Sustainable assessment in energy planning and management in Indian perspective

Renewable and Sustainable Energy Reviews 47 (2015) 58–73

Contents lists available at ScienceDirect

Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Sustainable assessment in energy planning and management in Indian perspective Sunil Luthra a,n, Sachin Kumar Mangla b,1, Ravinder K. Kharb c,2 a

Department of Mechanical Engineering, Government Polytechnic, Jhajjar-124103, Haryana, India Department of Mechanical & Industrial Engineering, Indian Institute of Technology, Roorkee-247667, Uttarakhand, India c Department of Electronics Engineering, Government Polytechnic, Jhajjar-124103, Haryana, India b

art ic l e i nf o

a b s t r a c t

Article history: Received 13 September 2014 Received in revised form 26 December 2014 Accepted 1 March 2015

Currently, energy systems are not fully sustainable due to the heavy power demands provoked by high population and increasing level of the living standard and economic growth. Hence, it requires significant efforts to conserve the resources and ensure the sustainability in energy demand and in its management from a nation perspective. The Indian economy is growing at a good pace and there is an enormous demand for resources and energy in the country. In this regard, this paper aims to identify and prioritize the indicators to develop a decision-support framework for the sustainability assessment in energy planning and management in Indian context. To achieve it, this work proposes to use a twophase research methodology. In first phase, a comprehensive literature review and expert’s inputs are used to identify the important five key dimensions of indicators and twenty-five indicators for assessing the sustainability in energy planning and management. In second phase, fuzzy Analytical Hierarchy Process (AHP) is used for prioritizing of indicators to develop an integrated sustainability assessment framework for energy systems. ‘Environmental’ indicator dimension has been reported as the most important dimension for assessing the sustainability in energy planning and management. The proposed decision framework may offer some valuable guidelines for policy makers to develop their plan of action in terms of design of both short term and long term flexible decision strategies, to assess the sustainability in energy systems. Sensitivity analysis has also been made to investigate the robustness and priority ranking stability of the proposed sustainability assessment framework. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Indicators Sustainability assessment Energy planning and management Fuzzy Analytical Hierarchy Process (Fuzzy AHP) Sensitivity analysis India

Contents 1.

2. 3.

4.

Introduction and background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 1.1. Research gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 1.2. Research objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Current status of energy scenario in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.1. Current status of CO2 emissions in India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 3.2. Sustainability assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Identification of indicators for assessing the sustainability in energy planning and management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1. Economic indicators (Eco) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.1. Payback period (Eco1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.2. Economic effectiveness (Eco2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.3. Savings on greenhouse gas (GHG) emissions (Eco3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.4. Capital and energy levelized cost (Eco4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.5. Investment opportunities (Eco5). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

n

Corresponding author. Tel.: þ 91 9466594853. E-mail addresses: [email protected] (S. Luthra), [email protected] (S.K. Mangla), [email protected] (R.K. Kharb). 1 Tel.: þ91 9458354826. 2 Tel.: þ91 9416312461. http://dx.doi.org/10.1016/j.rser.2015.03.007 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

59

4.2.

Environmental indicators (env). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.2.1. Land use and management (Env1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.2. Carbon emissions/CO2 emissions (Env2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.3. Climate related problems and issues (Env3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.4. Waste reduction and management (Env4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2.5. Adoption of independently audited environmental management systems (Env5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3. Social indicators (Soc) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.1. Societal equity (Soc1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.2. Human health and safety (Soc2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.3. Social acceptability for energy planning & management projects (Soc3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.4. Energy security and safety (Soc4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.5. Cultural heritage protection (Soc5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.6. Risk analysis and management (Soc6) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.3.7. Raising employment and living standard (Soc7) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4. Operational indicators (Opr) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.1. System flexibility to respond demands (Opr1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.2. Optimal resource allocation and usage (Opr2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.3. Diversification in source of provisioning (Opr3). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.4. System’s resilience and reliability (Opr4) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.4.5. Reserve/buffer capacity (Opr5) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5. Technological indicators (Tec) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5.1. Capacity utilization and effectiveness (Tec1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5.2. Adoption and continuous up gradation of smart technologies (Tec2). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.5.3. Flexibility to incorporate latest technological advancements (Tec3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5. Methodology of research. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 5.1. Fuzzy AHP method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 6. An application example. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.1. Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 6.1.1. Step 1: selection of indicators in assessing the sustainability in energy planning and management . . . . . . . . . . . . . . . . . . . . . . 66 6.1.2. Step 2: prioritizing the finalized energy planning and management sustainability assessment indicators using fuzzy AHP. . . . 66 6.2. Develop the structural decision framework for the problem in question . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.3. Develop the fuzzy pair wise evaluation matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 6.4. Devise the indicator’s importance weights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 7. Sensitivity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 8. Discussion of findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 8.1. Implications of the research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 9. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 10. Research limitations and scope for future work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Appendix A. The details of essential calculations of Extent Analysis method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

1. Introduction and background The world’s population is expected to grow, and at the same time, the swiftly increasing standards of living in some developing countries, along with the escalating accumulation of greenhouse gases in the atmosphere, has made the goal of sustainability increasingly urgent [1,2]. With a population around 1.25 billion, India is second most populated country in the world after China. It is expected that India would become a developed country by 2020 [3]. An unprecedented growth in human population in India is putting a huge pressure on available natural resources. Thereby, the fear of scarcity of natural resources and the goal of becoming a developed country, is forcing governments and enterprises to initiate corporate and industrial environmental management measures [4]. Energy has been identified as an essential need in every sector for any country for its economic development. In addition to that, increased demands for energy and climate change mitigation have become major concerns in last few years [5,6]. Consequently, the world is giving much attention to sustainable energy future [7]. A similar situation has been noticed in India as well. Therefore, India must rapidly come across towards new and energy efficient technologies to develop a sustainable path of energy development [8]. More efficient energy usage and a new

energy planning and management system should be needed to achieve so as to shift the structure of conventional energy systems towards sustainability [9]. ‘Sustainability’ is a latest buzz word appearing in the recent literature [1,2,3,4,10,11,12]. Consequently, the sustainable development topic has brought a broad conversation across various sectors (like, development and design, purchasing, manufacturing, supply chain management, etc.) in our society [12,13]. The World Commission on Environment and Development (WCED) put environmental issues on the socio-political agenda and entered into universal currency the term of ‘sustainability’ [14]. In 1987, UN WCED defined sustainability as “economic practices which meet up the requirements of the present without compromising the ability of future generations to meet their own requirements” [15]. In the literature, sustainability has been recognized as a multifacet concept and the triple bottom line approach has been generally adapted to highlight that developments need to not only economically sound and sustainable, but also socially and environmentally sustainable [16,17]. In India, approximately 69% of total energy generations have been made using fossil fuels, like coal, which have the highest CO2 emissions per kWh and also emits other pollutants at very high levels. However, still, coal and other fossil fuels are dominating in the energy production due to its easy availability and low cost,

60

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

while at the same time challenging the philosophy of sustainability [18]. If major efforts are not made to lower the amount of CO2 emissions produced, then the number of coal fueled energy stations will definitely increase, and hence, developing countries alone will produce more CO2 emissions than the entire OECD energy sector for the year 2030 [19,20]. Current energy generation approaches have been found as not fully sustainable with respect to environmental, economic, social, operational and technological considerations. Therefore, the need has been felt to find feasible alternatives to meet the current and future global energy demands in a most sustainable way [21]. Sustainable energy development is the decisive target of this modern world to conserve our conventional energy reserves and resources [22]. Sustainability assessment in energy planning and management has drawn an important research attention in energy sectors [23]. To achieve this, there is a need to address the goals and important challenges to the scientific community for initialing and/or adopting sustainability in energy planning and management. In chorus, it is also needed to analyze the efficient but reliable techniques/tools to assess the sustainability in energy systems logically.

1.1. Research gaps Due to recent economic growth and the fact that India still use large quantities of fossil fuels for electricity generation, emissions are expected to increase further and contribute to global warming. No consistent approach has been reported to evaluate energy sector sustainability [24]. In developed countries, the sustainability is mainly focused on environmental dimension, while in a developing country context, societal issues like poverty and equity should also be considered when making sustainability assessment [25]. Although, some studies are available on sustainability assessment in developed countries [26], no such significant research work on sustainability assessment in energy planning and management has been reported in developing countries, especially in India where energy demands are so high. The Indian energy sector is not on a sustainable path as a consequence of high dependence on fossil fuels, ignorance of environmental and social aspects of energies, and inefficient technologies. Therefore, providing reasonably priced, consistent and clean energies to the entire population as well as energy supply security and reducing environmental damages will remain a challenging mission for sustainable energy viewpoint of India [27]. This calls for developing a decision framework for assessing the sustainability in energy planning and management in Indian perspective. Hence, it is considered as a clear gap of research in sustainability assessment in energy planning and management dimension. To address the above stated research gaps, this research proposes a systematic approach that helps in identifying and prioritizing indicators for sustainability assessment in energy planning and management. In particular, a two-step methodology has been introduced and used in the present work. Step 1: identification of the sustainability assessment indicators based on literature resource and inputs received from field and industrial experts. Step 2: Prioritization of finalized indicators with an aim to develop a decision-support framework for an integrated sustainability assessment of energy systems under vague surroundings by means of a fuzzy AHP approach.

1.2. Research objectives This research work helps in achieving the objectives, as follows:

 Identification of indicators for assessing the sustainability in energy planning and management in Indian context.

 Prioritization of indicators to develop a decision-support framework for an integrated sustainability assessment of energy systems where environmental, economic, social, technological and operational aspects are considered collectively, which may enable the decision makers to incorporate different preferences for sustainability criteria, and thus, identify the most sustainable option. The work carried out in this research effort to identify and prioritize/rank the indicators to develop a framework for assessing the sustainability in energy planning and management for Indian energy sectors. Whilst, prioritizing the indicators is a kind of multi-criteria analysis decision. And, there are several difficulties in the process of prioritizing the indicators owing to the presence of vagueness and lack of clarity in the data. Thus, a fuzzy-based Analytic Hierarchy Process (AHP) approach is used in this research work [28]. The fuzzy AHP extends the AHP for decision making to cases carried out in an uncertain and fuzzy environment. Hence, it has the ability or advantage to deal with the uncertainty of human judgments not only in establishing the importance weights of the indicators [29], but also in prioritizing or ranking indicators in assessing the sustainability in energy planning and management. The rest of this paper is organized as The state-of-the-art review of related literature (Current status of energy scenario in India, CO2 emissions in India, and Sustainability assessment) has been covered (see Section 2). The identification of indicators assessing sustainability in energy planning and management based on exhaustive literature review is presented in the Section 3. The methodology of the research has been explained in Section 4. An application example of the proposed model along with data analysis and related results is illustrated in Section 5. Sensitivity analysis has been conducted to examine the proposed model robustness and to test ranking stability (see Section 6). Discussions of findings along with managerial implications are provided in Section 7. The research conclusions are provided in Section 8. Finally, in the last section, research limitation and future research scope are given (Section 9).

