The goal of this book is to improve the ability of enterprises to implement the green growth model and value chain reconstruction. China’s environmental development strategies, such as carbon peak emission and carbon neutrality, have created new challenges and requirements for enterprises to “go green.” In addition, anti-globalization and the complex dynamic uncertainty caused by COVID-19 have changed the operational environment that enterprises face. The application of new technologies, including the new generation of information technologies and the whole process management technology, provides solutions for the implementation of enterprises’ green growth model and value chain reconstruction. Based on China’s enterprise management cases, this book reveals the connotative features of enterprises’ green growth model and their evolutionary regularities, the overall framework and decision optimization of value chain reconstruction under the green growth model, and the approach to implementing the green growth model and value chain reconstruction. The theoretical framework of the green growth model and value chain reconstruction established in this book has enriched and developed the research results in this field. Cases of enterprises implementing the green growth model can provide references for the green transformation of enterprises and help enterprises appreciate the synergy between sustainability and growth. This book can also serve as a research reference for scholars engaged in the field of sustainable operations, as well as decision-makers and managers of relevant government departments.
Nengmin Wang is a professor of Xi’an Jiaotong University and the President of Zhengzhou College of Finance and Economics. His research interests include sustainable operation management and green supply chain management. He has presided over 7 national projects such as the National Natural Science Foundation of China, including 1 key project of the National Natural Science Foundation of China and 1 major project of the National Natural Science Foundation of China. He has authored 60 articles in refereed academic journals, including Journal of Management Information Systems, IEEE Transactions on Engineering Management, European Journal of Operational Research, Annals of Operations Research, International Journal of Production Research, Computers & Operations Research, Computers and Industrial Engineering.
Qi Jiang is a Ph.D. Candidate at School of Management in Xi’an Jiaotong University.Her main research field is supply chain management. She has authored several articles in peer-reviewed academic journals, such as International Journal of Information Management.
Bin Jiang is the inaugural Driehaus Fellow and a professor of management at DePaul University. He has authored over 50 research articles in journals, such as Decision Sciences, the Journal of Operations Management, Production and Operations Management, the International Journal of Operations and Production Management, the International Journal of Production Economics, the International Journal of Production Research, the Omega, the Academy of Management Perspectives, and the Business Horizons. His research focuses on outsourcing and supply chain management. Dr. Jiang received the Best Paper Award for his publications in the Journal of Operations Management, the Stan Hardy Award, the APICS Best Paper Award, and the Finalist of the POMS’ Wickham Skinner Best Unpublished Paper Award.
Zhengwen He is currently a professor of Industrial Engineering Department of Management School, Xi’an Jiaotong University, Xi’an, China. His research interests include project management optimization and engineering management optimization. He has authored 30 articles in refereed academic journals, including European Journal of Operational Research, IEEE Transactions on Engineering Management, Computers & Operations Research, Annals of Operations Research, International Journal of Production Research, Journal of the Operational Research Society, Computers and Industrial Engineering, Journal of Combinatorial Optimization.
