s2fieldsofstudy:
Business
Computer Science

text:
Optimal management of distance-based location registration using embedded Markov chain

In this study, we deal with a Distance-Based Registration with Implicit Registration, which is an enhanced scheme of the Distance-Based Registration in mobile-cellular networks. In comparisons with other Location Registration schemes, various studies on the Distance-Based Registration scheme and performance have been performed. However, a real network hierarchy has not been properly reflected in the performance evaluation of the Distance-Based Registration. To accurately evaluate the registration and paging costs of the Distance-Based Registration, a real network hierarchy should reflect that a mobile network is made up of many Visitor Location Register areas. Furthermore, we use an embedded Markov-Chain model in the Visitor Location Register hierarchy, which can reflect not only the Implicit Registration effect of the outgoing calls of user equipment but also cell staying time of the user equipment that may follow a general distribution. Without consideration of the Visitor Location Register, the paging cost decreases due to a small paging area, but the location registration cost rises because of frequent inter Visitor Location Register. The numerical results according to the various conditions show an accurate evaluation of the Distance-Based Registration performance in a real network hierarchy and the general cell staying time. Generally, the total signaling cost will increase when we consider the Visitor Location Register. However, for more appropriate evaluation of the Distance-Based Registration performance, it is necessary to consider the Visitor Location Register hierarchy.

Introduction
Nowadays, a lot of users can send queries to get valuable information from various web objects through mobile devices in real world. 1 They receive many location-based services (such as navigation, gas station, hotel, and restaurant) through mobile devices. 2 With some technologies such as Big Data, Cloud Computing, and Internet of Things (IoT) in the mobile environment, tremendous amounts of data have been collected in databases and employed for the users. 3 Continuous mobility management for User Equipment (UE) is the core function of mobile communication. Location Registration (LR) is the process that allows an UE to register its location area on a network database like the Visitor Location Register (VLR) or the Home Location Register whenever the UE moves to new location area. When a call arrives at the UE, the mobile network must find the UE and identify its current cell to connect the call because a location area comprised many base stations (cells). This is called paging. Generally, the best trade-off exists between the registration and paging costs, which lead to optimal usage of the radio resources. [4][5][6] The numerous LR schemes have been proposed so far: zone-based, 4,5 movement-based, [7][8][9][10][11] timerbased, 8,11,12 distance-based, [13][14][15][16] and tracking area list (TAL)-based [17][18][19] registrations. In zone-based registration, UE registers a new location area on the network whenever it moves to a new zone. In movement-based registration, UE registers a new location area whenever the number of entering cells reaches the specified number. In timer-based registration, UE registers a new location area whenever its timer reaches the specified interval. In TAL-based registration, UE registers a new location area whenever it enters a new TAL area.
In our study, Distance-Based Registration (DBR), which is a typical dynamic LR, [13][14][15][16] is considered. DBR scheme is known to have sound performance. DBR is always superior to movement-based registration which is another typical dynamic LR. 14 Furthermore, in certain situations, DBR shows better performance than zone-based registration which is the most typical LR in most mobile-cellular networks. 20 In comparisons with other LR schemes, various studies on the DBR scheme and performance have been performed. [13][14][15][16] In the DBR scheme, when an UE enters a cell, the UE's location is updated if the distance from the last registered cell to current cell is equal to or greater than a predetermined distance threshold. Significant merits of the DBR scheme are that the UE registers its location less frequently compared to the movement-based registration scheme, and the ping-pong phenomenon, which is a main drawback of the zone-based registration scheme, is nonexistent. To reduce the registration cost of the DBR scheme, a DBR with implicit registration was proposed, and its performance was analyzed using stochastic models. 14 However, a real network hierarchy has not been properly reflected in the performance evaluation of the DBR. To perform a more realistic and exact evaluation of both the registration and paging costs of the DBR, a real network hierarchy should reflect that a mobile network is made up of many VLR areas. In addition, an embedded Markov-Chain (MC) model is adopted to consider the implicit registration effect of UE's outgoing calls and the general cell staying time.

DBR
In DBR, every time an UE registers, the distance between the current cell and the last-registered cell is equal to or greater than the threshold D. [13][14][15][16] The UE has not only the latitude and longitude (X r , Y r ) of the last-registered cell but also the latitude and longitude (X c , Y c ) of the current cell. The distance is computed by the difference between the latitudes and longitudes of those two cells.
In this study, for convenience sake, the distance between two cells is simply defined as the smallest number of cells to be crossed to reach one from the other. [13][14][15] Figure 1 shows a location area in the hexagonal cell configuration, given that the threshold D is 3, and a UE registers in the ring 0 cell.
According to the technical requirements, when an UE sends a page response message or an origination message successfully, the cell could find out the location of the UE. We call this implicit registration. [13][14][15] That is, when a call to/from the UE occurs successfully, the mobile network could realize the UE's cell by the page response message or the origination message without an actual LR message. In other words, if a network uses a DBR and implicit registration concurrently, the network could know the UE's cell without an actual registration process; therefore, it can establish a new location area, which reduces the number of LR. Thus, the LR cost of the DBR can be reduced through implicit registration. In this study, only the DBR with implicit registration which is an enhanced scheme of the original DBR is considered. Hereafter, DBR indicates the DBR with implicit registration.

Network architecture
A mobile-cellular network is composed of many mobile switching systems, and each switching system has network databases such as VLR and Home Location Register. In terms of the VLR, a mobile network is composed of VLRs as shown in Figure 2.
In this study, we assume that a mobile network is composed of square-shaped VLR areas, and each VLR area consists of the same-sized hexagonal cells. The followings are also assumed in this study.
When an UE enters a neighboring cell, the probability of choosing one of the neighboring cells is one-sixth for any neighboring cell. The incoming and outgoing calls are generated with the rates l ic and l oc , respectively. According to the Poisson processes and the staying time in a cell, T m is generally distributed with the mean 1=l m .
Note that with the addition of the Poisson processes, the incoming calls with the rate l ic and the outgoing calls with the rate l oc form overall calls with the rate l c (= l ic + l oc ). Figure 1 shows a location area of the DBR with the distance threshold D = 3. Generally, the location area for the distance threshold, D, depends on D rings (0, 1, ..., D 2 1). Figure 3 shows a VLR area which is made up of 10 3 10 hexagonal cells. In this hierarchy, an UE performs LR when it enters a new VLR as well as when its distance value is equal to or greater than the threshold, D. In other words, if an UE enters a neighboring VLR, it performs LR regardless of its current distance value, which will be explained later with the use of some examples in this article.

Random walk mobility model
We assume that a random walk mobility model 3,13,14,16 is employed in this study. In this model, the probability of choosing one of the neighboring cells is one-sixth when an UE enters a neighboring cell.
The Fluid-flow model was used in some studies 10,18,21 to obtain the rate at which an UE moves to another VLR. The authors suggested that as a cell staying time is exponentially distributed and further, the Fluid-flow model is adopted, then the location area staying time will also be exponentially distributed. However, this assertion is evidently incorrect since the location area staying time of the UE, which is composed of various combinations of cell staying times, generally cannot follow an exponential distribution. 22 Nevertheless, they analyzed the performance of some of the registration schemes by using their assertion. 10,18 In this study, the distribution of the cell staying time is not limited to an exponential distribution. It is feasible to use a general cell staying time; therefore, an incorrect assertion (such as, if the cell staying time is exponentially distributed and a fluid-flow model is adopted, then the location area staying time will also be exponentially distributed) is not needed.
The rate at which an UE moves to another VLR becomes different depending on the UE's position in  the VLR area. For instance, the probability of moving to another VLR is one-third, and the probability of the UE moving to another VLR regarding the center cell ''b'' is 0 if the UE is in the boundary cell ''a'' in Figure 3. Therefore, to accurately evaluate the registration cost, it is necessary to take into account these different characteristics depending on the UE's location in the VLR area.

