The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector

2021 ◽  
pp. 101002
Author(s):  
Jun Liu ◽  
Liang Liu ◽  
Yu Qian ◽  
Shunfeng Song
Antibiotics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1376
Author(s):  
Liliana David ◽  
Anca Monica Brata ◽  
Cristina Mogosan ◽  
Cristina Pop ◽  
Zoltan Czako ◽  
...  

Over recent decades, a new antibiotic crisis has been unfolding due to a decreased research in this domain, a low return of investment for the companies that developed the drug, a lengthy and difficult research process, a low success rate for candidate molecules, an increased use of antibiotics in farms and an overall inappropriate use of antibiotics. This has led to a series of pathogens developing antibiotic resistance, which poses severe threats to public health systems while also driving up the costs of hospitalization and treatment. Moreover, without proper action and collaboration between academic and health institutions, a catastrophic trend might develop, with the possibility of returning to a pre-antibiotic era. Nevertheless, new emerging AI-based technologies have started to enter the field of antibiotic and drug development, offering a new perspective to an ever-growing problem. Cheaper and faster research can be achieved through algorithms that identify hit compounds, thereby further accelerating the development of new antibiotics, which represents a vital step in solving the current antibiotic crisis. The aim of this review is to provide an extended overview of the current artificial intelligence-based technologies that are used for antibiotic discovery, together with their technological and economic impact on the industrial sector.


Author(s):  
Ritvars Purmalis ◽  

Digital innovations such as artificial intelligence systems, although limited in their current operational capacity, can be considered to be part of our daily life. Various ways in which these systems are implemented into day-to-day aspects directly affect not only the further development of the industrial sector but the society as a whole. The purpose of this article is to provide a brief insight into the current situation and the various initiatives of the European Union institutions in relation to the methodology for the application of civil liability in the case of damage caused by artificial intelligence systems, as well as to assess the content of future regulatory framework that has been published by the European Parliament, with whom it is intended to establish a common methodology throughout the European Union for the application of civil liability regime, if the damage is caused by artificial intelligence systems.


Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 825 ◽  
Author(s):  
Shining Zhang ◽  
Fang Yang ◽  
Changyi Liu ◽  
Xing Chen ◽  
Xin Tan ◽  
...  

The industrial sector dominates the global energy consumption and carbon emissions in end use sectors, and it faces challenges in emission reductions to reach the Paris Agreement goals. This paper analyzes and quantifies the relationship between industrialization, energy systems, and carbon emissions. Firstly, it forecasts the global and regional industrialization trends under Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway2 (SSP2) scenarios. Then, it projects the global and regional energy consumption that aligns with the industrialization trend, and optimizes the global energy supply system using the Model for Energy Supply Strategy Alternatives and their General Environmental Impact (MESSAGE) model for the industrial sector. Moreover, it develops an expanded Kaya identity to comprehensively investigate the drivers of industrial carbon emissions. In addition, it employs a Logarithmic Mean Divisia Index (LMDI) approach to track the historical contributions of various drivers of carbon emissions, as well as predictions into the future. This paper finds that economic development and population growth are the two largest drivers for historical industrial CO2 emissions, and that carbon intensity and industry energy intensity are the top two drivers for the decrease of future industrial CO2 emissions. Finally, it proposes three modes, i.e., clean supply, electrification, and energy efficiency for industrial emission reduction.


Author(s):  
Geetha Prahalathan ◽  
Senthil Kumar Babu ◽  
Praveena H. D.

The industrial production has experienced a technological revolution in the recent past decades. The technological revolution influenced the agriculture industry too. The important areas in the change are not limited to innovation in farming, novel production of agriculture-based tools and equipment, transportation and consumption of food across the globe, marketing the agriculture products, and digitalization. Digitalization is the involvement of digital technology in the existing field for easing the mechanism of handling, processing, recording the data. Digitalization enables sustainable farming. It is required desperately to develop this technology because there is a substantial reduction of clean water and depletion of aquifers effects the cultivation. With the technology, the quantity and quality of the food has to be managed to feed the global population. The familiar digitization technology that makes the agri-industrial sector to experience growth are artificial intelligence, machine learning, sensor networks, internet of things, robotics, cloud data.


