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242-253 

242 

 

 

 

Article 

Intellectual property protection, digital economy 

development, and corporate green innovation: 

threshold effects and regulatory mechanisms 
Jun Pan1,2, Rusmawati Said1*, Normaz Wana Ismail1  

1School of Business and Economics, University Putra Malaysia, Serdang 43400, Selangor, Malaysia 
2College of Digital Intelligence and Financial Management, Minnan University of Science and Technology, Quanzhou 

362700, China 

A R T I C L E   I N F O 
 

Article history: 
Received 23 August 2025  
Received in revised form 
26 October 2025 
Accepted 28 November 2025 
 
Keywords:  
Intellectual property protection, Digital economy, 
Green innovation, Threshold effects, 
Corporate governance 
 
*Corresponding author 
Email address: 
rusmawati@upm.edu.my 
 
DOI: 10.55670/fpll.futech.5.1.21 

A B S T R A C T 
 

This study examines the complex interactions among intellectual property 
protection (IPP), digital economy development, and corporate green innovation 
using panel data from Chinese listed firms (2014-2022). Employing threshold 
regression models and moderated regression analysis, we identify significant 
nonlinear relationships and regulatory mechanisms. Results reveal a U-shaped 
relationship between IPP and the quantity of green innovation, with identifiable 
threshold effects across multiple protection regimes. However, IPP exhibits 
predominantly negative effects on innovation quality across protection levels, 
with varying intensities observed in different regimes (zones 1-4). Digital 
economy development demonstrates dimension-specific moderating effects, 
significantly amplifying the promotional effect on innovation quantity 
(coefficient 0.711**) but showing minimal impact on innovation quality (-0.085, 
insignificant), functioning as an "efficiency amplifier" rather than a "quality 
enhancer." Green agency costs exhibit complex regulatory mechanisms that 
vary across institutional regimes, resulting in compensatory effects in weak 
protection environments and triggering institutional overload in strong 
protection contexts. These findings challenge the linear "more protection 
equals more innovation" assumption and highlight fundamental distinctions in 
how technological and institutional drivers affect different dimensions of green 
innovation. The results have crucial implications for policymakers in designing 
differentiated IPP regimes and targeted digital economy policies optimized for 
specific development stages. 

1. Introduction 

Climate warming and ecological pollution have 
worsened, and the concept of carbon peak and carbon 
neutrality has gained worldwide consensus, whereas green 
development is the most strategic objective for all nations, 
particularly China, to achieve sustainable development [1]. 
Here, green technology innovation, as a key driving force for 
the realization of a symbiotic development between economic 
growth and ecological environment protection, is extremely 
important for firms to reduce pollution emissions and 
accelerate their green transformation [2,3]. Yet, green 
innovation is often faced with "double externalities" [4] - the 
intersection point of innovation spillovers and environmental 
externalities, which can deter the market mechanism from 
adequately stimulating firms to invest in green innovation 
[5,6]. Intellectual property rights (IPR) protection is a crucial 
institutional tool for encouraging green innovation and 

marketization [7,8]. Theoretically speaking, good intellectual 
property protection can internalize the positive spillovers of 
innovation by granting innovators exclusive rights, thereby 
increasing the value that green R&D outcomes can capture, 
and hence inducing enthusiasm among enterprises to conduct 
green innovation. However, the impact of IPR protection on 
green innovation is not a simple linear relationship; overly 
strong protection may inhibit knowledge flow and technology 
diffusion through the 'tragedy of the anticommons' effect [9], 
where fragmented IP rights create barriers to cumulative 
innovation, while protection that is too weak may fail to 
provide sufficient incentives for innovation. Meanwhile, the 
booming development of the digital economy is profoundly 
changing the economic and social landscape. It is regarded as 
a new type of productivity that promotes green 
transformation and enhances innovation capacity [10,11]. 
The digital economy can significantly reduce the search cost 

 

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February 2026| Volume 05 | Issue 01 | Pages 242-253 

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J. Pan et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 242-253 

243 

 

and coordination cost of green innovation, optimize the 
allocation of innovation resources, and promote green 
innovation through the spillover effect of data knowledge, 
enhancing information transparency, and reducing 
transaction costs [11,12]. Above all, the evolution of the 
digital economy can revolutionize the external environment 
upon which IPR protection is embedded, altering or adding to 
the mode of institutional policy impact. Though the role of 
intellectual property protection and the digital economy in 
promoting green innovation has drawn extensive academic 
attention, there are some obvious shortcomings in previous 
research. First, intellectual property protection or the digital 
economy itself is usually considered in most research, 
excluding its interactive mechanism. Second, most research is 
linear in type, failing to fully display policy effects under 
varying protection levels. Third, the multi-dimensional 
characteristics of green innovation are often overlooked, with 
a limited distinction between innovation quantity and quality. 
Fourth, the internal corporate governance elements that act 
as moderators are not explored well, particularly the role of 
agency costs in influencing how firms respond to external 
institutional incentives. According to these research gaps, this 
paper meticulously examines the dynamic regulatory effect 
mechanism of digital economy growth between intellectual 
property protection and green innovation, highlighting 
particular attention to threshold effects and nonlinearity. 
Using threshold regression models and moderated regression 
analysis with panel data from Chinese listed companies 
spanning 2014-2022, this study aims to: identify the 
nonlinear features and critical values of IPR protection's 
impact; analyze the moderating role of digital economy 
development; distinguish between quantitative and 
qualitative dimensions of green innovation; and examine the 
moderating effect of agency costs. This study makes three key 
contributions: (1) Theoretical innovation - we enrich green 
innovation theory by identifying threshold effects and 
distinguishing quantity-quality dimensions, challenging the 
linear 'more protection equals more innovation' assumption; 
(2) Methodological advancement - we provide a novel 
analytical framework integrating threshold regression with 
triple and quadruple moderated regression analysis to 
capture complex interaction effects; (3) Policy implications - 
we offer empirical evidence for implementing regime-specific 
IPR strategies and targeted digital economy policies across 
different development stages, recognizing that optimal policy 
configurations vary with institutional maturity. 

