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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]

Retiring coal plants as part of a decarbonization strategy has started to become a reality in the last five years and is reflected in the least-cost plans of many countries. However, major issues go beyond the scope of such planning, including commercial contracts, markets and asset stranding. This paper proposes a new model formulation that endogenously calculates stranded assets taking into consideration commercial and market issues. It can be used to develop insights into economic and financial issues surrounding stranded costs of coal retirement. An illustrative example around the existing 209 GW coal fleet, including an aging part of the coal fleet of 100 GW, in India is used to show how the model can be implemented with relative ease. A second case study is constructed for the Philippines, which has a much younger and uniform coal fleet. Comparing and contrasting the two case studies is valuable for understanding the critical drivers of stranded costs. • Achieving decarbonization requires finding estimates of stranded coal plant assets. • Proposed model provides a practical way for policymakers, regulators, and planners. • The model can be built off the country/utility generation expansion plans. • For India, the stranded asset costs do not exceed $0.45 m/MW. • For Philippines stranded costs do not exceed $0.4 m/MW for both price regimes. [ABSTRACT FROM AUTHOR]

Globally, there are 2 billion 'informal' workers, who lack access to social protection while facing profound health risks and socioeconomic exclusions. The informal economy has generated most jobs in Low and Middle-Income Countries (LMICs), but few studies have explored informal workers' complex health vulnerabilities, including in the face of climate change. This paper will discuss recent action-research in Indore (India), Harare, and Masvingo (Zimbabwe) with informal workers like vendors, waste-pickers, and urban farmers. We conducted qualitative interviews (N = 110 in India), focus group discussions (N = 207 in Zimbabwe), and a quantitative survey (N = 418 in Zimbabwe). Many informal workers live in informal settlements ('slums'), and we highlight the interrelated health risks at their homes and workplaces. We explore how climate-related threats—including heatwaves, drought, and floods—negatively affect informal workers' health and livelihoods. These challenges often have gender-inequitable impacts. We also analyse workers' individual and collective responses. We propose a comprehensive framework to reveal the drivers of health in the informal economy, and we complement this holistic approach with a new research agenda. Our framework highlights the socioeconomic, environmental, and political determinants of informal workers' health. We argue that informal workers may face difficult trade-offs, due to competing priorities in the face of climate change and other risks. Future interventions will need to recognise informal workers' array of risks and co-develop multifaceted solutions, thereby helping to avoid such impossible choices. We recommend holistic initiatives to foster health and climate resilience, as well as participatory action-research partnerships and qualitative, intersectional data-collection with informal workers. • Climate change is exacerbating many risks to informal workers' health and livelihoods. • WASH access may foster gender equality and decent work, while tackling heat stress. • Policymakers can partner with informal workers to create inclusive, multi-pronged interventions. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to diagnose the impact of climate change with the analysis of its scenario, causes and its solutions in relation to energy sector. Initially, this paper discusses the scenario of climate change in India. Afterward, a comparative difference analysis of selected climatic variables is performed on the chosen areas and also determines the relationships among these variables. Both these objectives are performed by ANOVA, post-hoc, correlation and linear regression analysis. Subsequently, the forecasting of several climatic variables is performed from 2021 to 2025 by time series analysis utilizing the ARIMA (Autoregressive Integrated Moving Average) model. Then, this paper investigates the interdependency scenario between the climate change and the electricity sector. Afterward, this paper discusses some major impacts of climate change on the electricity sector. Then, this paper presents the present electricity scenario of India and the renewable power potential of India. Subsequently, this paper suggests some open research challenges with recommendations for improving the energy sector of India. Results of this diagnoses analysis concludes that the climate change and energy sector are mutually connected, and the climate change impacts can be reduced with the improvement in energy sector using renewable energy. • A comparative difference analysis of selected climatic variables is performed on the chosen seven areas and also determines the relationships among these variables. • Forecasting of selected climatic variables is performed from 2021 to 2025 by time series analysis. • Interdependency scenario between the climate change and the electricity sector is analyzed. • Present electricity scenario of India and the renewable power potential of India are illustrated. • Open research challenges with recommendations are suggested for improving the energy sector of India. [ABSTRACT FROM AUTHOR]

