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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.

Abstract Castor seed is an important oil seed crop of dryland agriculture. To estimate the energy needs of this crop, two experiments were conducted with three tillage treatments under two farming systems, namely bullock and tractor farming. Under all the selected tillage treatments both for tractor and bullock farming systems, it was found that threshing, harvesting, manual weeding and seed bed preparation were the energy intensive operations. No-tillage treatments was found not only the least energy consuming package of practices but also resulted i n a comparatively high output-input energy ratio despite higher weed intensity. Further, tractor cultivation consumed a higher amount of energy as compared to bullock cultivation but did not result in higher production. The farmer may, therefore, continue with bullock cultivation unless he were to cover larger areas which cannot be commanded by the bullock power resources available to him.

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.

The article contains laboratory data comparing the rate of gasification of five types of woody plants—beech, oak, willow, poplar and rose. The gasification rate was determined thermogravimetrically. Carbon dioxide and steam were used as gasification gases. Willow wood was the most gasifiable, whereas rose wood the least.

Abstract Sugarcane is a major crop cultivated globally and the residue left over after the crop harvest and extraction of juice is a good biomass source that can be used for the production of several useful chemicals. The sugarcane bagasse is an excellent substrate for the production of various biochemicals and enzymes through fermentation. Now major interest is focused on the utilization of these residue for biofuel production. The sugarcane crop residue is rich in cellulose and hemicellulose, hence it can be used for the production of bioethanol and other liquid transportation fuels. The present review gives a detailed account of the availability of sugarcane residue and various commercially important products that can be produced from this residue. It also provides recent developments in R&D on the bioconversion of sugarcane crop residue for value added products.

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.

A field study was conducted on two texturally different soils to determine the influences of biosolids application on selected soil chemical properties and carbon dioxide fluxes. Two sites, located in Manildra (clay loam) and Grenfell (sandy loam), in Australia, were treated at a single level of 70 Mg ha-1 biosolids. Soil samples were analyzed for SOC fractions, including total organic carbon (TOC), labile, and non-labile carbon contents. The natural abundances of soil δ13C and δ15N were measured as isotopic tracers to fingerprint carbon derived from biosolids. An automated soil respirometer was used to measure in-situ diurnal CO2 fluxes, soil moisture, and temperature. Application of biosolids increased the surface (0-15 cm) soil TOC by > 45% at both sites, which was attributed to the direct contribution from residual carbon in the biosolids and also from the increased biomass production. At both sites application of biosolids increased the non-labile carbon fraction that is stable against microbial decomposition, which indicated the soil carbon sequestration potential of biosolids. Soils amended with biosolids showed depleted δ13C, and enriched δ15N indicating the accumulation of biosolids residual carbon in soils. The in-situ respirometer data demonstrated enhanced CO2 fluxes at the sites treated with biosolids, indicating limited carbon sequestration potential. However, addition of biosolids on both the clay loam and sandy loam soils found to be effective in building SOC than reducing it. Soil temperature and CO2 fluxes, indicating that temperature was more important for microbial degradation of carbon in biosolids than soil moisture.

Abstract Biodiesel offers to be one of the renewable fuel that is in great demand due to hike in price and exploitation of various conventional resources. Biodiesel has gained wide attention as it emits less carbon monoxide and other pollutants. Biodiesel is known as mono-alkyl esters of long fatty acid, produced by transesterification in the presence of triglycerides and alcohol. The choice of catalyst and feedstock are the most important criteria for effective production of biodiesel. Catalyst and raw material selection play a significant role in the cost of biodiesel production. The heterogeneous catalyst offers a wide option for the catalytic selection because of its high selectivity and reusability characteristics. Recent advances in the field of catalytic technology for biodiesel production using nanosized catalyst is because of high stability over the repeated use. Nanocatalyst solves various bottleneck problem associated in the production of biodiesel. The present review is focused on various technologies and challenges linked to the production process, including some important aspects of feedstock selection. Thus the present review is maily focused on various catalytic technology used for biodiesel production using different production methods and potential feedstocks.

Citrus is an important horticultural crop of India and is often prone to diseases, particularly under increased temperature scenarios. For developing disease-resistant Citrus varieties, conservation of wild relatives is extremely important. However, our knowledge on temperature tolerance of these wild relatives of Citrus to varied climate change scenarios is extremely limited. Therefore, we determined the climatic niche of six wild relatives of cultivated Citrus species (C. indica Tanaka, C. karna Rafin., C. latipes (Swingle) Tanaka, C. macroptera Montrouz., C. medica L., and C. sinensis (L.) Osbeck.) and identified the geographical areas in India that would remain climatically stable in future through ecological niche modeling (ENM). Raster data on 19 bioclimatic variables with a resolution of 0.04° were used to generate niche models for each Citrus species that delineated their potential distribution areas. Future species distribution predictions for the year 2050 were made using the climate change scenarios from the most appropriate climate models, i.e., IPSL-CM5A-LR and NIMR-HADGEM2-AO with four Representative Concentration Pathways (RCPs). Ensemble of current and future projections was used to identify climatically stable areas for each species. Precipitation-related bioclimatic variables were the key climatic determinants for the modeled distribution pattern. The consensus of current and future projections suggests that most areas with stable climates for the species in the future would be available in the northeastern states of Arunachal Pradesh, Meghalaya, Mizoram, and Tripura. Efforts for in situ conservation and establishment of germplasm banks and citrus orchards may be encouraged in these identified areas.

Residential buildings constitute roughly one-fourth of the total energy use across the globe. Numerous studies have shown that providing an energy breakdown increases residents' awareness of energy use and can help save up to 15% energy. A significant amount of prior work has looked into source-separation techniques collectively called non-intrusive load monitoring (NILM), and most prior NILM research has leveraged high-frequency household aggregate data for energy breakdown. However, in practice most smart meters only sample hourly or once every 15 minutes, and existing NILM techniques show poor performance at such a low sampling rate. In this paper, we propose a TreeCNN model for energy breakdown on low frequency data. There are three key insights behind the design of our model: i) households consume energy with regular temporal patterns, which can be well captured by filters learned in CNNs; ii) tree structure isolates the pattern learning of each appliance that helps avoid magnitude variance problem, while preserves relationship among appliances; iii) tree structure enables the separation of known appliance from unknown ones, which de-noises the input time series for better appliance-level reconstruction. Our TreeCNN model outperformed seven existing baselines on a public benchmark dataset with lower estimation error and higher accuracy on detecting the active states of appliances.