Embedded MC model
We introduce an embedded MC model to reflect the network hierarchy, the general cell staying time, and the implicit registration effect of the outgoing calls. For the convenience of the explanation, we consider a very small VLR area that comprises 4 3 4 hexagonal cells, and it is assumed that D = 2, as shown in Figure  4. It is also assumed that each cell has its own ID from 1 to 16 (= 4 3 4).
In Figure 4, for an improved understanding, the same cell ID is used for different cells in the different neighboring VLRs. Note that cells 1 and 13 show the same probabilistic characteristics since both of them are border cells. Assuming D = 2, the location area of the UE registering in the cell 7 is composed of the cells 7, 8, 9, 10, 6, 2, and 1, as shown in Figure 4. The UE registers its location when it moves out of this location area. If the UE moves out of this location area from the cell 6, it registers in one of the cells 3, 5, and 11, which is called intra-VLR LR, since the UE registers in the same VLR. Let us consider the other case. If an UE moves out of this location area from the cell 2, and it registers in one of the two cells in the neighboring VLR, then this is called inter-VLR LR since the UE registers in a different VLR.
Intra-VLR LR: LR for areas which are within the same VLR when an UE registers. Inter-VLR LR: LR for areas which are in the different VLR when an UE registers.
Alternatively, note that location area of an UE registering in the cell 2 is composed of the cells 2, 1, 7, 6, and 3, as shown in Figure 4. If the UE in a cell 2 moves to the left cells in the neighboring VLR, the UE must perform the inter-VLR LR, even if his or her distance is less than 2.
Since the signaling cost of the inter-VLR LR is larger than the signaling cost of the intra-VLR LR, 10,23 to exactly evaluate the signaling cost of the LR, the inter-VLR LR and the intra-VLR LR must be analyzed separately, which is very complicated. Without considering the inter-VLR LR, an analysis of the LR cost is rather simple. 13,14 When the VLR hierarchy is considered, the following two factors help modeling through the MC theory in comparison with the case where the VLR hierarchy is not considered: The position of a cell in the VLR should be included in the state of an UE since the UE's movement to the neighboring cells may or may not cause a inter-VLR LR according to the position of the UE's cell.
The state of the UE should be defined so that it can reflect the distance change in a new cell when the UE moves to the neighboring cells.
For instance of Figure 4 where D = 2, if it is assumed that the distance of an UE in a cell 14 is 1 when the UE moves to the right cell, it performs the inter-VLR LR. If this UE then moves to the cell 15, does it register its location? The following two outcomes are possible: (1) If the UE's last registered cell is 11 or 13, then the UE in the cell 15 will register its location.
(2) Alternatively, if the UE's last registered cell is 10, then the UE in the cell 15 will not register its location.
From the above cases, it is evident that an UE's distance in a new cell depends on the cell where the UE finally registered. Therefore, it is necessary to define the UE's state in the current cell so that the UE can include the cell of the last registration since the UE's distance in a new cell is directly affected by the cell where it finally registered. Now, assuming D = 2, we show a MC model for the consideration of the inter-VLR LR. In the proposed model, the state (i, j) is defined as a state of an UE where the UE who last registered in the cell j and is now in the cell i.
Concerning an UE in one of the states (2, 7), (2, 6), or (2, 3), a similar state transition diagram can be obtained. In Figures 5 and 6, the transition probability to the state (2, 0) is 1 À m = 1 À P½T c .T m , and this means that a call to/from the UE generates before the movement to other cells occurs. We can obtain the probability that an UE moves to other cells before a call occurs as follows 4,5 The UE's staying time in a cell, T m , affects the transition probability. We assume that T m follows a Gamma distribution, the mean is 1=l m , and the variance is V . In this case, P½T c .T m is  Especially, when the T m follows an exponential distribution with the rate l m , then g = 1 and P½T c .T m is given as Note that, when an UE is in the state (2, 0), it can transfer to other states as shown in Figure 7. Since the state (2, 0) is a state where the current cell of the UE becomes the ring-0 cell through an incoming-or outgoing-call generation, the cell staying time in this state is the residual cell staying time of the UE when the UE stays in the current cell after a call generation, and this is denoted by R m in this article. Then, the transition probability from the state (2, 0) to other cells is expressed as P½T c .R m , and it can be derived as follows In addition, the transition probability from (2, 0) to (2, 0) itself is expressed by P½T c \R m = 1 À P½T c .R m = 1 À (1 À f Ã m (l c ))=r, which represents the probability that an incoming or outgoing call to/ from the UE generates before the UE moves to other cells.
Note that, even when we assume a 4 3 4-cell VLR area and D = 2, the number of states (\16 3 8) is too large to draw a state transition diagram for the whole states. However, it is possible to express the transitions among the states by using mathematical equations, and the Limiting Probability (for an MC) of each state can be obtained. A number of mathematical equations for the attainment of the Limiting Probability of each state will be explained, and they are used to calculate some performance measures like the LR and the paging costs.
Let p (i, j) be the Limiting Probability of (i, j) with the transition-probability matrix P. Then, we can derive the Limiting Probability of (i, j), p (i, j) , using the balanced equation 24 Now, let us obtain the total signaling cost using these Limiting Probabilities.

Total signaling cost
The total signaling cost consists of the LR and the paging costs. A number of notions that are used to calculate the signaling cost are defined as follows: : probability that UE performs the intra-VLR LR when he or she moves to a cell from the state (i, j) p 2 (i, j) : probability that UE performs the inter-VLR LR when he or she moves to a cell from the state (i, j). p 0 (i, j) : probability that UE does not perform the LR when he or she moves to a cell from the state (i, j). N(j) : the number of paged cells when UE is in the state (Á , j) or (j, 0) Then, for instance, the following values can be obtained: p 1 (3, 2) = 1=3 (when UE moves to the cell 4 or 5) p 2 (3, 2) = 1=3 (when UE moves to the cell 13 or 14) p 0 (3, 2) = 1=3 (when UE moves to the cell 2 or 6) p 1 (7, 2) = 1=2 (when UE moves to the cell 8, 9, or 10) p 2 (7, 2) = 0 p 0 (7, 2) = 1=2 (when UE moves to the cell 1, 2, or 6) If an UE is in the state (3, 2), (7, 2), or (2, 0), the number of paged cells is N (2) = 5, and, if an UE is in the state (6, 7), (7, 7), or (7, 0), the number of paged cells is N (7) = 7.
Suppose that U is the LR cost for one registration and Dist½i, j is the distance between cell i and cell j. Then, the LR cost per an incoming call C dbr U can be given as follows where n is the number of cells that form one side of a VLR area (i.e. n = 4 in Figure 4). In the above equation, it is assumed that the inter-VLR LR cost is three times of the intra-VLR LR cost. 10,23 The number of paged cells is 1 + 3D(D À 1) since most mobile networks adopt simultaneous paging. 10,13,14,18 However, in this study, wherein the VLR hierarchy is considered, the number of paged cells decreases when an UE is around the boundary of the VLR. In the example, if the UE last registered in the cell 2, the number of cells to be paged is not 7 but 5. Therefore, considering the VLR, the decrease in the number of cells to be paged should be considered.
By letting V be the paging cost for one cell, we can obtain the paging cost per an incoming call as follows where p j = P i, Dist½i, j ł D p (i, j) + p (j, 0) . In this example (4 3 4-cell VLR area and D = 2), the paging cost is given as follows f3 Á (p 4 + p 16 ) + 4 Á (p 1 + p 8 + p 12 + p 13 ) + 5 Á (p 2 + p 3 + p 14 + p 15 ) + 6 Á (p 5 + p 9 ) where p j = P i, Dist½i, j ł 2 p (i, j) + p (j, 0) . Finally, the total signaling cost is obtained by Numerical results For the numerical results, a 12 3 12 VLR area is assumed with the following assumptions U = 10, V = 1, l m = 1, l c = l ic + l oc = 0:5 + 0:5 = 1:0 It is supposed that both an incoming call and an outgoing call generate according to the Poisson processes with the rates l ic and l oc , respectively. Also, it is assumed that the staying time of an UE follows a Gamma distribution with a shape parameter of 0.5 and a scale parameter of 2.
The total signaling costs for various thresholds are shown in Figure 8. The total signaling cost is made up of the LR and paging costs. Generally, as the distance threshold increases, the LR cost decreases because of the infrequency of the registrations, but the paging cost increases due to the large paging area. Finally, in this example, the optimal threshold D is 2 that minimizes the total signaling cost. The optimal threshold D that results in the minimal signaling cost can be derived for any other circumstance. Figure 8 shows the total signaling costs for two different conditions (in consideration of the VLR or not). Note that C u is the LR cost, C p is the paging cost, and C t is the total signaling cost for each incoming call. When the threshold D is small such as D = 1;3, the total signaling cost with the VLR hierarchy is larger than the cost without the VLR hierarchy. This is mainly because the number of LR increases especially around the VLR boundary with the VLR hierarchy. Note that, since the paging area around the VLR boundary decreases, the expected paging cost with the VLR hierarchy is less than the cost without the VLR hierarchy. However, in this example, it is evident that when the threshold D is small such as D = 1;3, the rise of the LR cost is more crucial than the reduction of the paging cost.
Alternatively, when the threshold D is large such as D = 4, the total signaling cost without the VLR hierarchy is larger than the cost with the VLR hierarchy. This is because the expected paging cost without the VLR hierarchy for a large D increases sharply. However, the cost with the VLR hierarchy does not increase significantly compared to the case where the VLR hierarchy is not considered due to the decreased paging area around the VLR boundary. Figure 9 shows the LR costs of different VLR sizes for various thresholds. Generally, because of the infrequency of the registrations, the more the distance threshold is, the less the LR cost is. In addition, it is clear that as the VLR size decreases, the LR cost increases since the inter-VLR LR also increases. Figure 10 shows the paging costs of different VLR sizes for various thresholds. In general, as the distance threshold increases, the paging cost increases very sharply due to the large paging area. Furthermore, as the VLR size decreases, the paging cost decreases due to the small paging area around the VLR boundary. Figure 11 shows the total signaling costs for different VLR sizes. In the circumstances, regardless of the VLR size, the optimal threshold D that results in a minimal signaling cost is always 2. Also, it is evident that, in Figure 11, if the optimal threshold D = 2 becomes the focus, a consideration of the VLR hierarchy results in an incremental increase of the total signaling cost, regardless of the VLR size. Figure 12 shows the total signaling costs for various VLR sizes when D = 2. Note that C u is the LR cost, C p is the paging cost, and C t is the total signaling cost for each incoming call. Obviously, if the optimal threshold D = 2 becomes the focus, the total signaling cost for a small VLR is larger than the cost for a large VLR.
Therefore, the total signaling cost is less than that of any VLR sizes if the VLR hierarchy is ignored (no VLR). In other words, if the VLR hierarchy is ignored, the total signaling cost for real mobile networks is underestimated; that is, when the VLR hierarchy is ignored, its performance seems to be better than its actual performance. Figure 13 shows the signaling costs for different Call-to-Mobility Ratios (CMR = l ic =l m ) when D = 2. It is evident that, as the CMR increase (in other words, as a call generates frequently), the paging cost increases sharply, but the registration cost decreases gradually. As a result, the total signaling cost increases almost proportionally to the CMR. Figure 14 shows the signaling costs for different cell staying time of an UE when D = 2 and l oc = l ic = 1.
It is shown that, as the cell staying time increases (in other words, as the number of cell entrance decreases), the registration cost decreases sharply, but the paging cost is unchanged. As a result, the total signaling cost decreases steeply especially when cell staying time is small compared to the paging interval.    From the various results, the following findings are evident: (1) The consideration of a real network hierarchy results in an incremental increase of the total signaling cost and (2) there exists the optimal threshold that minimizes the total signaling cost, since the best tradeoff is between the registration cost and the paging cost.
Even if the total signaling cost rises due to the network hierarchy, VLR hierarchy should be necessary considered to obtain a more accurate performance evaluation of the DBR in the real world.