Author(s):  
María Luisa Villasano Jain ◽  
Héctor Cuellar Hernández ◽  
Rosa Alejandra Reyes Rizo ◽  
Helga Elena Roesner García

El presente estudio se realizó en una empresa de giro industrial en el municipio de Atotonilco El Alto, Jalisco y tiene como objetivo analizar los efectos laborales en el recurso humano, por el uso de la inteligencia artificial (IA) (robótica y los sistemas de información) en una organización de giro industrial. Se fundamenta en el método cuantitativo, se aspira exponer la situación a partir de la medición y cuantificación de las realidades humanas; es una investigación no experimental. Los instrumentos que se utilizaron para la recolección de datos fueron cuestionario y entrevista, en donde se profundizó en las técnicas de IA. Una de las principales conclusiones a las que se llega es que la incorporación de la IA en la organización desencadena en los colaboradores cierto nivel de estrés por temor al remplazo. AbstractThis research was performed in a company which commercial activities are carried out in the industrial sector in the municipality of Atotonilco El Alto Jalisco and aims to analyze the effects artificial intelligence (AI) (robotic and systems information) has in workforce collaborators in industrial organizations. Based on a quantitative method, this research aspires to set forth the situation through the measurement and quantification of human reality; this is a non-experimental investigation. The instrument used for data recollection consisted of a questionnaire developed with AI techniques. One of the main conclusions reached was, when AI was incorporated in an organization, stress due to the fear of being replaced was triggered.


2020 ◽  
Vol 10 (4) ◽  
pp. 194-205
Author(s):  
Rabab Benotsmane ◽  
László Dudás ◽  
György Kovács

Nowadays, in the age of Industry 4.0 the Artificial Intelligence (AI) and Machine Learning capabilities have important role in the implementation of this new paradigm in the industrial sector. Especially in industrial robotics technology where the main target is improving the productivity, which requires the improvement on the rigid, inflexible capabilities of industrial robots. This article presents an overview of AI algorithms used in industrial robotics. In the first part of the article an overview about the Machine Learning algorithms used for industrial robots will be discussed. In the second part of the study we will introduce the most important AI algorithms used to optimize and improve the trajectory of robotic arms.


New IT technologies will help enterprises across all industries to master future challenges. As AI emerges from science fiction to become the frontier of world changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Today, the term “industrial automation” is generally referred to in the context of Industry 4.0 and the Industrial Internet of Things the two most recent technological revolutions in the industrial sector. The core principles of Industry 4.0 focus on increasing productivity, cost efficiency, quality, and safety by utilizing innovative technologies enabled by the IIoT, such as cyberphysical systems, cloud computing, big data, artificial intelligence and machine learning. When implementing this technology by using data centric digital business model. Internet of Things & Artificial Intelligence are poised to transform industrial operations.


Author(s):  
Rindra Yusianto ◽  
Marimin Marimin ◽  
Suprihatin Suprihatin ◽  
Hartrisari Hardjomidjojo

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1019
Author(s):  
Rui Li ◽  
Hong Jiang ◽  
Iryna Sotnyk ◽  
Oleksandr Kubatko ◽  
Ismail Almashaqbeh Y. A.

CO2 emissions have become a key environmental contaminant that is responsible for climate change in general and global warming in particular. Two geographical groups of countries that previously belonged to the former bloc of socialist countries are used for the estimations of CO2 emissions drivers. The research covers such Eastern European countries as Bulgaria, Czech Republic, Hungary, Russian Federation, Poland, Romania, Slovak Republic, and Ukraine and such Central Asian states as Kazakhstan and Uzbekistan during the period 1996–2018. The main goal of the research is to identify common drivers that determine carbon dioxide emissions in selected states. To control for the time fixed effects (like EU membership), random effect model was used for the analysis of the panel data set. Results: It is found that energy efficiency progress reduces per capita CO2 emissions. Thus, an increase in GDP by 100 USD per one ton of oil equivalent decreases per capita CO2 emissions by 17–64 kg. That is, the more energy-efficient the economy becomes, the less CO2 emissions per capita it produces in a group of selected post-communist economies. Unlike energy efficiency, an increase in GDP per capita by 1000 USD raises CO2 emissions by 260 kg per capita, and the richer the economy becomes, the more CO2 emissions per capita it generates. The increase in life expectancy by one year leads to an increase in CO2 emissions per capita by 200−370 kg, with average values of 260 kg per capita. It was found that an increase in agriculture, forestry, and fishing sector share (as a % of GDP) by one percentage point leads to the decrease in CO2 emissions by 67–200 kg per capita, while an increase in industrial sector share by one percentage point leads to the increase in CO2 per capita emissions by 37–110 kg. Oil prices and foreign direct investment appeared to be statistically insignificant factors in a group of selected post-communist economies. Conclusions: The main policy recommendation is the promotion of energy efficiency policy and the development of green economy sectors. The other measures are the promotion of a less energy-intensive service sector and the modernization of the industrial sector, which is still characterized by high energy and carbon intensity.


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