2. Current status of energy scenario in India Currently, India is the ninth largest economy in the world with growth rate of 8% and above [18], driven by a real GDP growth of 8.7% in the last 5 years. In the year 2010, India was the 5th highest in the world based upon the real GDP growth. This economic growth has been placing massive demand on its energy resources. This demand and supply imbalance in energy has pervasive across all sources requiring serious efforts by the Government of India to enhance energy supplies as India faces possible severe energy supply constraints [30]. According to the International Energy Agency (IEA), a 53% increase in global energy consumption has been foreseen by 2030. Therefore, energy security has been fetching a serious issue as fossil fuels like coal, gas, and diesel are consuming at extremely faster rate, and so, may deplete soon [31]. Electricity has been identified as a key driver of any developing country in its economic growth. India’s net cumulative installed power generation capacity from various sources is 250256.99 MW as on 31 July, 2014 [32] (see Table 1). Thermal based power generation (coal, gas and diesel) contributes 69% in total, hydro energy contributes 16%, Renewable energy contributes 13% and nuclear energy contributes only 2% as on July, 2014 (see Fig. 1). Further, this installed capacity of energy in India has been classified on the basis of sector contribution. The state government

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

61

Table 1 India’s installed power generation capacity in MW as on 31-07-2014. Region

Northern Western Southern Eastern North-East Islands India

Thermal Coal

Gas

Diesel

Total

39143.50 57464.51 26582.50 25927.88 60.00 0.00 149178.39

5331.26 10915.41 4962.78 190.00 1208.50 0.00 22607.95

12.99 17.48 939.32 17.20 142.74 70.02 1199.75

44487.75 68397.40 32484.60 26135.08 1411.24 70.02 172986.09

Nuclear

Hydro

Renewable

Grand Total

1620.00 1840.00 1320.00 0.00 0.00 0.00 4780.00

16598.11 7447.50 11398.03 4113.12 1242.00 0.00 40798.76

68641.63 11271.07 13784.67 432.86 256.67 11.10 31692.14

68641.63 88955.97 58987.30 30681.06 2909.91 81.12 250256.99

Source: Ministry of Power Central Electricity Authority, Government of India [28].

Renewable Energy 13%

Private Sector

Installed Capcity of Electricity in India( in Mega Watt) 35%

38% 27%

Hydro Energy 16%

Cental Government Sector State Government Sector

Thermal Energy 69%

Fig. 2. Installed energy share in India as on 31-07-2014 (Sector wise).

Nuclear Energy 2%

Fig. 1. Installed energy share in India as on 31-07-2014 (Energy-wise).

has been contributing about 38%, the private sector, contributing 35% and the central government, contributing 27% of the total installed capacity (for details please see Fig. 2).

3. Literature review This section has covered the relevant literature on the current status of CO2 emissions in India and sustainability assessment for this study. 3.1. Current status of CO2 emissions in India No argument is needed to understand that the world is today facing the problem of global warming due to rapid industrialization and urbanization. In terms of per capita equity, India is 145th in the world with a release of 1.25 million tons of CO2 per annum [33]. According to IEA estimates, India per capita CO2 in 2009 was 1.37 million tons of CO2 emissions. The predications of India per capita green house gas emissions in 2030  31 vary from 2.77 t to 5.00 t of CO2 emissions. Various studies estimated that India’s green house gas emissions per capita will stay under 4 t per capita and this may be compared to the 2005 global average per capita GHG emissions of 4.22 t of CO2 per capita [18,34]. 3.2. Sustainability assessment Currently, human beings have been using 50% more resources than the Earth’s finite reserves. It may provide and put considerable pressure on business due to resource scarcity. Observed increased/over utilization of natural resources due to a growing demand may escalate costs, raise supply chain risks and adversely affect businesses [35]. Thus, there is an obvious need arises for an urgent action to change the pattern of consumption of natural resources. The processes for perceiving planning, designing,

purchasing, manufacturing and distributing products/services may account for a bulk of the resources consumed and a huge environmental impact [36]. Sustainability based planning aims to reverse trends that are leading us away from an enviable and resilient future by taking consideration of the interdependence of environmental, economic and social objectives in global imperatives as well as in local context [37]. Sustainability assessment may be defined as a procedure to understand and integrate sustainability issues into decision-making by identifying and assessing the sustainability impacts to foster sustainability objectives [38,39]. Several researchers have been contributed towards sustainability assessment in the energy sector as reported in the literature. Bhattacharyya (2010) focused on previous literatures to identify challenges to sustainable energy future in India. The researcher has identified five strategies (Management of energy demand; Adaptation of internationally best-practice technologies; Good Governance and modern management practices; Management of indigenous resources effectively and Ensuring access to Clean energies by deploying decentralized and distributed supply options) and four major challenges (Supply management, Managing energy investment projects effectively, Resource management and Environmental and social responsibility management) for sustainable in India [27]. Gallego-Carrera and Mack (2010) have reported the social sustainability assessment of energy technologies and they evaluated nine indicators based upon four criteria named as Security and reliability of energy provision; Political stability and legitimacy; Social and individual risks and Quality of life [40]. Winfield et al. (2010) examined the integrated power system plan developed by the Ontario Power Authority in 2007. They discussed about assessing the sustainability in the design and evaluation of energy sector; and developed a sustainability assessment framework based upon set of evaluation criteria reflecting need to observe sustainability’s progress [37]. Doukas et al. (2012) reported the issue of assessing energy sustainability of rural communities. They identified nine indicators and developed Energy Sustainability Index (ESI). They suggested that developed ESI may help in monitoring communities’ progress and will help in implementation of future sustainable energy action plans [41]. Dombi et al. (2012) discussed the sustainability assessment framework. The researchers have reported ten technologies of power generation and seven technologies of heat supply; and identified

62

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

seven sustainability attributes to describe the sustainability performance of each identified technology [9]. Liu et al. (2013) have reported the issue of sustainability in hydropower development and covered environmental, economic and social sustainability issues; and identified ten factors for better sustainability performance. The experiences gained from the three Gorges projects applied into other hydropower developments in China [16]. Sovacool (2013) developed energy security index for international energy security performance based upon interrelated factors of availability, affordability, efficiency, sustainability, and governance by taking case study of Japan. Researcher suggested that there should be strategies that achieve 30% of total energy supply from renewable energy by 2025 [42]. Liu (2014) discussed development of a general sustainability indicator. He has identified nine general sustainability indicators for evaluating sustainability of renewable energy systems. The results of the study suggested that the proposed framework may be used as a direction towards development of sustainability indicator for various renewable energy systems [43]. Maxim (2014) developed sustainability assessment framework and ranked thirteen electricity generation technologies based on developed framework. He has identified four dimensions and ten indicators of sustainability assessment. The results of the study suggested that large hydroelectric projects reported as the most sustainable [24]. Milutinović et al. (2014) discussed the sustainability assessment framework for waste management based on multi-criteria analysis AHP methodology. They have reported four scenario and nine indicators of sustainability assessment based upon three dimensions. They reported ‘composting of organic and recycling of inorganic waste’ as the best sustainable scenario [44]. SantoyoCastelazo and Azapagic (2014) developed economic, environmental and social multi-criteria decision assessment framework by taking seventeen indicators and using a life cycle approach in electricity production of Mexico. The results suggested that tradeoffs are obligatory to identify the ‘most sustainable’ option. [45].

4. Identification of indicators for assessing the sustainability in energy planning and management Indicators are as physical parameters that can be measured and available as the data to obtain in quantitative or qualitative form [46]. An indicator is something that provides evidence to a matter of larger significance or makes noticeable a trend or occurrence that is not immediately detectable [3] and should be Specific, Measurable, Achievable, Relevant and Time bound [47]. There are certain key parameters that need to be recognized and analyzed correctly to ensure sustainability assessment in energy planning and management systems. Indicators for sustainability assessment in energy planning and management can be identified through extensive literature review and expert inputs. Regarding the present research work, we have explored various available literature, like national and international journals; the proceedings of national and international conferences; authentic websites of government and non government organizations; and energy reports published by various government/non government departments and research organizations. Important twenty-five indicators for sustainability assessment in energy planning and management have been identified from extensive literature review and from expert inputs. Further, these indicators have been categorized into five key dimensions through expert judgments; other details regarding data collection are given in Section 5.1. However, the identified dimensions and indicators have been explained as follows:

