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Published: 3 May 2025
Citation: Lu, G.; Li, B. Artificial
Intelligence and Green Collaborative
Innovation: An Empirical
Investigation Based on a
High-Dimensional Fixed Effects
Model. Sustainability 2025,17, 4141.
https://doi.org/10.3390/
su17094141
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Article
Artificial Intelligence and Green Collaborative Innovation: An
Empirical Investigation Based on a High-Dimensional Fixed
Effects Model
Guanyan Lu * and Bingxiang Li
School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China
*Correspondence: 1210511010@stu.xaut.edu.cn
Abstract: This study focuses on the intrinsic mechanisms and sustainable value of artificial
intelligence (AI)-driven green collaborative innovation in enterprises amid the global
green low-carbon transition, revealing new pathways for digital technology-enabled green
development. Based on the data of China’s A-share listed companies jointly applying for
green patents with other entities from 2010 to 2023, this study used a high-dimensional
fixed effect model to empirically find that artificial intelligence significantly promotes green
collaborative innovation. This promoting effect proved more pronounced in the case of
high macroeconomic uncertainty, large enterprises and SOEs. A mechanism test revealed
that artificial intelligence drives green collaborative innovation primarily by reducing
transaction costs and optimizing the labor structure. A moderating effect analysis showed
that green investor entry and CEO openness can strengthen the facilitating effect of artificial
intelligence on green collaborative innovation. In addition, the facilitating effect of artificial
intelligence on green collaborative innovation helps companies reduce carbon emissions
and improve ESG performance, driving the transformation of business ecosystems toward
environmental sustainability. From a technology–organization–environment co-evolution
perspective, this research clarifies the micro-level operational chain of AI-enabled green
innovation, providing theoretical support for developing countries to achieve leapfrog
low-carbon transitions through digital technologies. Practically, it offers actionable insights
for advancing AI-enabled green industries, constructing collaborative green innovation
ecosystems, and supporting the realization of the United Nations Sustainable Development
Goals (SDGs).
Keywords: artificial intelligence; collaborative innovation; green innovation; transaction
costs; labor force structure
1. Introduction
Against the background of accelerating global climate governance and green eco-
nomic transformation, green collaborative innovation has emerged as a key pathway for
enterprises to crack the dilemma of low-carbon development and achieve the Sustainable
Development Goals [
1
]. Halfway through the United Nations 2030 Agenda for Sustainable
Development, global greenhouse gas emissions continue to rise. This trend is in significant
conflict with the IPCC (Intergovernmental Panel on Climate Change) Sixth Assessment
Report’s goal of temperature control—if the global temperature rise is to be controlled
within 1.5 degrees Celsius, a 43% reduction in emissions is required by 2030, or else we
will face the risk of an “irreversible climate tipping point”. As the world’s largest carbon
Sustainability 2025,17, 4141 https://doi.org/10.3390/su17094141
Sustainability 2025,17, 4141 2 of 38
emitter, China has clearly put forward the strategic goal of “promoting Chinese-style mod-
ernization through green and low-carbon development” and emphasized breaking through
the bottleneck of energy and industrial transformation through technological synergy [
2
].
This is not only about national carbon neutrality commitments, but also a key support
for the global sustainable development agenda. However, the existing research predomi-
nantly examines the enabling effects of conventional digital technologies such as big data
and blockchain in technological collaboration [
3
,
4
] while overlooking the transformative
potential of artificial intelligence (AI)—a more complex general-purpose technology—in
reshaping innovation ecosystems [
5
,
6
]. This research gap directly restricts the application
efficiency of technological leverage in sustainable development, making it difficult for
green collaborative innovation to break through the efficiency bottleneck.
Corporate green innovation faces dual challenges. First, the dynamic restructuring
of global green low-carbon industry value chains has intensified technological diffusion
barriers and core patent monopolies, driving up R&D costs and constraining independent
innovation efficiency [7]. AI technologies, leveraging intelligent data analytics and patent
semantic mining algorithms, can effectively identify technological complementarities and
reduce information search costs, thereby breaking the “low-end lock-in” dilemma in sup-
ply chain restructuring [
8
,
9
]. Second, insufficient inter-organizational knowledge sharing,
frequent transaction frictions, and resource misallocation undermine collaborative innova-
tion effectiveness [
10
]. AI-driven knowledge graphs and smart contract technologies can
mitigate transaction frictions caused by trust deficits and incomplete contracts through
standardized knowledge interaction protocols and automated execution of collaboration
terms [
2
,
11
,
12
]. The traditional closed innovation model makes it difficult to meet the
ecological needs of open innovation. In this context, as an important paradigm of open
innovation, green collaborative innovation urgently needs to use AI technology to inte-
grate multiple subject resources, disperse technical risks and cost pressure, and then break
through the bottleneck of enterprise green transformation [13,14].
The rapid advancement of AI presents new opportunities for reconstructing green
collaborative innovation networks [
5
]. As a frontier technology in the digital economy, AI
is reshaping corporate innovation ecosystems through smart data analytics (e.g., emission
hotspot identification), process automation optimization (e.g., carbon footprint tracking
systems), and intelligent resource allocation (e.g., cross-organizational computing power-
sharing platforms) [
8
,
15
]. The existing studies focus more on the green innovation effect of
digital transformation. They demonstrate that digital technologies facilitate collaborative
innovation by reducing information asymmetry and enabling cross-domain knowledge
flows [
2
,
16
]. However, the existing literature exhibits two limitations. First, most studies
focus on AI’s general innovation effects [
17
,
18
] while neglecting its technological adaptabil-
ity in green collaboration scenarios. As a result, its sustainable development value has not
been fully released. Second, given that green collaborative innovation involves complex
interactions between heterogeneous knowledge-based actors, can AI truly optimize synergy
efficiency by restructuring transaction cost frameworks and reconfiguring human capital
allocation to overcome traditional collaboration dilemmas? Current theories inadequately
address this, particularly regarding the unverified mediating pathways of transaction costs
and the labor force structure.
Based on this, we used the joint green patent data of China’s A-share listed companies
from 2010 to 2023 to systematically investigate the impact and mechanism of artificial
intelligence on green collaborative innovation. The results show that (1) artificial intelli-
gence has a promoting effect on green collaborative innovation. This conclusion is still
valid after the endogeneity problem is alleviated and the robustness test is conducted.
(2) The nonlinea
r relationship test suggests that threshold effects or diminishing returns of
Sustainability 2025,17, 4141 3 of 38
artificial intelligence on corporate green collaborative innovation have not yet occurred.
(3) The mechanism test shows that the promoting effect of artificial intelligence on green
collaborative innovation can be realized by reducing transaction costs and optimizing the
labor structure. (4) The moderating effect analysis shows that green investor entry and CEO
openness can strengthen the promoting effect of artificial intelligence on green collaborative
innovation. This promoting effect is more pronounced in regions with higher marketization
and stronger legal regulations. (5) The heterogeneity tests indicate that the positive impact
of artificial intelligence technology application on green collaborative innovation is more
significant in the case of high macroeconomic uncertainty, large-scale enterprises, and
SOEs. (6) In addition, the promoting effect of artificial intelligence on green collaborative
innovation helps firms reduce carbon emissions and improve ESG performance, promoting
sustainable business practices. We further revealed that this promoting effect exhibits
heterogeneity with different firm sizes, ownership types, sectors to which firms belong,
and the stringency of regional environmental penalties.
Compared with the existing research, the marginal contribution of this study is as
follows. First, unlike the existing research focusing on production efficiency [
19
], organiza-
tional performance [
20
], energy transition [
15
], specific emissions [
21
], and other perspec-
tives, the micro impact of artificial intelligence technology is examined. This study explores
the impact of artificial intelligence adoption from the perspective of green collaborative
innovation for the first time and examines the heterogeneous impact of macroeconomic un-
certainty and micro-characteristics of enterprises, which expands the research boundaries
of the existing literature on the application of artificial intelligence technology. Second, it
suggests viable options for promoting green collaborative innovation. It is different from
previous studies that focus on the impact of green bonds [
1
], digital dynamic capabili-
ties [
13
], supply chain networks [
22
], and other factors on green collaborative innovation.
From the perspective of external technology, we examine how AI can drive green collabora-
tive innovation by reducing transaction costs and optimizing the labor structure, opening
the “black box” of how AI affects corporate green collaborative innovation and, thus,
broadening the scope of the existing research. Third, this study enriches the literature on
green innovation by studying how external capital incentives (green investors) and internal
cognitive drivers (CEO openness) regulate the impact of artificial intelligence on green
collaborative innovation. The existing studies have paid more attention to the moderating
effect of factors such as digital adaptability [
18
] and industry concentration [
23
]. In contrast,
we identified the moderating effect of external capital incentives (green investors) and
internal cognitive drive (CEO openness), which provides new insights into the boundary
conditions of AI technology application driving corporate green collaborative innova-
tion and enriches the scenario mechanism research on the endogenous driving factors of
green collaborative innovation. From a technology–organization–environment co-evolution
perspective, this research clarifies the micro-level operational chain of AI-enabled green
innovation, providing theoretical support for developing countries to achieve leapfrog
low-carbon transitions through digital technologies. Practically, it offers actionable insights
for advancing AI-enabled green industries, constructing collaborative green innovation
ecosystems, and supporting the realization of the United Nations Sustainable Development
Goals (SDGs).
2. Literature Review
2.1. Research on Artificial Intelligence
Artificial intelligence (AI) is a branch of computer science that aims to develop systems
capable of simulating, extending, and expanding human intelligence [
5
]. Its core objective
is to enable machines to make rational decisions by perceiving environments, processing
Sustainability 2025,17, 4141 4 of 38
information, learning from experience, and executing goal-oriented behaviors. With the
rapid advancement of AI technologies, an increasing number of enterprises are leveraging
AI’s capabilities in deep learning, multi-source data integration, and intelligent decision-
making to drive innovation.
The existing research primarily focuses on how AI impacts innovation in the manu-
facturing and industrial sectors. For instance, Rammer et al. [
9
] empirically demonstrated,
based on data from German industrial firms, that AI technologies significantly promote
innovation in product and production process domains. Wang et al. [
24
] found that AI
can replace labor while driving both general and sustainable enterprise innovation, with
absorptive capacity acting as a mediating factor. Tian et al. [
25
] confirmed through a multi-
period difference-in-differences model that the deep integration of AI in manufacturing
effectively reduces innovation costs, providing critical evidence for AI-driven process
innovation. Additionally, Fu et al. [
26
] revealed in a study of overseas-listed Chinese
digital enterprises that AI substantially enhances digital enterprise innovation, mediated
by absorptive capacity. However, idle resources negatively moderate this relationship,
weakening the link between absorptive capacity and corporate innovation. Song et al. [
14
],
analyzing China’s A-share listed companies, discovered that AI can advance corporate
green innovation by strengthening digital finance, thereby opening new pathways for
research on AI-enabled ecological transformation. Zhong et al. [
27
] further demonstrated
that AI adoption promotes corporate green innovation, with financing constraints and
agency costs serving as mediating mechanisms.
2.2. Research on Green Collaborative Innovation
Green collaborative innovation is a collaborative mode for enterprises to carry out
green innovation with external partners such as the government, suppliers, customers,
research institutions, and intermediaries. It means that enterprises jointly carry out green
innovation activities with external partners across organizational boundaries and propose
new products, processes, services, or management methods to solve environmental prob-
lems so as to achieve a common green value proposition [
22
,
28
]. Compared with general
collaborative innovation, green collaborative innovation has the following characteristics:
on the one hand, it achieves an effective balance between economic development and envi-
ronmental protection through coordination and complementarity among partners and joint
green innovation practices [
29
,
30
]. On the other hand, driven by external partners, green
collaborative innovation highlights corporate social responsibility and green image [1].
The literature exploring the drivers of green collaborative innovation is still in its
development stage. The existing research mainly focuses on the following aspects. Firstly,
from the perspective of innovation resource allocation, green collaborative innovation is
characterized by a long cycle and a high risk coefficient. It must rely on continuous financial
support. Maintaining sound financial liquidity can reduce the risk of innovation inter-
ruption, ensure the smooth development of green collaborative innovation projects, and
strengthen the management’s willingness to invest in cross-organizational collaboration
projects [
1
]. Secondly, from the perspective of the stability of innovation alliances, establish-
ing a reasonable proportion of benefit sharing and cost sharing among the main parties
of collaborative innovation and setting a moderate penalty for collaborative innovation
can inhibit opportunistic behavior, thus reducing the cost of collaborative governance [
31
].
Finally, from the perspective of innovation capability, enterprises’ digital capabilities, such
as digital infrastructure capability, digital perception capability, digital operation capability,
and digital collaboration capability, can all have a positive impact on green collaborative
innovation [13].
Sustainability 2025,17, 4141 5 of 38
A review of the existing literature reveals two critical points: (1) research on the impact
of artificial intelligence (AI) on green collaborative innovation in enterprises remains limited.
No studies have specifically addressed the relationship between AI technology applications
and green collaborative innovation practices in the new era. A theoretical gap persists at the
intersection of AI-driven technological innovation and corporate sustainable development
practices. (2) The mechanisms and boundary conditions through which AI influences green
collaborative innovation remain unclear. In the AI era, understanding how to harness its
potential to advance corporate green collaborative innovation represents both an urgent
research question and a significant opportunity for further exploration.
3. Research Hypotheses
3.1. Artificial Intelligence and Green Collaborative Innovation
As an innovation paradigm integrating “synergy” and “green”, the realization of
green collaborative innovation depends on multi-agent knowledge integration and green
value co-creation [
32
]. Hence, as the core technology of digital transformation, artificial
intelligence can become the key engine to promote green collaborative innovation by
optimizing collaborative network structures, strengthening the efficiency of knowledge
integration, and driving the goal of green innovation.
From the dimension of “synergy”, artificial intelligence improves synergy efficiency
by reconstructing innovation networks and mechanisms. First of all, AI-driven data
mining and analytics capabilities can break through the geographical and organizational
boundaries of traditional innovation networks. For example, a knowledge graph technology
based on a machine learning algorithm can dynamically match heterogeneous knowledge
resources and promote the connection of innovation subjects across regions and fields [
9
,
22
].
Secondly, artificial intelligence reduces information asymmetry and goal conflict in industry–
university–research collaboration by standardizing data interfaces and intelligent decision-
making systems. For example, natural language processing technology can parse multi-
agent unstructured knowledge, generate a standardized knowledge base, and improve the
efficiency of cross-system knowledge integration [
18
]. Finally, AI-enabled smart contracts
and blockchain consensus mechanisms can automatically execute collaborative agreements
and trace data trajectories, inhibit free-riding and opportunistic behaviors in the process
of green collaborative innovation [
4
], strengthen the trust mechanism, and, thus, stabilize
collaborative relationships.
