Improving energy efficiency and CO2 emissions reduction in the telecom sector: from exploration to development of framework for material flow costing

doi:10.21203/rs.3.rs-2215322/v1

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Improving energy efficiency and CO2 emissions reduction in the telecom sector: from exploration to development of framework for material flow costing

https://doi.org/10.21203/rs.3.rs-2215322/v1

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Telecommunication provides the infrastructure to deliver core services but contributes to extensive CO2 emissions. This study aims to demonstrate the effectiveness of Material Flow Cost Accounting (MFCA) in reducing carbon emissions in the telecom sector through energy optimization and improving operational efficiency by using electricity consumption as raw material and quantifying energy waste. An extensive literature review is conducted to identify the critical energy conservation initiatives in the telecom sector, followed by a questionnaire survey among Indian telecom managers to rate the identified initiatives. Finally, the outcome of the factor analysis was used as input for the successful implementation of MFCA in one of the large telecom industries in India. The study demonstrates a successful case of MFCA implementation with some promising results. The present case study may help promote the implementation of MFCA in sectors where material quantification of intangible products makes the identification of waste a hurdle for MFCA adoption.

MFCA

CO2 emission

energy efficiency

telecommunication

With around one billion installed devices, India is on its way to becoming the second-largest telecommunication industry globally by 2025 (GSMA, 2020). The contribution of telecommunication to the country's economic growth is immense, as it provides the backbone infrastructure to deliver core services that enhance citizens' quality of life (Runhaar & Lafferty, 2009). In addition, telecom services offer solutions to environmental issues using smart grids, smart homes, and smart offices (Marco et. al. 2016; Bu et al., 2012; Erol-Kantarci & Mouftah, 2011). However, they also deepen environmental issues, as energy use leads to Green House Gas (GHG) emissions from network equipment and an increase in electronic waste (Kuehr, 2012). The Indian telecommunication industry accounts for approximately 1% of India's total carbon dioxide emissions, higher than the total carbon dioxide global emissions standard of 0.7%. Furthermore, the emergence of technologies such as 5G and the Internet of Things (IoT) artificial intelligence (AI) is expected to increase global network energy consumption by 150%-170% by 2026 (Gurnaney, 2019). Increased energy consumption eventually contributes to increased carbon emissions (Lean & Smyth, 2010).

Carbon emission can be minimized if better measurement practices identify excessive use and wastage of energy (Labuschagne et al., 2005; Fang et al. 2020). However, there is no consensus on the indicators or standardized methods to assess carbon reduction (Helleno et al., 2017; Chen et al. 2019). Considering the rising energy consumption of mobile networks, an increasing number of base station sites, and steadily increasing energy costs, it has become critical for telecom service providers to reduce their operational expenditures by monitoring and controlling energy usage. Indian telecom sector contributed 58.3 million tonnes of CO2 emission in 2014-15 (TRAI, 2017). Telecom companies are finding ways to reduce operational electricity consumption; however, high carbon emissions still remain unnoticed due to the legacy accounting system. As the existing accounting system in the telecom sector does not elaborate on energy and material flows, a new kind of knowledge is required to promote Environmental Management Assessment (EMA) which can promote carbon emission reduction (Schaltegger & Burritt, 2017).

The Telecom sector, in a sense, is a unique service sector that requires electricity as raw material. It takes in electricity as input energy to run telecom infrastructure and, depending upon the efficiency of individual equipment, offers telecom signals as output. As output signals are also represented in electrical capacity, there is a possibility to trace the entire energy flow within the telecom network. As an EMA tool, the MFCA methodology is currently being used to calculate the avoidable greenhouse gas emissions (GHG) in many industries (Fakoya & van der Poll, 2013; J Hyrslova M Vagner, 2011; Schmidt & Nakajima, 2013; Tailor et al., 2017). However, this study goes a step further and aims to explore the effectiveness of MFCA in the service sector, specifically through energy optimization in the telecom sector. It would only be possible to reduce the carbon emission if MFCA could be coupled with information related to each telecom equipment's electricity consumption and identification of 'heat centres’ (point of higher operational electricity consumption). This technical information shall help immensely to identify hidden costs of losses due to operational inefficiency and discover the hidden environmental impacts of losses due to wastage of energy. The study's objective is to integrate the energy information of telecom networks with MFCA and develop a uniform methodological approach that can be assimilated with existing accounting methods used by the company. Keeping these objectives at forefront, our study intends to explore the following research question:

RO1: To identify the critical initiatives in the telecom sector that can reduce electricity consumption.

RO2: To expand the entire telecom network to identify 'heat centre' and develop 'Flow Model' as per MFCA

RO3: Extending the MFCA tool for utilization in the telecom sector for carbon emission reduction by allocating identified initiatives to heat centres.

The case study is based on the implementation of MFCA in Bharat Sanchar Nigam Limited (BSNL). BSNL is a large 'A Category' Government-owned telecom public sector enterprise, with its operations spread across India. 'Darbhanga' district of Bihar state in the East Zone has the highest energy consumption and is taken as a case to reduce carbon emission by focusing on energy efficiency and analyzing the improvement using MFCA.

The remaining article is arranged as follows: Section 2 describes the problem of CO2 emission in the telecom sector and MFCA with the relevant associated literature. The research method is described in section 3. Section 4 contains the descriptive case study. Section 5 discusses the improvement practices. This is followed by discussion section in Section 6. The last section summarizes the paper along with future directions.

There has been a body of research on energy saving in the telecom sector, and the focus has shifted toward developing low energy consuming telecom systems (Chen et al. 2012). In the telecom sector, telecom operators evaluate energy-saving and emission reduction work on technical advancement in various parts of the telecommunications network (Duan et al., 2011). On the operational front, energy efficiency improvement requires a comparison of the energy-saving potential of different technologies of components (Duan et al., 2011; Yang et al., 2013; Andreas & Jan,2019) as well as a rapid transformative change to support 'Operation and Quality improvement methodologies' (Arturo, 2015), transparent Environment Management Accounting (EMA), and effective Environmental Management control systems (EMCS) (Gibassier & Alcouffe, 2018). Energy conservation and efficiency improvement in Telecommunication systems can be carried out at three levels, namely-facility level (relates to deployed equipment), equipment level (relates to the evaluation of the performance of individual equipment), and network-level metrics (assess the performance of equipment) (Huan et al. 2020; Chen T. et al., 2010).