4.1. Economic indicators (Eco) Economic indicators may be defined as the cost and benefit analysis of the power projects by taking care of economic viability and profit viewpoints [3,43]. Economic indicators may be very helpful for comparing different sectors at an aggregated level [48,49]. Regarding economic sustainability, the normal procedure used in a project or an industry to analyze the cost of a product is to adopt financial methods such as cost-benefit analysis, life cycle costing, the net present values, etc. [50,51]. Economic indicators may be categorized in the following indicators: 4.1.1. Payback period (Eco1) Payback period has been identified as a measure to check the recovery of the initial investment. Payback period means how quickly cash returns are generated by the installed capacity of energy generation system. Shorter payback period is preferred by investors as compared to a longer payback period [43]. The payback period has been identified as an important indicator for sustainability assessment in economic dimension [16]. 4.1.2. Economic effectiveness (Eco2) Economic effectiveness related to the thermodynamic efficiency of the system and includes the energy efficiency conversion from the energy resources to the final energy [52]. Economic effectiveness has been focused to reduce energy dependency by enabling the activities that create business and wealth [53]. 4.1.3. Savings on greenhouse gas (GHG) emissions (Eco3) Less GHG emissions will help to reduce environmental compliance costs and decrease the threat of civil and criminal liability for polluting by preventing pollution to the source [54]. That is going to ultimately save costs associated with the GHG emissions [16,43]. Therefore, savings on GHG emissions may be a significant indicator for sustainability assessment in energy planning and management. 4.1.4. Capital and energy levelized cost (Eco4) Capital costs comprise costs of construction and installation of power plants. In the present research work, they are calculated as ‘overnight’ costs, i.e. costs without paying any interest on the borrowings [45,55]. Energy levelized cost accounts for investment costs, land costs, costs of design, operational costs, maintenance costs, labor costs and other operating costs, transportation costs, decommissioning and even CO2 emission costs were all taken into account [16,24,44]. 4.1.5. Investment opportunities (Eco5) Sustainable and environmental practices in the energy sector should be needed to attract more domestic as well as foreign investments and to export more products [56,57]. In India, a company named Carbon Credit Capital is planning to develop approximately 1000 MW of clean power capacity. This company is looking for an attractive investment opportunity in India for a clean power generation, which is bolstered by government incentives, strong demand for power and carbon credits [58]. 4.2. Environmental indicators (env) Environmental indicators deal with the environmental compatibility of the energy project with the surrounding territory and environmental science [59]. Environmental indicators must be focused on reducing the side effects caused by inefficient energy use and the energy supply chain, i.e. GHG emissions, biodiversity, noise, landscape, land use, air pollution and depletion of natural

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

resources, etc. [53,60]. In the light of climate changes and energy security issues, there are strongly needed environment friendly policies are required in energy planning and management [60]. Environmental indicators are given as follows: 4.2.1. Land use and management (Env1) Landfills require huge amounts of land that usually cause extensive environmental and human health problems due to leachate and methane emissions [61]. Land use and management can be understood as the effective use of land throughout the lifecycle of the energy generating plant. Effective land use and management becomes important because energy generation firms require sufficient land which may be agricultural arable land and ineffective land use may cause biodiversity change [20]. 4.2.2. Carbon emissions/CO2 emissions (Env2) Hazardous/toxic gas emissions are increasingly becoming one of the critical parameters to define and measure sustainability of energy generation systems [20]. CO2 emissions have been identified as an indicator of climate change and mainly released through the combustion of fossil fuels in energy generation systems [8,25,43,52]. The key to reducing CO2 emissions includes avoidance or reduction of usage of fossil and other harmful fuels etc. [62]. 4.2.3. Climate related problems and issues (Env3) Human activities threaten the sustainability of environmental and socio-economic systems. The international community, realizing where this situation may lead, is compelled to take measures aimed at reducing hazardous/toxic gas emission and tackling climate change [63,64]. However, in today’s global era, the world has been observed climate change as a serious concern for modern society [25]. 4.2.4. Waste reduction and management (Env4) Effective waste management in energy generation systems is key criterions to achieve environmental and social sustainability [16]. Implementing waste flow mapping proved to be functional and effective as a framework for analyzing the waste management process, revealing value loss and identifying sustainable improvement potentials [65]. 4.2.5. Adoption of independently audited environmental management systems (Env5) Implementing independently audited environmental management systems may be an effective and a better way to reduce energy consumption and related CO2 emissions. The integration of environmental management systems into power production management could be one significant force to guide energy production systems towards energy efficiency [16,49]. 4.3. Social indicators (Soc) The social indicators reflect the social aspects of planning and management of energy systems [46,53]. Social impacts may be vital to identify, enumerate the human risks and consequences, which will help to better understanding and acceptance of some technologies like nuclear energy technologies that are often subjected to community objection [20]. Social indicators are strongly related to that of social impact assessment. Social impact assessment is a methodology to monitor and analyze the inadvertent consequences of planned interventions, such as the implementation of new technologies in social context [40,41]. Some social dimension indicators have been explained as

63

4.3.1. Societal equity (Soc1) In developing countries, societal equity has the greatest importance and must be taken into account when measuring sustainability. Sustainability in energy planning and management cannot be achieved without ensuring reliable supply to the entire population [27]. Social equity is linked with satisfying the essential requirements of individuals in terms of easy accessibility, affordability and no disparities at some minimum level [25]. 4.3.2. Human health and safety (Soc2) A significant effect on human health and quality of life has been observed due to climate change in some of the world's largest developing countries [24,66,67]. Carbon or CO2 produced during the burning of fossil fuels (coals, gases and oils) has been causing considerable changes in the ecosystem and leading to nearly 150,000, additional deaths every year [8,68]. Human health impacts in this work are quantified as human toxicity potential (HTP) estimated in LCA, while the safety is mostly related to avoiding occupational accidents and public hazards [45]. 4.3.3. Social acceptability for energy planning & management projects (Soc3) Social acceptability is preference given by society for the deployment of a particular electricity generation technology [24]. Public perception and acceptability is a key to the implementation of any energy technology, be it fossil fuels with or without carbon capture and storage (CCS), renewable or nuclear power [40,45,69]. 4.3.4. Energy security and safety (Soc4) Energy security is a key concern in India due to very huge demands of energy in every sector. Depletion of fossil fuel reserves is an important indicator for the security of energy supply in many countries because of the fast depletion of their national reserves [45]. Energy security is to recognize the rationing signal embodied in escalating energy prices and to promote demand-side solutions [70]. Energy security and safety will help to provide affordable priced and consistent energies to all population [42]. Therefore, many countries in the world, subsidizing renewable on the basis of energy security is justified [71]. 4.3.5. Cultural heritage protection (Soc5) The uses of energy are essential particularly in urban areas for transportation, industrial production, household and office activities, and in rural areas for farming. Current dependence on conventional energy generation resources are leading to climate change, air pollution and consequent environmental and human health related problems; and thus, raising a severe danger for sustainable development [72]. Cultural heritage protection has been reported an important indicator for sustainability assessment [16,73,74]. 4.3.6. Risk analysis and management (Soc6) Environmental and social risk assessments have been added in recent years for betterment of society [16,40,75–77]. Risk analysis and management of the energy generation technologies engaged in power and energy sector can be very important to understand the full financial and environmental costs associated with their deployment and to know the development of mitigating strategies of social and environmental issues throughout the project development stages [37,76]. 4.3.7. Raising employment and living standard (Soc7) Employment appears to be a much cited social impact in electricity generation impact assessment [78]. Raising employment and living standard means that creation of employment and

64

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

improving lifestyle, of society over the entire life cycle of the unit [24]. In most of developing countries like India, societal issues like poverty and living standards play a significant role in improving its economy and should be incorporated in the sustainability assessment of energy planning and management [25,37].

4.4. Operational indicators (Opr) Operational indicators are used to measure the operational activities leading to improvement in the energy efficiency. Today’s operational systems may be integrated to provide useful guidance towards sustainability assessment in energy planning and management [79,80]. However, energy efficiency may be achieved by using optimized operational practices, i.e. optimal use and allocation of resources, system flexibility to incorporate changes, increasing system resilience, as well as reliability and buffer capacity to overcome the fluctuating demands [49]. The operational dimension indicators recognized from literature are discussed as follows:

4.4.1. System flexibility to respond demands (Opr1) In order to reduce the gap between the demand and supply of power/energy/electricity in India, adequate actions or measures are taken to manage power both at the supply side and also at the demand side [81]. Demand supply management should be considered as a key indicator of sustainability assessment that may help in reducing the greenhouse gas emissions, directly or indirectly and thus, provides several indirect economic and environmental benefits [40,81].

4.4.2. Optimal resource allocation and usage (Opr2) Optimal resource allocation and usage needed to optimize the system to minimize energy cost, available material, government regulations, financial resources, protection of the environment, together with safety, reliability, availability and maintainability of the system, etc. [40,46,52]. At the same time, resource management ensures sustainable development for all while reducing threats to long-term integrity of socio-ecological systems by reducing extractive damage, avoiding waste and reducing overall material and energy use per unit of benefit [37].

4.4.3. Diversification in source of provisioning (Opr3) Diversification in sources is required for provisioning the uninterrupted energy supply, i.e. adding new units of renewable energy sources [60]. India has already started to take some steps to increase the utilization of solar power and other sources of renewable energies in order to save fossil fuels for future generations and to reduce the pollutant emission [82]. In the context of global warming, climate change problems and electricity shortage problems are emerging at a very high rate in India; therefore, an urgent need is required to diversification in sources in the energy mix [83]. The substitution of fossil-fuel with renewable biomass based on efficient, clean and convenient technologies will reduce the CO2 emission from the energy system [84].

4.4.4. System’s resilience and reliability (Opr4) It is recognized as an important indicator in assessing the sustainability in energy systems [85]. As the conservation of resources and demand management increases system’s resilience and reliability by reducing path dependency, volatility associated with fuel and energy generation technology related market and geopolitical risks [37].

4.4.5. Reserve/buffer capacity (Opr5) It is a significant factor in assessing the sustainability in energy systems. Thus, there is a strong need to know reserve capacity and time span for reserve capacity for ensuring a continuous supply and service of energy over time [40]. 4.5. Technological indicators (Tec) Technology may play a significant role in assessing sustainability in energy planning and management. Technology selection is greatly influenced by the cost of technology, product targets and GHG emission reduction targets. It would also be influenced by governmental policies such as fossil carbon taxes or some other such as stimuli to reduce the fossil carbon footprints [61]. Technological indicators may be classified as Capacity utilization and effectiveness; Adoption and continuous up gradation of smart technologies; and Flexibility to incorporate latest technological advancements and are explained as 4.5.1. Capacity utilization and effectiveness (Tec1) Significant variations in capacity factors have been observed based on geographical location and energies that should be effectively utilized according to their capacity factors [24]. Due to the economical and technological developments around India, demand for energy is constantly increasing. Development and diffusion of more efficient technologies may entail costs, but the benefits can be adequate to justify the costs [70]. 4.5.2. Adoption and continuous up gradation of smart technologies (Tec2) India has an advantageous position compared to developed countries and may set a new development trends by continuous up gradation of smart technologies, adopting highly efficient, reliable, world class technologies in all spheres of life [27]. Smart grid technology adoption is a requirement of today’s scenario to achieve uninterrupted power supply in a sustainable way [86]. 4.5.3. Flexibility to incorporate latest technological advancements (Tec3) Old and incompetent technologies and the dilapidated state of assets, compounded by poor operations and maintenance may reduce efficiency and effectiveness of energy utilization [14]. Today’s energy systems must be flexible to react to market changes, in particular abrupt fuel price fluctuations and to incorporate new technological developments [40]. India must look towards new and emerging energy efficient latest technologies to add flexibility in meeting the energy demands of the country [8]. Utilizing of the renewable energy resource can be an important solution to achieve flexibility [16]. 5. Methodology of research In this research, to achieve the aim of this work, the fuzzy AHP is used a solution methodology. Selected energy planning and management sustainability assessment indicators are analyzed to determine their relative importance or priority through fuzzy AHP technique. The detail of fuzzy AHP technique is given in subsequent sub-section. 5.1. Fuzzy AHP method The AHP is a multi-criterion decision making (MCDM) approach which was coined by Thomas L. Saaty in 1980. It assists in decomposing, organizing and analyzing a complex nature problem and converting it into a multi-level hierarchy structure comprising