From the dimension of “green”, artificial intelligence drives green value creation by
accurately identifying user needs and promoting the integration of supply-side technolo-
gies. On the one hand, AI’s user portrait and behavior prediction functions can capture
dynamic and personalized green needs in real time and guide collaborative innovation
to focus on low-carbon product development and scene-based solutions [
18
,
33
]. On the
other hand, artificial intelligence can accelerate green technology research and develop-
ment and achievement transformation [
11
,
34
]. For example, deep learning models can
simulate complex ecological and environmental systems, thus optimizing green technology
paths. Furthermore, the intelligent matching platform promotes the efficient diffusion of
green patents in the industrial chain and then promotes the deep integration of the green
innovation chain and the industrial chain.
To sum up, artificial intelligence forms a multi-dimensional drive for enterprise green
collaborative innovation by improving the resilience of the collaborative network, the
efficiency of knowledge integration, and the targeting of green innovation. Based on the
above analysis, this study proposes the following research hypothesis:
H1. Artificial intelligence can significantly promote corporate green collaborative innovation.
Sustainability 2025,17, 4141 6 of 38
3.2. Artificial Intelligence, Transaction Costs, and Green Collaborative Innovation
The transaction cost theory reveals that the efficiency of inter-organizational collab-
orative innovation is subject to multiple transaction costs caused by information asym-
metry [
35
]. In the field of green innovation, the positive externality of technology R&D
and the non-excludability of environmental benefits intensify the interest game among
collaborative subjects. Therefore, the traditional governance model faces a double dilemma.
That is, it not only needs to overcome the explicit cost of knowledge search and contract
enforcement, but also needs to resolve the implicit loss caused by collaboration friction.
By reconstructing information interaction and contract execution mechanisms, artificial
intelligence significantly reduces transaction costs and becomes a key path to drive green
collaborative innovation.
In terms of search and matching costs, relying on machine learning and big data
analysis, artificial intelligence can efficiently match the supply and demand sides of green
technology. For example, an intelligent platform based on natural language processing
can analyze the global green patent database, the technology trading platform, and the
scientific research literature resources in real time and then precisely locate complementary
knowledge resources so as to achieve the accurate matching of cross-regional innovation
subjects. This undoubtedly reduces the time and economic cost for enterprises to search for
partners [
36
]. Meanwhile, this data-driven matching mechanism not only transforms the
traditional linear search path into a network interaction pattern, but also breaks the geo-
graphical boundary constraints through virtual collaboration networks. The reorganization
of innovation resources under the dynamic co-opetition mechanism effectively reduces the
risk of path dependence of enterprises on specific technology tracks and provides a flexible
organizational foundation for green technology portfolio innovation.
In terms of contract execution costs, artificial intelligence can optimize contract gov-
ernance through smart contracts and algorithm supervision mechanisms. Specifically,
in the process of green collaborative innovation, the distributed ledger system enabled
by blockchain can encode key performance elements such as research and development
progress and capital flow into verifiable smart contract terms, forming a visual supervi-
sion system for the whole process [
12
]. Furthermore, the risk prediction model based on
machine learning analyzes historical performance data and establishes a risk assessment
matrix with multidimensional characteristics to improve the identification efficiency of
opportunistic behaviors. This technological governance paradigm not only reduces the
cost of traditional supervision, but also reduces the frequency of contract renegotiation by
establishing a credible execution environment.
In terms of collaborative friction costs, artificial intelligence technology reshapes the
distribution mechanism of knowledge spillovers. Blockchain technology ensures the confir-
mation and traceability of data elements [
37
], while a federated learning framework realizes
knowledge sharing under privacy protection. By constructing an incentive-compatible
mechanism that transforms from Nash equilibrium to Pareto optimum, this “competitive
cooperation” mode enables all parties to share the network benefits brought by green
technology innovation on the premise of maintaining the sovereignty of core data and
significantly reduces the institutional transaction costs caused by collaboration friction.
Therefore, artificial intelligence can break the transaction cost barrier of green collab-
orative innovation and enhance the stability of multi-subject collaboration by reducing
the transaction costs of information search, contract execution, and collaboration friction.
Based on the above analysis, this study proposes the following research hypothesis:
H2. Artificial intelligence reduces transaction costs, which promotes corporate green
collaborative innovation.
Sustainability 2025,17, 4141 7 of 38
3.3. Artificial Intelligence, Labor Structure, and Green Collaborative Innovation
As the core driving force of green collaborative innovation, knowledge creation is, in
essence, a dynamic process of deep interactions between interdisciplinary
R&D subjects [38,39]
.
Under the constraints of the dual carbon target, the radical innovation of green technology
presents a significant feature of multidisciplinary coupling, which requires the integration
of heterogeneous knowledge systems in environmental science, material engineering, and
digital technology. This paradigm of knowledge integration puts forward higher requirements
for the heterogeneous allocation and dynamic synergy ability of enterprise human capital.
However, there are clear structural imbalances in the current global labor market. Data from
LinkedIn’s Global Green Skills Report 2024 show that the demand for green-skilled jobs
grew at 11.6% from 2023 to 2024, significantly outpacing the 5.6% growth rate in the supply
of skilled labor, resulting in a six-percentage-point gap between labor demand and supply.
This green skill mismatch not only makes it difficult for enterprises to build interdisciplinary
innovation teams, but also highlights that the traditional human capital cultivation model has
been unable to match the dynamic needs of green innovation.
In this context, artificial intelligence establishes a novel paradigm for resolving
structural contradictions through the reconstruction of labor factor allocation mecha-
nisms while simultaneously creating pathways for enterprises to overcome bottlenecks
in green collaborative innovation. Specifically, three mechanisms demonstrate this
technological empowerment.
First of all, an intelligent matching system based on a deep learning algorithm can
empower enterprises to break through geographical and industrial barriers, accurately
locate innovative talents with interdisciplinary backgrounds, and, thus, reduce the time
expenditure of traditional talent search. Second, by using an adaptive learning system,
enterprises can analyze the gaps in employees’ knowledge graphs in real time and con-
struct personalized training programs, thus shortening the skill iteration cycle of green
technical talents [
40
]. Third, by employing the semantic analysis and knowledge graph
technology of the intelligent collaboration platform, enterprises can automatically identify
the complementary knowledge domains of cross-institutional R&D personnel, increase
the communication between enterprise R&D personnel and researchers in universities
and research institutions, and, thus, significantly improve the efficiency of cross-boundary
knowledge integration. For example, through this technology, Ningde Times has realized a
targeted docking with the research team of Tsinghua University’s School of Materials, which
has improved the knowledge conversion efficiency of the lithium recycling technology by
22% (according to the 2023 CSR report). This technology empowerment induces the human
capital structure of enterprises to exhibit the characteristics of dynamic optimization, which
not only alleviates the binding constraints of the shortage of green technology talents, but
also reduces the organizational friction costs in collaborative innovation. Furthermore, this
restructuring of human capital establishes a novel knowledge production function. By
enhancing comprehension of critical challenges and core technologies, it drives collabo-
rative innovation processes while creating a sustaining mechanism for environmentally
sustainable innovation in enterprises. Based on the above analysis, this study proposes the
following research hypothesis:
H3. Artificial intelligence optimizes labor structure, which promotes corporate green
collaborative innovation.
Sustainability 2025,17, 4141 8 of 38
4. Methods
4.1. Data and Sample
We chose China’s A-share listed enterprises from 2010 to 2023 as the research sample.
The data on firm artificial intelligence technology patents came from the patent database of
the China National Intellectual Property Administration (CNIPA). The green collaborative
innovation data came from the China Research Data Service Platform (CNRDS). The annual
report of listed companies came from the China Securities Information Disclosure Platform
(CNINFO). The basic information and financial data of companies were obtained from the
CSMAR database.
In addition, in order to ensure the quality of the data, we carried out the following
processing. (1) Excluding samples from the financial industry. Given that the financial
industry takes capital intermediary business as the core, its unique prudential regulatory
framework and differentiated accounting standards (such as financial asset classification
and measurement rules) lead to a financial data structure that is significantly different
from that of non-financial enterprises, which may lead to cross-industry comparability
problems. (2) Excluding enterprises in ST and *ST status. According to the regulatory
regulations of the China Securities Regulatory Commission, listed companies subject to
Special Treatment (ST) and Delisting Risk Warning (*ST) generally have the characteristics
of questionable ability to continue operations and intensified financial distress. Abnormal
financial indicators of such companies may interfere with the validity of empirical results
and can effectively avoid the biased impact of extreme values on parameter estimates.
(3) Eliminating samples
with serious data missing, so as to avoid the impact on the validity
of the estimation results. (4) Winsorizing continuous variables at the levels of 1% and 99%
to minimize the influence of extreme values. Following these procedures, we retained
29,128 valid observations for analysis.
4.2. Variable Definitions
4.2.1. Dependent Variable: Green Collaborative Innovation (GCI)
This study refers to Lian et al. [
1
] and used the total number of green patents jointly ap-
plied by listed companies and other entities such as enterprises, universities, and scientific
research institutions to measure the green collaborative innovation of enterprises.
4.2.2. Independent Variable: Artificial Intelligence (AI)
The existing research mainly uses industrial robot data and the number of AI-related
word frequencies in enterprise annual reports to measure the application of artificial
intelligence in enterprises. The former typically utilizes datasets from the International
Federation of Robotics (IFR) and the China Robotics Industry Alliance (CRIA) to analyze
AI’s economic impacts at the national, regional, and industry levels. However, the above
data fail to disclose the information on the use of robots at the enterprise level, and the
macro-level data are difficult to accurately reflect the heterogeneity characteristics at the
micro-enterprise level. To address this limitation, some scholars have tried to use the
data of enterprises’ imported robots in the database of the Chinese Customs to measure
the adoption of artificial intelligence at the enterprise level [
15
,
41
]. Nevertheless, this
measurement method may not fully reflect the comprehensive nature of AI technologies,
given that artificial intelligence includes not only robots, but also large amounts of data,
algorithms, and mathematical models. The latter involves text analysis of listed companies’
annual reports by quantifying the artificial intelligence adoption degree through keyword
frequency counts [
14
,
17
,
42
–
44
]. While facilitating large-sample empirical analysis, this
method suffers from potential strategic disclosure biases, as enterprises might artificially
inflate AI-related terminologies to align with national policy trends or investor expectations.
Sustainability 2025,17, 4141 9 of 38
To overcome these measurement limitations, referring to Choi et al. [
45
], Jin and
Yu [
46
], and Cao et al. [
47
], this study used the number of artificial intelligence technology
patents applied by firms in the patent database of the China National Intellectual Property
Administration (CNIPA) as the measurement index of the artificial intelligence application
degree. Specifically, we measured the firm artificial intelligence adoption degree using a
natural logarithm: artificial intelligence patent applications + 1. The advantages of this
measurement method are as follows: first, the International Patent Classification number
(IPC) in the patent data directly describes the technical field in which R&D activities
belong, which can more accurately identify the innovation output in the field of artificial
intelligence. Second, the title and abstract of AI-related technology patents must be strictly
examined by relevant personnel to enhance the objectivity and reliability of the data.
At the same time, in order to further verify result robustness, we constructed three
alternative indicators for cross-validation: (1) industrial robot penetration rates calculated
through the Bartik instrumental variable methodology; (2) artificial intelligence keyword
density in full-text firm annual reports; and (3) artificial intelligence terminology frequency
in the MD&A section of firm annual reports. This multi-dimensional validation framework
effectively ensures the consistency and reliability of the research conclusions.
4.2.3. Control Variables
In the process of empirical analysis, this study controls for the firm-level, industry-
level, and macro-level factors that may have a significant impact on green collaborative
innovation, including Size, Lev, Age, Roa, Growth, Rdintensity, Pollution, HHI, and GDP.
The variables’ definitions are shown in Table 1.
Table 1. Variable definitions.
Type Variable Name Symbol Measurement
Dependent variable Green collaborative innovation GCI
The natural logarithm of the number of green
patents jointly filed by listed companies and
other entities + 1
Independent variable Artificial intelligence AI The natural logarithm of the number of
artificial intelligence patent applications + 1
Control variables
Firm size Size The natural logarithm of the total
corporate assets
Leverage ratio Lev Total liabilities/total assets
Firm age Age The natural logarithm of the number of
firm ages
Firm profitability Roa Net profit/total assets
Firm growth Growth Operation revenue growth rate
R&D intensity Rdintensity R&D investment/operation revenue
Key pollution monitoring unit Pollution
If the firm is the key pollution monitoring unit,
the value is 1, otherwise it is 0
Industry competition degree HHI
Sum of squared proportions of the total assets
of each listed company relative to the
industry’s total assets
GDP growth rate GDP The GDP growth rate of the city where the
listed company is located
4.3. Empirical Model
In order to investigate the impact of artificial intelligence on green collaborative
innovation, we set the following empirical model:
GCIi,t=α0+α1AIi,t+αControl +λi+µt+εi,t(1)
Sustainability 2025,17, 4141 10 of 38
where i and t represent the firm and the year, respectively; GCI
i,t
represents green collabo-
rative innovation; AI
i,t
represents artificial intelligence; Control means a series of control
variables;
λi
and
µt
are the firm fixed effect and the year fixed effect, respectively; and
εit
is
a random disturbance term. Meanwhile, to control the unobservable industry impact and
year impact, we also clustered standard errors to the industry–year level.
4.4. Descriptive Statistics
Table 2reports the descriptive statistics of the variables. The maximum value of GCI
is 3.2581, and the minimum value is 0, indicating that there is a large gap in the level of
green collaborative innovation among listed firms. AI has a mean of 0.8281 and a standard
deviation of 1.2268. This shows that the degree of artificial intelligence application varies
greatly among firms. In addition, the descriptive statistics of other variables show no
significant differences from the existing studies and are all within the normal range.
Table 2. Descriptive statistics of variables.
Variable N Mean SD Min Max
GCI 29,128 0.1556 0.4743 0.0000 3.2581
AI 29,128 0.8281 1.2268 0.0000 5.5568
Size 29,128 22.2214 1.3646 19.5113 26.6019
Lev 29,128 0.4494 0.2177 0.0536 0.9768
Age 29,128 2.2854 0.8134 0.0000 3.4012
Roa 29,128 0.0325 0.0680 −0.3109 0.1999
Growth 29,128 0.1759 0.4908 −0.6235 3.3170
Rdintensity 29,128 0.0142 0.0300 0.0000 0.1689
Pollution 29,128 0.1725 0.3778 0.0000 1.0000
HHI 29,128 2.0048 0.5470 1.0000 3.2493
GDP 29,128 0.0928 0.0610 −0.1463 0.2599
5. Results
5.1. Baseline Regression
Table 3shows the estimated results of the baseline regression. Column (1) shows
regression results without controlling for any control variables and fixed effects. The
coefficient of AI is significantly positive, indicating that the application of artificial intelli-
gence technology can improve the level of green collaborative innovation in enterprises.