Conclusion
In our study, we dealt with a DBR with implicit registration, which is an enhanced scheme of the DBR. For an accurate analysis of the registration and paging costs of the DBR, the real network hierarchy where a mobilecellular network is made up of VLRs should be considered. In addition, an embedded MC model is adopted to consider the implicit registration effect of the outgoing calls and the general cell staying time.
The various numerical results for diverse conditions show that the consideration of a real network hierarchy results in an incremental increase of the total signaling cost. That is, the rise of the registration cost is larger than the reduction of the paging cost compared to the simple case where the VLR hierarchy is not considered. Even if the total cost rises due to consideration of the network hierarchy, it is necessary to take into account the VLR hierarchy to accurately analyze the performance of the DBR in the real world.

Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Explaining the Di ﬀ usion of Energy-E ﬃ cient Lighting in India: A Technology Innovation Systems Approach

: Electricity consumption from lighting accounts for about 15% of total power demand and 5–6% of greenhouse gas emissions in developing countries. It is therefore a promising avenue to achieve considerable energy savings through technological innovation and upgrading. India has been very successful in recent years with a nationwide roll-out of modern light-emitting diode (LED) applications. This study uses the framework of technology innovation systems to identify the actors, institutions, and processes behind the di ﬀ usion of this technology. Our ﬁndings indicate that national innovation strategies, along with low-carbon technology (LCT) transfer policies, helped to bring down the cost of LED lamps in a rapidly expanding domestic market. Based on the ﬁndings, we further explore lessons for broader issues of low-carbon technology transfer and suggest an emerging intermediate step between north–south and south–south technology transfer.

Introduction
2015 was an historic year for global sustainable development efforts, with the Paris Agreement at COP21, and the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDGs), coming into life. Both agreements place a strong emphasis on clean energy development, in particular in energy-poor developing countries, where the lack of modern energy services affects a wide range of economic and social issues, and is disproportionally impacting women and children [1,2]. With more than 860 million people in the world today without access to electricity and 2.6 billion people that rely on the traditional use of biomass for cooking and heating [3], this is not an easy challenge. Moreover, the problem will be further exacerbated by a growing world population and increasing economic development, and thus energy demand, in many parts of the world. Future solutions must also emphasize the consumption side of energy systems in order not to waste additional energy supply by using outdated energy-intensive technologies. This is also being reflected in a recent assessment by the International Energy Agency that suggests that more than two-thirds of the global economically viable energy efficiency potential will remain unrealized under existing policies [4]. A promising sector for energy savings from technological upgrading is lighting. Globally, electricity from lighting accounts for about 15% of total power consumption and 5% to 6% of worldwide CO 2 emissions [5]. While developed countries like Japan have managed to reduce their lighting footprints with advanced energy-efficient lighting technologies, such as light emitting diodes (LEDs), to as low as 10% of their

Theoretical Framework
Barriers to the diffusion of energy-efficient lighting have been studied at various levels, ranging from general discussions over barriers towards diffusion of compact fluorescent light (CFL) lamps [15] to more detailed research on technological innovation in solid-state lighting at the pilot scale [16]. However, until now, there has been limited research focusing specifically on large-scale diffusion of energy-efficient LED lighting technology in developing countries. This study builds on the growing empirical literature of the technology innovation systems (TISs) approach [17][18][19][20][21][22], which aims to understand the barriers and enablers to the adaption and diffusion of certain types of environmental friendly technologies, such as renewable energy and energy efficiency technologies. Carlsson and Stankiewicz [23] (p. 94) define TIS as "a network of agents who interact in a specific economic/ industrial area under certain institutional infrastructure or set of infrastructures and are involved in generation, utilization and diffusion of technology". It argues that all innovations are a collective activity that take place within the setting of specific innovation systems, which can be subdivided into national/regional, sectoral, and technological innovation systems. Every technological innovation is embedded in its own specific TIS, with associated actors, structures, and processes that influence the outcome of the innovation process. A well-functioning system will be highly supportive of the development of a certain innovation, while unsuccessful innovation processes point to certain barriers within the TIS. Painuly [24] developed a useful set of analytical categories for the identification of barriers to the diffusion of renewable energy technologies, which can also be applied to our case of technological innovations systems of LEDs: (1) market failure/imperfections; (2) market distortions; (3) economic and financial factors; (4) institutional factors; (5) technical factors; and (6) social, cultural, and behavioral factors). A typical TIS analysis starts with the identification of the structural elements that make up the TIS environment, followed by a functional analysis of processes within the TIS to systematically identify factors that drive or hinder the development of a particular technological field.
The structural elements of any TIS environment include actors, networks (interactions), institutions, and infrastructure (technological artefacts) [25,26]. However, structural factors alone cannot explain the success or failure of a technological innovation. It is necessary to also investigate the dynamic interactions among different structural components of TIS to understand the outcome of a specific technology innovation and diffusion process (such as the successful market penetration of LEDs in India). The functions of innovation systems approach has helped researchers overcome this limitation [27]. Using the functions for the analysis of TIS allows not only for a systemic mapping of innovation determinants but also for the identification of the system's strengths (enabling factors) and weaknesses (barriers). Such an analysis in turn allows for the formulation of policy targets and the development of policy instruments to meet those targets [28]. Certain 'event types' linked to one of the functions can influence the trajectory of the innovation process and potentially even derail it. Since the nature of these events differ considerably between technologies, each study needs to identify the relevant key events for its specific research focus to guide the analysis and to allow for an assessment of the performance of the TIS [28,29]. Table 1 showcases the functions and associated event types that we adapted specifically for the LED sector development in our Indian case study. Table 1. Event types associated with the functions of innovation systems in the Indian LED sector. Source: modified after [21,25,27,28]. Another important factor in the analysis of technological innovation systems in developing country settings is the international dimension, in particular the role of technology transfers between countries. Traditionally thought of as a unidirectional movement of knowledge and products from developed countries (the "north") to developing countries (the "south"), the growing importance and capabilities of industries in emerging economies created another movement between developing countries, the so-called "south-south" technology transfer [30]. The international dimension of innovation through technology transfer has gained significant recognition in recent years and added an additional component of comparability in sustainability transition analyses [17,18,20]. During the literature review in the initial stages of our study, it became apparent that certain key barriers to technology transfer issues, especially intellectual property rights (IPRs) and standardization [31], also played an important role in the domestic innovation and diffusion processes of a cutting-edge technology, such as LEDs, in India. We therefore followed the examples of previous studies, such as Binz et al. [20] or Bento and Fontes [18], and included an additional layer of analysis into our functions, which integrates the spatial dimension of international technology transfer with the local barriers to implementation and adaptation.

Methodology
Our qualitative research design followed a multi-step approach, including desk research, the construction of an online questionnaire, and expert interviews with key actors. The first step involved an in-depth literature review of a variety of sources, including research papers, newspaper articles, as well as grey literature, such as working papers or policy briefs from governments and think tanks. This was followed by the development and distribution of an online questionnaire among Energies 2020, 13, 5821 5 of 16 36 member companies of the Electric Lamps and Components Manufacturers (ELCOMA), which is a non-profit umbrella organization for the lightning industry in India and actively involved in the process of LED lighting diffusion. The questionnaire was meant to complement the initial literature research and to help identify relevant stakeholders and experts about LED lighting technology in India. Although only 6 out of 36 companies responded to the online survey, this preparatory step helped to further sharpen the analytical framework and to identify appropriate partners for the following 17 face-to-face interviews with Indian government representatives, academics, and relevant stakeholders in industry and non-governmental organizations ( Table 2). The strength of in-depth qualitative research is simultaneously also its weakness, as common constraints, such as limited resources, only allow the gathering of small interview samples. We tried to address this issue by choosing a heterogenous mix of interviewees, representing different positions across the value chain of LED technology, from development and manufacturing to marketing and sales. The semi-structured interviews were the key part of the study and included general questions about the Indian LED sector, as well as questions specifically tailored to the role of the interview partner in the process of domestic LED technology development. A semi-structured interview format has the advantage that it allows the inclusion of topics brought up spontaneously by the interviewees themselves and that it gives the researcher the opportunity to conduct the interview in a more flexible way. For instance, during the interview with international business actors, it became apparent that some of them did not have very detailed knowledge about the government policy for large-scale diffusion of LED lights in India. In these cases, the questions asked during the interview were transformed into hypothetical settings. The questionnaire was guided by the seven TIS functions and corresponding event types that were adapted to our specific case study of LED lighting technology in India (Table 1). It also addressed barriers and challenges that different actors were facing and what influence government policies had on improving the effectiveness of the system as a whole. The interviews lasted between one and two hours and were conducted by the lead author in September and December 2016. The conversations were recorded and later transcribed. The interviews were conducted in line with the stipulations of the United Nations University's ethical research guidelines. All interview partners were informed about the aims of the research project and provided verbal informal consent to participate. Since not all interviewees were comfortable to be named in the final article, only the name of the organization and their position are mentioned in the text.