India's agricultural systems are increasingly affected by climate change's adverse effects. The Government of India has an impressive set of programs to address this issue, but they have substantial shortcomings, especially in reaching women farmers. We aim to understand policy and implementation gaps in reaching women farmers with climate-smart agriculture (CSA) practices and study how women and men farmer's different roles in agriculture shape their needs and access to complementary services needed to adapt to climate change. An extensive review of India's agriculture and climate policies and program and a series of focus group discussions with farmers in Gujarat, India to discuss constraints and potential entry points for better reaching women farmers with CSA practices. Women's increased vulnerability to climate change and reduced access to CSA practices can be attributed to limited land ownership, poor access to credit, reduced access to information and formal extension, and multiple pressures on their time. Village cooperatives and self-help groups can be leveraged to support women's access to agricultural information and adoption of CSA practices. This paper highlights constraints to information and extension access by Indian women farmers that could impede the widespread adoption of CSA practices. It fills an important knowledge gap in designing gender-responsive policies and inclusive agricultural extension systems to promote adoption of CSA practices among smallholder farmers. [Display omitted] • Women farmers have limited land ownership, access to credit, access to information and formal extension, and time, constraining adoption of climate smart practices. • Building gender-responsive extension systems is critical for promoting adoption of climate smart agriculture practices. • Knowledge of gendered experience of climate change, availability of gender-disaggregated data and gendered analyses are important aspects. • Participatory approaches to policy making and involvement of local and community organizations needs to be strengthened in India. • Village cooperatives and self-help groups can be key intermediary organizations to support women's access to information and adoption of CSA practices. [ABSTRACT FROM AUTHOR]

In view of the climate emergency, it is necessary to model and forecast road transport emissions for implementing more effective climate policy. This paper fills an important research gap by quantifying the challenges, barriers and remedies of achieving road transport emission reduction and air quality improvement in developing countries. It contributes to the debate on top-down and bottom-up approaches to reconcile carbon and other transport related emissions. China and India are chosen as case studies. Using various official statistics and proxy variables, we estimate and compare the road transport carbon emissions of the two countries systematically from 2009 to 2020. Insights from detailed mode-specific estimations of the bottom-up approach are valuable for targeted policies and measures. An analysis of the major components of road transport carbon emissions points to strategies of promoting electric vehicles, reducing transport demand, accelerating modal shift to low-carbon modes, and cleaner electricity to achieve climate policy. [ABSTRACT FROM AUTHOR]

• Countries with populations and economic activity concentrated in low-lying coastal regions are vulnerable to sea-level rise. • As the ocean advances inland, millions of coastal inhabitants will have to retreat, either by choice or force majeure. • Linkages among environmental stress, public assistance and population change are assessed for coastal India and Bangladesh. • Compensatory public assistance strongly dampens the migration response to rising environmental stress. • Damage compensation should direct public resources to households that choose to relocate as the coastal threat mounts. Linkages among climate change–related environmental stress, public assistance, and the spatial pattern of population change are assessed for neighboring coastal areas of India and Bangladesh. Environmental stress is measured using historical cyclone impacts, salinization, and land loss from erosion. Household migration decisions are based on current and expected future income streams in different locations. Rising environmental damage raises costs, but it may also induce increased public assistance that moderates or neutralizes those costs, diminishing migration incentives, even in areas hard-hit by climate change. Econometric estimates for the Sundarbans region shared by India and Bangladesh suggest that endogenous public assistance strongly dampens the migration response to rising environmental stress in both countries, though the assistance response and migration dampening are lower in Bangladesh. A broader analysis for the coastal region from India's Odisha State to eastern Bangladesh finds that present and past cyclone impacts are highly significant for explaining coastal population changes, although responses are lower in India because of lower environmental stress and greater public-assistance intensity. A counterfactual simulation suggests that, as a result of cyclones since 1970, the affected regions are 8–10% less populous in Bangladesh but only 2% less populous in India. The paper's findings motivate a discussion of the implications for alternative policy regimes as land erosion increases, and sea-level rise and salinization continue with climate change. After comparing the efficiency and equity of regimes that provide universal damage compensation or leave coastal households to fend for themselves, the paper suggests an alternative approach that focuses public resources on compensation for households that choose to relocate as the coastal threat mounts. [ABSTRACT FROM AUTHOR]

• Developed a Decision Support Framework to classify uncertainties and management options using participatory scoping for scenarios analysis. • DSS includes: reservoirs water balance, statistical downscaling, hydrological modelling, sediment assessment, water management and decision support for water management plans. • DSS framework was applied to Tandula reservoir Complex, India and performance was evaluated using multi-criteria decision support in the three-layer hierarchal process. • DSS framework provides a solution for strategic water resources planning and management in a basin using the participation of farmers, technocrats, and water resource managers. The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers. [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]