Column (2) adds control variables at the firm level. The coefficient of AI is significantly
positive at the 1% level. This further illustrates that the application of artificial intelligence
technology promotes green collaborative innovation in enterprises. Columns (3) and (4)
gradually increase the year and firm fixed effects, and the coefficient of AI is still signif-
icantly positive. From the statistical significance results, in all regressions, the impact of
AI on corporate green collaborative innovation is always positive at the significance level
of 1%. This shows that the higher the application degree of AI technology in enterprises,
the higher the level of green collaborative innovation. From the perspective of economic
significance, the coefficient of AI in Column (4) shows that when other conditions remain
unchanged, every unit increase in the application degree of AI technology will increase
the level of enterprise green collaborative innovation by about 11.93%. This indicates
that the application of AI technology has a significant positive impact on enterprise green
collaborative innovation. First, from the horizontal comparison across the industry, this
11.93% enhancement substantially exceeds the average innovation effect (by approximately
8.2%, according to the China Intelligent Manufacturing Development Report 2023) of dig-
ital transformation in manufacturing. In particular, considering that green collaborative
innovation involves cross-departmental and cross-industrial chain technology integration,
Sustainability 2025,17, 4141 11 of 38
the systemic efficiency gains enabled by AI carry greater strategic value. Second, it makes
dynamic inference based on the practice of coordinated Beijing–Tianjin–Hebei development.
According to the Beijing–Tianjin–Hebei Regional Development Index (2023), the annual
growth rate of the Beijing–Tianjin–Hebei collaborative index is 12.9%. If the application
degree of artificial intelligence technology in Beijing–Tianjin–Hebei firms increases by one
unit, the annual growth rate of the expected index will increase from 12.9% to 26.28%
((1 + 0.1193) ×(1 + 0.129) −1)
. That is, the average annual growth rate will have increased
by 13.38 percentage points. This acceleration effect could enable the Beijing–Tianjin–Hebei
region to achieve its 14
th
Five-Year Plan green technology R&D targets 2–3 years ahead of
schedule, strongly validating the practical policy value of AI empowerment. In summary,
both statistically and economically, artificial intelligence demonstrably promotes corporate
green collaborative innovation. Hypothesis H1 is supported.
Table 3. Results of baseline regression.
Variable (1) (2) (3) (4)
GCI GCI GCI GCI
AI 0.1242 *** 0.1083 *** 0.1087 *** 0.1193 ***
(0.0089) (0.0078) (0.0078) (0.0065)
Size 0.0745 *** 0.0749 *** 0.0335 ***
(0.0052) (0.0050) (0.0053)
Lev 0.0145 0.0078 0.0287
(0.0159) (0.0158) (0.0185)
Age −0.0108 ** −0.0147 *** −0.0272 **
(0.0043) (0.0041) (0.0110)
Roa −0.0491 −0.0582 0.0163
(0.0388) (0.0381) (0.0354)
Growth −0.0107 ** −0.0099 ** 0.0014
(0.0048) (0.0050) (0.0039)
Rdintensity −0.4948 ** −0.5976 *** −0.3051 **
(0.2021) (0.2038) (0.1451)
Pollution 0.0840 *** 0.0818 *** 0.0416 ***
(0.0131) (0.0132) (0.0096)
HHI −0.0126 −0.0101 −0.0117 **
(0.0106) (0.0107) (0.0057)
GDP 0.0185 −0.0206 0.0371
(0.0599) (0.0548) (0.0501)
Constant 0.0527 *** −1.5521 *** −1.5477 *** −0.6226 ***
(0.0035) (0.1081) (0.1045) (0.1180)
Year fixed effect No No Yes Yes
Firm fixed effect No No No Yes
N 29,128 29,128 29,128 29,128
R2_adj 0.1032 0.1571 0.1578 0.4869
Note: ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the industry-year level. The
following tables are the same.
Empirical analysis of control variables reveals that (1) larger enterprises exhibit a
stronger propensity for green collaborative innovation. This trend can be attributed to
their substantial financial capacity to absorb the capital costs associated with green innova-
tion. Moreover, the realization of innovation outcomes requires sufficient organizational
resilience to withstand potential market entrants and creative destruction threats. Even
when large enterprises possess singular resource advantages, cross-sector collaboration not
only facilitates resource complementarity, but also generates economies of scale through
synergistic innovation. (2) The estimated coefficient of firm age shows a significant nega-
tive impact on green collaborative innovation. This could be attributed to mature firms’
culture being more inclined toward maintaining their existing market share, and they
are cautious about high-risk and long-term green collaborative innovation. In contrast,
Sustainability 2025,17, 4141 12 of 38
younger firms typically demonstrate greater risk tolerance and organizational flexibility,
showing stronger receptiveness to open collaboration and cross-domain technology inte-
gration. (3) R&D investment intensity is significantly negatively correlated with corporate
green collaborative innovation. This relationship may stem from resource concentration
patterns in R&D-intensive industries (e.g., pharmaceutical manufacturing and information
technology), where enterprises prioritize internal technological breakthroughs to sustain
competitive barriers. Such closed-loop R&D models potentially divert resources from cross-
organizational collaboration infrastructure development (e.g., supply chain coordination
and industry–academia platforms), thereby impeding the formation of open innovation
networks essential for green collaboration. Furthermore, the decision-making tendency of
management may also have an impact. Firms with high R&D investments may strategically
attach more importance to technological leadership than environmental protection, thus
neglecting green collaborative innovation. (4) Key pollution monitoring units also show a
statistically positive impact on green collaborative innovation. This may stem from the fact
that focus monitoring firms face stricter environmental regulations and must adopt green
innovations to meet standards or face possible penalties. This pushes companies to invest
in green technologies and collaborative innovation projects. Moreover, as a monitoring
unit, the environmental performance of an enterprise directly affects its market reputa-
tion. Through green collaborative innovation, companies can enhance their brand image
and meet the environmental protection needs of consumers and investors, thus gaining
competitive advantages.
5.2. Threshold Effect of Artificial Intelligence on Green Collaborative Innovation
President Xi Jinping has emphasized that while technology serves as a powerful driver
for development, it may also become a source of risks. It is imperative to proactively
judge the regulatory conflicts, social risks, and ethical challenges arising from technological
advancement. This requires maximizing the benefits of rapidly evolving cutting-edge
technologies while mitigating their potential negative impacts. Therefore, we further
investigated the nonlinear impact of artificial intelligence on corporate green collaborative
innovation. We added the quadratic term of artificial intelligence (AI
2
) into Model 1 for
regression analysis, and the results are shown in Table 4. We found that the coefficient of
AI was still significantly positive at the 1% level. However, the regression coefficient of AI
2
was not significant. This shows that there is no nonlinear relationship between artificial
intelligence and enterprise green collaborative innovation.
Potential explanations are as follows. First, the application of artificial intelligence in
green innovation remains at an early stage, with most enterprises having yet to reach the
critical threshold for technological adoption. At present, the marginal utility of artificial
intelligence technology has not yet exhibited diminishing returns, as companies continue to
accumulate benefits through foundational applications without approaching the resource
investment boundaries required for deep technological integration. Second, green collab-
orative innovation involves cross-firm and cross-domain technological convergence. In
this process, artificial intelligence technology primarily acts as an “enabler” rather than a
“dominant driver”. Its value realization depends on synergies with other technologies (e.g.,
IoT, blockchain), so the independent contribution of artificial intelligence technology has
not reached the threshold to trigger nonlinear changes. Third, government departments
continue to encourage enterprises to engage in green collaborative innovation through
carbon neutrality policies and green technology subsidies. These policy-driven benefits
partially offset rising marginal costs of technological investments. For instance, China’s
“Dual Carbon” strategy provides long-term certainty for enterprises, enabling linear growth
in AI-related investments rather than premature saturation. Consequently, threshold effects
Sustainability 2025,17, 4141 13 of 38
or diminishing returns of artificial intelligence on corporate green collaborative innovation
have not yet occurred.
Table 4. Results of the threshold effect.
Variable (1)
GCI
AI 0.1082 ***
(0.0095)
AI20.0034
(0.0024)
Size 0.0334 ***
(0.0053)
Lev 0.0276
(0.0185)
Age −0.0267 **
(0.0110)
Roa 0.0156
(0.0354)
Growth 0.0016
(0.0040)
Rdintensity −0.3290 **
(0.1461)
Pollution 0.0416 ***
(0.0096)
HHI −0.0118 **
(0.0057)
GDP 0.0369
(0.0501)
Constant −0.6175 ***
(0.1181)
Year fixed effect Yes
Firm fixed effect Yes
N 29,128
R2_adj 0.4870
Note: ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the industry-year level.
5.3. Endogeneity Test
5.3.1. Missing Variable Bias Test
Due to the diversity and complexity of factors affecting green collaborative innovation,
it may lead to endogeneity problems caused by missing variables. Therefore, we tested for
missing variable bias and inferred the extent to which missing variables affect the baseline
results. Referring to Altonji et al. [
48
], we used observable variables to measure the degree
of bias in unobservable variables. That is, by controlling a set of finite observable variables,
we calculated the difference ratio of explanatory variable coefficients and assessed the
possibility of bias from missing variables to the baseline results. The greater the ratio value,
the stronger the explanatory power of the observed variables, and the lower the potential
bias resulting from omitted variables.
According to Table 5, the minimum value of the ratio is 24.3622, and the mean value
is 31.7840. This shows that if unobservable variables can cause errors in the baseline
estimation results of this study, their explanatory power should be at least 24.4 times that of
the selected control variables. Given that our baseline regression rigorously controlled for
firm-level and year-level fixed effects, as well as variables related to artificial intelligence
and green collaborative innovation, there is strong reason to believe that the potential
Sustainability 2025,17, 4141 14 of 38
bias caused by unobservable omitted variables is likely minimal. Therefore, the empirical
findings presented earlier are robust.
Table 5. Results of the missing variables bias test.
Finite Set, Control
Variable
Finite Set, Regression
Coefficient
Total Set, Regression
Coefficient Variance Ratio
No control variables
and no fixed effects 0.1242 0.1193 24.3622
Only firm or year
fixed effect, no
control variables
0.1230 0.1193 32.1901
Only control variables
and firm fixed effect 0.1224 0.1193 38.7996
5.3.2. Selection Bias
The adoption of artificial intelligence technology by enterprises may not be random,
but it is affected by human capital, technological level, industry characteristics, data
infrastructure, external policy shocks, and other factors. Therefore, the problem of sample
self-selection bias may exist in empirical research. Hence, we used propensity score
matching (PSM) and the Heckman two-stage model to deal with the selective bias problem.
First, we used the propensity score matching method to overcome self-selection bias.
According to whether the firms had artificial intelligence patent applications, we divided
the samples into a treated group and a control group, and we took the control variables
in Model 1 as the matching criteria using the 1:1 nearest neighbor matching method with
put-back. Figure 1presents the kernel density plots of propensity scores for the treated
and control groups before and after PSM matching. We observed that prior to matching,
the propensity score distributions of the two groups differed substantially. After matching,
however, the curves aligned more closely, with a significantly increased overlap area. This
suggests that the covariates were well-balanced between the treated and control groups
in the post-matching sample. Moreover, the results of the balance test show that the
standardized deviations of the covariates after matching were less than 8%, and the results
of the t-test did not reject the null hypothesis that the coefficients of the treated group
and the control group were not significantly different. This shows that the characteristic
difference between the experimental group and the control group was eliminated to a large
extent, and the matching effect was good. The t-value of the average treatment effect (ATT)
after matching was 34.29, which was significant at the 1% level, indicating that matching
was effective. We used the matched samples to conduct regression analysis on Model 1
again, and the test results of the matched samples are shown in Column (1) of Table 6. The
regression coefficient of AI was significantly positive. The results show that the conclusions
of this study are still robust when the self-selection bias problem is mitigated.
Sustainability 2025, 17, 4141 16 of 40
Hansen J statistic 4.179
Hansen J statistic p-value 0.1237
N 13,254 27,217 27,217 29,128 29,128
R2_adj 0.4270 0.5006 0.8151
Note: * p < 0.10, ** p < 0.05, *** p < 0.01. The values in brackets are standard errors clustered to the
industry-year level.
Figure 1. Kernel density curves pre- and post-PSM matching.
Second, we further used the Heckman two-stage model to control the estimation bias
of the sample selection problem. In the first stage, the probit model was used to estimate
the probability of whether enterprises carry out green collaborative innovation.
In order to ensure that the inverse Mills ratio (IMR) was uncorrelated with the ran-
dom disturbance term of the main regression, it was necessary to introduce exogenous
variables into the first-stage regression model. The existing research has found that exec-
utives with overseas backgrounds have been exposed to stricter environmental regula-
tions (such as EU carbon tariffs and US ESG standards) and have a deeper understanding
of the sustainable development concept. This experience drives companies to incorporate
green collaborative innovation into their strategies to meet international standards and
market demands [49]. However, executives with overseas backgrounds do not have a di-
rect and obvious impact on the application degree of artificial intelligence, so it largely
meets the independence and exclusivity assumptions for exogenous variable selection. To
this end, referring to Li et al. [50], we selected the proportion of executives with an over-
seas background (Overseas) as the exogenous variable, ran the regression together with
the control variables above, and calculated the inverse Mills ratio (IMR).
Column (2) of Table 6 reports the first-stage probit regression results. The regression
coefficient of Overseas was found to be 0.1541, and it was significant at the level of 5%.
This shows that Overseas is highly correlated with whether listed companies conduct
green collaborative innovation, which passes the test of weak instrumental variable and
meets the selection conditions of exogenous variables. In the second stage, we introduced
the inverse Mills ratio (IMR) into the baseline regression model to mitigate sample selec-
tion bias caused by the non-random behavior of firms. Column (3) of Table 6 shows the
regression results after considering the sample selection bias. It can be seen that after add-
ing IMR, the regression coefficient of AI is still significantly positive at the 1% level. The
results show that the findings of this study remain robust after controlling for the sample
selection problem.
Figure 1. Kernel density curves pre- and post-PSM matching.
Sustainability 2025,17, 4141 15 of 38
Table 6. Results of the endogeneity test.