Results
In 2006, the LED technology sector in India was still rather underdeveloped. There was just one local LED packaging company and no multinational company was thinking about entering the market [32]. In 2016, the Indian LED sector had already evolved into a market worth more than US$ 1 billion annually, shared by 27 large and medium, and more than 600 small-size manufacturers, while Energies 2020, 13, 5821 6 of 16 all major international chip producers had established branch offices in India [33]. According to a senior-level representative at ELCOMA, the major challenges to LED development in the early days of the Indian LED sector were the lack of localized standards, high technology costs, intellectual property rights (IPRs), and the negligible market size (Interview, ELCOMA management, 16 September 2016). However, only a few years later, India had managed to successfully establish a domestic LED technology industry. The following subsections will examine the key actors and processes behind this development by disaggregating it through the lens of TIS functions (Table 1).

Entrepreneurial Activities
This function describes activities by existing or emerging industrial actors to create and market technological innovations [28]. A major obstacle to the entrance into the Indian LED market was initially the lack of industrial standards. This was eventually resolved in 2012, when the IEA en.lighten initiative successfully facilitated the adoption of international LED standards localized for Indian usage through a collaboration of the Bureau of Indian Standards (BIS), the Bureau of Energy Efficiency (BEE), ELCOMA, and the International Electro-Technical Commission (IEC) [34]. In total, 12 international LED lighting standards were adopted in India, out of which 8 were modified to local conditions [35]. The establishment of local standards enabled the Indian LED manufacturing industry to evolve from merely importing final LED products to the creation of a considerable domestic industrial capacity in the latter stages of the value chain. For this reason, the LED lighting products imported to India saw a growth of 59% and 69% during 2014 and 2015 (worth USD 131 million and 222 million, respectively). The supplier nations included China with a share of 85%, followed by 5.4% from South Korea and 2.4% from Thailand [36]. During our interaction with LED companies in India, each one of them said that they imported LED chips from overseas due to the high investment cost of setting up LED chip manufacturing facilities and India's lack of R&D in this sector. One of our interviewees from the manufacturing sector said: "Why should I invest in making my own LED chips when I can import them at cheaper cost" (Interview, Rama Industries, senior level executive, 20 September 2016). Another Indian-based LED luminaire company expressed the common sentiment this way: "Setting up a LED chip manufacturing facility in India is difficult, because it needs huge investments and we are at least eight years late in R&D. Given the technology and IPR cost we cannot compete with the rest of the world, especially with China and Taiwan" (Interview, Sieger LED Industries, senior level technician, 14 September 2016). Indian manufacturers are thus heavily dependent on imports for LED chips, which represent around 15% to 20% of the total lamp cost. However, at the same time, they have been quite successful in finding opportunities in niche markets, such as LED luminaire design, retrofitting, or customization, for instance, the Indian-made LED driver circuit, which is currently one of the cheapest in the world (Interview, ELCOMA management, 16 September 2016). Although the Indian LED sector initially lacked domestic research and development capacity and faced restricted access to technology due to intellectual property rights (IPRs) [31], this did not stop the industry from growing at a tremendous pace. LED sales in India jumped from less than 5 million per year in 2014 to more than 520 million in 2018 [37].

Knowledge Development
This function describes all activities aimed at generating and applying knowledge in a certain field of technology development [28]. One of the most noteworthy efforts taken by the government towards local knowledge development in energy-efficient products in India was the launch of the Energy Standards and Labeling Scheme (S&L) by the Bureau of Energy Efficiency (BEE). The BEE was created in 2002 as part of the Ministry of Power under the provision of the Energy Conservation Act 2001. The agency was tasked with developing policies and programs to increase the energy conservation and energy efficiency provisions in India. The S&L scheme set minimum energy performance standards for electronic appliances and enabled consumers to make informed choices about these appliances. The S&L scheme was designed by the BEE in 2006 and has been regularly updated ever since, with the Energies 2020, 13, 5821 7 of 16 latest revision released in 2016. The "BEE STAR" labeling system under the S&L programme is similar to the international "ENERGY STAR". Another important step was the publishing of a feasibility report on Economic case to stimulate LED lighting in India by the government in 2010. The report discussed issues pertaining to the economics of manufacturing for LED technology in India and concluded with important recommendations at the local policy level [38]. Arguably, the most important step regarding knowledge development was the introduction of local LED standards in 2012. During our discussion with one of the senior-level participants in the former LED standardization committee, he emphasized that there was a need for even further localization of standards (Interview, ELCOMA management, 16 September 2016). To exemplify his argument, he pointed out that internationally accepted standard LED models have holes for the purpose of heat dissipation, which need to be closed in designs for the Indian market to prevent damage from high humidity and dust.

Knowledge Exchange
This function includes the exchange of information about certain technologies between key actors within the TIS [28]. In India, knowledge exchange about energy efficiency and LED technology at the national level is primarily the responsibility of three entities: The private sector association Electric Lamps and Components Manufacturers (ELCOMA), as well as the government entities Bureau of Energy Efficiency (BEE) and Energy Efficiency Services Ltd. (EESL). ELCOMA decided in 2009 to place a special emphasis on the development of the LED lighting sector in India. The first step was the creation of a new LED chapter to collect information about all LED technology-related activities in the country. The LED chapter was very successful in bringing together all the LED manufacturers and to establish a platform for peer learning in which various domestic barriers for the development of LED technology could be discussed. In addition to serving as a new umbrella body for the domestic LED industry, ELCOMA has been actively involved in the knowledge exchange about LED technology through many workshops and a dedicated white paper series on barriers to LED technology. The Bureau of Energy Efficiency (BEE) is another important actor in the field of knowledge exchange. It was the only government institution responsible for knowledge development and implementation of policies concerning LED technology. It focused in particular on educational activities, such as the organization of regular stakeholder workshops or the production of newspaper articles and TV commercials. However, based on the Energy Conservation Act of 2001, the Indian Ministry of Power established the Energy Efficiency Services Ltd. (EESL) in 2009 to take over the implementation functions of the BEE [39]. The EESL was set up as an energy service company that also served as a consultancy organization for CDM, as well as a resource and learning center for the capacity building of state designated agencies, financial institutions, and utilities in India [7]). Learning the lessons from its predecessors, EESL developed a new and rather innovative strategy for knowledge exchange by combining conventional techniques like workshops and awareness campaigns with a more targeted approach of utilizing public facilities, such as post-offices, banks, and community-level health centers, as product distribution centers. Ordinary citizens, who would visit these facilities on a routine basis, could be reached far more effectively with specialized advertisements and had the opportunity to inquire about additional information directly with the staff of the facility.

Policy Support
Policy support plays an important role for the development of a TIS, especially considering that policy decisions are always subject to efficient use of scarce resources [28]. The first step towards building an enabling policy environment for energy efficiency activities in India was the Energy Conservation Act of 2001, which was vital for the subsequent establishment of the BEE in 2002 and EESL in 2009. In 2008, the Indian government formulated its National Action Plan on Climate Change (NAPCC) with one of the mission statements focusing on the "enhancement of energy efficiency in demand side energy management" [40]. Additionally, in 2008, the BEE started a nationwide initiative called Bachat Lamp Yojana (BLY) (Hindi for "save per bulb scheme") that aimed to enhance  [41]. The Indian government also undertook several fiscal measures, such as a tax rebate policy for LED products in its 2011-2012 fiscal budget. Initially, it reduced the excise duty on LED lamps and LED chips from 10% to 5%. Even though it was increased again to 6% later in the following financial year, the tax rebate to this day remains one of the most lucrative rebates provided for LED technology development by international comparison. The special additional duty was also fully exempted on LED chips used in the manufacturing process of LED lamps in 2012-13. In addition, several state governments took the initiative to reduce their own value added tax (VAT) on LED lamps, for instance, the Delhi government, which reduced VAT on LEDs from 12.5% to 5% in the 2013-14 budget [7]. In 2015, the Ministry of Power simultaneously launched two new initiatives called Unnat Jyoti by Affordable LEDs for All (UJALA) (Hindi for "Domestic Efficient Lighting Programme"), an energy efficiency program for household lighting, and Street Lighting National Program (SNLP), with EESL as the program implementation body. Both initiatives proved to be very successful, with more than 366.4 million domestic LEDs and 10 million LED street lights diffused in the Indian market as of August 2020 [12,13]. Looking at policy support for LED technology by the national and state governments in India from a historical perspective, it becomes evident that the events in 2015 are a result of 15 years of policy work, with the basis laid by the Energy Conservation Act back in 2001. Initially, the national government only half-heartedly supported an already existing bottom-up movement and successfully utilized international instruments like the CDM, before making lighting efficiency a top political priority.

