Abstract
This paper analyzes the efficiency of thermal power plants in Angola by means of a two-stage Data Envelopment Analysis (DEA) approach. In the first stage, a novel super-efficiency DEA model for undesirable outputs (CO2 emission levels and discharge of polluted water) is initially used to measure their efficiency levels. Then, in the second stage, relevant cost structure variables frequently used to describe a productive technology are employed as analytical thresholds for assessing energy production performance either in terms of capital or labor-intensity levels. Precisely, bootstrapped regression trees are used to discriminate super-efficiency scores yielding an energy production performance predictive model based on the technology type as proxied by its cost structure and their respective thresholds, since Angolan thermal plants are heterogeneous. Findings suggest that Angolan power plants are old and labor intensive, as some of them date back to the colonial era, and that lack of capital investment should be revised in favor of installing carbon capture devices. The approach developed here consists of a valuable approach for identifying priorities when technologically updating a heterogeneous thermal industry to face pollutant concerns.
Highlights
This research focuses on a relatively understudied topic in thermal plants, which is the relationship between its productive efficiency and cost structure in a developing country such as Angola, a formerPortuguese colony and an important oil producer and exporter in Africa
The efficiency of thermal power plants at the machine or equipment level is a well-defined and parameterized research stream based on the laws of thermodynamics
While scores are computed using a super-efficiency model, the cost structure variables are tested as efficiency thresholds in bootstrapped regression trees, allowing for the discrimination of different technological patterns in thermal energy production
Summary
This research focuses on a relatively understudied topic in thermal plants, which is the relationship between its productive efficiency and cost structure in a developing country such as Angola, a former. While scores are computed using a super-efficiency model, the cost structure variables are tested as efficiency thresholds in bootstrapped regression trees, allowing for the discrimination of different technological patterns in thermal energy production It is worth mentioning why the set of Angolan thermal power plants represents a controlled environment for conducting this search. This paper adds to this beginning research stream by applying a novel super-efficiency DEA model for undesirable outputs altogether with statistical learning techniques such as bootstrapped regression trees, allowing to explore the impact of different productive technologies by proxying them to the cost structure This is the first time that DEA and machine learning are complementarily used to explore productive technologies by means of their underlying labor and capital cost structure.
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