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Technical progress has a tremendous potential to reduce carbon dioxide emissions by reducing energy consumption, a major concern across production units. However, the existing empirical literature concerning technical efficiency and carbon intensity is scanty. Thus, this paper examines the relationship between technical efficiency and carbon intensity for the organized manufacturing sector of two states, Maharashtra and Odisha, and the all-India level from 2001 to 2018. The paper uses data envelopment analysis to estimate technical efficiency scores. It applies the 2006 Intergovernmental Panel on Climate Change Tier 1 methodology for estimating carbon intensity for each 3-digit manufacturing industry in all three sample cases. The study has used static panel regression and fractional logit regression techniques to examine the deterministic relationship between technical efficiency and carbon intensity. The result shows that technical efficiency is highly sensitive to carbon intensity in the Indian manufacturing industries. The findings also addressed that the size of the industries also reduces the technical performance of manufacturing units. This paper also confirmed that increased profit could boost the Indian manufacturing industries' technical efficiency. Thus, this study addresses that carbon intensity as a proxy for the manufacturing sector's potential to affect climate change plays a crucial role in explaining the technical efficiency variations across industries. Thus, it calls for better policies aimed at reducing the emissions of industries specifically to achieve sustainable growth for the Indian manufacturing sector. [ABSTRACT FROM AUTHOR]

This comparative study analyzed the impact of loan portfolio composition on the efficiency of different types of banks in India—public sector, old private, and new private banks—in the period between 2013 and 2022. Efficiency was evaluated using data envelopment analysis (DEA). The study considered four loan variables—term lending, working capital, priority sector lending, and secured lending in proportion to the overall loans—as independent factors against the efficiency score as the dependent variable, using a random-effects generalized least squares (GLS) regression framework. The results indicate that there were no significant effects on the efficiency of old private banks, except for working capital, which had a marginally negative impact on bank efficiency. Working capital, priority sector lending, and term lending have been found to significantly impact the efficiency of new private banks. Only term and working capital loans significantly affected the efficiency of public sector banks. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers. [ABSTRACT FROM AUTHOR]

Objective: This research paper aims to evaluate the relative efficacy of pharmacognosy research on the top 40 tertiary hospitals and its pharmacy in India according to Newsweek's 2021 ranking. Purpose: India's pharmaceutical market is now estimated to be worth $50 billion. India is a significant pharmaceutical exporter, reaching more than 200 nations with its exports. This study will help to identify whether the pharma industry is working efficiently in selected Indian hospitals or not. Thus, to create a bench marking for the hospital pharmacies in India. Design/methodology/approach: Data for the study came from public and private hospitals' official websites, financial records, and government notifications. Crosssectional data analyses were done using a Slack-Based model and data envelopment analysis (DEA). STATA was the software used to scrutinize the results effectively. Results: Thirteen hospital pharmacies out of a total of 40 are judged to be relatively technically efficient overall, according to the research. The findings of the slack analysis demonstrate that government hospitals and pharmacies are better able to handle slack and effectively manage input and output. Conclusion: The researchers also set upped a benchmarking hospital both for private and government healthcare settings for pharmacognosy research. Thus, it can be utilized for the betterment of the pharmacies. [ABSTRACT FROM AUTHOR]

The United Nations' (UN) Sustainable Development Goals (SDG) are a recognised metric for measuring environmental, economic, and societal progress. However, national or multinational-level analyses are more prevalent than sub-national types. The performance of 14 SDGs for 56 Indian cities (grouped into 6 regions) with the available 77 indicators (2020–2021) have been analysed. Pearson's correlation, hierarchical clustering, data envelopment analysis, Theil index, etc. were used to infer existing status, interactions, inequality, efficiency, and interrelationships. Finally, policy suggestions have been offered coupled with limitations to mitigate the drawbacks of the Indian city SDG framework. The findings reveal the asynchronous nature of the SDGs. 18% of Indian cities register a poor track record of converting environmental performance into socioeconomic prosperity, while 55% of cities are lagging in performance compared to their respective states. Significant inequality exists among cities in various regions towards achieving the SDGs. The environment is adversely affected in a race to be economically powerful. So, mainstreaming the environment into development planning is urgently warranted. [ABSTRACT FROM AUTHOR]

