China is the world's largest user of industrial robots. In 2016, sales of industrial robots in China reached 87,000 units, accounting for around 30 percent of the global market. To put this number in perspective, robot sales in all of Europe and the Americas in 2016 reached 97,300 units (according to data from the International Federation of Robotics). Between 2005 and 2016, the operational stock of industrial robots in China increased at an annual average rate of 38 percent. In this paper, we describe the adoption of robots by China's manufacturers using both aggregate industry-level and firm-level data, and we provide possible explanations from both the supply and demand sides for why robot use has risen so quickly in China. A key contribution of this paper is that we have collected some of the world's first data on firms' robot adoption behaviors with our China Employer-Employee Survey (CEES), which contains the first firm-level data that is representative of the entire Chinese manufacturing sector.

https://pubs.aeaweb.org/doi/pdf/10.1257/jep.33.2.71

The Rise of Robots in China

Artificial intelligence (AI) can revolutionize production process by improving energy efficiency, reducing costs, and developing green technology. Among the most important applications of AI technology, industrial robots are increasingly being used in production and have become significant feature of high-quality ecinomic development. Concerning this issue, this study uses the provincial panel data from China from 2010 to 2019 to explore how adopting industrial robots affects regional pollution emissions. We find that industrial robots significantly reduce the pollution emissions intensity in various provinces of China, and this conclusion holds following a series of robustness tests. Mechanism analysis shows that industrial robots reduce pollutant emissions intensity by improving energy efficiency and enhancing pollution reduction technologies. Heterogeneity analyses reveal that industrial robots significantly reduce pollution emissions intensity in the eastern and western regions of China, as well as in regions outside the Yangtze River Economic Belt, while this effect is significant for the central region and the Yangtze River Economic Belt. Industrial robots primarily promote pollution reduction in the eastern region, rather than enhancing regional energy efficiency. For the western region and regions outside the Yangtze River Economic Belt, the reduction in pollution emissions intensity appears to be attributable to both mechanisms. The above findings enrich the study on the impact of industrial robots on green production behavior and provide insights into pollution emissions control for some provinces of China. 

How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology

In the digital age, most state-of-the-art research has investigated the influence of industrial robot applications (IRAs) on labor and income distribution, but its environmental impact remains underexplored. This paper uses regional panel data in China from 2006 to 2017 to explore the nonlinear effect of IRAs on carbon emission (CE), as well as its optimal conditions by using fixed effect model with quadratic term and threshold regression model, respectively. The results show that: (1) An inverted U-shaped relationship was verified between IRAs and CE, which means when the IRAs exceed a certain threshold, carbon emissions will be curbed. (2) IRAs accelerate the advance of the inverted U-shaped turning point by improving the factor allocation efficiency and optimizing industrial structures. (3) The CE reduction effect of IRAs depends on the scale of the highly skilled labor force, digital endowment and environmental regulation intensity in the region. These conclusions not only broaden the theoretical horizon of existing effects of IRAs in the environmental fields, but also provide practical implications to maximize the carbon reduction effect in IRAs. 

Nonlinear effect of industrial robot applications on carbon emissions: Evidence from China

The presence of pharmaceutical pollutants in water has emerged as a significant public health concern due to their potential adverse impacts, including the development of antibiotic resistance Consequently, advanced oxidation processes based on photocatalysis have garnered considerable attention for treating pharmaceutical contaminants in wastewater. In this study, graphitic carbon nitride (g-CN), a metal-free photocatalyst, was synthesized by the polymerization of melamine and assessed as a potential candidate for the photodegradation of acetaminophen (AP) and carbamazepine (CZ) in wastewater. Under alkaline conditions, g-CN demonstrated high removal efficiencies of 98.6% and 89.5% for AP and CZ, respectively. The relationships between degradation efficiency and catalyst dosage, initial pharmaceutical concentration, and photodegradation kinetics were investigated. Increasing the catalyst dose facilitated the removal of antibiotic contaminants, with an optimum catalyst dose of 0.1 g, achieving a photodegradation efficiency of 90.2% and 82.7% for AP and CZ, respectively. The synthesized photocatalyst removed over 98% of AP (1 mg/L) within 120 min, with a rate constant of 0.0321 min−1, 2.14 times faster than that of CZ. Quenching experiments revealed that g-CN was active under solar light and generated highly reactive oxidants such as hydroxyl (•OH) and superoxide (•O2−). The reuse test confirmed the good stability of g-CN for treating pharmaceuticals during three repeated cycles. Finally, the photodegradation mechanism and environmental impacts were discussed. This study presents a promising approach for treating and mitigating pharmaceutical contaminants in wastewater. 

Graphitic carbon nitride metal-free photocatalyst for the simultaneous removal of emerging pharmaceutical pollutants in wastewater.

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Air pollution, water pollution, and robots: Is technology the panacea.

Spatial and seasonal variations in the carbon and lead isotopes of PM2.5 in air of residential buildings and their applications for source identification.