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

65

of objective function, criteria and sub-criteria. This method has an ability to handle both subjective as well as objective functions and also allows the active participation of decision makers, thus, helps the managers to analyze the relative importance of the set of variables in decision problem [87,88]. Nevertheless, there are several other methods, like ELECTRE and TOPSIS methods of decision-making are presented in literature to solve the multi-criteria’ analysis problem. For both of these methods, a limited acceptance has been observed among the community of practitioners and scholars [89]. AHP has particularly been applied in numerous group decision making situations and has applications in varied fields like, SCM, Engineering/Design, Education, Healthcare, etc [90]. However, there is limited applicability of AHP is reported in literature due to its incompetence in handling with human based qualitative assessments [91]. Thus, the fuzzy concepts can be blended with the AHP methodology. In that case, fuzzy AHP extends the AHP for decision making to cases conducted in a fuzzy environment. Hence, it has the ability or advantage to deal with the uncertainty of human judgments in prioritizing or ranking indicators in assessing sustainability in energy planning and management. The flow diagram for the fuzzy AHP technique is illustrated in Fig. 3; however, the steps which summarize the fuzzy AHP are given below:

according to the studies of Dubois and Prade, 1979; Kauffman and Gupta, 1991 [92–93], in the domain of fuzzy sets and concepts, the problem can be analyzed as, i.e., if, X corresponds to a cluster of objects and the generic element of X is represented by x having values (x1 ; x2 ; x3 ………xn ). In that case, the fuzzy set M for X is given as fðx; μM ðxÞÞ j x A X g, and the corresponding membership function is known by theμM ðxÞ. The triangular fuzzy number (TFN) is a typical kind of fuzzy number, and is mostly suited in practical situations. Thus, triangular fuzzy numbers are preferred in this work. For any triangular fuzzy number (a, b, c), its membership function is expressed as mathematically through Eq. (1), where a rbr c and represents the lower, mean and upper boundary of the TFN (see Fig. 4). In case, if there are two triangular fuzzy number, given by O1 ¼ ðl; m; nÞ andO2 ¼ ðs; t; u;Þ, then, the algebraic operations for these two TFNs are given as follows:

(1) Formulate the research work aim: the aim of the present research, i.e., to prioritize the indicators in assessing the sustainability in energy planning and management Is defined. (2) Apply the fuzzy concepts: In this era of mechanization, it is difficult to make accurate decisions due to the presence of human bias and vagueness associated with the available data. In any MCDM problem, a human assessment commonly consists of qualitative judgments. That is considered to be very difficult to represent in crisp estimation. Thereby, the concepts of fuzzy set theory are used for helping the decision makers in analyzing the problem under such vague surrounds [28]. According to the theory of fuzzy sets, each number between 0 and 1 symbolizes a fractional correctness to a fuzzy set, whereas crisp values stand for the binary logic [0, 1]. Further,

O1 CO2 ¼ ðl; m; nÞCðs; t; uÞ ¼ ðlCs; mCt; nCuÞ

O1 þ O2 ¼ ðl; m; nÞ þ ðs; t; uÞ ¼ ðl þ s; m þ t; n þ uÞ O1  O2 ¼ ðl; m; nÞ  ðs; t; uÞ ¼ ðl  s; m  t; n  uÞ O1  O2 ¼ ðl; m; nÞ  ðs; t; uÞ ¼ ðl  s; m  t; n  uÞ

Fig. 4. Triangular fuzzy sets and intersection between O1 and O2 .

Literature

Prioritizing indicators to assesssustainability in

Expert

survey

energy planning and management

judgments

Applying fuzzy concepts to deal vagueness and human bias

Triangular

Develop fuzzy pair wise assessment matrix

fuzzy numbers

Chang’s Extent

Devise the energy planning and management

Analysis

sustainability assessment indicators importance

method

weights and establish their priority of concern Fig. 3. Flow diagram for preparing the fuzzy AHP based analysis model.

Improving the system continually

Develop a structural decision hierarchy

66

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

 O1 ¼  ðl; m; nÞ ¼ ð  n;  m;  lÞ

μM ðxÞ ¼

8 0; > > > < xl

; ml xn > > m  n; > : 0;

9 x r l; >
 > > x A l; m ; = x A ½m; n; > > > ; x 4 n;

ð1Þ

(3) Develop a structural decision framework: With regard to the study objective, a structural decision framework is constructed. It is developed strictly in accordance with the judgments of the professionals in an expert panel. (4) Construct a fuzzy pair wise comparison matrix: The pair wise comparison matrix to be formed for each identified dimension and indicator by means of linguistic statements as given in the Table 2. The basis of this should be inputs received from the experts. Further, there is a need to convert these linguistics statements into distinct values, and therefore, positive TFNs to be used in correspond to each linguistic judgments made by the experts. And, the resultant fuzzy pair wise assessment matrix is
 illustrated as X ¼ kij nm . In case, kij denotes the entries in the constructed fuzzy assessment matrix, i.e., ðlij ; mij ; nij Þ, and consequently fuzzy numbers should pursue the subsequent relation: lij ¼

1 1 1 ; mij ¼ ; nij ¼ alji mj i nji

where, i and j¼1, 2…z. (5) Devise the indicator’s importance weights: In order to determine the indicators’ importance weights, the fuzzy assessment matrix to be analyzed, and consequently, the entries in the fuzzy assessment matrix are converted into crisp values. However, for this, some appropriate calculation procedures should be followed. For this reason, we used Chang's Extent Analysis method. The method has been well accepted in literature in determining the importance weights for the fuzzy input pair wise assessment matrix [93,94]. The details of essential calculations of the Extent Analysis method as described by [94–96] are given in an Appendix A.

6. An application example The climate change and energy security problem has gained due importance in India [83]. However, a consideration regarding sustainability assessment in energy systems has also become a rapidly developing research area among researchers and practitioners [23]. For this, it needs a strategic approach for good understanding of the indicators to achieve sustainability in implementing an effective energy planning and management system in India. That is the reason that we aim to develop a decision support Table 2 Fuzzy linguistic scale used for assessing the sustainability in energy planning and management. Uncertain judgment

Fuzzy score

Approximately equal Approximately a times more significant Approximately a times less significant Between b and c times more significant Between b and c times less significant

1/2,1,2 a 1, a, a þ1 1/aþ 1, 1/a, 1/a  1 b, (bþ c)/2, c 1/c, 2/(b þc), 1/b

Note: The value of a ranges from 2, 3…9, whereas the values of b and c can be 1, 2… 9, and b o c. Source: Modified Mangla et al. (2014) [96].

framework for assessing the sustainability in energy planning and management in Indian context. It will not only help in identifying and prioritizing the indicators associated with the sustainability assessment in energy systems, but also facilitate in improving the efficiency by considering the environmental, economic, operational, technological and social concerns collectively. It enables the decision makers to segregate the sustainability assessment criteria in accordance with their importance level and recognize the most sustainable options. After analyzing the problem and making its relevancy with the aim of this research, the proposed fuzzy AHP based model is applied to resolve the issue. The other details are given in the next section. 6.1. Data collection There is a need for data to achieve the aim of this work, and it is gathered in two steps. First, indicators relevant to sustainability assessment in energy planning and management are identified as detailed in Section 6.1.1. Second, the selected indicators were analyzed to determine their priorities of concern as detailed in Section 6.1.2. Prior to this, to analyze the problem, a decision group of ten experts is formed. There are three senior project managers; three academicians; two environment and forest ministry members (environmental representatives who handle energy management related environmental concern); and two statistics and program implementation members (representatives who handle the climate change related environmental concern). The experts selected are highly skilled in their domains and are proficient in decisionmaking. The selection of experts was decided on the basis of certain criteria like their individual academic, industrial and consultancy experiences (should have an experience of minimum 10 years), decision making skills (a respective designation in hierarchical structure), expertise in area (individual profiles) etc. After the formation of decision group, we initiate the data collection process. In that way, we have collected the data needed for this research work. 6.1.1. Step 1: selection of indicators in assessing the sustainability in energy planning and management Identification of the indicators associated with the sustainability assessment in energy planning and management is conducted in two steps. First, conducting a critical review of literature helps in listing 25 energy planning and management sustainability assessment indicators. Second, the validation of listed indicators has been carried out from decision group inputs. For this, an idea engineering workshop, in which the experts formed the decision group (as mentioned in Section 3) were invited for a brainstorming session to inquire about their judgment on how significant a role the indicator play in the sustainability assessment of Indian energy planning and management. In that way, 25 energy planning and management sustainability assessment indicators were confirmed for their utility in Indian context. Next, these finalized indicators were grouped into five key dimensions, namely Economic indicators; Environmental indicators; Social indicators; Technological indicators; and Operational indicators. These key dimensions of indicators were finalized through inputs received from the experts of the decision group. 6.1.2. Step 2: prioritizing the finalized energy planning and management sustainability assessment indicators using fuzzy AHP In this stage, the finalized energy planning and management sustainability assessment dimensions and indicators were subjected to further analysis. In which, we analyze them to know their priority. However, the prioritizing of the indicators could be