Energy conservation is broadly classified into three categories: technological measures, social or behavioral measures, and policy-related measures(Azad, 2017). As technological measures increase energy efficiency and in turn, reduce CO2 emissions by measuring and controlling energy use in real-time (Erol-Kantarci & Mouftah, 2011), an extensive literature review in the technological measure category in the telecom sector was done, and 32 significant initiatives were identified (Table 1) that play a crucial role in reducing CO2 emissions through energy efficiency. The initiatives thus identified were used for energy efficiency, and their impact on overall CO2 emission reduction was analyzed using MFCA in a case study.

Table 1. Initiatives to reduce energy consumption and CO2 emission

#	Initiatives	Reference
V1	Green Information System strategy	Imasiku et al 2019; Crago and Chernyakhovskiy, 2017;
V2	Real Time information sharing during construction period	Tao et al., 2018; Hong et al.,2015;
V3	Use of alternate energy	Nugent and Sovacool, 2014;
V4	Removing air conditioning equipment from BTS site	Balshe, 2011; Sirohi, 2013;
V5	Green Procurement initiatives	Marron, 1997; Thomson and Jackson, 2007;
V6	Reduction of movement and transportation of equipment	Teoh et al., 2018; Brandenburg and Rebs, 2015;
V7	Investing in phasing out obsolete equipments	Huisingh et al., 2015; Prasad,2012;
V8	Fuel theft monitoring	Savadi and Arvind, 2012; TRAI,2011;
V9	Optimum AC temperature	Tarutani et al., 2016; Annaur et al., 2018;
V10	Waste Management at construction site	Kucukvar et al., 2016; Lu et al, 2018;
V11	Deployment of Hybrid Generator	Afgan and Carvalho, 2008; Celik, 2003; Hemmes et al., 2007;
V12	IT enabled smart logistics	Prasad, 2012; TRAI, 2011;
V13	Use of sustainable material in construction	Mao et al., 2013; Cole,1998;
V14	Traffic Offloading from cellular networks to WiFi	Laoutaris et al.,2009; Hossain et al.,2017
V15	Buildup of outdoor sites on RRH	Rueterbusch,2009; Jahid et al.,2018;
V16	Preventive and corrective maintenance	Ba, 2016; Hajej et al. 2019;
V17	Tie up with a renewable energy service company	Dornan, 201; Pätäri and Sinkkonen, 2014;
V18	FIWI-OFC based transmission network	Liu et al., 2012; kantarci and Muftah, 2011;
V19	Solar powered sites	TRAI,2011; Nugent and Benjamin, 2014;
V20	Use of DC Fan Coil Units	Prasad, 2012; TRAI, 2011;
V21	Use of outdoor DAS (Distributed Antenna Systems)	Deronne et al., 2015; Josse et al., 2012;
V22	Adopting Free cooling system	Sisk and Allen, 2014; Nadjahi et al.,2018; Sun et al., 2015;
V23	Extensive use of sleep mode in equipment	Lannoo et al.,2015; Ileyas et al., 2016;
V24	improved Fuel logistics	Wang et al., 2011; So and Sun,2010;
V25	Diesel Generator Set parameter monitoring	Dua Vikas, 2012; Masood et al., 2019;
V26	Use of smaller size generator	Jakhrani et al., 2012; Ali et al., 2018;
V27	Peak Load reduction of GRID transformer	Anusooya et al., 2019; Almeida et al.,2001;
V28	Use of Inverter Diesel Generator set	Prasad, 2012; TRAI, 2011;
V29	Adopting eco design for tower and shelter construction	Pelser et al.,,2018; Kuo et al., 2018;
V30	Use of ICT tool to detect critical events.	Lu, 2018; Andrae and Edler, 2015;
V31	GIS enabled cable fault location tracing	Chaoju and Rui,2009; Zhong-xuan et al. 2010;
V32	IP based remote monitoring	Rotharmel,2007; Hasan et al., 2009;

{Insert table 1 here}

2.1 Material Flow Cost Accounting

Among the EMA tools, Material Flow Cost Accounting (MFCA) and Life Cycle Analysis (LCA) implementation have been placed close to the assessment of economic and ecological benefits and decision-making about sustainable development (Wang et al., 2017; Zeng et al., 2019). MFCA and LCA also follow generally accepted accounting principles (GAAP). MFCA and LCA both have distinguished advantages; however, MFCA's role as a catalyst for operational efficiency (Schaltegger et al., 2012), targeted approach for waste reduction (Viere et al., 2011), the capability to coexist with any legacy accounting system (Strobel & Redmann, 2002) makes MFCA a suitable tool for carbon emission reduction by improving energy efficiency. The increased role of environment and accounting practices has improved the adaptation and testing of the MFCA tool in developed as well as developing countries (Parker, 2011Ali et al., 2017; Tilt, 2018). However, MFCA application in the service sector is yet to pick up; out of 28 MFCA case studies from nine Asian developing countries between 2009 and 2018, only one is from the service sector (Tran & Herzig, 2020). The contribution of service industries to the economy of developing countries is immense. Therefore, there is a need to maximize additional tools like MFCA in the service sector to control CO2 emissions (Dilvin et. al. 2022; Parker, 2011). The situation in India is similar. More than 100 companies have adopted environmental management accounting (Ramesh & Madegowda, 2019); however, MFCA has been primarily implemented in the SME sector (Shah et al., 2018; Tailor et al., 2017). Large enterprises in the service sector are yet to adopt MFCA.

