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The purpose of this paper is to examine the dynamics between banking penetration, infrastructure development and regional growth within a multivariate framework in 23 Indian states over the period 2000-2012.

The study employs the multivariate panel data framework to analyze the dynamics between banking penetration, infrastructure development and regional growth within the vector error correction model (VECM) framework.

The findings confirm the long-run equilibrium relationship between banking penetration, infrastructure and income for the panel. Long-run income elasticity of infrastructure, estimated using Panel dynamic ordinary least square, is positive, statistically significant and has a value of 0.1531. Further, results show bidirectional causality between income and aggregate infrastructure and unidirectional causality running from banking penetration to income and aggregate infrastructure in the long run. However, there is unidirectional causality running from income to banking penetration and aggregate infrastructure and from banking penetration to aggregate infrastructure in the short run.

The study mainly concentrates on the 2000-2012 period and includes transportation (roadways and railways), energy (including electricity) and telecommunication as indicators for infrastructure, as the data for these sectors are easily available at the state level. Second, this study employs the panel data technique as it has a shorter data count.

In order to minimize the existing regional disparity in a developing India, national infrastructure policies should be aimed toward improving the overall access to as well as the quality of infrastructure (existing as well as newly planned). Further, widening the banking outreach at the bottom level may further help the economy as well as the infrastructure sector in mobilizing long-term finances for productive investments, in order to have a balanced, more inclusive and faster growth in the long run.

The study employs panel unit root, cointegration and Granger causality tests within the panel VECM framework to explore the dynamics among the system variables. Further, the study creates a composite index of infrastructure with principle component analysis.

The purpose of this paper is to estimate the economic (namely cost, revenue and profit) efficiency and its association with intellectual capital of 37 BSE-listed Indian banks over the period 2005–2018.

This study employs truncated Tobit regression to compute the relationship between intellectual capital and estimated cost, revenue and profit efficiency using Data Envelopment Analysis (DEA) for the 37 BSE-listed Indian banks within the panel data framework.

Estimates suggest that banks’ overall annual average cost, revenue and profit efficiency are 0.4466–0.7519, 0.4825–0.8773 and 0.4905–0.8803, respectively, during the sample period. Further, Tobit regression results indicate that the aggregate intellectual capital (value-added intellectual coefficient or Modified Value-added Intellectual Capital) has a positive but minimal impact on these efficiency parameters at 1 percent significance level for the overall sample as well as public sector banks. Among all the sub-components of intellectual capital, human capital, structural capital and relational capital have a positive and moderate impact on these efficiency measures for the overall sample. Control variables, particularly bank size, are significant drivers of the estimated efficiency of banks.

Findings suggest that banks should invest adequately to enhance their overall intellectual capital to further augment these economic efficiency measures in the long run.

This study computes cost, revenue and profit efficiency of 37 BSE-listed banks based on DEA followed by intellectual capital using the Pulic approach (1998 and 2000) and the Bontis (1998) approach in the first stage. Later, it examines the dynamics between the computed efficiency parameters and intellectual capital using Tobit regression within the panel data framework.

The purpose of this paper is to examine the dynamics between income diversification and performance (cost, profit, revenue, technical, pure technical and scale efficiency) for 38 listed Indian banks within panel data framework during the period 2004-2005 to 2015-16.

This study computes bank’s cost, profit, revenue, technical, pure technical and scale efficiency within intermediation approach with data envelopment analysis (DEA) as a performance indicator, followed by exploring the association between income diversification and bank performance using truncated Tobit regression within panel data framework.

Tobit regression results revealed inverted U-shaped relationship between the income diversification and estimated efficiency parameters for the overall panel. Size and bank intermediation ratio seems to be a major factor in exploiting the potential benefits of income diversification. The author reconfirmed the inverted U-shaped relationship with these efficiency parameters for exclusive subsamples consisting of government-owned and private sector banks.

Inverted U-shaped relationship between the income diversification and estimated efficiency parameters suggest that banks should go for limited diversification to improve performance. Thus, regulators and banks should pursue limited diversification strategy for improving banking efficiency.

This study computes bank performance (cost, profit, revenue, technical, pure technical and scale efficiency) based on DEA followed by exploring the association between performance and income diversification for 38 Bombay stock exchange listed banks.

The purpose of this paper is to explore and explain the linkage between intellectual capital (IC) efficiency of banks and their performance.

