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For India, with its low agricultural productivity and huge population, land acquisition has always been a serious policy challenge in the installation of land-intensive power projects. India has experienced a large number of projects getting stalled because of land conflict. Yet, there is a paucity of literature pertinent to India that tries to estimate future land requirements taking into consideration of land occupation metric.

In the present study, the dynamic land transformation and land occupation metrics of nine energy sources, both conventional and renewable, are estimated to further determine the magnitude of land requirement that India needs to prepare itself to fulfil its Intended Nationally Determined Contribution (INDC) commitments. This is illustrated through two different scenarios of energy requirement growth rates, namely, conservative and advanced.

This analysis suggests that, while nuclear energy entails the lowest dynamic land transformation when land occupation metric is taken into account, waste to energy source possesses least land requirement, followed by coal-fired source. Hydro energy source has highest requirement both in terms of dynamic land transformation and land occupation. It is also seen that land requirement will be 96% and 120% more in INDC scenario than business as usual (i.e. if India continues with its current share of renewables in its energy portfolio in 2030) considering a conservative and an advanced growth rate, respectively.

Some policy recommendations are provided that may aid policymakers to better address the trade-off between clean energy and land and incorporate it into policy planning. This study has not been able to consider future technical efficiency improvement possibilities for all energy sources, which can be incorporated in the proposed framework for further insight.

This paper provides a framework for estimation of future land requirement to fulfil India’s INDC energy plans which is not available in existing literature. The authors confirm that this manuscript is an original work.

The purpose of this paper is to identify various risk and sub-risk drivers that affect the supply chain (SC) performance and to propose a framework to quantify the overall SC risk index by considering the importance of each risk and sub-risk drivers and their mutual interactions.

A hybrid method based on decision-making trial and evaluation laboratory and analytical network process has been proposed to develop the risk quantification framework. A case study of Indian petroleum supply chain (PSC) has been illustrated to explain the proposed method.

The results of this study found that transportation/logistics (delivery system), quality of the petroleum products, crude supply, customer’s order and legal/political regulations are the most significant risk drivers of a typical PSC. It is also found that the Indian PSC possesses a risk score of 34 percent.

The quantification of risk in operational measure provides an unblemished representation of the overall SC risk. Unlike the existing financial measure, it takes complex subjective operational effectiveness like product quality, customer satisfaction, etc., into consideration. Identifying the high-prioritized risks helps the decision and policy makers to merely focus on the most prominent risk drivers, and reduce the impact of overall SC risk. Planning a risk mitigation strategy at a given level of risk is however beyond the scope of this research.

The paper develops a risk quantification framework in the context of a PSC.

The purpose of this paper is to explore the relationship between various risk management strategies and risk management practices in order to design and hence enact a suitable supply chain risk mitigation (RM) plan. Additionally, this study proposes a hierarchical framework to explain the mutual relationship between supply chain risk management (SCRM) practices and strategies by considering the underlying dimensions between them.

An amalgamation of systematic literature analysis (SLA) and correspondence analysis (CA) has been performed to develop the conceptual framework. A real-life case of Indian petroleum supply chain has been considered to validate and explain the proposed model.

The results reveal three underlying dimensions, which associate the relationship between RM strategies. They are, risk adaptability of SC managers with a variance of 34.71%, followed by resource capability of the firm and the degree of sophistication of RM practices, with variances of 27.72 and 20.35%, respectively. Risk avoidance strategy comprises of practices such as supplier evaluation, technology adaption, flexible process and information security. On the other extreme, the risk sharing strategy includes revenue sharing, insurance, collaboration, public–private partnership and so on as essential RM practices.

The study not only focuses on the distinction between RM strategies and practices, which were used interchangeably in the prior literature, but also provides an association between the same by exploring the underlying dimensions. These underlying dimensions perform a crucial role while developing a risk management plan. This study explicitly focuses on the RM step of SCRM process. Pre and post risk mitigation phases of SCRM process, such as risk assessment and risk monitoring, are beyond the scope of the current research.

The paper develops a framework for mapping various RM strategies with their corresponding practices by considering the Indian petroleum supply chain as a viable case study. Various theoretical and business implications are derived in the context of the developing country.

An amalgamation of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) has been performed to develop a decision-making framework for improving the overall performance of the microfinance institutions. A primary survey was conducted to collect real-time data from the heterogeneous stakeholders of microfinance institutions across India. The validation of the proposed framework is performed by comparing the results against the conventional method of Analytical Hierarchy Process (AHP).

This study identifies various dimensions and indicators for measuring the performance of Indian microfinance institutions. Additionally, the ranking and prioritisation of the performance dimensions and indicators is obtained by considering the mutual interrelation between them.

The study indicates that there exists a significant dyadic relationship between financial performance and social performance for improving the overall performance of the microfinance institutions. Governance is found to unidirectionally influence both financial and social performance. Among all the considered dimensions, financial performance of a microfinance institution is the most critical dimension for improving the overall performance. The top five performance indicators of the Indian microfinance institutions are funding source, borrowing and overhead cost, size of the firm, end-use of the money and depth of outreach.

The study was conducted in the context of Indian microfinance institutions; hence the scope of generalisation of the results is limited. This research considers both subjective and objective aspect of the performance dimensions and indicators from the perspective of multiple stakeholders (i.e. firm, society and regulator). The integrated framework is expected to aid in improving overall performance of microfinance institutions by focusing on the most critical (high prioritised) performance indicators.

An integrated DEMATEL-ANP framework is used in the domain of microfinance to assess the performance dimensions. This study is unique in terms of analysing performance of microfinance institutions from the perspective of heterogeneous stakeholders.