The cost and profit efficiency of all Indian life insurance businesses is examined in this article using the data development analysis technique for the years 2017 through 2021. The Tobit regression is also used in this study to look into how well life insurance performs in relation to company factors. Cost efficiency scores were higher than profit efficiency ratings, as shown by the study. According to this, one of the most important inefficiencies for Indian life insurance is revenue inefficiency. In addition, state-owned life insurance firm in India is more profitable and economical than private ones, according to this study. In general, joint life insurers are more cost-effective than domestic life insurance companies, whereas domestic life insurance companies are more profit-efficient than joint venture insurers. The profitability and cost-effectiveness of life insurers are positively impacted by the claims ratio. While there are favorable effects of the distribution ratio and market share, respectively, effects on profit and cost-effectiveness. During the study period, the Indian life insurance businesses were relatively cost and profit efficient, according to empirical evidence. This study is the first to examine the profit and cost efficiencies of all Indian life insurance businesses. Decision-makers, top management, regulators, and other industry stakeholders will all gain from the research. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a non-parametric approach for the estimation of production frontier that is used to calculate the performance of a group of similar decision-making units (DMUs) which employ comparable inputs to produce related outputs. However, observed values might occasionally be confusing, imprecise, ambiguous, inadequate, and inconsistent in real-world applications. Thus, disregarding these factors may result in incorrect decision-making. Thus neutrosophic sets have been created as an extension of intuitionistic fuzzy sets to represent ambiguous, erroneous, missing, and inaccurate information in real-world applications. In this study, we have proposed a technique for solving the neutrosophic form of the Charnes-Cooper-Rhodes (CCR) model based on single-value trapezoidal neutrosophic numbers (SVTrNNs). The possibilistic mean for SVTrNNs is redefined and applied the Mehar approach to transforming the neutrosophic DEA (Neu-DEA) model into its corresponding crisp DEA model. As a result, the efficiency scores of the DMUs are calculated using different risk parameter values lying in [0, 1]. A numerical example is given to analyze the performance of the all India institutes of medical sciences and compared it with Abdelfattah's ranking approach. [ABSTRACT FROM AUTHOR]

Revenue risk is the most critical factor that hampers the long-term viability of highway infrastructure service projects. This paper develops an integrated multi-method decision framework to evaluate and compare multiple revenue risk mitigation strategies for build-operate-transfer (BOT) highway infrastructure concessions. Using the project data of a real highway BOT case project in India, this paper models and evaluates various risk mitigation strategies on different performance metrics using Monte Carlo simulation analysis. The technical efficiencies of the risk mitigation strategies are estimated using data envelopment analysis (DEA) and efficient strategies are identified. The findings suggest that strategies comprising appropriate combinations of minimum revenue guarantee, excess revenue share, and revenue shortfall loan mitigate the revenue risk most efficiently. The decision framework and the findings facilitate efficient public infrastructure contract design by policymakers and set multiple research directions. [ABSTRACT FROM AUTHOR]

Across the world, achieving sustainable water resource development and managing limited natural resources like water have proven to be significant issues, and India is no exception to the same. At present, irrigation consumes about 80 percent of the total available water in India and is predicted to remain the dominant water user. India is already a country with water stress and is on its way to becoming a country with water scarcity. Rising agricultural productivity, expanding urbanisation, growing industrialisation, and accelerating industry create competing demands for water. Most irrigation system issues in India are attributable to poor management of the built infrastructure. The present study aims to rank the irrigation canals based on their efficiencies and inefficiencies to improve the overall performance of WUAs in Jharkhand. The ranking is done for nine irrigation canals based on financial inputs like investments in training and development of WUAs, maintenance and repairs of canals, and outputs like users' charges collection and yield in crop production. The Comprehensive Efficiency score analysis suggests that while there is room for improvement in the performance, the Water Users' Associations have generally been successful in improving the technical efficiency of the irrigation system. The findings can be used for further investigation to identify best practises and areas for improvement. [ABSTRACT FROM AUTHOR]

The Indian Railways is India's biggest employer and undeniably influences the country's transportation network, economy, and social and cultural systems. The network is split into zones for operational reasons. It is vital to evaluate these railway networks to identify their strengths and shortcomings and to improve their performance. Past works often use Data Envelopment Analysis to evaluate railway services. Our contribution lies in the inclusion of several aspects not previously considered, such as (i) both tangible and intangible criteria, (ii) the hierarchical nature of the problem, and (iii) additional useful criteria and data to analyze the performance of the zones, including physical assets, operating ratio, accidents, comfort, travel experience, flexibility, transparency, etc. We use the novel Hierarchical Fuzzy Axiomatic Design method to evaluate the performance of sixteen zones in the Indian Railways since it suits our problem well. We find that the Southern Railway zone performs best, while the Northeast Frontier zone is ranked last. We also identify the strengths and weaknesses of all railway zones. [ABSTRACT FROM AUTHOR]

