AI-Driven Intent-Based Networking for 5G Enhanced Robot Autonomy

Sophocleous, Marios; Lessi, Christina; Wang, Chao; Špaňhel, Jakub; Qiu, Renxi; Lendinez, Adrian; Chondroulis, Ioannis; Belikaidis, Ioannis

doi:10.1007/978-3-031-08341-9_6

Cited by 3 publications

(1 citation statement)

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“…Digital technologies, including big data, blockchain, digital twin, artificial intelligence (AI), and the Internet of Things (IoT), can attract an influx of high-level production factors, including high-end talents, cutting-edge knowledge, advanced technological processes and production procedures, thereby optimizing the factor structure of conventional agriculture and expanding the scale and quality of agricultural technological innovation (Klerkx et al, 2019;Bolfe et al, 2020), and ultimately promote agricultural carbon emission reduction (Ali et al, 2021;Luo et al, 2023). Robots are loaded with AI (Rajan and Saffiotti, 2017), blockchain (Aditya et al, 2021), big data (Zhang, 2021), 5G (Sophocleous et al, 2022), digital twins (Hoebert et al, 2019) and a host of other digital technologies as physical carriers (Sodikjanov and Khayitboyev, 2023). Its unique potential for cutting carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Robotics, environmental regulation, and agricultural carbon emissions: an examination of the environmental Kuznets curve theory and moderating effects

Li,

Chen

2024

Front. Sustain. Food Syst.

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IntroductionReducing carbon emissions from agriculture is essential to ensuring food security and human prosperity. As a country with approximately 20% of the global population, China has begun actively practicing the low-carbon agricultural development conception. Against the backdrop of disruptive technologies that continue to be integrated into various industries, the massive application of agricultural robots has opened the way to intelligent agriculture. This paper tries to answer whether there is some non-linear nexus between the application of agricultural robots and agricultural carbon emissions in China. As an essential tool for carbon emission reduction in China, does environmental regulation moderate the nexus between agricultural robot applications and agricultural carbon emissions? If so, how does this effect manifest itself?MethodsThis work takes China as an example by collecting macro-regional panel data from 30 provinces from 2006 to 2019. The environmental Kuznets curve theory is extended to agricultural carbon emissions, and we carried out empirical tests utilizing the panel fixed effects model and the moderating effects model.ResultsThis study verifies the inverted U-shaped nexus between agricultural robotics applications and agricultural carbon emissions in Chinese provinces, i.e., the agricultural carbon emissions (ACE)-Kuznets curve holds. The higher the level of formal environmental regulation, the larger the peak of the ACE-Kuznets curve and the more the inflection point is pushed back. The higher the level of informal environmental regulation, the lower the peak of the ACE-Kuznets curve and the later the inflection point.DiscussionThe findings in this paper represent the first exploration of the environmental Kuznets curve in agricultural carbon emissions. It is noteworthy that the moderating effect of formal environmental regulation does not lower the peak of the curve as we expect. This appearance is attributed to the reality that China is still in a phase of rising agricultural carbon emissions, which is exacerbated by the overlapping positive effects of agricultural robotics applications and formal environmental regulations. Informal environmental regulation is more effective than formal environmental regulation in reducing agricultural carbon emissions at this stage.

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