Variable
(1) (2) (3) (4) (5)
PSM Heckman Instrumental Variable Method
GCI GCI_Dum GCI AI GCI
AI 0.1107 *** 0.3125 *** 0.6235 *** 0.1149 ***
(0.0082) (0.0085) (0.0525) (0.0086)
Overseas 0.1541 ** 0.3072 ***
(0.0776) (0.0333)
IMR 2.0705 ***
(0.2037)
AI_ind 0.1209 ***
(0.0172)
AI_pro 0.0993 ***
(0.0337)
AI_iv 0.0569 ***
(0.0021)
Size 0.0448 *** 0.2472 *** 0.4600 *** 0.1833 *** 0.0348 ***
(0.0099) (0.0104) (0.0438) (0.0094) (0.0057)
Lev −0.0357 0.0583 0.1291 *** −0.0967 *** 0.0284
(0.0403) (0.0699) (0.0216) (0.0340) (0.0185)
Age −0.0337 * −0.0524 *** −0.0995 *** 0.0762 *** −0.0270 **
(0.0183) (0.0168) (0.0147) (0.0194) (0.0110)
Roa −0.1259 * 0.0802 0.1883 *** 0.0143 0.0168
(0.0754) (0.2014) (0.0415) (0.0698) (0.0355)
Growth 0.0133 * −0.0356 −0.0587 *** −0.0000 0.0014
(0.0073) (0.0253) (0.0072) (0.0076) (0.0040)
Rdintensity 0.0749 0.3086 0.1310 0.5332 ** −0.2895 **
(0.2727) (0.4579) (0.1411) (0.2450) (0.1445)
Pollution 0.0419 *** 0.3025 *** 0.5367 *** 0.0028 0.0416 ***
(0.0148) (0.0287) (0.0524) (0.0126) (0.0096)
HHI −0.0184 ** −0.0165 −0.0408 *** −0.0041 −0.0116 **
(0.0080) (0.0209) (0.0068) (0.0080) (0.0057)
GDP 0.0676 0.0320 0.0960 * 0.0866 0.0378
(0.0880) (0.2403) (0.0524) (0.0728) (0.0502)
Constant −0.8297 *** −7.1811 *** −14.2481 *** −3.7262 ***
(0.2226) (0.2209) (1.3748) (0.2093)
Year fixed effect Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes
Kleibergen–Paap
rk LM statistic 175.253 ***
Cragg–Donald
Wald F statistic 4314.569
Hansen J statistic
4.179
Hansen J statistic
p-value 0.1237
N 13,254 27,217 27,217 29,128 29,128
R2_adj 0.4270 0.5006 0.8151
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
Second, we further used the Heckman two-stage model to control the estimation bias
of the sample selection problem. In the first stage, the probit model was used to estimate
the probability of whether enterprises carry out green collaborative innovation.
In order to ensure that the inverse Mills ratio (IMR) was uncorrelated with the random
disturbance term of the main regression, it was necessary to introduce exogenous variables
into the first-stage regression model. The existing research has found that executives
Sustainability 2025,17, 4141 16 of 38
with overseas backgrounds have been exposed to stricter environmental regulations (such
as EU carbon tariffs and US ESG standards) and have a deeper understanding of the
sustainable development concept. This experience drives companies to incorporate green
collaborative innovation into their strategies to meet international standards and market
demands [
49
]. However, executives with overseas backgrounds do not have a direct
and obvious impact on the application degree of artificial intelligence, so it largely meets
the independence and exclusivity assumptions for exogenous variable selection. To this
end, referring to
Li et al. [50]
, we selected the proportion of executives with an overseas
background (Overseas) as the exogenous variable, ran the regression together with the
control variables above, and calculated the inverse Mills ratio (IMR).
Column (2) of Table 6reports the first-stage probit regression results. The regression
coefficient of Overseas was found to be 0.1541, and it was significant at the level of 5%. This
shows that Overseas is highly correlated with whether listed companies conduct green
collaborative innovation, which passes the test of weak instrumental variable and meets
the selection conditions of exogenous variables. In the second stage, we introduced the
inverse Mills ratio (IMR) into the baseline regression model to mitigate sample selection bias
caused by the non-random behavior of firms. Column (3) of Table 6shows the regression
results after considering the sample selection bias. It can be seen that after adding IMR, the
regression coefficient of AI is still significantly positive at the 1% level. The results show that
the findings of this study remain robust after controlling for the sample
selection problem
.
5.3.3. Reverse Causality Test
In addition to the endogeneity problems caused by missing variables and the selec-
tion bias mentioned above, this study may also have reverse causality problems. That
is, firms with a higher level of green collaborative innovation may be more willing to
apply artificial intelligence technology. This reverse causality can also bias the estimated
coefficients. As a result, we used the instrumental variable method to further mitigate the
endogeneity problems caused by reverse causality, missing variables, and selection bias,
thereby strengthening the credibility of the benchmark conclusions.
First of all, referring to Lewbel [
51
], Choi et al. [
45
], Jin and Yu [
46
], and Cao et al. [
47
],
we selected the mean of the artificial intelligence technology application degree of other
firms in the same industry (AI_ind), the mean of the artificial intelligence technology
application degree of other firms in the same province (AI_pro), and the third power of the
artificial intelligence deviation (AI_iv) as instrumental variables for testing. The specific
logic was as follows: (1) the application degree of artificial intelligence at the industry
level and province level may significantly affect the firm’s own emphasis on artificial
intelligence technology, but it will not have a direct impact on the green collaborative
innovation behavior of a single enterprise, so this instrumental variable basically meets
the requirements of relevance and externality. (2) According to Lewbel [
51
], using the
third power of artificial intelligence deviation to construct instrumental variables can help
eliminate endogeneity bias to a certain extent.
Columns (4) and (5) in Table 6report the regression results of 2SLS. It can be found that
in the first-stage regression, the coefficients of AI_ind, AI_pro, and AI_iv are all significant
at the 1% level. This result is consistent with the theoretical expectation and supports
the correlation hypothesis for instrumental variables. At the same time, based on the
Kleibergen–Paap rk LM test, the null hypothesis of “insufficient recognition of instrumental
variables” is significantly rejected, which confirms that the model does not have the problem
of insufficient recognition. The Cragg–Donald Wald F statistic is 4314.569, significantly
exceeding the standard critical value of the Stock–Yogo weak instrument–variable test.
This shows that the instrumental variables used exclude weak instrumental variables. The
Sustainability 2025,17, 4141 17 of 38
Hansen J statistic is 4.179, and the corresponding p-value is 0.1237, indicating that the
instrumental variables used are relatively exogenous, and there is no evidence of over-
identification issues. In addition, in the second-stage regression, the regression coefficient
of AI is significantly positive at the 1% level, once again verifying that the application of
artificial intelligence technology can promote the green collaborative innovation of firms.
5.3.4. Placebo Test
To further eliminate potential interference from unobservable factors in the estimation
results, we conducted a placebo test. Specifically, if the positive impact of artificial intelli-
gence (AI) on corporate green collaborative innovation (GCI) were driven by unobservable
factors, such an effect would persist even when artificial intelligence indicators were ran-
domly assigned to target firms. Therefore, we randomly assigned the annual artificial
intelligence indicators to listed companies in the sample and then repeated the regression
of artificial intelligence (Random_AI) and corporate green collaborative innovation (GCI) in
random order 1000 times according to Model 1. The results of the placebo test are shown in
Figure 2. As illustrated in the left panel of Figure 2, the regression coefficients derived from
the randomized simulations exhibited a normal distribution centered around zero, and the
absolute values of these coefficients were consistently smaller than the empirically observed
coefficient of 0.1193 (see Column (4) in Table 3). Furthermore, the right panel of Figure 2
reveals that only a minimal proportion of the regression coefficients were statistically sig-
nificant (either positive or negative). These findings indicate that the virtual treatment
effect constructed in this study does not exist, which fully indicates that the conclusion that
the application of artificial intelligence technology can significantly improve the level of
enterprise green collaborative innovation is not caused by other unobservable factors. This
further validates the robustness of our findings.
Sustainability 2025, 17, 4141 18 of 40
indicates that the conclusion that the application of artificial intelligence technology can
significantly improve the level of enterprise green collaborative innovation is not caused
by other unobservable factors. This further validates the robustness of our findings.
Figure 2. Results of the placebo test.
5.4. Robustness Test
5.4.1. Changing Variable Measurement Methods
For the possible variable measurement bias problem, on the one hand, we changed
the measurement method of the dependent variable and used the total number of green
patent grants jointly applied by listed companies and other entities such as enterprises,
universities, and scientific research institutions to measure the green collaborative inno-
vation of enterprises (GCI1). The regression results are shown in Table 7. The coefficients
of AI are all positive and significant at the 1% level, supporting the conclusions in the
baseline regression.
Table 7. Results of changing the dependent variable measurement methods.
Variable (1) (2) (3) (4)
GCI1 GCI1 GCI1 GCI1
AI 0.0868 *** 0.0731 *** 0.0728 *** 0.0662 ***
(0.0070) (0.0059) (0.0058) (0.0053)
Size 0.0598 *** 0.0601 *** 0.0346 ***
(0.0042) (0.0041) (0.0043)
Lev 0.0033 0.0024 0.0136
(0.0133) (0.0132) (0.0159)
Age −0.0059 * −0.0126 *** −0.0065
(0.0035) (0.0034) (0.0086)
Roa −0.0720 ** −0.0744 ** 0.0091
(0.0316) (0.0308) (0.0289)
Growth −0.0140 *** −0.0134 *** −0.0065 *
(0.0043) (0.0044) (0.0037)
Rdintensity −0.3354 * −0.2388 0.0332
(0.1729) (0.1864) (0.1438)
Pollution 0.0706 *** 0.0687 *** 0.0407 ***
(0.0111) (0.0107) (0.0085)
HHI −0.0024 −0.0004 −0.0040
(0.0081) (0.0082) (0.0049)
GDP 0.0285 −0.0335 0.0368
(0.0458) (0.0444) (0.0474)
Constant 0.0403 *** −1.2649 *** −1.2554 *** −0.7055 ***
Figure 2. Results of the placebo test.
5.4. Robustness Test
5.4.1. Changing Variable Measurement Methods
For the possible variable measurement bias problem, on the one hand, we changed
the measurement method of the dependent variable and used the total number of green
patent grants jointly applied by listed companies and other entities such as enterprises,
universities, and scientific research institutions to measure the green collaborative inno-
vation of enterprises (GCI1). The regression results are shown in Table 7. The coefficients
of AI are all positive and significant at the 1% level, supporting the conclusions in the
baseline regression.
Sustainability 2025,17, 4141 18 of 38
Table 7. Results of changing the dependent variable measurement methods.
Variable (1) (2) (3) (4)
GCI1 GCI1 GCI1 GCI1
AI 0.0868 *** 0.0731 *** 0.0728 *** 0.0662 ***
(0.0070) (0.0059) (0.0058) (0.0053)
Size 0.0598 *** 0.0601 *** 0.0346 ***
(0.0042) (0.0041) (0.0043)
Lev 0.0033 0.0024 0.0136
(0.0133) (0.0132) (0.0159)
Age −0.0059 * −0.0126 *** −0.0065
(0.0035) (0.0034) (0.0086)
Roa −0.0720 ** −0.0744 ** 0.0091
(0.0316) (0.0308) (0.0289)
Growth −0.0140 *** −0.0134 *** −0.0065 *
(0.0043) (0.0044) (0.0037)
Rdintensity −0.3354 * −0.2388 0.0332
(0.1729) (0.1864) (0.1438)
Pollution 0.0706 *** 0.0687 *** 0.0407 ***
(0.0111) (0.0107) (0.0085)
HHI −0.0024 −0.0004 −0.0040
(0.0081) (0.0082) (0.0049)
GDP 0.0285 −0.0335 0.0368
(0.0458) (0.0444) (0.0474)
Constant 0.0403 *** −1.2649 *** −1.2554 *** −0.7055 ***
(0.0028) (0.0885) (0.0851) (0.0955)
Year fixed effect No No Yes Yes
Firm fixed effect No No No Yes
N 29,128 29,128 29,128 29,128
R2_adj 0.0759 0.1287 0.1315 0.4711
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
On the other hand, we replaced the measurement method for the independent variable.
First, referring to Islam et al. [
41
] and Huang et al. [
15
], we used the penetration of industrial
robots at the firm level to measure the artificial intelligence application degree (AI1). Second,
referring to Chen et al. [
43
], Song et al. [
14
], and Qu and Jing [
44
], we employed text
analysis methods to extract the number of artificial intelligence keywords from listed firms’
annual reports. We used a natural logarithm (number of artificial intelligence keywords
+ 1) to measure the application degree of artificial intelligence technology in the firm
(AI2). Specifically, the selection of keywords refers to artificial intelligence terminology
from Chen and Srinivasan [
52
], AI industry reports by Ping An Securities and Shenzhen
Forward-Looking Industry Research Institute, and the AI vocabulary list provided by the
World Intellectual Property Organization (WIPO). Through manual screening, we initially
identified 52 seed words. Using Word2vec technology and the Skip-gram model, we trained
these seed words along with textual materials from annual reports and patent documents
as corpora. For each seed word, we identified 10 semantically closest terms based on
cosine similarity between the seed word and other vocabulary. Subsequently, we removed
duplicate terms, AI-irrelevant words, and low-frequency terms. Finally, this process yielded
a total of 73 words to form the artificial intelligence dictionary for this study. The specific
artificial intelligence dictionary is shown in Appendix A, Table A1. Third, we also used
the word frequency of “artificial intelligence” in the MD&A section of the annual report to
re-measure the application degree of artificial intelligence technology in enterprises (AI3).
The regression results are shown in Table 8. It can be found that the results did not change
substantially, which proves the robustness of the benchmark conclusion.
Sustainability 2025,17, 4141 19 of 38
Table 8. Results of changing the independent variable measurement methods.
Variable (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)
GCI GCI GCI GCI GCI GCI GCI GCI GCI GCI GCI GCI
AI1 0.0058
***
0.0024
***
0.0025
*** 0.0010 *
(0.0007) (0.0007) (0.0007) (0.0006)
AI2 0.0335
***
0.0232
***
0.0241
***
0.0177
***
(0.0047) (0.0039) (0.0037) (0.0042)
AI3 0.0338
***
0.0229
***
0.0231
***
0.0138
***
(0.0054) (0.0046) (0.0044) (0.0048)
Size 0.1061
***
0.1062
***
0.0684
***
0.1041
***
0.1047
***
0.0651
***
0.1047
***
0.1051
***
0.0665
***
(0.0062) (0.0060) (0.0060) (0.0063) (0.0061) (0.0060) (0.0063) (0.0061) (0.0061)
Lev −
0.0397
**
−
0.0387
** 0.0218 −
0.0320
*
−
0.0343
*0.0217 −
0.0329
*
−
0.0344
*0.0216
(0.0180) (0.0180) (0.0193) (0.0176) (0.0177) (0.0193) (0.0177) (0.0178) (0.0193)
Age −
0.0189
***
−
0.0234
***
−
0.0227
*
−
0.0159
***
−
0.0197
***
−
0.0234
**
−
0.0165
***
−
0.0205
***
−
0.0222
*
(0.0047) (0.0046) (0.0117) (0.0047) (0.0046) (0.0116) (0.0047) (0.0046) (0.0116)
Roa −
0.0285
−
0.0293
0.0307 −
0.0166
−
0.0223
0.0307 −
0.0199
−
0.0243
0.0291
(0.0401) (0.0392) (0.0378) (0.0398) (0.0390) (0.0378) (0.0398) (0.0390) (0.0378)
Growth −
0.0054
−
0.0058
−
0.0008
−
0.0071
−
0.0072
−
0.0006
−
0.0067
−
0.0068
−
0.0006
(0.0053) (0.0054) (0.0043) (0.0053) (0.0054) (0.0042) (0.0053) (0.0055) (0.0043)
Rdintensity
0.9450
***
1.0766
*** 0.1190 0.6569
***
0.8025
*** 0.0434 0.7329
***
0.8646
*** 0.0683
(0.1740) (0.2002) (0.1493) (0.1748) (0.2027) (0.1512) (0.1760) (0.2032) (0.1513)
Pollution 0.0631
***
0.0638
***
0.0415
***
0.0718
***
0.0750
***
0.0442
***
0.0703
***
0.0723
***
0.0431
***
(0.0140) (0.0142) (0.0102) (0.0137) (0.0138) (0.0102) (0.0138) (0.0139) (0.0102)
HHI 0.0067 0.0086 −
0.0105
*0.0038 0.0062 −
0.0110
*0.0045 0.0067 −
0.0108
*
(0.0110) (0.0115) (0.0063) (0.0106) (0.0112) (0.0063) (0.0107) (0.0112) (0.0063)
GDP 0.0839 0.0529 0.0556 0.0928 0.0346 0.0526 0.0846 0.0403 0.0545
(0.0618) (0.0564) (0.0521) (0.0620) (0.0563) (0.0519) (0.0624) (0.0563) (0.0520)
Constant 0.1186
***
−
2.2002
***
−
2.1957
***
−
1.3235
***
0.1329
***
−
2.1582
***
−
2.1650
***
−
1.2520
***
0.1381
***
−
2.1668
***
−
2.1699
***
−
1.2800
***
(0.0062) (0.1302) (0.1268) (0.1298) (0.0062) (0.1302) (0.1266) (0.1290) (0.0063) (0.1303) (0.1266) (0.1301)
Year fixed
effect No No Yes Yes No No Yes Yes No No Yes Yes
Firm fixed
effect No No No Yes No No No Yes No No No Yes
N 29,128 29,128 29,128 29,128 29,128 29,128 29,128 29,128 29,128 29,128 29,128 29,128
R2_adj 0.0022 0.0942 0.0953 0.4605 0.0058 0.0961 0.0972 0.4609 0.0043 0.0955 0.0965 0.4607
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
5.4.2. Hysteresis Effect
Since the impact of artificial intelligence on green collaborative innovation may take
more than 1 year to emerge, we treated AI as lagging for one period and multiple periods.