2. Literature review 

2.1 Intellectual property protection and green 
innovation 
As a core institutional arrangement of the modern 

innovation system, the mechanism through which intellectual 
property protection impacts green innovation has received 
increasing attention from academics. From a theoretical 
perspective, intellectual property protection can effectively 
internalize the positive externalities of innovation activities 
by granting exclusive rights to innovators and providing 
incentives for enterprises to increase R&D investment [13]. 
Such incentives are particularly important in the case of green 
innovation, which often faces the challenge of "double 
externalities" - the knowledge spillovers of general 
technological innovation and the social benefits of 
environmental protection - making it difficult for the market 
mechanism to incentivize firms to invest on their own 
adequately [5]. Empirical studies generally support the 
positive effect of IPR protection on green innovation [14]. 

Strengthening IPR protection can significantly increase the 
commercial value of environmental R&D results, reduce the 
risk of imitation by competitors, and thereby enhance the 
endogenous motivation of enterprises to undertake green 
innovation [2,13]. However, a growing number of studies 
have revealed the complexity and non-linear nature of the 
impact of IPR protection on green innovation. Excessive IPR 
protection may create an "innovation lock-in" effect, 
inhibiting the flow of knowledge and the diffusion of 
technology [6,15]. Meanwhile, different strengths of IPR 
protection may have differentiated impacts on the quantity 
and quality of innovations, as an increase in patent 
applications does not always equate to high-quality 
technological breakthroughs [3]. Based on the above 
literature, this study proposes hypothesis H1. Initial 
enforcement costs may deter innovation at lower protection 
levels, but accumulated benefits emerge as protection 
strengthens, creating threshold turning points [2]. 
H1a: IPR protection exhibits a non-linear U-shaped 
relationship with green innovation quantity, with identifiable 
threshold effects. Strong protection may create "patent 
thickets" that fragment knowledge and incentivize 
incremental over breakthrough innovations [3,6]. 
H1b: IPR protection negatively affects green innovation 
quality in regimes 1-4, with varying intensities. 

2.2 Digital economy and green innovation 
The digital economy, as the core driving force of the new 

round of technological revolution, is profoundly reshaping 
enterprise innovation patterns and resource allocation 
mechanisms. Existing studies generally agree that the 
development of the digital economy has a significant role in 
promoting green innovation [16,17]. In terms of the 
mechanism of action, digital technology can significantly 
improve information transparency, optimize the efficiency of 
resource allocation, and promote knowledge sharing and data 
overflow through big data analysis, artificial intelligence 
algorithms, and cloud computing platforms, thus reducing the 
search cost, coordination cost, and trial-and-error cost of 
green innovation [11]. Particularly, enterprise digital 
transformation can support green innovation through 
various channels, including realizing actual environmental 
observation and resource management, integrating 
innovation organizations to facilitate industry-university-
research collaboration, and offering financial and policy 
incentives [18]. Empirical evidence suggests that enterprise 
digital transformation will substantially promote the degree 
of green innovation, particularly substance-based green 
innovation [12]. However, the impact of the digital economy 
on green innovation is characterized by pervasive 
heterogeneity among variables such as enterprise size, 
industry, and technology platform type. For instance, Xu et al. 
[18] found that larger firms benefit more from digital 
economy development through enhanced resource allocation 
efficiency and stronger innovation network effects, while 
smaller enterprises face greater implementation barriers. 
Different dimensions of green innovation may respond 
differently to digital tools, as the advantages of digital 
technologies in facilitating innovation scale-up may not be 
fully applicable to quality innovations that require long-term 
accumulation and deep insights [19]. 

2.3 Regulatory mechanisms and threshold effects in the 
digital economy 
Although relatively few studies have directly explored 

the moderating role of the digital economy in the relationship 
between intellectual property protection and green 



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innovation, the existing literature provides important 
theoretical foundations and empirical clues for 
understanding this complex interaction mechanism. 
Theoretically, the development of the digital economy may 
have a moderating influence on the incentive effect of green 
innovation by altering the implementation environment and 
the transmission mechanism of IPR protection. The digital 
economy can significantly enhance the actual effectiveness of 
IPR protection by improving information transparency and 
regulatory efficiency. In a highly digitalized environment, IPR 
infringements can be more easily identified, tracked, and 
punished, thereby better protecting the exclusivity rights of 
IPRs and allowing the incentive effect to be fully realized [7]. 
This "technology-enabling" effect enables more digitized 
enterprises to obtain better IPR protection even at relatively 
low levels of institutional protection. The digital economy 
facilitates the flow of knowledge and the formation of 
innovation networks, which may have a differentiated impact 
on the effectiveness of IP protection at different stages of 
development. At the stage of low digitization, IPR protection 
primarily serves as an incentive; whereas in a highly digitized 
environment, overly strong IPR protection may conflict with 
the digital economy's features of open innovation and 
knowledge sharing, creating institutional friction [20]. This 
dynamic feature implies that the level of digital economy 
development may constitute an important threshold variable 
for the effect of IPR protection. 

Recent studies have begun to directly test the interaction 
effect of digital technology and IPR protection. Huang and Lau 
[11] explicitly incorporate the interaction term between 
digital transformation and IPR protection (DT × IPP) in their 
analysis of the quality of firms' green innovations, providing 
direct empirical evidence of the moderating effect of the 
digital economy. This suggests that the level of digital 
economy development may indeed change the intensity or 
direction of the impact of IPR protection on green innovation, 
and this moderating effect may be characterized by 
heterogeneity across different dimensions of innovation. 
Based on the above literature, this study proposes hypothesis 
H2: 
H2a: The level of digital economy development can 
significantly modulate the promotion effect of intellectual 
property protection on the number of green innovations. 
H2b: There are dimensional differences in the moderating 
effect of the level of digital economy development on the 
relationship between intellectual property protection and the 
quality of green innovation. 

2.4 The moderating role of corporate governance 
factors 
In the research on the relationship between intellectual 

property protection and green innovation, the internal 
governance factors of enterprises, especially the issue of 
agency cost, have gradually received attention from scholars. 
Agency cost reflects the degree of conflict of interest between 
owners and managers of enterprises, which directly affects 
the investment decision and resource allocation efficiency of 
enterprises. In green innovation decision-making, as green 
projects are typically characterized by long investment cycles 
and high uncertainty of returns, the agency problem may be 
more pronounced, and managers may reduce green 
innovation investments due to risk aversion or short-term 
performance considerations [21]. Existing research suggests 
that agency costs not only directly affect the level of firms' 
investment in innovation, but may also have a complex impact 
on the incentive effects of IP protection through interaction 

with the external institutional environment. In firms with 
high agency costs, managers may fail to respond adequately 
to institutional incentives due to internal governance 
problems, even if the level of external IP protection is strong. 
In contrast, the incentive effects of IP protection can be better 
realized in well-governed firms. This interaction between 
internal and external institutions offers a new perspective on 
understanding firm heterogeneity in the effects of IP 
protection. Based on the above literature, this study proposes 
hypotheses H3 and H4: 
H3: Green agency costs play a complex regulatory role in the 
relationship between intellectual property protection and 
green innovation. 
H3a: Green agency costs weaken the promotion effect of IP 
protection on green innovation. 
H3b: The moderating effect of green agency costs is 
significantly different under different levels of IP protection. 
H4: There is a triple interaction effect among digital economy, 
intellectual property protection, and green agency costs. 
H4a: The triple interaction of the development level of the 
digital economy, the intensity of intellectual property 
protection, and green agency costs has a significant effect on 
the number of green innovations. 
H4b: The triple interaction effect exhibits different modes of 
action and regime characteristics in the dimension of green 
innovation quality. 

