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

Bhattacharya, M., Inekwe, N. and Sadosky, P. (2020). "Convergence Of Energy Productivity In Australian States And Territories: Determinants And Forecasts", Energy Economics, 85.

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Abstract The Australian government has recently launched a National Energy Productivity Plan that calls for a 40% increase in energy productivity (economic output divided by energy use) before 2030. Improving energy productivity would help boost economic competitiveness, reduce energy costs, and reduce carbon dioxide emissions in Australia. Understanding energy productivity dynamics at the state level is essential for the success of this program. This research analyses the convergence path of energy productivity in Australian states and territories. Club convergence analysis applied to data over the period 1990–2015 reveals two converging energy productivity clubs. Initial energy productivity, industry structure, and automobile fuel prices are important determinants of higher energy productivity. Based on Australian state energy productivity forecasts to 2030, New South Wales and Victoria will be the forerunners in maintaining higher energy productivity in 2030. Australia will not achieve a 40% increase in energy productivity before 2030 without significant changes to its fuel mix and industry structure.

Bhattacharya, M., Inekwe, N. and Sadosky, P. (2020). "Consumption-based And Territory-Based Carbon Emissions Intensity: Determinants And Forecasting Using Club Convergence Across Countries", Energy Economics, 86.

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Abstract Understanding the dynamics of carbon emissions across time and space is important when formulating energy policies to minimise climate changes in the future. If per capita carbon emissions (or carbon intensity) converge over time, then any negotiation of multilateral agreements will be easier than if convergence is absent. It is also possible for club convergence to exist where countries within a club converge but countries between clubs do not converge. We examine the convergence of consumption-based and territory-based carbon emissions intensity across 70 countries. We find two convergent clubs for consumption-based emissions and three convergent clubs for territory-based emissions. Increases in each of total factor productivity, renewable energy consumption and urbanisation increase the odds of belonging to a low carbon emissions intensity club. An increase in industry value added reduces the odds of belonging to a low carbon intensity club. Increases in total factor productivity can be obtained through effective macroeconomic policy, while increasing renewable energy consumption may require structural changes in economies focused on transitioning to a low carbon economy. Under a business as usual forecasting scenario the number of consumption-based carbon emissions intensity clubs increases between 2014 and 2030 making it more difficult to negotiate multilateral agreements on climate change in the future.

Sadorsky, P. (2012). "Modeling Renewable Energy Company Risk", Energy Policy, 40, 39-48.

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Abstract The renewable energy sector is one of the fastest growing components of the energy industry and along with this increased demand for renewable energy there has been an increase in investing and financing activities. The tradeoff between risk and return in the renewable energy sector is, however, precarious. Renewable energy companies are often among the riskiest types of companies to invest in and for this reason it is necessary to have a good understanding of the risk factors. This paper uses a variable beta model to investigate the determinants of renewable energy company risk. The empirical results show that company sales growth has a negative impact on company risk while oil price increases have a positive impact on company risk. When oil price returns are positive and moderate, increases in sales growth can offset the impact of oil price returns and this leads to lower systematic risk.

Sadorsky, P. (2012). "Correlations And Volatility Spillovers Between Oil Prices and The Stock Prices of Clean Energy and Technology Companies", Energy Economics, 34(1), 248-255.

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Abstract In this paper, multivariate GARCH models are used to model conditional correlations and to analyze the volatility spillovers between oil prices and the stock prices of clean energy companies and technology companies. Four different multivariate GARCH models (BEKK, diagonal, constant conditional correlation, and dynamic conditional correlation) are compared and contrasted. The dynamic conditional correlation model is found to fit the data the best and generates results showing that the stock prices of clean energy companies correlate more highly with technology stock prices than with oil prices. On average, a $1 long position in clean energy companies can be hedged for 20 cents with a short position in the crude oil futures market.