As can be seen from Columns (1)–(3) in Table 9, the coefficients of L_AI, L2_AI, and
L3_AI are all significantly positive. This not only verifies the robustness of the conclusion,
but also shows, to a certain extent, that the promoting effect of artificial intelligence on
enterprise green collaborative innovation has not only a short-term impact, but also a
long-term impact.
Sustainability 2025,17, 4141 20 of 38
Table 9. Results of considering hysteresis effects, adding fixed effects, and adjusting the
clustering level.
Variable (1) (2) (3) (4) (5) (6)
GCI GCI GCI GCI GCI GCI
AI 0.1122 *** 0.1182 *** 0.1193 ***
(0.0064) (0.0063) (0.0069)
L_AI 0.0658 ***
(0.0057)
L2_AI 0.0488 ***
(0.0063)
L3_AI 0.0319 ***
(0.0064)
Size 0.0520 *** 0.0660 *** 0.0789 *** 0.0423 *** 0.0344 *** 0.0335 ***
(0.0058) (0.0072) (0.0084) (0.0058) (0.0055) (0.0077)
Lev 0.0164 0.0030 −0.0077 0.0386 ** 0.0326 * 0.0287
(0.0203) (0.0231) (0.0260) (0.0194) (0.0194) (0.0268)
Age −0.0409 ** −0.0519 ** −0.0712 * −0.0430 *** −0.0246 ** −0.0272 **
(0.0172) (0.0255) (0.0407) (0.0114) (0.0113) (0.0134)
Roa 0.0595 0.0444 0.0246 −0.0486 0.0020 0.0163
(0.0391) (0.0412) (0.0437) (0.0372) (0.0363) (0.0418)
Growth 0.0035 0.0055 0.0045 0.0004 0.0011 0.0014
(0.0043) (0.0047) (0.0052) (0.0041) (0.0041) (0.0043)
Rdintensity −0.1280 −0.0355 0.0174 −0.0066 −0.2875 ** −0.3051 *
(0.1549) (0.1655) (0.1685) (0.1636) (0.1465) (0.1671)
Pollution 0.0316 *** 0.0258 ** 0.0237 ** 0.0155 0.0350 *** 0.0416 ***
(0.0098) (0.0108) (0.0113) (0.0098) (0.0097) (0.0106)
HHI −0.0076 −0.0114 * −0.0122 −0.0205 ** −0.0113 ** −0.0117 **
(0.0062) (0.0067) (0.0075) (0.0086) (0.0057) (0.0048)
GDP 0.0387 0.0241 0.0425 0.0262 0.0854 0.0371
(0.0552) (0.0600) (0.0638) (0.0517) (0.0674) (0.0530)
Constant −0.9567 *** −1.2025 *** −1.4113 *** −0.7582 *** −0.6533 *** −0.6226 ***
(0.1294) (0.1665) (0.2108) (0.1284) (0.1217) (0.1668)
Year fixed effect Yes Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes Yes
Industry ×year fixed effect No No No Yes No No
Province ×year fixed effect No No No No Yes No
N 25,376 22,360 19,473 29,128 29,128 29,128
R2_adj 0.4650 0.4651 0.4806 0.4956 0.4871 0.4869
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets in Column (6) are standard errors for clustering
to the firm level, and the values in brackets in the remaining columns are standard errors for clustering to the
industry-year level.
5.4.3. Adding Fixed Effects and Adjusting the Clustering Level
In view of the possible model setting bias in this study, we added fixed effects and
adjusted the cluster level as follows. (1) Increasing industry
×
year fixed effect and
province ×year fixed effect
. Based on the original controls for firm fixed effects and year fixed
effects, we further added the industry
×
year fixed effect and province
×
year fixed effect to
control the impact of time-varying unobservable factors at the industry and provincial levels.
(2) Adjusting
the clustering to the enterprise level. The previous estimates reported results
with standard errors clustered at the industry-year level, which, to some extent, captured both
industry-specific differences and annual shocks, thereby avoiding underestimating standard
errors. However, since the data on artificial intelligence and corporate green collaborative
innovation are both at the firm level, we employed firm-level clustered standard errors to
more accurately reflect the variability of the estimated coefficients. This method better handles
the correlation within the cross-sectional dimension or the time-series dimension. As can be
seen from Columns (4)–(6) in Table 9, the coefficients of AI are all significantly positive. This
indicates that the findings of this study remain robust.
Sustainability 2025,17, 4141 21 of 38
5.5. Mechanism Analysis
According to the previous theoretical analysis, we believe that artificial intelligence can
promote enterprise green collaborative innovation by reducing transaction costs and optimiz-
ing the labor force structure. Referring to Jiang [
53
], we focused on the impact of artificial
intelligence on these two paths and built the following model to test the action mechanism:
Mi,t=α0+α1AI i,t+αControl +λi+µt+εi,t(2)
where M
i,t
represents the mediating variable, including the transaction cost and labor
force structure.
5.5.1. Mechanism I: Reducing Transaction Cost
In order to verify research hypothesis H2, referring to Collis and Montgomery [
54
],
we used the proportion of intangible assets in the total assets to measure transaction costs
(Cost1). To enhance the reliability and robustness of the conclusions, we also employed
the ratio of the total sum of fixed assets, construction in progress, intangible assets, and
long-term prepaid expenses to the total assets as a measure of transaction costs (Cost2) [
55
].
The results in Columns (1) and (2) of Table 10 show that the application of artificial
intelligence technology can significantly reduce enterprise transaction costs. Moreover, the
existing research shows that due to bounded rationality, opportunism, and asset-specific
characteristics of green innovation, the process of green collaborative innovation generates
transaction costs [
36
]. Lower transaction costs are the key to improving the efficiency
and effect of green synergy [
35
]. Therefore, the empirical results of this study support
Hypothesis H2, indicating that the application of artificial intelligence technology helps
firms reduce transaction costs, thus promoting green collaborative innovation.
Table 10. Results of the mechanism analysis.
Variable (1) (2) (3) (4)
Cost1 Cost2 Labor1 Labor2
AI −0.0006 ** −0.0024 ** 0.0006 ** 0.0091 ***
(0.0003) (0.0011) (0.0003) (0.0015)
Size 0.0002 −0.0111 *** 0.0011 ** 0.0123 ***
(0.0007) (0.0026) (0.0005) (0.0022)
Lev 0.0015 0.0466 *** −0.0024 −0.0552 ***
(0.0026) (0.0096) (0.0020) (0.0095)
Age 0.0083 *** 0.0385 *** −0.0046 *** −0.0050
(0.0010) (0.0045) (0.0008) (0.0061)
Roa −0.0336 *** −0.1249 *** 0.0023 0.0089
(0.0051) (0.0180) (0.0038) (0.0174)
Growth 0.0000 −0.0041 ** 0.0011 * 0.0027
(0.0005) (0.0021) (0.0006) (0.0022)
Rdintensity −0.0088 0.0066 0.0034 0.2891 ***
(0.0118) (0.0445) (0.0111) (0.0687)
Pollution 0.0030 *** 0.0243 *** −0.0035 *** −0.0375 ***
(0.0007) (0.0036) (0.0005) (0.0038)
HHI −0.0014 *** −0.0067 ** −0.0001 0.0010
(0.0004) (0.0030) (0.0004) (0.0024)
GDP 0.0059 −0.0261 0.0007 −0.0035
(0.0051) (0.0184) (0.0034) (0.0194)
Constant 0.0237 * 0.4413 *** 0.0095 0.0867 *
(0.0140) (0.0573) (0.0115) (0.0494)
Year fixed effect Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes
N 29,128 29,128 29,128 29,128
R2_adj 0.6941 0.7975 0.6479 0.7671
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level
.
Sustainability 2025,17, 4141 22 of 38
5.5.2. Mechanism II: Optimizing Labor Force Structure
In order to verify research hypothesis H3, referring to Dou et al. [
56
] and Zhang and
Peng [
57
], we used the ratio of the number of employees with Master’s degrees and above
to the total number of employees to measure the labor structure of a firm (Labor1). To
enhance the reliability and robustness of the conclusions, we additionally measured the
labor force structure using the proportion of the high-skilled workforce to the total number
of employees (Labor2). Specifically, we categorized the firm workforce into six groups:
production workers, administrative and support staff, technical personnel, marketing and
sales personnel, financial personnel, and others. The proportion of high-skilled labor
(including technical, marketing/sales, and financial personnel) to the total number of
employees was then calculated [58].
The results in Column (2) of Table 10 show that the application of artificial intelli-
gence technology can significantly optimize the labor structure. The existing literature
confirms that high-quality human capital is key to knowledge accumulation and techno-
logical innovation [
59
,
60
]. When a firm has higher-quality human capital, it can improve
the efficiency of understanding green technology’s nature and the accuracy of innovation
decision-making, thus improving the interaction efficiency with other collaborative inno-
vation subjects [
61
]. Moreover, green collaborative innovation is essentially a dynamic
reconstruction process of a multi-agent value network. High-quality human capital can
significantly improve the operational efficiency of innovation networks by shaping trust re-
lationships and optimizing collaboration models [
62
,
63
]. In addition, Alfalih and Hadj [
64
]
pointed out that high-quality human capital contains stronger environmental awareness,
which is conducive to stimulating the demand for green products, thus promoting the
green collaborative innovation of enterprises. Therefore, the empirical results of this study
support Hypothesis H3, indicating that the application of artificial intelligence technol-
ogy helps optimize the labor structure and improve the quality of human capital, thus
promoting green collaborative innovation.
5.6. Further Analysis
5.6.1. Green Investor Entry, Artificial Intelligence, and Green Collaborative Innovation
As a special category of institutional investors, when selecting investment objects,
green investors consider whether the project conforms to environmental testing standards,
pollution control effects, and ecological protection as important preconditions [
65
]. As
a fund investor taking into account environmental and social responsibility, a green in-
vestor comprehensively considers multiple performances, such as economic, social, and
environmental performance, in the investment process. They play a strong supervisory
and governance role in the process of enterprise development, which helps to achieve
the dual effect of economic benefits and environmental protection [
66
]. According to the
resource dependence theory and stakeholder theory, the entry of social capital with green
investment preference into enterprises can provide solid capital support for enterprises to
effectively carry out high-quality green collaborative innovation [
67
]. More importantly,
green capital has the attribute of long-term investment, which helps to reduce the risk of
green technology transformation, promotes enterprises to fulfill their green governance
obligations, and strengthens the willingness for green collaborative innovation. In addition,
green investor entry can influence the green innovation decisions of the management
through the direct decision of “voting with hands” and the indirect intervention of “voting
with feet”, thus increasing the possibility and initiative of enterprises to carry out green
collaborative innovation activities and promoting the connection between AI R&D goals
and the SDGs [
68
]. Based on this, we expected that green investor entry can strengthen the
positive effect of artificial intelligence on green collaborative innovation.
Sustainability 2025,17, 4141 23 of 38
In order to identify the moderating effect of green investor entry on the relationship
between artificial intelligence and green collaborative innovation, green investor entry
(Greninvest) and the interaction term (Greninvest
×
AI) were added to Model 1, where
Greninvest was the logarithm of the number of green investors plus 1 [
69
]. Column (1) of
Table 11 reports the regression results of the moderating effect of green investor entry. It
can be seen that the coefficients of AI and Greninvest
×
AI are significantly positive at the
level of 1%. This means that green investor entry can significantly enhance the promoting
effect of artificial intelligence on green collaborative innovation.
Table 11. Cross-sectional test: the moderating effect of green investor entry.
