A survey of data envelopment analysis in energy and environmental studies

Urban energy use and carbon emissions from cities in China and policy implications

Economic Growth, Carrying Capacity, andtheEnvironment

A literature study for DEA applied to energy and environment

Transport sector CO2 emissions growth in Asia: Underlying factors and policy options

http://directory.umm.ac.id/Data%20Elmu/jurnal/E/Energy%20Economics/Vol22.Issue4.2000/1107.pdf

Indicators of carbon emission intensity from commercial energy use in India

Purpose




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.




Design/methodology/approach




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.




Findings




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.




Research limitations/implications




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.




Originality/value




The paper develops a risk quantification framework in the context of a PSC.

A hybrid approach for quantifying supply chain risk and prioritizing the risk drivers: A case of Indian petroleum supply chain

In this paper long run structural relationship for freight transport demand is derived for railways in India using annual time series data for 1960–1995. Some of the recent developments in multivariate dynamic econometric time series modelling have been employed such as estimation of long-run structural cointegrating relationship, short-run dynamics and measurement of the effects of shocks and their persistence during the evolution of dynamic freight transport demand system. The models are estimated using a cointegrating vector autoregressive (VAR) modelling framework, which allows for endogeneity of regressors. Results indicate high GDP elasticity and low price elasticity, with real freight rate, i.e. the price variable behaving exogenously with respect to the system. Any disequilibrium in the short-run is likely to be corrected in the long run via adjustments in freight transport demand and GDP. Further, the demand system seems to be stable in the long run and converges to equilibrium in a period of around 3 years after a typical system-wide shock.

Barnali Nag

Papers

Indicators of carbon emission intensity from commercial energy use in India

A hybrid approach for quantifying supply chain risk and prioritizing the risk drivers: A case of Indian petroleum supply chain

A multivariate cointegrating vector auto regressive model of freight transport demand: evidence from Indian railways

Carbon emission coefficient of power consumption in India: baseline determination from the demand side

Estimation of carbon baselines for power generation in India: the supply side approach