Research EngineerPatricio Gallardo Ocampo
Link - Room 203
Internal Phone: 91536
“His interests focus on assesing cost-effective pathways to reduce fossil fuel dependency in energy and transportation systems".
Patricio has over 10 years of experience in industry and academia. His area of research focuses on transportation as an energy system. During his PhD project he developed a strategic planning platform, integrating different methodologies (transition engineering, energy systems planning, GIS-based network analysis and simulation) to identify cost-effective infrastructure options to decarbonize freight transport, using New Zealand as a case of study. Nowadays, Patricio is working as a postdoctoral researcher at the EPECentre, becoming involved with projects and initiatives aiming to transition to a low carbon energy/transport system.
- Williams B., Gallardo P., Bishop D. and Chase G. (2023) Impacts of electric vehicle policy on the New Zealand energy system: A retro-analysis. Energy Reports 9: 3861-3871. http://dx.doi.org/10.1016/j.egyr.2023.02.080.
- Andrade I., Land J., Gallardo P. and Krumdieck S. (2022) Application of the InTIME Methodology for the Transition of Office Buildings to Low Carbon—A Case Study. Sustainability (Switzerland) 14(19) http://dx.doi.org/10.3390/su141912053.
- Gallardo P., Murray R. and Krumdieck S. (2021) A sequential optimization-simulation approach for planning the transition to the low carbon freight system with case study in the North Island of New Zealand. Energies 14(11) http://dx.doi.org/10.3390/en14113339.
- Gallardo P., Díaz JP., Quintana P., Cevallos I., León P. and Guillén J. (2018) Energy intensity of road freight transport of liquid fuels for automotive use in Ecuador: Assessment of changes in logistics. Case Studies on Transport Policy 6(2): 289-296. http://dx.doi.org/10.1016/j.cstp.2017.12.001.
- Gallardo Ocampo P., Murray R., Bishop D. and Krumdieck S. (2020) A freight distribution exercise. In.
- Energy Systems Modeling
- Transport Modeling
- Agent-based simulation
- GIS programming
- Inferential and Regression Statistics