The article computes cost, revenue and profit efficiency scores of Indian Life Insurance companies during the period 2013-14 to 2020-21 by employing a Non-Parametric Approach, namely, Data Envelopment Analysis (DEA). The paper also examines the efficiency of Life Insurers across ownership by comparing Public and Private Insurance Sector. The concrete findings suggest that the overall performance of Life insurance companies in terms of economic efficiencies is not satisfactory. The average cost, revenue and profit efficiency is .527, .402 and .589 respectively which is far less than the yardstick of 1. The highest level of inefficiency subsists on the revenue side, which is in turn reflected in the average of profit inefficiency. Significantly inferior efficiency scores in the Private Sector seem to have marred the average efficiency scores of Insurance sector in India. [ABSTRACT FROM AUTHOR]

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency. [ABSTRACT FROM AUTHOR]

Using disparate datasets, one on the quality of higher education institutions (HEIs) in India comprising 348 HEIs that were accredited over the years 2004–2008, and another on the investment potential of states based on four indicators, we fail to find a correlation between the investment potential of a state and the mean efficiency of its HEIs computed using nonparametric data envelopment analysis (DEA). However, interactions of investment potential indicators with HEI scores on seven quality assessment criteria exhibit a significant impact on efficiency scores thereby suggesting a latent moderating effect. We conclude that the investment potential moderates the quality assessment of HEIs, but these indicators do not affect the efficiency of HEIs. A direct policy implication of the study is a latent link between HEI quality and investment potential of a state but not an overt one. Moreover, a state's infrastructure interacts with teaching and learning to affect HEI efficiency positively, whereas research output and good political governance interact to affect efficiency positively and significantly. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the influence of intellectual capital on changes in total factor productivity of 36 BSE-listed banks in India from 2005 to 2019. This study employs a two-stage analysis that begins by investigating changes in total factor productivity using the Malmquist Productivity Index estimated through Data Envelopment Analysis, and then computes intellectual capital and its sub-components within the Value Added Intellectual Coefficients model framework. Then, using the System Generalised Method of Moments, we investigate the impact of intellectual capital on changes in total factor productivity. According to our findings, productivity growth is primarily driven by efficiency changes rather than technological changes. Furthermore, regression results show that the intellectual capital index and its two sub-components, human capital and capital employed, have a strong positive impact on bank productivity. This research could help bank senior executives measure their productivity and intellectual capital, identify relevant intellectual capital elements that contribute to productivity and develop future policies to encourage and improve their intellectual potential. Furthermore, this is one of the few studies in the Indian context that examines the nexus between intellectual capital and productivity using the Malmquist Productivity Index. [ABSTRACT FROM AUTHOR]

The paper adopts the conventional data envelopment analysis (DEA) and bootstrap procedure to analyze the technical efficiency, while tobit regression is applied to identify the factors affecting efficiencies of exotic fish polyculture in Jammu and Kashmir (India). According to the statistical analysis of the variables utilized, there was a lot of variability in the inputs being used by the farmers, with the most variation being in the lime input. The DEA estimated technical efficiency for the sample farms in Jammu and Kashmir is 0.9771 and 0.9741, respectively, with least technical inefficiency of 3%. The bias-corrected (bootstrapped) technical efficiencies found were slightly lower than the ones estimated by conventional DEA. Mean allocative and cost efficiencies for sample farms in the Jammu region were 92% and 75%, respectively, and 84% and 74%, respectively, for farmers in the Kashmir region. Farming experience, age, and education have favorable impacts on technical efficiency of farmers in the state, but family size showed negative impacts. Efficiency improvement will eventually lead to increase in the production providing better scope for marketed surplus. More fisheries extension is suggested for expanding the exotic fish culture in the state making the enterprise a more profitable venture. [ABSTRACT FROM AUTHOR]

