• Energy Research
• Open Access close
• Closed Access close
• Open Source close
• Embargo close
• 11. Sustainability close
• 3. Good health close
• IN close

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.CCCR-IITM.IITM-ESM.ssp126' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The IITM-ESM climate model, released in 2015, includes the following components: aerosol: prescribed MAC-v2, atmos: IITM-GFSv1 (T62L64, Linearly Reduced Gaussian Grid; 192 x 94 longitude/latitude; 64 levels; top level 0.2 mb), land: NOAH LSMv2.7.1, ocean: MOM4p1 (tripolar, primarily 1deg; 360 x 200 longitude/latitude; 50 levels; top grid cell 0-10 m), ocnBgchem: TOPAZv2.0, seaIce: SISv1.0. The model was run by the Centre for Climate Change Research, Indian Institute of Tropical Meteorology Pune, Maharashtra 411 008, India (CCCR-IITM) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km.

Rapid urbanization combined with high economic growth, industrialization, and changes in socio-economic conditions increase the quantity of municipal solid waste. Cities located in South-Asia are facing serious issues due to waste, with countries like India, Bangladesh, and Pakistan top of the list of bad waste management. The increasing generation of solid waste and also the improper management of waste in Bangladesh leads to environmental degradation. Current waste management practice in Bangladesh is so weak that day by day it is harming the climate and creating a lot of unwanted situations. This research consists of an examination of the current administrative measures and presents another proposition for the executive cycle to decrease ecological contamination. The research study aims to decrease the amount of waste being dumped into municipal sanitary landfill sites & converting the waste into energy which is both financially and environmentally suitable by involving unemployed people in the management system. The results of this study will give an idea of how waste can be utilized as a resource and how this resource can be a capital good as well as how the local level problems can be solved by taking some strategies and making our environment suitable for future generations.

Biogas is a viable alternative for supplying clean and sustainable energy. Despite all manner of policy measures introduced by the Government of India, biogas is not widely used in India. This article tries to identify factors that influence the decision to adopt biogas at household level. We examine a conceptual framework empirically in which a household wants to maximize utility from biogas by using the India Human Development Survey (IHDS) I, which is a nationally representative, multi-topic survey. By applying both maximum likelihood and penalized likelihood methods (Firthlogit) of logistic regression on a sample size of almost 10,384 households, it has been found that wealthy people are more likely to adopt biogas compared to the marginalized section of the society. We recommend more inclusive policy measures for the weaker section of the society to create an enabling environment to make it a self-promoting technology.

This chapter focuses on the evaluation of adaptive capacities of community-level human systems related to agriculture and food security. It highlights findings regarding approaches and domains to monitor and evaluate behavioral changes from CGIAR’s research program on climate change, agriculture and food security (CCAFS). This program, implemented in five West African countries, is intended to enhance adaptive capacities in agriculture management of natural resources and food systems. In support of participatory action research on climate-smart agriculture, a monitoring and evaluation plan was designed with the participation of all stakeholders to track changes in behavior of the participating community members. Individuals’ and groups’ stories of changes were collected using most significant change tools. The collected stories of changes were substantiated through field visits and triangulation techniques. Frequencies of the occurrence of characteristics of behavioral changes in the stories were estimated. The results show that smallholder farmers in the intervention areas adopted various characteristics of behavior change grouped into five domains: knowledge, practices, access to assets, partnership and organization. These characteristics can help efforts to construct quantitative indicators of climate change adaptation at local level. Further, the results suggest that application of behavioral change theories can facilitate the development of climate change adaptation indicators that are complementary to indicators of development outcomes. We conclude that collecting stories on behavioral changes can contribute to biophysical adaptation monitoring and evaluation.

This review presents recent advancements in high-voltage rechargeable aqueous batteries employing water-in-salt and modified water-in-salt electrolytes.

Project: High Resolution dynamically downscaled CMIP5 climate data over India - The datasets in this project are developed as a part of a project associated with the Indian Institute of Technology Delhi, India, and the National Mission for Clean Ganga, Government of India. We have dynamically downscaled a coarser resolution CMIP5 GCM (CESMv1) climate data using the Weather Research and Forecasting (WRF) model for the current (2006-2015) and future (2091-2100) RCP8.5 emission scenario to produce a 10km resolution dataset over India. This dataset is expected to be of massive value to fine-scale regional modelling based climate change adaptive and mitigative studies in fields of water resources, energy, agriculture, and forestry over India. This project is financially supported by the National Mission for Clean Ganga (NMCG), Ministry of Jal Shakti, Department of Water Resources, River Development and Ganga Rejuvenation, Government of India, through grant number TE-16015/02/2019/NMCG. Summary: The Bias Corrected CESMv1 data for mid-century (2041-2050) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 30 files (10 variables x 3 temporal resolutions) packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