3. Research methodology 

3.1 Data 
This study employs five main variables to test the 

research hypotheses. Green innovation is measured from two 
dimensions: quantity (GreenInnN) using the natural 
logarithm of green patent applications plus one, and quality 
(GreenInnQ) using the natural logarithm of green patent 
citations plus one, with self-citations excluded. Intellectual 
property protection level (IPProtect) is calculated based on 
the ratio of intellectual property cases in a city to its GDP, 
normalized by the national average [22]. The digital economy 
level (dig_level) is a comprehensive index constructed using 
the entropy weight method, incorporating digitization-
related vocabulary frequency from annual reports, including 
keywords such as big data, artificial intelligence, and cloud 
computing. Green agency cost (GreenAgCst) is measured by 
the ratio of environmental management expenses to 
operating revenue [23]. Detailed variable definitions are 
presented in Table 1. The choice of control variables was 
based on past literature. The specific settings are shown in 
Table 2. Descriptive statistics of the data are shown in Table 
3. The main variables are characterized as follows. The mean 
values of green innovation quantity (GreenInnN) and quality 
(GreenInnQ) are 1.129 and 0.915, respectively, with large 
standard deviations, indicating significant differences in the 
level of green innovation among enterprises. The mean value 
of intellectual property protection level (IPProtect) is 0.661, 
and the mean value of digital economy development level 
(dig_level) is 0.226. The mean value of green agency cost 
(GreenAgCst) is close to 0, and the maximum value is only 
0.083, which indicates that the cost is relatively low. The 
control variables are characterized as follows. The average 
ROA of the sample enterprises is 5.9%, with moderate 
profitability; the mean value of sales growth rate is 22.5%, but 
the variation is extremely large (standard deviation of 3.369), 
reflecting the disparity of enterprise growth; the mean value 
of TobinQ is 2.139, and the maximum value reaches 56.664, 
showing that the market valuation is obviously differentiated. 



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The distribution of financial indicators such as enterprise size 
and leverage ratio is relatively stable. 
Table 1. Main variables 

Variable 
name 

Interpretation 

GreenInnN ln(green patent applications + 1) 

GreenInnQ ln(green patent citations + 1), excl. self-citations 

IPProtect (City IP cases/GDP) / (National IP cases/GDP), 
nationally normalized 

dig_level Entropy-weighted digitalization index (keywords: 
big data, AI, cloud computing) 

GreenAgCst Environmental management expenses / 
Operating revenue 

Note: ln(x+1) transformation handles zero values following standard 
patent study practices. GreenInnQ excludes self-citations for quality 
validity. dig_level uses frequency of digitalization terms from annual 
reports. IPProtect is normalized for cross-region comparability. 

 

Table 2. Control variables 

Variable 
name 

Interpretation 

Size Company size 

Lev Ratio of liability and asset, expressed as gearing 

ROA Net interest rate on total assets 

Liquid Current ratio 

Growth Sales growth rate 

Invest Company investment level 

Board Number of Board of Directors 

Indep Proportion of independent directors 

Top5 Shareholding ratio of top five shareholders 

TobinQ Tobin's Q 

FirmAge Company age 

ATO Total asset turnover 

CEOHoldR Number of shares held by CEO 

KZIndex Financing constraints KZ index 

PollEmis Company Pollution Levels 

EconDevLvl Level of urban economic development, log of 
urban GDP per capita 

SecInd Logarithm of urban secondary sector output 

OpenDegree Degree of openness of the city's economy, total 
city imports and exports/city GDP 

FinDevLvl Level of urban financial development, total 
urban savings and loans/urban GDP 

HumanCapLv Level of urban human resources, number of 
urban university students/total urban 
population 

GovRevenue Logarithm of municipal revenues 

UrbanLvl Urbanization rate of cities 

Note: Variables are measured as ratios (Lev, ROA, Liquid, etc.), 
frequency-normalized indices (dig_level), or logarithmic 
transformations (Size, SecInd, GovRevenue) to ensure comparability 
and normalize distributions. 

Figures 1-3 reveal the distributional characteristics of 
the key variables. Both the quantity of green innovation and 
the quality of green innovation show typical right-skewed 
long-tailed distributions, with a large number of firms (about 
3,000-3,400 samples) concentrating their green innovation 
values around 0, indicating that the majority of firms have 
fewer or missing green innovation activities, while a few firms 
have a high level of green innovation, which reflects a 
significant differentiation in green innovation capability 
among firms. In contrast, the distribution of intellectual 
property protection level is relatively uniform, showing an 
approximate normal distribution, with the peak value 
concentrated in the 0.5-0.8 range, indicating that the 

differences in intellectual property protection level among 
enterprises in the samples are relatively small, and the overall 
level is in the middle of the range. Figure 4 shows the 
trajectory of the three key variables over the 2014-2022 
period: the quantity of green innovation shows a stable linear 
growth trend, more than doubling from 0.7 to 1.5, reflecting 
the continued activity of green innovation activities by firms.  

Table 3. Descriptive statistics for variables 

Note: This table reports descriptive statistical information for all 
main and control variables in the study. GreenInnQ is constructed 
from patent citation data (natural logarithm of citations plus one), 
with self-citations excluded to ensure quality measurement validity. 
Patent data are sourced from the China National Intellectual Property 
Administration (CNIPA) database. 

