Energy Sector Research Articles

Industry-specific stock valuation methods – a literature review

PurposeSurprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.Design/methodology/approachThis paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.FindingsThe use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.Originality/valueThis is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

Assessing the impact of renewable energy policies on decarbonization in developing countries

This study offers the first consistent attempt to identify how energy sector decarbonization policies have affected the energy mix over the last four decades across more than 100 developing countries. It applies systematic regression analysis to five energy sector decarbonization outcomes and 75+ policy instruments aggregated into seven policy packages. Combining instrumental variables with country interactions and country and time-fixed effects in regional panels helps address potential endogeneity issues. Only a handful of energy policy packages significantly affect decarbonizing developing countries' energy mix, and they more often achieve a negligible or opposite result than intended three years after implementation. Policies that address counterparty risk have the highest immediate effect. Effects of renewable policies on different decarbonization outcomes improve slightly five and seven years after their implementation.

Value creation or political trick? An event study on anti-ESG regulations

This paper examines the effect of the anti-ESG regulations approved in 18 states from 2021 to 2023. Focusing on the fuel energy sector, we find that the regulatory intervention leads to 0.8 % to 3.5 % cumulative abnormal stock returns (CARs) around the regulation approval dates. The market reactions are found to be more positive and pronounced when the firm has poor previous ESG performance, when the firm is financially more constrained, and when the regulation is less controversial. Meanwhile, although fuel energy firms have more institutional ownership afterwards, their carbon emission also increases, indicating potential negative economic effects of anti-ESG policies.

A Reviewed Turn at of Methods for Determining the Type of Fault in Power Transformers Based on Dissolved Gas Analysis

Since power transformers are the most important pieces of equipment in electricity transmission and distribution systems, special attention must be paid to their maintenance in order to keep them in good condition for a long time. This paper reviews the main steps in the process of diagnosing the health of power transformer insulation, which involves the science of analysing the gases dissolved in power transformer oil for effective identification of faults. An accurate diagnosis of incipient faults is favourable to sustainable development and necessary to maintain a reliable supply of electricity. The methods presented for fault diagnosis in mineral-oil-immersed power transformers are divided into analytical and graphical methods and have been found to be simple, economical and effective. After describing the methods, both their strengths and weaknesses were identified, and over the years, the methods were complemented to provide highly accurate information, validated by field inspections. This paper focuses on practical information and applications to manage maintenance based on accurate and up-to-date data. The contents of this paper will be of particular use to engineers who manufacture, monitor and/or use high-power transformers in the energy sector, as well as to undergraduate, master’s and PhD students interested in such applications.

Driving innovation in energy and telecommunications: next-generation energy storage and 5G technology for enhanced connectivity and energy solutions

