A light source is the most significant component of a lighting system because it merely defines visual value, cost, and efficiency of the lighting system [18]. Four important features in light source selection include illumination level required by a particular road according to codes and standards, colour rendering properties of the lamp, lamp lifetime and lamp efficacy [18]. Previously, the high intensity discharge (HID) lighting systems were widely used for road lighting as opposed to incandescent and fluorescent lighting. HPS, LPS, MH and MV lights are classified as the main road lighting technologies of HID lighting systems for road lighting use [19]. Table 1 presents the characteristic of five (5) main road lighting technologies which are HPS, LPS, MH, MV, and LED.

Table 1 compares colour rendering index (CRI) (an index to quantify the ability of a light source to render colour of surfaces accurately), luminous efficacy (a measure of how well a light source can produce visible light), which is measured in lumens per watt (lm/W), and lifetime (the life expectancy until the end-of-life) [18,19,20,21]. MH has the best colour rendering properties, and its luminous efficacy is considerably high, but the lifetime is relatively low. MV consumes high energy, and it also has poor lifetime. Among these HID lighting systems, the HPS lights are the most commonly used for conventional and high mast road lighting due to their excellent luminous efficiency, power usage, and long lifespan [19]. Nevertheless, LED has good CRI and efficacy, and the striking feature of LED is found through its highest lamp lifetime, which is ranging from 40,000 to over 100,000 h.

An HPS lamp commonly composed of four fundamental components, namely a sealed, translucent ceramic arc tube, main electrodes, an outer bulb, and a base. While a basic LED is made up of optical, electrical and mechanical, and thermal components [19,22]. An HPS lamp requires ballasts or capacitors to regulate the arc current flow and deliver the proper voltage to the arc, hence it is powered by an alternating current (AC) source [19]. LEDs utilise an electronic arrangement that converts the supply voltage to low-voltage direct current (DC), making it more energy efficient [18].

Data envelopment analysis (DEA) is a non-parametric approach that measures the performance of decision-making units (DMUs) in a single index value taking multiple inputs and multiple outputs. Pioneered by Charnes et al. [23] and developed from Farrell [24], DEA uses linear programming to estimate relative efficiency, a non-negative value, based on linear relationships between the inputs and outputs of DMUs under analysis. This data-driven performance measurement technique determines how efficient a DMU is in producing a certain level of output, based on the amount of input it uses as compared to other similar DMUs [25]. The Charnes, Cooper and Rhodes (CCR) DEA benchmarking model provides the information by locating and understanding the nature of inefficiencies of a DMU through comparisons with selected efficient DMUs of a similar profile.

Several studies have examined efficiencies in energy by adopting the DEA approach. Ashuri et al. [26] established the DEA energy benchmarking model by taking into account criteria such as total energy usage, building attributes, and local meteorological conditions for a more energy-efficient facility management work. In a large scale, Ervural et al. [27], who employed DEA to analyse renewable energy efficiencies of 81 cities in Turkey, discovered that regions with a high renewable energy potential can turn out to be efficient depending upon their regional technical advancement.

Nevertheless, the conventional efficiency formulation of the DEA tends to improve the performance of inefficient DMUs as soon as the efficient frontier is identified, by either increasing the current level of outputs or decreasing the current level of inputs. In the circumstances when the efficiency can be increased by maximising inputs or the efficiency can be reduced by minimising outputs [28,29]. These are usually referred to as ‘undesirable’ or ‘bad’ input and output variables. The production of ‘undesirable’ by-products such as pollutants and wastes are considered dangerous because of their negative impacts on the environment. Moreover, not considering the production of undesirable outputs in the DEA model development may result in biased efficiency measurement. During the evaluation process of the production performance, particularly when inefficiency exists, the desirable and undesirable outputs should be treated differently in order to improve the inefficiency [30].

There are four common methods to treat undesirable outputs [31]: neglecting undesirable outputs, treating undesirable outputs as inputs, treating the undesirable outputs in the nonlinear model, and applying necessary transformations to the undesirable outputs. An earlier study by Yang and Pollitt [32] showed that enforcing a technically correct disposability features on undesirable outputs makes a substantial impact on the final efficiency evaluation. Zhang et al. [33] established an eco-efficiency analysis on regional industrial systems in China by treating undesirable outputs as inputs. One of the nonlinear models developed for undesirable outputs is the directional distance function (DDF), which has been applied successfully on two separate occasions by Alfredsson et al. [34] and Halkos and Papageorgiou [35].

Techno-economic analysis (TEA) in general is a cost-benefit comparison resulting from the consideration of both technological and economic factors [36]. Zimmermann et al. [37] defined TEA as ‘a methodology framework to analyse the technical and economic performance of a process, product or service and includes studies on the economic impacts or research, development, demonstration, and deployment of technologies’. The TEA approach is used to conduct a cost-benefit analysis by utilising several methodologies to accomplish objectives such as determining the economic feasibility of a particular project, analysing cash flows over the duration of a project’s lifespan, determining the scales and applications of a technology, and comparing the economic quality of various technologies [38].

