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A declining trend in Indian summer monsoon precipitation (ISMP) in the latter half of the 20th century is a scientifically challenging and societally relevant research issue. Heavy aerosol loading over India is one of the key factors in modulating the ISMP. Using the state-of-the-state-of-the-art chemistry-climate model, ECHAM6-HAMMOZ, the impacts of South Asian anthropogenic sulfate aerosols on the Indian summer monsoon precipitation were investigated against: (1) 2010 La Niña (excess monsoon), (2) 2015 El Niño (deficit monsoon) in comparison to (3) normal monsoon 2016. Sensitivity simulations were designed with 48% enhancement in South Asian SO 2 emissions based on a trend estimated from Ozone Monitoring Instrument (OMI) satellite observations during 2006–2017. The model simulations showed that sulfate aerosols reduce ISMP by 27.5%–43.3 %, while simulations without sulfate loading enhanced ISMP by 23% in 2010 La Niña and reduction by 35% in 2015 El Niño. This paper reports that sulfate aerosols loading over India reduce precipitation by aerosol-induced direct and indirect effects by inducing atmospheric cooling, weakening in the convection, and reduction in moisture transport to Indian landmass. This paper emphasizes the necessity of alternate use of energy to reduce sulfate aerosol emissions to solve water issues in South Asia. [Display omitted] • Sulfate emission reduces Indian summer monsoon precipitation through aerosol-radiation and aerosol-cloud interactions. • Over 27% of the summer monsoon rainfall deficit in a normal year are linked to Asian anthropogenic sulfate emissions. • Sulfate-induced rainfall reduction is the maximum for La Niña year. • SO 2 loading increases atmospheric stability and weakens moisture transport to India. [ABSTRACT FROM AUTHOR]

Irrigation is an important adaptation strategy to cope with climate change which reduces vulnerability to water stress and improves crop productivity to feed millions. There is evidence of crop yield stagnation in many developing countries, and irrigation efficiency is claimed to increase crop productivity. Therefore, this paper uses data envelopment analysis to evaluate the farmer's productivity through technical efficiency (TE), i.e., the relationship between resource inputs and outputs of 513 paddy farmers in Eastern India. The results show that the farms are, on average operating at 14% TE, leaving a considerable scope to improve up to 86% to reach the optimal level. A significant difference is observed between irrigated and rain-fed paddy farmers, such that10% of the irrigated farms achieved efficiency scores over 40% and only 2% of rain-fed farms achieved the same. The tobit and beta fit regression models are estimated to find out the factors that influence the TE. Both surface water and groundwater sources of irrigation are used as predictors, along with other socio-demographic factors. Access to surface water irrigation is identified to be a significant determinant of farm efficiency, however, surface water irrigation, such as canal irrigation, is accessible only to farmers living on plain land. Farmers living on highlands need to explore other sources of irrigation practices, such as drip and sprinkler, that can increase TE and farm productivity. Therefore, this paper calls for government intervention to provide extensive training and facilities for these micro-irrigation practices. • Data Envelopment analysis was estimated to derive the paddy efficiency among the tribal villages of eastern India. • The DEA estimates show that average Technical efficiency in the study region is much below the national average. • Irrigated farmers are more efficient than the rain-fed farmers. Surface irrigation is the dominate factor. • Rain-fed paddy cultivators' farm efficiency would improve by 6.5%, if they had irrigated. • Semi-mountainous regions with little access to surface irrigation must explore micro-irrigation practices such as drips and sprinklers. [ABSTRACT FROM AUTHOR]

Artificial intelligence has evolved into a mechanism that can predict environmental changes and investigate climatic conditions. In recent days, extreme precipitation and flooding have resulted in huge damage to the society of human beings and farmers. In real-life, the climatic conditions were predicted using huge collected data. However, the collected data are not utilized properly due to the presence of uncertainty. Most of the research was carried out in time series data for classification and prediction. However uncertain data leads to erroneous predictions. Thus, an integrated model is required to overcome the uncertainty and to improve the prediction accuracy. The paper aims in integrating the Rough Set on Fuzzy Approximation Space (RSFAS) with a Deep Learning (DL) techniques to predict the precipitation level in the southern coastal areas of India in a seasonal way. The proposed model can handle uncertainty using an RSFAS whereas the DL technique is utilized for classification and prediction. The developed model is estimated based on various evaluation metrics and optimizers, benchmarked with existing Deep Learning and Machine Learning (ML) techniques to demonstrate the prediction accuracies obtained with minimized error rate. Consequently, the proposed model assists in understanding the climatic conditions in extreme events to provide a prior warning about heavy rainfall to the people who live near the coastal areas. • Towards the development of a low-cost solution for seasonal precipitation prediction. • Rough Set on Fuzzy Approximation Space with LSTM used for precipitation prediction. • High accuracy as impact of tuning hyperparameters with adam optimizer for prediction. [ABSTRACT FROM AUTHOR]