Driving innovation in energy and telecommunications involves leveraging next-generation energy storage and 5G technology to enhance connectivity and energy solutions. This review explores the intersection of these two domains, highlighting the importance of advancements in energy storage and 5G technology for a sustainable and connected future. Energy storage is crucial for balancing the supply and demand of electricity in modern power systems. Traditional energy storage methods, such as batteries and pumped hydro, have limitations in terms of scalability, efficiency, and cost-effectiveness. Next-generation energy storage technologies, including advanced batteries, hydrogen storage, and thermal storage, offer promising solutions to overcome these limitations. These technologies enable efficient energy storage at scale, facilitating the integration of renewable energy sources like solar and wind into the grid. By storing excess energy generated during periods of low demand, next-generation energy storage systems ensure a reliable and stable power supply, reducing the reliance on fossil fuels and lowering greenhouse gas emissions. In parallel, the evolution of telecommunications technology, particularly the advent of 5G networks, is revolutionizing connectivity and communication. 5G technology offers significantly higher data transfer speeds, lower latency, and increased network capacity compared to its predecessors. These capabilities are essential for supporting emerging technologies such as the Internet of Things (IoT), autonomous vehicles, and smart grids. With 5G-enabled IoT devices, utilities can monitor energy consumption in real-time, optimize grid operations, and detect and respond to faults more efficiently. Moreover, 5G connectivity enhances the efficiency and reliability of energy storage systems by enabling seamless communication between distributed energy resources and grid operators. The convergence of next-generation energy storage and 5G technology presents numerous opportunities for driving innovation in both energy and telecommunications sectors. One of the key areas of innovation is the development of smart energy storage systems equipped with 5G connectivity. These systems can autonomously adjust their operation based on grid conditions, weather forecasts, and energy demand patterns, optimizing energy storage and distribution in real-time. Furthermore, advanced energy management algorithms leveraging artificial intelligence (AI) and machine learning (ML) algorithms can optimize energy usage and storage, further improving the efficiency and reliability of the grid. Another area of innovation lies in the integration of renewable energy resources with 5G-enabled microgrids. Microgrids are localized energy systems that can operate independently or in conjunction with the main grid. By combining renewable energy sources with energy storage and 5G-enabled communication, microgrids can provide reliable, clean, and resilient power to remote or urban areas. These microgrids can also facilitate peer-to-peer energy trading, allowing consumers to buy and sell excess energy within their communities, fostering energy independence and sustainability. Furthermore, advancements in battery technology, such as solid-state batteries and flow batteries, are enhancing the performance and reliability of energy storage systems. Solid-state batteries offer higher energy density, faster charging rates, and improved safety compared to conventional lithium-ion batteries. Flow batteries, on the other hand, provide scalability and long-duration storage capabilities, making them suitable for grid-scale applications. Integrating these advanced battery technologies with 5G-enabled monitoring and control systems enhances the overall resilience and flexibility of the energy infrastructure. In addition to technological advancements, driving innovation in energy and telecommunications requires collaboration among various stakeholders, including policymakers, regulators, industry players, and research institutions. Policies and regulations should incentivize the deployment of next-generation energy storage and 5G infrastructure, promote interoperability standards, and ensure data privacy and security. Public-private partnerships can facilitate the investment and deployment of innovative solutions, while research and development initiatives can spur further technological advancements. Driving innovation in energy and telecommunications through next-generation energy storage and 5G technology is essential for building a sustainable, connected, and resilient future. By leveraging advanced energy storage systems, smart grids, and 5G-enabled communication networks, we can optimize energy usage, reduce carbon emissions, and enhance the reliability and efficiency of our energy infrastructure. Collaboration and investment across various sectors are key to unlocking the full potential of these transformative technologies and achieving a brighter, more sustainable future for generations to come. Keywords: Innovation, Energy, Telecommunications, Next-Generation, 5G technology, Enhanced connectivity.

Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

This study presents an in-depth analysis of gasoline price forecasting using the adaptive network-based fuzzy inference system (ANFIS), with an emphasis on its implications for policy-making and strategic decisions in the energy sector. The model leverages a comprehensive dataset from the U.S. Energy Information Administration, spanning over 30 years of historical price data from 1993 to 2023, along with relevant temporal features. By combining the strengths of fuzzy logic and neural networks, the ANFIS approach can effectively capture the complex, nonlinear relationships present in the data, enabling reliable price predictions. The dataset’s preprocessing involved decomposing the date into year, month, and day components to enhance the model’s input features. Our methodology entailed a systematic approach to ANFIS regression, including data preparation, model training with the inclusion of the previous week’s prices as an additional feature, and rigorous performance evaluation using MSE, RMSE, and correlation coefficients. The results indicate that incorporating previous prices significantly enhances the model’s accuracy, as reflected by improved scores and correlation metrics. The findings have significant implications for the energy sector, where stakeholders can leverage the ANFIS model’s insights for strategic decision-making. Accurate gasoline price forecasts are instrumental in devising pricing strategies, managing risks associated with price volatility, and guiding policy formulation. The model’s predictive capability enables energy companies to optimize resource allocation, plan for future investments, and maintain competitive advantage in a market influenced by fluctuating prices. Moreover, policymakers can utilize these predictions to assess the impact of energy policies on market prices and consumer behavior, ensuring that regulatory measures align with market dynamics and sustainability goals. In addition to the ANFIS model, we also employed Vector Autoregression (VAR) and Autoregressive Integrated Moving Average (ARIMA) models to validate our approach and provide a comprehensive understanding of time series forecasting within the energy sector. Notably, the ANFIS model achieves a score of 0.9970 and a robust correlation of 0.9985, demonstrating its ability to accurately forecast gasoline prices based on historical data and features. The integration of these traditional techniques with advanced ANFIS modeling offers a robust framework for accurate and reliable gasoline price prediction, which is vital for informed policy-making and strategic planning in the energy industry.

