Job Market Paper:

Integration of renewable energy in electricity system: Economic challenges and stakeholder impacts.

The Province of Ontario has had an aggressive program of introducing wind electricity generation technologies into its generation supply mix. This, combined with the rigid baseload production by nuclear and hydro plants, has for 20 years created a surplus baseload electricity supply. Pumped hydro storage (PHS) is suggested as an economically viable technology for storing energy from non-dispatchable wind energy sources. An analytical framework has been developed to explore the feasibility of the PHS facility to manage the surplus supply of electricity and compare its cost performance with the alternative gas power plants. Two situations are analyzed. First, the PHS plant uses only surplus energy for the first 20 years of operation. Second, an additional 20 years of PHS usefulness is added by making investments in wind electricity generation to provide energy for pumping. Given the capital costs of building PHS in Ontario, the PHS expansion is not economically cost-effective for utilizing the projected off-peak surpluses. The economic analysis also illustrates that in the context of Ontario, the integration of PHS with wind power generation will have a negative impact on the Canadian economy in all circumstances. This loss is borne mainly by the electricity consumers of Ontario. Even considering the cost of CO2 emissions from a world perspective, this investment is not cost-effective. It would be much better socially from a world perspective and economically from Canada’s perspective if the surplus baseload electricity from Ontario were given away free to the USA. It could then be used to reduce generation by natural gas plants in the USA, hence reducing CO2 emissions globally, without any incremental economic cost to Canada.

My Most Recent Published Papers in Refereed Journals:

Bahramian, P., Jenkins, G. P., and Milne, F. (2021). A stakeholder analysis of investments in wind power electricity generation in Ontario. Energy Economics, 105569.

This study uses an ex-post evaluation of the grid-connected wind projects in Ontario, Canada, to quantify the stakeholder impacts of such renewable energy projects. Our study includes a financial, economic and stakeholder analysis of a sample of three wind farms. The analysis sheds light on the distributional impacts that arise when there is a significant gap between the incentives created by the financial price paid for electricity generation and the economic value of the electricity generated. The analysis shows that the negotiated power purchase agreements (PPAs) have resulted in a negative outcome for the economy in all circumstances. It is found that the present value of the economic costs is at least three times the present value of the economic benefits, including the global benefits from the reduced CO2 emissions. This loss is borne by all the stakeholders of the electricity system, except the private owners of the wind farms. The losers are primarily the electricity consumers followed by the governments. The Ontario Electricity Rebate (OER) programme, which is financed by increased government borrowing, has the effect of transferring a large share of the costs incurred to promote investments in wind power to future generations of taxpayers in Ontario.

Bahramian, P., Jenkins, G. P., and Milne, F. (2021). The displacement impacts of wind power electricity generation: Costly lessons from Ontario. Energy Policy, 152, 112211. 9.

The displacement impacts of wind power generation on other generation technologies are estimated for Ontario. In addition, their annual financial benefits, costs, and international stakeholder impacts are measured. For every 100 MWh generated, almost 53 MWh of gas output is displaced, and 19 MWh of power is exported. Due to inadequate storage capacity hydropower generation is reduced by 23 MWh. Ontario on average loses about 859 million USD annually from having wind power generation in the system, while the US gains approximately 10 million USD through electricity exported from Ontario. Wind power generation has produced an estimated 109 million USD of benefits by reducing CO2 emissions in the US and Ontario through displacing thermal generation. Comparing the environmental benefits with the net cost to consumers shows the promotion of wind power generation to be largely a waste of Ontario’s resources.

Some Selected Publications

Bahramian, P., & Saliminezhad, A. (2021). Revisiting purchasing power parity in the ASEAN-5 countries: evidence from the Fourier quantile unit root test. Applied Economics Letters, 28(13), 1104-1109.

Bahramian, P., & Saliminezhad, A. (2020). Does capacity utilization predict inflation? Wavelet-based evidence from United States. Computational Economics, 1-23.

Seraj, M., Bahramian, P., Alhassan, A., & Shahabad, R. D. (2020). The validity of Rodrik’s conclusion on real exchange rate and economic growth: factor priority evidence from feature selection approach. Palgrave Communications, 6(1), 1-6.

Bahramian, P., & Saliminezhad, A. (2020). On the relationship between export and economic growth: A nonparametric causality-in-quantiles approach for Turkey. The Journal of International Trade & Economic Development, 29(1), 131-145.

Kanda, P. T, Balcilar, M., Bahramian, P., Gupta, R. (2016). Forecasting South African Inflation Using Non-Linear Models: A Weighted Loss-Based Evaluation, Applied Economics, Vol 48, Issue 26, 2016.