Resource Mobilization
The gathering and mobilization of human, financial, and material resources is central to the evolution and maturing of any TIS [23,27]. The Delhi government took the lead in 2006 by introducing a "Buy one get one free CFL scheme", with the purpose of enhancing energy efficiency among households [7]. While the Indian market price for CFL lamps in 2008 was INR 80-100 (USD 1.22-1.53) per lamp, it was reduced to only INR 15 (USD 0.23), under the subsidized BLY scheme. The government initially aimed to replicate the BLY model for LED lamps in India, but with the crash in carbon markets and dim prospects for the CDM mechanism, the model now faced severe limitations [7]. Commenting on BLY in an interview, a representative from EESL said "BLY had its days, but for large-scale diffusion of LED lights we needed a better model" (interview, EESL management, 18 September 2016). As a result, the government launched the UJALA and SNLP schemes in 2015, by far the biggest resource mobilization attempts for energy-efficient lighting in India. In contrast to its predecessor, the BLY scheme, UJALA and SNLP both worked with a "demand aggregation-price crash model", which basically meant reducing cost by stimulating economies of scale. In 2015, EESL invited open bidding from manufacturers for large-scale procurement of LED lamps, and paid all upfront costs. EESL further approached state governments and electricity production and distribution utilities, to sign agreements for creating a value chain for: (1) the public distribution of these LED lamps under the UJALA scheme, and (2) the replacement of existing street lights with LED bulbs under the SNLP scheme. The result of this demand aggregation was a drastic decrease in LED retail prices, which dropped as low as USD 0.8 (INR 56) (Figure 1).
Energies 2020, 13, 5821 9 of 16 bidding from manufacturers for large-scale procurement of LED lamps, and paid all upfront costs. EESL further approached state governments and electricity production and distribution utilities, to sign agreements for creating a value chain for: (1) the public distribution of these LED lamps under the UJALA scheme, and (2) the replacement of existing street lights with LED bulbs under the SNLP scheme. The result of this demand aggregation was a drastic decrease in LED retail prices, which dropped as low as USD 0.8 (INR 56) (Figure 1).  The UJALA scheme provided two different payment options. In a first option, consumers could simply choose to pay the whole cost upfront at once [14]. The second option was a "pay as you wish /on-bill financing" scheme, for which EESL cooperated with the distribution companies to overcome the first-time investment barrier. Consumers were given the choice to pay USD 0.15 (INR 10) upfront per bulb, while the remaining sum was recovered through the monthly electricity bill, charging USD 0.15 (INR 10) per month. The scheme gave each consumer an opportunity to purchase up to eight LED bulbs on one electricity bill. Here, a noteworthy point is that these LED bulbs were not subsidized under the UJALA scheme. Explaining about the cost breakup, our representative from EESL mentioned: "If per bulb selling cost is USD 1.55 (INR 100), USD 0.35 (INR 23) would be our managing cost, and further working with supply chain, marketing, and distribution cost, we finally are left with around USD 0.11 (INR 7) as our profit margin per bulb" (interview, EESL management, 18 September 2016). The working model of the SNLP is slightly different from UJALA. EESL approaches the municipalities and does the upfront investment for replacing the street lights with LED lights, the municipalities in-turn save on their electricity bills and use these savings to repay EESL over a payback period of seven years. Beyond this, in 2012, there were three CDM projects implemented in India related to LED lighting [43,44]. While LED technology is constantly improving and getting ever more popular, the true game changer in India in successfully diffusing LEDs in society has been the innovative business model developed by EESL, rather than the technology alone.

Market Formation
Market formation is a crucial function of any TIS. It aims to shield emerging technologies from the dominance of incumbent technologies and to stimulate demand for the new product [28,29]. In our case study, we found that the BLY scheme in 2008 along with the three CDM projects in 2012 helped with building consciousness among people for energy-efficient lighting. However, the comparatively smaller scale of these events limited their impact for market formation. We already discussed the important role entrepreneurs played in the development of LED technology TIS in India. The tax rebate policy for LED products also played an important role because it incentivized domestic Indian manufacturers to enter the market. As a result, we saw the manufacturing sector developing niche markets for themselves in later stages of the value chain, which included LED luminaire design, retrofitting, and customization. In addition, the demand aggregation by EESL in 2015 greatly supported the market formation of the Indian LED industry. An interview partner representing one of the premier LED manufacturing companies in India mentioned that the Indian market before 2015 was too small and fragmented to support large-scale manufacturing (interview, Kwality Photonics management, 28 September 2016). The establishment of EESL as a large-scale buyer and distributor had quickly expanded the market, thus enabling an economy of scale in India. Another important role that the government scheme played was the creation of a parallel market for differentiated LED products. Under the UJALA scheme, a person can only buy 9-watt LED bulbs; for LED lamps other than 9 watts, the consumer must buy it from the open market, outside the UJALA scheme. Since consumers who had used the UJALA scheme were now aware about the benefits of LED lighting, they were more likely to use other LED lighting products as well. This had the effect of opening a wider market for other LED-based products that fall outside the government's LED distribution scheme, thus helping with the market creation of LED technology in general. The new market opportunities did not go unnoticed in the private sector, at home and abroad. In an interaction with a senior-level representative from a UK-based lighting products firm, he said "The Indian market in LED luminaries is growing fast. We are here to enter the market" (interview, Orange Tek management, 21 September 2016). A representative from Japan explained that the "huge market size and growth opportunities" (interview, Toyoda Gosai management, 27 September 2016) led his company to scale up their business activities in India.

Advocacy Support
This function describes the social acceptance of technology and its corresponding compliance with relevant institutions [29]. The BLY scheme in 2008 was instrumental to counter initial social resistance to change by improving public perception about energy-efficient lighting (interview, EESL management, 18 September 2016). The involvement of ELCOMA starting in 2009 was an important step for engaging LED manufacturers from the private sector. As discussed above, ELCOMA as a lobbying group has played a major role in building advocacy support of LED technology development in India, both at the national and international level. The involvement of EESL in energy-efficient lighting in 2015 was arguably the most important and defining event in garnering political support towards LED technology development in India. However, our representative from EESL told us that the initiatives still had to face many challenges (interview, EESL management, 18 September 2016). While it was relatively easy for the domestic lighting scheme to convince people to buy LED lamps by emphasizing their personal economic benefits in terms of cheaper price and saving on their electricity bills, it proved to be much more difficult to persuade organizations and local governments to install street lights under the SNLP initiative. Local governments in developing countries are more susceptible to corruption and often mistrust higher government levels due to a perceived tendency of top-down policy making by national elites and special interest groups. One such challenge we witnessed in our case study was the conflict of interests at the organization level (interview, SDMC management, 25 September 2016). Indian municipalities receive an annual allowance for the maintenance of street lighting, but the shift to LED-based street lights would have cut down the allowance and led to fears of budgetary deficits, even though the long-term electricity savings would have easily compensated for the short-term losses. Local municipalities tried different strategies to overcome the barrier of conflicting organizational interests. One example was the South Delhi municipal corporation (SDMC), which had already outsourced their maintenance service to a local electricity utility, thereby creating an organizational buffer. The SDMC example also demonstrates the substantial long-term cost reduction made possible by the shift in lighting technology. Before the installation of LED street lights, the municipal corporation used to pay around USD 1.5 (96 INR) per street light to the distribution company for line maintenance, which was reduced to a mere USD 0.45 (23 INR) per street light afterwards. In combination with the savings on the monthly electricity bill, SDMC was able to save approximately USD 250,000 per year (160,000,000 INR) (interview, SDMC management, 25 September 2016).

Discussion
Our findings show that the key to the rapid expansion of the Indian LED market was the development of innovative strategies that helped with creating an enabling environment, which either directly addressed or helped to circumvent the major barriers. Although not all barriers that can have a negative effect on low-carbon technology transfer, such as intellectual property rights, restricted access to technology, or the lack of a domestic R&D environment, were addressed in the Indian case, the large-scale diffusion of LED technology has been very successful for the following three main reasons: Firstly, technological localization through standard setting, knowledge exchange, and the creation of niche markets played the most decisive role for the knowledge development in the Indian LED sector. The key event was the retrofitting of international LED standards for localized Indian usage under the auspices of the IEA, which created a regulatory basis that helped to stimulate large-scale private sector investment in the market. Even though India still relies on the import of raw materials for LED production, many new businesses and jobs, as well as local industrial capacity, were created in niche markets in later stages of the LED value chain, such as packaging or the retrofitting of LED lights. Furthermore, with its out-of-the-box approach, Energy Efficiency Services Limited (EESL) developed innovative strategies for knowledge exchange and the raising of public awareness in a developing country in which people have comparatively lower access to traditional print media or internet. The technological localization through local entrepreneurs is also interesting from another perspective. The rapid development of the Indian LED sector, despite the initial lack of domestic research and development capacity and intellectual property right issues [31], is at odds with the literature on low carbon technology transfer, which consistently cites IPR as a prime barrier for development and diffusion of low-carbon technologies in developing countries [32,[45][46][47][48]. However, when asked during our interviews how the capacity to build subsidiary elements for LED lamps (driver circuits and luminaire frames) had been developed, most local manufacturers claimed it to be indigenous capacity building without any foreign help or assistance. One explanation is that an enabling TIS environment can lead to high rate of technology diffusion even with some key barriers like IPRs remaining unaddressed in a focal country. Another explanation is offered by studies such as Rai and Funkhouser [49], who argue that one of the most important dynamic drivers of international low-carbon technology transfer is in fact reverse-engineering, especially in the context of technology localization. This example shows that the role of reverse-engineering of clean energy technologies as a driver of low-carbon technology transfer is not yet properly understood and invites further study.
A second important factor in the Indian success story was the creation of innovative economic and fiscal support policies. Many of the financial barriers to the alleviation of energy poverty through rural electrification identified by Sovacool [2] also hampered previous attempts to diffuse energy efficiency solutions in India, such as lower income levels of the target group, their low ability to pay, low levels of consumption, geographic dispersion, and the high costs of initial connection and maintenance. The Indian government played a decisive role by creating policy instruments like tax rebates and subsidization schemes that helped to increase demand, which in turn stimulated the initially fragile domestic industrial base. However, rather than technology progress and fiscal support policies, the true game changer in India has been the innovative business model developed by EESL, which had reduced the price of LED lamps for individual customers by providing upfront procurement on an enormous scale and by developing an innovative end-user payment model. The stepping-in of EESL as a large-scale buyer had quickly expanded the market and made domestic large-scale manufacturing economically sustainable in India. The Indian example also shows that this does not necessarily imply the development of domestic capacity in all stages of the manufacturing process, especially for high-end technological solutions, such as modern LED lamps. Instead, it is crucial to stimulate job creation and new business opportunities in niche markets across the value chain of low-carbon technologies.
A third important factor was the establishment of institutional support mechanisms, such as the EESL, as an autonomous implementation agency with clear responsibilities. Previous studies have shown that the best support policies in developing states can fail without independent and accountable institutions to back them up [50]. In political systems with high corruption levels and political frictions between competing levels of government, it is imperative to build different buffers into the design of the implementation agencies and support policies. Even though India encountered many challenges in this regard, EESL and several municipal governments proved to be quite effective in finding innovative ways to bridge or circumvent political conflicts that would otherwise hamper the program implementation. However, the success of the EESL rested on a much longer historical learning process. It can be argued that without the Energy Conservation Act of 2001, neither the EESL, nor the UJALA and SNLP initiatives would have come into existence.

