Green Cloud? An Empirical Analysis of Cloud Computing and Energy Efficiency

被引:29
作者
Park, Jiyong [1 ]
Han, Kunsoo [2 ]
Lee, Byungtae [3 ]
机构
[1] Univ N Carolina, Bryan Sch Business & Econ, Greensboro, NC 27412 USA
[2] McGill Univ, Desautels Fac Management, Montreal, PQ H3A IG5, Canada
[3] Korea Adv Inst Sci & Technol, Coll Business, Seoul 02455, South Korea
关键词
cloud computing; software-as-a-service; infrastructure-as-a-service; IT outsourcing; energy efficiency; green IT; green IS; sustainability; stochastic frontier analysis; INFORMATION-TECHNOLOGY INVESTMENTS; TECHNICAL EFFICIENCY; MANAGEMENT-PRACTICES; PANEL-DATA; PRODUCTIVITY; SYSTEMS; IMPACT; INNOVATION; INTENSITY; SOFTWARE;
D O I
10.1287/mnsc.2022.4442
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
The rapid, widespread adoption of cloud computing over the last decade has sparked debates on its environmental impacts. Given that cloud computing alters the dynamics of energy consumption between service providers and users, a complete understanding of the environmental impacts of cloud computing requires an investigation of its impact on the user side, which can be weighed against its impact on the vendor side. Drawing on production theory and using a stochastic frontier analysis, this study examines the impact of cloud computing on users' energy efficiency. To this end, we develop a novel industry-level measure of cloud computing based on cloud-based information technology (IT) services. Using U.S. economy-wide data from 57 industries during 1997-2017, our findings suggest that cloud-based IT services improve users' energy efficiency. This effect is found to be significant only after 2006, when cloud computing started to be commercialized, and becomes even stronger after 2010. Moreover, we find heterogeneous impacts of cloud computing, depending on the cloud service models, energy types, and internal IT hardware intensity, which jointly assist in teasing out the underlying mechanisms. Although software-as-a-service (SaaS) is significantly associatedwith both electric and nonelectric energy efficiency improvement across all industries, infrastructure-as-a-service (IaaS) is positively associated only with electric energy efficiency for industries with high IT hardware intensity. To illuminate the mechanisms more clearly, we conduct a firm-level survey analysis, which demonstrates that SaaS confers operational benefits by facilitating energy-efficient production, whereas the primary role of IaaS is tomitigate the energy consumption of internal IT equipment and infrastructure. According to our industry-level analysis, the total user-side energy cost savings fromcloud computing in the overall U.S. economy are estimated to be USD 2.8-12.6 billion in 2017 alone, equivalent to a reduction in electricity use by 31.8-143.8 billion kilowatt-hours. This estimate exceeds the total energy expenditure in the cloud service vendor industries and is comparable to the total electricity consumption in U.S. data centers.
引用
收藏
页码:1639 / 1664
页数:26
相关论文
共 101 条
[1]   Material efficiency: A white paper [J].
Allwood, Julian M. ;
Ashby, Michael F. ;
Gutowski, Timothy G. ;
Worrell, Ernst .
RESOURCES CONSERVATION AND RECYCLING, 2011, 55 (03) :362-381
[2]  
[Anonymous], 2013, case study
[3]  
[Anonymous], 2010, Cloud Computing and Sustainability: The Environmental Benefits of Moving to the Cloud
[4]  
[Anonymous], In a sense, Er, who is a warrior of "every race
[5]   Three-Way Complementarities: Performance Pay, Human Resource Analytics, and Information Technology [J].
Aral, Sinan ;
Brynjolfsson, Erik ;
Wu, Lynn .
MANAGEMENT SCIENCE, 2012, 58 (05) :913-931
[6]   SOME TESTS OF SPECIFICATION FOR PANEL DATA - MONTE-CARLO EVIDENCE AND AN APPLICATION TO EMPLOYMENT EQUATIONS [J].
ARELLANO, M ;
BOND, S .
REVIEW OF ECONOMIC STUDIES, 1991, 58 (02) :277-297
[7]   A View of Cloud Computing [J].
Armbrust, Michael ;
Fox, Armando ;
Griffith, Rean ;
Joseph, Anthony D. ;
Katz, Randy ;
Konwinski, Andy ;
Lee, Gunho ;
Patterson, David ;
Rabkin, Ariel ;
Stoica, Ion ;
Zaharia, Matei .
COMMUNICATIONS OF THE ACM, 2010, 53 (04) :50-58
[8]  
AWS, 2018, PRED SCAL EC2 POW MA
[9]  
AWS, 2008, AN SCAL VIR GROWTH
[10]  
Baer A, 2020, 20127 IMF