Jenkins, G. P., Amala Anyabolu, H., & Bahramian, P. (2019). Family decision-making for educational expenditure: new evidence from survey data for Nigeria. Applied Economics, 51(52), 5663-5673.

Papers Under Review

Clean energyconsumption economic growth nexus in China:Fresh evidence from the rolling and recursive causality approaches (R&R at Studies in Nonlinear Dynamics & Econometrics).

The current study investigates the dynamic linkages among clean energy usage and economic growth in China for the timespan between 1965 and 2019. Dissimilar to previous studies that have assumed parameter stability in the causality analysis, we apply the rolling and recursive rolling Granger causality techniques that allow time-varying behavior of the model parameters and offer increased reliability and inclusive inference on the causal relationship among the variables. Utilizing the conventional linear Granger causality test, we fail to detect a causation between clean energy usage and growth of the economy. However, we show that the causal relationships experience substantial time variation. Therefore, the inferences according to the linear framework are vulnerable to unreliability. Nevertheless, our study detects a bi-directional and some unidirectional causality evidence over the sample period through the time-varying methodologies and their date-stamping feature. The findings of our study emphasize the significance of considering time-varying causality tests to avoid the risk of misleading inferences.

Revisiting the CO2-growth nexus: A symbolic transfer entropy analysis in the OECD.

This study revisits the causal linkages between CO2 emission level and economic growth in the selected OECD economies spanning the period 1870–2019. The study utilizes various panel-based granger causality methodologies to provide a more comprehensive picture of the variables’ dynamic patterns. Using a symbolic transfer entropy causality test that accounts for cross-sectional dependence, nonlinearities, and parameter heterogeneity, only the evidence of causality running from economic growth to CO2 emission is confirmed for the panel as a whole. This contradicts the findings of the other panel-based causality methods, in which the bivariate causality linkages are supported. The results imply the need for adopting more cost-effective environmental policies to prevent sustainable economic growth from being jeopardized while addressing environmental concerns.

LoLiMoT Based framework in Causality Analysis.

Causality analysis is an essential and sometimes challenging step that plays a vital role in detecting the dynamic interrelationships between variables. The standard approach of causality (Granger causality method) assumes a straightforward nature of the dependence relations (linear relationships) subjected to a broad spectrum of criticism. In this study, I develop an effective connectivity metric that uses a local linear model trees (LoLiMoT) framework in the context of causality analysis. The proposed technique benefits from LoLiMoT’s divide-and-conquer strategy, allowing for a more complex structural setting and detecting linear and nonlinear causal information flows. The performance of the LoLiMoT based Granger Causality measure (LoLiMoTGC) is evaluated and compared with those of the Linear Granger Causality (LGC), Multilayer perceptron neural network Granger Causality (MLPGC), and Adaptive Neuro-Fuzzy Inference System Granger Causality (ANFISGC) methods. Numerical simulation indicates that LoLiMoTGC outperforms its counterparts in detecting both linear and nonlinear linkages. Besides, it illustrates that this new approach performs decent size and power.

Bubble Detection in US Energy Market: Application of Log Periodic Power Law (LPPL) Models.

This study aims to investigate the ability of LPPL models to identify bubble (s) and its corresponding termination point (s) [𝑡𝑐] in the US energy market using two US major energy prices, namely, the daily US dollar crude oil price of West Texas Intermediate (WTI), and the US dollar natural gas price covering the 1986:01:02-2018:03:19 periods daily. This study is the first comprehensive work in the literature showing the possibility to develop LPPL models in US energy market over the long horizon. To raise the reliability of the estimation process, the new constraint on the Augmented Dickey-Fuller and Philips–Perron values are introduced to the model to accept fits that are nonstationary. Our findings reinforce that energy market prices during bubble periods oscillate with decreasing amplitude around a faster-than-exponential growth. Besides that, our results indicate that applying the LPPL model in the US energy market is error-free, where the confidence interval of termination point encloses the actual regime shift dates.

Working Papers:

Jenkins, G. P., Anyabolu, H. A., Bahramian, P. (2019). Family Decision Making on Healthcare Spending: New Evidence for Nigeria (No. 2019-12). JDI Executive Programs.

Jenkins, G. P., Bahramain, P., & Miklyaev, M. (2019). Estimation of the Economic Opportunity Cost of Labor:An Operational Guide for Mozambique (No. 2019-04). JDI Executive Programs.

Works in Progress:

Out of Sample Forecasting of Electricity Prices: Application of the GMDH Neural Network.

Automatic Clustering in Big Data:Application of Metaheuristic Algorithms in US Energy Market.

Economic Policy Uncertainty and Housing Market Returns: A Nonparametric Panel Data Analysis for G7 Countries.

Economic application of advanced binary ant colony algorithm for feature subset selection.

Testing unemployment hysteresis: A provincial analysis in Canada.