 

 

Figure 1. Distribution of green innovation quantity (Note: Data 
source: EPS database, CSMAR database, and wind database) 

 

 

 Obs Mean SD Min Max 
GreenInnN 6032 1.129 1.448 0.000 7.439 
GreenInnQ 6032 0.915 1.328 0.000 7.322 
IPProtect 6032 0.661 0.536 0.000 3.751 
dig_level 6032 0.226 0.097 0.061 0.566 

GreenAgCst 6032 0.000 0.002 0.000 0.083 
Size 6032 22.790 1.322 18.370 28.636 
Lev 6032 0.412 0.190 0.028 0.943 
ROA 6032 0.059 0.047 0.000 0.466 

Liquid 6032 2.289 2.097 0.079 38.253 
Growth 6032 0.225 3.369 -0.940 251.211 
Invest4 6032 0.126 0.186 -0.791 6.443 
Board 6032 2.146 0.192 1.099 2.890 
Indep 6032 0.374 0.057 0.222 0.800 
Top5 6032 0.528 0.147 0.164 0.985 

TobinQ 6032 2.139 1.866 0.641 56.664 
FirmAge 6032 2.998 0.282 1.609 3.714 

ATO 6032 0.655 0.441 0.011 5.116 
CEOHoldR 6032 0.029 0.085 0.000 0.667 
KZIndex 6032 0.701 1.925 -9.417 7.196 
PollEmis 6032 0.145 0.003 0.138 0.152 

EconDevLvl 6032 11.441 0.467 9.671 12.293 
SecInd 6032 26.409 0.909 22.780 27.766 

OpenDegree 6032 0.452 0.352 0.001 1.697 
FinDevLvl 6032 3.960 1.692 0.802 13.530 

HumanCapLv 6032 0.042 0.030 0.001 0.140 
GovRevenue 6032 25.129 1.365 21.159 27.379 

UrbanLvl 6032 0.730 0.131 0.257 0.971 



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246 

 

 

Figure 2. Distribution of green innovation quality (Note: Data source: 
EPS database, CSMAR database, and wind database) 

 

 
Figure 3. Distribution of intellectual property protection level (Note: 
Data source: EPS database, CSMAR database, and wind database) 

 

 
Figure 4. Annual trends of main variables (Note: Data source: EPS 
database, CSMAR database, and wind database) 

The quality of green innovation maintains the same 
upward trend but with a relatively moderate growth rate 
from 0.6 to 1.1; and the level of intellectual property rights 
(IPRs) protection shows an " The level of intellectual property 
protection, on the other hand, is characterized by an "inverted 
U-shaped" change, with a significant decline after reaching a 
peak in 2017 (about 0.85), and then dropping to a low point 
in 2019-2020 (about 0.45), followed by a slight rebound. This 
trend suggests that, despite fluctuations in the intellectual 
property protection environment over the study period, 
firms' green innovation inputs and outputs have continued to 
grow, possibly reflecting the roles of other factors, such as 
policy-driven initiatives, market demand, or technological 
advances, in driving green innovation. 

Figure 5 shows that the level of IP protection is 
negatively correlated with the number of green innovations 
(solid line decreasing) in a low digital economy environment 
(blue dots), while the relationship is relatively flat (dashed 
level) in a high digital economy environment (pink triangles), 
implying that the level of digital economy development may 
modulate the effect of IP protection on green innovation. 

 

 
Figure 5. Moderating Effect of Digital Economy Level (Note: Data 
source: EPS database, CSMAR database, and wind database) 

3.2 Model specification 
Based on the research hypotheses and theoretical 

analysis, this study develops a series of progressive 
econometric models to test the complex relationships among 
intellectual property protection, digital economy 
development, and green innovation.  To assess the 
fundamental impact of intellectual property protection on 
green innovation, this study first develops the following 
benchmark regression model. 

 

,

0 1 , 2 , ,

i t

i t i t i t i t

GreenInnovation

IPProtect Controls     

=

+ + + + +
       (1) 

where GreenInnovationi,t represents the level of green 

innovation of firm i in year t, which is measured by the 
quantity of green innovation and the quality of green 
innovation, respectively; IPProtecti,t denotes the level of 

intellectual property protection of the region where firm i is 
located in year t; Controlsi,t is the set of control variables; 
μiand λt represent firm fixed effects and time fixed effects, 
respectively; εi,t is a randomized disturbance term. 

In order to test the moderating effect of the level of 
digital economy development and green agency costs, this 
study constructs a moderating effect model with interaction 
terms. Firstly, the moderating effect of the digital economy is 
tested: 

𝐺𝑟𝑒𝑒𝑛𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝛽0 + 𝛽1𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 + 𝛽2𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 

+𝛽3𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 + 𝛽4𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝜇𝑖 + 𝜆𝑡 + 휀𝑖,𝑡             

             (2) 

Further incorporate green agent costs to construct a triple 
interaction model: 

𝐺𝑟𝑒𝑒𝑛𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝛾0 + 𝛾1𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 + 𝛾2𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 +

𝛾3𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 +𝛾4𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 +

𝛾5𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡  +𝛾6𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 ×

𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 + 𝛾7𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 

+𝛾8𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝜇𝑖 + 𝜆𝑡 + 휀𝑖,𝑡              (3) 



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247 

 

where digleveli,t denotes the digitization development level 

of firm i in year t, and GreenAgCti,tdenotes the level of green 

agency costs of firm i in year t. 
In order to identify the nonlinear characteristics and 

threshold effects of the impact of intellectual property 
protection on green innovation, this study adopts the 
threshold regression model proposed by Hansen [24]. Taking 
the level of intellectual property protection as the threshold 
variable, the following model is constructed. When 
IPProtecti,t ≤ τ (regime 1): 

𝐺𝑟𝑒𝑒𝑛𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝛿0
(1)

+ 𝛿1
(1)

𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 +

𝛿2
(1)

𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 + 𝛿3
(1)

𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 + 𝛿4
(1)

𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 ×

𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 + 𝛿5
(1)

𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 +

𝛿6
(1)

𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 +

𝛿7
(1)

𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 + 𝜇𝑖 + 𝜆𝑡 + 휀𝑖,𝑡                  (4) 

In practical application, this study identifies five IPR 
protection zone systems (Regime 1-5), corresponding to 
different thresholds, to form a more refined segmented 
regression model. 

