Urban Energy Efficiency Assessment Using Stochastic and Deterministic Data Analysis: A Proposed Sustainable Urban Energy Assessment

Introduction

In recent years, increased energy consumption has led to social, economic and environmental problems that are detrimental to healthy urban sustainability, and that prevent the development of appropriate urban energy policies. The accurate measurement of urban energy consumption is vital if metropolitan areas want to achieve sustainable urban energy development and energy efficiency. Urban energy efficiency necessitates the use of less energy for the production of similar outputs, and the sustainment of a healthy urban life. Daily urban life activities and economic factors are both influential n the energy consumption of those living in metropolitan areas (CCICED, 2009).

Urban energy efficiency data is usually incomplete and imprecise, as previous studies that make use of classical approaches provide only limited insight into the randomness and complexity of urban energy efficiency data. In neglecting to make use of stochastic techniques in their measurement of the noise and uncertainty in data sets, it is necessary to include probability theory in their measurement techniques.

Energy policy makers should identify and implement more robust approaches to energy efficiency that include factors that are sensitive to the daily variations associated with energy investment, energy demand, data availability, local circumstances and any other local policy options. The frequent use of uncertain and poor quality data in policy making and planning have compelled researchers to make use of stochastic techniques in the study of urban energy efficiency (Keirstead, et al., 2012).

Benchmarking the efficiency of urban energy use within electricity consumption is thus an important area of study in the energy field. Among the parametric and nonparametric approaches to benchmarking, the Data Envelopment Analysis (DEA) method has emerged as the leading methodology for the benchmarking of the efficiency of various systems, being based on multiple factors, and has entered into widespread use for the calculation of the efficiency of the related decision-making units (DMUs) (Azadeh et al., 2015).

Since urban energy consumption is a leading factor in industrial development, increasing energy efficiency in the urban field is essential for urban sustainability. Accordingly, the present study makes use of stochastic approaches in its assessment of Turkey’s 30 largest metropolitan areas, as decision making units (DMUs), over the 2018–2019 period in an evaluation of urban energy efficiency. The main purpose of the present study is to carry out a deterministic and stochastic measurement of the urban energy efficiency of these cities based on six indicators, being total invoiced consumption (MWh), total installed power (MW), line length (Km), population and GDP per capita as inputs, and total generation (MWh) as the sole output.

For the purpose of the study, all indicators were determined as random in the stochastic models, and are frequently applied to the performance benchmarking of energy efficiency in literature (Jamasb et al., 2001). The present study extended input oriented deterministic and stochastic CCR DEA and two SFA models to consider the stochastic variations in the data and in the efficiency score of each DMU. It was found in the study that uncertainty in the data leads to fluctuations in the efficiency analysis results, which hinders the establishment of actual and consistent energy policies. These results suggest a need to place more importance in the error scores when measuring stochastic efficiency. The identified technical efficiencies of cities differ when measured using deterministic or stochastic methods. While in the deterministic DEA approach there is no consideration of randomness in the data, the Stochastic Data Envelopment Analysis (SDEA) and SFA method allowed for the calculation of the noise in the data. These approaches can be referred to as parametric and nonparametric methods.