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

Deviatkin, I., Kozlova, M. and Yeomans, J.S. (2021). "Simulation Decomposition for Environmental Sustainability: Enhanced Decision-Making in Carbon Footprint Analysis", Socio-Economic Planning Sciences, 75, 1, 1-10 .

Open Access Download

Abstract Environmental sustainability problems frequently require the need for decision-making in situations containing considerable uncertainty. Monte Carlo simulation methods have been used in a wide array of environmental planning settings to incorporate these uncertain features. Simulation-generated outputs are commonly displayed as probability distributions. Recently simulation decomposition (SD) has enhanced the visualization of the cause-effect relationships of multi-variable combinations of inputs on the corresponding simulated outputs. SD partitions sub-distributions of the Monte Carlo outputs by pre-classifying selected input variables into states, grouping combinations of these states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since it is a straightforward task to visually project the contribution of the subdivided scenarios onto the overall output, SD can illuminate previously unidentified connections between the multi-variable combinations of inputs on the outputs. SD is generalizable to any Monte Carlo method with negligible additional computational overhead and, therefore, can be readily extended into most environmental analyses that use simulation models. This study demonstrates the efficacy of SD for environmental sustainability decision-making on a carbon footprint analysis case for wooden pallets.

Hunt, C., & Weber, O. (2019). "Fossil Fuel Divestment Strategies: Financial and Carbon Related Consequences", Organization & Environment, 32(1), 41–61.

Open Access Download

Abstract Fossil fuel divestment is discussed controversially with regard to its financial consequences and its effect on decarbonizing the economy. Theory and empirical studies suggest arguments for both financial underperformance and outperformance of divestment. Therefore, our first research objective is to understand the financial effect of divestment. The second objective is to analyze the influence of divestment strategies on the carbon intensity of portfolios. Empirically, our analysis is based on the Canadian stock index TSX 260 for the time between 2011 and 2015. The results of the study suggest higher risk-adjusted returns and lower carbon intensity of the divestment strategies compared with the benchmark. We conclude that divestment is not only an ethical investment approach but also that it is able to address financial risks caused by climate change and, at the same time, is able to reduce the carbon exposure of investment portfolios.