Towards Optimized ARMGs’ Low-Carbon Transition Investment Decision Based on Real Options

Abstract

As a critical node of the global transportation network, ports have great potential in promoting transportation emission reduction. Promoting the low-carbon transition of ports by using clean energy is effective. Using hydrogen energy in automated container terminals (ACTs) has become popular in port emission-reduction research. The research object is the main port equipment—the automated rail-mounted container gantry crane (ARMG). This research designs a staged investment decision-making scheme for ARMGs’ hydrogen energy transition. The Internet of Things (IoT) architecture in ACTs collects ARMG equipment operation and carbon emission data. It provides a basis for data acquisition in ARMGs’ hydrogen energy transition. Furthermore, ports can adopt big data technology to analyze the correlation between equipment operation and carbon emissions. Finally, the digital twin platform will visualize the ARMG equipment operation and carbon emission behavior to remote operators. These advanced technologies can achieve status monitoring and simulation prediction, which will support ARMGs’ hydrogen energy transition. However, the ARMGs’ hydrogen energy transition has a long cycle, large investment, and strong variability. Ports should make staged investment decisions based on the digital twin platform’s status monitoring and simulation prediction analysis results. Therefore, this research establishes an optimization model for ARMGs’ low-carbon transition investment decision based on the real options method, and analyzes the staged investment scale and timing of ARMGs’ hydrogen energy transition. The results provide a popularized decision-making scheme for the low-carbon transition of ports’ equipment, which could facilitate the low-carbon transition of ports’ equipment.

Exploring Digital Twins in the Transport and Energy Fields: A Bibliometrics and Literature Review Approach

“…Digital Twins are being adapted into all pores of various industries [13], from the beginning in manufacturing [17], after in supply chains [18][19][20], logistics [14], the process industry [21,22], and transport [23,24] with a focal point on energy efficiency [25][26][27]. Specifically in the transport industry, digital twins are anticipated to drastically change the transport systems' technology, as they enable monitoring of the whole transport system life cycle [24].…”

“…Framework and case study (1); As for the other publications (27), each had one implementation level type: review (11), theoretical implementation (8), conceptual model (4), case study (2), framework (1) and prototype (1). Only implementation levels repeated at least five times are presented (Table 11).…”