Net present value (NPV), internal rate of return (IRR), payback period, and return on investment (ROI) are some of the techniques. Several studies have employed these techniques to measure the efficiency of energy-efficient technology through their investments in lighting technology by techno-economic analysis. Yoomak et al. [39], for example, evaluated the performance of their investment in LED lighting to replace HPS road lighting in Thailand using the payback period and IRR. Their findings agreed that LED lighting meets the investment targets and has good potential quality, energy savings, and powerful lighting. Beccali et al. [40] carried out NPV and payback period for the return on investment by considering the on/off control and dimming control of LED retrofit funded through loan, self-funded, and incentive provided by the Italian Government. They found that LED retrofits with dimming control funded through the incentive provided by the Italian Government give the highest NPV value and a return time of 4 to 5 years.

There are two important types of cost to be considered in analysing the investment made on road lighting technology, namely the initial investment cost and operating cost. The initial investment cost is the installation cost [41], and this is one of the important aspects to consider in adopting LED lighting technology [42]. The fact that price of LED lighting is much higher compared to the conventional lighting, LED lighting systems would require a higher initial investment cost [43,44], which then could result in a low penetration rate of LED lighting.

The other fundamental segment of road lighting costing is the operation cost. Operation cost comprises energy consumption and maintenance cost. While the energy price makes up the consumption cost, the maintenance cost consists of the cost of cleaning and upkeeping of the lanterns [45,46]. Therefore, economic feasibility of the streetlight depends heavily on electricity prices [47], as revealed by Duman and Güler [48]. Meanwhile, Beccali et al. [49] found that the annual capital savings on a new LED lighting system could be attributed to maintenance cost and reduction in energy consumption.

Energy saving and efficiency have received high affinity among researchers specifically in road lighting installation. The literature on energy savings and efficiency for road lighting installation is analysed in three aspects, namely by optimising the design of road lighting system with the best parameter combination to ensure lighting regulating and installation efficiency, improving lamp and luminaire technology, and improving lamp and luminaire control systems [4,5,50]. Owing to these efforts, more sustainable road lighting technology has been developed and improved [2]. Previous studies also associated reduced energy consumption to energy savings with new LEDs’ design status. Djuretic and Kostic [51], for instance, underlined the need to choose similar photopic or mesopic luminance levels and they found that the high-quality LED luminaires could save 31 to 60% of energy as compared to the high-quality HPS luminaires applicable to multi-stage scenarios. Yoomak, Jettanasen, Ngaopitakkul, Bunjongjit and Leelajindakrairerk [39], on the other hand, studied power quality and energy savings of LED road lighting from the DIALux simulation programme, where the energy efficiency index difference was approximately 40%.

In a study on pedestrian street lighting, Juntunen et al. [52] developed smart LED luminaires with higher luminous efficacy to demonstrate energy savings without sacrificing user visual comfortability. In the passive mode, where the streetlights function without added intelligence, energy savings of 19 to 44% were achieved as compared to commercial luminaires. There is also huge potential for power consumption reduction in the LED lighting system with a control system [53,54]. Energy savings without comprising user visual comfortability has achieved a reduction of 38% energy consumption in an indoor experiment on the control of SPDs of LED lighting [55].

Pipattanasomporn, Rahman, Flory and Teklu [54] found that an intelligent lighting control system may reduce approximately 74% of energy usage. On the other hand, Bunjongjit, Ananwattanaporn, Ngaopitakkul, Jettanasen and Patcharoen [53] proposed a control circuit for LED luminaire based on the amount of daylight which has been able to reduce the lighting system’s power consumption while preserving lighting quality for the user. Smart Grid technology equally has the potential to further increase the efficiency and operational reliability of outdoor lighting systems as discussed by Filimonova et al. [56] on an LED outdoor lighting system for a university campus in Russia.

Lifetime is the useful life of luminaire. LED advances have resulted in significant improvements in light source efficiency and lifetime [57]. In the market, the lifetime of LED lights involving LED drivers and source packages is regularly quoted as 25,000 to 50,000 h [58,59,60,61]. In general, the lifetime of LED lights is two to six times more than conventional lights which will reduce maintenance costs [49]. Nonetheless, poorly designed LED driver and high temperatures might shorten the lifetime of LED lights [62,63].

The assessment of environmental impact is the other well-researched topic in LED road lighting. Tannous et al. [64] utilised life cycle assessment (LCA) in measuring environmental impact to compare the usage of solar-LED and traditional-HPS road lighting. They considered LCA from the raw material extraction until the end-of-life, with two end-of-life scenarios modelled, landfilling and recycling. They concluded that the solar system has fewer overall environmental impacts than the traditional system for both landfilling and recycling scenarios. Many others looked into carbon dioxide emission reduction when assessing the environmental impact of the LED and conventional LED for road lighting illumination [4,18,44,48,49]. Carbon dioxide emission can be computed directly from electricity consumption by applying the multiplication of the carbon dioxide emission factor of a country.