Variable
(1) (2) (3) (4) (5)
GCI
High
Marketization
Low
Marketization
High Legal
Regulations
Low Legal
Regulations
GCI GCI GCI GCI
AI 0.1087 *** 0.1159 *** 0.0975 *** 0.1224 *** 0.0931 ***
(0.0062) (0.0098) (0.0071) (0.0102) (0.0071)
Greninvest ×AI 0.0144 *** 0.0149 ** 0.0139 ** 0.0173 ** 0.0136 **
(0.0040) (0.0061) (0.0056) (0.0075) (0.0054)
Greninvest −0.0045 −0.0129 0.0000 −0.0142 0.0021
(0.0054) (0.0087) (0.0069) (0.0090) (0.0070)
Size 0.0316 *** 0.0373 *** 0.0320 *** 0.0309 *** 0.0320 ***
(0.0053) (0.0097) (0.0070) (0.0097) (0.0071)
Lev 0.0285 0.0478 0.0163 0.0159 0.0217
(0.0185) (0.0317) (0.0242) (0.0354) (0.0243)
Age −0.0288 *** −0.0394 ** −0.0203 −0.0391 ** −0.0208
(0.0110) (0.0167) (0.0146) (0.0179) (0.0131)
Roa 0.0026 −0.0765 0.0663 −0.0541 0.0185
(0.0362) (0.0619) (0.0431) (0.0749) (0.0461)
Growth 0.0018 −0.0004 −0.0006 0.0108 −0.0013
(0.0040) (0.0073) (0.0046) (0.0077) (0.0046)
Rdintensity −0.2875 ** −0.7364 *** 0.0400 −0.7020 *** 0.1343
(0.1443) (0.2219) (0.1874) (0.2530) (0.2061)
Pollution 0.0409 *** 0.0530 *** 0.0289 ** 0.0667 *** 0.0271 **
(0.0097) (0.0154) (0.0123) (0.0159) (0.0119)
HHI −0.0120 ** −0.0157 * −0.0102 −0.0191 ** −0.0127 **
(0.0057) (0.0085) (0.0062) (0.0088) (0.0060)
GDP 0.0371 0.2534 *** −0.0459 0.1722 0.0017
(0.0501) (0.0963) (0.0612) (0.1089) (0.0619)
Constant −0.5731 *** −0.6806 *** −0.5972 *** −0.5134 ** −0.6008 ***
(0.1175) (0.2130) (0.1493) (0.2116) (0.1550)
0.1087 *** 0.1159 *** 0.0975 *** 0.1224 *** 0.0931 ***
Tests for between-group
differences p-value = 0.000 *** p-value = 0.000 ***
Year fixed effect Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes
N 29,128 12,692 16,436 12,376 16,752
R2_adj 0.4876 0.5094 0.4969 0.5373 0.4721
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
The uneven regional economic and sociocultural development levels in China have led
to significant disparities in marketization degrees and legal environments across different
regions. Green investors exhibit substantial variations under these distinct external con-
ditions. To further investigate how the moderating effect of green investor entry changes
under different economic conditions and regulatory environments, we employed the mar-
ketization index and legal environment index developed by Wang Xiaolu et al. (data
sourced from the China Provincial Marketization Index Database) to measure regional
Sustainability 2025,17, 4141 24 of 38
marketization levels and legal regulatory enforcement. Samples were categorized based on
the industry–year medians.
As shown in Columns (2) and (3) of Table 11, the coefficients of Greninvest
×
AI are
significantly positive at the 5% level, with the coefficient in the high marketization group
being significantly higher than that in the low marketization group (validated by between-
group differences tests). This indicates that the positive moderating effect of green investor
entry on the relationship between artificial intelligence and green collaborative innovation
is more pronounced in regions with higher marketization levels. A plausible explanation
is that compared to low-marketization regions, high-marketization regions possess more
efficient factor mobility mechanisms. Price signals and competitive mechanisms enable
rapid alignment between green technology innovation demands and capital supply, thereby
better guiding green investors to prioritize funding for AI-driven green collaborative
innovation projects.
Columns (4) and (5) of Table 11 reveal that the coefficients of Greninvest
×
AI are also
significantly positive at the 5% level, with the coefficient in the high legal regulatory group
significantly exceeding that in the low legal regulatory group (validated by between-group
differences tests). This suggests that the positive moderating effect of green investor entry
is further amplified in regions with high legal regulations. The underlying rationale lies in
the fact that regions with high legal regulations typically establish clearer property rights
definitions and infringement accountability mechanisms. Such legal certainty alleviates
green investors’ concerns about technology plagiarism or patent disputes, encouraging
them to fund high-risk, long-cycle AI-driven green collaborative innovation initiatives.
Additionally, high legal regulations facilitate complete commercialization chains for tech-
nological achievements. Green investors can leverage mechanisms such as patent pledge
financing and technology licensing to accelerate the application of artificial intelligence in
green collaborative innovation fields.
5.6.2. CEO Openness, Artificial Intelligence, and Green Collaborative Innovation
CEO openness refers to a CEO’s characteristics of actively changing the current situa-
tion of the organization, pursuing diversity, and being willing to explore new governance
systems and strategic directions [
70
]. In general, CEOs with high openness are willing to
accept new ideas, actions, and experiences and actively explore innovative activities [
71
].
Therefore, CEOs with higher openness are more likely to accept the open innovation
paradigm and more actively absorb other innovators to carry out collaborative innovation,
which helps to enhance the willingness of multiple partners to carry out green collaborative
innovation [
72
]. In addition, CEO openness can reduce opportunistic and short-sighted
behaviors in cooperation, enhance the willingness of external partners to maintain long-
term cooperation, and help enterprises establish a stable and close cooperative relationship
with green partners [
10
]. Based on this, we expected that a high level of CEO openness can
strengthen the positive effect of AI on green collaborative innovation.
In order to identify the moderating effect of CEO openness on the relationship be-
tween artificial intelligence and green collaborative innovation, CEO openness (Open) and
interaction term (Open
×
AI) were added to Model 1. Referring to Datta et al. [
70
] and
Gal [
71
], we used the CEO’s education level, CEO’s age, and CEO’s tenure to measure CEO
openness (Open). Column (1) of Table 12 reports the regression results of the moderating
effect of CEO openness. It was found that the coefficients of AI and Open
×
AI were
significantly positive. This means that the higher the degree of CEO openness, the more
significant the promoting effect of artificial intelligence on green collaborative innovation.
Sustainability 2025,17, 4141 25 of 38
Table 12. Cross-sectional test: the moderating effect of CEO openness.
Variable
(1) (2) (3) (4) (5)
GCI
High
Marketization
Low
Marketization
High Legal
Regulations
Low Legal
Regulations
GCI GCI GCI GCI
AI 0.1204 *** 0.1227 *** 0.1138 *** 0.1241 *** 0.1108 ***
(0.0066) (0.0083) (0.0082) (0.0081) (0.0090)
Open ×AI 0.0048 *** 0.0067 *** 0.0032 0.0054 *** 0.0037
(0.0015) (0.0021) (0.0022) (0.0020) (0.0024)
Open −0.0017 −0.0004 −0.0030 −0.0026 −0.0023
(0.0016) (0.0023) (0.0022) (0.0022) (0.0026)
Size 0.0335 *** 0.0319 *** 0.0347 *** 0.0305 *** 0.0341 ***
(0.0053) (0.0080) (0.0079) (0.0080) (0.0079)
Lev 0.0299 0.0586 ** 0.0114 0.0243 0.0262
(0.0185) (0.0266) (0.0286) (0.0277) (0.0278)
Age −0.0263 ** −0.0325 ** −0.0134 −0.0368 *** −0.0113
(0.0110) (0.0140) (0.0182) (0.0140) (0.0178)
Roa 0.0185 −0.0529 0.0861 * −0.0554 0.0917 *
(0.0355) (0.0509) (0.0486) (0.0511) (0.0497)
Growth 0.0013 0.0002 0.0000 0.0038 −0.0014
(0.0039) (0.0063) (0.0050) (0.0065) (0.0051)
Rdintensity −0.3009 ** −0.6493 *** 0.0983 −0.4856 ** 0.1873
(0.1468) (0.2013) (0.2059) (0.1945) (0.2467)
Pollution 0.0405 *** 0.0517 *** 0.0213 * 0.0552 *** 0.0280 **
(0.0096) (0.0133) (0.0127) (0.0134) (0.0130)
HHI −0.0114 ** −0.0131 * −0.0127 * −0.0127 * −0.0106
(0.0057) (0.0074) (0.0069) (0.0072) (0.0068)
GDP 0.0382 0.1996 ** −0.0567 0.1120 0.0025
(0.0501) (0.0820) (0.0662) (0.0881) (0.0668)
Constant −0.6263 *** −0.5882 *** −0.6708 *** −0.5356 *** −0.6759 ***
(0.1176) (0.1784) (0.1691) (0.1766) (0.1660)
Tests for between-group
differences p-value = 0.000 *** p-value = 0.000 ***
Year fixed effect Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes
N 29,128 16,315 12,813 16,748 12,380
R2_adj 0.4873 0.5129 0.4714 0.5198 0.4609
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
The unbalanced level of regional economic, social, and cultural development in China
leads to great differences in the degree of marketization and the legal environment in
different regions. CEO openness varies greatly in different external environments. To
identify how the moderating effect of CEO openness changes under distinct regulatory
environments or economic conditions, we employed the marketization index and the
legal institutional environment index developed by Wang Xiaolu et al. for analysis and
categorized samples based on the industry–year medians.
According to Columns (2) and (3) of Table 12, the coefficient of Open
×
AI is signifi-
cantly positive only in the high-marketization group, with the coefficient in this group being
significantly larger than that in the low-marketization group (validated by between-group
differences tests). This indicates that in regions with higher marketization levels, CEO
openness plays a more pronounced positive moderating role in the relationship between
artificial intelligence and green collaborative innovation. Possible reasons are described
below. (1) Highly marketized regions typically feature intense competition, where com-
petitive pressures compel enterprises to adopt differentiated innovation pathways. CEOs
with greater openness are more inclined to break away from traditional technological path
dependencies and drive the cross-integration of artificial intelligence with green collabo-
Sustainability 2025,17, 4141 26 of 38
rative innovation. (2) Consumers and enterprises in high marketization regions are more
willing to pay for green products/services, and CEOs with greater openness can keenly
perceive changes in market demand and apply artificial intelligence technology to green
collaborative innovation. (3) In addition, these regions often feature dense innovation
networks (e.g., technology parks, industry–university–research alliances), enabling CEOs
with greater openness to establish cross-sector collaborations more easily.
Columns (4) and (5) of Table 12 reveal that the coefficient of Open
×
AI is significantly
positive only in the high legal regulatory group, with the coefficient in this group being
significantly larger than that in the low legal regulatory group (validated by between-
group differences tests). This suggests that the positive moderating effect of CEO openness
is further amplified in regions with high legal regulations. The key explanations are
as follows. (1) High legal regulations reduce uncertainty in innovation, thereby creating
strategic trial-and-error space for highly open CEOs. This institutional environment enables
CEOs with greater openness to boldly promote cross-domain integration between artificial
intelligence and green technologies. (2) Regions with high legal regulations establish
secure boundaries for technology-sharing through intellectual property protection and
antitrust regulations. Within such governance parameters, open-minded CEOs may be
more proactive in advancing AI-driven green collaborative innovation initiatives.
5.6.3. Heterogeneity Test
The impact of artificial intelligence on corporate green collaborative innovation may
be constrained by internal and external factors, such as macroeconomic uncertainty, firm
size, and the nature of firm ownership. First, uncertainty has become a defining feature
of the current global economic development. Under varying levels of macroeconomic
uncertainty, corporate strategic orientation and resource allocation capabilities undergo
dynamic adjustment, which may lead to differential effects of artificial intelligence on green
collaborative innovation. Second, there are differences in the application degrees of artificial
intelligence and the development needs of green innovation in enterprises of different sizes,
so the impact of artificial intelligence on green collaborative innovation is heterogeneous
in terms of enterprise size. Third, state-owned enterprises (SOEs) and non-SOEs differ in
management models and innovation decision-making due to their distinct ownership struc-
tures, which may lead to different effects of artificial intelligence on improving enterprise
green collaborative innovation. Accordingly, we conducted a heterogeneity analysis on the
relationship between artificial intelligence and green collaborative innovation based on
three perspectives: macroeconomic fluctuations, firm size, and nature of firm ownership.
The heterogeneity test results are presented in Table 13.
Regarding macroeconomic uncertainty, we measured it using the annual arithmetic
average of China’s economic policy uncertainty index developed by Baker et al. [
73
]. The
sample was divided into high and low macroeconomic uncertainty groups based on the
median value. Columns (1) and (2) show that while AI coefficients remain significant in
both groups, the coefficient for the high macroeconomic uncertainty group is significantly
larger than that of the low macroeconomic uncertainty group (validated by between-group
differences tests). This suggests that the promoting effect of artificial intelligence on green
collaborative innovation becomes more pronounced under higher macroeconomic uncer-
tainty. A plausible explanation lies in uncertainty serving as a latent driver of corporate
innovation. Specifically, in highly uncertain environments, firms exhibit stronger motiva-
tion to adopt technological transformations to enhance operational performance and secure
sustainable competitive advantages. Macroeconomic uncertainty can drive the application
of artificial intelligence technology and green collaborative innovation with the help of
Sustainability 2025,17, 4141 27 of 38
incentive and selection effects and then strengthen the positive correlation between artificial
intelligence and green coordinated innovation.
Table 13. Results of the heterogeneity test.
Variable
(1) (2) (3) (4) (5) (6)
High
Macroeconomic
Uncertainty
Low
Macroeconomic
Uncertainty
Large-Scale
Firms
Small-Scale
Firms SOEs Non-SOEs
GCI GCI GCI GCI GCI GCI
AI 0.1115 *** 0.0980 *** 0.1478 *** 0.0569 *** 0.1702 *** 0.0767 ***
(0.0097) (0.0090) (0.0090) (0.0057) (0.0115) (0.0062)
Size 0.0745 *** 0.0264 *** 0.0461 *** 0.0228 *** 0.0570 *** 0.0298 ***
(0.0156) (0.0069) (0.0128) (0.0064) (0.0104) (0.0065)
Lev −0.0180 0.0366 0.0271 −0.0315 * 0.0093 0.0322
(0.0435) (0.0261) (0.0481) (0.0190) (0.0398) (0.0204)
Age −0.0839 *** −0.0016 −0.0287 0.0235 ** 0.0661 ** −0.0045
(0.0265) (0.0141) (0.0241) (0.0096) (0.0334) (0.0109)
Roa −0.0538 −0.0568 0.1535 * −0.0400 0.1840 ** −0.0477
(0.0545) (0.0430) (0.0834) (0.0315) (0.0808) (0.0395)
Growth 0.0014 −0.0028 0.0083 −0.0008 0.0043 −0.0037
(0.0064) (0.0042) (0.0072) (0.0035) (0.0081) (0.0043)
Rdintensity −0.0842 1.0272 −0.7227 *** 0.2536 * −0.1682 0.0339
(0.1955) (6.1077) (0.2722) (0.1537) (0.3311) (0.1511)
Pollution −0.0091 0.0243 0.0346 ** 0.0217 ** 0.0294 * 0.0361 ***
(0.0134) (0.0181) (0.0151) (0.0085) (0.0153) (0.0118)
HHI −0.0068 −0.0061 −0.0182 * −0.0089 ** −0.0170 * −0.0025
(0.0073) (0.0061) (0.0099) (0.0042) (0.0093) (0.0058)
GDP 0.0124 0.1228 * 0.0868 0.0445 0.0062 0.0733
(0.0678) (0.0692) (0.0945) (0.0417) (0.0901) (0.0545)
Constant −1.3606 *** −0.5394 *** −0.9036 *** −0.4802 *** −1.3808 *** −0.6118 ***
(0.3398) (0.1496) (0.2904) (0.1314) (0.2419) (0.1407)
Tests for
between-group
differences
p-value = 0.000 *** p-value = 0.000 *** p-value = 0.000 ***
Year fixed effect Yes Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes Yes
N 13,891 15,237 14,578 14,550 11,654 17,474
R2_adj 0.5577 0.5162 0.5076 0.4070 0.5449 0.4312
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
For firm size, we divided the sample into large-scale firms and small-scale firms
based on the industry–year medians of the total assets of firms and further examined
the differences in the impact of artificial intelligence on green collaborative innovation
at different sizes. Columns (3) and (4) reveal that the regression coefficients of AI are all
significant, and the coefficient of the large-scale firm group is significantly larger than
that of the small-scale firm group (validated by between-group differences tests). This
indicates that artificial intelligence exerts a stronger promoting effect on green collaborative
innovation in larger firms. The technology adoption theory provides a rationale: large
resource-rich firms typically act as early adopters of emerging technologies, whereas small
resource-constrained firms face limitations in fully leveraging artificial intelligence for
green innovation. Moreover, large firms’ superior slack resources enhance their risk-bearing
capacity and confidence in pursuing green collaborative innovation.