Evaluating Preparedness and Overcoming Challenges in Electricity Trading: An In-Depth Analysis Using the Analytic Hierarchy Process and a Case Study Exploration

The economy of South Asia is experiencing growth, yet it faces constraints due to heavy reliance on fossil fuels and frequent power outages. Access to diverse energy sources, particularly electricity, is crucial for sustaining this growth. One feasible solution involves neighbouring countries engaging in the trade of renewable electrical energy. Hydropower stands as one of the many energy sources available in South Asia. However, sectorial constraints pose significant challenges to energy trade initiatives. This study utilises the Analytic Hierarchy Process (AHP) to evaluate Nepal’s readiness and identify obstacles to its cross-border energy trade with India and Bangladesh. A comprehensive analysis of these obstacles is imperative for formulating effective strategies and policies. Additionally, this study offers recommendations for enhancing preparedness and resolving issues related to energy trading, which may apply to similar cross-border situations. This study ranks energy trading obstacles with neighbouring nations using the AHP, offering key insights for stakeholders and policymakers. Using a non-probabilistic purposeful sampling technique, 25 expert respondents from the energy sector and prominent academicians were selected as part of the data collection procedure. At every level of the interview process, their perspectives were invaluable in guaranteeing a thorough and rigorous investigation.

Single-Atom based Metal-Organic Framework Photocatalysts for Solar-Fuel Generation.

The growing demand for fossil fuels and subsequent CO2 emissions prompted a search for alternate sources of energy and a reduction in CO2. Photocatalysis driven by solar light has been found as a potential research area to tackle both these problems. In this direction, SAC@MOF (Single-atom loaded MOFs) photocatalysis is an emerging field and a promising technology. The unique properties of single-atom catalysts (SACs), such as high catalytic activity and selectivity, are leveraged in these systems. Photocatalysis, focusing on the utilization of Metal-Organic Frameworks (MOFs) as platforms for creating single-atom catalysts (SACs) characterized by metal single-atoms (SAs) as their active sites, are noted for their unparalleled atomic efficiency, precisely defined active sites, and superior photocatalytic performance. The synergy between MOFs and SAs in photocatalytic systems is meticulously examined, highlighting how they collectively enhance photocatalytic efficiency. This review examines SAC@MOF development and applications in environmental and energy sectors, focusing on synthesis and stabilization methods for SACs on MOFs and also characterization techniques vital for understanding these catalysts. The potential of SAC@MOF in CO2 Photoreduction and Photocatalytic H2 evolution is highlighted, emphasizing its role in green energy technologies and advances in materials science and Photocatalysis.