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

subjective due to human involvement and fuzziness in data. Therefore, we used the fuzzy AHP technique in this work; other details are given in subsequent sections. 6.2. Develop the structural decision framework for the problem in question Taking the experts’ opinions into account, a structural decision framework for assessing the sustainability in energy planning and management has been developed (please see Fig. 5). There are three distinct levels in this developed framework, namely, prioritizing the energy planning and management sustainability assessment indicators (at Level-1), five dimensions of indicators (at Level-2) and twenty- five energy planning and management sustainability assessment indicators (at Level-3). 6.3. Develop the fuzzy pair wise evaluation matrix In this step, the experts were asked to make pair wise comparisons for both the dimensions and the indicators. And, they have been provided linguistic expressions to finalize these pair wise comparisons. Next, we transform the expert’s linguistic views into a fuzzy evaluation matrix through classical TFNs. Hence, it provides the fuzzy pair wise assessment matrix for the dimensions of the indicators (please see Table 3). 6.4. Devise the indicator’s importance weights Following the procedural steps of Chang’s Extent Analysis method, the importance weight in regard to each dimension and their specific indicators were devised. The computed dimension importance weight estimates their priority or rank (please see Table 4). “Environmental indicator dimension (0.2374)” has been reported as the most important dimension of indicators for assessing the sustainability in energy planning and management followed by “Economic dimension (0.2161)”; “Social dimension (0.1958)”; “Technological dimension (0.1825)”; and “Operational dimension (0.1681)” are shown in Table 4. In the next level, indicators under each dimension have been rated by experts. Relative importance weights of indicators have

67

been calculated within the dimension and checked for hierarchy (please see Table 5). Global importance weights of indicators have been obtained by multiplying the importance weight values of dimension with the relative weight of each specific indicator. Based upon global importance weights of indicators, ranking of indicators for assessing the sustainability in energy planning and management is determined; for details please see Table 5. It will be helpful for decision analysts and policy makers to better understand and distinguish the essential sustainability assessment energy planning and management indicators and guarantee continuous improvement for the system likewise.

7. Sensitivity analysis Analyzing of the multi-criteria’ decision problems and its based frameworks may be difficult due to involvement of human subjectivity and fuzziness in data. It may distort the stability and robustness of the developed framework. Thus, it is important to check the robustness of the developed framework, so sensitivity analysis has been suggested as a suitable means for this [97]. Sensitivity analysis helps to determine the smallest change in the weights of the criteria, which may alter the existing weights of the other criteria and sub-criteria. Consequently, the final ranking may change significantly. Among all dimensions of indicators, the “Environmental indicators (Env)” dimension got the maximum priority weight. By virtue of this, it is capable enough to influence the other dimensions of indicators. Given that small change in relative weights would give large changes in the final rank [98]. Considering this, we tested the final ranking through sensitivity analysis by varying the weights of environmental indicator dimension [99]. The sensitivity analysis thus may provide a further insight in the assessment of sustainability in energy planning and management. To illustrate the sensitivity analysis the effect of an incremental change in value from 0.1 to 0.9, to the “Environmental indicator (Env)” dimension, was devised (please see Table 6). The results of the sensitivity analysis indicate that the maximum relative change happened in the ‘Economic’ dimension; other details are given in Table 6. Further, due to variation in indicator dimension weights, the specific indicator weights and their final ranking also changed. At

Prioritizing for sustainability assessment indicators in Indian energy planning and management systems

Economic (Eco)

Eco1

Environmental (Env)

Social (Soc)

Operational (Opr)

Technological (Tec)

Soc1 Env1

Opr1

Tec1

Opr2

Tec2

Soc2 Eco2

Env2 Soc3

Eco3

Env3

Opr3 Soc4

Eco4

Eco5

Tec3

Env4 Soc5

Opr4

Soc6

Opr5

Env5

Soc7 Fig. 5. The developed decision hierarchy of indicators for assessing the sustainability in energy planning and management.

68

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

Table 3 Triangular fuzzy number based pair-wise evaluation matrix for dimensions of indicators for assessing the sustainability in energy planning and management. Dimension

Eco

Eco Env Soc Opr Tec

1.00 0.33 0.50 2.00 0.33

Env 1.00 0.40 1.00 2.50 0.50

1.00 0.50 2.00 3.03 1.00

2.00 1.00 0.33 0.20 2.00

Soc 2.50 1.00 0.50 0.25 2.50

3.00 1.00 1.00 0.33 3.03

Opr

0.50 1.00 1.00 0.33 0.50

1.00 2.00 1.00 0.50 0.67

2.00 3.00 1.00 1.00 1.00

0.33 3.00 1.00 1.00 0.50

Tec 0.40 4.00 2.00 1.00 1.00

0.50 5.00 3.00 1.00 2.00

1.00 0.33 1.00 0.50 1.00

2.00 0.40 1.50 1.00 1.00

3.00 0.50 2.00 2.00 1.00

Source: Expert inputs.

Table 4 Ranking of dimensions for assessing the sustainability in energy planning and management. Indicator dimension

Importance weights

Ranking

Env Eco Soc Tec Opr

0.2374 0.2161 0.1958 0.1825 0.1681

1 2 3 4 5

Source: Fuzzy AHP analysis.

0.1 value of “Environmental indicators (Env)” dimension, the first rank is acquired by ‘Capacity utilization and effectiveness (Tec1)’ indicator, while, ‘Cultural heritage protection (Soc5)’ holds the last rank. ‘Capacity utilization and effectiveness (Tec1)’ indicator holds the first rank until the environmental dimension indicator value reaches to the normal value (i.e.). From 0.3 to 0.9, the first rank is acquired by ‘Climate related problems and issues (Env3)’, and the ranks of other indicators vary likewise (for details please see Table 7). Ranking or priority for specific indicators based on sensitivity analysis is illustrated in Fig. 6. Under these considerations, it may be concluded that ‘environmental’ dimension indicators are very important in assessing the sustainability in energy planning and management. Thus, it needs greater decision-makers’ attentiveness accordingly.

Table 5 Final ranking for indicators for assessing the sustainability in energy planning and management. Indicator dimension

Specific indicators

Relative weights

Relative ranking

Global weights

Global ranking

Eco

Eco1 Eco 2 Eco 3 Eco 4 Eco 5

0.2271 0.2503 0.1439 0.2103 0.1685

2 1 5 3 4

0.0491 0.0541 0.0311 0.0454 0.0364

7 5 19 10 15

Env

Env1 Env2 Env3 Env4 Env5

0.2585 0.2002 0.2819 0.1825 0.0769

2 3 1 4 5

0.0614 0.0475 0.0669 0.0433 0.0183

3 8 2 12 23

Soc

Soc1 Soc2 Soc3 Soc4 Soc5 Soc6 Soc7

0.1387 0.2293 0.2018 0.1710 0.0298 0.1000 0.1293

4 1 2 3 7 6 5

0.0272 0.0449 0.0395 0.0335 0.0058 0.0196 0.0253

20 11 13 17 25 22 21

Opr

Opr1 Opr2 Opr3 Opr4 Opr5

0.2059 0.3050 0.2262 0.1874 0.0755

3 1 2 4 5

0.0346 0.0513 0.0380 0.0315 0.0127

16 6 14 18 24

Tec

Tec1 Tec2 Tec3

0.4238 0.3246 0.2515

1 2 3

0.0773 0.0592 0.0459

1 4 9

8. Discussion of findings

Source: Fuzzy AHP analysis.

Sensistivity analysis 30 25 Ranking

20 15 10 5 0

Eco1 Eco2 Eco3 Eco4 Eco5 Env1 Env2 Env3 Env4 Env5 Soc1 Soc2 Soc3 Soc4 Soc5 Soc6 Soc7 Opr1 Opr2 Opr3 Opr4 Opr5 Tec1 Tec2 Tec3

According to Table 4, the order of priority for the dimensions of indicators for sustainability assessment in Indian energy planning and management is given as Env – Eco – Soc – Tec  Opr. While the priority for specific indicators within their respective dimensions are given in Table 5. The global ranking for the specific indicators, based on their respective global fuzzy weights, is also established, and is also shown in Table 6. In addition, the research findings were discussed with the consolations team of experts in order to have a better understanding of the contemporary issues associated with the management of energy systems in Indian context, which may improve the system effectiveness and sustainability accordingly. The “Environmental (Env) indicator dimension” holds first rank, and consequently, occupies the highest priority in reference to other indicator dimensions. There are five specific indicators for this dimension. Among them, ‘Climate related problems and issues (Env3)’ obtain the highest priority. Ecological problems are major concerns for current energy systems and one of the most important phenomena is the climate change [9,100]. Thus, decision makers should consider issues relevant to the climate related problems logically and considerably. ‘Land use and management (Env1)’ is ranked after Env3. Meaning that, a significant need is recognized for effective management of land because Indian economy is highly dependent on agriculture. The effective use of land for the energy

Specific indicator

Fig. 6. Sensitivity analysis of indicators for assessing the sustainability in energy planning and management.

generation will be beneficial for ecology and human habitation [24,40]. ‘Carbon emissions/CO2 emissions (Env2)’ come next in the priority list. Next is ‘Waste reduction and management (Env4)’ in this

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

69

Table 6 Dimension values when increasing ‘Environmental indicators (Env)’ dimension indicators. Env¼0.1

Env¼0.2

Env¼ 0.2347 (Normal)

Env¼ 0.3

Env¼ 0.4

Env¼0.5

Env¼ 0.6

Env¼0.7

Env¼ 0.8

Env¼0.9

0.2551 0.1000 0.2311 0.1984 0.2154 1

0.2267 0.2000 0.2054 0.1764 0.1915 1

0.2161 0.2374 0.1958 0.1681 0.1825 1

0.1984 0.3000 0.1797 0.1543 0.1675 1

0.1701 0.4000 0.1541 0.1323 0.1436 1

0.1417 0.5000 0.1284 0.1102 0.1197 1

0.1134 0.6000 0.1027 0.0882 0.0957 1

0.0850 0.7000 0.0770 0.0661 0.0718 1

0.0567 0.8000 0.0514 0.0441 0.0479 1

0.0284 0.9000 0.0257 0.0221 0.0239 1

Source: Sensitivity analysis.