In Telecom, there is hardly any implementation of MFCA in the sector for carbon emission reduction. MFCA's contribution in Telecom is still limited to green manufacturing, where ICT-related industries have used it to improve resource efficiency (Huang et al., 2019; Schmidt & Nakajima, 2013; Trappey et al., 2013). This gap provides an excellent opportunity to develop a framework for Telecom service providers who wish to reduce CO2 emissions using MFCA. Material flow cost accounting (MFCA) was first developed in the late 1990s in Augsburg, Germany, under the name 'flow cost accounting' (METI, 2011), and it is among the most fundamental and well-developed systems (Jasch, 2006; Lang et al.,2005). Germany and Japan were the prime instigators of global MFCA development (Jasch & Savage, 2008; Jindrichovska & Purcarea, 2011; Kokubu & Tachikawa, 2013). MFCA also quantifies and assesses the potential savings costs of waste and stimulates companies to invest more in avoiding such losses (Schmidt, 2015). Furthermore, re-evaluation of MFCA may provide a catalyst for developing new and more efficient technologies (Hyrslova et al., 2011; Schaltegger et al., 2012). In addition, MFCA supports the implementation of other future-oriented EMA tools, such as various environmental budgeting formats (Bennett et al., 2013; Burritt & Schaltegger, 2001; Heupe & Wendisch, 2003). Finally, the MFCA also provides a declarative understanding of improved eco-efficiency opportunities (METI, 2011; Scavone, 2006) and helps policy decision-making (Papaspyropoulos et al., 2012).

Material Flow Cost Accounting (MFCA) has emerged as a strategic tool for e-waste management (Islam & Huda, 2019). It helps reduce energy and material waste and constructs a low-carbon supply chain to promote resource efficiency (Nakajima et al., 2015). MFCA is an ISO 14051 standard applicable to any organization that uses material and energy, regardless of its products, services, structure, and existing accounting systems (Christ & Burritt, 2015; ISO, 2011; Papaspyropoulos et al., 2012). MFCA quantifies, allocates, and then evaluates each material (and material losses) in monetary terms in stock, inflow, and output (Doorasamy, 2015; ISO, 2011).

2.2 Application and Implementation of MFCA, based on ISO 14051

As per ISO 14051 clauses, MFCA implementation requires the following stages.

Engagement and Roles and Responsibilities of Management (Clause 6.2, 6.3)
Scope and Scale of the Process of Material Flow Model (Clauses 6.4, 6.5)
Allocation of Costs (Clauses 5.3, 6.8)
Analysis, Interpretation &Reporting of MFCA Results (Clauses 6.9, 6.10)
Improvement Practices and Loss Reduction through MFCA final Results (Clause 6.11)

This study is split into two parts. In the first part of the study, an extensive literature review is done to identify initiatives in the telecom sector for operational electricity consumption reduction. Then, a survey among telecom experts was carried out to rate the usefulness of the identified initiatives, and factor analysis was carried out to identify dimensions to which they belong.

In the second part, the entire telecom infrastructure is expanded to measure electricity consumption, and carbon emission is calculated for each heat center. The expanded telecom network with detailed information about energy consumption is crucial as MFCA implementation requires an understanding of material and energy flows across the organization (Kokubu & Tachikawa, 2013; Scavone, 2006; Strobel & Redmann, 2002). The flow representation is referred to as the 'flow model'. Further initiatives which help in electricity consumption reduction are mapped with the heat center of the flow model and year-wise Carbon emission, and its reduction is measured and analyzed using MFCA. Once the material flow model is ready, technological initiatives are implemented, and overall efficiency improvement is measured. The research flow is represented in Fig. 1.

An exploratory cum empirical research-based case study on India's national telecom carrier BSNL is carried out to analyze MFCA implementation's benefits in terms of energy efficiency, carbon emission reduction, and waste reduction. BSNL has been offering Fixed Line, Broadband, and Leased Line services to consumers on copper cable networks; 'Fibre To The Home' (FTTH) service is provided on the optical fiber network. In addition, services such as 2G, 3G, WIFI, WIMAX, and WLL are also provided wirelessly to consumers.

Each city has many mobile towers and telephone exchanges where access network equipment is installed and directly connects to consumers. Approximately 4000 Telephone-Exchanges and 80000 Mobile Towers are first aggregated on main exchanges of individual districts and then connected to core network nodes in significant cities. This immense network is woven together using 750 thousand kilometers of optical fiber network known as network internet backbone (NIB). The vast network infrastructure of BSNL is built with essential telecom equipment and powered by supporting power equipment.

The study belongs to the district 'Darbhanga' of the eastern state of India. As the scope of the study is limited to carbon emission reduction through energy efficiency, the system costs, which comprise the cost of transport, labour, depreciation, and Maintenance, are not included in the study. Details of the telecom equipment installed in the district are listed in Table 2. (Appendix 'C' may be referred to for technical terms and abbreviations used).

Table 2

Details of Equipment
SNO	Name of the Equipment	Services Offered	Quantity
1	2G	GSM Based 2G Network	75
2	3G	GSM Based 3G Network	76
3	COMBO	GSM Based 4G Network	70
4	EXCHANGE	Landline Network	55
5	DSLAM	BroadBand Network	40
6	RADIO MODEM	Leased Circuit Network	8
7	LEASE CIRCUIT MODEM	Leased Circuit Network	75
8	VMUX	Leased Circuit Network	13
9	PMUX	Leased Circuit Network	20
10	DXC	Transmission Network	1
11	EDGE ROUTER	Transmission Network	1
12	AGGREGATION SWITCH	Transmission Network	6
13	OCLAN	Transmission Network	3
14	CPAN (B2)	Transmission Network	7
15	CPAN (A1/B1)	Transmission Network	43
16	MADM	Transmission Network	27
17	CPE	Transmission Network	54
18	MINILINK	Transmission Network	104
19	WIFI	Alternate Data Network	48
20	OLT GOVERNMENT	Alternate Voice Network	15
21	OLT PRIVATE	Alternate Voice Network	1
22	WIMAX	Alternate Data Network	19
23	WLL	Alternate Voice Network	15

{Insert Table 2 here}

Telecom networks rely on a combination of electric supply from grid and diesel generator, which is converted to DC (current) by a power plant (also known as Switch Mode Power Supply-SMPS). Telecom equipment receives DC (-48V) from the power plant. The telecom network also requires battery banks and air conditioners. Battery banks are used as power backup to provide redundancy against equipment crashing due to power failure(s), which is very common in rural India. Air conditioners reduce the heat generated from the continuous operation of equipment. Details of the overall cumulative power capacities of the BSNL in the district of Darbhanga are listed in Table 3.