Concerning the nature of firm ownership, we divided the sample firms into state-
owned firms and non-state-owned firms based on the enterprise ownership structure and
further investigated the difference in the impact of artificial intelligence on green collabora-
tive innovation under different property rights. Columns (5) and (6) demonstrate that while
Sustainability 2025,17, 4141 28 of 38
AI coefficients are significantly positive for both groups, the coefficient of SOEs significantly
exceeds that of non-SOEs. This highlights that, compared with non-state-owned firms, the
promoting effect of artificial intelligence on green collaborative innovation in state-owned
firms is more significant. A plausible explanation is that developing artificial intelligence
technology requires substantial hardware investments and specialized talent, imposing
significant demands on financial resources and human capital. SOEs’ superior resource
advantages and higher risk tolerance enable them to make complementary investments
that amplify AI’s benefits for green innovation. Furthermore, SOEs’ inherent advantages in
information access, policy support, and resource allocation provide more robust safeguards
for green collaborative innovation activities.
5.6.4. Economic Consequences Test
The previous part confirms that artificial intelligence can promote green collaborative
innovation by reducing transaction costs and optimizing the labor structure. We further
explored the economic consequences. That is, we aimed to examine whether the promoting
effect of artificial intelligence on green collaborative innovation can help firms reduce
carbon emissions, improve ESG performance, and promote sustainable business practices.
Hence, we constructed the following model to analyze economic consequences:
Carboni,t/ESGi,t=α0+α1GCIi,t+αControl +λi+µt+εi,t(3)
where Carbon
i,t
represents the carbon emission level of a firm, which is measured by the
ratio of the pollutant discharge fee or environmental protection tax paid by the firm to the
total profit and ESG
i,t
represents the ESG performance of the firm. We used the ESG rating
of Huazheng to measure the ESG performance.
Table 14 reports the test results of the economic consequences analysis. It can be found
in Column (1) that the coefficient of GCI is significantly negative at the 5% level. From an
economic perspective, after controlling for other factors, each unit increase in a company’s
green collaborative innovation level reduces its carbon emission levels by 0.05%. This
finding demonstrates the positive role of artificial intelligence technology in the long-term
development of enterprises. Artificial intelligence technology facilitates the enhancement
of green collaborative innovation, which ultimately contributes to the reduction of cor-
porate carbon emissions. In Column (2), the coefficient of GCI is significantly positive
at the 1% level, which means that a higher level of green collaborative innovation can
significantly improve ESG performance. From an economic perspective, after controlling
for other factors, each unit increase in a company’s green collaborative innovation level
improves its ESG performance by 5.17%. The findings demonstrate the positive role of
artificial intelligence technology in corporate sustainable development. Artificial intelli-
gence technology enhances enterprises’ ESG performance by promoting green collaborative
innovation, ultimately fostering better sustainability outcomes. In conclusion, after the
application of artificial intelligence technology promotes green collaborative innovation, it
can significantly reduce corporate carbon emissions and improve ESG performance so as to
help firms better achieve the sustainable development goals.
In addition, this study further analyzed the heterogeneity of the impact of green collab-
orative innovation on carbon emissions and the impact of green collaborative innovation
on ESG performance. Specifically, we examined four types of heterogeneity: firm scale,
firm property rights nature, sector to which the firm belongs, and regional environmental
protection penalty enforcement. The grouping methods for firm scale and firm property
rights nature are consistent with those in the previous section. Regarding the sector to
which the firm belongs, we classified the samples into three categories, primary-sector firms,
secondary-sector firms, and tertiary-sector firms, for testing based on the International
Sustainability 2025,17, 4141 29 of 38
Standard Industrial Classification of All Economic Activities and the Chinese National
Economic Industry Classification System. Meanwhile, we used the number of environmen-
tal administrative penalty cases in each province to measure the regional environmental
protection penalty enforcement and group them according to the industry–year medians.
The results of the heterogeneity test are shown in Tables 15 and 16.
Table 14. Results of the economic consequences test.
Variable (1) (2)
Carbon ESG
GCI −0.0005 ** 0.0517 ***
(0.0002) (0.0145)
Size −0.0001 0.2608 ***
(0.0002) (0.0123)
Lev 0.0012 ** −0.6498 ***
(0.0006) (0.0557)
Age 0.0012 *** −0.3197 ***
(0.0002) (0.0271)
Roa 0.0040 *** 0.0366
(0.0012) (0.1179)
Growth −0.0003 ** −0.0713 ***
(0.0001) (0.0113)
Rdintensity 0.0101 *** 0.8592 **
(0.0027) (0.3361)
Pollution 0.0002 0.0176
(0.0003) (0.0183)
HHI −0.0001 0.0143
(0.0001) (0.0161)
GDP −0.0023 0.1356
(0.0017) (0.1208)
Constant 0.0021 −0.7767 ***
(0.0035) (0.2686)
Year fixed effect Yes Yes
Firm fixed effect Yes Yes
N 29,128 29,128
R2_adj 0.4770 0.3412
Note: ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the industry-year level.
As shown in Columns (1) and (2) of Table 15, compared with small-scale firms, green
collaborative innovation has a more significant inhibitory effect on the carbon emissions
of large-scale firms. The possible reason for this result is that large-scale firms are more
willing to assume social responsibilities and have stronger capabilities for green collabo-
rative innovation. Moreover, compared with small firms, large-scale firms have resource
advantages and can increase green R&D investments, actively carry out green collaborative
innovation, etc., thus making it easier to reduce carbon emissions.
As shown in Columns (3) and (4) of Table 15, compared with non-SOEs, green collabo-
rative innovation has a more significant inhibitory effect on the carbon emissions of SOEs.
The possible reason for this result is that, compared with non-SOEs, SOEs can obtain more
government support and resources, which is conducive to their implementation of green
collaborative innovation and environmental policies, and they can more effectively play
the inhibitory role of green collaborative innovation on enterprises’ carbon emissions.
As shown in Columns (5)–(7) of Table 15, green collaborative innovation only has an
inhibitory effect on the carbon emissions of secondary-sector firms. A possible explanation
is that the secondary sector covers industry and construction, which are the main areas of
energy consumption and carbon emissions. Its production process relies on a large amount
of fossil energy, such as in industries including steel, cement, and chemicals, which have a
large carbon emissions base. Green collaborative innovation can target these high-emission
Sustainability 2025,17, 4141 30 of 38
links and effectively reduce carbon emissions through technological improvement and
management optimization. In contrast, the primary sector is mainly agriculture, and its
carbon emissions mainly come from land use changes and agricultural activities, such as
livestock breeding and fertilizer use. Its emission sources are scattered and difficult to
manage centrally. The tertiary sector is mainly the service industry, with relatively low
energy consumption. Its carbon emissions are mainly concentrated in energy use in office
spaces, transportation, etc., and the overall emission scale is relatively small. Therefore, the
impact of green collaborative innovation on these carbon emissions is relatively limited.
As shown in Columns (8) and (9) of Table 15, green collaborative innovation has a
more significant inhibitory effect on the carbon emissions of enterprises in regions with
stronger environmental penalty enforcement. A possible explanation is that in regions with
stronger environmental protection penalty enforcement, enterprises face a stronger impact
of environmental costs, which forces more enterprises to increase green R&D investments
and actively carry out green collaborative innovation to reduce carbon emissions. Moreover,
in regions with stronger environmental protection penalty enforcement, enterprises are
more inclined to improve environmental performance through green collaborative innova-
tion to achieve carbon emission reduction, thereby obtaining innovation compensation and
market competitiveness.
Table 15. Results of the heterogeneity test (green collaborative innovation on carbon emissions).
Variable
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Large-
Scale
Firms
Small-
Scale
Firms
SOEs Non-
SOEs
Primary
Sector
Secondary
Sector
Tertiary
Sector
Strong Envi-
ronmental
Penalty
Enforcement
Weak
Environmental
Penalty
Enforcement
Carbon Carbon Carbon Carbon Carbon Carbon Carbon Carbon Carbon
GCI −0.0004 * −0.0005 −0.0007 ** −0.0002 −0.0004 −0.0005 ** 0.0001 −0.0005 ** 0.0001
(0.0002) (0.0004) (0.0003) (0.0002) (0.0030) (0.0002) (0.0001) (0.0003) (0.0003)
Size 0.0005 * −0.0004 0.0005 −
0.0006 ***
−0.0006 −0.0003 0.0000 −0.0003 −0.0002
(0.0003) (0.0003) (0.0003) (0.0002) (0.0009) (0.0003) (0.0001) (0.0002) (0.0003)
Lev 0.0013 0.0011 0.0022 0.0010 −0.0046 0.0019 ** 0.0000 0.0016 ** 0.0027 **
(0.0011) (0.0009) (0.0014) (0.0006) (0.0042) (0.0009) (0.0003) (0.0008) (0.0013)
Age 0.0015 *** 0.0010 *** 0.0012 * 0.0010 *** 0.0020 * 0.0018 *** 0.0001 0.0017 *** 0.0012 **
(0.0004) (0.0003) (0.0007) (0.0003) (0.0010) (0.0003) (0.0002) (0.0003) (0.0005)
Roa −0.0009 0.0071 *** 0.0101 *** 0.0019 * 0.0100 * 0.0069 *** 0.0012 ** 0.0044 *** 0.0059 ***
(0.0024) (0.0013) (0.0034) (0.0011) (0.0060) (0.0019) (0.0006) (0.0015) (0.0022)
Growth −0.0004 ** −0.0001 −0.0003 −0.0003 ** −0.0008 −0.0004 * −0.0001 * −0.0003 ** −0.0001
(0.0002) (0.0002) (0.0002) (0.0001) (0.0006) (0.0002) (0.0000) (0.0001) (0.0002)
Rdintensity 0.0123 *** 0.0085 *** 0.0246 *** 0.0009 0.0415 0.0208 *** 0.0002 0.0082 *** 0.0134 **
(0.0043) (0.0032) (0.0060) (0.0024) (0.0341) (0.0052) (0.0010) (0.0029) (0.0054)
Pollution 0.0003 0.0006 −0.0002 0.0002 0.0012 0.0004 0.0004 0.0001 −0.0000
(0.0004) (0.0005) (0.0004) (0.0004) (0.0017) (0.0003) (0.0004) (0.0004) (0.0006)
HHI −0.0000 −0.0003 * −0.0001 −0.0001 −0.0015 −0.0001 −0.0002 ** −0.0001 −0.0002
(0.0002) (0.0002) (0.0002) (0.0001) (0.0013) (0.0002) (0.0001) (0.0002) (0.0002)
GDP −0.0012 −0.0021 0.0003 −0.0043 ** −0.0037 −0.0018 −0.0017 * −0.0027 0.0010
(0.0029) (0.0022) (0.0035) (0.0021) (0.0056) (0.0024) (0.0009) (0.0026) (0.0026)
Constant −0.0122 ** 0.0079 −0.0122 * 0.0129 *** 0.0134 0.0053 −0.0004 0.0045 0.0022
(0.0061) (0.0055) (0.0073) (0.0040) (0.0193) (0.0056) (0.0019) (0.0047) (0.0074)
Tests for between-group
differences p-value = 0.000 *** p-value = 0.000 *** p-value = 0.000 *** p-value = 0.000 ***
Yearfixedeffect Yes Yes Yes Yes Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 14,577 14,551 11,654 17,474 389 20,157 8582 17,933 11,195
R2_adj 0.3848 0.2880 0.3377 0.3813 0.2226 0.3419 0.1838 0.3497 0.3590
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
According to Columns (1) and (2) of Table 16, green collaborative innovation demon-
strates a more significant enhancement effect on ESG performance in large-scale firms
compared with small-scale firms. This outcome may arise because large firms tend to
improve their ESG performance to gain legitimacy. Relative to smaller firms, large firms
face greater public scrutiny and broader stakeholder engagement. The need to effectively
balance diverse interests and formulate key responses to meet stakeholder demands drives
them to more actively adopt green collaborative innovation to comply with ESG standards.
Sustainability 2025,17, 4141 31 of 38
Table 16. Results of the heterogeneity test (green collaborative innovation on ESG performance).
Variable
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Large-
Scale Firms
Small-
Scale Firms SOEs Non-SOEs Primary
Sector
Secondary
Sector
Tertiary
Sector
Strong Envi-
ronmental
Penalty
Enforcement
Weak
Environmental
Penalty
Enforcement
ESG ESG ESG ESG ESG ESG ESG ESG ESG
GCI 0.0432 *** 0.0188 0.0428 ** 0.0224 0.0633 0.0397 ** 0.0705 ** 0.0412 ** 0.0358
(0.0166) (0.0296) (0.0188) (0.0239) (0.1252) (0.0159) (0.0343) (0.0198) (0.0266)
Size 0.2643 *** 0.2648 *** 0.2624 *** 0.2938 *** 0.3493 ** 0.2643 *** 0.2796 *** 0.2483 *** 0.2964 ***
(0.0231) (0.0223) (0.0209) (0.0169) (0.1430) (0.0159) (0.0255) (0.0164) (0.0227)
Lev −0.7025 *** −0.6114 *** −0.5852 *** −0.6693 *** −
1.7739 ***
−
0.5964 ***
−
0.6173 ***
−0.6231 *** −0.5927 ***
(0.0954) (0.0772) (0.0895) (0.0710) (0.4133) (0.0694) (0.1084) (0.0711) (0.0972)
Age −0.1609 *** −0.3448 *** 0.0252 −0.2575 *** −0.5965 * −
0.3493 ***
−
0.2929 ***
−0.3366 *** −0.3558 ***
(0.0440) (0.0373) (0.0680) (0.0351) (0.3166) (0.0321) (0.0539) (0.0344) (0.0565)
Roa 0.1245 −0.1729 −0.6310 *** 0.1415 −
2.0703 ***
−0.0241 −0.0168 −0.1351 0.1388
(0.2310) (0.1480) (0.2014) (0.1487) (0.7795) (0.1518) (0.2034) (0.1636) (0.1948)
Growth −0.0667 *** −0.0471 *** −0.0776 *** −0.0717 *** −0.0008 −
0.0774 ***
−
0.0605 ***
−0.0512 *** −0.0809 ***
(0.0165) (0.0158) (0.0183) (0.0148) (0.1049) (0.0141) (0.0197) (0.0153) (0.0169)
Rdintensity 0.7763 * 0.7872 * 2.0543 *** 1.1294 *** −3.1858 0.9285 ** 0.4474 1.7155 *** −0.3105
(0.4662) (0.4751) (0.5956) (0.3611) (3.4490) (0.4276) (0.4260) (0.4706) (0.5663)
Pollution −0.0355 0.0690 ** −0.0349 0.0504 * −0.1095 −0.0043 −0.0632 −0.0053 0.0329
(0.0227) (0.0286) (0.0251) (0.0270) (0.2257) (0.0195) (0.0562) (0.0248) (0.0308)
HHI 0.0111 0.0152 0.0252 0.0173 −0.1023 −0.0072 0.0599 0.0037 0.0264
(0.0196) (0.0182) (0.0217) (0.0175) (0.0790) (0.0147) (0.0384) (0.0171) (0.0232)
GDP 0.4024 ** −0.2008 0.3430 * 0.0371 0.9173 0.0637 0.2629 0.1159 0.0384
(0.1674) (0.1757) (0.1814) (0.1704) (1.1335) (0.1402) (0.2549) (0.1746) (0.1911)
Constant −1.1836 ** −0.8377 * −1.6862 *** −1.6972 *** −1.6869 −0.8218 ** −1.1952 ** −0.4356 −1.5401***
(0.5257) (0.4697) (0.5106) (0.3655) (3.0509) (0.3389) (0.5536) (0.3528) (0.5057)
Tests for
between-group
differences
p-value = 0.000 *** p-value = 0.000 *** p-value = 0.000 *** p-value = 0.000 ***
Year fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes
Firm fixed effect Yes Yes Yes Yes Yes Yes Yes Yes Yes
N 14,577 14,551 11,654 17,474 389 20,157 8582 17,933 11,195
R2_adj 0.4571 0.5009 0.5068 0.4694 0.3625 0.4712 0.5028 0.4825 0.5057
Note: * p< 0.10, ** p< 0.05, *** p< 0.01. The values in brackets are standard errors clustered to the
industry-year level.