The COVID-19 pandemic has dramatically altered global energy consumption, particularly affecting investment in renewable energy projects. In India, strict shelter-in-place orders enforced during March 2020 have since led to a considerable change in public and private sector investments in planned renewable energy installations. In this paper, we attempt to highlight trends in energy consumption and installed renewable energy capacity noted in India during a period concurrent with the shelter-in-place orders. We discuss recent policy measures and additions to installed renewable energy capacity, and propose key policy recommendations that may help the sector adopt a growth trajectory similar to one noted pre-pandemic. This paper is organized into four main parts. In the first section, we draw focus to India's renewable energy policies and pay special emphasis on recent interventions and campaigns targeted towards achieving high growth rates in the sector. We briefly discuss the need for effective public-private partnerships in order to meet these targets. In the second part, we quantitatively characterise the growth of renewables in India. We present an overview of several mechanisms and missions the government has launched in line with their policy to mitigate the environmental impact of India's energy mix. In the third part, we analyse the decrease in electricity demand in India from 24 March to 30 June 2020, a period concurrent with shelter-at-home orders issued by the Government. We also characterise changes in installed renewable energy capacity between March to December 2016–2020 to provide causal evidence of the effect of the pandemic on the growth of renewables. In this section, we also compile and analyse data on state-wise stressed assets across renewable energy generators in the country. Lastly, in the fourth and final portion of this paper, we highlight policy recommendations that may help the sector overcome logistical and financial bottlenecks in the short-term. We do this with the hope of outlining key measures that decision makers may employ to achieve pre-COVID sectoral growth in the long term. Our recommendations cover three different policy instruments: investment subsidies, operational subsidies, and recommendations for DISCOMs. • The increase in Indian renewable energy capacity installations between February to May 2020 hasseen an increment of 0.75%. • Daily electricity demand has decreased by 18.8% after the announcement of the shelter at home orders. • Immediate effects of the pandemic on the Indian renewable energy sector have been quantified. • A 3 tier system may be adopted to regulate incentives among renewable energy power producers. • Key policy recommendations to improve the fitness of the renewable energy sector have been discussed. [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]

A growing concern for the environment has raised the need for green computing, which calls for the responsible use of electronic devices and safe disposal/recycling of e-waste. The aim of this study is to find out the level of awareness and the adoption of green computing in India. Descriptive research was undertaken on the basis of primary data, which were collected from 250 Indian respondents using a structured questionnaire. Results of the study show a low level of awareness about green computing among the respondents and a moderate level of effort by them for judicious use of electronic devices and their safe disposal/recycling. The paper offers fresh insights into the relatively slow pace of green computing practices in India and the new ways and means to accelerate them for creating a safer environment. • People are aware about the harmful effects of the indiscriminate use of electronic/digital devices. • Perceptions of males and females are homogenous with respect to various objectives for adopting green computing. • People who are aware of the benefits of green computing are making efforts to contain irrational use of electronic devices. • They are also making efforts to reduce power consumption and emission of greenhouse gases to mitigate climate change. [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]

• Hydrological responses to land use and climate change are simulated using integrated modelling. • Scoring method is applied to select representative climate models. • Mean temperature is likely to increase by 1.79 °C (3.57 °C) under mid (high) emission scenarios. • Annual and monsoon flow is likely to increase during the 2050s and 2080s. • Hydrological sensitivity of the basin is analysed against changes in climatic variables. Accurate spatial and temporal measurement of water budget play a crucial role in achieving the sustainable planning and management of water resources. This paper presents a comprehensive integrated approach to simulate hydrological responses under dynamics of land use land cover (LULC) and climate change scenarios over Upper Narmada Basin, India. In order to select the representative climate models, scoring method was applied at regional scale. Best five performing climate models (MIROC5, CNRM-CM5, MPI-ESM-LR, GFDL-ESM2G and IPSL-CM5A-MR) were coupled with semi-distributed hydrological model to simulate the hydrological responses. Considering the heterogeneity of the large basin, multi-site calibration and parameters sensitivity analysis were performed using Sequential Uncertainty Fitting (SUFI-2) algorithm. To compute the hydrological sensitivity against land use change, calibrated model was coupled with historical and futuristic land use scenarios (1990, 2000, 2010 and 2030). The results indicate intensified precipitation towards the late 21st century, whereas annual mean temperature could be raised by 1.79 °C and 3.57 °C under mid and high emission scenarios respectively, at the end of century. Annual and monsoon flow in the basin likely to increase during the 2050 s (2041–2070) and 2080 s (2071–2100). Moreover, the relation between climate variables and water budget components were identified to analyse the hydrological sensitivity of the basin under changing climate. [ABSTRACT FROM AUTHOR]