Publications Database

Welcome to the new Schulich Peer-Reviewed Publication Database!

The database is currently in beta-testing and will be updated with more features as time goes on. In the meantime, stakeholders are free to explore our faculty’s numerous works. The left-hand panel affords the ability to search by the following:

  • Faculty Member’s Name;
  • Area of Expertise;
  • Whether the Publication is Open-Access (free for public download);
  • Journal Name; and
  • Date Range.

At present, the database covers publications from 2012 to 2020, but will extend further back in the future. In addition to listing publications, the database includes two types of impact metrics: Altmetrics and Plum. The database will be updated annually with most recent publications from our faculty.

If you have any questions or input, please don’t hesitate to get in touch.

 

Search Results

Syed Abul Basher and Perry Sadorsky (2024). "Do Climate Change Risks Affect The Systemic Risk Between The Stocks Of Clean Energy, Electric Vehicles, And Critical Minerals? Analysis Under Changing Market Conditions", Energy Economics, 138, 107832.

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Abstract This paper analyzes the impact of climate change risks—specifically from natural disasters, global warming, international summits, and U.S. climate policy—on the return connectedness (systemic risk) of a network consisting of the stocks of clean energy, electric vehicles, and critical minerals in bear, bull, and normal market conditions. Employing a quantile vector autoregression (QVAR) approach, we find significant temporal variations in the total connectedness index, with notable spikes during the COVID-19 pandemic and the Russia-Ukraine war. Total connectedness is higher but less variable under bear and bull market conditions. Concerns about global warming has a positive and significant impact on systemic risk during bear and normal market conditions while international summits have a negative impact during normal market conditions. However, the effects of these climate change risks are small in magnitude. Economic policy uncertainty and stock market volatility have the largest positive impacts on systemic risk under most market conditions. Our results reveal a nonlinear (inverted U-shaped) relationship between variable importance and systemic risk quantile, showing that the impact on connectedness is largest in magnitude under normal market conditions.

Yeomans, J.S. (2021). "A Multicriteria, Bat Algorithm Approach for Computing the Range Limited Routing Problem for Electric Trucks", WSEAS Transactions on Circuits and Systems, 20(13), 96-106.

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Abstract As a result of increasing urban intensification, civic planners have devoted additional resources to more sustainability-focused logistics planning. Electric vehicles have proved to be both a lower cost alternative and more environmentally friendly than the more ubiquitous internal combustion engine vehicles. However, the predominant decision-making approaches employed by businesses and municipalities are not necessarily computationally conducive for the optimization and evaluation of urban transportation systems involving electric vehicles. An innovative modelling and planning approach is proposed to enable urban planners to more readily evaluate the contribution of electric vehicles in city logistics and to support the decision-making process. Specifically, this paper provides a multicriteria modelling-to-generate-alternatives (MGA) decision-support procedure that employs the Bat Algorithm (BA) metaheuristic for generating sets of alternatives for electric vehicle planning in urban transshipment problems. The efficacy of this multicriteria, BA-driven MGA approach for creating planning alternatives is demonstrated on an urban transshipment problem involving electric trucks.

Gunalay, Y. and Yeomans, J.S. (2020). "An Algorithm for Computing Solutions to the Range Limited Routing Problem Using Electrical Trucks", WSEAS Transactions on Computers, 19(7), 47-53 .

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Abstract Increasing urban intensification has caused civic planners to devote additional resources to more appropriate logistics planning. Electric vehicles have proved to be both a lower cost alternative and more environmentally friendly than the more ubiquitous internal combustion engine vehicles. However, prevailing decision-making formulations employed by municipalities and businesses are not necessarily computationally conducive for the evaluation and optimization of urban transportation systems using electric vehicles. An innovative computational approach, the range limited routing problem, is introduced that enables urban planners to more readily evaluate the contributions of electric vehicles to the city logistics decision-making process. While there is no generalized solution technique for solving this new formulation, this paper employs the Firefly Algorithm (FA) metaheuristic to solve the range limited routing problem using electric trucks.