EFFECT OF GREEN TECHNOLOGY INVESTMENT ON CRUDE OIL INVENTORY SYSTEM - A CASE STUDY BASED ON ONGC DATA Abhibasu Sen, Karabi Dutta Choudhury, Tapan Datta Yugoslav Journal of Operations Research, 2024 The carbon footprints are increasing in the environment at an alarming rate mainly due to unplanned human activities. The world?s population will continue to grow at a rapid pace in the future. As a result, our future generations may find it difficult to live on this planet in a healthy manner. The world?s developed and developing countries began to investigate various methods for reducing their carbon footprint. However, it will not be sustainable if it is also not economically viable. In this scenario, maintaining a good profit for businesses while reducing their carbon footprint necessitates a pragmatic strategy. In this article, we will try to find a way out that will provide us with a practical solution. We will simulate the profitability of an upstream oil manufacturer that has invested heavily in green technologies. Under a carbon tax system, we will use the production-inventory model. This system assumes that capital investment in green technology can reduce emissions, and increase profits. We used data from the Oil and Natural Gas Corporation of India, available in its annual reports. As a matter of fact, the Oil and Natural Gas Corporation of India accounts for 70% of crude oil production in India and is a major player in India?s upstream oil companies. The results were quite encouraging, with deviations between expected and actual values being less than 10%. The findings also led us to believe that the excise duty and the pollution control tax levied in India can be regarded as a Green Tax or Carbon Tax. We used a published research model to find the optimal solution.
A sustainable bi-objective inventory model with source-based emissions and plan-based green investments under inflation and the present value of money Tapan Kumar Datta, Sayantan Datta, Adrijit Goswami Operations Research and Decisions, 2024 The paper develops a finite-horizon inventory model with source-based emissions, plan-based green investments under inflation, and the present value of money. The cap-and-trade policy is used as the carbon policy. The model is solved in a bi-objective scenario where the two objectives are maximization of the present value of net profit and minimization of the total emission. We find the Pareto optimal solutions represented by a Pareto front using the ε-constraint method. A flowchart is provided to find the non-dominated solutions. Pareto solutions for three special cases (no inflation, carbon tax, and no green investments) are also derived. In our sensitivity analyses, we observe that the carbon quota does not affect the optimal policy. It only affects the optimum profit. Our model shows that green investment is beneficial for the polluting firm and also for the environment.
An analysis of crude oil prices in the last decade (2011-2020): With deep learning approach Abhibasu Sen, Karabi Dutta Choudhury, Tapan Kumar Datta Plos One, 2023 Crude Oil is one of the most important commodities in this world. We have studied the effects of Crude Oil inventories on crude oil prices over the last ten years (2011 to 2020). We tried to figure out how the Crude Oil price variance responds to inventory announcements. We then introduced several other financial instruments to study the relation of these instruments with Crude Oil variation. To undertake this task, we took the help of several mathematical tools including machine learning tools such as Long Short Term Memory(LSTM) methods, etc. The previous researches in this area primarily focussed on statistical methods such as GARCH (1,1) etc. (Bu (2014)). Various researches on the price of crude oil have been undertaken with the help of LSTM. But the variation of crude oil price has not yet been studied. In this research, we studied the variance of crude oil prices with the help of LSTM. This research will be beneficial for the options traders who would like to get benefit from the variance of the underlying instrument.