In order to deeply explore the differential moderating 
effects of the digital economy and green agency costs under 
different IPR protection regimes, this study constructs an 
extended model containing a fourfold interaction term: 

 𝐺𝑟𝑒𝑒𝑛𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑖,𝑡 = 𝜃0 + 𝜃1𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 + 𝜃2𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 +

𝜃3𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 + 𝜃4𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 +
𝜃5𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 + 𝜃6𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 ×

𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 + ∑ 𝛿0+𝑗
5
𝑗=1 𝐼𝑃𝑃𝑟𝑜𝑡𝑒𝑐𝑡𝑖,𝑡 ×

𝑑𝑖𝑔𝑒𝑣𝑒𝑙𝑖,𝑡 × 𝐺𝑟𝑒𝑒𝑛𝐴𝑔𝐶𝑡𝑖,𝑡 × 𝑟𝑒𝑔𝑖𝑚𝑒𝑗 + 𝜃12𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖,𝑡 +

𝜇𝑖 + 𝜆𝑡 + 휀𝑖,𝑡              (5) 

where regimej is a dummy variable that takes the value of 1 

when the level of IPR protection in the region where the firm 
is located in ith tth year is in the jth zone system, and 0 
otherwise. 

4. Analysis of empirical results 

4.1 Basic regression  
The baseline regression results (Table 4) show that the 

direct effect of intellectual property protection on green 
innovation is not statistically significant. Neither the linear 
term (IPProtect) nor the squared term (IPProtect_sq), whose 
coefficients do not reach the level of statistical significance, 
indicates that there is no significant direct relationship 
between intellectual property protection and the quantity 
and quality of green innovation. Among the control variables, 
enterprise size (Size) has a significant positive effect on green 
innovation, leverage (Lev) has a significant negative effect, 
the financing constraint index (KZIndex) is significantly 
positive, and the urbanization level (UrbanLvl) has a 
significant promotion effect on the quantity of green 
innovation. The adjusted R² of the model reaches 0.801 and 
0.882, respectively, indicating strong explanatory power. This 
result implies that there may be a more complex mechanism 
for the effect of intellectual property protection on green 
innovation, and other nonlinear models need to be used to 
find the causal relationship between them. 

 

 

 

 

Table 4. Results of basic regression 

 (1) (2) (3) (4) 

 Green 
innovations 

Number 

Green 
innovations 

Number 

Green 
Innovatio
n Quality 

Green 
Innovation 

Quality 

IPProtect 0.002 0.023 -0.011 -0.080 

 (0.036) (0.084) (0.026) (0.061) 

IPProtect_sq  -0.009  0.030 

  (0.032)  (0.022) 

Size 0.464*** 0.464*** 0.232*** 0.232*** 

 (0.063) (0.063) (0.051) (0.051) 

Lev -0.385* -0.385* -0.366** -0.368** 

 (0.200) (0.200) (0.147) (0.147) 

ROA -0.479 -0.480 -0.213 -0.208 

 (0.395) (0.395) (0.311) (0.311) 

Liquid 0.004 0.004 -0.010* -0.010* 

 (0.008) (0.008) (0.005) (0.005) 

Growth -0.001 -0.001 0.002* 0.002* 

 (0.001) (0.001) (0.001) (0.001) 

Invest4 0.126** 0.126** -0.059 -0.059 

 (0.058) (0.058) (0.044) (0.044) 

Board -0.156 -0.155 -0.069 -0.071 

 (0.141) (0.141) (0.109) (0.109) 

Indep -0.159 -0.156 -0.448 -0.455* 

 (0.374) (0.374) (0.274) (0.273) 

Top5 0.140 0.139 -0.280 -0.278 

 (0.245) (0.245) (0.188) (0.188) 

TobinQ 0.012 0.012 0.007 0.008 

 (0.009) (0.009) (0.008) (0.008) 

FirmAge 0.207 0.208 0.361 0.360 

 (0.392) (0.392) (0.276) (0.276) 

ATO 0.127* 0.127* 0.013 0.012 

 (0.070) (0.070) (0.049) (0.049) 

CEOHoldR 0.304 0.305 0.180 0.177 

 (0.269) (0.268) (0.197) (0.196) 

KZIndex 0.031*** 0.031*** 0.032*** 0.032*** 

 (0.008) (0.008) (0.008) (0.008) 

PollEmis -13.825 -13.815 -7.462 -7.496 

 (8.597) (8.598) (5.931) (5.943) 

EconDevLvl -0.136 -0.140 0.152 0.164 

 (0.168) (0.168) (0.119) (0.119) 

SecInd -0.091 -0.087 -0.093 -0.104 

 (0.130) (0.130) (0.105) (0.106) 

OpenDegree 0.048 0.046 -0.017 -0.010 

 (0.173) (0.173) (0.156) (0.156) 

FinDevLvl 0.004 0.004 0.039 0.039 

 (0.039) (0.039) (0.030) (0.030) 

HumanCapLv 0.391 0.448 -0.847 -1.028 

 (2.477) (2.487) (2.390) (2.388) 

GovRevenue -0.045 -0.042 0.141 0.131 

 (0.132) (0.132) (0.098) (0.098) 

UrbanLvl 1.108** 1.115** 0.025 0.003 

 (0.526) (0.529) (0.372) (0.373) 

Constant -3.515 -3.652 -6.711** -6.270* 

 (4.465) (4.500) (3.258) (3.316) 

Observations 6031 6031 6031 6031 

Adj. R-
squared 

0.801 0.801 0.882 0.882 



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248 

 

Note: ***, **, and * indicate significant at the 1%, 5%, and 10% levels, 
respectively; standard errors in parentheses; all regressions control 
for firm, province, region, and year fixed effects and adjust for 
clustered standard errors at the firm level. 

4.2 Threshold regression 
The threshold regression results (Table 5) reveal 

significant nonlinear characteristics in the impact of IPR 
protection on green innovation, with F-statistics of 16.94 and 
11.88, respectively, indicating that the threshold effect is 
highly significant and verifying the hypothesis of the 
existence of multiple thresholds for IPR protection. The 
impact is still positive but decreases significantly in the 
second zone (0.373**), suggesting a diminishing marginal 
effect; the third zone turns negative but insignificant (-0.214), 
suggesting that overprotection may inhibit the diffusion of 
innovation; and the fourth zone (the highest level of 
protection) turns positive again but with a weak impact 
(0.116), reflecting a complex equilibrium in a high protection 
environment. In contrast, the impact of IPR protection on the 
quality of green innovation is consistently negative and 
increasing: the coefficients are negative and mostly 
significant in all zones, ranging from -0.414 in the first zone to 
-1.431* in the second zone, showing a strong dampening 
effect. This counterintuitive result may reflect the 
phenomenon of the quantity-quality trade-off. Firms are more 
inclined to pursue a large number of patents than 
breakthrough innovations under a strong protection 
environment, or the high cost of protection forces firms to 
reduce the quality of individual innovations. 