Energy Sector Evolution: Perspectives on Energy Platforms and Energy Transition

Digital platforms are becoming more important in transforming the energy industry and altering the way we produce, distribute, and use energy. This paper explores the role of energy platforms in the transition towards renewable energy. We highlight, through real-life examples, that these platforms foster a participatory approach, convert consumers into proactive participants, democratize energy production, and encourage innovation in areas such as storage, electric mobility, and renewable project investments. Through a comprehensive review of the current literature, technological advancements, and emerging business models, we identify the possible key contributions of digital platforms to the energy sector. These platforms offer personalized user experiences, mutual benefits for users and companies, adaptability to market changes, support for peer-to-peer trade, and a reduction in bureaucracy. We then present a pioneering conceptual model by Liu et al. (2022), which integrates the energy cloud, digital platform, and transaction platform and we explore the business model of energy platforms. This business model is characterized by connectivity, innovative pricing, and revenue strategies independent of physical asset ownership. Advanced technologies like artificial intelligence and blockchain facilitate peer-to-peer energy trading, dynamic pricing, and a focus on transaction and access fees over traditional cost structures. Drawing on the business model and previous analysis we update the conceptual model for energy platforms to present a practical vision through a holistic approach.

A Single-Buyer Model of Imbalance Cost Pass-Through Pricing Forecasting in the Malaysian Electricity Supply Industry

The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Meanwhile, if an increment in the ICPT price signal can be provided to electricity providers and consumers, early preparation for operation budgeting can be realised, and energy management program development can be properly prepared. Due to this reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia that will benefit the stakeholders. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed using the mean absolute percentage error (MAPE). In light of our findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the moving average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July–December 2022 and January–June 2023 is MYR 0.21/kWh for the ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for forecasting, the ICPT price is expected to drop in the next announcement. The findings of this study may have a positive impact on the sustainability of the energy sector in Malaysia.

Can China’s Regional Industrial Chain Innovation and Reform Policy Make the Impossible Triangle of Energy Attainable? A Causal Inference Study on the Effect of Improving Industrial Chain Resilience

This study used a double machine learning model (based on the random forest algorithm) and spatial Durbin DIDs model to conduct quasi-natural experiments. The results are as follows: (1) innovation and reform policy regarding regional industrial chains as well as their resilience can significantly and positively address the development of China’s impossible triangle coupling of energy; (2) implementing the innovation and reform policy for regional industrial chains in other regions can have a significant positive spatial transmission effect on the impossible triangle coupling coordinated development of energy in the region; (3) regional industrial chain resilience can produce a significant positive mediating effect between the innovation and reform policy of regional industrial chains and the safety, reliability, and economic feasibility of green and clean energy systems; (4) under the counterfactual framework, the mechanism path “innovation and reform policy of the regional industry chain→regional industry chain resilience→coordination degree of impossible triangle coupling of energy” has significantly positive direct and indirect effects in both the treatment group and the control group. However, “innovation and reform policy of the regional industrial chain→regional industrial chain resilience→the energy sector’s impossible triangle coupling coordination degree” and “innovation and reform policy of the regional industrial chain→leading power of the regional industrial chain→the energy sector’s impossible triangle coupling coordination degree” have significantly positive direct and indirect effects in the treatment group, but only the direct effect is significant in the control group.

Numerical analysis of the heat transfer application on a convective tangent hyperbolic nanofluid flow over a porous stretching cylinder with stratification effects

The nanofluids has attractive applications in the industrials and engineering field because nanofluids offer the improvement in the thermal efficiencies of flow, which is very fruitful in the energy sector. This study emphases on the numerical exploration of heat and mass transfer efficiencies on a mixed convective flow of tangent hyperbolic nanofluid under the consideration of gyrotactic microorganisms. To analyze the magnetized boundary layer flow, the stratification boundary conditions are considered on the surface of the stretching cylinder. The thermal analysis involves the important features such as activation energy, nonuniform heat source/sink, and nonlinear thermal radiation. The flow model is altered into system of nonlinear ordinary differential equations by utilization of suitable similarity variables and Bvp4c solver on the MATLAB to find numerical solutions. The graphical and tabulated investigation is presented for various developing parameters thoroughly. The results indicate that by the increment of mixed convection parameters the fluid velocity accelerate consequently, whereas raising values of the Weissenberg number reduce the fluid velocity. TThe greater values of the Prandtl number lead to a decrease in fluid temperature, while the concentration of nanoparticles rises with the increase in activation energy parameters. Additionally, the heat transfer rate reduces due to stronger values of thermophoresis parameter, although reverse tendency is observed for the greater values of Brownian motion parameter.