AbstractBackgroundBinge alcohol exposure during adolescence results in long‐lasting alterations in the brain and behavior. For example, adolescent intermittent ethanol (AIE) exposure in rodents results in long‐term loss of functional connectivity among prefrontal cortex (PFC) and striatal regions as well as a variety of neurochemical, molecular, and epigenetic alterations. Interneurons in the PFC and striatum play critical roles in behavioral flexibility and functional connectivity. For example, parvalbumin (PV) interneurons are known to contribute to neural synchrony and cholinergic interneurons contribute to strategy selection. Furthermore, extracellular perineuronal nets (PNNs) that surround some interneurons, particularly PV+ interneurons, further regulate cellular plasticity. The effect of AIE exposure on the expression of these markers within the PFC is not well understood.MethodsThe present study tested the hypothesis that AIE exposure reduces the expression of PV+ and choline acetyltransferase (ChAT)+ interneurons in the adult PFC and striatum and increases the related expression of PNNs (marked by binding of Wisteria floribunda agglutinin lectin) in adulthood. Male rats were exposed to AIE (5 g/kg/day, 2‐days‐on/2‐days‐off, i.e., P25 to P54) or water (CON), and brain tissue was harvested in adulthood (>P80). Immunohistochemistry and co‐immunofluorescence were used to assess the expression of ChAT, PV, and PNNs within the adult PFC and striatum following AIE exposure.ResultsChAT and PV interneuron densities in the striatum and PFC were unchanged after AIE exposure. However, PNN density in the PFC of AIE‐exposed rats was greater than in CON rats. Moreover, significantly more PV neurons were surrounded by PNNs in AIE‐exposed subjects than controls in both PFC subregions assessed: orbitofrontal cortex (CON = 34%; AIE = 40%) and medial PFC (CON = 10%; AIE = 14%).ConclusionsThese findings indicate that, following AIE exposure, PV interneuron expression in the adult PFC and striatum is unaltered, while PNNs surrounding these neurons are increased. This increase in PNNs may restrict the plasticity of the ensheathed neurons, thereby contributing to impaired microcircuitry in frontostriatal connectivity and related behavioral impairments.

Abstract Globally, three-quarter of a billion people live without electricity. Besides, hundreds of million use a solar lantern for less than 4 h a day. Most of the access-deprived are in the Global South, predominantly in Sub-Saharan Africa and South Asia. The United Nations framed the seventh Sustainable Development Goal to improve access. Solar photovoltaic powered mini-grids are increasingly extending better service to deprived regions. However, poor load-factor and expensive storage adversely affect viability. Also, these mini-grids do not support infrequent large loads to avoid further loss of load-factor. Electric cooking is efficient and non-polluting; water treatment facilities can save millions from contaminant and pathogen by providing clean water. Besides, both electric cooking and water treatment are less expensive than alternatives. But mini-grids frequently do not support these. Indeed, the presence of sustained productive loads favourably influences the mini-grid economy. This study investigates the role of critical household loads to deliver similar bearing on the mini-grid economy. Results underscore realisation of desirable impact with household collaboration under a demand-response program. Collaborative consumption can lower initial investment by 62% and reduce the unit energy cost to $0.23. Also, cooperation improves the mini-grid load factor and promotes viability. Additionally, fast deployment needs during and after Covid-19 remains inherently supported while mitigating the pandemic induced financial stress of both consumer and mini-grid operator. This study of 88 nation-states underscores that demand response in a mini-grid can not only improve affordability for all consumers, but it can also bring 186 million people within affordable access.

With the fast growth of the global economy, energy supply and demand have a strong impact on social, economic, and environmental aspects. As a consequence, this has pushed the decision-makers to formulate objectives, guiding economic policies toward sustainable goals. The process is known as Sustainable Development Goals (SDGs) that have been proposed by the United Nations. This being said, the energy sector is a vital domain with a vast potential for improvments in terms of technologies and ligistalations. Solar energy is among the most efficient solutions proposed to reduce the economic and environmental footprints of energy. In this frame, the current paper aims to localize solar energy within SDGs and analyze the contribution of the solar energy towards the achievement of the SDGs. Moreover, the current work highlights the contributions of Mohammed bin Rashid Al Maktoum (MBR) Solar Park in the United Arab Emirates to achieving the SDGs. Indeed, the MBR Solar Park concept offers valuable insights of environmental impacts by deploying clean and affordable energy sources in place of conventional fossil fuel power plants that are still heavily used in the region. The MBR Solar Park operation has already mitigated 6.5 million tonnes of carbon dioxide equivalent and this number will likely rise when all phases are installed and operational. Moreover, it has been shown that MBR Solar Park achieve several SDGs such SDG 8: decent work and economic growth, SDG 9: industry, innovation and infrastructure, SDG 11: sustainable cities and communities, and SDG 15: life on land.

Abstract Natural resources exist independent of human intervention. Although these interventions can and do affect the balance between ecological and biological diversity conditions these resources support, and their use to promote economic development. Currently, the unsustainable use of these resources threatens this balance, calling for more sustainable patterns of natural resource use and conservation. The primary responsibility for ensuring the proper balance lies with governments, leading to various policies and programs to preserve natural resources. The ultimate goal is to make the masses aware of natural assets’ importance and encourage their sustainable use. To successfully implement, however, these government practices require public communication and participation, and the full consideration of public opinion at various levels of governance. A predictive analytics framework is proposed for understanding public opinion on government policies to improve sustainable water governance. An integrated policy initiative to balance water resources use and conservation launched by the Indian government served as a test case for applying the framework in an attempt to accurately classify the opinion polarity related to the policy. The conventional feature extraction is applied to pre-processed datasets to extract the relevant features. Subsequently, swarm-based feature selection is applied to filter out optimal features. Lastly, opinion mining and textual analysis are performed to determine the most relevant water management factors that need immediate attention. The proposed framework serves as a policy evaluation strategy in the water management domain. The paper closes with a discussion of the general applicability of the proposed framework.