Table 5. Threshold regression results 

 (1) (2) 
 Green innovations 

Number 
Green Innovation 

Quality 
IPProtect_sq -0.038 0.070*** 

IPProtect @ regime1 3.862*** -0.414** 
 (1.190) (0.204) 

IPProtect @ regime2 0.373** -1.431*** 
 (0.178) (0.367) 

IPProtect @ regime3 -0.214 -0.113* 
 (0.179) (0.061) 

IPProtect @ regime4 0.116 -0.174*** 
 (0.090) (0.062) 

Observations 6032 6032 
Adj. R-squared 0.198 0.168 

F-statistic 16.94 11.88 
Note: ***, **, and * indicate significance at the 1%, 5%, and 10% 
levels, respectively; standard errors in parentheses; control variables 
are included but not reported; all regressions control for firm, 
province, region, and year fixed effects and adjust for clustered 
standard errors at the firm level. 

4.3 Results of moderated effects regression  
The results reveal an important effect reversal: the direct 

effect of IPR protection is negative (-0.096), but the 
interaction term with digital economy is significantly positive 
(0.711**), suggesting that digital economy fundamentally 
changes the direction of IPR protection's impact. When the 
digitization level is low, IPR protection may inhibit green 
innovation; when digitization increases, this negative effect is 
significantly offset or reversed. The value of 0.711 denotes 
that by a one-unit increase in digitization, the marginal effect 
of protection of IPR to green innovation increases by around 
0.71 units, which determines the causal importance of the 
digital economy as an "institutional enhancer" (Table 6). 
Triple interaction analysis produces nonlinear relations of 
regulation between regimes. The reinforcing regime with a 

coefficient of 6.854 is the first regime, and the digital economy 
is "compensatory" in environments of poor protection for 
IPRs. The middle regimes (2-3) are stable with coefficients of 
0.000. The high protection regimes (4-5) produce 
differentiation effects with coefficients of -0.024 and 1.111 
and have complex dynamics between institutional excess and 
technological empowerment (Table 7). 

In contrast with innovation quantity outcomes, the 
digital economy's moderating effect on innovation quality is 
significantly ineffective. The value of the interaction term's 
coefficient (IPProtect × dig_level) is -0.085 and is not 
significantly effective, essentially the opposite of the 
significantly positive 0.711 for innovation quantity. This 
finding indicates dimension-specificity: digital technologies 
excel best in supporting innovation scale extension but are 
less effective in stimulating innovation depth and 
breakthrough quality. The information economy is more of an 
"efficiency amplifier" and not a "quality enhancer." The 
regime triple interaction terms also confirm this trend. The 
initial regime coefficient is 2.551, but has a very high standard 
error of 3.537, i.e., exceedingly uncertain. The fourth regime 
coefficient is -0.882 and suggests that the digital economy 
may even negatively regulate innovation quality in high 
protection settings, possibly an indication of built-in 
competition among digital standardization tools and 
innovation peculiarity. 

Table 6. Moderating effects of the digital economy (number of green 
innovations) 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Variables (1)  
Basic 

model 

(2) 
Regime 

1 

(3) 
Regime 

2 

(4) 
Regime 

3 

(5) 
Regime 

4 

(6) 
Regime 

5 

IPProtect -0.096 -0.140 -0.096 -0.096 -0.116 -0.064 

 (0.100) (0.101) (0.100) (0.100) (0.113) (0.155) 

IPProtect×dig_level 0.711** 0.772
** 

0.711*
* 

0.711*
* 

0.766 0.454 

 (0.295) (0.268) (0.295) (0.295) (0.465) (0.462) 

Triple interaction 
by regime: 

      

IPProtect×dig_level
×regime1 

- 6.854 - - - - 

IPProtect×dig_level
×regime2 

- - 0.000 - - - 

IPProtect×dig_level
×regime3 

- - - 0.000 - - 

IPProtect×dig_level
×regime4 

- - - - -0.024 - 

IPProtect×dig_level
×regime5 

- - - - - 1.111 

Standard errors - (3.907) (.) (.) (0.525) (0.627) 

Model statistics:       

Observations 6028 6028 6028 6028 6028 6028 

Adj. R-squared 0.803 0.803 0.803 0.803 0.803 0.803 

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; 
standard errors in parentheses; control variables are included but not reported; all 
regressions control for firm, year, industry, province, and region fixed effects. 

 



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249 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 6 clearly shows the divergence pattern between 
the level of IPR protection and the quantity and quality of 
green innovations: the quantity of green innovations shows a 
"U-shaped" trajectory, dropping from 1.25 at the low level of 
protection to a low of 1.00 at the medium level, and then 
rebounding to 1.06 at the medium-high level of protection; 
whereas the quality of green innovations shows a continuous 
monotonically decreasing trend, dropping from 1.09 at the 
low level to 0.47 at the medium-high level, a drop of more 
than 50%. to 0.47 at medium-high protection, a decrease of 
more than 50%. This comparison validates the core finding of 
the threshold regression. IPR protection has distinct impacts 
on the "quantity" and "quality" of innovation - moderate 
protection is conducive to quantitative growth, but a strong 
protection environment may incentivize firms to pursue 
easily accessible incremental innovations at the expense of 
breakthrough quality innovations, resulting in structural 
changes in innovation that "compensate for quantity and 
dilute quality". 

The results reveal a complex multilevel moderating 
mechanism (Table 8). The moderating effect of digital 
economy remains significant (0.763**), but the addition of 
green agency costs produces a "moderating the moderator" 
effect: the triple interaction term 
IPProtect×dig_level×GreenAgCt coefficient is -217.524, 
implying that green agency costs may systematically weaken 
the positive moderating effect of digital economy. Being a 
mirror of governance problems within, green agency fees act 
as a "friction brake" on the synergies of intellectual property 
rights and the digital economy. 

The quadrupled interaction term discloses regime 
heterogeneity at the extremes. For regime 1 (lowest 
protection), the coefficient is 4726.206*, indicating a 
"compensatory explosion" effect—where there is a lack of 
external institutional protection, internal agency conflicts 
push firms to implement more robust internal control 
mechanisms, and digital technology offers useful 
coordination tools, which leads to unexpected synergistic 
improvement. By way of comparison, regime 4 (medium-high 
protection) has a highly negative coefficient (-1266.740**), 
resonating with "institutional overload"—where IPR 
protection is highly developed externally, the interaction 
between high green agency costs and digitization may create 
negative synergies via excessive institutional intricacy. 