Thermochemical Activation of Wood with NaOH, KOH and H3PO4 for the Synthesis of Nitrogen-Doped Nanoporous Carbon for Oxygen Reduction Reaction.

Carbonization of biomass residues followed by activation has great potential to become a safe process for the production of various carbon materials for various applications. Demand for commercial use of biomass-based carbon materials is growing rapidly in advanced technologies, including in the energy sector, as catalysts, batteries and capacitor electrodes. In this study, carbon materials were synthesized from hardwood using two carbonization methods, followed by activation with H3PO4, KOH and NaOH and doping with nitrogen. Their chemical composition, porous structure, thermal stability and structural order of samples were studied. It was shown that, despite the differences, the synthesized carbon materials are active catalysts for oxygen reduction reactions. Among the investigated carbon materials, NaOH-activated samples exhibited the lowest Tafel slope values, of -90.6 and -88.0 mV dec-1, which are very close to the values of commercial Pt/C at -86.6 mV dec-1.

Validation Assessment of Turbulent Reacting Flow Model Using the Area-Validation Metric on Medium-Scale Methanol Pool Fire Results

Abstract Accident analysis and ensuring power plant safety are pivotal in the nuclear energy sector. Significant strides have been achieved over the past few decades regarding fire protection and safety, primarily centered on design and regulatory compliance. Yet, after the Fukushima accident a decade ago, the imperative to enhance measures against fire, internal flooding, and power loss has intensified. Hence, a comprehensive, multilayered protection strategy against severe accidents is needed. Consequently, gaining a deeper insight into pool fires and their behavior through extensive validated data can greatly aid in improving these measures using advanced validation techniques. A model validation study was performed at Sandia National Laboratories (SNL) in which a 30-cm diameter methanol pool fire was modeled using the SIERRA/Fuego turbulent reacting flow code. This validation study used a standard validation experiment to compare model results against, and conclusions have been published. The fire was modeled with a large eddy simulation (LES) turbulence model with subgrid turbulent kinetic energy closure. Combustion was modeled using a strained laminar flamelet library approach. Radiative heat transfer was accounted for with a model utilizing the gray-gas approximation. In this study, additional validation analysis is performed using the area validation metric (AVM). These activities are done on multiple datasets involving different variables and temporal/spatial ranges and intervals. The results provide insight into the use of the area validation metric on such temporally varying datasets and the importance of physics-aware use of the metric for proper analysis.

Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.

Advanced fusion of MTM-LSTM and MLP models for time series forecasting: An application for forecasting the solar radiation

Accurate time series forecasting has become increasingly important across various domains such as finance, energy, and medicine. This study introduces an innovative hybrid model that leverages the power of neural networks, precisely Many To Many LSTM (MTM LSTM) and Multilayer Perceptron (MLP), to improve time series forecasting accuracy. In this new combination, we trained network MTM LSTM to approximate the target at each step, and finally, we used network MLP to combine these approximations. To perform the evaluation, we made a forecast for the amount of solar energy radiation in the city of Mashhad, Iran. The experiment results concerning MSE and MAE showed that the proposed method with five lags outperforms the standard models.We hypothesize that MTM LSTM can effectively capture solar radiation's intricate temporal dependencies and nonlinearity. At the same time, MLP can enhance function approximation by modeling complex interactions, resulting in improved forecast accuracy. By employing the hybrid MTM LSTM and MLP model, we achieved improved accuracy in predicting solar energy radiation, which has significant implications for the renewable energy sector and its energy management and planning applications. This research advances time series forecasting techniques, highlighting the effectiveness of combining neural networks to address complex and dynamic patterns in time-dependent data.Overall, our findings underscore the potential and efficacy of the proposed hybrid model as a robust tool for accurate time series forecasting in various domains, supporting effective decision-making and planning processes.