In total, 39 public and private banks listed in Bombay Stock Exchange from 1999 to 2015 were considered for the study. Panel fixed effects technique is used to draw inferences.

Results of the study provide evidence of positive association between IC and performance of banks; however, only human capital and structural capital have shown instances of significant positive linkage with banks performance. The results also indicate that the IC efficiency of private sector banks is better than public sector banks in India.

This study may enable Indian banking firms to measure their IC efficiency and develop policies to promote and improve upon their intellectual potential to enhance banks performance.

It is a novel study in Indian context that considers interaction variables in extending the prior understanding of the role of IC in enhancing banks performance, which may build sustainable advantage for banks in emerging economies like India.

The purpose of the paper is to empirically examine the relationship between energy consumption and economic growth for a panel of five South Asian economies, namely, India, Pakistan, Bangladesh, Sri Lanka and Nepal over the period from 1971 to 2010 within a multivariate framework.

The study uses Pedroni cointegration and Granger causality test based on panel vector error correction model to examine long-run equilibrium relationship and direction of causation in the short and long run between energy consumption and economic growth using energy inclusive Cobb–Douglas production function for a panel of five South Asia countries, namely India, Pakistan, Bangladesh, Sri Lanka and Nepal.

Pedroni’s panel cointegration test indicates the long-run equilibrium relationship between economic growth per capita, energy consumption per capita and real gross fixed capital formation per capita for panel. Further, 1 per cent increase in energy consumption per capita increases the gross domestic product (GDP) per capita by 0.8424 per cent for the panel. Causality results suggest bidirectional causality between energy consumption per capita, gross fixed capital formation per capita and GDP per capita in the long run and unidirectional causality running from energy consumption per capita and gross fixed capital formation per capita to GDP per capita in the short run.

These South Asian countries should implement an expansionary energy policies through improving the energy infrastructure, energy efficiency measures and exploiting massive renewables’ availability for low-cost, affordable clean energy access for all, especially in the yet unserved rural and remote areas for further stimulating economic growth.

Implementing energy efficiency measures and massive renewables development (wind, solar and hydropower) may help the affordable and clean energy access and reducing fossils fuel dependence and its associated greenhouse emissions in South Asia.

The purpose of this paper is to empirically examine the relationship between energy consumption, carbon emissions and economic growth for a panel of five South Asian economies namely India, Pakistan, Bangladesh, Sri Lanka and Nepal over the period 1972-2009 within multivariate framework.

The study uses Pedroni cointegration and Granger causality test based on panel vector error correction model to examine long-run equilibrium relationship and direction of causation in short run and long run between energy consumption, carbon emissions and economic growth in South Asia.

Cointegration result indicates the long-run equilibrium relationship between economic growth, energy consumption and carbon emissions for panel. Causality results suggest that bidirectional causality exist between energy consumption-GDP, and unidirectional causality from carbon emissions to GDP and energy consumption in long run. However, energy consumption causes carbon emissions in short run.

Implementing energy efficiency measures and reducing dependence on fossils fuels by scaling up carbon free energy resources like nuclear, renewables including hydropower in energy mix is necessary for sustainable and inclusive growth in the region.

South Asia economies need to sacrifice economic growth for reducing the carbon emissions in long run if the region dependence on fossils fuels including coal, oil and natural gas in energy mix continues at same pace.

The purpose of this paper is to empirically examine the long run and causal relationship between energy consumption, carbon emissions and economic growth in India over the period 1971-2009 within multivariate framework.

The study uses the Johansen cointegration test to examine the possible long-run equilibrium relationship followed by Granger causality test based on vector error correction model to explore short- and long-run causality between energy consumption, carbon emissions and economic growth in India.

Cointegration result indicates the long-run equilibrium relationship between economic growth, energy consumption and carbon emissions. Further causality results suggest unidirectional causality running from energy consumption and carbon emissions to economic growth in long run, energy consumption to carbon emissions, carbon emissions to economic growth and economic growth to energy consumption in short run.

There is urgent need of policy development toward boosting energy efficiency, developing alternative carbon-free energy sources like nuclear, renewables and expansion of affordable energy for faster, sustainable and more inclusive growth for India in upcoming years.

India, an energy-dependent economy needs to effectively implement energy efficiency measures, super critical technologies in power plants, and investment in renewable energy resources in order to minimize the dependence on fossil fuels and carbon emissions for faster, more inclusive and sustainable growth.