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The wide-spread dependence of transportation sector on the ever-depleting and greenhouse gas emitting fossil fuels when burnt has raised considerable energy security and environmental concerns. Consequently, countries around the world have positioned biofuels as a center stage alternative to fossil fuels in their national policies to provide energy security and carbon emission control. In this regard, the paper conducts a feasibility assessment of setting-up large-scale supply chain of bioethanol (i.e., a biofuel variant) based on the regional availability of agricultural residues. Critical resources and capabilities requirements and governance attributes for bioethanol supply chains are explored using the resource-based view and institutional theory lenses. An agricultural residues (as biomass inputs) based bioethanol supply chain (BESC) model is proposed to incorporate the economic and environmental optimality considerations for an agro-climatic region. A case study of the Punjab State in India is used as an application of the proposed BESC model. Findings suggest that sustainable BESC configurations can be achieved differently for different agricultural residues. Comparative analyses, trade-off scenarios, and sustainability assessments of various agricultural residues-based BESC configurations are conducted. The policy implications of the priority areas are discussed within the bounds of India's biofuel policy. • Designed biomass-based bioethanol supply chains from a policy perspective. • Resource-based view and institutional theory are applied. • Developed large-scale supply chain model for energy security and carbon-neutrality. • Performed comparative, trade-off, and sustainability analyses for biomass types. • Priority areas for creating a nationwide bioethanol economy are identified. [ABSTRACT FROM AUTHOR]

In recent years, lignocellulosic biomass has gotten a lot of coverage, especially for the captured energy through the anaerobic degradation. The conversion of lignocellulosic biomass to biogas through an anaerobic digestion process has been facing challenges such as low biogas production, low buffering ability, poor end quality of products, and potential heterogeneity of biomass which have difficulties to define an optimized unit operation. These deficiencies may be related to the recalcitrant disposition of agricultural wastes or low mass transfer. India has a large amount of lignocellulosic biomass, and this biomass remains unused in the harvested field. However, it has a scope of utilization by conversion into useful biofuel. Furthermore, it could be enhanced the farmer's income as well as helps to contribute reduce the loss of environmental impact. Hence, the present review script discusses the potential, composition, biomethane production, pretreatment methods and technology related to lignocellulosic biomass, challenges, progress, and the latest program initiated in India. However, only 10 major types of lignocellulosic biomass have been reviewed in this paper. [Display omitted] • Lignocellulosic biomass has a scope to convert into biofuel. • The conversion of lignocellulosic biomass to biogas facing some challenges. • Pretreatment necessary for conversion of lignocellulosic biomass to biogas. • Out of all the pretreatments, alkali pretreatment (NaOH) is the most promising. • The combination of two or more pretreatments provides a better result. [ABSTRACT FROM AUTHOR]

The power sector of developing countries is the backbone of the economy, and robust implementation of energy policies can play a significant role in its expansion. India is blessed to have ample natural resources; the only necessity is to identify these unseen resources comprehensively. India is an agricultural country with plenty of livestock generated biomass, which is renewable and easily accessible in rural India. The paper estimated the potential of electrical energy through biogas production from livestock generated dung in all states of India. Estimation of biogas from the gross livestock dung generation from all states and largely populated livestock species of the country is annually 2633 million tons which correspondingly projects the annual biogas potential of 265,542 million m3. This magnificent potential unbolts the proposals and implementation of biogas plants as a distributed generation to strengthen India's power sector. [ABSTRACT FROM AUTHOR]

Accurate estimates of spatial above ground biomass (AGB) in tropical forests are important in understanding the global carbon cycle. Microwave and optical remote sensing datasets have been used extensively for AGB estimation, but their uses are restricted due to saturation in high biomass region. To overcome saturation issues of single sensor based models, the current paper uses a non-parametric Random Forest based approach to spatially estimate biomass over Indian forests using field inventory data in combination with optical (MODIS) and Microwave (L-band ALOS-PALSAR) images along with other bio-climatic parameters (e.g., rainfall, temperature) which significantly influence the biomass accumulation in an ecosystem. Plot level biomass estimates for 6678 sample plots (0.1 ha size), inventoried as part of robustly designed National Forest Inventory (NFI), are computed using volumetric equations, wood density and biomass expansion factors. Spatial above ground biomass estimates were generated using random forest model over two scenarios. Firstly, a single nation-wide model using all the available plot data and secondly, physiographic zone (14 zones over India) wise models with plot data over respective zones. AGB stored in Indian forest is estimated as 7952.3 million tonnes (Carbon equivalent: 3737.58 TgC) with a root mean square error (RMSE) of 31.2% using national level model. Physiographic zone level models estimated the country's biomass as 7597.45 million tonnes (Carbon equivalent: 3570.8 TgC). The above ground biomass estimates from our study indicates that the estimation error in physiographic zone model varies from 25.24% to 54.15% depending upon the sample size and biomass range. We observed that L-band microwave saturated in 140–160 Mg ha−1 range and currently available microwave wavelengths alone is not sufficient to predict entire range of biomass over Indian forests. Inclusion of multisource data using random forest regression model increase the saturation range to 350 Mg ha−1 which is a significant improvement as 94.7% of Indian forests are covered in this range. Model estimation error reduces to 25.6% in AGB range up to 350 Mg ha−1. • L-band ALOS PALSAR backscatter saturates in biomass range140–160 Mg ha−1. • Inclusion of multi source data improved the saturation point to ~350 Mg ha−1. • 94.7% of Indian forest cover is covered in AGB range below 350 Mg ha−1. • Biomass stored in Indian forest estimated as 7952.3 Mt. (carbon equivalent: 3737.58 TgC). [ABSTRACT FROM AUTHOR]