration and innovation-driven strategies. First, the study
recommends an advance innovation in green financial products.
Developed provinces such as Zhejiang and Guangdong can
introduce green credit securitization instruments tailored to the
LI, such as carbon-neutral bonds or green logistics special-purpose
bonds. This will facilitate the promotion and application of green
technologies. Second, digitalization and intelligent technology
should be further empowered. Provinces like Shanghai and
Jiangsu can expand the adoption of smart logistics technologies,
including AI-based dispatching systems and blockchain-based
traceability solutions. This will optimize logistics routes and
enhance operational efficiency. Lastly, the study suggests that the
role of policy as a demonstration tool should be strengthened. For
instance, leveraging the collaborative development of the Beijing-
Tianjin-Hebei region as a model, cross-regional mechanisms for GF
and logistics cooperation can be established, to encourage local
governments to share experiences in green projects and amplify the
spillover effects of effective policies.
Central provinces exhibit steady development but require
improvements in resource allocation efficiency, necessitating
more precise policy support and wider adoption of green
financial tools. To this end, this paper proposes an acceleration
on the inclusiveness of GF. Provinces such as Hunan and Henan
should enhance green credit support for small and medium-sized
logistics enterprises, particularly through low-interest loans or
subsidies for the purchase of new energy logistics vehicles and
energy-efficient warehousing equipment. Additionally, the
development of green logistics infrastructure is crucial. Drawing
on the experiences of transportation hubs like Wuhan, investment in
green freight corridors should be increased, along with the creation
of comprehensive logistics parks to improve regional resource
integration. Furthermore, fostering green logistics pilot
enterprises is essential. In provinces like Jiangxi and Anhui,
eligible logistics companies could be selected for GT pilot
programs, supported by financial incentives and tax reductions to
promote the adoption of green logistics models.
Western provinces, characterized by a lower integration level,
should prioritize infrastructure development and leverage policy
support and technological advancements to enhance GT efficiency.
Greater policy-driven financial support is essential, with the central
government allocating dedicated green development funds to
underdeveloped regions like Guizhou and Yunnan for the
procurement and maintenance of new energy logistics vehicles
and clean energy warehousing equipment. Additionally, directing
financial resources toward green projects is crucial. Policy-based
financial institutions, such as the China Development Bank, can
establish specialized green funds to support the development of
green logistics infrastructure in provinces like Gansu and Qinghai.
Promoting clean energy technologies should be a key priority, with
Sichuan and Chongqing serving as pilot regions for the adoption of
new energy solutions, particularly in electric logistics vehicles and
clean energy supply stations, to facilitate the localization of green
technologies. Lastly, fostering regional industrial collaboration is
vital. Establishing an interprovincial LI cooperation platform in
western China could help integrate dispersed resources, enhance
economies of scale, and advance GT levels.
The northeastern provinces face challenges in GT due to
sluggish economic growth and a high proportion of traditional
industries. To address this, the focus should be on advancing the
application of green financial tools and transforming the traditional
LI. It is recommended to promote the transformation and upgrading
of old industrial bases. In northeastern provinces like Liaoning and
Jilin, more support should be provided for the technological
renovation of traditional logistics enterprises, with a focus on
promoting new energy freight vehicles, energy-efficient loading
and unloading equipment, and other green technologies.
Additionally, GF service platforms should be established. Cities
like Harbin can set up GF information service platforms to
connect logistics enterprises with green financial products,
enhancing the accessibility of GF. Attracting external investment
and fostering technological cooperation is also crucial. Domestic and
international green technology companies should be encouraged to
collaborate with logistics enterprises in the northeastern region, to
accelerate GT through technology transfer and localized innovation.
Lastly, nurturing emerging green LI should be prioritized.
Policymakers should combine the advantages of agricultural
resources in Heilongjiang, explore logistics models for green
supply chain and smart agriculture, and promote the
construction of a green logistics system for agricultural products.
7.3 Limitations and future recommendations
Although this study reveals the dynamic impact mechanism of the
integration of GF and the LI, there are still several limitations that
should not be overlooked. First, some of the indicators in Tables 4,5
primarily rely on domestic data sources, such as the National Bureau
of Statistics and industry-specific survey databases. The availability of
these indicators on a global scale are limited, especially in
international databases like those of the World Bank. This restricts
the applicability of the research methods in an international context.
Secondly, while the threshold effect model used in this study is suitable
for exploring nonlinear relationships, its ability to address the
complex endogeneity issues between variables is limited. Although
robustness checks have been conducted to mitigate potential biases
from endogeneity, further optimization of model selection is needed
in the future, possibly by integrating causal inference methods to
enhance the credibility of the conclusions. Lastly, the impact
mechanism of the integration of the two industries on the GT of
the LI involves multiple factors, however, this study mainly focuses on
direct effects. There is insufficient consideration of indirect influences,
such as technological innovation, energy efficiency, and industrial
structure, which lead to an incomplete explanation of the underlying
mechanisms.
Based on the aforementioned limitations, future research can
deepen and improve this study from the following directions. First,
further enhance the applicability of the proposed method on a global
scale. Specifically, adjustments to the indicator system and model for
different countries or regions should be explored to improve the
method’s universality. This will help reveal both the commonalities
and differences in the integration of GF and the LI across various
Frontiers in Environmental Science frontiersin.org16
Dai et al. 10.3389/fenvs.2025.1556580
economies. Second, at the model level, it is recommended to
introduce more advanced methods such as dynamic panel
models, causal inference models (e.g., regression discontinuity,
difference-in-differences), to explore the causal mechanisms
between the integration of the two industries and the GT of the
LI, while addressing potential endogeneity issues. Additionally,
employing methods such as multilevel models and quantile
regression can facilitate a more detailed analysis of the
heterogeneous impacts across different development stages and
regions. Third, the indirect effects of GF on the GT of the LI
should be explored. This includes focusing on the mediating
effect of technological innovation and analyzing how GF
enhances the greening of logistics through investments in
research and development and the diffusion of technology. The
study should also examine the synergistic effect of policies and
market mechanisms, uncovering the combined impact of GF
policies and market-based mechanisms such as carbon trading.
Finally, attention should be given to the dynamic adjustment of
enterprise behavior and decision-making, analyzing how
companies’low-carbon actions, incentivized by GF, contribute to
the GT. By adopting multiple perspectives, future research will
further illuminate the complex mechanisms between GF and the
GT of the LI, providing scientific evidence for policy-making and
industry practice.
Data availability statement
The raw data supporting the conclusions of this article will be
made available by the authors, without undue reservation.
Author contributions
LD: Methodology, Validation, Formal Analysis, Writing –review
and editing. DT: Conceptualization, Formal Analysis, Project
administration, Supervision, Writing –review and editing. EA:
Visualization, Writing –review and editing. MZ: Formal Analysis,
Software, Validation, Writing –review and editing. HZ: Formal
Analysis, Software, Validation, Writing –review and editing. YK:
Conceptualization, Formal Analysis, Data curation, Investigation,
Methodology, Validation, Writing –original draft. LZ: Data
curation, Formal Analysis, Investigation, Writing –review and editing.
Funding
The author(s) declare that no financial support was received for
the research and/or publication of this article.
Conflict of interest
Authors MZ and HZ were employed by Jiangsu Trendy
Information Technology Co., Ltd.
The remaining authors declare that the research was conducted
in the absence of any commercial or financial relationships that
could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the
creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
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