The Role of Low-Carbon Technology Transfer
Low-carbon technology transfer is widely considered a vital component in international development strategies to combat energy poverty and to provide access to climate-friendly technologies to developing countries [46,49,[51][52][53][54]. It has the potential to bridge some of the gaps between the developed and the developing world and to pave the way to a future in which the costs and benefits of modern energy services are more evenly distributed in and between countries. Our findings not only demonstrated that it is indeed possible to utilize LCT transfer between countries to help address the challenges of energy poverty, but it also shows that it is even possible in a country like India, with a huge and diverse population, various developmental challenges, and domestic political frictions. Furthermore, although it took India 15 years from the Energy Conservation Act in 2001 to the creation of the EESL, which eventually spurred the widespread diffusion and localization of LED lighting, we argue that is possible to shorten this transition time in other developing countries by learning some of the lessons from the Indian example. Perhaps the most influential factor in India was the development of domestic regulatory and institutional mechanisms for supporting the localization of energy efficiency technology. The international community can help to facilitate this process, as seen in the localization of LED standards in India, but each country needs to find its own local strategies adapted to its own unique conditions.
To our knowledge, India is to date still the only country that has developed localized LED standards. However, the Indian success story had implications beyond its borders, since the resulting interest in parts of the developing world has already strengthened the role of Indian knowledge institutes as international knowledge brokers on energy efficiency strategies in developing states. This development is also actively facilitated by the Indian government, which is well aware of the success of its lighting efficiency policies and is actively trying to propagate its domestic experiences and best-practice examples as part of its south-south capacity building strategy with other developing states. The representative of EESL mentioned that his organization had been approached by many countries to share their experiences about the large-scale diffusion of LED lighting. Commenting on the scope of technology and knowledge transfer, the EESL representative explained: "If we compare with advanced countries like USA, Germany, Japan, clearly this model may not be much beneficial, but for developing countries with similar economies, similar practices and life style, the model can be easily replicated and re-engineered. For instance, we are currently helping Indonesia in developing their LED standards. I am personally coordinating assignments in Myanmar, Indonesia, Thailand, Cambodia and Vietnam, where we have been given the mandate to showcase our best practices and facilitate similar programs in their countries." (interview, EESL management, 18 September 2016).
In this way, the Indian case also challenges conventional models of international low-carbon technology transfer, which usually only consider the transfer of technology from developed to developing countries (north-south transfer) [55], or the growing cooperation between developing countries, recently accelerated by an increasingly assertive China (south-south transfer) [30,56,57]. In our case study, we found that there is something in between the north-south (N-S) and south-south (S-S) transfer, which is currently missing in the literature. While the developed world ("north") helped India ("south") with improving its domestic LED sector capacity by providing assistance with Energies 2020, 13, 5821 13 of 16 localizing international LED standards and through investments of foreign companies in the Indian market, this capacity was further transferred to other developing countries ("south"), making the flow of knowledge and technology in essence a north-south-south connection (N-S-S). The novelty of the N-S-S transfer is the adaptation of technological knowledge to local conditions by one developing country, and the provision of this modified knowledge and reengineered technology to countries in similar socio-economic circumstances. This creates higher chances of acceptance in recipient countries than the traditional north-south route, thereby potentially opening a new avenue for quicker clean energy technology transfer processes. We believe the north-south-south perspective might be helpful for the further exploration of the dynamics of technology transfer processes across a wide range of environmentally friendly technologies.

Conclusions
Developing countries face many challenges in innovation and deployment of modern energy technologies. This article therefore set out to explore the reasons behind India's largely successful nationwide rollout of modern LED lighting in the span of only a few years. We learned that the story of India's lighting modernization began already more than two decades ago with the first energy efficiency policies. It took India 15 years to reach from the Energy Conservation Act 2001 to the beginning of the wide diffusion and localization of LED lighting in 2015. Although the first attempts to enhance lighting efficiency in India were met with only limited success due to a range of factors, such as a lack of financial resources and domestic know-how, policy makers and industrial players learned at each step from previous failures. The result was the eventual development of innovative strategies that helped the rapid expansion of the Indian LED market. This happened through the creation of an enabling environment in which the major barriers, such as the initial lack of a domestic industrial capacity or a lack of public awareness, were either directly addressed or circumvented. The main factors for success were the adaptation of the technology to the local needs and capabilities of a developing country, the creation of tax rebates and subsidization schemes tailored specifically to the diffusion of particular technologies, in combination with an autonomous implementation body that had a strong mandate and clear responsibilities. Another important finding was that India greatly benefitted in its early LED development from the support of international actors, for instance, through the help of international organizations in local standard settings or through international businesses that invested in the Indian LED market. Since India demonstrated that it is possible to create a cutting-edge energy technology industry in a relatively short amount of time even in a developing country setting, many other countries seem to be keen to learn from its experiences. The Indian government and industry are equally interested in exporting their newly acquired know-how, thereby creating a new link in the discussion on international technology transfer that sees a technological innovation being imported into a developing country, adapted to local circumstances, and, in turn, further exported as a modified version to other developing countries with similar needs. We believe that this emerging north-south-south cooperation pathway certainly invites future studies to investigate if the Indian mechanism for shortening the transition time could be replicated in other technological innovation sectors and localities.


Requirement Area Review: Flexibile Study Area for Traffic Impact Assessments

New developments will contribute to increased traffic to the external environment. Therefore, the majority of nations, in planning and building regulations and guidelines have adopted steps to control them beforehand. Similarly, in Sri Lanka, new developments in the approval process should be undertaken, based on their legal specifications and through a Traffic Impact Assessment (TIA). Although the management of traffic from new developments is exceedingly positive, one of the problematic sectors noted in the TIA study is a fixed study area. Consequently, a 500 m buffer area from the proposed site edges is currently being taken as the study area in Sri Lanka. The geographical extent of the traffic impact area is not a fixed bound-ary. Particularly based on a set of parameters, the true catchment area could be changed. A fixed boundary may, therefore, misdirect developers, consultants and planning authorities in decision making in situations of transport planning. Owing to this very debatable selection, almost all studies have been incorrect. Consequently, this research focuses on investigating the requirement of flexible study areas for traffic impact assessments of distinctly unique developments.

Introduction
Urban road traffic congestion is a dangerous global issue existing in every major city (Wen, Chin, & Lai, 2017). A new development, thus, essentially attracts vehicle demand for the accessible and adjacent roads and linked junctions in the vicinity (Weerasekera, 2011). Therefore, new constructions or alterations to the existing development or renovations has an impact on the extent of external traffic (Land Transport Authority, 2011). If not properly diagnosed, challenges would become increasingly severe (Ponnurangama&Umadevib, 2014). Therefore, once this was identified, numerous countries have taken different planning and building regulations and guidelines to detect and eliminate adverse effects beforehand (Cooley, Gruyter, &Delbosc, 2016). A Traffic Impact Assessment (TIA) is needed to carry out according to the legal conditions, to obtain a development permit for new developments. It identifies potential traffic impacts from the proposed development for the surrounding road network and provides corrective action to minimise them (Teodoro&Regidor, 2005).
Mega constructions in Sri Lanka too, have to submit a traffic impact assessment as laid down by the parking & traffic control (Regulation 34, Schedule III) under the City of Colombo Development Plan for development permits from 1986 onwards. While there is an enormous positive movement, questionable areas may still emerge in some areas referred to the Term of Reference (TOR) in traffic impact assessments (Director, Enforcement Division of Urban Development Authority, 2019). Delineating the true catchment area for traffic impact analysis is one of the debatable areas. The 500m buffer zone from site edges is currently taken as the study area for traffic impact assessments. This is a fixed figure similar to most other countries (Ponnurangama&Umadevib, 2014). However, the area of influenced traffic after a new development varies with different indicators (Abley, Durdin, & Douglass, 2010).
Even though there are numerous different TIA guidelines in most countries, a flexible boundary for transport impact analysis of the TIA is not well understood through the guidelines, or even at practice (Cooley, Gruyter, &Delbosc, 2016). Besides, the guidelines are included in a reasonable study area, depending on the type and scope of the development, especially in the United States. In the TIA guidelines and the TORs, a minimum study area limit should be recommended as the initial (Weller, 2007). Therefore, the flexible boundary limit should be clearly defined (Cooley, Gruyter, &Delbosc, 2016) and to understand how much the new development will affect the environment, a geographical demarcation of the traffic impact area is needed (Wen, Chin, & Lai, 2017). If not, due to this very arbitrary selection, many findings would be erroneous. In addition, numerous experts in the transport field believe that it is essential for increasing development to have an acceptable catchment area for the evaluation of traffic impacts. Therefore, this research objects to investigate the requirement of a flexible study area for traffic impact assessments of each unique development.