Table 3

Details of Power Capacity
Equipment Name	Function	Capacity
Battery	Power Backup	89300 AH
Power Plant	AC to DC Convertor	18875 Amp
Engine	Power Generator	2282 KVA
AC Load	Cooling	191.5 Tn

{Insert Table 3 here}

4.1 Engagement and identification of initiatives by MFCA project Group

Telecom is a unique service sector where electric energy is the raw material, intermediate products, and output. Because each process consumes electricity, it is of utmost importance to identify processes that consume or waste maximum energy to minimize the wastage of electricity, thereby devising a standard corrective action. A multi-disciplinary MFCA project implementation group was formed by management. A cross-functional team comprising Deputy General Manager, Executive Engineer (Electrical), Divisional Engineer (Planning), and Account Officer was formed to identify the most critical initiatives related to the telecommunication sector which positively impact energy efficiency and carbon emission reduction. The extensive literature review identified 32 initiatives in the telecom sector which help in energy efficiency (Appendix A). This list was validated further by the MFCA project group, who agreed on its importance related to the installation, up-gradation, or replacement of telecom equipment (Table 1). The development of the questionnaire followed this; the first section had demographic details, while the second section had 32 questions, which were based on the 32 initiatives approved by the MFCA project team. Respondents were asked to rate the importance of these energy efficiency initiatives on 7- points Likert scale. Questionnaires were distributed among 400 middle-and senior-level experts and technical executives of the telecom industry, of which 250 respondents participated. Written consent from participants was gained, and it was ensured that the study was approved by the IRB (Internal Review Board) of BSNL. Factor analysis was used to identify the most critical initiatives that played a significant role in reducing carbon emissions. The value of the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.872. Appendix -B contains the output of factor analysis. The total variance explained by the 32 initiatives was 85.958%. The first three factors explained the maximum variance: Factor − 1 explained 25.881% of the variance and had ten initiatives, Factor-2 explained 21.588% of the variance, had eight initiatives, and factor-3 explained 18.344% of the variance and had six initiatives. Finally, 24 initiatives were found to be the most important in the rotated component matrix. The total variance explained by six factors (32 initiatives) was 85.958%, and the variance explained by the first three factors was 65.813%. Out of the most critical 24 initiatives, six initiatives (V5, V7, V15, V18, V19, V29) were related to equipment purchase; hence, they were grouped as procurement policy initiatives. The remaining 18 initiatives (V3, V4, V8, V9, V12, V14, V16, V17, V21, V22, V23, V25, V26, V27, V28, V30, V31, V32) related to operation and Maintenance, named operation and maintenance initiatives.

4.2 Identification of Material Flow

For MFCA implementation, identifying energy flow and quantity centre is a must. Then, in between the flow, Quantity Centres are identified, and the cost of materials, energy, and system are assigned to them, after classifying them into 'positive' products and 'negative' products (Kasemset et al., 2015). The material flow model of the BSNL is drawn based on the flow of electricity, as shown in Fig. 2. In the figure, 'rectangles' represent Quantity Centers (QC), while small circles represent input, wastage, and output associated with quantity centers. A QC may consist of one or more processes and quantify electricity consumption and carbon emissions in physical and monetary units. Grid and Diesel Generated Power are joined at the AC Distribution BOX (ACDB). Part of the electricity from the ACDB is consumed by the cooling system and other auxiliary requirements. The rest of the power moves to the power plant. Electricity from the power plant is distributed between the battery and DC Distribution BOX (DCDB). DCDB distributes power supplies to the following network equipment: Transmission Network, Alternate Access Network, Fixed Access Network, 2G Access Network, 3GAccess Network, and Combo/4G Access Network. (Appendix 'D' may be referred to for process details; Quantity Center details; input flow of electricity, electricity wastage, and output generated from each QC).

Overall twenty-four Quantity Centre, were identified across the telecom operation of the BSNL, and energy usage at each quantity centre was measured. At each quantity centre, electricity consumption, carbon emissions due to electricity consumption, and expenditure on power consumption were calculated. If we compare it with legacy accounting system, this detail is very critical. In existing accounting system only two cost were indefinable- a) overall cost of electricity consumption and b) overall cost of diesel consumption. After identification of energy flow and assigning electricity consumption pattern to 24 quantity centers, telecom network reveals energy consumption and wastage pattern more explicitly.

4.3 Allocation of Cost-Analysis of CO2 Emission

Once the process identification of the telecom setup is complete, the power consumption for each Quantity Centre was measured using ‘FLUKE-325, 400A AC/DC True RMS Clamp Meter', and yearly electricity consumption was calculated. The yearly time duration was chosen so that effect of cost reduction achieved due to MFCA can be reflected in the financial statements of the company. Yearly carbon emission 'Cc' (in Tons) can be calculated using Eq. 1.

The yearly calculation was based on IPCC 2006 and the Greenhouse Gas Emission Accounting Methods. As per guidelines received from the Telecom Regulatory Authority of India, GRID consumption emits 0.84 Kgs CO₂/kWh, and diesel generator sets emit 1.27 Kgs CO₂/kWh (TRAI, 2011). It is also assumed that the daily availability of electricity from the grid is P_GRID (in kWh) for H_GRID'hours, and off-grid electricity consumption from diesel generators is 'P_GEN' (in KVA) for 'H_GEN' hours. Eq. 1 also helps in determining the cost of electricity consumption as the average cost of grid power ( $ .101/ kWh) and diesel ( $ .2038/ kWh) is known (BERC, 2020). The calculation provides the necessary information to help management identify telecom equipment, processes, and Quantity Centre, which consume high energy.