Both energy and education have been positioned as priority objectives under the itinerary of UN development goals. Hence, it is necessary to address the implicit inter relationship between these two development goals in the context of developing nations such as India who are trying to grow in both per capita income and socio economic factors whilst struggling with the challenges of a severe energy supply constrained economy.

In the present study, the causal relationship between energy consumption per capita and education index (EI) as a proxy of educational advancement is investigated for India for 1990–2016 using the Johansen-Juselius cointegration test and vector error correction model.

The empirical results infer although energy consumption per capita and EI lack short run causality in either direction, existence of unidirectional long run causality from EI to per capita energy consumption is found for India. Further, it is observed that energy consumption per capita takes around four years to respond to unit shock in EI.

The findings from this study imply that with the advancement of education, a rise in per capita energy consumption requirement can be foreseen on the demand side, and hence, India’s energy policy needs to emphasize further its sustainable energy supply goals to meet this additional demand coming from a population with better education facilities.

The authors hereby confirm that this manuscript is entirely their own original study and not submitted elsewhere.

The purpose of this paper is to develop a decision support system (DSS) to assist supply chain (SC) risk managers to select a suitable risk management (RM) strategy and expedite the implementation of corresponding RM enablers. The relationship between RM strategies and RM enablers is explored by identifying the underlying factors between them, which is further used to build the DSS.

The DSS is built by integrating heterogeneous techniques. A systematic review approach is employed to explore both proactive and reactive RM enablers, and they are further mapped to various RM strategies by using correspondence analysis (CA). An in-depth interview is conducted to develop the rules for constructing the decision system. A rule-based fuzzy inference system (FIS) is utilized to counteract the uncertainty involved in the decision variables. The efficacy of the proposed DSS is demonstrated by considering two conjectural scenarios in the case of Indian petroleum SC (IPSC).

The results reveal three primary underlying factors between the risk mitigation strategies viz. SC managers' preparedness to face risk, organization's resource capability to deal with risk and the sophistication of the implementation of the RM enablers; with explained variances of 37%, 29% and 22%, respectively. Risk avoidance strategy comprises of RM enablers such as supplier evaluation, technology adaption, information security, etc. Whereas, the risk-sharing strategy includes revenue sharing, insurance, collaboration, public-private-partnership, etc. as essential RM enablers. The DSS recommends risk-mitigation and risk-sharing as effective RM strategies for the IPSC under the considered scenarios.

This paper develops a decision support framework for recommending an effective risk mitigation strategy and outranking the corresponding enablers. The study explicitly focuses on the risk mitigation step of the supply chain risk management (SCRM) process. Pre- and post-risk mitigation steps of the SCRM process, such as risk assessment and risk monitoring are beyond the scope of this research.

The operational procedure of the proposed DSS is explained by considering a real-life case of petroleum SC in the Indian scenario. The unique contributions of this study are presented as theoretical implications and managerial propositions in the context of a developing country.

The objective of this study is threefold–first, to develop a Regional Competitiveness Index (RCI) for measuring competitiveness of sub-national regions for India; second, to test this index for its ability to explain regional growth, which validates usage and applicability of this index; and third, to further investigate if the competitiveness of states is in turn caused by economic growth, i.e. it is tested if there is a bidirectional causality between competitiveness and regional growth.

The data of indicators used in the index are from sources available freely in public domain. The competitiveness index is constructed using equal weightage supported by principal component analysis (PCA) technique. The causal relationship analysis is done using panel data of 10 years from 2008 to 2017 for 32 Indian states/union territories. The generalized method of moments (GMMs) is used for this dynamic regression estimation.

Based on RCI score, states have been ranked and through rank analysis, the authors observe the performance status of these sub-national regions and are able to categorize them as improving, no change or deteriorating in regional competitiveness. Using the GMM estimation, the association between RCI and economic growth is found to be significant at 10% level. This shows that regional competitiveness as captured through the RCI score is able to explain regional economic growth and economic disparity among the sub-national units. Further, that RCI score is found to Granger-cause growth, while growth does not lead to better RCI scores. This establishes the usefulness of RCI as an important policy variable to compare states and provide direction for sectoral reforms.

The limitations of the study include (1) broad assumption that these sub-national regions belong to a uniform macro-economic and technology environment, and (2) data constraints as it is a longitudinal study. The study implies that the composite index could capture differences in regional competitiveness explaining regional economic disparity and that competitiveness causes higher economic growth and not vice versa.

The RCI score can prove to be a useful indicator of economic performance of different states and can be used by national and state policymakers to compare and assess regional disparity among different states. The pillar-wise scores will be useful for in-depth study of weakness and strength of the sub-national territories.

Construction of an RCI for sub-national territories and analysis of panel data for longitudinal study of ten years is unique in the regional competitiveness literature.

Developing composite index-regional entrepreneurship, technological readiness and institution quality index (RETRIQ) of regional entrepreneurship, technology readiness and quality of institution to measure regional competitiveness. This study, also, aims to test econometrically the effectiveness of the index in capturing the economic performance of the sub-national regions.

The data of eight indicators used in the index are from sources available freely in the public domain. The causal relationship analysis is done using panel data of 10 years from 2008 to 2017 for 32 Indian states/union territories. The generalized method of moments (GMM) is used for this dynamic regression estimation.

Based on RETRIQ, 32 states and union territories of India have ranked. The estimation using GMM shows a significant association between the composite index and economic growth.

The limitations of the study include the broad assumption that these sub-national regions belong to a uniform macro-economic and technology environment and data constraints as it is a longitudinal study. Then, the implication of the study is that the composite index-RETRIQ could capture differences in regional competitiveness explaining regional economic disparity.

The index will be useful for policy implications in the assessment of competitiveness disparity.

It is a composite index of regional entrepreneurship, technological readiness and quality of the institution. The panel data across states along 10 years series is novel.