Costs of Reducing Greenhouse Gas Emissions: A Case Study of India’s Power Generation Sector

If India were to participate in any international effort towards mitigating CO2 emissions, the power sector which is one of the largest emitters of CO2 in the country would be required to play a major role. In this context the study estimates the marginal abatement costs, which correspond to the costs incurred by the power plants to reduce one unit of CO2 from the current level. The study uses an output distance function approach and its duality with the revenue function to derive these costs for a sample of thermal plants in India. Two sets of exercises have been undertaken. The average shadow prices of CO2 for the sample of thermal plants for the period 1991-92 to 1999-2000 was estimated to be respectively Rs.3380.59 and Rs.2401.99 per ton for the two models. These shadow prices can be used for designing environmental policies and market-based instruments for controlling pollution in the power sector in India.Marginal Abatement Costs, Distance Function, CO2 Emissions, Shadow Prices, Power Generation Sector

Estimation of marginal abatement costs for undesirable outputs in India's power generation sector: An output distance function approach.

Many production activities generate undesirable byproducts in conjunction with the desirable outputs they produce. The present study uses an output distance function approach and its duality with the revenue function to estimate the marginal abatement cost of CO2 emissions from a sample of thermal plants in India. Two sets of exercises have been undertaken. The marginal abatement cost is first estimated without considering the distinction between the clean and the dirty plants (model-1) and then by differentiating between the two (model-2). The shadow prices of CO2 for the coal fired thermal plants in India for the period 1991-92 to 1999-2000 was found to be Rs. 3,380.59 per ton of CO2 as per model-1 and Rs. 2401.99 per ton of CO2 as per model-2. The wide variation noticed in the marginal abatement costs across plants is explained by the ratio of CO2 emissions to electricity generation, the different vintages of capital used by different plants in the generation of electricity and provisions for abatement of pollution. The relationship between firm specific shadow prices of CO2 and the index of efficiency (ratio of CO2 emission and electricity generation) points to the fact that the marginal cost of abating CO2 emissions increases with the efficiency of the thermal plant.Power sector

Interpretation of Semantic Tweet Representations

Research in analysis of microblogging platforms is experiencing a renewed surge with a large number of works applying representation learning models for applications like sentiment analysis, semantic textual similarity computation, hashtag prediction, etc. Although the performance of the representation learning models has been better than the traditional baselines for such tasks, little is known about the elementary properties of a tweet encoded within these representations, or why particular representations work better for certain tasks. Our work presented here constitutes the first step in opening the black-box of vector embeddings for tweets. Traditional feature engineering methods for high-level applications have exploited various elementary properties of tweets. We believe that a tweet representation is effective for an application because it meticulously encodes the application-specific elementary properties of tweets. To understand the elementary properties encoded in a tweet representation, we evaluate the representations on the accuracy to which they can model each of those properties such as tweet length, presence of particular words, hashtags, mentions, capitalization, etc. Our systematic extensive study of nine supervised and four unsupervised tweet representations against most popular eight textual and five social elementary properties reveal that Bi-directional LSTMs (BLSTMs) and Skip-Thought Vectors (STV) best encode the textual and social properties of tweets respectively. FastText is the best model for low resource settings, providing very little degradation with reduction in embedding size. Finally, we draw interesting insights by correlating the model performance obtained for elementary property prediction tasks with the highlevel downstream applications.Comment: Accepted at ASONAM 2017; Initial version presented at NIPS 2016 workshop can be found at arXiv:1611.0488