Circular economy principles can lead to a series of opportunities to tackle climate change, particularly in cities of developing countries where the generation of waste, consumption and demand for energy is growing rapidly. This paper aims to advance the understanding of the expansion of circularity for tackling climate change using biomass for energy at the urban scale. In addition, little is known about how Circular Economy (CE) flows can be quantitatively integrated, what potential trade-offs exist in promoting Circular Economy using biomass, and how governance models can integrate these flows and contribute to a Circular Economy transition in cities. Based on an empirical study of a city in India, where the private sector participation through an innovative Public-Private Partnership (PPP) model enabled the use of urban wastewater in thermal power plants, the study presents the viability of Circular Economy models at the city scale. The feasibility of a further expansion of this circularity by co-firing biomass in local thermal power plants was analyzed, as a potential solution for reducing pollution and carbon emissions in a hard-to-abate sector. The result indicated an average 20.5 MJ kg−1 calorific value for torrefied mixed biomass pellets from crop residues, which, if applied to about 447 kt y−1 surplus biomass available in Nagpur would amount to 9.18 PJ of energy (per year). Overall, local power plants demanded about 19.85 Mt of coal in 2017–18, equivalent to 297.8 PJ in that fiscal year. Thus, the supply of torrefied pellets from peri-urban crop residues can meet between 2 and 3% of the demand from coal-fired power plants, resulting in a 0.3% decrease in the emissions from the power sector in India in 2017–18 (i.e., 922 MtCO 2), benefiting different sectors and levels of government. The analysis provides a framework for understanding the technical and institutional possibilities for strengthening the relationship between circular economy and climate change in the cities of developing countries by connecting flows and systems within and beyond urban centers. Therefore, the expansion of Circular Economy in cities can occur by integrating urban sectors and policy coordination at different levels of governance. Partnerships between local governments, utilities, and civil society for co-managing energy, water, and waste in cities can reduce the financial burden on national governments, including meeting national emission targets. [Display omitted] • Urban circular economy can be advanced using biomass in thermal power plants. • Our model estimates biomass contribution to national decarbonization targets. • Biomass pellets can meet between 2 and 3% of the demand from local coal-fired power plants. • Such replacement accounts for a 0.3% decrease (or 922 MtCO 2) in the emissions from India's power sector. • Results contribute to the design of CE models for cities and regions by including different levels of government and sectors. [ABSTRACT FROM AUTHOR]

There is anxiety that has been rising vis-à-vis the climate changes in the entire world. Notwithstanding this, the primary purpose of this paper is to examine the board characteristics and carbon emission performance. This study applied the fixed effect model, 2SLS, and multiple regression analyses data from 220 non-financial listed companies on the BSE index and covering a period of 7 years from FY 2014-15 to FY 2020-21. The estimated results confirmed that board size, board meetings, board gender diversity, CEO duality, and ownership concentration have a negative association with carbon emission performance. In addition, board independence and the environmental committee are found to be positively associated with carbon emission performance based on 2 SLS and multiple regression analyses, and these results imply that they improve carbon performance. Overall, this study's shreds of evidence suggest that governing boards tend to pay attention to company's carbon performance and without mitigating original carbon performance is not likely to reduced GHGs emissions. This study's outcomes have significant implications for policymakers, corporate bodies, creditors, and regulators regarding the efficiency of corporate governance mechanisms in communicating climate change risks. However, corporate boards can use these results to find positive aspects of companies and formulate possible corporate governance modifications in relation to carbon policies. [Display omitted] [ABSTRACT FROM AUTHOR]

Coal-fired energy generation is the backbone of India's power sector and considered a driver of its economic development. However, it is associated with detrimental environmental and health impacts in India and its fleet is currently struggling with overcapacity and inefficiency problems. One solution to address these challenges is the early retirement of some of India's coal-fired power plants. In this paper, we introduce multidimensional indices that identify plants for retirement based on comprehensive criteria that include technical and economic characteristics of plants as well as their environmental impacts. We implement an ensemble approach, where we formulate 8008 indices based on all possible combination of seven relevant parameters and rank plants accordingly. This approach facilitates a comprehensive analysis of the plants' performance on different parameters and provides a new outlook on plant retirements that differs from the common approach of retiring plants based solely on technical characteristics such as age, capacity, and heat rate. Our results show that top plants recommended for early retirement are typically 7 years older, 13% more expensive and have around 40% higher population exposure to emissions compared to an average plant in India. We estimate the potential costs saved from the retirement of the worst-performing 50 GW of generating capacity to be $21 billion resulting from shifting ownership towards a cheaper cost of capital and replacing coal by more competitive sources such as solar power. • Identify plants for early retirement based on technical, economic and environmental indicators. • Worst performing 50 GW capacity are older, more expensive to operate and have higher population exposure. • Costs saved from retiring 50 GW capacity and replacing them with solar are $21 billion. • A multidimensional approach is essential for a politically feasible phaseout of coal. [ABSTRACT FROM AUTHOR]