IJERPH | Free Full-Text | How Does Climate Policy Uncertainty Affect Green Innovation? Evidence from China
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Due to the frequent extreme climate problems in recent years, scholars have paid attention to the impact of climate change. Several scholars have investigated the impact of climate change and climate policies on various innovations. For example, Hu et al. (2022) [29] discovered a negative correlation between temperature extremes and GI and a more significant impact on green invention innovation. Since climate change often does not affect enterprises directly but somewhat through climate policy, Pan et al. (2022) [30] used a low-carbon city pilot program as a quasi-natural experiment to explore the impact of climate policy on low-carbon innovation. Closest to our study, Ren et al. (2022) [31] investigated the impact of the U.S. CPU index on the total factor productivity of A-share listed companies in the mining, energy, and manufacturing sectors of China. However, there remains a research gap regarding the impact of CPU on enterprises’ GI. This study aims to examine the impact of Chinese CPU on enterprise GI. First, we work to investigate whether CPU inhibits enterprise GI. Second, we intend to clarify the influencing mechanism of CPU on enterprise GI. Eventually, we analyze whether government subsidies mitigate the negative impact of CPU on enterprise GI.
Certain research shortcomings in this paper deserve further exploration in future studies. First, due to data availability, we mainly used data from listed companies in China and no data from non-listed companies. Future research could include a broader sample of enterprises to study. Second, our study only examined the impact of direct government subsidies on enterprise GI. Future research needs to more comprehensively consider the heterogeneous impact of different forms of government subsidies, which may become an important direction for future research. Eventually, our study only considers the adverse effects of government subsidies that can mitigate CPU. Future research could explore more influencing factors to mitigate the harmful effects of CPU and enrich and improve the existing studies.
The adverse effect of CPU has caused widespread concern in recent years. We constructed an index of CPU in China based on two major Chinese newspapers, adopting the panel regression model, the mediating effect model, and the moderating effect model. We investigated the impact of CPU on enterprise GI using data from A-share-listed Chinese companies from 2010 to 2021. It was found that CPU has a dampening effect on enterprise GI, i.e., the higher the CPU, the lower the enterprise GI. The findings were subjected to a series of robustness tests to ensure reliability. Second, the financing constraint faced by enterprises is a mediating path through which uncertainty in enterprises’ climate policies affects their GI. A rise in CPU exacerbates enterprises’ financial constraints, which in turn inhibits enterprises’ GI activities. Third, government subsidies can significantly mitigate the inhibitory effect of CPU on enterprises’ GI. Ultimately, the negative effect of CPU differs across heterogeneous enterprises, with CPU mainly hurting the GI of non-SOEs and having no significant effect on SOEs.
Variables Type | Variables | Full Name | Obs. | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|---|---|---|
Dependent variable | lnCPU | Climate policy uncertainty index | 12 | 88.070 | 23.430 | 66.945 | 140.074 |
Independent variable | lnIPG | The number of green invention patents granted | 25,513 | 0.141 | 0.456 | 0.000 | 5.043 |
lnUMPG | The number of green utility model patents granted | 25,513 | 0.251 | 0.612 | 0.000 | 5.844 | |
Mediating variable | KZ | Financing constraints | 25,513 | −3.742 | 0.267 | −5.600 | −0.271 |
Moderating variable | Subsidies | Government subsidies | 25,513 | 0.014 | 0.060 | 0.000 | 8.380 |
Controlled variables | lnSize | Enterprise size | 25,513 | 22.081 | 1.368 | 13.076 | 28.636 |
lnAge | Enterprise age | 25,513 | 9.533 | 7.207 | −1.000 | 29.000 | |
Roa | Enterprise profitability | 25,513 | 0.039 | 0.798 | −48.316 | 108.366 | |
TobinQ | Enterprise investment opportunity | 25,513 | 2.356 | 13.447 | 0.674 | 52.705 | |
Lev | Enterprise leverage | 25,513 | 0.445 | 0.644 | −0.195 | 63.971 |
Variable | lnIPG | lnUMPG | ||||
---|---|---|---|---|---|---|
(1) | (2) | (3) | (4) | (5) | (6) | |
lnCPU | −0.051 *** | −0.039 *** | −0.033 *** | −0.208 *** | −0.200 *** | −0.037 *** |
(0.012) | (0.012) | (0.008) | (0.016) | (0.016) | (0.011) | |
lnSize | 0.109 *** | 0.019 *** | 0.118 *** | 0.018 ** | ||
(0.003) | (0.005) | (0.004) | (0.007) | |||
lnAge | 0.007 *** | 0.009 *** | −0.014 *** | 0.011 *** | ||
(0.000) | (0.001) | (0.001) | (0.001) | |||
Roa | −0.108 *** | −0.022 | −0.027 | 0.001 | ||
(0.028) | (0.021) | (0.037) | (0.028) | |||
TobinQ | 0.112 *** | 0.005 | 0.072 *** | −0.002 | ||
(0.009) | (0.009) | (0.012) | (0.011) | |||
Lev | −0.059 *** | −0.029 | 0.154 *** | 0.086 ** | ||
(0.021) | (0.026) | (0.029) | (0.034) | |||
Cons | −0.088 * | −2.585 *** | −0.221 * | −0.676 *** | −3.229 *** | −0.436 *** |
(0.053) | (0.086) | (0.124) | (0.070) | (0.116) | (0.163) | |
N | 25,513 | 25,513 | 25,513 | 25,513 | 25,513 | 25,513 |
Enterprise FE | NO | NO | YES | NO | NO | YES |
Adj. R2 | 0.010 | 0.151 | 0.253 | 0.017 | 0.164 | 0.259 |