In an article recently published in the journal Scientific Reports, researchers investigated the impact of industrial robot adoption on corporate green innovation in China.
Study: Impact of Industrial Robots on Corporate Green Innovation. Image credit: Generated using DALL.E.3
Background
In recent years, green innovation has gained significant attention as a suitable solution to increasing environmental pollution and a strategy for sustainable economic growth. Enterprises are crucial for environmental and ecological management as they can be key in green technological development and independent innovation. However, enterprises are currently facing both technological and financial issues with green innovation due to the need for higher investment in green innovation and greater risks associated with the initiative.
Artificial intelligence (AI) technology can effectively address these challenges faced by enterprises and facilitate green innovation. Specifically, industrial robots can promote green innovation and technology and enable the eco-friendly transformation of manufacturing enterprises as these robots are becoming more automated and intelligent. However, studies have not sufficiently investigated the potential impacts of adopting industrial robots, a prominent example of AI technology, on corporate green innovation.
The study
In this study, researchers investigated the impact of industrial robot adoption on corporate green innovation and its underlying mechanisms. Specifically, they analyzed the role of industrial robots in improving/promoting corporate green innovation based on the environmental management effect and productivity effect.
Researchers used data from Chinese A-share listed manufacturing companies/enterprises between 2007 and 2019 due to extensive industrial robot adoption since 2007 and the availability of the latest industrial robot data till 2019. The industrial robot data was obtained from the International Federation of Robotics (IFR).
The database contains reliable data on the industries, applications, and types of robots utilized worldwide. Researchers manually matched a two-digit code in the Chinese manufacturing industry with the IFR-provided industry codes to ensure that the data of the listed company matched the industrial robot data.
GIS and GIZ were the dependent variables representing corporate green innovation, while ROBOT was the independent variable representing industrial robot penetration in enterprises. SIZE, LEV, GROW, OCF, AGE, LOSS, SHRCR, OTHREC, STAFF, INDUSTRY, and YEAR were the control variables representing an enterprise’s total market value, total liabilities/total assets, income growth rate, operating cash flow/total assets, listing age, loss, shares held by the largest shareholder/total shares, other receivables/total assets, number of employees, industry, and year, respectively.
Researchers employed regression models to evaluate the effect of industrial robot adoption on green innovation. They used instrumental variable (IV) estimation as a robustness test to address the endogeneity concerns. The industrial robot penetration in the United States (US) was utilized as an IV to assess the robustness using the two-stage least squares method (2SLS). The persistence of industrial robot adoption was investigated by delaying the independent variable by two and three periods.
Researchers also performed heterogeneity analysis to identify the differences in the impact of industrial robot adoption on green innovation across enterprises based on their characteristics and locations.
Significance of the study
Descriptive statistics indicated that industrial robots were already used extensively in enterprises. However, robot adoption was low in a significant number of enterprises. Additionally, the green innovation capabilities varied across several enterprises, and 10.51% experienced negative net profit.
The Spearman correlation coefficients displayed a significant correlation between GIZ/GIS and ROBOT coefficients, which indicated that industrial robot adoption indeed promotes corporate green innovation. Moreover, the coefficients of GIS/GIZ demonstrated positive correlations with STAFF, AGE, GROW, and SIZE, and negative correlations with LOSS, OCF, LEV, and SHRCR. Results from baseline regressions showed that industrial robot adoption can substantially improve the quality and quantity of green innovation.
In the IV regression, the findings of the first-stage regression displayed a strong correlation between Chinese industrial robots and US robots, while the findings of the second-stage regression indicated that green innovation was significantly impacted by the adoption of industrial robots, which mitigated the potential endogenous problems.
The outcomes of the persistent test demonstrated the reliability of the research findings that industrial robot adoption can promote corporate green innovation. Moreover, the results of the mechanism tests displayed that industrial robot adoption can effectively enhance green innovation by promoting environmental management capability and improving productivity.
Heterogeneity analysis showed that the positive impact of industrial robot adoption on corporate green innovation was more pronounced among Chinese state-owned enterprises, enterprises located in regions with higher carbon emissions intensity, and enterprises with intense market competition.
To summarize, the findings of this study demonstrated the feasibility of using industrial robots to effectively promote corporate green innovation. Additionally, the findings also provided a reference to achieve a low-carbon economy in emerging markets and improve environmental management.
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