After primary MFCA analysis, material flows, stocks, costs, and initiatives are re-evaluated continually, facilitating the comparison of planned activities to actual results. Re-evaluation is necessary to improve eco-efficiency and reduce costs and adverse environmental impacts (Lang-Koetz et al., 2006). It was decided to measure the impact of a group of initiatives separately. The Impact of 'Procurement Policy initiatives' and 'Operation & Maintenance Initiatives' were measured in 2019-20 and 2020-21. The impact of the initiatives on electricity consumption reduction is discussed in detail in the 'Improvement Practices and Loss Reduction' section. The MFCA implementation was initiated in 2019, and yearly power consumption, CO2 emissions, and expenditure on electricity for each QC are described in Table 4. The telecom network sources its power requirement from the grid (QC-RAW_GRID) and diesel generator (QC-RAW_GEN). Out of the overall consumption of 6503687.61 kWh (QC-ACDB),2227720.72kWh goes as waste due to inefficiency. As a result, the network generated 6861390.43 Ton of CO2 and incurred $ 989557.1 in power expenditure during FY 2019–20.

Table 4

Power Consumption and CO2 emission before MFCA Implementation
Quantity Centre	Yearly power consumption (Kwh)			CO₂ emission (Tons)			Power Exp (Dollars)
Quantity Centre	INPUT	WASTE	OUTPUT	INPUT	WASTE	OUTPUT	Power Exp (Dollars)
F_2G	88768.00	44384.00	44384.00	93650.24	46825.12	46825.12	13506.34
R_2G	245221.60	156453.60	88768.00	258708.79	165058.55	93650.24	37311.26
TRX_2G	1438041.60	1192820.00	245221.60	1517133.89	1258425.10	258708.79	218802.69
F_3G	113425.78	56712.89	56712.89	119664.20	59832.10	59832.10	17258.10
R_3G	374305.07	260879.29	113425.78	394891.85	275227.65	119664.20	56951.73
TRX_3G	1224998.40	850693.33	374305.07	1292373.31	897481.47	394891.85	186387.48
F_COMBO	0.00	0.00	0.00	0.00	0.00	0.00	0.00
R_COMBO	0.00	0.00	0.00	0.00	0.00	0.00	0.00
TRX_COMBO	0.00	0.00	0.00	0.00	0.00	0.00	0.00
TXP	260252.30	260245.00	7.30	274566.18	274558.48	7.70	39598.23
AGGREGATOR	328930.70	68678.40	260252.30	347021.89	72455.71	274566.18	50047.87
CORE	328938.00	7.30	328930.70	347029.59	7.70	347021.89	50048.98
ALT2	281378.50	281371.20	7.30	296854.32	296846.62	7.70	42812.65
ALT1	323426.50	42048.00	281378.50	341214.96	44360.64	296854.32	49210.39
LC	78146.50	78139.20	7.30	82444.56	82436.86	7.70	11890.24
BB	220408.90	142262.40	78146.50	232531.39	150086.83	82444.56	33535.93
LL	532965.70	312556.80	220408.90	562278.81	329747.42	232531.39	81092.46
COOLING_W	1158729.00	1158729.00	0.00	1222459.10	1222459.10	0.00	176304.37
DCDB	3848370.20	0.00	3848370.20	4060030.56	0.00	4060030.56	585542.00
PP	4275966.89	427596.69	3848370.20	4511145.07	451114.51	4060030.56	650602.22
ACDB	6503687.61	2227720.72	4275966.89	6861390.43	2350245.36	4511145.07	989557.14
RAW_GRID	3251843.81	0.00	3251843.81	2731548.80	0.00	2731548.80	326907.27
RAW_GEN	3251843.81	0.00	1821032.53	4129841.63	0.00	2312711.31	662649.87
GEN	4064804.76	812960.95	3251843.81	5162302.04	1032460.41	4129841.63	828312.34

{Insert Table 4 here}

5.1 Improvement by Procurement Policy Initiatives

The power consumption requirement of the 2G network (QC-TRX_2G) was the highest at 1438042 kWh. Power consumption requirement of the 3G network (QC-TRX_3G), the power needed for cooling telecom equipment (QC-COOLING_W), power consumption to run Landline network (QC-LL) are other higher power consuming quantity centers. A total of 70 4G BTS and transmission equipment were purchased to reduce excess energy consumption and related high cooling requirements of the 2G and 3G networks. Details of the equipment identified for procurement under green initiatives are listed in Table 5.

Table 5

Details of Green Procurement
SNO	Green Purchase	Qty	Unit Price ($)	Total Price ($)
1	COMBO	70	1797.47	125822.62
2	EXCHANGE	16	3297	52752.04
3	RADIO MODEM	8	408.72	3269.75
4	CPAN (B2)	7	4670.49	32693.43
5	CPAN (A1/B1)	43	390.78	16803.39
6	WIFI	48	943.23	45274.99
7	OLT GOVERNMENT	15	Nil	Nil
8	OLT PRIVATE	1	Nil	Nil
			0	276616.23

{Insert Table 5 here}

4G COMBOBTS offers 2G and 4G services from a single cabinet and consumes significantly less energy. Replacement of 2G and 3G BTS with 4G COMBO BTS reduced carbon emissions by 92.11% and 53.95% in 2G and 3G mobile networks, respectively. Electricity consumption was reduced from 1438042kWh to 113529.6 kWh (QC-TRX_2G) in the 2G network and from 1224998kWh to 564144kWh (QC-TRX_3G) in the 3G network.

BSNL laid a length of 700 kilometers of optical fiber cable (OFC), replaced MADM/ CPE/Minilinks with 43 CPAN and 100mbps NEC Mini links. These changes fulfilled the bandwidth requirement of the 4G network and helped the transmission network (QC-CORE) reduce electricity consumption from 328938kWh to 282670.6 kWh. As a result, approximately 14% CO2 emission reductions and savings of $7038.62 were achieved in the transmission network. The BSNL signed agreements with the local service providers to offer fibre to the home (FTTH) and WIFI services under a revenue-sharing scheme. Local vendors purchased equipment (OLT and WIFI hotspot), and BSNL provided fibre connectivity and space to install this equipment within its network. Revenue from service(s) was shared equally. Such arrangements avoided capital investment and reduced significant electricity consumption, as FTTH and WIFI technologies consume significantly less power. Improved alternate access networks helped increase subscriber counts by four times. The power consumption in the alternate network (QC-ALT1) was reduced from 323426.5 kWh to 58874.50 kWh. A total of 81.8% CO2 emissions reduction resulted in savings of $ 40252.44.

