Page 158 - IJOCTA-15-4

Mengxue Li et al. / IJOCTA, Vol.15, No.4, pp.686-705 (2025)
(iv) In Table 7, the coefficient of environmen- obtained in Table 7, that is, the regression co-
tal input intensity in the regression re- efficients of R&D investment, COORP, ES, and
sults is not significant, which may be be- GOVNM are all significant at the 5% level.
cause most of the specialized enterprises
are SMEs, and there is a contradiction be- As shown in Table 8, the regression coef-
tween short-term cost crowding out and ficients of R&D investment, COORP, ES, and
long-term technological forcing in the role GOVNM are 0.156, 0.117, 0.726, and 0.827, re-
of environmental inputs on green innova- spectively, which are all significant at 5% level.
tion. In the short term, environmental in- R&D investment, COORP, and GOVNM are pos-
vestment may consume funds that could itively correlated with GIE. There is a negative
have been used for green R&D, leading to correlation between ES and GIE.
a mismatch of resources between innova-
tion and compliance, and there is a cycli-
cal lag in the transformation of environ-
mental investment into green innovation 5. Conclusion and recommendations
results. Simultaneously, the environmen-
tal investment of some enterprises may be 5.1. Conclusion
allocated only to meet the requirements Panel data are used from 40 SNEs in Zhejiang
of policy compliance rather than based from 2017 to 2021 as a sample. The CCR input-
on the active investment of green techno- oriented super-SBM model is employed to mea-
logical upgrading, and a lack of synergies sure the GIE of these 40 SNEs. Subsequently,
with R&D activities and cooperation be- using the systematic GMM dynamic model, this
tween industry, universities, and research paper finds that influencing factors, such as R&D
institutes, which makes it difficult to ef- investment, ES, COORP, and GOVNM, have an
fectively transform environmental invest-
important impact on GIE. Previous studies have
ment into green innovation, resulting in an
found that government subsidies can enhance the
insignificant effect in the regression model.
ability of enterprises in green technology inno-
(V) Government financial support and the
vation, as R&D investment is positively affected
GIE are notably positively correlated.
by government financial subsidies. While gov-
The regression coefficient value of
ernment subsidies can help to improve the green
GOVNM on the GIE is 0.326 overall, sur-
technology innovation ability of SNEs, this paper
passing the 5% significance level. This
does not take R&D investment as a mediating
means that GIE of SNEs increases by
variable, and by directly analyzing R&D invest-
0.326% for every 1% increase in GOVNM. ment and policy support, we focus more on these
Therefore, H5 is verified. Liu 49 found that two core factors, and thus more accurately assess
government science and technology fund- their role in enhancing GIE. The core explanatory
ing has a direct incentive effect on corpo- variables are replaced to conduct tests, resulting
rate green innovation. Through direct fi- in two conclusions.
nancial assistance and other methods, the
government lowers the risk of technolog- Firstly, this paper evaluates the GIE of SNEs
ical innovation for businesses, promotes in Zhejiang province. Overall, the GIE of SNEs in
innovation activities to a certain extent, Zhejiang is relatively low. From 2017 to 2021, the
creates a favorable environment for inno- average value of the CRS of SNEs was 0.807,
vation, and lessens the cost-cutting im- 0.773, 0.779, 0.724, 0.704, showing a general
pact of green innovation.
downward trend, and a small number of them
were in the state of inefficiency, reflecting the de-
4.2.3. Robustness test
cline in the level of the ability of the SNEs to
To enhance the credibility of the conclusion, the configure and utilize the resources for innovation.
core explained variable, GIE, is replaced with the There are spaces available to improve the GIE.
VRS decomposition variable, and the BCC model Additionally, both VRS and scale efficiency fail
is adopted to measure GIE for SNEs in Zhejiang. to reach optimal levels. As emerging enterprises
The results are shown in Table 8. It can be seen in recent years, GIE has not been much empha-
that whether the CRS decomposition variable in sized. However, this also indicates that there is
the CCR model or the VRS decomposition vari- still significant room for improvement in the GIE
able in the BCC model is used as the replacement of SNEs in Zhejiang.
variable of GIE, it is consistent with the results
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