Literature Review
Unanticipated growth is a massive concern since traffic in most countries is unbalanced (Azra&Hoque, 2014). Therefore, some kind of serious transport studies, which are commonly known as the Traffic Impact Assessment (TIA), should be carried out before the approval for construction . Different countries and different researchers use a variety of names for this, such as transport assessment, transport impact assessment, transportation assessment, traffic impact assessment, traffic impact study, traffic report and integrated transport assessment (ITA) (Abley, Durdin, & Douglass, 2010). With certain mitigation actions and directives, this study may be rejected or accepted.
The vehicle demand from each new construction activity is usually increased in the neighbouring road network (Khade, Khode, &Bhakhtyapuri, 2017). Thus, the accumulative vehicle trip attraction from new developments will generate many adverse impacts on the street network (Ponnurangama&Umadevib, 2014). The extent of traffic impact should, therefore, be correctly estimated from any new construction of the adjacent roads and intersections. Unless properly managed, serious planning and transportation problems can arise (Abley, Durdin, & Douglass, 2010). Therefore, most cities have undertaken a traffic impact study to approve new developments and improvements to existing developments . The TIAs is used to seek an impact on all aspects of the transportation system due to new developments (Sarkar, Maitri, & Joshi, 2015). Therefore, this assessment is more cost-effective and time-efficient to make future development decisions at the planning stage (Cooley, Gruyter, &Delbosc, 2016). The required inputs and its scope should be clearly described under the TIA study to make a sustainable transport system (Abley, Durdin, & Douglass, 2010). Accordingly, the study area or impact area is the base to conduct any impact study (Cooley, Gruyter, &Delbosc, 2016). Therefore, in a TIA study, the most important and most difficult stage is the delineation of the study area, which is also called the "impact area" (Abley, Durdin, & Douglass, 2010).
Nevertheless, planning and building approving agencies of most countries such as Bangladesh (Azra&Hoque, 2014), Philippines (Teodoro&Regidor, 2005), Canada (Engineering and Capital Infrastructure Services Infrastructure, Development & Enterprise, April 2016), China (Weller, 2007) and United Arab Emirates (Department of Transport, November 2009) etc., have failed to identify a proper mechanism to delineate the study area for the transport study. For generalization, most agencies both local and international have instructed TIA consultants to undertake a certain study area, irrespective of the development type and scale. As an example, the Land Transport Division of the Ministry of Public Infrastructure & Land Transport in Malaysia, through the TIA Guideline in November, 2015 instructs that the study area should be a 1km radius adjacent to the site. Likewise, impact levels to all the access roads, nearby roads and junctions within 1km radius from edge of the site should be studied as the direct traffic impact area under the guidelines in Mauritius (Ministry of Public Infrastructure & Land Transport, November 2015). In this instance, there is a fixed impact area under these regulations and the consultant may discuss the extent of the study area with the Ministry of Public Infrastructure & Land Transport, Mauritius. Locally, the Urban Development Authority in Sri Lanka instructs to get a 500m radius buffer area from the site edges as the traffic impact study area (TOR for TIA, 2019). A minimum study area is an essential aspect under the TIA guidelines (Weller, 2007) and it should define the detailed study area limits for all development types (Cooley, Gruyter, &Delbosc, 2016). Abley, Durdin, & Douglass (2010), clearly explained that the design years are generally changing by the location, scope, and development type and therefore, it is recommended that there should be at least a 10-year space as "future assessment year". Nevertheless, all demarcations are static and no rigid dynamic ways for demarcation could be observed elsewhere. Abley, Durdin, & Douglass (2010), have explained that the scope of the TIA study should be described based on the different development types. At the same time, the minimum study area for all developments should be specified according to its use and development size (Cooley, Gruyter, &Delbosc, 2016). Subsequently, he illustrated that the flexible impact areas should be defined, based on the "scale of activity and the extent of impact". Also, many research studies discuss that the impactable boundary of each development will differ and is not fixed. Therefore, due to variations in travel patterns, socio-economic and cultural conditions, and locational characteristics, the same guideline cannot be applied to all developments (Azra&Hoque, 2014). Thus, a large number of developments will attract massive traffic around (Yayat K.D., Kombaitan B., Pradono, Purboyo H.P.H., 2015). The bare land available around is also one of the factors to change the impact proportion of the area (Chen Y. & Liu A., 2019).
According to most scholars, the extent of the true catchment area after a new development, varies with different parameters such as the proposed development type / use (Abley, Durdin, & Douglass, 2010), size / scale of the proposed development, location of the proposed development (Azra&Hoque, 2014), scale of activity of the proposed development, around land use pattern (chen y. &liu a., 2019), modes of transport facilities around, nature of transport network around, trip generation from the proposed development (Cooley, Gruyter, &Delbosc, 2016), intersection performance around, road link performance around (Weerasekera, 2011),topographical barriers, connectivity of road network, nearby trip production / attraction points, population density around (Sarkar, Maitri, & Joshi, 2015), prevailing traffic conditions on the existing road systems (Ministry of Public Infrastructure & Land Transport, 2015), considerable distance from the site, close proximity of new developments to the site (Land Transport Authority, 2017), site access points (PMK Associates, Inc., July 2006), transport related infrastructure developments (Department of Planning, August 2016), pedestrian routes and cycle routes, availability of bare land (chen y. &liu a., 2019), access road classifications, access road width and speed limits, number of lanes of the access road, walking distances to public transport connections (Engineering and Capital Infrastructure Services Infrastructure, Development & Enterprise, April 2016), drive distance (Dramowicz, 2005), drive time (Segal, 1998), trading hours , perception of safety, parking facilities (Wegener &Fuerst, 2004), travel cost, employment, density, urban density, neighbourhood design ( Leszczyc, Sinha, &Sahgal, 2004) and accessibility (Delloye, 2018) etc. Hence, a fixed border might also point in the wrong direction for the planning authorities to take transport planning decisions.

Methodology
This research reviewed 50 TIA reports in Sri Lanka through the cornel method, and they were analyzed to recognize the requirement of a flexible study area or fixed study area for traffic impact assessments of each development. All the TIA reports have been collected from the Urban Development Authority and randomly selected for this study. All reports, prepared by different consultants for a variety of development categories, have been submitted within the past 5 years, to obtain traffic planning clearance for a development permit.
This study used Google Earth Pro Software, Microsoft Excel and IBM SPSS Statistics software. Google Earth Pro Software was used to identify the traffic count locations and to find the direct distances from each site edges to traffic count locations. Microsoft Excel and IBM SPSS Statistics software were applied to identify the radius of the adopted impact areas and the gap between the true catchment and adopted catchment in TIA studies.