Background: India is the largest producer as well as consumer of pulses in the world due to higher relance of their people on vegetarian sources of proteins, however, we have been dependent on import of pulses for meeting our demand for pulses over several decades. As a strategy to be self-reliant in pulses production two important initiatives were taken by the government of India viz. implementation of Accelerated Pulses Production Program (A3P) and inclusion of pulses in the National Food Security Mission (NFSM-Pulses) during 2010-11. Pigeon pea has tremendous medicinal and pharmacological properties and uses in addition to the good quality source of proteins. Significant improvement observed in production and productivity of pigeon pea after the A3P and NFSM-Pulses was questioned for its Total Factor Productivity (TFP) growth. Methods: This study was designed to assess impact of A3P and NFSM-Pulses on TFP of Pigeon pea in major Indian producing states. TFP was estimated with the help of Malmquist Productivity Index (MPI) using Data Envelopment Analysis Program (DEAP) version-2.1 in five states of India viz., Gujarat, Karnataka, MP, Maharashtra and UP which contribute more than three-fourth pigeon pea production in India. Result: TFP change in period-II (after A3P and NFSM-Pulses) was invariably higher than the TFP change in period-I (before the initiatives) in all the studied states. It indicates that these government initiatives contributed to the enhancement of production and productivity of pigeon pea with the support of better technology adoption rather than just led by higher use of inputs. [ABSTRACT FROM AUTHOR]

This study examines internal greener operations in Indian healthcare during COVID-19 pandemic. Specifically, the research explores the interrelationships among three parallel healthcare lines: general care, emergency care, and biomedical waste (BMW) management to fulfill greener healthcare's targeted sustainable development goals. We use the three parallel stage data envelopment analysis (DEA) approach model to assess efficiencies of the overall system and the individual parallel stages. We collected panel data for cities in the state of Maharashtra, India during the second wave of pandemic in 2021. We find that the effectiveness of general and emergency medical services depends on the requirements of healthcare resources such as beds and equipment in times of pandemics. By contrast, the capacity of individual BMW treatment processes such as incineration, autoclave, shredder, and deep burial would have a bearing on the efficiency of both BMW management and the total system for greener operations. In the second phase, we evaluated the impact of context-specific exogenous factors using a regression model. We found that literacy level was the most important factor at all three parallel stages separately. Additionally, the total population and number of female vaccinated persons were significant across the system while population density and number of male vaccinated persons were inconsequential factors. Interestingly, the Wilcoxon rank-sum hypothesis test offered no evidence to suggest that performance of general care and BMW management, and emergency care and BMW management were equivalent. Implications of these findings for academic theory, practitioners, and policy makers are offered. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is an important fiber crop with high energy and C footprints. This study aimed at tracking C footprints through emission of greenhouse gases and energy flow nexus in cotton cultivation to frame policy to reduce C and energy footprints, while enhancing net ecosystem C budget and C sequestration in soils. We attempted to quantify and optimize C footprints and energy flow in cotton cultivation using integrated Life Cycle Assessment and Data Envelopment Analysis approach. The total input energy of 23,960 MJ ha−1 produced total output energy of 73,964 MJ ha−1. The energy use efficiency and energy productivity were 3.1 ± 0.1 and 0.083 ± 0.003 kg MJ ha−1, respectively. The Charnes–Cooper–Rhodes model based Data Envelopment Analysis approach elucidated 37 (out of total 65), while Banker–Charnes–Cooper model revealed 55 decision-making units as energy efficient. The energy footprints expressed as specific energy were significantly higher under current production situation (13.0 MJ kg−1), compared with optimum production situation (12.0 MJ kg−1). The Data Envelopment Analysis based optimized total energy input was significantly reduced by 1522 MJ ha−1 through chemical fertilizers (2.7–51.5%) and biocides (7.7–25.1%), which led to a significant reduction of ~9.2% of total C equivalent emissions having a technical mitigation potential of 114.3 kg CO2e ha−1. The negative values of net ecosystem C budget for efficient (− 4.04 Mg C ha−1) and inefficient (− 4.94 Mg C ha−1) decision making units revealed that these ecosystems act as net C source. The average technical efficiency of 0.87 ± 0.02 revealed that ~13% of total energy input could be saved without any impact on cotton productivity and environment. These results underpin the overwhelming significance of intensified extension efforts for efficient use of chemical fertilizers and discouraging farmers from unwarranted use of biocides in cotton in the north-western India. [ABSTRACT FROM AUTHOR]