Under the government of India's rural mission, 17 exchanges were electrified, which were running earlier on the diesel generator. The electrification of telephone exchanges reduced diesel consumption from 4064805 KVA to 2470813 KVA (QCGEN). As a result, a total of 39.21% carbon emission reduction and savings of $ 324818.3 against diesel purchase was achieved. The Policy procurement Initiatives helped the BSNL network to reduce the overall electricity consumption (QC-ACDB) from 6503688 kWh to 3953301 kWh. A significant $388049.61 annual saving potential with a one-time investment of $1029360 reduced 2690658Ton of CO2 emissions. The result of 'Procurement Policy Initiatives’ is listed in Table 6.

Table 6

Result of Procurement Policy Initiatives
Quantity Centre	Yearly power consumption (Kw)			CO₂ emission (Tons)			Power Exp (Dollars)
Quantity Centre	INPUT	WASTE	OUTPUT	INPUT	WASTE	OUTPUT	Power Exp (Dollars)
F_2G	7008.00	3504.00	3504.00	7393.44	3696.72	3696.72	1066.29
R_2G	19359.60	12351.60	7008.00	20424.38	13030.94	7393.44	2945.63
TRX_2G	113529.60	94170.00	19359.60	119773.73	99349.35	20424.38	17273.90
F_3G	52235.56	26117.78	26117.78	55108.51	27554.26	27554.26	7947.81
R_3G	172377.33	120141.78	52235.56	181858.09	126749.58	55108.51	26227.77
TRX_3G	564144.00	391766.67	172377.33	595171.92	413313.83	181858.09	85836.34
F_COMBO	51100.00	10220.00	40880.00	53910.50	10782.10	43128.40	7775.03
R_COMBO	204400.00	153300.00	51100.00	215642.00	161731.50	53910.50	31100.12
TRX_COMBO	613200.00	408800.00	204400.00	646926.00	431284.00	215642.00	93300.37
TXP	199976.20	199968.90	7.30	210974.89	210967.19	7.70	30427.03
AGGREGATOR	282670.60	82694.40	199976.20	298217.48	87242.59	210974.89	43009.25
CORE	282677.90	7.30	282670.60	298225.18	7.70	298217.48	43010.36
ALT2	16826.50	16819.20	7.30	17751.96	17744.26	7.70	2560.21
ALT1	58874.50	42048.00	16826.50	62112.60	44360.64	17751.96	8957.95
LC	85680.10	85672.80	7.30	90392.51	90384.80	7.70	13036.50
BB	164870.50	79190.40	85680.10	173938.38	83545.87	90392.51	25085.58
LL	379665.70	214795.20	164870.50	400547.31	226608.94	173938.38	57767.37
COOLING_W	1158729.00	1158729.00	0.00	1222459.10	1222459.10	0.00	176304.37
DCDB	2012091.70	0.00	2012091.70	2122756.74	0.00	2122756.74	306146.27
PP	2235657.44	223565.74	2012091.70	2358618.60	235861.86	2122756.74	340162.52
ACDB	3953300.81	1717643.36	2235657.44	4170732.35	1812113.75	2358618.60	601507.53
RAW_GRID	1976650.40	0.00	1976650.40	1660386.34	0.00	1660386.34	198712.31
RAW_GEN	1976650.40	0.00	1106924.23	2510346.01	0.00	1405793.77	402795.22
GEN	2470813.00	494162.60	1976650.40	3137932.51	627586.50	2510346.01	503494.02

{Insert Table 6 here}

5.2 Improvement by Operation and Maintenance Initiatives

The impact of operation and maintenance initiatives on energy waste and carbon emission reduction was measured for the second stage of MFCA implementation in FY 2018–19. All the 4G COMBO Base Transceiver Station (BTS) have a Radio Remote Head mounted on top of mobile towers, allowing BTS to function at a higher temperature. Air conditioners were removed from all the mobile tower locations, and the environmental temperature was set to 28°C for the collocated sites. Building up a 4G Combo BTS network, switching off air conditioners from tower locations, using free cooling at six exchanges, and setting the environmental temperature at 28°C reduced cooling requirements. As a result, the overall electricity consumption for cooling requirements (QC-COOLING_W) decreased from 1158729kWh to 645612 kWh. A 51.1% CO2 emission reduction and cost savings of $ 98069.63 were achieved.

ECO modes on 2G, 3G, and 4G networks were enabled, and traffic offloading from the mobile network to the Wi-Fi network was enabled. The peak traffic of the mobile network was analyzed, and BTS with low traffic during peak hours was optimised with a reduced number of channels and racks. Regular cleaning and Maintenance created a dust-free environment, reducing faults in the equipment, ultimately reducing cooling requirements; improved operations and Maintenance reduced 43.11% CO2 emission in the 2G network (QC-TRX_2G), 29.4% CO2 emission in the 3G network (QC-TRX_3G), and 26.27% CO2 emissions in the 4G combo network (QC-TRX_COMBO). As a result, the entire mobile network (sum of quantity centres TRX_2G, TRX_3G, TRX_COMBO) could save $49500.

