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Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level.

In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years.

From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies.

The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company.

In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

This study aims to focus on identifying the business risks that cause a delay in the oil and gas projects and suggest the way forward toward the better development of the city gas distribution (CGD) sector in India by suggesting the appropriate mitigation strategies.

The study is a systematic review of literature on risks causing a delay in oil and gas projects. Comprehensive literature was carried out following a seven-step model to develop an exhaustive list of risk classifications and factors, risk identification methods and strategies to mitigate the risks. Weighted average ranking method is used to identify the top ten risks affecting oil and gas projects.

This research identifies the top ten risks frequently impacting the oil and gas projects, which are project cost, improper project management, change in economic parameters, currency exchange rate, government regulations and laws, contractor and subcontractors issues, lack of skilled labor, delay in approvals, health and safety issues and force majeure. These risks are primarily responsible for cost overrun and project delay. Additionally, this study recommends the implementation of joint risk management to avoid CGD project delay.

The CGD industry is in the growing stage with many projects under construction. However, there is a lack of research to manage risks in the CGD project. This study contributes to the limited literature available on risk management in oil and gas projects. Additionally, it highlights the need for further research to explore the different risks factors affecting the CGD business and its operations and subsequently develop appropriate mitigation strategies.

The purpose of this paper is to identify and model critical barriers to sustainable practices implementation in Indian oil and gas sectors by the interpretive structural modeling (ISM) approach.

In this paper, through exhaustive literature survey and experts opinions, 14 critical barriers were identified, and the ISM tool, which is a multi-criteria decision-making approach, was used to establish interrelationship among the identified barriers and to determine the key barriers having high driving power.

After analyzing, it was found that six barriers, namely, market competition and uncertainty (B7), shortage of resources (B8), governmental rules and regulations (B1), knowledge and training (B2), financial implications (B3), and management commitment and leadership (B5) were found to have high influencing power. These barriers need the maximum attention and organizations need to overcome these hindrances for the effective implementation of sustainable practices. From the driving and dependence power diagram, two barriers, namely, management commitment and leadership (B5) and knowledge and training (B2) were found to have the highest driving power and two barriers, namely, lack of green initiatives (B9) and lack of corporate social responsibility (B14) were found to have highest dependence power.

The presently developed model is based on the experts’ opinions, which may be biased, influencing the final output of the structural model. The research implications of the developed model are to help managers of the organization in understanding significance of the barriers, to prioritize or eliminate the same for the practical implementation of sustainability.

This is for the first time an attempt has been made to apply the ISM methodology to explore the interdependencies among the critical barriers for Indian oil and gas industries. This paper will guide the managers at various levels of an organization for effective implementation of the sustainable practices.

In this paper, the authors aim to investigate the return, volatility and correlation spillover effects between the crude oil market and the various Indian industrial sectors (automobile, financial, service, energy, metal and mining, and commodities sectors) in order to investigate optimal portfolio construction and to estimate risk minimizing hedge ratios.

The authors compare bivariate generalized autoregressive conditional heteroskedasticity models (diagonal, constant conditional correlation and dynamic conditional correlation) with the vector autoregressive model as a conditional mean equation and the vector autoregressive moving average generalized autoregressive conditional heteroskedasticity model as a conditional variance equation with the error terms following the Student's t distribution so as to identify the model that would be appropriate for optimal portfolio construction and to estimate risk minimizing hedge ratios.

The authors’ results indicate that the dynamic conditional correlation bivariate generalized autoregressive conditional heteroskedasticity model is better able to capture time‐dynamics in comparison to other models, based on which the authors find evidence of return and volatility spillover effects from the crude oil market to the Indian industrial sectors. In addition, the authors find that the conditional correlations between the crude oil market and the Indian industrial sectors change dynamically over time and that they reach their highest values during the period of the global financial crisis (2008‐2009). The authors also estimate risk minimizing hedge ratios and oil‐stock optimal portfolio holdings.

This paper has empirical originality in investigating the return, volatility and correlation spillover effects from the crude oil market to the various Indian industrial sectors using BVGARCH models with the error terms assumed to follow the Student's t distribution.

The case describes the policies followed by the Government of India to attract private investments for Oil & Gas exploration. This case is based around observations made by the Comptroller and Auditor General of India on some of the petroleum sharing contracts and the remedial measures suggested by a committee appointed by the Government. The case describes how such contracts are structured elsewhere and raises issue about how such contracts can be structured and managed by the state.

The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.

This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.

This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.

In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.

Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

The purpose of this paper is to demonstrate importance of usage of sector indices which provides insight for sector specific investment strategies and direction for suitable policy formulation for the Indian industry. It investigates long run, short run and causality relationships between eight identified sector indices and Sensex for the post subprime period.

The study uses Vector Error Correction Model (VECM) for econometric analysis. It employs Generalized Impulse Response and Variance Decomposition analysis for developed multivariate framework in order to provide information about precise interplay of the sector indices.

Long-term relationships between sector indices were determined by the usage of VECM indicating minimal benefits from diversifying investments to different sectors. Limited lead – lag short run relationships between sector indices were observed. Banking index played a predominant and integrating role in moving other indices. During this period of recovery; most sectors were protected and provided marginally better returns due to robust Banking policy. Realty and Metal were other significant drivers influencing remaining sectors contemporaneously. The study for the post subprime crisis period helps to understand the importance and behavior of interrelated sector indices and Sensex in the dynamic economic environment.

The study clearly provides direction for sector specific investment strategies and policy formulation.

The study highlights utility and importance of usage of sector indices. No study using sector indices for the Indian economy have been done earlier employing VAR for the post subprime crisis period.

Rising energy demand and the quest for achieving climate change targets have been pushing emerging markets like India to bolster the natural gas share in their energy mix. The country has set an aggressive target of increasing natural gas share in the energy mix to 15% by 2030. The purpose of this study is to acknowledge the need for adopting and developing strategies for natural gas business market development to ensure a reliable supply at an affordable price. Hence, this study explores the natural gas market business development strategies and assesses them through cause/effect analysis.

This study proposed an integrated framework based on the Grey concept and Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique to assess and determine the interdependence among the natural gas business market development strategies by cause-and-effect group analysis. The application of Grey theory reduced the uncertainty and subjectivity involved in the decision-making process. Later, sensitivity analysis is also performed to check the robustness of the framework.

The natural gas business market development strategies are identified through a systematic literature search and contributions from industry experts. The findings of this study highlight the importance of developing pipeline and storage infrastructure facilities, ensuring supply security through long-term imports and overseas investment, implementing free-market-based pricing, simplification and standardization of regulatory processes at state and national levels, etc., for the development of the natural gas market development in India.

This study acknowledges the natural gas market development strategies and evaluated them into cause-and-effect groups which are limited to Indian context. All evaluations in the Grey-based DEMATEL method were made in this study based on the decision team inputs which limits the generalization to other geographies. Moreover, the opinions of the experts can be subjective and differ. The selection of the experts is done through non-probability sampling process.

This study could support the government and decision-makers in formulating the appropriate strategies to develop the domestic natural gas market. The cause-and-effect relationships are helpful for the companies, management, government, regulators and other stakeholders to understand the criticality of the causal strategies that must be implemented for developing the favorable natural gas business market scenario.

This study explores and evaluates the strategies that successfully bolster the natural gas business demand in India using Grey-based DEMATEL framework. By focusing on those critical strategies, relevant stakeholders would ensure a reliable natural gas supply at affordable prices.