This study estimates the effect of productivity on export performance of selected sectors and countries. Specifically, this study analyzes both theoretically and empirically, the nexus between productivity and export performance of selected emerging Asian economies including China, India, Indonesia, South Korea, and Japan. Total Factor Productivity (TFP) is used as a proxy for productivity and measured using two different techniques, growth accounting framework and data envelopment analysis. The study uses Auto Regressive Distributed Lag technique to estimate the effect of productivity on export performance of agricultural and manufacturing sectors of emerging Asian economies over the period of 1990 to 2016. Results show that TFP significantly and positively affect the agricultural and manufacturing sectors' export performance of selected emerging Asian economies both in long and short run. Hence, policies and economic strategies promoting adoption of advanced production technologies will increase exports of the emerging Asian economies in the long run. [ABSTRACT FROM AUTHOR]

This paper aims to empirically analyse the efficiency of public and private sector banks in India. The paper uses data envelopment analysis to measure and compare the efficiency of banks. Three measures of efficiency viz. total technical efficiency, pure technical efficiency and scale efficiency are computed. The paper evaluates and compares the efficiency of public and private sector banks by utilizing the RBI published data set for the period 2015 to 2019. The paper concludes with empirical results that public sector banks are less efficient in terms of total technical efficiency and pure technical efficiency than private sector banks. However, both these sectors have equal scale efficiency. [ABSTRACT FROM AUTHOR]

Ensuring that the benefits of growth are shared equitably requires a strong focus on greater convergence of regions in terms of output and productivity. The present study examines the regional disparities in Total Factor Productivity (TFP) in India by using the Data Envelopment Analysis (DEA) based non-parametric, Malmquist Productivity Index (MPI), especially in the post-crisis period. It utilises panel data for 3-digit industries from Annual Survey of Industries (ASI) for the period from 2003-04 to 2017-18 for 18 Indian states. The study period has been divided into a pre-crisis period (2003-2008) and post-crisis period (2008-2017) to see the impact of global financial crisis (GFC) on productivity as well. The results show that at the all India level, TFP of Organised Indian Manufacturing has declined in the post-crisis period as compared to pre-crisis period. The decomposition of the productivity growth further reveals that technological changes propelled growth in the pre-crisis period and efficiency changes were responsible for driving post-crisis growth. However, the inter-regional variations in productivity reduced during the post-crisis years signalling towards convergence of growth which is a welcome change. The author found that TFP is positively impacted by education, health and road infrastructure whereas relationship with telecommunication was found to be statistically insignificant. [ABSTRACT FROM AUTHOR]

Microfinance institutions (MFIs) are instrumental in enabling the economic empowerment of women. We examine the efficiency and performance of 84 Indian MFIs from 2016 to 2018 using a two-stage double bootstrap approach. Our results show that MFIs with increased outreach and actively target female borrowers achieve higher efficiency. Furthermore, we find larger MFIs and higher leverage intensity to be positively associated with efficiency. Government policies should be encouraged to support current MFIs to grow larger, actively target female borrowers and increase outreach to the poor to support India's financial inclusion agenda and facilitate the economic empowerment of women whist revitalizing less efficient MFIs. [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]

In the current study, flood risk assessment of densely populated coastal urban Surat City, on the bank of the lower Tapi River in India, was conducted by combining the hydrodynamic model-based flood hazard and often neglected socioeconomic vulnerability. A two-dimensional (2D) hydrodynamic (HD) model was developed using physically surveyed topographic data and the existing land use land cover (LULC) of the study area (5248 km2). The satisfactory performance of the developed model was ascertained by comparing the observed and simulated water levels/depths across the river and floodplain. The 2D HD model outputs with geographic information system (GIS) applications were further used to develop probabilistic multiparameter flood hazard maps for coastal urban city. During a 100-year return period flood (Peak discharge = 34,459 m3/s), 86.5% of Surat City and its outskirt area was submerged, with 37% under the high hazard category. The north and west zones are the worst affected areas in Surat City. The socioeconomic sensitivity and adaptive capacity indicators were selected at the city's lowest administrative (ward) level. The socioeconomic vulnerability was evaluated by employing the robust data envelopment analysis (DEA) technique. Fifty-five of 89 wards in Surat City, covering 60% of the area under the jurisdiction of the Municipal Corporation, are highly vulnerable. Finally, the flood risk assessment of the city was conducted using a bivariate technique describing the distinctive contribution of flood hazard and socioeconomic vulnerability to risk. The wards adjoining the river and creek are at high flood risk, with an equal contribution of hazard and vulnerability. The ward-level hazard, vulnerability, and risk assessment of the city will help local and disaster management authorities to priorities high risk areas while planning flood management and mitigation strategies. [Display omitted] • A 2D HD model has been developed for the lower Tapi basin, India, and its performance is evaluated from past flood events. • Probabilistic multiparameter flood hazard obtained through 2D HD model output and GIS application. • Ward-level socioeconomic vulnerability is computed using census-based indicators and the robust DEA technique. • A bivariate risk technique is used to develop flood risk maps by integrating flood hazard and socioeconomic vulnerability. • Developed flood risk maps helpful in identifying and prioritizing high risk areas. [ABSTRACT FROM AUTHOR]