Table 7 Ranking for indicators by sensitivity analysis when ‘Environmental indicators (Env)’ dimension changes from 0.1 to 0.9. Specific indicators

Env¼ 0.1

Env¼0.2

Env¼ 0.2347 (Normal)

Env¼0.3

Env¼0.4

Env¼ 0.5

Env¼ 0.6

Env¼ 0.7

Env¼0.8

Env¼0.9

Eco1 Eco2 Eco3 Eco4 Eco5 Env1 Env2 Env3 Env4 Env5 Soc1 Soc2 Soc3 Soc4 Soc5 Soc6 Soc7 Opr1 Opr2 Opr3 Opr4 Opr5 Tec1 Tec2 Tec3

5 3 15 7 11 19 21 18 22 24 16 8 9 13 25 20 17 12 4 10 14 23 1 2 6

7 3 19 9 14 6 12 4 15 23 20 10 11 17 25 22 21 16 5 13 18 24 1 2 8

7 5 19 10 15 3 8 2 12 23 20 11 13 17 25 22 21 16 6 14 18 24 1 4 9

9 7 19 11 15 2 4 1 5 22 20 12 13 17 25 23 21 16 8 14 18 24 3 6 10

9 7 20 11 16 2 3 1 4 14 21 12 13 18 25 23 22 17 8 15 19 24 5 6 10

10 8 20 12 16 2 3 1 4 7 21 13 14 18 25 23 22 17 9 15 19 24 5 6 11

10 8 20 12 16 2 3 1 4 5 21 13 14 18 25 23 22 17 9 15 19 24 6 7 11

10 8 20 12 16 2 3 1 4 5 21 13 14 18 25 23 22 17 9 15 19 24 6 7 11

10 8 20 12 16 2 3 1 4 5 21 13 14 18 25 23 22 17 9 15 19 24 6 7 11

10 8 20 12 16 2 3 1 4 5 21 13 14 18 25 23 22 17 9 15 19 24 6 7 11

Source: Sensitivity analysis.

dimension. Finally, the Adoption of independently audited environmental management systems (Env5) last in the list. In a country like India, efficient CO2 emission management to be required because the population is increasing at a very rapid pace and climate change can impact on fertility of agriculture lands, rise in sea level leading to submergence of coastal areas, as well as increased frequency of natural calamities, which may be a serious threat to food security as well as to human survival [101]. “Economic indicator dimension (Eco)” acquires second place among other indicator dimensions. It plays an important part in improving the sustainability of the energy systems. This particular dimension category has five specific indicators. ‘Economic effectiveness (Eco2)’ indicator attains the utmost importance. This implies that the energy policy makers’ need to put directed efforts in the form of cost-effective ways to improve energy efficiency in the economy is to combine investments in energy-efficient technologies with the encouragement of good energy management systems [102]. Following this, there is ‘Payback period (Eco1)’ indicator in the list. The time span required to pay back the energy invested is a critical consideration for evaluating the sustainability of energy systems [103]. Next to this, is ‘Capital and energy levelized cost (Eco4)’ indicator, it shows that Effective planning and control of goals will help to achieve effective utilization of resources in addition to reducing costs of energy generation.

‘Investment opportunities (Eco5)’ indicator is next in terms of priority. Lastly, ‘Savings on Greenhouse Gas (GHG) emissions (Eco3)’ indicator is there according to the priority sequence. “Social indicator dimension (Soc)” acquired the third importance level in the main hierarchy. In the particular dimension, ‘Human health and safety (Soc2)’ indicator holds the highest priority in this dimension. The emitted NOx, SOx, VOC and particulate matter are spoiling human health and harvest losses, and are also responsible for surface ozone and acid rain [25]. For this reason, it is suggested to design healthy facilities and built in a resource-efficient manner using ecologically based principles for energy planning and management systems [104]. Afterwards, ‘Social acceptability for energy planning and management projects (Soc3)’ indicator comes in this dimension. In India, few energy systems like nuclear energy and larger wind turbines face social protest and opposition. Therefore, social acceptability must be considered in the sustainability assessment of energy planning and management. ‘Energy security and safety (Soc4)’ indicator comes next according to the rank indicated in the list. Then is ‘Societal equity (Soc1)’ indicator in the list. Leadtime management is important to survive in the market as well as attract new customers. Afterward, ‘Raising employment and living standard (Soc7)’ indicator is there in accordance with the priority. Next to this is ‘Risk analysis and management (Soc6)’. Finally, ‘Cultural heritage protection (Soc5)’ is last in the list.

70

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

“Technological dimension (Tec)” occupies fourth place in the main priority list. Thereby, managers are suggested to look the issues associated with technological advancements in improving the energy management system efficiency. This dimension has three specific indicators. ‘Capacity utilization and effectiveness (Tec1)’ indicator is at the top ranking. While, assessing the sustainability of energy planning and management, the ease of use and limitations of every technology must be considered because some technologies may be profoundly resource constraint [20]. Afterwards, ‘Adoption and continuous up gradation of smart technologies (Tec2)’ indicator comes in this dimension. Finally, the ‘Flexibility to incorporate latest technological advancements (Tec3)’ indicator is at the end in the list. It represents that sustainability of technological systems must be assessed and technologies should be flexible in terms of demand fluctuations. “Operational indicators dimension (Opr)” hold the last place in priority list. There are five specific indicators within this particular dimension. The ‘Optimal resource allocation and usage (Opr2)’ indicator is ranked first. For many emerging economies like India, keeping up with a thriving electricity demand is an enormous investment challenge. Current investment trends present some warning signs for the sufficiency of power supply [105] and managing resources in the term of natural, human and financial resources, capital assets, etc. [27]. ‘Diversification in source of provisioning (Opr3)’ indicator comes next to this. At present, most of the energy generation is from fossil fuels and only 14% of total energy generation from renewable sources. India is very rich in renewable energy sources and many of these resources have a great potential for exploitation in India. These renewable resources may also provide commercially smart options to meet up specific requirements for energy services, especially in rural areas, generate new employment opportunities, and offer opportunities to manufacture most of the equipment locally [83]. The next indicator, i.e. ‘System flexibility to respond demands (Opr1)’ that will help in demand management, and may also increase energy security. Next is, ‘System’s resilience and reliability (Opr4)’ indicator in the priority list. The last place of importance is attained by ‘Reserve/buffer capacity (Opr5)’ indicator. Further, an overall ranking of all indicators has also been made by assigning global weights. Capacity utilization and effectiveness (Tec1), Climate related problems and issues (Env3), Land use and management (Env1), Adoption and continuous up gradation of smart technologies (Tec2), and Economic effectiveness (Eco2) have been reported top five indicators for sustainability assessment in energy planning and management from Indian perspectives.

8.1. Implications of the research The findings of this work will surely aid to prepare well for initiation and implementation of sustainability in energy planning and management in India efficiently and effectively. In addition, it will help energy management professionals and policy makers to improve the consistency of the sustainability initiatives in energy planning and management projects in the energy sector in India. Further, Indicators with higher priority, provide an opportunity for the policy makers to continuously improve their performance on a timely basis. However, the strategic results can be achieved by continuously improving the high ranked indicators. Our model will help to assess and achieve sustainability in energy planning and management practices and will improve a nation environmental, social, operational, technological as well as economic performance in the energy/power sector. To end with, the proposed framework may provide some valuable guidelines to decision analysts and policy makers to develop their plan of action in terms of design of both short term and long term, flexible decision strategies for assessing the sustainability in energy planning and management.

9. Conclusions Due to an exponential increase in demand for energy, significant efforts are needed to conserve the resources and guarantee sustainability in energy demand and its management in Indian perspective. In India, energy systems are not fully sustainable due to the heavy power demands provoked by high population, increasing level of living standard and exceptional economic growth. In addition to that, short-and long-term insecurity with regard to the availability of resources is another reason for not fully sustainable energy systems [9]. Approximately 80% of the total estimated power and energy potential in India is yet to be exploited [3]. Taking this into account, the present research work attempts to develop a decision-support framework for integrated sustainability assessment energy systems in India where environmental, economic, social, technological and operational aspects are considered all together, which assists decision makers to incorporate different preferences for sustainability criteria and identify most sustainable options. More specifically, the research contribution is twofold: (1) to identify indicators for assessing the sustainability in energy planning and management; (2) to prioritize the indicators related to sustainability assessment in energy planning and management that will be helpful in improving its efficiency and effectiveness in the sector. The work conducted in this research may guide on how the identified dimensions and specific indicators to assess sustainability in energy planning and management in India are prioritized. This is imperative for the reason that normally decision making team focuses on few indicators whom it thinks are significant without considering the effect of other indicators, which may be very important and significant indicators for assessing the sustainability in energy planning and management. For prioritizing indicators in assessing the sustainability in energy planning and management, a fuzzy based AHP approach is suggested and used in this study. The fuzzy AHP based framework will enable policy makers to understand different indicators to assess sustainability in energy planning and management in India. This method will also help in conquering the difficulty of human bias in prioritizing the indicators in assessing sustainability to energy systems. Besides, the fuzzy AHP method allows the determination of relative importance of accepted dimensions and indicators in energy systems. These dimensions and their specific indicators were recognized from literature and from experts from industry and academia backgrounds. The priority wise importance order of identified dimensions of indicators for assessing sustainability in energy planning and management is presented as Env – Eco – Soc – Tec – Opr. It shows that the Environmental (Env) dimension indicators acquire maximum importance. Consequently, there is great attention is required for this dimension indicator for the perspectives of policy makers and decision analysts from both short-term and long-term viewpoints. Additionally, the indicator belongs to their specific dimension has also been prioritized. Lastly, we tested the robustness of the proposed sustainability assessment framework, as well as checked the stability of ranking of the dimensions of indicator and specific indicators by means of sensitivity analysis.

10. Research limitations and scope for future work This work also has some limitations. In this study, a fuzzy AHP based structural model has been suggested with five dimensions and 25 indicators in order to assess sustainability in energy planning and management. Other indicators have not been documented and classified. The study findings are based on expert’s

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

opinion, therefore, the evaluation procedure need to be carried out carefully. Different multi-criteria decision making models may be applied to the same problem and results can be compared in further studies. A real life case example may be conducted to validate this research work. Additionally, various energy generation technologies may be added as alternatives and can be analyzed for future perspectives.

Acknowledgements The authors are very much thankful to the unanimous reviewers of the paper and editors of the journal for their constructive and valuable comments/suggestions that improved the quality of the paper.

