Date of Graduation
Davis College of Agriculture, Natural Resources and Design
Division of Resource Economics & Management
This dissertation examines topics related to renewable energy development and investment planning, energy markets, environment degradation and economic development. The substantial ecological costs of deforestation are well known and considered globally important due to biodiversity loss, land degradation, soil erosion, and contributions to climate change. The first essay focuses upon understanding the tradeoff between development and deforestation in Africa. In the second essay, spatial analysis and Geographic Information System (GIS) are applied to determine potential locations for wind farms development in the state of West Virginia. Lastly, the third essay examines the role of wind power penetration on wholesale electricity market.
The first essay explores the relationship between economic growth and deforestation in African countries. During the past half-century, the continent of Africa has suffered massive losses of forested areas due to the changing structure of economies, increasing population, and expanding globalization. This research examines statistical evidence for the Environmental Kuznets Curve (EKC) hypothesis as applied to deforestation occurring within Africa from 1990 to 2016. Changes in forest cover data are explained with Generalized Method of Moments (GMM) estimators to overcome the endogeneity problems arising from reverse causality between deforestation and explanatory variables. The empirical results of a panel GMM confirm the EKC hypothesis is valid for deforestation in Africa with a turning point estimated to be US $3,000. Heterogenous panel non-causality findings suggest that Africa could deter and reverse deforestation through appropriate land-use and forest products trade policies, and the consequences of these policies would not impact their economic growth.
In the second essay, a multi-criteria decision analysis employing Analytic Hierarchy Process (AHP) and GIS are used for assessment of potential sites for future utility-scale wind farms in West Virginia. Worldwide, demand is increasing for renewable energy. While wind power is a proven, sustainable energy source, siting can be challenging. Identifying potential sites for wind turbines is a significant step in renewable energy resource planning. Wind turbine site suitability is primarily dependent upon wind speed at a location along with other environmental, social, and economic factors. It is critical to arrive at a robust wind farm decision to improve public acceptance, preserve the environment, and maintain pristine habitats. This research builds upon previous studies and contributes to the literature by incorporating two important components into siting: (1) inclusion of critical wildlife habitat for birds and bats as an elimination criterion within the AHP, and (2) the participation by wind power experts in the AHP decision-making process. By incorporating expert opinions with the weighing of ten siting factors, about 70,000 hectares of land are identified as 'highly suitable' for wind power development throughout the state of West Virginia. This area represents the potential to yield an estimated 29,000 MW of future utility-scale wind power capacity, which is larger than the current coal dominated electricity generation capacity in West Virginia.
The third essay examines the wind power penetration impacts on wholesale electricity markets. Using data from two wholesale electricity markets (Pennsylvania – New Jersey – Maryland (PJM) and Electric Reliability Council of Texas (ERCOT)), four energy policy questions are addressed: (1) How much does wind power integration impact wholesale electricity prices under different markets? (2) Does the merit-order effect (MOE) exist at different quantiles of wholesale electricity prices? (3) What drives the day-ahead market (DAM) and real-time market (RTM) prices at different market conditions in both markets? (4) Does the increasing penetration of wind power undermine its market value along with the market values of other generating technologies? To answer these questions, quantile regression is used to obtain coefficient estimates that indicate wind penetration has unequal impacts on wholesale electricity prices and market values across quantiles, reinforcing the need for this type of analysis. The empirical analyses confirmed the existence of the merit-order effect across different quantiles of the conditional distribution of wholesale prices for both DAM and RTM, implying that the increasing deployment of wind power for electricity generation significantly suppresses the wholesale electricity prices in the PJM market. Contrary to the PJM estimations, merit-order effects are confirmed across quantiles of wholesale prices for only the DAM in the ERCOT market. Furthermore, the findings show that as wind generation expands within the market, the revenue earned by wind power producers reduces across all quantiles of the conditional distribution of its unit revenue. Specifically, each additional GWh increase in electricity from wind is associated with a fall in its unit revenues across quantiles by an amount that ranges from approximately $0.01/MWh to $0.06/MWh. Results also confirm cross-cannibalization impacts such that each additional GWh increase from wind is associated with decreased gas and baseload unit revenues across all quantiles ranging from $0.02/MWh to $0.06/MWh. Contrary to unit revenue results, there is weak evidence of increasing wind supply's cannibalization effect for value factor as positive impacts occur below the 90% quantile and negative impacts occur at quantiles 90% and greater. The negative impacts of wind power on gas and baseload generators demonstrate the need for corrective policies.
Ajanaku, Bolarinwa, "Three Essays on “Energy , Environment, and Developmental Economics”" (2021). Graduate Theses, Dissertations, and Problem Reports. 8134.
Agricultural and Resource Economics Commons, Econometrics Commons, Energy Policy Commons, Forest Management Commons, Geographic Information Sciences Commons, Growth and Development Commons, Spatial Science Commons