A multi-criteria decision-making approach to rank the sectoral stock indices of national stock exchange of india based on their performances Dev Gupta, , Akanksha Parikh, Tapan Kumar Datta, , and National Accounting Review, 2021 The ideal sector for an investment is a challenge for any capital market investor. This complexity is primarily attributed to the dynamic and volatile nature of public policies and macroeconomic factors that indirectly impact a sector's growth. In recent years, India in particular, has witnessed dynamic policies being implemented by the Government, such as Demonetization and Goods and Services Tax (GST), which led to sudden changes in the market forces. Thus, it is imperative that researchers focus on developing new scientific techniques for selecting the ideal sector to invest in capital markets. Understanding and analyzing a sector's behaviour is of prime importance to investors in any emerging capital market. This task appears to be complex as an investor needs to decide from a diverse set of sectors, where performance ranking can conflict with another for different variables. A volatile sector might be ranked higher on pure returns but would be ranked lower if risk-adjusted performance was considered. This is commonly referred to as a multiple criteria decision-making (MCDM) problem. In this paper, we consider the data of eleven Nifty sectoral indices from the National Stock Exchange (NSE) of India from January 2017 to December 2018. We apply three MCDM methods—COPRAS, SAW, and TOPSIS to rank the sectors and provide a holistic overview of their performance. Additionally, we propose a hybrid-ranking approach to solve the issue of divergent rankings from different MCDM techniques. We conclude that Nifty Financial service is the best performer in this volatile period. Once the rankings were obtained, we confirmed our results with actual fundamental events that took place in the economy. Through our research, potential investors can utilize our technique to rank the performance of sectoral indices for their specific region at any given time period.
An integrated production policy with defective items and stock-out based substitution under triangular dense fuzzy lock set environment Snigdha Karmakar, Sujit Kumar De, Tapan Kumar Datta, Adrijit Goswami RAIRO Operations Research, 2021 Brand substitution is common observed phenomenon in daily life. It is the decision makers’ economic understanding and potential scheme for business-industries. Also, it provides the flexibility in management and increases the ability to control the production. This works proposes an integrated supplier–retailer inventory model for substitutable products. Two suppliers work not works with two different brand products with their corresponding demand are involved and one retailer sells each of the products. To nullify the complexities of the joint optimization problem, we first develop a deterministic model for three cases: no substitution, partial substitution and full substitution, then we go for its fuzzification. Keeping the financial constraint of each producer, we have studied over the elasticity of the cost parameters by means of triangular dense fuzzy lock set approach with its locking and unlocking property for final decision making. Finally, sensitivity analysis and graphical illustrations are made to justify the model.
A New Green Efficiency-Based Carbon Taxing Policy and Its Effects on a Production-Inventory System with Random Carbon Emissions and Green Investment Tapan Kumar Datta Advances in Operations Research, 2020 In this study, the author proposes a new carbon taxing policy. This proposed carbon tax has two tax components. The first component is constant, and the second component depends on the green efficiency of production. The green efficiency of production is measured by the average amount of emissions per unit production in an assessment year. The green efficiency-based tax component can be reset every year. Lesser average emission rate indicates better green efficiency. The second component of this proposed carbon tax forces the firm to improve the green efficiency of production, which results in cleaner production. The author incorporates this new carbon tax policy in a production-inventory system with a price-sensitive demand rate. A rule is provided for the implementation of this new tax. Emissions during setup, production, and storage are considered as independent random variables. The firm has the opportunity of investing in green technologies to improve green efficiency. A profit maximization policy is adopted to solve the developed model. A solution algorithm is also provided. The model is illustrated by numerical examples with randomly generated model parameters. The results of numerical examples show the environmental benefits of the proposed carbon tax.
Effect of Green Technology Investment on a Production-Inventory System with Carbon Tax Tapan Kumar Datta Advances in Operations Research, 2017 Carbon emissions play the central role in global warming. Manufacturing firms are significant contributors to carbon emissions. In many countries, regulatory authorities are taking actions to reduce emissions. Carbon taxation and cap-and-trade schemes are two mechanisms implemented in many countries. In the present paper, the author analyzes a production-inventory model under a carbon tax system. The production rate is assumed to be a decision variable and can be set at any level within machine limits. A proportion of items produced are defective, and this proportion depends on the production rate. Demand depends on the selling price. Unit price is a decreasing function of the production rate. Emissions can be reduced to some extent by capital investment on green technology, and this capital investment amount is a decision variable. Customers are categorized as retail customers and wholesale customers. A discount is offered to the wholesale customers on the regular selling price. The results are illustrated by a numerical example and a sensitivity analysis is performed.