Appendix A. The details of essential calculations of Extent Analysis method The details of some essential calculations of extent analysis method as described by Mangla et al. (2014) are given as following [96]: If extent analysis values for the ith object is represented by O1gi ; O2gi ; O3gi ; ……::Opgi , in this case their corresponding fuzzy synthetic extent would be represented as below in Eq. (2). 2 3 o n X o X X ð2Þ Si ¼ Ojgi  4 Ojgi 5 j¼1

i¼1j¼1

In addition, for considering the minimum and maximum values for fuzzy number, the degree of possibility for two fuzzy numbers is represented as below: h  i VðO1 Z O2 Þ ¼ sup min μo1 ðxÞ; μo2 ðyÞ ; x; y A R; and x Z y ð3Þ Noted that, if, x Zy and f o1 ðxÞ ¼ f o2 ðyÞ ¼ 1; then VðO1 Z O2 Þ ¼ 1. Since O1 and O2 are two convex fuzzy numbers, therefore, we should have the following: VðO1 Z O2 Þ ¼ 1

if o1 Z o2 ;

ð4Þ

VðO1 Z O2 Þ ¼ hgt ðO1 \ O2 Þ ¼ f o1 ðmÞ

ð5Þ

while, 'm' could view as the ordinate of the highest intersection point M between μo1 μo2 (see Fig. 4), and further, M is given as VðO1 Z O2 Þ ¼ hgt ðO1 \ O2 Þ ¼ ða  r Þ=ðq  r Þ  ðb  aÞ

ð6Þ

Moreover, the degree of possibility for 'k' convex fuzzy numbers Oi ði ¼ 1; 2…kÞ should be calculated as


 V ðO Z O1 ; O2 ; …k Þ ¼ V ðO Z O1 Þ and ðO Z O2 Þ and … and ðO Z Ok Þ ¼ min V ðO Z Oi Þ; i ¼ 1; 2; 3; …; k:

ð7Þ

□

Furtheʹrmore, by assuming, z ðC i Þ ¼ min V ðSi Z Sk Þ; for k ¼ 1; 2; …; n; k ai; the weight vector is given as  T W □ ¼ z□ ðC 1 Þ; ðC 1 Þ; ðC 1 Þ…ðC 1 Þ where C i (i¼ 1, 2…n) are the elements. After normalizing, the normalized fuzzy weight vectors for the system is given as below, while, 'W' is a non-fuzzy number.  T W ¼ z□ ðC 1 Þ; ðC 1 Þ; ðC 1 Þ…………ðC 1 Þ ð8Þ

References [1] Mangoyana RB, Smith TF, Simpson R. A systems approach to evaluating sustainability of bio fuel systems. Renew Sustain Energy Rev 2013;25:371–80.

71

[2] Cucchiella F, D’Adamo I, Gastaldi M, Koh SL. Implementation of a real option in a sustainable supply chain: an empirical study of alkaline battery recycling. Int J Syst Sci 2014;45(6):1268–82. [3] Kumar D, Katoch SS. Sustainability indicators for run of the river (RoR) hydropower projects in hydro rich regions of India. Renew Sustain Energy Rev 2014;35:101–8. [4] Luthra S, Garg D, Haleem A. Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry. Prod Plan Control, (ahead-of-print) 2014. http://dx.doi.org/10.1080/09537287.2014.904532. [5] Tsoutsos T, Frantzeskaki N, Gekas V. Environmental impacts from the solar energy technologies. Energy Pol 2005;33(3):289–96. [6] Kotcioglu I. Clean and sustainable energy policies in Turkey. Renew Sustain Energy Rev 2011;15(9):5111–9. [7] Yang Q, Chen GQ, Liao S, Zhao YH, Peng HW, Chen HP. Environmental sustainability of wind power: an energy analysis of a Chinese wind farm. Renew Sustain Energy Rev 2013;25:22939. [8] Kumar A, Kumar K, Kaushik N, Sharma S, Mishra S. Renewable energy in India: current status and future potentials. Renew Sustain Energy Rev 2010;14(8):2434–42. [9] Dombi M, Kuti I, Balogh P. Sustainability assessment of renewable power and heat generation technologies. Energy Pol 2014;67:264–71. [10] Frame B, O’Connor M. Integrating valuation and deliberation: the purposes of sustainability assessment. Environ Sci Pol 2011;14(1):1–10. [11] Mangla S, Madaan J, Sarma PRS, Gupta MP. Multi-objective decision modeling using interpretive structural modeling(ISM) for green supply chains. Int J Logist Syst Manag 2014;17(2):125–42. [12] Roldán C, Martínez M, Peña R. Scenarios for a hierarchical assessment of the global sustainability of electric power plants in México. Renew Sustain Energy Rev 2014;33:154–60. [13] Mangla S, Madaan J, Chan FTS. Analysis of flexible decision strategies for sustainability-focused green product recovery system. Int J Prod Res 2013;51 (11):3443. [14] Koroneos CJ, Nanaki EA. Electric energy sustainability in the Eastern Balkans. Energy Pol 2007;35(7):3826–42. [15] Burton I. Report on reports: our common future: The world commission on environment and development. Environ: Sci Pol Sustain Dev 1987;29 (5):25–9. [16] Liu J, Zuo J, Sun Z, Zillante G, Chen X. Sustainability in hydropower development  a case study. Renew Sustain Energy Rev 2013;19:230–7. [17] Mangla S, Kumar P, Barua MK. Flexible decision approach for analyzing performance of sustainable supply chains under risks/uncertainty. Global J Flex Syst Manag 2014;15(2):113–30. [18] IEC. Sustainable sources of energy. India Energy Congress; 2013. Online available at: 〈http://www.deloitte.com/assets/Dcom-India/Local%20Assets/ Documents/IEC%202013/India Energy _Congress_2013.pdf 〉 [accessed 22.03.14]. [19] IEA. World energy outlook 2004. International Energy Agency; 2004. [20] Evans A, Strezov V, Evans TJ. Assessment of sustainability indicators for renewable energy technologies. Renew Sustain Energy Rev 2009;13 (5):1082–8. [21] Phillips J. Determining the sustainability of large-scale photovoltaic solar power plants. Renew Sustain Energy Rev 2013;27:435–44. [22] Afgan NH, da Graça Carvalho M. Energy system assessment with sustainability indicators. Sustainable assessment method for energy systems. US: Springer; 83–125. [23] Ness B, Urbel-Piirsalu E, Anderberg S, Olsson L. Categorizing tools for sustainability assessment. Ecol Econ 2007;60(3)498–508. [24] Maxim A. Sustainability assessment of electricity generation technologies using weighted multi-criteria decision analysis. Energy Pol 2014;65:284–97. [25] Kemmler A, Spreng D. Energy indicators for tracking sustainability in developing countries. Energy Pol 2007;35(4):2466–80. [26] Banos R, Manzano-Agugliaro F, Montoya FG, Gil C, Alcayde A, Gómez J. Optimization methods applied to renewable and sustainable energy: a review. Renew Sustain Energy Rev 2011;15(4):1753–66. [27] Bhattacharyya SC. Shaping a sustainable energy future for India: management challenges. Energy Pol 2010;38(8):4173–85. [28] Zadeh LA. Fuzzy sets. Inform Control 1965;8(3):338–53. [29] Saaty TL. The analytic hierarchy process: planning, priority setting, resources allocation. New York: McGraw; 1980. [30] MOSPI. Energy statistics 2013. Ministry of statistics and programme implementation Government of India; 2013. Online available at: 〈http://mospi.nic. in/mospi_new/upload/Energy_Statistics_2013.pdf〉 [accessed 13.07.14]. [31] Boudghene Stambouli A, Khiat Z, Flazi S, Tanemoto H, Nakajima M, Isoda H, et al. Trends and challenges of sustainable energy and water research in North Africa: Sahara solar breeder concerns at the intersection of energy/ water. Renew Sustain Energy Rev 2014;30:912–22. [32] Online available at: 〈http://cea.nic.in/reports/monthly/executive_rep/jul14. pdf〉 [accessed 31.08.14]. [33] WEC. Survey of energy resources. World Energy Council, London; 2010. [34] Ministry of Environment and Forests Government of India. India’s GHG emission profile-results of five climate modeling studies. Climate Modeling Forum, India; 2009. Online available at: 〈http://www.moef.nic.in/downloads/ home/GHG-report.pdf〉 [accessed 19.04.14]. [35] Online available at: 〈http://www.wwfindia.org/wwf_publications/?10521/Glo bal-Practices-in-Promoting-Environmental-Sustainability〉 [accessed 02.03.14].

