Low-carbon technology transfer between rival firms under cap-and-trade policies

Abstract

We investigate the effects of low-carbon technology transfer between two rival manufacturers on their economic, environmental, and social welfare performance under a cap-and-trade policy. We model alternative licensing arrangements of technology transfer and evaluate the model performance from the perspectives of different stakeholders, including manufacturers, customers, and policy makers. Our findings show that the contractual choice on low-carbon technology licensing is dependent on the trade-off between the benefits gained from the licensing of technology and the consequential losses incurred from competition with a strengthened competitor, which is influenced by a combination of factors, including internal technological abilities, the interfirm power relationship, external market competition, and the carbon emission control policy. Among them, the interfirm power relationship is most influential in determining the optimal contractual decision. In addition, we extend the analysis of technology licensing strategies to different carbon emissions caps with additional cost incurred from purchasing emission allowances through auction, and a two-period model considering emissions cap reduction, respectively. Finally, our analyses show it is critical for policy makers to develop appropriate emissions control policies to promote the agenda of a sustainable, low-carbon economy.

Keywords:

• Low-carbon manufacturing
• technology licensing
• cap-and-trade
• sustainability

Additional information

Funding

The first author is supported by the National Key R&D Program of China (No. 2020YFB1711900), Major Program of National Social Science Foundation of China (No. 20&ZD084), and National Natural Science Foundation of China (No. 91646109).

Notes on contributors

Xu Chen

Xu Chen is a professor of operations management at the School of Management and Economics, University of Electronic Science and Technology of China, Chengdu, China. His current research interests include coopetition management, supply chain management, and operations management. His publications have appeared in Production and Operations Management, IIE Transactions, European Journal of Operational Research, OMEGA-International Journal of Management Science, and other journals. His research has been supported by grants from the National Sciences Foundation of China (NSFC), National Social Science Foundation of China (NSSFC), and National Key R&D Program of China.

Xiaojun Wang

Xiaojun Wang is a professor of operations management at the School of Management, University of Bristol. His current research predominantly focuses on low carbon manufacturing, supply chain management, coopetition, and social media research using analytical approaches including game theory, optimization, and machine learning techniques. He has published over 70 peer-reviewed papers in international renowned journals including Production and Operations Management, European Journal of Operational Research, Omega, International Journal of Production Economics, Journal of Business Ethics, and British Journal of Management. He has received funding for research from a range of funding bodies including NERC, ESRC, the Royal Society, the Newton Fund, and the National Natural Science Foundation of China (NSFC).

Yusen Xia

Yusen Xia is Bradford and Patrica Ferrer Professor in Analytics in the Robinson College of Business, Georgia State University. He received his PhD in supply chain and operations management from the McCombs School of Business at the University of Texas at Austin in 2004 and has been working at Georgia State University since then. His research interests include structured and unstructured data analytics, algorithm design and machine learning, blockchain technology, and operations and supply chain management. He has been applying and advancing different analytics methods such as machine learning, deep learning, and text/image analytics to solve business problems in different industries. He has published papers in various journals such as Management Science, Manufacturing and Service Operations Management, Operations Research, Production and Operations Management, Journal of Operations Management, Informs Journal on Computing, Decision Sciences, etc. His research has been supported by grants from the National Sciences Foundation (NSF) and companies such as Amazon.com, Ryerson Inc. etc.