 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Figure 6. The moderating effect of the digital economy level (Note: 
Data source: EPS database, CSMAR database, and wind database) 

As for innovation quality results, green agency costs have 
entirely dissimilar moderating processes in regard to quality 
(Table 9). The net moderating effect of electronic economy 
(IPProtect × dig_level) is -0.084 and not significant, merely 
different from the highly significant positive 0.763 in 
quantity. The IPProtect×dig_level×GreenAgCt three-way 
interaction term is -86.955, indicating that agency costs can 
work through a "quality dilution effect"—the management is 
likely to seek short-term tangible outputs instead of long-
term quality breakthroughs. The quadruple interaction term 
is extremely polarized. In regime 1, the coefficient stands at -
583.668, and this indicates a "quality trap" in that, under 
weak institutional contexts, companies are subject to greater 
uncertainty, and agency costs impose short-termism, 
rendering quality innovation problematic in spite of digital 
facilitation. On the other hand, with regime 5 (greatest 
protection), the coefficient strongly becomes positive 
(1302.802*), depicting a "quality burst" effect—at the 
greatest level of institutional protection, safe property rights 
ensure long-term investment in quality, and agency costs no 
longer deter innovation but potentially create quality 
breakthroughs under competitive pressure. The substantial 
negative direct effect of IPR protection (-0.178*) in regime 4 
and the substantial positive quadruple interaction in regime 
5 imply a high institutional threshold effect on quality 
innovation.  

 

Table 7. Moderating effects of the digital economy (green innovation quality) 

Variables (1) Basic model (2) Regime 1 (3) Regime 2 (4) Regime 3 (5) Regime 4 (6) Regime 5 

Main effects:       

IPProtect -0.066 -0.076 -0.066 -0.066 -0.170* -0.041 

 (0.072) (0.085) (0.072) (0.072) (0.091) (0.117) 

IPProtect×dig_level -0.085 -0.249 -0.085 -0.085 0.432 0.142 

 (0.173) (0.145) (0.173) (0.173) (0.281) (0.496) 

Regime-specific 
interactions: 

      

IPProtect×dig_level×regime - 2.551 0.000 0.000 -0.882 0.027 

 - (3.537) (.) (.) (0.480) (0.612) 

Model statistics:       

Observations 6028 6028 6028 6028 6028 6028 

Adj. R-squared 0.883 0.883 0.883 0.883 0.883 0.883 

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; standard errors in parentheses; control variables are included 
but not reported; all regressions control for firm, year, industry, province, and region fixed effects. 

 

1.25

1.09
1.14

0.94

1.00

0.72

1.06

0.47

0

.5

1

1.5

A
v
e

ra
g

e
 G

re
e

n
 I

n
n

o
v
a

ti
o

n

Low IP Med-Low IP Medium IP Med-High IP

Based on threshold regression analysis
Data Source: EPS Database, CSMAR Database, and Wind Database

Threshold Model Results

Green Innovation Performance Across IP Protection Regimes

Innovation Quantity Innovation Quality



J. Pan et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 242-253 

250 

 

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
The gigantic coefficient 1302.802 in regime 5 implies a 

high sensitivity of quality innovation choices, displaying a 
characteristic "quality leverage effect"—quality innovation is 
more susceptible to steep fluctuations from institutional, 
governance, and technological situations than the relative 
stability of quantitative innovation. 

5. Discussion and policy recommendations 

This research illustrates the non-linear, intricate effect of 
intellectual property protection on green innovation, refuting 
the conventional linear hypothesis of 'more protection equals 
more innovation'.  

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Threshold regression estimates confirm a "U-shaped" 

relationship between intellectual property protection and the 
number of green innovations, yet the quality of innovation 
remains negatively impacted. This quantitative-qualitative 
asymmetry is representative of the structural asymmetry of 
the current system of patents to overweight measurable 
outputs of innovation and not to establish strong incentives 
for innovative breakthroughs. The policymakers have to 
create a differentiated protection system for intellectual 
property, with standard protection for progressive green 
innovations and shorter protection terms and weaker 
protection for breakthrough innovations, and a mechanism 

Table 8. Moderating effects of green agency costs (green innovation numbers) 

Variables (1) Basic (2) Regime 1 (3) Regime 2 (4) Regime 3 (5) Regime 4 (6) Regime 5 

Main and 
interaction 
effects: 

      

IPProtect -0.106 -0.165 -0.106 -0.106 -0.120 -0.078 

 (0.092) (0.099) (0.092) (0.092) (0.102) (0.159) 

IPProtect×dig_le
vel 

0.763** 0.859** 0.763** 0.763** 0.793 0.482 

 (0.281) (0.295) (0.281) (0.281) (0.446) (0.563) 

IPProtect×Green
AgCt 

30.718 73.740 30.718 30.718 16.247 -4.582 

 (48.099) (40.508) (48.099) (48.099) (49.173) (118.256) 

IPProtect×dig_le
vel×GreenAgCt 

-217.524 -499.035* -217.524 -217.524 -118.789 -144.291 

 (255.891) (240.717) (255.891) (255.891) (265.735) (435.164) 

Quadruple 
interactions: 

      

Regime-specific 
coefficient 

- 4726.206* 0.000 0.000 -1266.740** 323.989 

Standard error - (2323.924) (.) (.) (476.706) (483.216) 

Model statistics:       

Observations 6028 6028 6028 6028 6028 6028 

Adj. R-squared 0.803 0.803 0.803 0.803 0.803 0.802 

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; standard errors in parentheses; control variables are included 
but not reported; all regressions control for firm, year, industry, province, and region fixed effects. 

 
Table 9. Moderating effects of green agency costs (green innovation quality) 

Variables (1) Basic (2) Regime 1 (3) Regime 2 (4) Regime 3 (5) Regime 4 (6) Regime 5 

Main and interaction effects:       

IPProtect -0.063 -0.063 -0.063 -0.063 -0.178* -0.041 

 (0.072) (0.087) (0.072) (0.072) (0.093) (0.117) 

IPProtect×dig_level -0.084 -0.272 -0.084 -0.084 0.498 0.142 

 (0.170) (0.146) (0.170) (0.170) (0.280) (0.496) 

IPProtect×GreenAgCt 7.916 -0.198 7.916 7.916 26.102 76.102 

 (8.973) (18.269) (8.973) (8.973) (20.973) (45.401) 

IPProtect×dig_level×GreenAgCt -86.955 -67.974 -86.955 -86.955 -192.288 -295.110* 

 (50.729) (136.337) (50.729) (50.729) (134.370) (135.367) 

Quadruple interactions:       

Regime-specific coefficient - -583.668 0.000 0.000 209.071 1302.802* 

Standard error - (1309.582) (.) (.) (296.253) (588.810) 

Model statistics:       

Observations 6028 6028 6028 6028 6028 6028 

Adj. R-squared 0.883 0.884 0.883 0.883 0.883 0.883 

Note: ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively; standard errors in parentheses; control variables are included 
but not reported; all regressions control for firm, year, industry, province, and region fixed effects. 