The transmission team avoided using mini-links as primary transmission equipment and shifted the traffic load on the newly laid 700 km of optical fibre. As a result, load shifting to the OFC network helped the transmission network (QC-CORE) reduce electricity consumption from 282677.9 kWh to 266055.8kWh. In addition, the optimization of the transmission route helped BSNL reduce 51857.51-ton CO2 emissions. EWSD and CDOT Exchanges were installed 20 years ago and did not have the facility for internet protocol (IP)-based monitoring. Fifty exchanges were replaced with NGN exchanges, which use less space and have reduced electricity requirements. BSNL also shifted the leased-line circuit from the copper network to the OFC and radio modem for remote monitoring. Remote monitoring of network equipment raises immediate alarms to the concerned cluster in charge and reduces unnecessary movement of field staff. A considerable increase in the overall network uptime (7%) without increasing power consumption corroborates the relationship between carbon emission reduction and remote monitoring. Furthermore, optimization of the Landline network (QC-LL) reduced electricity consumption from 379665.7 kWh to 225489.7kWh. As a result, the landline network reduced 47.87% CO2 emission and saved $30442.

The energy efficiency reduced the diesel generator requirement from 4064805KVA to 714479.4 KVA. Because various energy reduction initiatives decreased the peak demand, BSNL replaced 20 KVA or higher capacity DG sets with smaller 15 VK DG sets at 37 locations. Remote monitoring of DG set through alarm extension, and ECO setting in inverter DG has shown a 17% reduction in fuel consumption. Measures such as size reduction of the diesel generator, fuel theft monitoring, and ECO mode setting reduced the diesel generator requirement (QC-RAW_GEN) from 3251844 kWh to 116475.4 kWh. Optimum utilization of DG capacity reduced 82.42% CO2 emission from DG operation and saved $546174.

From an initial load of 3251844 kWh, the grid load was reduced to 2286334 kWh. Demand reduction necessitates a reduction in contract demand from grid supply. Except for three significant locations, contract demand in the remaining exchanges was reduced to 12 kW, and only mobile tower sites were shifted to 5 kW. The reduction in contract demand reduced 811028.2 Ton CO2 emissions and saved $97062. The 'Operation & Maintenance Initiatives' result is listed in Table 7.

Table 7

Result of ‘Operation and Maintenance Initiatives
Quantity Centre	Yearly power consumption (Kw)			CO₂ emission (Tons)			Power Exp (Dollars)
Quantity Centre	INPUT	WASTE	OUTPUT	INPUT	WASTE	OUTPUT	Power Exp (Dollars)
F_2G	4542.22	2271.11	2271.11	4206.10	2103.05	2103.05	550.42
R_2G	12547.89	8005.67	4542.22	11619.35	7413.25	4206.10	1520.54
TRX_2G	73584.00	61036.11	12547.89	68138.78	56519.44	11619.35	8916.85
F_3G	42015.56	21007.78	21007.78	38906.40	19453.20	19453.20	5091.41
R_3G	138651.33	96635.78	42015.56	128391.13	89484.73	38906.40	16801.66
TRX_3G	453768.00	315116.67	138651.33	420189.17	291798.03	128391.13	54987.24
F_COMBO	42924.00	8584.80	34339.20	39747.62	7949.52	31798.10	5201.50
R_COMBO	171696.00	128772.00	42924.00	158990.50	119242.87	39747.62	20805.98
TRX_COMBO	515088.00	343392.00	171696.00	476971.49	317980.99	158990.50	62417.95
TXP	183354.10	183346.80	7.30	169785.90	169779.14	6.76	22218.70
AGGREGATOR	266048.50	82694.40	183354.10	246360.91	76575.01	169785.90	32239.54
CORE	266055.80	7.30	266048.50	246367.67	6.76	246360.91	32240.43
ALT2	16826.50	16819.20	7.30	15581.34	15574.58	6.76	2039.02
ALT1	58874.50	42048.00	16826.50	54517.79	38936.45	15581.34	7134.36
LC	85680.10	85672.80	7.30	79339.77	79333.01	6.76	10382.64
BB	164870.50	79190.40	85680.10	152670.08	73330.31	79339.77	19978.87
LL	225489.70	60619.20	164870.50	208803.46	56133.38	152670.08	27324.66
COOLING_W	645612.00	645612.00	0.00	597836.71	597836.71	0.00	78234.74
DCDB	1592860.00	0.00	1592860.00	1474988.36	0.00	1474988.36	193021.48
PP	1769844.44	176984.44	1592860.00	1638875.96	163887.60	1474988.36	214468.31
ACDB	2857917.56	1088073.11	1769844.44	2646431.66	1007555.70	1638875.96	346320.13
RAW_GRID	2286334.04	0.00	2286334.04	1920520.60	0.00	1920520.60	229844.75
RAW_GEN	571583.51	0.00	320086.77	725911.06	0.00	406510.19	116475.38
GEN	714479.39	142895.88	571583.51	907388.82	181477.76	725911.06	145594.22

{Insert Table 7 here}

This paper provides a better understanding of initiatives in the telecom sector to adopt MFCA for CO2 emission reduction through energy efficiency. However, material quantification of intangible and abstract products in the service sector makes identifying waste a hurdle for MFCA adoption. The present work fills this gap, uses electricity consumption as raw material, and quantifies energy waste as a negative cost. This research started in agreement with previous studies advocating for the application of MFCA in different sectors (Tran & Herzig, 2020). As the present literature does not indicate how MFCA can draw value in a service sector like Telecom and get acceptance among service providers, this study builds on this gap. The present work starts with an extensive literature review to identify the critical initiatives in telecom sectors worldwide in energy conservation. The reviewed studies indicated a high relevance for twenty-four initiatives identified for improving energy efficiency, which was further validated by the MFCA team from the case organization. This was followed by a questionnaire survey among Indian telecom managers to rate the initiatives. Finally, the outcome of the factor analysis was used as input for the successful implementation of MFCA in one of the large telecom industries in India. As MFCA has been widely used in production, it requires modification for its acceptance in the telecom sector. Identification of cost centers and energy reduction initiatives for the telecom sector are industry-specific activities. Despite their prevalence in the telecom sector, it has not been compiled. Assigning and matching initiatives with cost centers is the core of the study, and it is expected that the industry will get a direct benefit by avoiding reinventing the wheel. As overall electricity consumption is distributed among 24 cost center, the study is expanding hidden energy consumption, and it should immensely help the telecom sector to achieve energy efficiency. Due to the addition of specific processes to exploit the benefit of MFCA application in the telecom sector, we have named the process 'Extended MFCA for Telecom'. The implementation steps of MFCA are summarized in Fig. 3.