Modeling, analysis and forecasting of the Jordan’s transportation sector energy consumption using artificial neural networks

Modeling, analysis and forecasting of the Jordan’s transportation sector energy consumption using artificial neural networks

Going resilient with digital transformation, human capabilities and innovation readiness: empirical evidence from the energy sector

PurposeThe present study investigated organizational resilience (OR) in the United Arab Emirates (UAE’s) energy sector to identify impactful technological and human variables and assess the hermeneutic effect of digital transformation on value co-creation and OR. The study also investigates the mediating role of value co-creation on a few covariates of OR.Design/methodology/approachThe questionnaire was sent out to 311 professionals in the energy sector, all affiliated with governmental organizations, using quota sampling. A total of 206 collated responses corresponding to the tested variables regarding the influences of digital transformation, employee resilience, innovation readiness, cyber resilience and value co-creation on OR were analyzed using structural equation modeling. Accordingly, a model of eight constructs and their 27 indicators was tested.FindingsInstituting flexibility and adaptability to technological advancements, as well as cyber resilience, was found to enhance digital transformation. The sense of self-efficacy of the professionals who participated in the study led them to develop innovation readiness and thus embrace creativity and encourage co-creation while maintaining collaborative efforts with customers and stakeholders. This mediated several technological and human variables, such as the importance of managers' understanding of customer needs, preferences and pain points, which involves actively seeking and valuing customer feedback to inform decision-making.Practical implicationsWhen iterative prototyping, continuous learning, and OR are integrated into an organization’s culture, they create a robust foundation for a customer-centric mindset. This mindset becomes ingrained in how employees approach their work and make purposeful decisions.Originality/valueThe present study drew empirical insights into OR in the UAE’s energy sector from a resource-based theory perspective. By identifying potential vulnerabilities and implementing appropriate mitigation measures, organizations can reduce the likelihood and impact of disruptions, which can ultimately help them maintain customer satisfaction and loyalty.

Free-standing arrays of BaTiO3 nanotubes for non-enzymatic glucose sensing & hydrophobic coating applications

BaTiO3 nanostructures have been considered as a promising candidates in recent past for energy and biomedical sectors owing to their excellent physiochemical properties, such as high dielectric constant, excellent piezoelectric property, good biocompatibility, non-linear optical characteristics etc. Present study reveals on free-standing arrays of BaTiO3 nanostructures, were fabricated by hydrothermal conversion of anodic TiO2 nanotubes. Morphological and structural information of the BaTiO3 nanotubes were done using FESEM and XRD studies. FESEM analysis revealed that the fabricated samples were having tubular morphology of average length and pore diameter of 4.63 μm and 290 nm respectively. Cubical perovskite crystalline phase of BaTiO3 was confirmed through XRD analysis. The BaTiO3 nanotube samples had shown a higher sensitivity of 44.43 μA mM−1 cm−2 and a faster response of 0.1 s for glucose detection. The fabricated BaTiO3 nanotubes film also showed a higher contact angle of 122.70°. Therefore, our present fabrication on Titanium foil study emphasizes on arrays of BaTiO3 nanotubes which will open up a new window in the development of various types of sensing and hydrophobic coating applications.

Status of Biogas Programmes in India

The study makes an attempt to enquire into the strengths and weaknesses in implementing the programme. Another relevance of the study is that biogas programme is popularly believed to have deeply influenced energy and agricultural sectors. This study provided an opportunity to verify the basis of such popular notion. Looking at biogas programme as an activity of local change, the study makes an effort to find out the options of replicability and scaling up of the biogas programme. It is hoped that the study will bring forth useful clues that can go a long way in improving agriculture in Kerala, especially in the highranges of Kerala.