Technical progress is one of the best strategies to meet the dual challenges of economic growth and carbon emissions control in developing countries such as India. In this research, an empirical study of India's carbon emissions was conducted to understand the effects of various technical factors on total and industrial carbon emission intensity. Ten driving factors of carbon emission in 56 sectors of India are decomposed by combining Index Decomposition Analysis (IDA) and Production-theoretical Decomposition Analysis (PDA). The results reveal that (i) the pure carbon-reduction technical efficiency and the scale efficiency of carbon-reduction technology in India have not improved, and the technical progress of pure carbon-reduction and the scale efficiency of carbon-reduction technology has played a positive role in carbon emission. (ii) The scale efficiency of pure energy-saving technology progress is the most important factor in restraining carbon emissions, and pure energy-saving technology progress has had an overall promoting effect. However, the effect of energy-saving technology progress is not as effective as the scale efficiency through improving energy technology progress. (iii) Per capita value added and population size are powerful factors that promote carbon emissions. This study also makes a targeted analysis of the driving factors of carbon emissions in different industries, which provide a reference for the process of carbon-reduction in different industries. [Display omitted] • Investigating the effects of various technical factors on total carbon emission and carbon emission intensity. • Combining Index Decomposition Analysis and Production-theoretical Decomposition Analysis. • Ten driving factors of carbon emission in India's 56 sectors were decomposed. • Technique factors have not effectively offset Indian carbon emission. • The effect of technological progress on carbon efficiency needs to be improved. [ABSTRACT FROM AUTHOR]

In the process of analyzing and taking action toward a low-carbon production, understanding the effect kind of farm management options is critical. Currently, a sustainable production system entails effective energy use with reduced environmental impact. Considering the quantum of inputs in coffee production, this study deals with the evaluation of energy flow and environmental footprints of coffee-pepper farms of Karnataka, India. The aim is to ensure the socio-economic and environmental sustainability of coffee-pepper production systems. The evaluation was made for 108 farms, categorized into conventional farming (CF), integrated production (IP), and organic farming (OF) systems, using both data envelopment analysis (DEA) and Life cycle Assessment (LCA) methodologies. A cradle-to-gate perspective was used to evaluate the life cycle environmental impacts. The functional unit is one ha of the cropping system, used for producing both crops (coffee and pepper). The energy analysis revealed higher energy use efficiency (2.3), net energy gain (18,890 MJ/ha), and farm autonomy (0.3) in the OF system. Higher energy consumption in CF and IP was attributed to the N fertilizer use (38% & 32%, respectively), followed by lime (20% & 17%, respectively). The DEA indicated that the average technical efficiency of the farming system was 0.82 for the CF and 0.76 for the IP. This implies farmers can save 18 and 24% of the resources in the CF and the IP systems, respectively. These farms performed more efficiently with a mean economic saving of 497 and 540 USD, respectively compared to the CF. The on-farm greenhouse gas (GHG) emissions were the major contributor to the global warming potential; contributing 59%, 62%, and 62%, respectively in the CF, IP, and OF. The overall GHG emissions in the OF systems were 65% lower compared to the CF. The IP and OF systems showed better environmental and energy gains compared to the CF for most of the selected sustainability metrics. Hence, large-scale adoption of the OF practices or reducing the use of external inputs can help to achieve higher energy efficiency and to reduce environmental impacts associated with conventional farming systems. [Display omitted] • Coupled DEA + LCA approaches were used to assess energy flows & environmental impacts. • The organic system was energy efficient (2.3) with higher farm autonomy (0.3). • Enhancing EUE can save 18 & 24% of the resources in CF and IP systems, respectively. • Major contributor to the GWP were on-farm GHG emissions and fertilizer productions. [ABSTRACT FROM AUTHOR]