The diagram has used two colors scheme to demonstrate process of MFCA implementation. The overall process of CO2 emission reduction in telecom has been defined as ‘Extended MFCA for telecom sector’. Process written in ‘BLUE COLOR’ are well established MFCA steps and has been defend in ISO 14051.Additional steps suggested in the study has been written in ‘GREEN COLOR’

6.1 Implications

The government of India has made carbon emission disclosure only voluntary; thereby telecom sector in India has emphasized more on yearly carbon disclosure. However, best practices and benchmarking adopted by individual telecom companies have neither become part of academic discussion nor developed as an industry-specific strategy. This paper has tried to bridge this gap. In the present work, all carbon emission reduction initiatives related to the telecom sector are compiled through the identification of 24 quantity centres, which is the first major contribution of the paper. By using MFCA, telecom companies can monitor carbon emission at 24 checkpoints and analyse the impact of each quantity centre on overall carbon reduction for the desired time frame instead of yearly overall carbon disclosure.

Six of the twenty-four initiatives grouped as 'Procurement Policy initiatives' help save $ 388049 and reduce 2690658 tons (39.21%) of carbon emission annually. However, it requires high capital investment. The 'Operation and Maintenance Initiatives' help to save $255187 and reduce 2646432 ton (36.55%) of carbon emission annually. Operation and maintenance initiatives do not require much investment but still have the potential to reduce carbon emissions. Initiatives such as real-time information sharing, use of ICT tools to detect critical events, GIS-enabled cable fault location tracing, DG set parameter monitoring peak, reduction in contract load, use of smaller size generators, optimised use of air conditioning equipment, and fuel theft monitoring must be included as improvement initiatives.

An important insight drawn from the present case is that the MFCA in the telecom sector should be implemented in a phase-wise manner. Procurement policy initiatives, which involves investment, should be implemented first within the budgetary limits as Operation and maintenance initiative on equipment that are on the verge of obsolescence yield more minor improvement in terms of carbon emission reduction. Operation and Maintenance is a continual process, and it cannot be stopped even during the implementation phase of procurement policy initiatives. On the other hand, the improvement initiatives related to the 'Actives Infrastructure' yield better results as it reduces electricity requirement, reducing diesel consumption. This is the main reason why initiatives such as reducing the size of the generator, reducing the demand power of grid supply, removing the air conditioner, and setting up the optimal temperature as taken in the third phase.

In a case-based approach, unique insights can be drawn from the practicing managers working in sizeable energy-consuming service sectors. As MFCA implementation will be a high involvement exercise, due consideration should be given to

1. association with the business goal and environment management (ISO 14051),

2. the opportunity of connecting a product-based system to a service-based system,

3. the potential of the tool to provide a competitive advantage.

The present study exemplifies the implementation process and sets a platform to encourage other industries to adopt MFCA. Also, government and policymakers can set up directly in line with their sustainability goals to encourage other industries to adopt MFCA. The advent of ISO 14052 encourages this practice towards cleaner production and focusing on energy efficiency.

This work is one of the first to examine the issue of MFCA implementation in large service industries, especially the telecom sector. The study in phase one reveals the key initiatives taken by the telecom sector for controlling CO2 emissions. The main objective of this study was to apply the MFCA tool in a large Indian telecom company and examine the environmental and financial impact of implementation. The study demonstrates a successful case of MFCA implementation with some promising results. The study's outcome may inspire many large service organizations, especially in developing countries suffering from energy scarcity (Bashir 2019), to implement MFCA. This is in line with the findings of Christ and Burritt (2015), who stressed the need for MFCA applications in developing nations. In addition, the present case study may help generate awareness and promote the implementation of MFCA in the service sector and the manufacturing sector.

We present the contribution of our study in various directions. First, the study demonstrates the role of the MFCA tool in offering sustainable solutions to the extensive service organization that otherwise were struggling to meet CO2 emission standards as per regulations. Second, the study establishes how MFCA makes excellent sense in value and opportunity for the telecom sector, which otherwise found its application only in the manufacturing sectors. Thus, our study tries to answer one of the most critical questions from a sustainability point of view how the tools proven successful in one sector can be applied to other sectors to address sustainable solutions. This is particularly important for a sector like Telecom, the driving force behind the digital economy but, unfortunately, one of the most significant contributors to CO2 emission. Perhaps, one of the most significant barriers to the implementation of MFCA in the service sector was the material quantification of intangible and abstract products, which is electricity in the case of the telecom sector. This problem was addressed through the step-by-step mechanism of MFCA implementation, ultimately showing tangible results.

The case-based approach in the present work presents findings to demonstrate the opportunities of MFCA application in the service sector. This may help academicians and practitioners to explore the application of MFCA in different service sectors and enhance the effectiveness of decision-makers in the move toward sustainability.

On the limitation side, several initiatives, such as waste management, use of solar energy, sharing of activities, eco-design, tie-up with a renewable energy service company, and active/passive infrastructure sharing among telecom companies, have not been implemented. Earlier research has established their importance; however, their implementation and efficiency improvement analysis using MFCA involves the entire telecom supply chain and needs stakeholders, industry experts, and government agencies. Therefore, this may be taken as a future area of research.

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Improving energy efficiency and CO2 emissions reduction in the telecom sector: from exploration to development of framework for material flow costing

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Abstract

Figures

1. Introduction

2. Literature Review

2.1 Material Flow Cost Accounting

2.2 Application and Implementation of MFCA, based on ISO 14051

3. Research Method

4. Case Study

4.1 Engagement and identification of initiatives by MFCA project Group

4.2 Identification of Material Flow

4.3 Allocation of Cost-Analysis of CO2 Emission

5. Improvement Practices And Loss Reduction Through Mfca

5.1 Improvement by Procurement Policy Initiatives

5.2 Improvement by Operation and Maintenance Initiatives

6. Discussion

6.1 Implications

7. Conclusion And Policy Implications

References

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Supplementary Files

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