Analysis and Results
All 50 locations of TIA reports are within different local authorities in Sri Lanka. They were taken through a random sampling technique to analyze the need for a flexible study area for TIA studies. These TIA reports are prepared for different development purposes such as retail, shopping, religious, recreational, industrial, residential, office, educational and other utilities etc., and different development scales. Further, the area, where the traffic count locations (traffic data collected and analyzed roads and junctions) in these TIA reports, are considered as the adopted impact area or "Applied Impact Area" at the practice in this study. Moreover, the direct distances in each direction from the site edges to traffic count locations will be measured and the maximum value of the radius will be taken as the radius of the applied impact areas. The 500m radius is the study area or initial impact area under the Urban Development Authority guidelines and regulations for TIAs in Sri Lanka (Urban Development Authority, 2018). Although it is a fixed study area, there is a gap between the regulatory boundary and the adopted boundaries at the practice in TIAs. It is obviously proven as below.
The total floor area of the proposed development in Figure 1 is 5,785.16 sqm. The TIA report was prepared on 17th September 2018 and it is now at the operational stage. The boundary of traffic count locations is the practically adopted impact area throughout this TIA study. All the traffic planning decisions have been taken based on this study area. But, the regulated study area (500m radius) is larger than the adopted impact area at the practice. Accordingly, this TIA study has taken a maximum 240m radius impact area, which is lesser than the legal requirement. It is clearly shown in Figure 1.
The total floor area of the proposed development in Figure 2 is 1,160 sqm. The TIA report was prepared in December 2017 and it is now at the operational stage. Here also, TIA study has taken a maximum of 130m radius impact area and is much less than the legal requirement. It is clearly shown in Figure 2. The total gross area of the building in Figure 3 is 15,060 sqm. This TIA study has taken the maximum 500m radius impact area. Therefore, the legally required impact area and the practically adopted impact area are the same boundaries of this TIA study. Accordingly, this study has been carried out following the legal guidelines by the TIA consultant ( Figure 3).
The total gross area of the building in Figure 5 is 11,516.89 sqm. This TIA study also has taken the maximum 500m radius impact area. Therefore, this study has been carried out following the legal guidelines by the TIA consultant ( Figure 5).
The total floor area of the proposed development in Figure 4 is 1,151 sqm. The TIA report was prepared in February 2018 and is now at the operational stage. This study has taken a maximum of 740m radius impact area, and is larger than the legal requirement. It is clearly shown in figure 4.
The total floor area of the proposed development in Figure 6 is 1,186.0 sqm. The TIA report was prepared in June 2018 and it is now at the operational stage. This study has taken a maximum of 710m radius impact area and exceeds the legal requirement. It is clearly shown in Figure 6, and the table below shows the results of the study in a summary.    Figure 7 clearly shows the different radius values for the study area between the TIA guidelines and at practice. The scatter graph shows the different radius values of the study areas at the practice and dotted straight line shows 500m radius under the TIA guideline. Accordingly, very few studies have undertaken the 500m or a closer radius of the study area for their analysis. Besides, according to the 50 TIA case studies, most of them had been carried out with either more than or less than the 500m radius of the study area for traffic impact analysis. Hence, there is a strong gap between the true catchment areas and applied catchment areas in these TIA studies.
According to the results of the descriptive statistic analysis for the floor area of each development from BM SPSS Statistics software, the mean value is 7874.7638 sqm. There is a high range between maximum and minimum radiuses which is 123853.37 sqm. This data set which has a high variance is 357466269.668 sqm. A high standard deviation is in the data set, which is 18906.77841 sqm. Further, this data set is widely spread out.
According to the results of the descriptive statistics analysis for the radius of the applied study area from BM SPSS Statistics software, the middle value of the data set (median) is 370m. There are multiple modes in this data list and the smallest mode value is 320m and maximum mode value 500m. The applied average radius of the study area (mean) for each development is 378m. Thus, the maximum radius value of the applied study area is 750m and the minimum radius value of the applied study area is 20m. Thus, there is a high range between maximum and minimum radiuses which is 730m. This data set has a high variance, which is 33995.918m. A high standard deviation is in the data set, which is 184.37982m. Furthermore, this data set is widely spread out. Therefore, the application of a fixed radius for different development types misdirects the planning decisions in a city.

Conclusion
There are different problematic areas in TIA guidelines and it may negatively affect the future of town planning. Cooley, Gruyter, & Delbosc (2016), argue that although many national TIA guidelines are available, there is no proper scope to practice well. Accordingly, although planning authorities of different countries recommend adopting the TIA guidelines to prepare TIAs to assess the transport impacts (Abley, Durdin, & Douglass, 2010), many technical and operational disparities have been identified in the TIA guidelines (van Rensburg & van As, 2004). One of them is the absence of a flexible study area (Ponnurangama & Umadevib, 2014). It means that there is a gap between the regulatory boundary and the adopted boundaries at the practice in TIAs. Different kinds of literature and analytical results prove undoubtedly that there is a requirement of a flexible study area for traffic impact assessments of each distinctly unique development. Hence, this study not only shows the way to adopt or develop a new technique to define the true catchment area for traffic impact assessments, but has also helped to rectify reasonable decision making in the transport planning sector.


Calculation of Value-at-Risk Variance-Covariance with the Approach of Simple Cash Portfolio, Factor Models and Cash Flow

One way to calculate Value-at-Risk (VaR) is the variation-covariance method. The calculation of VaR covariance assumes stock data is normally distributed. The data needed to calculate VaR by the variance-covariance method is the covariance matrix of Bank Danamon and Bank Mandiri stock data. The main topics discussed in this paper are calculating VaR covariance with a simple cash portfolio approach, factor models and cash flow. For comparison of the use of the three approaches Backtesting, the backtest results indicate that the factor model is the best method.

Introduction
Financial activity is very volatile and risky. Risk can be interpreted as the possibility of undesired or opposite results being desired. To avoid losses, investors should be able to face all the risks that might occur to obtain the results as expected and can also calculate these risks (Fabozzi, 2000;Jogiyanto. 2007).
To control and calculate these risks, you can use Value at Risk (commonly abbreviated as VaR) which has many advantages, one of the main advantages of VaR is that it does not neglect the underlying risk factors and specific risks of the portfolio (Sukono et al., 2019). VaR can be used to compare market risks from all types of activities in a company and provide a single calculation that is easily understood. VaR can also be widely used and has become a standard in risk calculation because it can be applied to all types of risks. By using VaR, investors can calculate investment risk. One method of calculating VaR is variance and covariance. The covariance method has one advantage, i.e. it is very fast and easy to calculate VaR (Redhead, 1997).

Normal Distribution and Normal VaR Estimation
Normal distribution is often also called a Gaussian distribution. This normal distribution depends on three variables, namely the x value, the average distribution notated by μ, and the standard deviation notated by σ. Definition 1 ( Mood et al., 1963). The random variable X which has a mean μ and variance σ 2 < ∞ is said to be normally distributed, abbreviated X ~ N(μ, σ 2 ) if the probability density function is (1) Calculation of probability in the normal distribution model will involve searching for the integral value for each real number a. This integral can be simplified through the following theorem (Sudjana, 2005). So that for each real number a and b apply ) means a normal distribution that has a mean of 0 and a variance of 1. If Z is a normal standard, then the distribution of μ + σZ is called a normal distribution with mean μ and variance σ 2 and is denoted by N(μ, σ 2 ) (Walpole and Myers, 1978). ■ Based on the above normal distribution, so that the estimation for normal VaR can be written as follows ) , ( where P t is the stock price at time t, and . where μ t = 0, because VaR is only suitable for calculating short-term risks. Thus, the VaR value can be calculated by the formula (Dowd, 2002): . (2)

Estimation of VaR Variance-Covariance
The approach used to estimate VaR covariance is a simple cash portfolio, factor models and cash flow.

Simple Cash Portfolio
If p'Vp represents variance, then the VaR volatility value can be calculated with So the equation for the VaR estimate becomes (Dowd, 2002):

VaR Estimation with Factor Models
Factor models are statistical approaches that can be used to analyze the interrelationship of several variables and to explain what dimensions (called factors) underlie these variables and reduce them (Sukono et al., 2019).
The factor model is usually used for large scale equity portfolios. Suppose a large-scale equity portfolio with k assets, which is denoted by a factor model with m risk factors. The general equation for the factor model is (Klienbaum et al., 1988): (5) From the above equation, the VaR calculation can be done using the following equation: variance due to market risk factors = p'BV x B'p,

VaR estimation with cash flow map
In addition to simple cash portfolio and factor models, another approach that can be used to calculate VaR covariance is cash flow. Cash flow is the amount of balance of cash received and paid by the company during a certain period.
Cash flow maps can be used to calculate VaR covariance from loan portfolios, i.e.

 ,
, , The VaR of the original cash flow will not be the same as the VaR of the mapped cash flow. The variance of the original cash flow is the same as the variance of the portfolio, which can be written into the equation (Ruppert, 2004):

Backtest
This section will discuss the backtest that will be used to evaluate the performance of the VaR model. Backtest is an important part of the risk measurement process. In the process, the backtest has four important elements and a single output. The first element is a collection of n observations of loss or profit for each period that are interconnected with VaR forecasts. The second element is the value of the loss function depends on VaR forecasting in its period.
The backtest used in this paper is Dowd (2002) which states the loss function as follows: where L t is loss (profit if negative) in the form of return (r t ). The third element is the benchmark value, which is the expected value of the best model. The fourth element is the value of the function, which is taken as the input of the loss function and the reference value. If the C t reference value uses the null hypothesis, the QPS (quadratic probability score) value is where p = 1 -α and n are lots of stock data. The range of QPS values is at [0, 2]. For QPS values close to zero, the model is getting better.

Results and Discussion
For example, the initial investment value at Bank Danamon and Bank Mandiri is 1,00 IDR. Using a 95% confidence coefficient, Bank Danamon's share variance of 0.000250 and Bank Mandiri of 0.000241, and covariance of both shares of 0.000245, VaR will be calculated for each share with the three approaches (Sulaiman, 2003).

Calculation of VaR Covariance with Simple Cash Portfolio for Single Shares and Portfolios
Using equation (3)

Calculation of VaR Covariance with a Factor Model for Single Shares and Portfolios
The market shares used are the CSPI (IHSG) and the exchange rate of the Rupiah (IDR) to the Euro. IHSG variance is 0.000113 and 0.0000187 for the exchange rate, both covariance are 0.0000459. B is the matrix m × k, each element is a beta of the i-th stock and the j-th risk factor.
The value of variance for company shares and market shares is  

Calculation of VaR Covariance with Cash Flow for Single Shares and Portfolios
For example, cash flow on each Bank Danamon and Bank Mandiri shares is 1,00 IDR. r is the annual interest rate on SBI. Calculate the present value for each share using Equation (11)

Conclussion
VaR calculation with cash flow results in a smaller risk value than the other two methods. Whereas the calculation of VaR with the factor model produces the greatest VaR. This indicates that market shares have an influence on the VaR calculation. With market influence, the risk of loss also increases.
VaR calculation using the factor model approach is better than a simple cash portfolio and cash flow. This can be seen from the backtest calculation for each method, from the backtest calculation results, the QPS value of the factor model is smaller than the simple cash portfolio and cash flow.