Design of an Ultra-Compact and Highly-Sensitive Temperature Sensor Using Photonic Crystal Based Single Micro-Ring Resonator and Cascaded Micro-Ring Resonator

Silicon - In the present report, a photonic crystal based micro-ring resonator (MRR) structure is proposed which is very compact in size and has very fast response and is employed for temperature...     

Substitution and price elasticity estimates using inter-country pooled data in a translog cost model

Pooled data across several developing countries and the U. S. were used to estimate long-run substitution and price elasticities in a translog framework for the paper, iron and steel, and aggregate manufacturing industries. While the quality of the estimates varies across the several industry-specific models, the results suggest higher values for these elasticities than appear commonly used in integrated assessment models. Estimates of own-price elasticities of energy range from − 0.80 to − 1.76 and are comparable to estimates from previous econometric studies in the context of developed countries (− 0.77 to − 0.87). Substitution elasticities show wider variation across countries and industries. For energy and capital they range from − 1.96 to 9.80, for labor and energy from 2.61 to 7.11, and for energy and material from − 0.26 to 2.07.     

Industrial energy efficiency and climate change mitigation

Industry contributes directly and indirectly (through consumed electricity) about 37% of the global greenhouse gas emissions, of which over 80% is from energy use. Total energy-related emissions, which were 9.9 GtCO₂ in 2004, have grown by 65% since 1971. Even so, industry has almost continuously improved its energy efficiency over the past decades. In the near future, energy efficiency is potentially the most important and cost-effective means for mitigating greenhouse gas emissions from industry. This paper discusses the potential contribution of industrial energy-efficiency technologies and policies to reduce energy use and greenhouse gas emissions to 2030.

Analysing energy intensity trends and decoupling of growth from energy use in Indian manufacturing industries during 1973–1974 to 2011–2012

This paper has two research objectives: first, it derives and analyses energy intensity trends for seven energy intensive manufacturing industries and the aggregate manufacturing sector in India for the period 1973–1974 to 2011–2012 and compares the same with best practice benchmarks. Second, based on Index Decomposition Analysis, it studies the extent to which the energy efficiency has contributed in the decoupling of industrial activity growth from growth in energy use. The study finds faster decline in energy intensity in all the seven industries during the recent years (1998–1999 to 2011–2012). Aluminium, cement and fertilizer industries are found to operate close to the global best-practice energy intensities with transformational changes in process technology. Iron and steel and pulp and paper are found to be lagging behind with only incremental transformation in technology in place. The decomposition results show that activity growth is the major driver of growth in energy demand with marginal impact coming from structural change. However, declining energy intensity has been able to neutralize a major portion of the growing energy demand resulting in decoupling trends, especially in recent years, with more energy efficiency-related voluntary and mandatory policies in place).     

Estimating energy-augmenting technological change in developing country industries

Assumptions regarding the magnitude and direction of energy-related technological change have long been recognized as critical determinants of the outputs and policy conclusions derived from integrated assessment models. Particularly in the case of developing countries, however, empirical analysis of technological change has lagged behind simulation modeling. This paper presents estimates of sectoral productivity trends and energy-augmenting technological change for several energy-intensive industries in India and South Korea, and, for comparison, the United States. The key findings are substantial heterogeneity among both industries and countries, and a number of cases of declining energy efficiency. The results are subject to certain technical qualifications both in regards to the methodology and to the direct comparison to integrated assessment parameterizations. Nevertheless, they highlight the importance of closer attention to the empirical basis for common modeling assumptions.     

Cost of oil-based decentralized power generation in India: Scope for SPV technology

In India growth of oil-based decentralized (backup and non-backup) power-generating systems is an outcome of the increasing demand for power with security in supply from consumers. Given the projections on demand for and supply of power through the centralized grid, growth of these systems is bound to be on the rise. The present study, based on primary data collected from a field survey, builds up a database for this decentralized power-generating sector to assess its role in the context of the Indian economy. Cost calculations and on-the-spot measurements of sound pollution and a standard estimate of air pollution from conventional oil-based power generators bring out clearly the problems of the existing systems. It has been shown that if pollution abatement costs and the scarcity value of diesel are included in cost calculation for widely used conventional diesel-based decentralized systems, along with standard accounting costs, then solar photovoltaic (SPV) technologies may be an ideal alternative to conventional oil-based systems in the decentralized power-generating sector. However, to encourage existing private entrepreneurs to go for this new technology, government intervention is necessary in a number of ways.     

Economic benefits of arsenic removal from ground water--a case study from West Bengal, India

Endocrine Disrupting Chemicals (EDCs) are of increasing concern because of their potential impacts on the environment, wildlife and human health. Pesticides and some pesticide metabolites are an important group of EDC, and exposure to them is a poorly quantified source of human and environmental exposure to such chemicals generally. Models for estimating human exposure to Endocrine Disrupting (ED) pesticides are an important risk management tool. Probabilistic models are now being used in addition to deterministic ones in all areas of risk assessment. These can provide more realistic exposure estimates, because they are better able to deal with variation and uncertainty more effectively and better inform risk management decisions. Deterministic models are still used and are of great value where exposure data are scarce. Models or groups of models that provide holistic human ED pesticide exposure estimates are required if the risk posed to humans by ED pesticides is to be better assessed. Much more research is needed to quantify different exposure routes such as exposure from agricultural spray drift and the medical use of pesticides to develop such models. Most available probabilistic models of human exposure were developed in the USA and require modification for use elsewhere. In particular, datasets equivalent to those used to create and apply the American models are required. This paper examines the known routes of human pesticide exposure with particular reference to ED pesticides and their quantification as unlike pesticides generally, many ED pesticides are harmful at very low doses, especially if exposure occurs during sensitive stages of development, producing effects that may not manifest for many years or that affect descendants via epigenetic changes. It also summarises available deterministic and probabilistic models commonly used to calculate human exposure. The main requirement if such models are to be used in the UK is more quantitative data on the sources and pathways of human ED pesticide exposure.