 



J. Pan et al. /Future Technology                                                                                         February 2026| Volume 05 | Issue 01 | Pages 242-253 

251 

 

for assessing the quality of innovations, synthesizing 
technological advancement and ecological merits into a 
unified consideration, so as not to miss the problem of quality 
dilution due to sheer quantitative focus [25]. The digital 
economy's moderating role is typically dimension-specific: it 
operates very effectively to energize the amount of innovation 
but ineffectively to moderate the quality of innovation. Digital 
technologies primarily enhance substantive green innovation 
rather than serving as universal quality enhancers [12], as 
their advantages in facilitating innovation scale-up do not 
fully translate to breakthrough innovations requiring deep 
insights and long-term experimentation. Future research 
should explore AI-specific digital tools designed to support 
quality innovation through enhanced R&D analytics and 
knowledge integration [26]. From this consciousness, the 
Government needs to adopt a clear policy to promote 
digitalization [27]: on the one hand, for green innovation for 
quantitative growth, it shall emphasize building strong digital 
infrastructure construction and cultivating digital platform 
development; on the other hand, for quality breakthrough-
type innovation, it shall depend more on conventional R&D 
factors, human resource cultivation and foreign cooperation, 
and shun the tendency of short-termism most likely brought 
about by overdependence on digitalization tools. 

The green agency cost moderating effect highlights the 
central role of internal corporate governance and its intricate 
interaction with the institutional environment in the external 
environment. When there is poor IPR protection, agency costs 
can induce firms to create more efficient internal controlling 
systems, and new information and communication 
technologies offer effective coordinating mechanisms that 
yield synergistic benefits [28]. In a well-protected 
environment, high agency costs and the complexity of the 
external institutional environment may result in institutional 
overload and increase the burden of decision-making [29]. 
This dynamic game relationship between internal and 
external systems requires enterprises to establish 
governance mechanisms that are compatible with the 
external environment: weak systems need to strengthen 
internal control systems and digital applications, while 
perfect systems should focus on governance streamlining and 
long-term incentives. Threshold regression and moderating 
effect analysis together reveal the significant differences in 
policy effects under different institutional environments, 
providing a scientific basis for the implementation of district-
oriented and precise policies. Regions with a low level of IPR 
protection should focus on the construction of basic systems 
and digital empowerment by strengthening laws and 
regulations, improving enforcement mechanisms, and 
promoting the construction of digital infrastructure [30]. 
Regions with moderate levels of protection should turn to 
system optimization and quality enhancement, establish 
classified protection mechanisms, and develop high-end R&D 
services and technology transfer [31,32]. Regions with a high 
level of protection need to prevent over-protection by 
improving patent examination standards, compulsory 
licensing systems, and other measures, and at the same time 
strongly support basic research and original innovation [33]. 

The construction of a systemic green innovation policy 
framework needs to be coordinated across multiple 
dimensions, including intellectual property protection, digital 
economy development, and improved corporate governance. 
Such a framework should be differentiated and dynamic, 
taking into account the differences in the stages of 
development of different regions, as well as the different 
needs for the quantity and quality of innovation. In the long 

term, a dynamic adjustment mechanism for the level of 
intellectual property protection should be established [34], 
the deep integration of the digital economy and green 
innovation should be promoted [35], a new model of digital 
technology supporting quality innovation should be explored, 
and the long-term mechanism of corporate governance for 
green innovation should be improved, so as to promote the 
sustainable enhancement of the green innovation capacity of 
corporations through legal improvement, incentive 
optimization and monitoring and evaluation measures [36]. 

6. Conclusion 

This study systematically investigates the complex 
relationship between intellectual property protection, digital 
economy development, and corporate green innovation 
through threshold regression and moderated regression 
analysis of Chinese listed companies from 2014-2022. Our 
findings fundamentally challenge the conventional linear 
assumption that stronger IPR protection uniformly promotes 
innovation. The empirical evidence reveals a U-shaped 
relationship between IPR protection and green innovation 
quantity, with five distinct threshold regimes demonstrating 
varying policy effectiveness across different institutional 
maturity levels. Critically, IPR protection exhibits persistent 
negative effects on innovation quality across all regimes, 
suggesting a troubling quantity-quality trade-off in which 
enterprises strategically prioritize patent volume over 
breakthrough innovations in strong protection environments. 
The research makes three substantive contributions to 
innovation theory and policy design. Theoretically, we 
advance green innovation literature by identifying threshold 
effects and dimensional asymmetries between innovation 
quantity and quality, demonstrating that institutional impacts 
are regime-dependent rather than uniform across 
development stages. Methodologically, our integrated 
analytical framework combining threshold regression with 
triple and quadruple interaction analysis successfully 
captures the complex interplay among institutional 
protection, technological infrastructure, and corporate 
governance mechanisms. In practice, we provide robust 
empirical evidence for policymakers to design regime-
specific strategies: low-protection regions should prioritize 
strengthening institutional foundations and digital 
infrastructure; moderate-protection regions should optimize 
quality-oriented mechanisms and knowledge diffusion 
channels; while high-protection regions must prevent over-
protection that stifles knowledge flow and quality 
breakthroughs. Future research should extend this 
framework across different national contexts to test 
generalizability beyond the Chinese setting, examine the 
dynamic mechanisms underlying threshold transitions over 
longer time horizons, and explore industry-specific 
heterogeneities in how firms respond to institutional and 
technological changes. Additionally, investigating the role of 
emerging digital technologies, such as artificial intelligence 
and blockchain, in supporting quality innovation is a 
promising avenue for advancing our understanding of the 
effectiveness of innovation policy in the rapidly evolving 
digital era.  

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically regarding authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with research ethics policies. The authors adhere to 



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252 

 

publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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