According to Columns (3) and (4) of Table 16, green collaborative innovation exhibits
a stronger promoting effect on ESG performance in SOEs. A potential explanation lies
in SOEs’ dual focus: beyond profit generation, they bear social responsibilities, includ-
ing addressing wealth inequality, labor disputes, and environmental protection. Their
institutional characteristics enable swifter responses to policy guidance and government
signals. When national initiatives promote ESG and sustainable development, SOEs conse-
quently demonstrate stronger motivation to leverage green collaborative innovation for
ESG advancement.
Columns (5)–(7) of Table 16 reveal that green collaborative innovation significantly
enhances ESG performance in both secondary and tertiary sectors, with a more pronounced
effect in the tertiary sector. This disparity may stem from fundamental industrial character-
istics. The primary sector features extended production cycles and seasonal constraints,
delaying the observable impacts of technological adoption. In contrast, the secondary and
tertiary sectors benefit from shorter production cycles that facilitate quicker implementation
of collaborative innovation outcomes. Furthermore, the social image and reputation of
tertiary-sector firms are critical to their business development. Strong ESG performance
enables enterprises to establish a positive social image while strengthening consumer trust
and loyalty. In contrast, consumers of secondary-sector products tend to prioritize factors
such as price, quality, and functionality, showing relatively lower concern for corporate
ESG performance. Consequently, tertiary-sector enterprises exhibit greater motivation
than their secondary-sector counterparts to enhance their ESG performance through green
collaborative innovation, thereby securing societal recognition and support.
According to Columns (8) and (9) of Table 16, green collaborative innovation demon-
strates heightened effectiveness in improving ESG performance among enterprises located
in regions with stringent environmental penalties. This phenomenon likely occurs because
rigorous environmental regulations impose substantial compliance pressures. Potential
Sustainability 2025,17, 4141 32 of 38
violations risk severe consequences, including heavy fines and operational suspensions that
threaten both financial stability and corporate reputation. Green collaborative innovation
offers enterprises a dual solution: meeting environmental requirements while achieving
sustainable development. Through collaborative green innovation with suppliers and
partners, enterprises can jointly develop and implement eco-friendly technologies, pro-
cesses, and products. This approach not only enhances ESG performance but also reduces
environmental risks and regulatory penalties.
6. Conclusions and Police Implications
6.1. Conclusions
The new generation of information technology, artificial intelligence, and green envi-
ronmental protection technology is becoming a new engine of economic growth. Through
the application of artificial intelligence technology, the formation and development of new
quality productivity are promoted, thus enabling the development of green and low-carbon
industries. This is the key to promoting high-quality economic development. From the
perspective of collaborative innovation, using the data of green patents jointly applied by
Chinese A-share listed companies and other entities from 2010 to 2023, this study discusses
the effect and mechanism of artificial intelligence on green collaborative innovation. The
results show that (1) artificial intelligence has a promoting effect on green collaborative
innovation. This conclusion is still valid after the endogeneity problem is alleviated and a
robustness test is conducted. (2) The nonlinear relationship test suggests that threshold
effects or diminishing returns of artificial intelligence on corporate green collaborative
innovation have not yet occurred. (3) The mechanism test shows that the promoting effect
of artificial intelligence on green collaborative innovation can be realized by reducing
transaction costs and optimizing the labor structure. (4) The moderating effect analysis
shows that green investor entry and CEO openness can strengthen the promoting effect
of artificial intelligence on green collaborative innovation. This promoting effect is more
pronounced in regions with higher marketization and stronger legal regulations. (5) The
heterogeneity tests indicate that the positive impact of artificial intelligence technology
application on green collaborative innovation is more significant in the case of high macroe-
conomic uncertainty, large-scale enterprises, and SOEs. (6) In addition, the promoting
effect of artificial intelligence on green collaborative innovation helps firms reduce carbon
emissions and improve ESG performance, promoting sustainable business practices. We
further revealed that this promoting effect exhibits heterogeneity in different firm sizes,
ownership types, the sectors to which the firm belongs, and the stringency of regional
environmental penalties.
6.2. Policy Recommendations
According to the above conclusions, this study has the following policy implications.
For enterprises, firms should seize the opportunity of artificial intelligence technology
and take advantage of artificial intelligence technology to promote breakthroughs in the
key core technologies. Firstly, they can reduce transaction costs by building intelligent
collaborative platforms. For example, a blockchain-powered supply chain management sys-
tem can be deployed to automate environmental terms execution and technology matching
processes through smart contracts, reducing cross-organizational negotiation and informa-
tion search costs. Alternatively, an industry-level data sharing alliance can be established
to realize secure collaborative analysis by using privacy-computing technology and reduce
data flow barriers and compliance costs. Secondly, enterprises should actively integrate a
variety of artificial intelligence technologies, combine different tools and algorithms such
as machine learning, deep learning, knowledge graphs, and reasoning technology, en-
Sustainability 2025,17, 4141 33 of 38
hance the ability to identify green innovation opportunities, allocate innovation resources,
transform innovation strategies, and actively participate in green collaborative innovation.
Finally, enterprises should also attach importance to the construction of human capital,
constantly optimize the labor structure, enhance the learning and adaptation ability in the
process of green collaborative innovation through high-quality human capital, improve
the performance of green innovation, and accelerate the value transformation of artificial
intelligence technology into green innovation.
The government, first of all, should promote the clarification of data property rights
and improve the benefit distribution mode of green collaborative innovation. Secondly, in
order to give full play to the positive role of the artificial intelligence technology application
in green coordinated innovation, the government should intensify efforts to open up
and share diversified knowledge resources, establish cross-regional and cross-industrial
integration and interconnection mechanisms, and build data resource platforms for different
industries and enterprises. It provides strong support for enterprises, parks, universities,
research institutes, and green technology innovation alliances to drive green collaborative
innovation through digital transformation. Finally, the government needs to formulate
and implement long-term policies to support the artificial intelligence technology R&D
and application and give innovation alliances sufficient time and resources to conduct
technology trials, thus promoting green collaborative innovation. At the same time, the
government can also introduce relevant policies, tax and fee cuts, subsidies, and other
measures to increase the participation of financial institutions, intermediaries, and other
organizations in green collaborative innovation.
6.3. Further Discussion
First, an extended discussion on ethical issues must be carried out. This study acknowl-
edges the role of artificial intelligence in driving corporate green collaborative innovation
but emphasizes the ethical risks inherent in its deployment. (1) Artificial intelligence tech-
nology relies on vast datasets for model training, which may involve privacy breaches of
supply chain partners, consumer data, or environmental information (e.g., production pro-
cess data, emissions monitoring records). For example, when optimizing green technology
solutions via artificial intelligence, failure to anonymize sensitive partner data or obtain
proper authorization could violate data compliance laws such as China’s Personal Infor-
mation Protection Law, leading to legal disputes and eroding trust within the innovation
ecosystem. (2) Algorithmic bias may compromise fairness in green resource allocation.
If artificial intelligence models used to screen green technology suppliers or prioritize
innovation projects embed biases (e.g., regional or scale-based discrimination), they risk ex-
acerbating the “Matthew Effect” in environmental governance. To address this, enterprises
should establish ethical review mechanisms, integrate fairness metrics (e.g., counterfactual
fairness testing) during algorithm design, and enhance decision transparency through
explainable artificial intelligence technology.
Second, an extended discussion on regulatory barriers must be conducted. The
global regulatory landscape is dynamically tightening, posing three key challenges for
businesses. (1) Cross-border data flow restrictions (e.g., China’s Data Export Security
Assessment Measures) may hinder multinational corporations from consolidating global
R&D data, limiting the generalizability of artificial intelligence models. (2) Certification
barriers for environmental AI algorithms (e.g., the EU’s proposed “Green Algorithm”
compliance audits) could increase technical implementation costs. (3) Intellectual property
disputes (e.g., patent ownership of AI-generated green technology solutions) may dampen
enthusiasm for cross-enterprise collaboration. These regulatory hurdles could delay the
Sustainability 2025,17, 4141 34 of 38
scaled application of artificial intelligence solutions, particularly creating a crowding-out
effect for small and medium-sized enterprises.
Finally, an extended discussion on compliance strategies must be conducted. To
balance green innovation with regulatory requirements, enterprises should adopt the fol-
lowing approaches: (1) implementing a compliance-by-design framework that embeds
data minimization and anonymization modules at the artificial intelligence development
stage and leverages federated learning for distributed data training; (2) proactively en-
gaging in co-developing industry standards, such as collaborating on ethical guidelines
for green AI algorithms to align regulations with technological progress; (3) establishing
dynamic risk assessment systems to monitor AI-driven decisions’ environmental equity
impacts on stakeholders (e.g., communities, suppliers), iteratively optimizing algorithms
to enhance social responsibility. At the policy level, regulators should adopt a “regula-
tory sandbox” approach, enabling controlled testing of green AI applications to accelerate
compliance verification.
6.4. Limitations and Potential Future Study Areas
Although this study provides a systematic exploration of the impact of artificial in-
telligence on green collaborative innovation, there remain several limitations that require
improvement in future research. First, regarding the measurement of green collaborative
innovation, we adopted the widely accepted metric of quantifying corporate green col-
laborative innovation levels through the quantity of jointly applied for green patents and
the total number of green patent grants jointly applied for. Future studies could refine the
classification of green patents based on the International Patent Classification (IPC) codes
to enable deeper analysis of how artificial intelligence applications affect collaborative
innovation in different types of green technologies.
Second, concerning the measurement of mediating variables, transaction costs were
proxied using asset specificity [
54
]. However, this indicator only reflects static character-
istics of asset structure and fails to dynamically capture transactional frictions in green
collaborative innovation. Future research could improve this by incorporating process-
based metrics such as contract enforcement efficiency. Additionally, this study focused
on validating the mediating role of labor structure optimization between artificial intel-
ligence and green collaborative innovation; future investigations could explore both the
direct impacts of artificial intelligence on the labor structure (e.g., job displacement effects)
and the indirect influences (e.g., skill transformation), thereby further expanding the re-
search on the interdisciplinary interaction of artificial intelligence and economics at the
micro-enterprise level.
Third, this study primarily focused on verifying the direct effects of artificial intel-
ligence on green collaborative innovation. Future studies could delve into the follow-
ing:
(1) heterogeneity
in AI’s facilitative effects under varying intensities of data privacy
protection; (2) dynamic relationships between algorithm fairness metrics and corporate
ESG performance; (3) how the speed of AI technology iteration forces policy adaptation;
(4) combining
a spatial econometric model and a hierarchical network model, this paper
explores the dynamic law of artificial intelligence technology on cross-regional innovation;
and (5) identification of threshold effects of regulatory enforcement, thereby providing
more nuanced guidance for policy formulation.
Sustainability 2025,17, 4141 35 of 38
Author Contributions: Conceptualization, G.L.; data curation, G.L.; formal analysis, G.L.; method-
ology, G.L.; software, G.L.; supervision, B.L.; validation, B.L.; writing—original draft preparation,
G.L.; writing—review and editing, B.L. All authors have read and agreed to the published version of
the manuscript.
Funding: This work was supported by the National Natural Science Foundation of China (grant
No. 71772151).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data that support the findings of this study are available from the
corresponding author upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.
Appendix A
Table A1. Artificial intelligence dictionary.
1Artificial
intelligence 2 AI products 3 AI chip 4 Machine
translation 5Machine
learning
6Computer
vision 7
Human-
computer
interaction
8 Deep learning 9 Neural
network 10 Biological
recognition
11 Image
recognition 12 Data mining 13 Feature
recognition 14 Voice synthesis 15 Voice
recognition
16 Knowledge
graph 17 Smart banking 18 Intelligent
insurance 19
Human-
computer
coordination
20 Intelligent
supervision
21 Intelligent
education 22
Intelligent
Customer
Service
23 Intelligent
retail 24 Intelligent
agriculture 25
Intelligent
investment
advisor
26 Augmented
reality 27 Virtual reality 28 Intelligent
medical care 29 Intelligent
speaker 30 Intelligent
voice
31 Intelligent
Government 32 Automatic
driving 33 Intelligent
transport 34 Convolutional
neural network 35 Voiceprint
recognition
36 Feature
extraction 37 Unmanned
driving 38 Smart home 39
Question
answering
system
40 Face
recognition
41 Business
intelligence 42 Smart finance 43 Recurrent
neural network 44 Reinforcement
learning 45 Intelligent
agent
46 Smart home
care 47 Big data
marketing 48 Big data risk
control 49 Big data
analysis 50 Big data
processing
51 Support vector
machine, SVM 52
Long
short-term
memory, LSTM
53
Robotic process
automation 54
Natural
language
processing
55 Distributed
computing
56 Knowledge
representation 57 Intelligent chip 58 Wearable
products 59 Big Data
Management 60 Intelligent
sensor
61 Pattern
recognition 62 Edge
computing 63 Big Data
Platform 64 Intelligent
computing 65 Intelligent
search
66 Internet of
things 67 Cloud
computing 68 Augmented
intelligence 69 Voice
interaction 70
Intelligent
environmental
protection
71 Man-machine
dialogue 72 Deep neural
networks 73 Big Data
Operations
Sustainability 2025,17, 4141 36 of 38
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