72

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

[36] Gupta S, Palsule-Desai OD. Sustainable supply chain management: review and research opportunities. IIMB Manag Rev 2011;23(4):234–45. [37] Winfield M, Gibson RB, Markvart T, Gaudreau K, Taylor J. Implications of sustainability assessment for electricity system design: the case of the Ontario Power Authority’s integrated power system plan. Energy Pol 2010;38(8):4115–26. [38] Gasparatos A, El-Haram M, Horner M. A critical review of reductionist approaches for assessing the progress towards sustainability. Environ Impact Assess Rev 2008;28(4)286–311. [39] Huge J, Waas T, Eggermont G, Verbruggen A. Impact assessment for a sustainable energy future  reflections and practical experiences. Energy Pol 2011;39(10):6243–53. [40] Gallego-Carrera D, Mack A. Sustainability assessment of energy technologies via social indicators: results of a survey among European energy experts. Energy Pol 2010;38(2):1030–9. [41] Doukas H, Papadopoulou A, Savvakis N, Tsoutsos T, Psarras J. Assessing energy sustainability of rural communities using principal component analysis. Renew Sustain Energy Rev 2012;16(4):194957. [42] Sovacool BK. An international assessment of energy security performance. Ecol Econ 2013;88:148–58. [43] Liu G. Development of a general sustainability indicator for renewable energy systems: a review. Renew Sustain Energy Rev 2014;31:611–21. [44] Milutinović B., Stefanović G., Dassisti M., Marković D., Vučković G. Multicriteria analysis as a tool for sustainability assessment of a waste management model. Energy (in Press); 2015. 10.1016/j.energy.2014.05.056. [45] Santoyo-Castelazo E, Azapagic A. Sustainability assessment of energy systems: integrating environmental, economic and social aspects. J Clean Prod 2014;80:119–38. [46] Afgan NH, Carvalho MH, Hovanov NV. Energy system assessment with sustainability indicators. Energy Pol 2000;28(1):603–12. [47] Morris P, Therivel R. Sustainable development and sustainability appraisal. Methods of environmental impact assessment. 3rd edition. London & New York: Rutledge (Taylor and Francis group; 2009. p. 467–81 [chapter 16]. [48] Phylipsen GJM, Blok K, Worrell E. International comparisons of energy efficiency-methodologies for the manufacturing industry. Energy Pol 1997;25:715–25. [49] Bunse K, Vodicka M, Schönsleben P, Brülhart M, Ernst FO. Integrating energy efficiency performance in production management–gap analysis between industrial needs and scientific literature. J Clean Prod 2011;19(6):667–79. [50] Huppes G, van Rooijen M, Kleijn R, Heijungs R, Koning A, van Oers L. Life cycle costing and the environment. Report of a Project Commissioned by the Ministry of VROM-DGM for the RIVM Expertise Centre LCA, CML; April 2004, p. 52. [51] Basurko OC, Mesbahi E. Methodology for the sustainability assessment of marine technologies. J Clean Prod 2012;68(1):155–64. [52] Afgan NH, Carvalho MG. Sustainability assessment of a hybrid energy system. Energy Pol 2008;36(8):2903–10. [53] Neves AR, Leal V. Energy sustainability indicators for local energy planning: review of current practices and derivation of a new framework. Renew Sustain Energy Rev 2010;14(9):2723–35. [54] Luthra S, Qadri MA, Garg D, Haleem A. Identification of critical success factors to achieve high green supply chain management performances in Indian automobile industry. Int J Logist Syst Manag 2014;18(2):170–99. [55] NEA IEA. Projected costs of generating electricity. edition. France: International Energy Agency and Nuclear Energy Agency; 2010. [56] Kaygusuz K. Energy for sustainable development: a case of developing countries. Renew Sustain Energy Rev 2012;16(2):1116–26. [57] Tessema Z, Mainali B, Silveira S. Mainstreaming and sector-wide approaches to sustainable energy access in Ethiopia. Energy Strat Rev 2014;2(3):313–22. [58] Online available at: 〈http://carboncreditcapital.com/dev/wp-content/uploads/ resources/India_Article_for_Marketracker_full_v2[1].pdf〉 [accessed 02.03.14]. [59] Rosso M, Bottero M, Pomarico S, Ferlita SL, Comino E. Integrating multi criteria evaluation and stakeholders’ analysis for assessing hydro power projects. Energy Pol 2014;67:870–81. [60] Jay S. Strategic environmental assessment for energy production. Energy Pol 2010;38(7):3489–97. [61] Wan Alwi SR, Manan ZA, Klemeš JJ, Huisingh D. Sustainability engineering for the future. J Clean Prod 2014;71:1–10. [62] Klemeš JJ, Varbanov PS, Huisingh D. Recent cleaner production advances in process monitoring and optimization. J Clean Prod 2012;34:1–8. [63] Li H, Jenkins-Smith HC, Silva CL, Berrens RP, Herron KG. Public support for reducing US reliance on fossil fuels: investigating house hold willingness to pay for energy research and development. Ecol Econ 2009;68(3):731–42. [64] Stigka EK, Paravantis JA, Mihalakakou GK. Social acceptance of renewable energy sources: a review of contingent valuation applications. Renew Sustain Energy Rev 2014;32:100–6. [65] Kurdve M, Shahbazi S, Wendin M, Bengtsson C, Wiktorsson M. Waste flow mapping to improve sustainability of waste management: a case study approach. J Clean Prod 2014. http://dx.doi.org/10.1016/j.jclepro.2014.06.076. [66] Pandey JS, Kumar R, Devotta S. Health risks of NO2, SPM and SO2 in Delhi, India. Atmos Environ 2005;39(36):6868–74. [67] Kan H, Chen R, Tong S. Ambient air pollution, climate change, and population health in China. Environ Int 2012;42:10–9. [68] World Energy Outlook. International energy agency; 2008. 〈http://www. worldenergyoutlook.org/2008.asp〉 [accessed 07.04.14].

[69] Onat N, Bayar H. The sustainability indicators of power production systems. Renew. Renew Sustain Energy Rev 2010;14(9):3108–15. [70] Gunatilake H, Roland-Holst D, Sugiyarto G. Energy security for India: bio fuels, energy efficiency and food productivity. Energy Pol 2014;65:761–7. [71] Sener C, Fthenakis V. Energy policy and financing options to achieve solar energy grid penetration targets: accounting for external costs. Renew Sustain Energy Rev 2014;32:854–68. [72] Bilgen S. Structure and environmental impact of global energy consumption. Renew Sustain Energy Rev 2014890–902. [73] Thygesen J, Agarwal A. Key criteria for sustainable wind energy planning  lessons from an institutional perspective on the impact assessment literature. Renew Sustain Energy Rev 2014;39:1012–23. [74] Pang X, Mörtberg U, Brown N. Energy models from a strategic environmental assessment perspective in an EU context  what is missing concerning renewables? Renew Sustain Energy Rev 2014;33:353–62. [75] García-Serna J, Martínez JL, Cocero MJ. Green HAZOP analysis: incorporating green engineering into design, assessment and implementation of chemical processes. Green Chem 2007;9(2):111–24. [76] McLellan BC, Corder GD. Risk reduction through early assessment and integration of sustainability in design in the minerals industry. J Clean Prod 2013;53:37–46. [77] Mangla S, Kumar P, Barua MK. A flexible decision framework for building risk mitigation strategies in green supply chain using SAP-LAP and IRP Approaches. Global J Flex Syst Manag 2014;15(3):203–18. [78] Ribeiro F, Ferreira P, Araújo M. Sustainability assessment of electricity production using a logic models approach. Renew Sustain Energy Rev 2013;28:215–23. [79] Swart RJ, Raskin P, Robinson J. The problem of the future: sustainability science and scenario analysis. Global Environ Change 2004;14(2):137–46. [80] Singh RK, Murty HR, Gupta SK, Dikshit AK. An overview of sustainability assessment methodologies. Ecol Indic 2012;15(1):281–99. [81] Harish VSKV Kumar A. Demand side management in India: action plan, policies and regulations. Renew Sustain Energy Rev 2014;33:613–24. [82] Ansari MF, Kharb RK, Luthra S, Shimmi SL, Chatterji S. Analysis of barriers to implement solar power installations in India using interpretive structural modeling technique. Renew Sustain Energy Rev 2013;27:163–74. [83] Luthra S., Kumar S., Garg D., Haleem A. Barriers to renewable/sustainable Energy technologies adoption: Indian perspective. Renew Sustain Energy Rev (in press); 2015. doi:10.1016/j.rser.2014.08.077. [84] Pazheri FR, Othman MF, Malik NH. A review on global renewable electricity scenario. Renew Sustain Energy Rev 2014;31:835945. [85] Dale VH, Efroymson RA, Kline KL, Langholtz MH, Leiby PN, Oladosu GA, et al. Indicators for assessing socioeconomic sustainability of bio energy systems: a short list of practical measures. Ecological Indicators 201387–102. [86] Luthra S, Kumar S, Kharb R, Ansari MF, Shimmi SL. Adoption of smart grid technologies: an analysis of interactions among barriers. Renew Sustain Energy Rev 2014;33:554–65. [87] Dey PK, Cheffi W. Green supply chain performance measurement using the analytic hierarchy process: a comparative analysis of manufacturing organizations. Prod Plan Control 2013;24(8 9):702–20. [88] Luthra S, Garg D, Haleem A. Identifying and ranking of strategies to implement green supply chain management in Indian manufacturing industry using analytical hierarchy process. J Ind Eng Manag 2013;6 (4):930–62. [89] Harputlugil T, Prins M, Gultekin T, Topcu I. Conceptual framework for potential implementations of multi criteria decision making (MCDM) methods for design quality assessment. In: Management and innovation for a sustainable built environment. June. Amsterdam, The Netherlands; 97890526939582011. p. 20–3. [90] Ordoobadi SM. Application of AHP and Taguchi loss functions in supply chain. Ind Manag Data Syst 2010;110(8):1251–69. [91] Ishizaka A, Labib A. Analytic hierarchy process and expert choice; benefits and limitations, or insight. Oper Res 2009;22(4):201–20. [92] Dubois D, Prade H. Operations in a fuzzy-valued logic. Inform Control 1979;43(2):224–40. [93] Kauffman A, Gupta MM. Introduction to fuzzy arithmetic: theory and application. New York: Van Nostrand Reinhold; 1991. [94] Chang DY. Extent analysis and synthetic decision. Optim Tech Appl 1992;1 (1):352–5. [95] Chang YH, Yeh CH, Cheng JH. Decision support for bus operations under uncertainty: a fuzzy expert system approach. Omega, Int J Manag Sci 1998;26(3):367–80. [96] Mangla SK, Kumar P, Barua MK. Flexible decision modeling for evaluating the risks in green supply chain using fuzzy AHP and IRP methodologies. Global J Flex Syst Manag 2014:1–17. http://dx.doi.org/10.1007/s40171-014-0081-x. [97] Herva M, Roca E. Review of combined approaches and multi-criteria analysis for corporate environmental evaluation. J Clean Prod 2013;39:355–71. [98] Chang CW, Wu CR, Lin CT, Chen HC. An application of AHP and sensitivity analysis for selecting the best slicing machine. Comput Ind Eng 2007;52(2) 296–307. [99] Govindan K, Kaliyan M, Kannan D, Haq AN. Barriers analysis for green supply chain management implementation in Indian industries using analytic hierarchy process. Int J Prod Econ 2014;147:555–68. [100] Haas R, Panzer C, Resch G, Ragwitz M, Reece G, Held A. A historical review of promotion strategies for electricity from renewable energy sources in EU countries. Renew Sustain Energy Rev 2011;15:1003–34.

S. Luthra et al. / Renewable and Sustainable Energy Reviews 47 (2015) 58–73

[101] Kang MS, Banga SS. Global agriculture and climate change. J Crop Improv 2013;27(6):667–92. [102] Backlund S, Thollander P, Palm J, Ottosson M. Extending the energy efficiency gap. Energy Pol 2012;51:392–6. [103] Espinosa N, Hösel M, Angmo D, Krebs FC. Solar cells with one-day energy payback for the factories of the future. Energy Environ Sci 2012;5(1):5117–32.

73

[104] Zuo J, Zhao ZY. Green building research – current status and future agenda: a review. Renew Sustain Energy Rev 2014;30:271–81. [105] World Energy Outlook. International energy agency; 2014. 〈http://www. worldenergyoutlook.org/〉 [accessed 22.08.14].