Date of Publication


Document Type


Degree Name

Doctor of Philosophy in Mechanical Engineering

Subject Categories

Energy Systems | Mechanical Engineering


Gokongwei College of Engineering


Mechanical Engineering

Thesis Advisor

Neil Stephen A. Lopez

Defense Panel Chair

Jose Bienvenido Manuel Biona

Defense Panel Member

Aristotle Ubando
Gerardo Augusto
Robby Manrique
Krista Danielle Yu


The Republic of the Philippines is an archipelagic country, accommodating 108.12 million people. With increasing population, there is a growing demand for transport leading to huge congestion in several regions of the country. These are as a result of poor transportation framework and infrastructural planning in the country. As transport becomes essential in our daily lives, there is a need to address several driving factors leading to the huge traffic flow with a rise in transport emissions. For decades, human activities and energy consumption have been linked to climate change, which has caused many worries. The transportation sector, in particular, contributes significantly to global emissions. This is owing to a growing reliance on private vehicles and a shoddy transportation system. This has substantial environmental and sustainability consequences in addition to economic effects. Transitioning from a product-based to a service-based approach, i.e., lowering private vehicle ownership and use, is one way to use circular economy ideas in transportation. This is evident in recent innovations in numerous countries, ranging from ridesharing, bike-sharing, and car-sharing programs. However, studies show that as income levels improve, private vehicle ownership will continue to outpace public transportation use in emerging countries over the next decade. Recent vehicle ownership statistics in the Philippines support this. Using an exemplary case study in the Philippines, this work provides an approach for analyzing drivers of energy use, traffic flow, and CO2 emissions in regions using spatial Logarithmic Mean Divisia Index (LMDI). Regional disparities in traffic flow are evaluated using plausible explanatory factors such as population, economic activity, travel intensity, and mode structure. Similar patterns emerge for drivers in terms of traffic flow and transportation emissions, yielding some intriguing results. In terms of the impact, the findings demonstrated that increased economic activity generally reduces traffic intensity and switching to cleaner energy is not a guarantee of lower carbon emissions. As the pandemic came into place, the study carried out an extensive review on how to address the spread of the virus in public transport, address supply and demand issues and lastly improve contact tracing in the country. In addition, as the pandemic skewed up recent findings with huge reduction of carbon emission in the country, the researcher further extended the study to cover the energy reduction during this event using LMDI analysis pre- and post-pandemic and the drivers were also revealed. Furthermore, Autoregressive Distributive Lag (ARDL) and Cointegration Analysis addressed the relationship between population, gross domestic product (GDP) and carbon emission, revealing there exists long term relationship between emissions of previous year and if these are not addressed, emissions will start to rebound until it catches up with us 4 years later. GDP on the other hand showed slight significance on the current year’s carbon emission but not in a long run. The novelty of this study is the introduction of an ARDL-modified Spatial LMDI to ensure that correlated previous-year data are also considered. The researcher provided a way forward towards the end of the study, which include the need to develop a roadmap to reduce the overall transport demand, equity on the appropriation of transport infrastructure projects, quality improvement of public transport services, promoting mixed-use development, and providing fiscal and non-fiscal incentive to companies adapting to telecommuting. This study further supports the Sustainable Development Goals (SDGs) of industry, innovation and infrastructure (SDG 9), sustainable cities and commodities (SDG 11), and climate action (SDG 13).

Keywords: Spatial LMDI; LMDI; Traffic flow; Emissions; ARDL; COVID-19; Transport; ARDL-modified Spatial LMDI; Philippines

Abstract Format




Physical Description

103 leaves


Automobiles—Philippines--Fuel consumption; Traffic congestion--Philippines; Atmospheric carbon dioxide--Philippines; Transportation--Philippines

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