Essential elements of the energy transition for freight movements: data mining, modelling tools and policy scenarios - with case study in New Zealand

Abstract

New Zealand’s commitment towards the Kyoto Protocol and Paris Agreement requires a 70% reduction of current gross emissions by 2050. Oil’s high energy return on investment, containerization, the expansion of the road network, and the deregulation of trucking have turned the freight sector heavily dependent on fossil fuel resources. This dependency represents a wicked problem whose solution requires a radical transformation of the system. This thesis is concerned with the development of a framework that channels the advantages of different modelling methodologies into delivering a future vision, a long term-strategic concept of a freight system that fully embraces feasible and systemic decarbonisation pathways.

The first part of the thesis presents a brief review on the historical evolution of the freight sector and provides current figures on both, energy and transportation sectors. Chapters 2 and 3 also explore state of the art energy and transport modelling methodologies, searching for limitations and advantages, which further lead into the conception of a new planning approach more aligned with the aim of the thesis. Chapter 4 covers the formulation of a framework for STRATegic COncept DEsign of Freight Systems (STRATCODE). STRATCODE combines three components: freight distribution, network analysis and agent based Discrete Event Simulation (DES). The consideration of different components enhances the realization of different objectives, specifically addressing issues on data availability, absence of logistics features in transport modelling and long-term planning of freight infrastructure capacity.

The remaining chapters cover each of the components of STRATCODE in more detail and are also implemented using the North Island of New Zealand as a case of study. Chapter 5 describes the implementation of the freight distribution component. The key contribution of the distribution component was the identification of representative facility locations through the exercise of web scrapping and sequential GIS processing tasks. Chapter 6 elaborates on the development of the optimization component, a GIS-based intermodal planning model. The contribution of the network planning approach relied on the architecture of the model’s algorithm which enhanced multifold functionality, mainly allowing to deliver locations for intermodal terminals and to build a database with optimal shipping plans to be tested during simulation. Chapter 7 presented the last component, and agent-based discrete event simulation model. The simulation component was complementarily utilized to interrogate the capacity of the analytic solution delivered through the previous optimization component. The model was tested for different simulation experiments leading to the formulation of different concepts. An economic assessment was also carried out in order to identify the most cost-effective setup.

The results suggest that future investments should prioritize the development of intermodal hubs. These developments could lead to reductions in energy savings and GHG emissions of approximately 48% and 47%, respectively. The electrification of a congested railway segment can increase emissions reduction up to 54%, which is still far from the 70% needed. The difference against the national target could be further achieved at the expense of a significant reduction in transport activity related to non-essential commodities.